Development and Validation of a Green Method for Simultaneous Determination of Alogliptin and Pioglitazone in Bulk, Combined Tablet Dosage Forms, and Spiked Human Plasma by High-Performance Thin-Layer Chromatography

Development and Validation of a Green Method for Simultaneous Determination of Alogliptin and Pioglitazone in Bulk, Combined Tablet Dosage Forms, and Spiked Human Plasma by High-Performance Thin-Layer Chromatography | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and Validation of a Green Method for Simultaneous Determination of Alogliptin and Pioglitazone in Bulk, Combined Tablet Dosage Forms, and Spiked Human Plasma by High-Performance Thin-Layer Chromatography Azza Sayed Tammam, Ahmed Abdelmogod Gahlan, Ahmed Mogahed Haredy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7804109/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract A high-performance thin-layer chromatography (HPTLC) technique that indicates stability was created and verified. to simultaneously quantify the active pharmaceutical ingredients pioglitazone, alogliptin, and their fixed-dose combination. This method is characterized by its clarity, directness, precision, and accuracy. Methods: Densitograms of alogliptin (ALO) and pioglitazone (PIO) were created using n-butanol: water: glacial acetic acid: ammonia (8.0:0.5:0.5:1.0v/v) as the mobile phase and HPTLC plates (silica gel 60 F254) as the stationary phase. Densitometric quantification was implemented at a wavelength of 254 nm. Results: The values of RF for Alogliptin and Pioglitazone were determined to be 0.28 and 0.75, respectively. The method's robustness, specificity, accuracy, and precision were all verified. With correlation coefficients of 0.9998 and 0.9990 for Alogliptin and Pioglitazone, respectively, the linearity was obtained in the concentration range of 125.0-2000.0 ng/band for Alogliptin and 250.0-4000.0 ng/band for Pioglitazone. The lowest quantity that could be quantified by the suggested method was found to be 0.31 and 2.75 ng /band for ALO and PIO, respectively. The detection limit for Alogliptin and Pioglitazone was found to be 0.1 and 0.9 ng/band, respectively. The environmental impact of the method was assessed using various greenness evaluation tools, confirming its eco-friendly nature. Conclusion: After validation, the method was determined to be specific, selective, and appropriate for analyzing these medications, whether in bulk form or fixed-dose combinations. Alogliptin Pioglitazone HPTLC Method validation T2DM Green Chemistry Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1- Introduction Thin-layer chromatography (TLC) studies are one of the most essential techniques for identifying compounds in pharmaceutical monographs. These monographs are used by industry to meet quality control (QC) requirements and current good manufacturing practices (cGMPs). The most advanced form of TLC is high-performance thin-layer chromatography (HPTLC), which is a fast, efficient, and reliable method for quantitatively analyzing compounds [ 1 ]. TLC is the foundation of High Performance Thin Layer Chromatography (HPTLC), an analytical method that has been enhanced to improve the resolution of the separated chemicals and facilitate quantitative analysis. These advancements involve the utilization of superior TLC plates featuring finer particle sizes within the stationary phase, significantly improving the resolution of compounds and enhancing overall analytical performance [ 2 ]. By developing the plate repeatedly, the separation can be further enhanced. Consequently, HPTLC provides reduced Limit of Detection (LODs) and enhanced resolution. [ 2 , 3 ]. Takeda Pharmaceutical Company Limited developed Alogliptin Benzoate (ALO) ( Fig. 1 ) , which received FDA approval on January 25, 2013, for treating T2DM. It is currently offered internationally under the brand name Nesina, and in Egypt under the names Oliptina and Prandglim [ 4 , 5 ]. Alogliptin, mostly manufactured as a benzoate salt, mainly presents as the R-enantiomer (> 99%). It exhibits little or no chiral conversion to the (S)-enantiomer in vivo. Alogliptin Benzoate, an oral dipeptidyl peptidase-4 (DPP4) inhibitor, demonstrates high potency and selectivity, with a Half-maximal inhibitory concentration (IC50) value less than 10 nM. Its remarkable selectivity, exceeding 10,000-fold for DPP8 (Dipeptidyl peptidase 8) and DPP9 (Dipeptidyl peptidase 9) genes, positions it as a highly promising agent in the realm of DPP4 inhibition [ 6 ]. Pioglitazone (PIO) ( Fig. 2 ) is an effective hypoglycemic drug widely utilized in the management of T2D to regulate glucose levels. It is categorized as a Thiazolidinedione and functions as a Peroxisome proliferator-activated receptor- gamma (PPAR-γ) activator [ 7 ]. PIO induces the ligand-activated transcription factor PPAR-γ to become active, which inhibits angiogenesis and cell proliferation and causes cell differentiation. Furthermore, PIO enhances adipocyte development, suppresses the activation of monocytes and macrophages and alters the transcription of genes that respond to insulin [ 8 ]. Pioglitazone's effectiveness in treating diabetes within adipose tissue is directly linked to its ability to block the phosphorylation of PPAR-γ at Ser-273 gene [ 9 ]. It enhances the effect of insulin and promotes nonoxidative glucose metabolism in the muscles, effectively inhibiting hepatic gluconeogenesis. Importantly, it does not stimulate insulin secretion. As a result, the drug effectively reduces glucose concentration, leading to a decrease in insulin plasma levels [ 10 ]. PIO is a member of the drug class called thiazolidinediones and is distinguished by its special five-membered ring containing sulfur and nitrogen atoms. Five carbon atoms and one nitrogen atom make up the six-membered pyridine ring of PIO, an aromatic heterocyclic molecule. Furthermore, the phenylmethyl group is a frequently used substituent in organic chemistry that is defined by a phenyl ring attached to a methyl group, whereas the ethoxy group is an organic functional group with an oxygen atom bound to an ethyl group[ 10 ]. ALO and PIO alone or in combination with other medications have been analyzed using different techniques, according to the literature study, these techniques include spectrophotometry[ 11 – 16 ], HPLC [ 8 , 17 – 27 ], UPLC method [ 28 – 31 ], HPTLC method[ 32 – 35 ], LC-MS/MS [ 36 , 37 ], and electrophoresis [ 38 , 39 ]. High Performance Thin-Layer Chromatography is the simplest technology available for separation for analysts today. It can handle multiple samples of different natures and compositions simultaneously [ 40 ]. The use of HPTLC offers numerous advantages for compound analysis when compared to other techniques, such as HPLC. HPTLC is a highly versatile and repeatable chromatographic method known for its exceptional sensitivity, specificity, and accuracy in estimating drug products. Its wide-ranging applications make it an ideal choice for estimation of quantitative and qualitative of active molecules [ 41 ]. In developing countries, the exorbitant costs of solvents (HPLC-grade) and columns, as well as the excessive utilization of solvents, significantly impede the timely release of crucial laboratory findings required for interventions [ 42 ]. Because of its many benefits, the HPTLC device has become essential in pharmaceutical analysis despite its high cost. The benefits include enhanced efficiency of the separation and limit of detections, lower analysis costs, shorter analysis times, and the removal of solvent pretreatment processes like filtration and degassing. Additionally, there is reduced consumption of the mobile phase per sample and the elimination of previous analyses analysis interference, as each utilizes fresh stationary and mobile phases [ 43 , 44 ]. 2- Materials and Methods 2.1- HPTLC system Samples were applied as 5.0 mm bands onto pre-coated aluminum-backed silica gel 60 F254 HPTLC plates (20.0 cm × 20.0 cm, 0.2 mm thickness; E. Merck, Germany) using a Camag Linomat applicator (Cat. No. 022.7808, Switzerland) and a 100.0 µL Camag sample syringe (Muttenz, Switzerland). Densitometric scanning of the chromatogram was conducted with a Camag TLC Scanner 3, employing a slit dimension of 4.0 × 0.45 mm. Using an autosampler, 5.0 µl of each standard solution was applied in triplicate as bands having a bandwidth of 4.0 mm onto HPTLC plates (20.0 × 10.0 cm). The bands were separated by 0.6 cm from each other and 0.5 cm from the plate's bottom edge. 2.2- Chemicals and Reagents 2.2.1 Drugs Alogliptin benzoate (lot no. 180889 R) and Pioglitazone hydrochloride (lot no. 198635 R), in authentic powder forms, were given as a gift from Rameda Pharmaceutical (10th of Ramadan, Cairo, Egypt). 2.2.2 Pharmaceutical formulations Oliptina® tablets which containing ALO (12.5 mg/tablet) from (Rameda, 10th of Ramadan, Cairo, Egypt), and Actozone® (Amoun Pharmaceutical Company, El-Obour City, Al Qalyubia, Egypt) containing PIO (30.0 mg/tablet), and Prandaglim tablets (EVA PHARM Company, Cairo, Egypt) containing (25/30 mg/tablet) Alogliptin/Pioglitazone were purchased from a local pharmacy. 2.2.3 Solvents N-butanol (99.0%), Glacial acetic acid (34–40%), Ammonia (25%), and methanol (99%) were purchased from the El-Nasr Chemical Company (Cairo, Egypt). 2.3- Preparation of standard solutions and calibration curves To prepare a final concentration of 1000.0 µg/mL, exactly 25.0 mg of Alogliptin benzoate was weighed and transferred to a 25.0 mL volumetric flask, dissolved, and diluted to the mark with methanol. Similarly, to achieve a final concentration of 2000 µg/mL, precisely 50.0 mg of pioglitazone hydrochloride was weighed, placed in a 25.0 mL volumetric flask, dissolved, and diluted to the mark with methanol. The two solutions were then mixed in a 1:1 ratio. A mixture of these standard solutions was applied to the HPTLC plate for calibration using a 100.0 µL Hamilton syringe and a Linomat V auto sprayer, resulting in concentrations of 250–4000 ng/band (25–400 µg/mL) for PIO and 125–2000 ng/band (50–800 µg/mL) for ALO The plates were developed and analyzed using a TLC scanner at 254 nm ( Fig. 3 ) . Six different sets of calibration curves were generated, and the correlation coefficient, slope, intercept, and regression coefficient were calculated and reported. 2.4- Analysis of Tablet Formulation Ten Prandaglim tablets, which contained 25.0/30.0 mg Alogliptin/Pioglitazone per tablet, were precisely weighed and ground into a fine powder. After weighing and transferring an amount of powder equal to 25.0 mg of Alogliptin and 30.0 mg of Pioglitazone to a 25.0 mL volumetric flask with around 15.0 mL methanol, the powder was sonicated to dissolve it, and the volume was adjusted with methanol. The Whatman no. 41 paper was utilized to filter the solution to obtain a final quantity of 1500.0 and 1800.0 ng per band for ALO and PIO, respectively. A 5.0 µL aliquot of the filtered solution was applied on an HPTLC plate. The peak areas of the bands were measured at 254 nm following chromatographic development, and the corresponding calibration plots were used to estimate the amount of medication contained in each tablet (Fig. 4 ) . Six repetitions of the process were conducted to analyze the homogeneous sample. ( Table 9 ) 2.5- System suitability We evaluated the performance of the proposed HPTLC-densitometric method by calculating system suitability parameters. As shown in Table 2 , the results were promising, with resolution (Rs) and selectivity factor (α) both exceeding 1, ensuring effective separation of the analyzed compounds. This demonstrates the method's strong reliability and potential for practical application [ 45 ]. 2.6- Content uniformity Ten tablets from Prandaglim, each weighed individually and then analyzed separately in the same steps mentioned under 2.4, analysis of tablet formulation. According to the official USP recommendations, the content uniformity was evaluated [ 46 ]. 2.7- ALO and PIO analysis in spiked human plasma Frozen human plasma samples were thawed at ambient temperature. Next, 1.0 ml of plasma was transferred into 5.0 ml tubes. Different volumes (0.5, 1.0, and 1.5 ml) of an ALO solution (100.0 µg/ml) and PIO (200.0 µg/ml) were added, and the total volume was brought to 5.0 ml with methanol to facilitate the deproteinization of the plasma. The tubes were then closed, vortexed, and centrifuged for 10.0 minutes at 4500.0 rpm and then filtered through a 0.45 µm Millipore membrane filter. Finally, 1.0 ml of the supernatant was transferred to a clean test tube, and the previous procedure was repeated. 3- Results and Discussion 3.1- Validation of Analytical Method Linearity, accuracy, precision, specificity, robustness, and quantification limitations were among the various characteristics assessed in the validation of the developed technique according to ICH Q2A and Q2B guidelines [ 47 ]. 3.1.1- Linearity : Strong linear relationships between peak area and concentration were observed for ALG and PIO using linear regression analysis of the calibration plots. The linear range was 125.0–2000.0 ng/band for ALO and 250.0–4000.0 ng/band for PIO Figs. 5 and 6 illustrate the standard curves for ALO and PIO, respectively, while the corresponding data for both compounds is presented in ( Table 1 ) . Table 1 Regression analysis data for ALG and PIO (n = 6) Parameter Alogliptin Pioglitazone Range of linearity (ng/band) 125–2000 250–4000 Regression Coefficient (r 2 ) 0.9998 0.999 Correlation Coefficient (r) 0.9999 0.999 Slope 2.70E-06 1.88E-06 Intercept 0.002764 0.0007 Table 2 System suitability parameters of the developed HPTLC method Parameters ALO PIO Reference value [ 48 ] Retardation factor (R f ) 0.28 0.75 The higher the capacity factor, the smaller the retardation factor Capacity factor (K') 2.57 0.33 Selectivity factor (α) a 7.78 α > 1 Resolution factor (R s ) b 6.7 R s >1 Tailing factor (T) 1.05 0.98 T = 1 for a typical symmetric peak Number of theoretical plates (N) c 635 4556 High N indicates better separation Height equivalent to theoretical plate (HETP) (mm) d 0.0035 0.0015 Low HETP indicates better separation a α = k' 2 /k' 1 , where the capacity factor k’ is equal to (1-R f )/R f . b R s = [2 (R f2 -R f1 )]/(W 1 + W 2 ), where R f is the retardation factor and W is the peak width. c N = 16 (z/ w ) 2 , where z is the spot width and z is the spot migration length d HETP = z/N, where z is the spot migration length. 3.1.2- Accuracy : To validate the method's accuracy, replicate analyses (n = 3) were carried out with three solutions made by adding pure Active Pharmaceutical Ingredients (API) at three different concentration levels in a regular manner: 50.0%, 100.0%, and 150.0%. Accuracy was determined by comparing theoretical (spiked) values with the actual measured values. The results, expressed as percentage recovery ± standard deviation (SD), are presented in Table 3 , confirming the method's reliability and precision. Table 3 Investigation of recovery using the standard addition method to determine alogliptin in Oliptina tablets and pioglitazone in Actozone tablets. Drug Pre-Analyzed Conc. Added Conc. Founded Recovery (mean ± SD) % SD ALO 500.0 0.0 499 100.8 ± 1.13 1.12 250.0 743 99.04 ± 1.03 1.04 500.0 998 99.75 ± 0.93 0.93 750.0 1265 101.16 ± 1.04 1.03 PIO 1000.0 0.0 999.5 99.9 ± 0.81 0.82 500.0 1504 100.3 ± 0.61 0.61 1000.0 1999 99.9 ± 0.79 0.80 1500.0 2509 100.3 ± 0.97 0.97 3.1.3- Precision: The precision of the method was rigorously evaluated through repeatability, intraday precision, and interday precision studies. For repeatability, 5.0 µL of a standard solution containing 1000.0 ng/band of ALO and 2000.0 ng/band of PIO were applied, and six replicates were analyzed to ensure consistency in the calculated peak areas. Three concentrations of ALO (250, 1000, and 1500 ng/band) and PIO (500, 2000, and 3000 ng/band) within the linearity range were selected for intraday and interday precision. Intraday precision was assessed by analyzing three replicates of each concentration on the same day, while interday precision involved analyzing three replicates over three consecutive days. The peak areas of the drug solutions were measured, and the results were expressed as percentage relative standard deviation (% RSD). The precision data for the method is detailed in Tables 4 , 5 , and 6 . Table 4 Repeatability data for ALG and PIO (n = 6) Drug Conc. Taken Conc. Found Recovery a ±SD %SD ALO 1000.0 986.4 98.6 ± 0.56 0.57 PIO 2000.0 1988.3 99.4 ± 1.16 1.17 Table 5 Inter-day precision data for ALG and PIO (n = 3) Drug Conc. Taken Recovery a ±SD %SD ALO 250.0 98.7 ± 1.48 1.46 1000.0 99.4 ± 1.4 1.39 1500.0 101.1 ± 1.24 1.23 PIO 500.0 101 ± 1.06 1.05 2000.0 99.3 ± 0.84 0.85 3000.0 99.7 ± 1.35 1.36 Table 6 Intra-day precision data for ALG and PIO (n = 3) Drug Conc. Taken Recovery a ±SD %SD ALO 250.0 101.1 ± 0.86 0.85 1000.0 99.4 ± 0.93 0.94 1500.0 100.2 ± 1.01 1.0 PIO 500.0 99.9 ± 1.09 1.09 2000.0 99.5 ± 0.67 0.67 3000.0 99.0 ± 0.54 0.55 3.1.4- Robustness: To assess the method's robustness, we intentionally varied the chromatography conditions. The specific changes included differing saturation times (20.0 ± 5.0 minutes) and varying wavelengths (254 ± 3 nm). During the robustness test, each condition was adjusted separately while keeping other parameters fixed at its optimal values. We observed no significant changes in the peak area or retention factor (R F ) values. These results strongly suggest that the method is not only robust but also reliably generates data of high quality suitable for further application, see Tables 7 , 8 . Table 7 Robustness saturation time study for alogliptin (1000.0 ng/band) and pioglitazone (2000.0 ng/band) by the proposed HPTLC method Drug Saturation Time (min.) Conc. Taken Recovery a ±SD %SD ALO 15.0 1000.0 99.0 ± 0.56 0.57 20.0 1000.0 98.6 ± 0.57 0.57 25.0 1000.0 98.5 ± 0.98 1.0 PIO 15.0 2000.0 98.9 ± 0.96 0.97 20.0 2000.0 99.7 ± 0.96 0.96 25.0 2000.0 99.3 ± 1.19 1.20 Table 8 Robustness wavelength study for alogliptin (1000.0 ng/band) and pioglitazone (2000.0 ng/band) by the proposed HPTLC method Drug Wavelength (λ) Conc. Taken Recovery a ±SD %SD ALO 251 1000.0 98.4 ± 0.56 0.57 254 1000.0 99.8 ± 0.93 0.93 257 1000.0 98.9 ± 0.74 0.75 PIO 251 2000.0 99.0 ± 0.81 0.82 254 2000.0 99.2 ± 1.07 1.08 257 2000.0 98.2 ± 1.31 1.33 3.1.5- Limits of detection and quantification : To ensure accurate and reliable results in analytical measurements, it is crucial to determine the limit of detection (LOD) and limit of quantification (LOQ) using established formulas derived from the calibration curve. These formulas are as follows: - LOD = 3.3 σ/s - LOQ = 10 σ/s In these equations, ‘σ’ signifies the standard deviation of the y-intercept of the regression line, while‘s’ represents the slope of the calibration curve. The method outlined in this study provides a practical, accurate, and precise approach for the performing simultaneous analysis of pioglitazone (PIO) and alogliptin benzoate (ALO) in both bulk and pharmaceutical dose forms. The HPTLC process for the simultaneous determination of ALO and PIO was optimized, with a solvent system consisting of n-butanol, glacial acetic acid, water, and ammonia in a ratio of (8:0.5:0.5:1). This system produced well-resolved, symmetrical peaks at R f values of 0.28 for ALO and 0.75 for PIO To ensure optimal TLC performance, plates were pre-washed with methanol, dried, and activated, while the TLC chamber was pre-saturated with the mobile phase for 20 minutes, enhancing reproducibility and peak shape. The method was thoroughly validated, addressing critical parameters such as linearity, accuracy (recovery), precision, limit of detection (LOD), limit of quantification (LOQ), robustness, and specificity. Regression analysis confirmed linearity over concentration ranges of 125.0–2000.0 ng/band for ALO and 250.0–4000.0 ng/band for PIO, with regression coefficients of 0.9998 and 0.999, respectively. Precision was demonstrated by % RSD values less than 2% for repeatability (n = 6), intraday precision (n = 3), and interday precision (n = 3). Accuracy, verified through recovery studies, showed percentage recoveries for ALO and PIO consistently within 98.0% to 102.0%, with low standard deviation and % RSD values, highlighting the method's reliability. The LOD values were highly sensitive at 0.10 ng/ band for ALO and 0.91 ng/ band for PIO, while the LOQ values were 0.3 ng/ band for ALO and 2.75 ng/ band for PIO Assay results for ALO and PIO were 99.96 ± 0.75 and 99.32 ± 0.88, respectively, with % RSD values below 2%, confirming the method's suitability for routine analysis. Robustness studies also yielded % RSD values below 2% for all parameters, further validating the method's reliability. Specificity was confirmed by the absence of interference from excipients in the formulation. A summary of validation parameters and regression analysis for ALG and PIO is provided in Table 9 . Table 9 Summary of ALG and PIO validation parameters and regression analysis Parameter Alogliptin Pioglitazone Range of linearity (ng/band) 125–2000 250–4000 Regression equation (y = mx + c) y = 2.70E-06x + 0.002764 y = 1.88E-06x + 0.0007 Regression Coefficient (r 2 ) 0.9998 0.9991 Correlation Coefficient (r) 0.9999 0.999 Slope 2.70E-06 1.88E-06 Intercept 0.002764 0.0007 R f value 0.28 0.75 Recovery (%) 98.6% 99.4% Repeatability (%RSD) 0.57 1.17 Intra-day precision (n = 3) (%RSD) 0.93 0.77 Inter-day precision (n = 3) (%RSD) 1.36 1.08 LOD (ng/band) 0.19 0.90 LOQ (ng/band) 0.31 2.75 Robustness Robust Robust Specificity Specific Specific Selectivity Selective Selective 3.2- Determination of Alogliptin and Pioglitazone in Pharmaceutical Formulation After optimization and validation, The suggested method was utilized to calculate the content of ALO and PIO in Prandaglim tablets. The recovery percentage was found to be 99.96%, with a standard deviation of 0.75 for ALO and 99.23% with a standard deviation of 0.88 for PIO These results further confirm that there were no interfering effects caused by the tablet excipients. ( see Table 10 ). Table 10 Evaluation in formulation Drug Conc. Taken Recovery a ±SD %SD ALO 1500.0 99.96 ± 0.75 0.75 PIO 1800.0 99.23 ± 0.88 0.89 3.3- Content uniformity Content uniformity is determined by analyzing the specific content of drug substances in dosage units to assess whether their concentrations fall within the permitted range. This study used the USP procedure to assess the uniformity of Prandaglim tablet ingredients. The acceptance value (AV) was calculated using the formula: AV = ǀM - x̄ǀ + KS Where x̄ represents the recovery of ten determinations mean; M is the reference value that corresponds to X if (98.5 ≤ x̄ ≥101.5), K is an acceptable constant (set at 2.4, when testing ten units), and S is the resulting Standard deviation (SD). As can be observed from Table 11 , the calculated acceptance values were below the maximum allowed AV of 15, showing a uniform distribution of the medications under study in Prandaglim, Table 11 Content uniformity testing results of Prandaglim tablets using the proposed high-performance thin-layer chromatography method. Tablets Alogliptin benzoate Pioglitazone hydrochloride Recovery a ±SD RSD Recovery a ±SD RSD 1 99.7 ± 1.74 1.74 100.5 ± 1.95 1.94 2 99.3 ± 1.18 1.2 101.1 ± 1.74 1.73 3 99.1 ± 0.78 0.78 99.1 ± 0.54 0.55 4 98.3 ± 0.75 0.76 101.0 ± 0.61 0.60 5 99.6 ± 1.65 1.66 98.0 ± 0.54 0.55 6 101 ± 1.12 1.11 98.7 ± 0.89 0.90 7 99.7 ± 1.33 1.34 100.2 ± 0.54 0.54 8 97.9 ± 1.97 2.02 100.5 ± 0.89 0.89 9 99.0 ± 1.36 1.37 98.4 ± 1.47 1.50 10 101.0 ± 0.62 0.61 101.4 ± 1.27 1.26 Mean 99.5 ± 1.25 1.26 99.9 ± 1.05 1.05 SD 0.97 1.21 Acceptance b value (AV) 2.33 2.90 Max allowed AV (L1) b 15 15 a Each result is calculated as the three separate measurements average. b The United States Pharmacopeia, 42nd edition, and National Formulary, 37th edition, 2019. 3.4- Proposed HPTLC method application to determine ALO and PIO drugs in spiked human plasma The proposed method demonstrated high sensitivity, successfully identifying ALO and PIO in spiked human plasma samples. As shown in Table 12 , the mean recovery ± SD values were 99.0% -100.9% (± 0.93–1.9) for ALO and 98.7% − 101.5% (± 1.06–1.6) for PIO These results indicate that the method is suitable for analyzing these drugs in real human plasma. Table 12 Proposed HPTLC method application to determine ALO and PIO in spiked human plasma Added Concentration (ng/band) Founded Concentration (ng/band) % Recovery ± SD Alogliptin Pioglitazone Alogliptin Pioglitazone Alogliptin Pioglitazone 500.0 1000.0 504 999 100.8 ± 1.9 99.9 ± 1.06 1000.0 2000.0 990 1974 99.0 ± 0.93 98.7 ± 1.33 1500.0 3000.0 1513 3044 100.9 ± 1.16 101.5 ± 1.6 3.5- Greenness evaluation The principles of green chemistry are increasingly applied across various fields, leading to the emergence of multiple tools designed to assess the environmental impact of different analytical procedures. Recently, several metrics have been extensively utilized for this purpose, including the Analytical Eco-Scale [ 49 ], the Analytical GREEnness calculator (AGREE) [ 50 ], AGREEprep [ 51 ], and ComplexMoGAPI [ 52 ]. A semi-quantitative evaluation tool called the Analytical Eco-scale was created to compare the environmental friendliness of various analytical techniques. For each stage in the analytical process that could have an effect on environmental sustainability, it determines a numerical score known as penalty points (PPs). The kind and quantities of solvents used, energy utilization, workplace risks, and waste generation are among the variables taken into consideration. These penalty points are deducted from 100, which is the score of an ideal green procedure, to get the Analytical Eco-scale's overall score. Excellent green analysis is indicated by a score above 75, acceptable green analysis is indicated by a score between 50 and 75, and insufficient green analysis is indicated by a score below 50. Additionally, an approach with a higher Eco-scale score is more cost-effective and environmentally friendly [ 53 ]. The results of the suggested High-Performance Thin-Layer Chromatography (HPTLC) method are shown in Table 13 . With an outstanding score of 86, this approach is categorized as an exceptional green analysis method. Twelve guiding principles of green analytical chemistry serve as the base for the AGREE criteria. A value between 0 and 1 is allocated to each of the twelve variables. At the center of a circular figure is the overall evaluation of these factors (see Table 14 ) . The suggested method's overall greenness is shown by the color of the center. AGREE is a comprehensive, adaptable, and easy-to-use assessment technique that yields lucid and instructive findings. The original AGREE tool was updated in 2022 with AGREEprep, a new greenness evaluation instrument. With an emphasis on sample preparation, AGREEprep provides thorough details on the method's overall environmental friendliness. Another significant approach is ComplexMoGAPI, which is a modification of GAPI and ComplexGAPI. ComplexMoGAPI is a tool that provides insights into the analytical method at every stage, from data collection to final analysis. It also assesses the procedures conducted prior to the general analysis. Finally, it generates a cumulative score that evaluates the overall environmental suitability of the method. Table 14 presents pictograms of the various greenness tools, and the results confirm the eco-friendly nature of the recommended analytical method. 3.6- Blueness evaluation The BAGI tool can be used to assess the method's performance and viability [ 54 ]. Four colors—dark blue, blue, light blue, and white—are used by this tool to assign grades. These colors correspond to grades of 10, 7.5, 5, and 2.5, respectively. Excellent performance is indicated with a dark blue score, whilst no performance is shown by a white score. The program creates a pictogram with a number between 25 and 100 in the middle. Poor practicality is represented by a score of 25, and high practicality is indicated by a score of 100. If a method's ultimate score is at least 60, it is deemed relevant. The current work's good applicability is indicated by its BAGI score of 82.5 (see Table 14 ) Table 13 The analytical eco-scale of the proposed HPTLC method. Item Penalty point n-butanol Glacial acetic acid Water Ammonia Instrument energy Occupational hazard Waste Total penalty points 4 5 0 4 0 0 3 16 Analytical eco-scale score 100 − 16 = 84 Table 14: Evaluation of the greenness and blueness of the proposed HPTLC method. Tool Pictogram Greenness evaluation AGREE AGREEprep ComplexMoGAPI Blueness evaluation BAGI 4- Conclusion The HPTLC analytical technique outlined for identifying pioglitazone HCl and alogliptin benzoate in bulk and tablets. Linearity, accuracy, specificity and robustness, were all evaluated for the analytical procedure. With regression coefficients (r 2 ) of 0.9998 and 0.9990 for alogliptin benzoate and pioglitazone HCl, respectively, within the working concentration range of 50.0% to 150.0%, the results demonstrated the linearity of the approach. Additionally, it was determined to be accurate within the 98.0% to 102.0% acceptable range. The method's validity as a stability-indicating approach was confirmed when it was examined for specificity and demonstrating selectivity for the active constituent without interference from excipients. Alogliptin benzoate and pioglitazone HCl in bulk, tablets, and human plasma can be determined using the HPTLC method outlined, and also content uniformity, which is also a reliable stability indicator. Finally, the environmental impact of this method was assessed using various assessment tools, including Analytical eco-scale, AGREE, AGREEprep, ComplexMoGAPI, and BAGI, which highlight its eco-friendly profile. Declarations Conflict of interest All authors declare that they have no conflicts of interest. Funding sources This study did not receive funding from any public, commercial, or non-profit agencies. Author Contribution All authors whose names appear on the submission1- made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data; or the creation of new software used in the work;2- drafted the work or revised it critically for important intellectual content; 3- approved the version to be published; and4- agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Acknowledgment I would like to thank Pharmaceutical Service and Bioavailability Center, Faculty of Pharmacy, Assiut University where I did my research work. References Galal, A.M., B. Avula, and I.A. Khan, Utility of thin-layer chromatography in the assessment of the quality of botanicals, in Instrumental Thin-Layer Chromatography. 2015, Elsevier. p. 479–504. Attimarad, M., K.M. Ahmed, B.E. Aldhubaib, and S. 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18:22:48","extension":"html","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":169097,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7804109/v1/6cc307564bbb88075217b2a8.html"},{"id":94590870,"identity":"02ea9503-475f-476d-955e-b775572d5ae4","added_by":"auto","created_at":"2025-10-28 18:21:35","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":7944,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAlogliptin benzoate structure\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7804109/v1/b727ca59795eb38fd9964fbe.jpg"},{"id":94591168,"identity":"f595adbe-5b81-4d92-868b-09c97a086b8a","added_by":"auto","created_at":"2025-10-28 18:21:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":7357,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePioglitazone hydrochloride structure\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7804109/v1/3cc2879ea32e74e1d3e1b95f.png"},{"id":94591581,"identity":"6201d187-2e2a-4eea-b6b5-a00eac1b0b03","added_by":"auto","created_at":"2025-10-28 18:22:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":149642,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBulk spectra of alogliptin and pioglitazone at 254nm\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7804109/v1/57f57cbeb406ecac0e8c93ee.png"},{"id":94591471,"identity":"58c615b2-7d30-44ef-a27b-f9919db5fd17","added_by":"auto","created_at":"2025-10-28 18:22:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":152416,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTablet dosage form spectra of alogliptin and pioglitazone at 254nm\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7804109/v1/67ae7b2099ef54f141ae6d45.png"},{"id":94591448,"identity":"89ca1fa4-1c09-4e13-a47d-cc30d43a7459","added_by":"auto","created_at":"2025-10-28 18:22:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":72245,"visible":true,"origin":"","legend":"\u003cp\u003eStandard calibration curve of alogliptin benzoate\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7804109/v1/d75ffdfd6249bc32a78b6709.png"},{"id":94596617,"identity":"8561df09-9f71-49b1-bde1-1e26bf63c4a7","added_by":"auto","created_at":"2025-10-28 18:43:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":38991,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStandard calibration curve of pioglitazone hydrochloride\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7804109/v1/39019c888214f49dc5d01238.png"},{"id":94591470,"identity":"621d9621-a973-410e-b440-9d38f9a89db3","added_by":"auto","created_at":"2025-10-28 18:22:16","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":672616,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7804109/v1/8ef122bf13917e9ef8476742.png"},{"id":96913986,"identity":"8a04d7b7-93f2-4d98-8de7-41ca4ffdd096","added_by":"auto","created_at":"2025-11-27 14:04:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2743168,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7804109/v1/8a6ea8b8-c858-497d-971b-4fcc8f866897.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Green Method for Simultaneous Determination of Alogliptin and Pioglitazone in Bulk, Combined Tablet Dosage Forms, and Spiked Human Plasma by High-Performance Thin-Layer Chromatography","fulltext":[{"header":"1- Introduction","content":"\u003cp\u003eThin-layer chromatography (TLC) studies are one of the most essential techniques for identifying compounds in pharmaceutical monographs. These monographs are used by industry to meet quality control (QC) requirements and current good manufacturing practices (cGMPs). The most advanced form of TLC is high-performance thin-layer chromatography (HPTLC), which is a fast, efficient, and reliable method for quantitatively analyzing compounds [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. TLC is the foundation of High Performance Thin Layer Chromatography (HPTLC), an analytical method that has been enhanced to improve the resolution of the separated chemicals and facilitate quantitative analysis. These advancements involve the utilization of superior TLC plates featuring finer particle sizes within the stationary phase, significantly improving the resolution of compounds and enhancing overall analytical performance [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. By developing the plate repeatedly, the separation can be further enhanced. Consequently, HPTLC provides reduced Limit of Detection (LODs) and enhanced resolution. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTakeda Pharmaceutical Company Limited developed Alogliptin Benzoate (ALO) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, which received FDA approval on January 25, 2013, for treating T2DM. It is currently offered internationally under the brand name Nesina, and in Egypt under the names Oliptina and Prandglim [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Alogliptin, mostly manufactured as a benzoate salt, mainly presents as the R-enantiomer (\u0026gt;\u0026thinsp;99%). It exhibits little or no chiral conversion to the (S)-enantiomer in vivo. Alogliptin Benzoate, an oral dipeptidyl peptidase-4 (DPP4) inhibitor, demonstrates high potency and selectivity, with a Half-maximal inhibitory concentration (IC50) value less than 10 nM. Its remarkable selectivity, exceeding 10,000-fold for DPP8 (Dipeptidyl peptidase 8) and DPP9 (Dipeptidyl peptidase 9) genes, positions it as a highly promising agent in the realm of DPP4 inhibition [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePioglitazone (PIO) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e is an effective hypoglycemic drug widely utilized in the management of T2D to regulate glucose levels. It is categorized as a Thiazolidinedione and functions as a Peroxisome proliferator-activated receptor- gamma (PPAR-γ) activator [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePIO induces the ligand-activated transcription factor PPAR-γ to become active, which inhibits angiogenesis and cell proliferation and causes cell differentiation. Furthermore, PIO enhances adipocyte development, suppresses the activation of monocytes and macrophages and alters the transcription of genes that respond to insulin [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Pioglitazone's effectiveness in treating diabetes within adipose tissue is directly linked to its ability to block the phosphorylation of PPAR-γ at Ser-273 gene [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. It enhances the effect of insulin and promotes nonoxidative glucose metabolism in the muscles, effectively inhibiting hepatic gluconeogenesis. Importantly, it does not stimulate insulin secretion. As a result, the drug effectively reduces glucose concentration, leading to a decrease in insulin plasma levels [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. PIO is a member of the drug class called thiazolidinediones and is distinguished by its special five-membered ring containing sulfur and nitrogen atoms. Five carbon atoms and one nitrogen atom make up the six-membered pyridine ring of PIO, an aromatic heterocyclic molecule. Furthermore, the phenylmethyl group is a frequently used substituent in organic chemistry that is defined by a phenyl ring attached to a methyl group, whereas the ethoxy group is an organic functional group with an oxygen atom bound to an ethyl group[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eALO and PIO alone or in combination with other medications have been analyzed using different techniques, according to the literature study, these techniques include spectrophotometry[\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], HPLC [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], UPLC method [\u003cspan additionalcitationids=\"CR29 CR30\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], HPTLC method[\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], LC-MS/MS [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], and electrophoresis [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHigh Performance Thin-Layer Chromatography is the simplest technology available for separation for analysts today. It can handle multiple samples of different natures and compositions simultaneously [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The use of HPTLC offers numerous advantages for compound analysis when compared to other techniques, such as HPLC. HPTLC is a highly versatile and repeatable chromatographic method known for its exceptional sensitivity, specificity, and accuracy in estimating drug products. Its wide-ranging applications make it an ideal choice for estimation of quantitative and qualitative of active molecules [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In developing countries, the exorbitant costs of solvents (HPLC-grade) and columns, as well as the excessive utilization of solvents, significantly impede the timely release of crucial laboratory findings required for interventions [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Because of its many benefits, the HPTLC device has become essential in pharmaceutical analysis despite its high cost. The benefits include enhanced efficiency of the separation and limit of detections, lower analysis costs, shorter analysis times, and the removal of solvent pretreatment processes like filtration and degassing. Additionally, there is reduced consumption of the mobile phase per sample and the elimination of previous analyses analysis interference, as each utilizes fresh stationary and mobile phases [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"2- Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1- HPTLC system\u003c/h2\u003e\u003cp\u003eSamples were applied as 5.0 mm bands onto pre-coated aluminum-backed silica gel 60 F254 HPTLC plates (20.0 cm \u0026times; 20.0 cm, 0.2 mm thickness; E. Merck, Germany) using a Camag Linomat applicator (Cat. No. 022.7808, Switzerland) and a 100.0 \u0026micro;L Camag sample syringe (Muttenz, Switzerland). Densitometric scanning of the chromatogram was conducted with a Camag TLC Scanner 3, employing a slit dimension of 4.0 \u0026times; 0.45 mm. Using an autosampler, 5.0 \u0026micro;l of each standard solution was applied in triplicate as bands having a bandwidth of 4.0 mm onto HPTLC plates (20.0 \u0026times; 10.0 cm). The bands were separated by 0.6 cm from each other and 0.5 cm from the plate's bottom edge.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2- Chemicals and Reagents\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 Drugs\u003c/h2\u003e\u003cp\u003eAlogliptin benzoate (lot no. 180889 R) and Pioglitazone hydrochloride (lot no. 198635 R), in authentic powder forms, were given as a gift from Rameda Pharmaceutical (10th of Ramadan, Cairo, Egypt).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 Pharmaceutical formulations\u003c/h2\u003e\u003cp\u003eOliptina\u0026reg; tablets which containing ALO (12.5 mg/tablet) from (Rameda, 10th of Ramadan, Cairo, Egypt), and Actozone\u0026reg; (Amoun Pharmaceutical Company, El-Obour City, Al Qalyubia, Egypt) containing PIO (30.0 mg/tablet), and Prandaglim tablets (EVA PHARM Company, Cairo, Egypt) containing (25/30 mg/tablet) Alogliptin/Pioglitazone were purchased from a local pharmacy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3 Solvents\u003c/h2\u003e\u003cp\u003eN-butanol (99.0%), Glacial acetic acid (34\u0026ndash;40%), Ammonia (25%), and methanol (99%) were purchased from the El-Nasr Chemical Company (Cairo, Egypt).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.3- Preparation of standard solutions and calibration curves\u003c/h2\u003e\u003cp\u003eTo prepare a final concentration of 1000.0 \u0026micro;g/mL, exactly 25.0 mg of Alogliptin benzoate was weighed and transferred to a 25.0 mL volumetric flask, dissolved, and diluted to the mark with methanol. Similarly, to achieve a final concentration of 2000 \u0026micro;g/mL, precisely 50.0 mg of pioglitazone hydrochloride was weighed, placed in a 25.0 mL volumetric flask, dissolved, and diluted to the mark with methanol. The two solutions were then mixed in a 1:1 ratio. A mixture of these standard solutions was applied to the HPTLC plate for calibration using a 100.0 \u0026micro;L Hamilton syringe and a Linomat V auto sprayer, resulting in concentrations of 250\u0026ndash;4000 ng/band (25\u0026ndash;400 \u0026micro;g/mL) for PIO and 125\u0026ndash;2000 ng/band (50\u0026ndash;800 \u0026micro;g/mL) for ALO The plates were developed and analyzed using a TLC scanner at 254 nm \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Six different sets of calibration curves were generated, and the correlation coefficient, slope, intercept, and regression coefficient were calculated and reported.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.4- Analysis of Tablet Formulation\u003c/h2\u003e\u003cp\u003eTen Prandaglim tablets, which contained 25.0/30.0 mg Alogliptin/Pioglitazone per tablet, were precisely weighed and ground into a fine powder. After weighing and transferring an amount of powder equal to 25.0 mg of Alogliptin and 30.0 mg of Pioglitazone to a 25.0 mL volumetric flask with around 15.0 mL methanol, the powder was sonicated to dissolve it, and the volume was adjusted with methanol. The Whatman no. 41 paper was utilized to filter the solution to obtain a final quantity of 1500.0 and 1800.0 ng per band for ALO and PIO, respectively. A 5.0 \u0026micro;L aliquot of the filtered solution was applied on an HPTLC plate. The peak areas of the bands were measured at 254 nm following chromatographic development, and the corresponding calibration plots were used to estimate the amount of medication contained in each tablet (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Six repetitions of the process were conducted to analyze the homogeneous sample. \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.5- System suitability\u003c/h2\u003e\u003cp\u003eWe evaluated the performance of the proposed HPTLC-densitometric method by calculating system suitability parameters. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the results were promising, with resolution (Rs) and selectivity factor (α) both exceeding 1, ensuring effective separation of the analyzed compounds. This demonstrates the method's strong reliability and potential for practical application [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.6- Content uniformity\u003c/h2\u003e\u003cp\u003eTen tablets from Prandaglim, each weighed individually and then analyzed separately in the same steps mentioned under 2.4, analysis of tablet formulation. According to the official USP recommendations, the content uniformity was evaluated [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.7- ALO and PIO analysis in spiked human plasma\u003c/h2\u003e\u003cp\u003eFrozen human plasma samples were thawed at ambient temperature. Next, 1.0 ml of plasma was transferred into 5.0 ml tubes. Different volumes (0.5, 1.0, and 1.5 ml) of an ALO solution (100.0 \u0026micro;g/ml) and PIO (200.0 \u0026micro;g/ml) were added, and the total volume was brought to 5.0 ml with methanol to facilitate the deproteinization of the plasma. The tubes were then closed, vortexed, and centrifuged for 10.0 minutes at 4500.0 rpm and then filtered through a 0.45 \u0026micro;m Millipore membrane filter. Finally, 1.0 ml of the supernatant was transferred to a clean test tube, and the previous procedure was repeated.\u003c/p\u003e\u003c/div\u003e"},{"header":"3- Results and Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.1- Validation of Analytical Method\u003c/h2\u003e\u003cp\u003eLinearity, accuracy, precision, specificity, robustness, and quantification limitations were among the various characteristics assessed in the validation of the developed technique according to ICH Q2A and Q2B guidelines [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1- \u003cb\u003eLinearity\u003c/b\u003e:\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eStrong linear relationships between peak area and concentration were observed for ALG and PIO using linear regression analysis of the calibration plots. The linear range was 125.0\u0026ndash;2000.0 ng/band for ALO and 250.0\u0026ndash;4000.0 ng/band for PIO Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrate the standard curves for ALO and PIO, respectively, while the corresponding data for both compounds is presented in \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRegression analysis data for ALG and PIO (n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAlogliptin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePioglitazone\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRange of linearity (ng/band)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e125\u0026ndash;2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e250\u0026ndash;4000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegression Coefficient (r\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCorrelation Coefficient (r)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSlope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.70E-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.88E-06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.002764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSystem suitability parameters of the developed HPTLC method\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePIO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference value [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRetardation factor (R\u003csub\u003ef\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eThe higher the capacity factor,\u003c/p\u003e\u003cp\u003ethe smaller the retardation factor\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCapacity factor (K')\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelectivity factor (α)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eα\u0026thinsp;\u0026gt;\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResolution factor (R\u003csub\u003es\u003c/sub\u003e)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR\u003csub\u003es\u003c/sub\u003e \u0026gt;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTailing factor (T)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT\u0026thinsp;=\u0026thinsp;1 for a typical symmetric peak\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of theoretical plates (N)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4556\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh N indicates better separation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight equivalent to theoretical plate (HETP) (mm)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow HETP indicates better separation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e α\u0026thinsp;=\u0026thinsp;k'\u003csub\u003e2\u003c/sub\u003e/k'\u003csub\u003e1\u003c/sub\u003e, where the capacity factor k\u0026rsquo; is equal to (1-R\u003csub\u003ef\u003c/sub\u003e)/R\u003csub\u003ef\u003c/sub\u003e .\u003c/p\u003e\u003cp\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e R\u003csub\u003es\u003c/sub\u003e = [2 (R\u003csub\u003ef2\u003c/sub\u003e -R\u003csub\u003ef1\u003c/sub\u003e)]/(W\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;W\u003csub\u003e2\u003c/sub\u003e), where R\u003csub\u003ef\u003c/sub\u003e is the retardation factor and W is the peak width.\u003c/p\u003e\u003cp\u003e\u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e N\u0026thinsp;=\u0026thinsp;16 (z/\u003cem\u003ew\u003c/em\u003e)\u003csup\u003e2\u003c/sup\u003e, where z is the spot width and z is the spot migration length\u003c/p\u003e\u003cp\u003e\u003csup\u003e\u003cb\u003ed\u003c/b\u003e\u003c/sup\u003e HETP\u0026thinsp;=\u0026thinsp;z/N, where z is the spot migration length.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2- \u003cb\u003eAccuracy\u003c/b\u003e:\u003c/h2\u003e\u003cp\u003eTo validate the method's accuracy, replicate analyses (n\u0026thinsp;=\u0026thinsp;3) were carried out with three solutions made by adding pure Active Pharmaceutical Ingredients (API) at three different concentration levels in a regular manner: 50.0%, 100.0%, and 150.0%. Accuracy was determined by comparing theoretical (spiked) values with the actual measured values. The results, expressed as percentage recovery\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, confirming the method's reliability and precision.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eInvestigation of recovery using the standard addition method to determine alogliptin in Oliptina tablets and pioglitazone in Actozone tablets.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrug\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePre-Analyzed\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eConc. Added\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eConc. Founded\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRecovery\u003c/p\u003e\u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e% SD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eALO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e500.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e100.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e250.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e99.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e500.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e99.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e750.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e101.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003ePIO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e1000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e999.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e99.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e500.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e100.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e99.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1500.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e100.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e3.1.3- Precision:\u003c/h2\u003e\u003cp\u003eThe precision of the method was rigorously evaluated through repeatability, intraday precision, and interday precision studies. For repeatability, 5.0 \u0026micro;L of a standard solution containing 1000.0 ng/band of ALO and 2000.0 ng/band of PIO were applied, and six replicates were analyzed to ensure consistency in the calculated peak areas. Three concentrations of ALO (250, 1000, and 1500 ng/band) and PIO (500, 2000, and 3000 ng/band) within the linearity range were selected for intraday and interday precision. Intraday precision was assessed by analyzing three replicates of each concentration on the same day, while interday precision involved analyzing three replicates over three consecutive days. The peak areas of the drug solutions were measured, and the results were expressed as percentage relative standard deviation (% RSD). The precision data for the method is detailed in Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRepeatability data for ALG and PIO (n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrug\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConc. Taken\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eConc. Found\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRecovery\u003csup\u003ea\u003c/sup\u003e \u0026plusmn;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e%SD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e986.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e98.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePIO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1988.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e99.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eInter-day precision data for ALG and PIO (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrug\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConc. Taken\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRecovery\u003csup\u003ea\u003c/sup\u003e \u0026plusmn;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%SD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eALO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e250.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e98.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e99.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1500.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e101.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ePIO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e500.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e101\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e99.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e99.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIntra-day precision data for ALG and PIO (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrug\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConc. Taken\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRecovery\u003csup\u003ea\u003c/sup\u003e \u0026plusmn;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%SD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eALO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e250.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e101.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e99.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1500.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e100.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ePIO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e500.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e99.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e99.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e99.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e3.1.4- Robustness:\u003c/h2\u003e\u003cp\u003eTo assess the method's robustness, we intentionally varied the chromatography conditions. The specific changes included differing saturation times (20.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0 minutes) and varying wavelengths (254\u0026thinsp;\u0026plusmn;\u0026thinsp;3 nm). During the robustness test, each condition was adjusted separately while keeping other parameters fixed at its optimal values. We observed no significant changes in the peak area or retention factor (R\u003csub\u003eF\u003c/sub\u003e) values. These results strongly suggest that the method is not only robust but also reliably generates data of high quality suitable for further application, see Tables\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, \u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRobustness saturation time study for alogliptin (1000.0 ng/band) and pioglitazone (2000.0 ng/band) by the proposed HPTLC method\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrug\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSaturation Time (min.)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eConc. Taken\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRecovery\u003csup\u003ea\u003c/sup\u003e \u0026plusmn;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e%SD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eALO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e99.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e98.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e98.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ePIO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e98.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e99.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e99.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRobustness wavelength study for alogliptin (1000.0 ng/band) and pioglitazone (2000.0 ng/band) by the proposed HPTLC method\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrug\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWavelength (λ)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eConc. Taken\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRecovery\u003csup\u003ea\u003c/sup\u003e \u0026plusmn;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e%SD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eALO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e98.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e99.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e98.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ePIO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e99.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e99.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e98.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e3.1.5- \u003cb\u003eLimits of detection and quantification\u003c/b\u003e:\u003c/h2\u003e\u003cp\u003eTo ensure accurate and reliable results in analytical measurements, it is crucial to determine the limit of detection (LOD) and limit of quantification (LOQ) using established formulas derived from the calibration curve. These formulas are as follows:\u003c/p\u003e\u003cp\u003e\u003cb\u003e- LOD\u0026thinsp;=\u0026thinsp;3.3 σ/s\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e- LOQ\u0026thinsp;=\u0026thinsp;10 σ/s\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn these equations, \u0026lsquo;σ\u0026rsquo; signifies the standard deviation of the y-intercept of the regression line, while\u0026lsquo;s\u0026rsquo; represents the slope of the calibration curve.\u003c/p\u003e\u003cp\u003eThe method outlined in this study provides a practical, accurate, and precise approach for the performing simultaneous analysis of pioglitazone (PIO) and alogliptin benzoate (ALO) in both bulk and pharmaceutical dose forms. The HPTLC process for the simultaneous determination of ALO and PIO was optimized, with a solvent system consisting of n-butanol, glacial acetic acid, water, and ammonia in a ratio of (8:0.5:0.5:1). This system produced well-resolved, symmetrical peaks at R\u003csub\u003ef\u003c/sub\u003e values of 0.28 for ALO and 0.75 for PIO To ensure optimal TLC performance, plates were pre-washed with methanol, dried, and activated, while the TLC chamber was pre-saturated with the mobile phase for 20 minutes, enhancing reproducibility and peak shape.\u003c/p\u003e\u003cp\u003eThe method was thoroughly validated, addressing critical parameters such as linearity, accuracy (recovery), precision, limit of detection (LOD), limit of quantification (LOQ), robustness, and specificity. Regression analysis confirmed linearity over concentration ranges of 125.0\u0026ndash;2000.0 ng/band for ALO and 250.0\u0026ndash;4000.0 ng/band for PIO, with regression coefficients of 0.9998 and 0.999, respectively. Precision was demonstrated by % RSD values less than 2% for repeatability (n\u0026thinsp;=\u0026thinsp;6), intraday precision (n\u0026thinsp;=\u0026thinsp;3), and interday precision (n\u0026thinsp;=\u0026thinsp;3). Accuracy, verified through recovery studies, showed percentage recoveries for ALO and PIO consistently within 98.0% to 102.0%, with low standard deviation and % RSD values, highlighting the method's reliability.\u003c/p\u003e\u003cp\u003eThe LOD values were highly sensitive at 0.10 ng/ band for ALO and 0.91 ng/ band for PIO, while the LOQ values were 0.3 ng/ band for ALO and 2.75 ng/ band for PIO Assay results for ALO and PIO were 99.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75 and 99.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88, respectively, with % RSD values below 2%, confirming the method's suitability for routine analysis. Robustness studies also yielded % RSD values below 2% for all parameters, further validating the method's reliability. Specificity was confirmed by the absence of interference from excipients in the formulation. A summary of validation parameters and regression analysis for ALG and PIO is provided in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of ALG and PIO validation parameters and regression analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAlogliptin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePioglitazone\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRange of linearity (ng/band)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e125\u0026ndash;2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e250\u0026ndash;4000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegression equation (y\u0026thinsp;=\u0026thinsp;mx\u0026thinsp;+\u0026thinsp;c)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;2.70E-06x\u0026thinsp;+\u0026thinsp;0.002764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ey\u0026thinsp;=\u0026thinsp;1.88E-06x\u0026thinsp;+\u0026thinsp;0.0007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegression Coefficient (r\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9991\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCorrelation Coefficient (r)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSlope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.70E-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.88E-06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.002764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csub\u003ef\u003c/sub\u003e value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecovery (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRepeatability (%RSD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntra-day precision (n\u0026thinsp;=\u0026thinsp;3) (%RSD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInter-day precision (n\u0026thinsp;=\u0026thinsp;3) (%RSD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOD (ng/band)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOQ (ng/band)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRobustness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRobust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRobust\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpecific\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpecific\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelectivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelective\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSelective\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.2- Determination of Alogliptin and Pioglitazone in Pharmaceutical Formulation\u003c/h2\u003e\u003cp\u003eAfter optimization and validation, The suggested method was utilized to calculate the content of ALO and PIO in Prandaglim tablets. The recovery percentage was found to be 99.96%, with a standard deviation of 0.75 for ALO and 99.23% with a standard deviation of 0.88 for PIO These results further confirm that there were no interfering effects caused by the tablet excipients. (\u003cb\u003esee\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEvaluation in formulation\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrug\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConc. Taken\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRecovery\u003csup\u003ea\u003c/sup\u003e \u0026plusmn;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%SD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1500.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e99.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePIO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1800.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e99.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.3- Content uniformity\u003c/h2\u003e\u003cp\u003eContent uniformity is determined by analyzing the specific content of drug substances in dosage units to assess whether their concentrations fall within the permitted range. This study used the USP procedure to assess the uniformity of Prandaglim tablet ingredients. The acceptance value (AV) was calculated using the formula:\u003c/p\u003e\u003cp\u003eAV = ǀM - x̄ǀ + KS\u003c/p\u003e\u003cp\u003eWhere x̄ represents the recovery of ten determinations mean; M is the reference value that corresponds to X if (98.5\u0026thinsp;\u0026le;\u0026thinsp;x̄ \u0026ge;101.5), K is an acceptable constant (set at 2.4, when testing ten units), and S is the resulting Standard deviation (SD).\u003c/p\u003e\u003cp\u003eAs can be observed from Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e, the calculated acceptance values were below the maximum allowed AV of 15, showing a uniform distribution of the medications under study in Prandaglim,\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eContent uniformity testing results of Prandaglim tablets using the proposed high-performance thin-layer chromatography method.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTablets\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eAlogliptin benzoate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003ePioglitazone hydrochloride\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRecovery\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e \u003cb\u003e\u0026plusmn;SD\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eRSD\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eRecovery\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e \u003cb\u003e\u0026plusmn;SD\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eRSD\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e101.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e99.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e101.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e98.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e101\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e98.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e98.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e101.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e101.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e99.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcceptance\u003csup\u003eb\u003c/sup\u003e value (AV)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMax allowed AV (L1)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e Each result is calculated as the three separate measurements average.\u003c/p\u003e\u003cp\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e The United States Pharmacopeia, 42nd edition, and National Formulary, 37th edition, 2019.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.4- Proposed HPTLC method application to determine ALO and PIO drugs in spiked human plasma\u003c/h2\u003e\u003cp\u003eThe proposed method demonstrated high sensitivity, successfully identifying ALO and PIO in spiked human plasma samples. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e, the mean recovery\u0026thinsp;\u0026plusmn;\u0026thinsp;SD values were 99.0% -100.9% (\u0026plusmn;\u0026thinsp;0.93\u0026ndash;1.9) for ALO and 98.7% \u0026minus;\u0026thinsp;101.5% (\u0026plusmn;\u0026thinsp;1.06\u0026ndash;1.6) for PIO These results indicate that the method is suitable for analyzing these drugs in real human plasma.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eProposed HPTLC method application to determine ALO and PIO in spiked human plasma\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAdded Concentration (ng/band)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eFounded Concentration (ng/band)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e% Recovery\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlogliptin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePioglitazone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAlogliptin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePioglitazone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAlogliptin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePioglitazone\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e500.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e99.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e990\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1974\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e99.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e98.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1500.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3000.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1513\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e101.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e3.5- Greenness evaluation\u003c/h2\u003e\u003cp\u003eThe principles of green chemistry are increasingly applied across various fields, leading to the emergence of multiple tools designed to assess the environmental impact of different analytical procedures. Recently, several metrics have been extensively utilized for this purpose, including the Analytical Eco-Scale [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], the Analytical GREEnness calculator (AGREE) [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], AGREEprep [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], and ComplexMoGAPI [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA semi-quantitative evaluation tool called the Analytical Eco-scale was created to compare the environmental friendliness of various analytical techniques. For each stage in the analytical process that could have an effect on environmental sustainability, it determines a numerical score known as penalty points (PPs). The kind and quantities of solvents used, energy utilization, workplace risks, and waste generation are among the variables taken into consideration.\u003c/p\u003e\u003cp\u003eThese penalty points are deducted from 100, which is the score of an ideal green procedure, to get the Analytical Eco-scale's overall score. Excellent green analysis is indicated by a score above 75, acceptable green analysis is indicated by a score between 50 and 75, and insufficient green analysis is indicated by a score below 50. Additionally, an approach with a higher Eco-scale score is more cost-effective and environmentally friendly [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The results of the suggested High-Performance Thin-Layer Chromatography (HPTLC) method are shown in Table\u0026nbsp;\u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003e13\u003c/span\u003e. With an outstanding score of 86, this approach is categorized as an exceptional green analysis method.\u003c/p\u003e\u003cp\u003eTwelve guiding principles of green analytical chemistry serve as the base for the AGREE criteria. A value between 0 and 1 is allocated to each of the twelve variables. At the center of a circular figure is the overall evaluation of these factors \u003cb\u003e(see\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab14\" class=\"InternalRef\"\u003e14\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The suggested method's overall greenness is shown by the color of the center. AGREE is a comprehensive, adaptable, and easy-to-use assessment technique that yields lucid and instructive findings. The original AGREE tool was updated in 2022 with AGREEprep, a new greenness evaluation instrument. With an emphasis on sample preparation, AGREEprep provides thorough details on the method's overall environmental friendliness.\u003c/p\u003e\u003cp\u003eAnother significant approach is ComplexMoGAPI, which is a modification of GAPI and ComplexGAPI. ComplexMoGAPI is a tool that provides insights into the analytical method at every stage, from data collection to final analysis. It also assesses the procedures conducted prior to the general analysis. Finally, it generates a cumulative score that evaluates the overall environmental suitability of the method. Table\u0026nbsp;\u003cspan refid=\"Tab14\" class=\"InternalRef\"\u003e14\u003c/span\u003e presents pictograms of the various greenness tools, and the results confirm the eco-friendly nature of the recommended analytical method.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e3.6- Blueness evaluation\u003c/h2\u003e\u003cp\u003eThe BAGI tool can be used to assess the method's performance and viability [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Four colors\u0026mdash;dark blue, blue, light blue, and white\u0026mdash;are used by this tool to assign grades. These colors correspond to grades of 10, 7.5, 5, and 2.5, respectively. Excellent performance is indicated with a dark blue score, whilst no performance is shown by a white score. The program creates a pictogram with a number between 25 and 100 in the middle. Poor practicality is represented by a score of 25, and high practicality is indicated by a score of 100. If a method's ultimate score is at least 60, it is deemed relevant. The current work's good applicability is indicated by its BAGI score of 82.5 \u003cb\u003e(see\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab14\" class=\"InternalRef\"\u003e14\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab13\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe analytical eco-scale of the proposed HPTLC method.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePenalty point\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003en-butanol\u003c/p\u003e\u003cp\u003eGlacial acetic acid\u003c/p\u003e\u003cp\u003eWater\u003c/p\u003e\u003cp\u003eAmmonia\u003c/p\u003e\u003cp\u003eInstrument energy\u003c/p\u003e\u003cp\u003eOccupational hazard\u003c/p\u003e\u003cp\u003eWaste\u003c/p\u003e\u003cp\u003eTotal penalty points\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e5\u003c/p\u003e\u003cp\u003e0\u003c/p\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e0\u003c/p\u003e\u003cp\u003e0\u003c/p\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnalytical eco-scale score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100\u0026thinsp;\u0026minus;\u0026thinsp;16\u0026thinsp;=\u0026thinsp;84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003cp\u003eTable 14: Evaluation of the greenness and blueness of the proposed HPTLC method.\u003c/p\u003e\n\u003ctable style=\"border-collapse: collapse;border: none;width: 638px;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:110.1pt;border:solid #4BACC6 1.0pt;border-bottom:solid #4BACC6 2.25pt;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:128.4pt;border-top:solid #4BACC6 1.0pt;border-left:none;border-bottom:solid #4BACC6 2.25pt;border-right:solid #4BACC6 1.0pt;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003eTool\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:240.3pt;border-top:solid #4BACC6 1.0pt;border-left:none;border-bottom:solid #4BACC6 2.25pt;border-right:solid #4BACC6 1.0pt;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003ePictogram\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width:110.1pt;border:solid #4BACC6 1.0pt;border-top:none;background:#D2EAF1;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;color:black;'\u003eGreenness evaluation\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:128.4pt;border-top:none;border-left:none;border-bottom:solid #4BACC6 1.0pt;border-right:solid #4BACC6 1.0pt;background:#D2EAF1;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;line-height: 150%;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:13px;line-height: 150%;font-family:\"Times New Roman\",serif;color:black;'\u003eAGREE\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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\" 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tglaEKpWr2ptJShlz2+//WYVF7IqBupSffBJniFvrnspaiNGr4Gs/kdNXgkYksxm9erVaNGiBWISYkCNuAMYzyrLoLriaCghMSURK1eu9H4CpSx55ZVXULFSRSsLofH+qJwvr9FL9TopUyv16BX/oiavlDmy3P3qq69ayWw8kR5rCY+GeDsAU8fgFMngI5Ws9/3LL794P41SFsjS7tKlS+2tHKlDYLo/quDRCFY6oVHjRlYxKcV/qMkrZcoPP/yAJUuWoHr16tZo3gqgkqAcpxv8ETUj1KpVS2cgZcz3339vZR2U7IMacOcCyYB5ECE8ORz9+/fXUyt+RE1eKROkQpzkG+/QoQNiUmJAdfghH+192E2dgFM1lBARFYFVq1Z5P5lSFki+hLxyeXZtAtN9UQWf5NnvS4iKj8K4ceOsQlOK71GTV/zOt99+a0VFJyYn2jOxU1llkanOH5JVhzxCv379vJ9OKQuuuOIKUDRfezlOabovquCUGD0P3DwxHixcuNCqS6H4FjV5xW9I5Pybb75pGWJMPM/eG/MDPZEVLEvzJsl5/eaElJQUK9pbKRtkBYgq87Wfme9eqNwhOUnThJCanmplxZOAXMV3qMkrPkfMXQLTpAKVlY5WqsT14Qc50AltfKX+PPOI8+DBBx/0fmLFn3z99deIiomysg4G3faOqmgSo69JKF++vHXiRs/Q+w41ecXnfPDBB1YxmbiEOCudpbXEGsyz94KSNJ3ZhGHDhumsowy47rrr7CDNoQXug8pdmsYqT6hcuTI2btzovftKaVGTV3yGzODlaFzTpk0RER9hV4qble8hdoskurshoXHjxvj888+9n17xBxKw2adPHysOwpVtSfU/yURAjtalETp37mwlyVJKj5q84hPkiNMFF1yAzKxMq2IbDWO5+ajTGYTk5GSr4IYMbhT/IDO6GjVqgNoY7oHKfRKj575DVm4GDhyIXbt2eVuCUlLU5JVSI8kszjjjDDtrnRyNk2U30wPsJs0jq9zp5MmTrcpoiu+RlKdSwz8uLk6j6kNJYvRdCNEJ0ZgzZ44OokuJmrxSYiQ4Zv369Wjbti3CE8JBnfjhDKUlVR7QSNlTTeThH3799VcMHz4clMzXOliPXKpKJgnEa0xISErAE088oUZfCtTklRIh51nluIsce5EgNKtinJuC64qigYSwyDA8+eST3qui+JKPP/7YiramVnytQ61tqezKjzUIVapUsSYTWsymZKjJK8VGZliXXHIJ0jPTQdX5QRzHCsVOWHKoZxImTpzovTKKL5Gsgp4ojxX/YLz+KvdrAsGT5EGnTp2wfft2b8tQioOavFIsfvrpJ8vUpBob1eeHUEbboTrLmsdqaB/50X153yLLs5LTnMrxNQ6FGA+VWdK39CVEJERYWzdy2kIpHmrySpGQpTJZPpXsdRTLD15b1nzvgxjK6k2IS4nTJXsfI1HV1kpRC77Guh8f2lrI6kJWBUJZQTxw4IC3lShFQU1eKRKSnlaWzCLjI0E9+aGTWazpgQw1TSKEp4fjrLPO0j1DHyJL9bGpsVZ2QeN1V4WWJBCvrr1qJoWuNBCv6KjJK8dEar+/8MILqFCxAsLSw0CD+WHT1KL/k+QCqEVo3bq17hn6CFmSHTp0KDxZHns7yHTdVaGnswieTA+aNWumiXKKgZq8clRkn3nFihUoV64cqCI/ZCNZWsu7sPoSMjIy8Pjjj3uvnFIaPvzwQzRq1MgqWqJR9ar/l7SFQax4stJm79u3z9tilGOhJq8YkTPw5513HjIzM22Dn+J9yEwPX6hrOiEsOgyLFi2yVj6UkiPLsPfffz8SExNBQwzXWhW6kglGD1YcoWrVqti6dau31SjHQk1eKcR3332H8ePHW4EudAI/VFre89iSwKDaZC0jSnpffyKDL5nB/Pjjj1ZwmiTi+eyzz7Blyxa89957eP311/HUU0/hjjvusGqwL1++HEuXLrUGbFKve/78+dZ/pb7/kiVLsGzZMlx66aW46aab8Oijj+Lll1/Gu+++a82mJZPhl19+iZ07d+KHH37A3r17/V6QZ//+/Rg7diwoia/p3HzXWBW6EnPngbT0RbEJsejWrRs2b96sMTBFRE1e+Q/SmY8ZM8Z6mKgDP1jneB801dElKxw9ySqH+sYbb3ivZOmRWe2ePXusDu2ZZ56xSveef/75GDdunJXXu3v37mjRogXq1q1rJY1JSEhAWFgYwsIJ8YmEtGxCVjlCTgVCbiVCuSqE8lX5vyz5t3w9qzwhI4eQmEKIiCR4PB7ExsYiNzcXNWvWtAYuXbt2xamnnopRo0ZhwYIFVlW4xx57DG+//Ta++eYb/PXXX953XHpkQFG9enV7qV6j6lWi01gVCSlpKdZq2bfffquBd8VATV75f+RoyoQJExATH2NnGdMjckXXmaxksmbIxUVmJGKUMsB67bXXsHLlSsyYMQO9evVC8+bNULt2bZQrl4fEpHg24jCks3lXb0ho2o7QqR/hlFGEoWcTJl9AOOc6wtI7CRevJlz+COHKxwhXPc56gnD1k4RrnuL/suTf8nX5/hWPEi55gHDhKsL8FYRpFxFGzCacOpbQdSCheSdCbTbdXO5oI6M8iIuLQU5OFmrUqI4mTZtYgwDJnSArB88//7xl/DLjl89V3M543bp1CI8KB/Hn0u2hEJeskPHgWZLhyPK8nLjQfBTFR01esZAZlASzSNIJOZNKC/I9bKrjS1Y86hAaNGhw3I5IUgLv2LHDOpYoHde0adOs10VFRSImjmfWuYSKNdhYmxJancRmO8423kseJNz9JuGRTwiPbg2M7nuPBwc8WJh1JeG0aYT2pxDqnUioUpuQXd5eQQiP8FhHnaQ9SeCmbAF88cUX1nL/sUxfvievoRy+lpJN0HSdVe6XDO7Gs7hNxcbHWm1C2o9SMtTkFStN7Zlnnmkv0bfnh0sNvmTiwVFGTgY2bNjgvbL/Q2bp8nVZ6pbldqmXLUvskVFhyOEZcqvubJpTCVOXE86/3Z513/lGYA29qLrnHcL1a+2VgBmXEYbPtlcYKtUiRMcQ0tPT0apVKwwbNgwXX3yxtfUgsQQFDV+ukWwRSBZBbYMhKtmikYBLHujKqR7ZnpLBoVJy1ORDHDF4WWq1guwki50muSm5eMYdlRpl7RvKbF4C4x588EFrAFWnTm1UrJSHjKx4a7m9STvCsJmEC+4mrHiWcNdGwkMfmU002PTwx2z8bxNuXE+49EHC+MWENj3sGIDU9GiUr5DFZl4Dffv2tbYm5MyzBNw99NBDSMlKsbIIGq+vyt2ShDf8XIQnh1uxILJ1o9ntSo+afAgjS/RiQNGJ0aDO/IDp7Kl0kihgnpXXqlXLMrD09DTEJRKq1CG0Z+MayZ3YBXfZS94mc3S7HvyQcNnDhAl8rboN5uvUmJCcToiLi8VJJ51kdezhqeF2JHXBa6tyryS51hhWVUJaWpoV+CsBp4pvUJMPUWRfWPa6rCV6mcGrwftGMlgiO4K9z5mE2VfZQW5icCbjC1XJbH/Fc4SFN9p7+9UbEMLC+NrVZGnAXehIludl5SaDrKyacoLkl19+8fZSii9Qkw8xZB9U9j4lij4yIdLeg9clet9pIiuS0PlUe6/aZHCq/+qBD+zZvUdMvm++a6lyr2Qg5z37HpMUgy5dulj5GfydhyEUUZMPMSTRjSyHWcfkJIpej8n5VpLAhWfxqZlq8kXV6s2Ell35usWzzvJeR5V7JdtaElxXnpCZlWkdO929e7e3h1J8jZp8CCHnlqdMmWIv0bfmh0yX6H0v2V/sZM9K5ey5ydRU/5UE6cnROzmCqKtKLpf0OSezkuzYlXvuuUeD6/yMmnyIIHvwksI0OiHaimDVztSPGs7iTqzTqWZTK0s9vIWw/D7CgAmEhi0JaVl2djuJFTD9fCA08wq+XuGsXizdj3en5L5yG5SBXFxSHIYPH26lTdbUtP5HTT4EOHToEG699VZkZ2fb6ULlqIrpQVT5RrNZVewEMaveMhtbWen+9wm9R9jbB3WaEirVtNPXnn2Z+ecDoRNl2yidJRHWpuupCm5J5jopUc0DzIoVK1q1FDRzXdmhJh8CvPLKK9bDReX4QdP63P6XzFraEJLYuCTNrMnYykoP8UxeEtXIefWVzxNOP4sQHesck7/lZUKF6ny9RLq65D7JPZWVw0RCo8aNsGbNGl2eL2PU5F2MRNJLxGqVqlWsIBdNFVqGGkvwRBP6jnZWkpvR8wkxDjJ5SZQTk8DXS0qImq6jKjglR+P4GaAKdsZDqWqpmesCg5q8i5E9L0kyEpYWZu8T635n2Uk6uQxCveZ25jeTwQVCTjJ5iarvcQZfJx4M0RTvdVMFv2R5XgZt6YRq1atZW4Vq8IFDTd6lSEKJIUOGIDI+EjSAHzg5tmJ6IFX+UyspsGFnuTOZXCDkJJOXingSI2CtMmn7DH7JJGImqxlZOTg6duyIt956S8++Bxg1eRfy22+/Yfr06aAYfuC6s+RYl+mhVPlXUn42kjBwotnkAiEnmbyUtQ2XqHppo6brpwoeSR8jZ9/zCNk52ViyZAl+/PFHb4+kBBI1eZdx8OBBXHXVVUhKSQK15IdOSqCaHkqV/yVBjuXsEqxOqSbnJJPvIitM/F6ksI/x+qmCQxJcd5Jd971+/fq499578eeff3p7JCXQqMm7iL///ht33XUXsrKzNLGIEyR7k23sxDg3rDMbXVnLKSYvUf8JSXx9arDkyKHp+qmcLVmelzTOdQlxyXE444wzsGXLFqsfUpyDmryLeO2116w0kZJWlc72PoiqwEqWMOMIfUabza6s5RSTX3InXxfJVS9n5HU/PvgkmesG8wA2zYPKlStbibZ09u5M1ORdwvbt29G1a1dQJj98bCgaSe8QyWArl9CgFeH+TWbDK0s5xeS7DuLrIjP5Yd7rpAoeyRZgG1YCoUWLFli7dq21Tag4EzV5F/DTTz9ZRWci4iNAp/PDpwbvHMm9ONFOJytlVU2G52/d9grhglWEpTx77jGUEBVtBwPKv+Xrt75sfp2/JH+vRkO+LpVZGjMSPPKeffdU8iA1PRXjxo2zKlpKPg7FuajJu4Arr7wS8Unx9uhaDd55GkEICycMm0l45GOz8flTU5exsctJCyos+frEJebX+UszLickJPPf78AyXS+V8yTL83L2PY1Qp04d3H777di3b5+3B1KcjJp8kPP+++8jLy/PDmDSfXhnSjrIWEKzjoQ7XjMbnz8lM+fz7yCcd1thyddv2WB+nT8kAXenTePZoIevyfh810jlXElg5Al8z2I9aN++Pd544w389ddf3h5IcTpq8kGKLJFJRrtWrVqBcvgh1JS1zlYzQkwc4bKHzeYXKpL8+ZVr8/WQ2BFddXK2jpx9zybk5ubiwgsvxK+//urtgZRgQU0+SJGR9IoVK5CQkACqxA8iz46003SwpApXBGHMQrP5hYouXk2IjOJr0TbftVE5T3NZ3QhhiWFo0qQJ7r//fs1cF6SoyQcpMpPfuXMnFixYYO/HV+SHcgRLjyM5U5KbnWdE1eqbzS9U1E/KyUqueqmlYLpOqsBKJgrSVusSohOjcdppp+GDDz7Q5fkgRk3eBTzyyCM44YQTEBEbYWWe0uQiDpTsy8uSfby9ZG0yQLfr4S2ELCl3LFH1WvLYeZrPGsyz95Qw6+z7pZdeqoltXICavAuQB3HTpk0YMWIEPFEeUD1+WHWP3nnqSwhnkx/GBmcyQbfrkgfsQY4U7rGOY5mukSowkolBaxbfnw4dOmDdunWa3MYlqMm7CKk8d9FFF6Fc+XIIywwDncYPraRWNT3UqrKXxE1kEFp2JTyw2WyEblafM2WPl6+BBHOZro+q7CXBdeMInsoeJKUmYezYsfjuu+/wzz//eHsVJdhRk3cZsnf2wgsvWNnvwuLY6KVITSgsjUosgnxO2fMVE+FZM0mtclkmN/287D1KWUz5mVNZEhgnebhNP+sryd+sbxesuXCV2QjdqjteJzSUGbycBCnr7SRpG2exJLtef1Y/lgyAQ72GvdS2OJmVTGjQoIFV9+KPP/7w9iSKW1CTdymfffaZlQUvLjEOJDW7pdKXm6PvxeDlc6azjiR+kVMHRxvgyBntaixZPo5kSTBYNksqo/nzOrHJhPPfm3yhcyrTlYXOvYmQKsfmmrHKuh3KgE9q1iew5F5HsKSN5LLE8EMxWFWeixO4LcaFo1u3bnj99dd1/92lqMm7mN9//x2rVq1CzVo1EZbKs3rJWOXWynTTWXL+WlYupFOXDv1oJj+HJR28ZF2Tn5V/S+13OaGQwpLZvb+MSP5WHKHlSYRVb5kN0W2SBDgj+XNLNT4ame9alJU6siRORWbv0h5kJUFWbqQNyPbBQJabB8D5JbEQch34s+fk5Vh13/fs2aOpaV2MmnwIsHHjRvTq1Qux8bGg5vyAT2W5rVMr+HnErI9m8j1ZMpOTzl/2JI98XZb6xYiasPw1GJJOtj4hngcYK9ebTdFXkmj2+98n3Psu4Z63CXdtJNz5hv1fGWDI1+/j7z/4ofn1vpIs1dfhWSOlsgIxyJQz3/nv8xHJYE7ut7w3iSwv+H23SQY33Xj2nhBuncZ56KGHdO89BFCTDwFklP71119j0aJFiIqN+t+ZelNH4BYdzeQlEFGKo8hMX2bv+b8nRpDBkmVlf6UIlsHIKSw2l5lXmk2xOBKjvn4t4cJ7+PddYVeZO3UsoUNfQtP2hAYtCPVOZDWzjbZ2U/4vqy7/W75en7/fqA2hLQ98TuFZ9gg2grMustPdXv2kPSgo7bbCDc/aRXEsM3VSVL2s/oSzZJYvAwHTz7hFMrDnzxkWE4YhQ4ZYp3F0eT40UJMPIcTsn376aTRt2hSRMZGgrvzgy+jejUuVRzN5MW/5uswqC3bsch14lm11/P4MwpvA4oFEfTZZkynm1+rNdhW5a54iLL2LMGkpoetAO6lOIn/G8Ai7yExcEiGJf2daHiGrMqFcLUKlBvxzbOg1mhNqtmRzZzOvx8Zfpy2hViv769XYeCvzoKd8bUJ2VUJ6eUJylr3SEB1r//7YeP5+NR4I9CKcyTPxxbcQrnyccPNL9iDjeIMAyVVPkuVOlolN1yMQknvdhyX3WrLvufVI35Gz74lhqFS5Eq655ho19xBDTT7EkOU5yWAlQXnh0eFWZivLdEwdRDDraCYvEdVshFaAXsElXOn42XitoL2Cs3xfSpaseVaVnlO4OIwY5orn7LK0kgK3xxl2YZsKbLJiulGs3Oo8QOhAaDWA0G0Mz8B5RjqYf3bkJYSJNxDOvpuwgE146XrCxa8dXxe+SFjEg4iZ9xIm898983I25sWEvjMJ3cezuQ/h2X43fg/cVmQwERHJA4IKhIat7QHH8FmEOVcTruK/KVsEBT9PdR5sWNfcSdHskkdCEvPIYG+092tuk7R9OdEQx+2kWzc899xzOHTokLcnUEIFNfkQRWrQL1u2DFk5WfBkeew69KZ9y2DV0Ux+MksCrsTkC0ZVi8m3YInJ+ztArDubNhvmOL7mD3xgz9anX0rocIpt6DIAkNm0qDKbZJtBhCH8s5NvYkNdTTj3acIFbOIX8etMxu0zvWoPAhavIZzzEOGs29nUlxM68fWpyQOipAye6ScQUnn2n1fZHpCMWUC4bq29CnHlY4QU2f5ozHJKzgYelFAdlkTa92a5qd2LpF3LwJ3vR1xSnDWgl+063X8PTdTkQxh56F999VXrTL0nmo1eDE72KU0dR7DpaCYv56V5Fip1sQsFW4nJS9CdlEGVI4f5v+dLSScsMRHxhIo1CSfwrFwq1MWxoZeTf/ckDDiHMPVWNvIXDMbrIC1/mTDzHsLp/LlkIFKpvr1tILP9Wnwta7K5h8fyZ5V0y05YEpfcCLVYfL0lCM2656afC1ZJe5cTJnz969evj3vvvRcHDx70PvFKKKImr+Duu++Gx8MmL5HGUpdeli+DvfM7msnLPrx8RjZUKxgp//fEfKuwxAAKfs8XkhmjXFtJH1qV5d1PF2PsxKY/bBlh1r22cZoMNRg07xHC6CsIJ/NMsiYPGmXgYu17y+qJBN7JilGgItmlLTRiyaBD4lHcFFEvz6ucDpEBjAxSiTBx4kSdvStq8qHOV199ZdWkb8ad8GiedWVJ1LnsU/ZiBXMneDSTl85QTFaMXLKf5R/MyBl2iQKXJDm+ysomxi6/V5Ls8OAinN9XQjqhSkM2womEabcRFj5pL4mbTDNYtXyDvaUgg5b+5xBqt7ED+iLkvkhiGpnZy6y6rGb3skQv8SdydFIM3k3pniWrowQR8rXN5Gs8mz9r796EatWqWNkv1ehDGzX5EObXX3+1RvtZiZF4fAg3BZ7JruGZVotybEZHzg9LsFSwzOrFUMU0RGImclRQIrvl3/n3XWUpXpKgyGxa9ujl84kRsxFZUeCSNKjgfn1xJSYis/ZOrBw7YK5iPULHYXZg3HJ/76U7TBe9yrP8Rwm9phJqnEiIl/sjkqXloSweCBivoy8kAz0xeBnYicE7YdvAV5IVp+aEyEhC82aEJx4n/PsP4ZOPCbVqedC5c2d8+eWX3ideCUXU5EMUOU4n2fDS0tJwFne6h7njE5P/h83wk0mEkY3s41OWUUrSEFMH4zTJDF06c5EEVUmnLkvz8m8x8CNGIgbemSWGLhHWEhRWnSWzPDlCJ7O+gr+7qBJzl6Ni8jvTuPNlcxdTO40HErPvt2e4JhMMFYnZS+T/qMsITbp7l/Nl9UgC4SR/gK/Pq8vgrj1Ltgzk70ikvySEyi9+H34dZPhLEtdRgZ9TbsejRhI+2Ez4+y9+jv+1desthOTkKMyePVtn8yGMmnyIsmPHDpQvl4euPJv9erpt8Pn1x3zCjT0JFZIIYbKELUFKTj9TL2Yu+edNEvPOvwQvZiyDF+n0xehlMCCdfUm3KMScxNy5043ka5bGv7PtYMLU2wjnP2ubm8n0QllyOmD2asLJPKjM4XYYwzN7j0Tiy1aRzL590dZk1t6OdaQdSIGcgmrK8tX2TFlI2tpJ/FzyILYCt7drrubn9Xd+br3mfkR7fyWcNY2QkBCNtWvXauraEIVbgxJqSE778ePHoyKb0bqhhH95tlPQ5EUyq3+FZwi9ZJYrs3qZbY1lmTqeUJXsh8pyc23JJkaoyIOJXtyxyqxdjb2I4uskcQmD2ZBrt+ZBkqzAyAmII3UFTNc9VDWBJYPZcG5nPAh/8QXCX4f5eS1g8Ef02aeEVq0I7du3twb2SujBLUEJJWQ0/8QTTyA3JwczWhIOskmZDD6/vuGZ/pRm3PnKkmcWS2as/HVjJxRKkkp2Eq2dSEjmGehJ4+yEMn4/u+5izX+MMGAeIbcGz+olCl4qC0rp4GCJC/GX5PPLc5dNiOHB5FgebO/4yt5/N5n7Ecn3772HkJgQh4svvliT4YQg3BKUUEKCcNq1a4ea6YRf5/LtL2DoR9NfPMt6eBChsSx5yl627GW65Ux9cSTL/BK4x58/PNpelpdgOpmJ6szdN5LrKNn6+swg5LHJS1yDZfZSDz4QBW4CLdm64AG5h5+7RjyoXL2a8OdBfi4Npm7SP38TBg6Upf3yVl4MJbTgVqCEEkuXLkVKXDhuP4VvvcHMj6dNPHvtV4sQJbN6WcYfxeKvGzsnt0n2bWV/N5UQz2rPM6spNwf3uXYnS8xe9ux7TCJkyPK9LONLsqIjJyJM98hNks8oJzRq2LN3ORb3ztv8HBqM/Hh6na9n1ar87Pbrp7P5EINbgBIqbN26FXm5ORhcl7BnFt/6AgZeVH0/k3Bee0KyzLAkc5wESrnp3HFB8We2ctlX48FNPKF+R8K4a4ueG15VOi3bYKfTlRz6sRKNL3nwJceBm9ucBAxKTAI/X8kphMXcBnd+e/zl+aPpwB/8O84lpKXF4cEHH9QgvBCCW4ASCvz8888YNGgQaqV78CbPDkzmXVxtGEFozh1upJypl71pOVPPXzd2WsEomUnJUikPaMJ4QJNVxU7fukxn7gGRzOwnrSRUbuxdwpeTEVJoxm1tTnI7NOHPGElo1ozw7Dp+3gzGXVzt/o7QuTOhSZMm+Pjjj709g+J2+O4rbkdKS8qZ+PT0dFzQifC390x8afUva+tkwvim3CFJ9L1kMnNLkBR/NitqvoZtKM1727nkdd898Jr7EKErD1Rly8RKlyvn62Xma7qPwSY51inHMKMJ48YSPvzA3lM3mXZJtOYZQmJiNObPn48//vjD20MobobvvOJ2JHVt8+bN0JQ7xB9n8y3PZ9S+0G/nEG7sRchO4BlvPHdSklVMzvIGo9nLe5ZjWzwYCvOed5ejXefzbMpkOKrASAr3jLvGPrIYIdkLJfmQzIC5PRrvq9Ml+Rn4uQnjz5KXR7j6Kvucu8moS6ODB+zEOdnZmXjttde8PYTiZvjOK25GZvGzZs1CZhzh0cFHPxNfWsnvfYNnV31qEjwSlCeFMiRoyNShOVVi8JJFrDIhhjvbE/vYFdZ09u5cyamGrmPsvPiUwZJ9bG6LxvvrVMlpDcmzEGEH173yyn8z1/lamzcRGjbk69a1K/bu3evtKRS3wnddcTMbN25EhXJ5OJNnOr/wDNVk0L7UzrMJ009kk5S0snLcbhArGDpdMXipkJZOSM0lnDpbZ+/BogtfIoy82J7Ve2QlSTIXSpIi0312kqTN8cBbsu7FJxAmTSR8vYOfI4Mx+1KH/iRcdikhMzMet9xyiwbhuRy+64pb+e233zBixAhUSw/D5vF8qw2m7A8dmE9YPYBnC2zyVkITNn1i8zd2dE6Q5C3vyu81iVC1qR3cpcfigkuy2jL3AULTkwkRkqNeiis5uc1JQGcr+/moz4OT++8j7N/Hz4/BlP0h2Qpo396Dtm3b4vPPP/f2GIob4TuuuBEZna9ZswZ5eXmY29p/y/THkgTl9a1FiJWgPKnTLmfqnTarl2I0bAgRcfbRuPmPmk1EFRxa+jyh3WnewjdSMphnx46KDeHnwjqOyc9DHLe5PqfYhWVMRuxvPfKwBOHF4KqrrsJff/3l7TkUt8F3W3EjEjk7atQo5CZ6rKpyJhMuC+2eyR0vm2eiFLlJZ/VkOcXopUxnPfvsu5zBlpSqJuNQBZekIFBfbndW9L0k0ZE4CycYPT8P1kmANDFXfp/8tW+/KfnZ99Lqxx8IHfnZbNiwIX766Sdvz6G4Db7biht5+eWXER8bhfM7EP5cyLe5gPmWpf5mU399NKFJDhuq7NU3ZInB8veMnaG/JX9XjsdlEpJYEj0vCVdMhqEKTkn9gMk3EbKrEsJk+V4SNgVqn14GGGexGnD7jyGccALhhfX+Da4rimRw8eADkiDHg8suu0z35l0K323FbRw8eBADBgxAgyzC++P4Fucz3EBJztR/NsUudBMtRi/lXQd6O0BTx+gvySoC/10Pz/LK1SKMuMg2BJNRqIJbsk8/7TZCnbaEcAnI68IKRO57yR1Rnts9G/zECYRPPvbt2ffS6PvdhFNOkZz4DbVKnUvhO624jXXr1qF8XjbmtSEcCvAsvqD2ziWs7ElI5Q7PIzMsqVNf0hruxRX/fRrASiFUqGunStUAO3dLjH7eo4TGJ3mDQDuyyiodriTokYDORIlkJ1x1JeGXn/k5MJhtoCSz+dX3y/uLw+WXX45//vnH24soboHvtOImfvjhB2sWXy3Vg50z+PYWMFmn6N1xdlBeuJyplwApOVPvz1m9dLh9+O9xR1+9md3xm0xB5U4teY7Q8lRClBh9a5acqDC1E19pDKsGISLSPvv+1puB23s/nmTboDsPgho3bowPP/zQ25MoboHvsuIWZE/tySeftNLXyl68yVydJCl0c3YLb1Aez3T8VnTEa/CSTaw2d/AzVnHHrwluQk6SOKftYB7oSTW7tizJymhqL6WRdztI2nNyMmHKZLuwjMlcnaQnn5D3G4mLLrpIZ/Mug++w4hb+/PNP9OzZE3W4g/l0Ct9ag7E6Tb/PJ9zH5l47g+CRzpdN3zrW5qtZPf8NMXg5A2/N4B+xl3BNJqByv85bR2jNJmwZvczofTmolHbbigeT8YSaNQn38GDyt73czg2m6jRJpH1/fg6l5rysBiruge+w4haeffZZxEZ6cEkX5+3FH09fTyecwh1jvATlVWSNZMkM3NSZFlXeWZUs0dfmzvech80dvyq0JJkMZUZvLd13YJU2GE/amSzPV+D2ywYvZ98/+5TbtcFMnSrZSnjicUJOjhztO19n8y6C77DiBg4cOGDtxTfKJnwcwHPxpdH3PBO6iAcoqdL5yhlnSU9aUqPn3ycpdT0phGonEGbcrTN41f8kS/eyR28F40lBpZIerxODl9wPadxuuc1eeAFh105uzwYjdbp+/YXQrx+hadMm2LZtm7dnUYIdvruKG1i/fj0qVayAhe0I/3DHYzLRYJCsQLx2JqF+Fs+0ZK9eztSXpLrYCO7AueMtX0uX6FVmSTBe425kH6/rzSrOFpH8rKSmlbPv/PrGjQnP8e/78yC3Y4OBBotkbz41NQHXX3+9zuZdAt9ZJdiRWfycOXOQEheOz6byLS1gnMGoL9nYpzUnxEVxRyo1w+Xom8yaTB1uQUnikWxCdjXClJvNHbxKJZI0xnXaeBPmFDUznvyMFF4qZxeWmTzJXp53avR8cSSDlLp15URAb+zevdvbwyjBDN9ZJdj54osv0KRJE/SqwR1NEM/iC0rq1MuZ+nQJkpJOuCjJTKQWPM/+E9IJpy/RTHaqY0tWeKbeZmfGs1LgSq57U7s6Iu/Zd2mP2TyQlLrvTjv7XlotX0ZISUnBhg0bvD2MEszwXVWCnZUrVyI+2oOnTuPbaTDLYJZsPciZegnKkzPHVJ0lQU6mGZcMAE4kRPKg4FQ2e010oyqKxOgn3+jNdS85G6Z721NBjWXxQDoyhtCzh332PdCpaf2hjz60TwcMHz7c28MowQzfVSWYkepRLVq0QKfKhG+4czIZpRv0y1zC9BaENAmUSmPJmfr8wVJi+t3to1EtTrVrjJs6dJXKJEltLEVtrOp1TVn5j9bJ/8vZdx4EpKcTpk2z08GaDNINkpK3sgWRmZmGb7/91tvTKMEK31UlmHnjjTeQkhCLCzsR/lrEt9NgkG7R7zxTlzP1VVK4w5Ul/JasIwlNhhE83EHXbqNH5VQl05Jn7TK1Vj36k1kSAyKZ8Vpz2+L2VpkH0nffFTxn30sqiS145BFCXl64VYZWCW74rirBiiS/GTlyJOpmerAlSI/NlUQ/zib0rkFIOBKU14OVSajUgDDnAXMHrlIVRVKPvim3J49E3PPAmXK4nbHp9+1D+OZrbn8GU3SjZDbfrRuhVatW+Oabb7w9jhKM8B1VgpX33nsPdevWxZgm7p/FF9QPbPSXcieUITP6MLtk7LhrzR23SlUcyUCxQj1uV2QXllm+nLD7O253BjN0s269RbYn0rF69WotQxvE8N1UgpHDhw9bNaDjYqKsWu0mI3S7DswnTG1GiIwm9JyikfQq30gC8UZcTEjOInToYCeJMZmg2/XTj4TcXA9GjRqFvXv3enseJdjgu6kEI7t27ULLli3RsTLhzyBLYesLSX16SZpTIZnQqCth0VPmDlulKomWvUToNpaQlkG4+SbC4UPc7gxG6GbJyYFzzpHVjAxr1VAJTvhuKsGIZLiLj43Gyl7BneGupNo9kzC4LiE1k2fzt5g7apWqNJLUt5XqE5o0IXz4Abc7gxG6XS++YOezl+p0SnDCd1IJRiZOnIi6bHDvj+dbaDBBN+vvRYRLefaeEEs4Zbq9vGrqpFWq0mrsNYTENMKwoYQ/fuf2ZzBCN0tiEU45RfLZN8WhQ4e8vY8STPCdVIKNn3/+GVWrVsGIhnZWOJMRulmbxhEyEwgNOhEWP2PunFUqX+gCnsl2HU2IjiOsWuWO1LXFkXzeiy6S2XwKXn31VW8PpAQTfCeVYOOJJ55AbkYSVvS096ZNRuhW/TqXMKAOIb0cYfx1OotX+V/nPESo0phwwgmELR9xOzSYoZv1Gl+DihUjce6552qUfRDCd1EJJmTJbOzYsaiRHmYVcTEZoVt1aBHhsq6E5HhCXylL+4q5U1apfCoeSE5aSUhIIYzmWX2oRdtL0GHnToROnTrpmfkghO+iEkx88sknaN68OQbV5Vl8iAXcfTKZ0CSHUJVnVbKMauyQVSo/SFaM2g2xz82vXcPt0WCGbtY1V8uSfQ7WrVvn7YmUYIHvoBJMPPbYY0hJTsY9/fjWGYzQzVrGs4nYOMLw5eaOWKXyp866g02+AqF3r9Dbm9/6CSEuLgzLly+3cnQowQPfQSVYOHjwIKZPn46seMJ3M/nWGYzQrfrqLEIqG/wJPQnnrTV3wiqVPyXJlnpMIsRwO3z8MW6XBjN0qyRf/8ndCW3atMGvv/7q7ZGUYIDvoBIsSFR9o0aNrMCzffP41hnM0I06vJAwpxUhJZNw5uXmDlilKgvNXk0oV5PQkwebbq5EV1B/HbbrzCckxFtbhkrwwHdQCRbefvttxERF4LqTCf+EUK56yWxXM92exV/4ornzVanKQrI3LyVpU1MJd97Bz+Hf3EYNpuhGPbvOToxz7bXXenskJRjgu6cECwsWLED1NMIbbHomMwykds0gvDCc8MhgwtOnEz6YQDjkg3S7kgdgXFNCbCJh1n3mjlelKktdsJ6QW4PQujXh6x3cTg2GWBz9sIfw7juE554lPPoIP0MPE9Y/b/9uJw0ivv2G0LmzqLPuywcRfPeUYEAeqtatW+PkatwpzOLbZjDEQOmtMYRe3OllJ9jlX1NiCI2zCZd1K33KXZnFZ8YTWvUnLNcjcyoniGfzA3jwGcft/caV3E4NhlgcLVtGqCO5H9IJ8dzWY/j5ycoidD+JTf9R5+TNlyX76dMJVatWxKeffurtmRSnw3dPCQa2bNmCqpUrYi7PHpx0dE7q2LetyDMb7vCuPZnw2RTCm2z6Q+qxOccR7uxbcqOX9LWDuPPLqkyYequhs1WpAqT5bL61+VmsXZvw80/cXg2mWFTddy/hNm7fb79F+Go74fPP7H+XL0/IyyN8sNn8ukDogdXyvlJw3333eXsmxenwnVOCgVWrVqFcZgoeGsS3zGCIgZCY8NXdCZFhhHlt/jv4kEQ9DXg20o4HAN/w6D//64qq98fx7IYHDx2GEi58ydzZqlSBkOzND+b2nypV6m7m9mowxNJIKsBddqld0/6aa8w/Ewht/5IH9DkRmDt3Lv7++29v76Q4Gb5zitP5559/MG/ePOQkRWCbg7Lc/SH13JvbHdGGkf/9nqTblVMA5RIJ69ik83+vKJLZ//y2hDQeKIy/3tzRqlSB1MIn2PCqEU49tfSz+YI6Es0uz9aKG8w/EwjJ+2rcmNC/f3/8+OOP3h5KcTJ85xSns2fPHvTp0wfN8kq/x+1L/TGPMLmZ3RG9YjB5ycons/xbe//3e0XRx5PI+rx123Fnt8HcyapUgdbJE9nocwnPPFP6BDlffE545227vOvSJYSMDNtQZfZs+vlA6ezphHr16mHTpk3eHkpxMnzXFKezefNm66Ga3oJvl8EQA6W/JJd8N0IEG/l5HWxjP/K9r7kjaJRtDwAu55/5uxiDE/nZW3hgkBBNGHGxuXNVqZygOQ8Q4pMJ088iHPiD26/BFIuqU/sRYmMJYfw8idq3J7z8srMi7EUPPSgBgvF46qmnvD2U4mT4rilOZ+3atdZDtXoA3y6DKQZSG0cTaqUTKqUQ7u1P2HcOYdtUwvim3BFE2iZ/SRd7/970epPk2FzfWoTsKoRznzZ3riqVEyTH6Zp05/ZfibDzW26/BlMsqj76kLDhJcJTT9pR7LVqEubO4WfqN/PPB0ofbyEk88Dm4osvtrYSFWfDd01xMhLcsmTJEqTzCF8i102mGGg9P4zQPI+s9xjLxp4RR+jInd5ZJxLCPYSVkuvb8LqjSY7NRfPv6cmDheU8kzF1riqVUzTqMns2f+UV3H4NplgSSeDdDdfbx+mcFHgnkkx/HTsSTj75ZKsqpuJs+K4pTkbOx/fs2RMd2DR3OzhfvczeZaVBatw/ONBerl/Uzjb8x4eYX2OSROhPaGoXApl8s7lTVamcpAWPE2rwgLZVKzvHu8kYS6LXXrVXCKQgjun7gdLv+3kAP01iBjKwf/9+b0+lOBW+a4qT+f33362HaVpzfrjm8+0yGKMTtfccQnvuoFqWtwcApp8x6Uv+2RrphKY9CEufN3eqKpWTJMfpenG7zc7lAS0bvskYS6Lnn7PTyEqefNP3AyUJMFy5UlYZPHj33Xe9PZXiVPiuKU7mjTfesJbAbznFnuWajDGQOryIsP0swq9z//f+dvC/z2ltZ6q7uZiR9ZKXPz2VMPJSc4eqUjlRkhwnlQ15wgTCnwe5LRvM0aSDBwiv8+tlCVyW6OVrkuHu062EIYMJERGE668r/LpAS2IHqlQhXHrppd6eSnEqfMcUJ3PFFVegcjLhxRF8qwymGGj9Mocw8QTCwLqEKc3tc/OdK5NVDvfsFoTfi1Etbz//7KhGbPJ5hMVaTlYVTOLZvGTAa9uWsO0Lbs8GYzRJ8tZL1rxevQjjxxNmzSSMGyslXe1I+0GDCN/tMr82kJK8+q1a2ufl//33X29vpTgRvmOKkxk6dChOLGfPlk3GGGj9uYBwdz87d71kuKufSRhan/DkEDvS3vSao0lS5Dbl2ZAs1Rs70gBJlmMl455EUssWgvx3mWTg46+bfl4VmurP7T0tjbCOB6gmYzTp0J+Em27iZ2aobZq1ahEa8UBXzP3BBwg//mAvj5teG0jJqsOggYQTTmiKn376ydtbKU6E75jiVA4cOIDWrVqhR3XCATZTkzG6RRJ9/zB3bPFRhGHLzJ1oWeqiVwhzHyKMvpJw6mxCp+GEljyYadbbLpbTeSRhIN+TcdcQFjxm/h2q0JJUSYxLIiw+184MZzJHN2kBt/9ataprUhyHw3dLcSpfffUVGjSob5VaNRmjmyR7+zN5JpOUYScYMXWiZaVzHiacNJZQqQEhkd+PJ8w+719Q4RGElGxC9RMIfWYQFj1p/n2BkkR9j7/OHoiUlRaW0TWYy22kBQ+6GnUx69Q5hAteML/WXzr3GULNFnYJ2tImxgkG3XYboWLFXDzzzDPeHktxIny3FKfyyiuvoGrlSrjyJL5NBmN0k2Q/XpLqNOpGOH+duRP1t5Y8Z5t1RnnbwE3GfjRFRBJyqhGGLS97czmaZKUhPoUQFVs2io4jnHae+b34Uuc/ywbflxAWbr4Xoma97J8zvd5fkpwOkuY2Morw2afcrg3G6CZtfEO2JxJw4403enssxYnw3VKcipRzzM5Iw9On820yGKOb9O44NkruHPucHZi68TID7Tj82MZRFMUmEXpNIZwXoIFKfp06lw0n2vw+/SGPhzD4XPN78ZUkPuL0JfbgxfQejigQJi8afQUhKdN5CWz8IYkXSEgIw6JFi7QinYPhu6U4lcsvvxyJcVH4dDLfJoMxuknLOxNSc+3lZVPn6U/JykH7M3g2GmM2jOIqNpHQk+9ZoAvrBMLkJ640vxdfSe5VuVrmv59fgTL5eY/Y2zy9e//vSJxbJTn1K1QgjB07Fvv27fP2WorT4LulOJGDBw/irLPOQgobj+xXm4zRLTown9CWO4uaJ/IMeI258/SXZNVA9t+lIEhBo5BZvQw8Wg8kjLvWLi0qkfXzHiWceTmhUVc7hkDMzfRamXFKAJ/p75aFytrkc6ub34evJCcc2p1u/tsFFSiTF7UZRKhRk/DBZm7fBnN0k3r0IHTr1g07d+709lyK0+A7pTgRqdU8cOBAnMAmYzJGN+mTSYRabJZtBps7TX9q8k123vGCJiGmX6ulvbJwtPz5svc+4hJC+dqFXy+SWuMzVplfWxY663buhPnadh/vO0mwm2mpXAY6PSab34cvJMv01lI4t5P8f1O2eExbLIE0+dPPJ+Twc/vQQ9y+DcboJkn1vfr162Pr1q3enktxGnynFCeyfft2tGrVCqfX51tkMEY36anTCOXYOAbyjN7UafpL560lNOxsnolXrGcfoRNzMb32iGSmPu1WQlpe4d8hwXsy81zKs3/Ta4NNci3OWEqIji38WSXb21S+DqbX+ULzHyPUacv3Kt+Ki2yLSKCmabUikCY/8x4eCCXx/1/M7dtgjG7Sihsk+C4Nb731lrfnUpwG3ynFiUgN+fLly2MZm5DJGN0iOR8v9eYTJEPe3YU7TH/JCuDiGVecYRYvS/TTbjO/ziT5XcOX82dILfy7krMIE1eYXxdsksFK/Y6FP6MMkiR/gD9Ntevo/xq8/M3WA/ge8qDDFEsRSJOXlR8JvhszhrB/H7dzgzm6RS++wIPZcMJjjz3m7bkUp8F3SnEiGzZsQFRUFB4ayLeogDG6SQcX2KlwE3gmv7QMj55JAFeTkwqbQ3gkocsowoXFfC+yKtD05P8a0RF1OMP8mmCT5C+Q2XPBzydfG7zI/BpfSLY8Cm4RZFW2Z8yysuA0kxfJefmuXQnffM3t3GCObtGmTYTsbM1h72T4TilO5I477rCyv23kGYzJHN2iXTMIPasTqjQ2d5b+0gxZUjXsLYt5FGcWn1+yZywZzwr+TpnVXfCi+TXBJDkxUPCzifJq2Il3TK8prSQQs0Gn//69mHg72Y3MmJ1q8p1HEKpWJWxmEzSZo1skefpPOEFWLcZ4ey7FafCdUpzI8uXLUTGZ8OFEvkUGc3SLPuLPVzOd0P50c2fpL51yVmFjEJ3Q8+iBdseT5LOvWN/8eyU9ruk1wSJZqahwlADDzqPMrymt5HqeMp0QzaZ+5G/JSknjboTF3lMYTjX54Rfx++JBenHy2Aejdu0kdD9Joux7eHsuxWnwnVKcyKxZs1CXZ4BuPyO/YSRZpXRP4/83dZb+Up3WhY1BDET26U0/X1R1PbPw7xXJOXzTzweLRl5ib6kU/FyyvTHDT7EUM+8lZFf579+LSbQDHY/8jFNNXk42yKrOlTy4M5mjWyQJcQYMILRo0cLbcylOg++U4kRGjBiBVuWdW33OV7q+h7386s/I7II692l7Cb2gMchxLPme6TVFlSSDMe3Ly3ZEoNL1llaSG0DSyJo+V+1W/tmKuJB/p+QhyH/yQU4rnDTuvycenGryUv+gQl0evJ7G7dxgjm7Rb3sJo0dLbfkq3p5LcRp8pxQnIstf3avZe9Ymc3SLpPiOJFEpy6I0cvbdFEAm+/Gmny+O5KhXSpb5d0+/0/wap2v6XYVn1CIZFMmxx+MdMyyurKN6SwiRBcy72gn29c3/s041+cVrCY15kFKnDrdzgzm6RQcPEM4+mwfNSUlaV96h8J1SnIgsfw3kmcCPs/kWGczRLTqZDV4y3fkrcMuk/nPNxiBn5k0/XxxJJbJqPHAp+LsT0+0lb9NrnCwxXDFyU8EeGZzJAMD0utJo9v08M2z031m8HE+UrZSCGQSdavKyEtF2CL/vBGfWg/eVJLXtokV8D6KisH//fm/vpTgJvlOKE5HlrzGNCb+dw7fIYI5uUb1MO5BKArtMnaU/JPvjpixpXceYf744Wvo8oXnvwr9bErZIRLjpNU7Wsg2Eyg0Kfx4PX7+2g+3vm15XUsn1a93/vwYvOtrfcqrJy+CouxRd4sHRzz9xWzcYpFt0+WWEuLhIqzS24jz4LilOQ5a9ZPnr7BaEPxfwLTKYoxv077mEjFhCK+7UZd/X1Fn6WmIUTU4ubAqiQQvNrymOJCK888jCv1tMy9pPDmAu+5JIEhSZBkSSREiODJpeUxrJ74zl2W/+vyXZ9OY/av55p5q8qO8sOzvgx1u4vRvM0S266UZCSkoE3n33XW8PpjgJvkuK05CKTtHR0VjUjvAPG6HJIN0gKUwTxQYiR7BKemytuJIVg7p8XQuagkiKzpheUxyJifeebv79MhstbpKdgIpno51GmD+LBJX5Ol2vHIuTZfr8f8cqPzzT/PMiJ5u8nBiRuggv8HUymaNbdN+9hKysCDz33HPeHkxxEnyXFKcheevjY6OsdK8mc3SLtk2VvTw2xbPMnaQ/tOAxQvVmhU0hIpowyUdlUgctMO9hyxn8YIqwl9z9UmSn4OcQ9Zttfk1JJcV+Og3n68bt4cjfkCJBkpXwWCcenGzyMmiUIMy77uT2bjBHt+gZvj/ly4fjnnvu8fZgipPgu6Q4DVn2SomPxE3cUZnM0S16/UxCXIJtiqZO0h+SKP7KDQubQmwSYcrN5tcUV6efx8ZjKOLSoCPPVp8xv8Zx4lm8pKo1nUJITCPMXm14TQkl+9dSyldqBuT/O2KQE24wv+aInGzyk28kZJSXlK/c3g3m6Ba9vIFQtUoYrr32Wm8PpjgJvkuK01i3bh2yEyNwX3++PQZzdIseGkRISieMuMjcSfpDs+4lVKhT2BTkfZQ0nW1BDbvQbI61WxMWPWl+jdN0JBc/FQiAE4mBSoCc6XUlkaxuSK73/MF2cia/29jjb+M42eSlreVUJcyQY7AGc3SL3n+P23btMCxevNjbgylOgu+S4jRWr16N8skReFISaRjM0S26hk0knWdvE643d5L+kBQ7KVersCkkZ9pZykyvKa6kIp0ph70Y2cIgMXkZ8Mg1KfgZZPAigxhfnY0/WgyDpAcuyokLJ5u8DOjK84ByyBBu7wZzdIu++JzQoIEH06ZN8/ZgipPgu6Q4jbvvvhsVU8Kx5gy+PQZzdIsWtiNkVSrbJDFn32UXVCloCinZbPJ3mF9TXI282Fz8RmIByjIfQGnUc0rhY2yiSg0J5zxofk1JJIOJTG4D+f+GXDtJhlOUgYSTTV7OyksgYbduhL8Oc5s3GKQb9NV2QqNGHkycONHbgylOgu+S4jSkAp2Y/LND+fYYzNEtkiOCspw56z5zJ+kPSZ71cjULm4LUfffVTF62H0x16iXpz8InzK9xki58yZzhToIJu4z23SxeAuoksC5/ulw5rtdqAGHJc+bXFJSTTV4kWfo6diT88Tu3eYNBukFf7yA0buzBuHHjvD2Y4iT4LilO49Zbb0Wl1HA8P4xvj8Ec3aKpbHoSvT3Hh0Fcx5PUIC9vqKZm7cn7yOSHLTv6nnwwLNePuYoQEVn4/csWhJybN72mJJLkQAUDFCX9r2S8M/28SU43+Rrcxjt0IOz7jdu8wSDdoG+/sU1+9OjR3h5McRJ8lxSncdNNN6Eym/wLw/n2GMzRLZrYzF469+Xy7/EkUeGmcrBynnnqLebXFFey1BwdV/hv1Gtf+gI4/pYEurXoZ16qr9Gcv++jZD6S/jc177+/X/5mcY/mOd3ka7UitGtH2Psrt3mDQbpBUm62aVMPRo4c6e3BFCfBd8m9bNiwAbfffnvQafjw4ciID8Ncnvnd3se9al/RNvl5j5g7SH9IMqdVP6GwKUgxFJ+dk19oPifftAfhPIefk5ctC9NSvSyjn3GB+TXFlZX695T//n5ZspfaAcVNb+x0k6/blgeVFQhz5vBn44GtGzVrJiE3l9CyZUtjf+Z0SaCzm3G1yZ9++un2ESBJsBEdRDIYhFuVV/PoKUv9ITmnLsvmBd+HmMzYq82vKZZeJfTlTs80E5a9Zl8ePfO1JNJd3rvk2S/43iV24pyHzK8rjuRvyEqHFJzJ//vTeFYvZ+WLu9/vdJOX1ZtwHiAlJhCSElWOEt+TyEhChQoVvI7hTlxt8sOGDQPl8EN/GmtcEGksa3QIiA0+V5brHzZ3kP6QRDxLCdCCpiCSKmem1xRHkhtfznebfn+XM8sufW9JJAMgY8pfHrC0G2JnpTO9rjiSVRuprZ///L2senQaad8b02uOJaebfB2eyTeuR1j/AOHtNSonaQO3xb7dCZUqVfI6hjtxtcmPGDECVJEfejmHyx24ymFqbpcrnVuGe/KidjzoMxVdkQIypp8vjmSmfmKfwr9bcrD3m2V+jVM0+abCNdxFcvJAgvFMrymuBpxT+Penl7PzF8gAoLiSIjCmlQdZ+j/a7yzLIkGSG6E9a++n3NV+p3KS9n/B7fp0HnRWqeJ1DHfCn9a9SCAIVeCHXk3emZIjdNV8myK1KJJc+ZH5cqQfUbOe5p8vjhavtYOtCv5uOfstSWRMr3GKOpxR+H2LJOBOiseYXlNcnWJIfJOUaecQKImk/UiO+0K/M4NQtYn5Nef56LMURdX52nXk9rDv88Imowqs5J6cyQP+atWqeR3DnfCndS9jxowB8SyBuFM3mowqsOLOL7uMz8mLTOVMRZIJz/TzxZEUwEkvX/h3Z/Bg01fR+/6QDE7kPRZ83xKrIPv0pteURCaTL2uVZa6Cqk0JndoQ/viysMmoAqu9nxFGDiLUrFnT6xjuhD+te5E0i9ae/FSWyWRUgVVHNpaKvssZX1RJ1rnEdP77BTp/WfYtbZW4qfxZTFsBlerbe96m1zhBciLAtLctSX18+b5DyeSlFG/lBoQenQh/f1vYZFSB1c8fE07rR2jSpInXMdwJf1r3MnfuXJDk357MMpmMKrDqTUjhQdi4a8ydpL8kEdymSnQyax1zpfk1RVWfGYV/r0iOjPkqU5yvJdnlrMxzhhMBjbuZX1NShZLJy2BSVoeGDyxsMKrA64ePCP17Elq3bu11DHfCn9a9LFmyBJTGD/YklslkVIHVaYT41MDsVUuQXSEDYJNr1b/kgVkSOS9pTAv9XpaT9+MlP4AEvxV8z5KNTrY2TK8pqaR8reSq95VkP980OIlJsLcfTK8pq4REkkJZcg6cM7WwwagCr+82E3p3JXTt2tXrGO6EP617ueyyy0BSDWwCy2QyqsCKjTY6nkfT55g7SX9Ktgjkbxc0B0nOM7uEMQJTbiIkGArTyJK3E45zmSRH/k6eaA5ekyC1ssxhUBI5+Qid1MJPzyNcw+/RZDKqwOrbdwnd2hH69OnjdQx3wp/Wvdxwww0gCbCSWZvJZFSB1Qw2l0i74pmpk/SnFvNsTo43FTQHmb1KoFlxz7Mve4nQ7nRzpjtZCje9xgmSGASJFyj4nsP5vnQbw9eBBwGm1zlFTjb5kZcSkjMI968sbDCqwGvH24QOLclKmuZm+NO6F6nmRtIBSHISk8moAqtzWeGEjsPsGaWpo/SXZH988EJCjGE2LxH/0+8yv84k+V1nXm6Xqy34uySz2/jrzK87miSvu5xZLyh5TxesN7+mpBp7jXm5WzLQTb3V/BonyckmL+0rLpHwymOFDUYVeG17g9CiCVmnsNwMf1r38sADD4CkmpZkVzOZjCrwSuAHrW9g0r1aeeyb8XsoYBCiRl2pyHnUJd2r1A0v+DskkO+EnsU/l32aXJcCv0sk2QGlHr7pNSUSD07EDE1/q0FHu+Ss8XUOkpNNXoIMo6IJX7xe2GBUgddn3P6b1CfrFJab4U/rXtauXWunzxzBKmguKmco185OFojjZTIDl7Kwko2uoEnI1yQITyKkjxYVL1+f84Cdn9w0G5ZMceOvN7/2WCork5f3bjobLxLzNL3GaXKqyUvwpmx3yPbNvs8KG4wq8PqIB7G1qhEWLlzodQx3wp/Wvbz55puIiIgADeQHv6C5qJyhWoRqTXlW/Zi5sywLtR1s3ksXSTS2pE6VpWupez/vYbveuQTZdR1tG7npdTJIkNeZ/t7xVBYmLwOUXtPMKWEl0t7JhXTyy6kmL3n+pSBRWkphc1E5Q++uI+Tw83vNNdd4HcOd8Kd1L5s2bUJKSgpIliQLmovKGWphG2lZZ73Lr3mP8my8nTmJjUi+npprB6jJEbkKddncJf+C4WdFErQm5+JLapRlYfKLnjpKMRqWxEg4uZBOfjnV5OWYXr0OhBMaFDYXlTP0+pOEhHjCqlWrvI7hTvjTupePP/7YqjBEnfjBL2guKmeonz3rnXSjubMsC8msVvLny7L70Yy+qBLDacGfSZb5TX+rKCoLk5dthARD1j/JBCglX02vcaKcavKyFSJ59ceeUdhcVM6QVAYM85C1retm+NO6ly+++AINGjQAncgPfkFzUTlDY1jRhH6zzZ1lWUrSkLYdYkfJm/bYjyUJsssobx8HlON0pt9fVPnb5Je/YlfiK/Q3+DPX71h2yWJ8Iaea/JSb7fd182WFzUXlDD2wkicYERHYuHGj1zHcCX9a97Jjxw60adMG1IAf/ILmonKGpK4Am6rMfk2dZVlLjqhJWdVmvQmxifzeCpiHSVLxTExz4grfHAX0t8nLNkKq1HQo8PvFlE45++iBhk6UU03+tMWEuFjCSw8XNheVM7TiIn4OUlPx/vvvex3DnfCndS+7d+9Gjx49QNX4wS9oLipnaA6rNqE8y9RZBkRscmKEsoQvnbUEUMlRu7yahKzKdj5yq074GXa6WgnGk0Ar4+8qgWRFwVQHXXKu+2IQISYucQgFf78cKZS/bXqNUzXzHsLJkwjdxv5XIy4hXPii+TVloTaDCXW5vWx5qbC5qJyhJbPJ2s7dsmWL1zHcCX9a9/Lzzz9b2Yxkpmg0GFXgJQlxWhOiEwjnlbICnErlFEn1OcmLLvnRTQajCrymjCI0bNjQ2tZ1M/xp3cvvv/+OKVOmgCSrmclgVM5QT0IEm7xkdTN1mCpVMElWdeKTuT2ziRzcXthcVM7QqT0IHTt2xLfffut1DHfCn9a9/PPPP1YlOk+4BzTPYC4qZ2gEITzVLtNq6jRVqmCS5FRIZpO/5oLCxqJyjhrXIwwZMgQ//fST1zHcCX9adyNFamLjYrWmvJM1neDJIpzo4JrrKlVRJVUV83IJT99V2FhUzlE6TyymTp2KP/74w+sW7oQ/rbu5//77kZaeBhpmMBeVMyT78tUIVRqX7ny5ShVoyfFEieyvW4Pw5cbCxqJyhvZ+RoiMJJx33nn4+++/vW7hTvgTu5sXXngBFStV1Kx3TldvOxHLqMvMnadKFQyaea99+mJof8K/uwqbi8oZ+mA99zcJsdZKr9vhT+xuNm/ejFq1a4EkhafJXFTO0FS7tnz3CWVfdlal8pWk4FFcMmHVdYWNReUcPbOKkJWZjtWrV3udwr3wJ3Y3EjnZtGlTUEODsah8o0WsISwptypVzSp6//90lnzP9JqCWsjKI9RpHZiKdCpVaSXn8juNIMTHE3a9X9hYiiKZ/UtO9W7t7TKoD99i/jlV6XTjJXJGvgLWr1/vdQr3wp/Y3fz555/WMQmq7DUSk8GoSq75rDYsqdufy+KOyZJkVJOvcWdFC1im1+bXeaxOhNgkwgweZZs6UZXKyVrI5ixFjHp1JRz8qrCxFEXfbSL07U5I4edAMveJGZl+TlU6zZlMqFu3Nj788EOvU7gX/sTuZ/jw4dYskc72GorKdzqTlcqS6zuJtdgr+X/5WgZrHMv02oKS4MgYwoB55k5UpXKypt1OiI4lXMED1r93FjaWouiqJYTa1QkDJYZITd4v+ofvTf+ehObNm+PHH3/0uoR74U/tfpYvXw5K44dGiqGYzEVVcg1mSU3ypiyZjR/5uvx/M5bkfx/u/drxdBarIqFOWz1Kpwo+9eCBbbkcwsuPFjaWoujz1wkVeGC8fD7hEll1VJP3i378iND2RELv3r2tXCpuhz+1+3n88cdBcfzQiCGZzEVVcsnASUqWFpzJT/R+rQprOsv02oKS/Xt++JIy7dzqpo5UpXKipP5+lUaE7h0JO0uwH//Tx4TBfXig0Nnez796KT8LavJ+0YcvEOrXIsyePdvrEO6GP7X7+eSTT0BSJ7yn10xUvpPMOE5iyWxe9uHreSUGX5U1mpV/hn88nUqISuEOjw3f1JmqVE7U2XcTUnhwOpsHun99U9hYjqW/vrVL0mbyYPnIKoCavP/03Gp7xeSmm27yOoS74U/tfiT4LjY21g4QK2q0t6romsmSa8vmTFksKQgkgUNi8uNZMrM3vc4kqUrHr6/XXqPsVcEhmcV3HEYox8bx6uOFTeV4emedXbFu1gTCP96z9Wry/tMdVxMS4glvvPGG1yHcDX9q9yMZjerXr2/XlT/HayYq32gGS66rmLscmZvLkmss/88zG2tGL0v3ptceTR0IyfzasfwwmjpVlcpJmn0/oWJ9e6n90I7CpnIs/fkVYfAphNbN7D35I19Xk/ePJCBy2TxCeHhYSATdCfzJ3Y8EV5x66qn2+W2ZdZqMRVV8yTJ8P1YUq6v33/m/140l2yQ9vP/O/9pjaTLBE8Ozo+H8QL5k7lhVKidIAkRP47YdFUu474bCpnI8PXILoXwuYeXFhH1fEA5st3W5rH6xyV+3zD6O9/e35teriqf92wiTRxJycnJw+PBhr0O4G/7k7ufff//FBRdcYJecnZLPTFSlk+Scl0yC3BnRad6v5ddQlnyvLas42ySyz99EA/BUzteS5wg1TyQ0aUD4YUthUzmeFvOkIyKC27s8J0dRUiJh9Urz61XFk9T379yGcPLJJ7s+Z/0R+JOHBo888ggiIiOKfpxLdXzJbENm6x6WGHnBmXwnVhiru/ff+V97PA1i8exIKnqZOleVygmadhshlicPS+cQDn1d2FSOJ9nDv4QHwBct+K96y3PFBi81z6/k5+GTl82vVxVPsiVSqTzhnHPOsSZ/oQB/8tDgzTffRLly5exI8IKGoiq5RrEkB4EE2kkCD9kOEfVmJbNkX74k+Qlkr7+KfSxp6XpzB6tSBVKyVN92MKFaZTbrxwobSmmke/L+0WtPECIjPLj33nu9zuB++JOHBl9++SXatWtnH+8ymYqqZJIZusy6y7F45m3twYvk/yWP/Rms4s7ij6g7IY4HEFL0w9TJqlSB1Kz7CRk8KxzFRn/gy8KGUhqpyftH11xg78eHSmS9wJ88NPjll19wxhln2GlWS2o6qqNrKkuK1PT1Svbop7FMP1tUTSeEJRFO7GPvfZo6WpUqELroFUJvbt+pPAh9/PbCZlJaffiibfAfbzB/X1UyjeZ+qVmzZti6davXGdwPf/LQQCLsFyxYgPD4cM1hHyySwVgLQhIPzPQ4ncpJmnWffWyuTXPfz+JV/pFU+GtUjzBw4ED89NNPXmdwP/zpQ4e77roLKZkp9hluk6monCc5DZFKqNtWj9OpnCHZiz9pLCExkWfxdxQ2E5UztXszISMtDHPmzAmZyHqBP33o8Prrr6NCpQqgzgWMROVcydG7doSIWMKEG8ydrkpVlpKSsnK8s9/JJTs2pwqMnl9NyMlOxI033uh1hNCAP33osHfvXjRu3BhUh41Da8sHj2Q2n0eo2YJw3lpzx6tSlYWWb7BT2OZlE566214CNhmKynlaOpdQtUp5bNy40esIoQF/+tDCqi0vkeClDQpTlZ1kb747ITbZLlwjQU+mDlil8rem3GxH1I8YRNj/RWEjUTlTf3xp1+iXSZ5M9kIJvgKhxYoVK4pX41zlDEk+/FRC7VaEBU+YO2CVyp+SfA3tTuPBZqxdVMZkJipn6nO+fy2aknXCKlSS4ByBr0BoIUcnrMx3J7NpFKc6mirw6kcIiyb0m23uhFUqf2rcdYREHmhOHlXYRFTO1pp7CNkZhNtvv93rBKEDX4HQQpZqqlevbuVGpwUFTETlbEkmvaqErMqEhU+ZO2KVyh9atoFQvyOhakXCW88UNhGVcyXFfa69kBAVFY6PP/7Y6wShA1+F0OLAgQM4/fTTQTlsGFK73GQmKmdK9ub782w+wQ5+ulCP1KnKQHJk7nRuf4lJPLicbleJM5mJypmSynNjTidrcidJ0UINvgqhhZyPvO666xAWGVb8OueqwEsq37UiJKTZHe/yl80ds0rlE7HBT72VkF2F0J1n8nLW2mQkKufq+w8I9WoRRo4ciYMHD3qdIHTgqxB6PPfcc8jMzLSLqBQ0EZXzJcVrMgiV6hPmPWLomFUqH+n8ZwnNehGSeBb/8qOFDUTlfL3P9zAmJgzXX399SCXBOQJfhdBj27ZtaNu2Lag2m4XmsQ9ODSCExRPaDLbPLps6aJWqVOJZvBzZjEsgLDiLcLgEpWRVgdfliwnly5fHSy+95HWA0IKvQuhx6NAhTJw4EWGpYXYwl8lEVM7WfFZLQnQiYcRF9r6psaNWqUqomffaZ+J7dCbseKeweaicLwm669KO0LVrV+zcudPrAKEFX4nQ5JZbbkFCRoJdLc1kIirnaxrBk02o1pQwe7W5o1apSqLz1xFO7EvIziKsZbPXzHbBqe1vEqpUDMfUqVPx119/eXv/0IKvRGjy2WefIa9cHqg1m4Uu2Qen5Agkj9LDwgltBhGWPm/usFWq4kiCOU+dQ4iJJ7RoQvjmXcI/avJBqVXXEsqXS8Lq1au9PX/owVciNJHSs9a+fDU2iln5jEMVHJLgOzZ4TywhLo6VSBi0UJftVaXXpBsJaXncvpII4ZGETjwReO5+NvqdhU1E5Vwd+pow9UxCxYrl8M0333h7/tCDr0bosnjxYitVKo3xGocqODSVVZXFHXDbtoTHHyN07EjIrKiV6lSlk5zWkO0fSmcNYnG78kQTKlcgrLxIl+2DSbIC05UnAl26dLEmdaEKX43Q5c0330RUdBSoFz/MumTvfM1lyb3iWXtuLmHpUsLeX7kJ/0vY+gmhQX1C+dr2uWZTB65SHUsLeLDYoBObOs/g/z9WR1Jfj2RVJkRGEYb0IWxe/18zUTlTzz9AyMogXHvttd4ePzThqxG67N69G02bNrVLz0rd8oKmonKOZHleUhHzrKphA8ITjxMOH+LmywZ/RPK1vDxCrZaExWvMHblKZZKkrW07hBARx22sC6vgoH86qzGLjb4Jt78n77Ijt03mogq85N5ctEDyGyRi06ZN3h4/NOErErpIitsZM2aAYvnhne19mFXOksykRrGqEKJjCIMHETa9z802n7kf0cEDhMsvJ6SkEtqdTrjgBXOHrlLll5QuHnIuISqe25ks1UvFQ1NblK/3ZPFMPzebBwbzCL9u5bZnMBlVYHXgS0KnNrKN1xE//PCDt8cPTfiKhDaPPPII0tLSQKfme5hVzpDUFujMYnOvWJFw/XU8Qv+Lm6zB4I9IjP7cRYR47oi7T1CjVx1bEkk/fDnP+DK5ndVjFSUIdwqrMiuC0JmN5P3ndFbvNH32Kk8KosOxfPlyHD582NvbhyZ8RUIbOUrXvHlz+wHXfXlnSO7DJFZjQlgUz8rbEZ55hvDvP9xcDcZeUL/8TDilN3fcGYShF5g7d5VKdNbthHK1uK1xW7ECOk3t0SRZvmeD9/AAtF5Nwp1XEw7t4PZnMBxV2esafu7z8vKwZs0ab08fuvAVCW1klDd27FiEZYYV7yFX+UcSGyFBT9zpJvBsfALPxnfzQ/vP39xUDYZ+NH34AaEtd8KpOYSJK3nG9oq5k1eFpuSo5fxHCRXqslGncHs7w9v+iiPJ0zCElUpI4d8xaQThp4/1TH2gdXA7oUtbe6l+165d3p4+dOGrojz55JOITY7VgjWBlsyO+OG0Zkf1CHfdVTi4rqiSWf+bGwktWxIyKhJGX6kV61S2xOBnrCJUb+Y1+IEsqW5oapPHk6w6TWY1tFed2p5IWHMP4W89Ux8wvfaEZLmLwMKFC709fGjDV0XZu3evtbRjRW9LTnTTw6zyr3jGbp19jyB070545+3j778fT2L0G98gVK5CyKlGmHyTudNXhZbkLHzt1oRwCbjtzyqpweeXxI9IVD7/zkrl7eXiv77hdmgwIZX/JAmLLuP7mZYah1dffdXbw4c2fGUUYdKkSaA0fkjFbEwPsco/kojl3jyjSiVkZxOW8Nd+2MPN0mDaJZEY/QvrCQ15ppVViY3+Rp3Rh6pkBj/vYUINnm2HJXK7O4XlC4M/ItlqGsHidhYXTzjjVMKHL3A7NJiRyj/atYnQiwdbTZo0Cdlc9QXhK6MI69evR3x8vH1ExvQAq3yvs1jes8fNmtnn3Iu7914Uye9c/zyhQQNCLs/oR1+hRh9yYoM/exWhVis2eEl2Iwa/kGVql6WV5HSQo3jRhDo1CE/drUF5ZaUXeRCXzhOGyy67zNuzK3xlFEFyG1u57HkUbnxwVb6T7GNKFrGqhIhIwpDBhM08AjcZtK8kRv8iz6qqVLH36CetNBiByrWSJXrZg7eW6Puw/J386siZesnOmE24YC5h32fcFg3GpPKdpO5/dnYW3n33XW/PrvCVUQRZ2lm2bBkioyLtQBrTg6sqvWTvUlKHxhAqSD5wNtuSBtcVV7J0/8brdjCeRN0PvZA73/VmU1C5Q5LJbsrNhPKS1VKC7AawJMGSqW36WjKYlRM7lXlwwbP61jzI+OhFbu+6V+8XHdhOKJfLz/XQofjll1+8PbvCV0c5wssvv8wzvSqg9t4H1PTgqkomuZ4S79CQZ+9RhE5s9M89W/Sz777Skah7KWwj5+g1YY57JccmT19iB11a8TZScMaXe/BF1UxWO1YcoXZ1wi2X24ZkMipVybX2Pn6mExNx0003hXRBmoLw1VGOsH//fnTv3h3EM0xNc+tDycxJzr5nscHzjGbSJMLXO8re4PPrww/thDmSGa/96YTz1pqNQhWcWvYS4dS5hESpJpfNGu5th6b2WRaSM/WDWckS+W2fqd/3OT8DeqbeJ5KMg2cOIdSvVw9btmzx9uiKwFdIyc+qVavsXPZydlZn86WXnH2XYCc2dzn7/sBqwl+HudkZjLesJZnxJAVuCs/ypKiNVq9zh855mNDyVEKkFJtpyJIAT1PbDISmsRoRPFGEExsTnr6bnwdNiVtqvfesvUoyYcIE/Pvvv97eXBH4Cin5+emnn5Calgo6gR9GPTNfcskAaSyrKndoEYR+/QhvvxXY2btJVlGby+zqdeVr2fXo5aiVyTxUztecBwh12hDCxeBPZEkMiKl9BlJSMrkrK55QLodw1RLCH19yezSYl+r4klm8XMPYGLK2XJX/wldJKYiMBj3JHjt/uukhVR1b81hyRCmDkM46b7GdmtZksk7QnwcJjz9GqF+fkFmRMGghYenzZhNROVMSYDf+WkIlnrl7krntdfe2Q1P7dIIkun8oqzwhIZEwYiBh6yvcHgsYmOr4+m4ToVsHQuPGjXHo0CFvL64cga+SUhAZDWZlZYE65nsoVUWTLI3KcmQkoUULwppiFJYJtD7dKvmu7Zz5bQbxrHC1zuqDQYueIvSc4q0kJxrGCpatNllpkDP13kx56+7VoLzi6ulVhPj4CKxYsUKX6g3wVVIKsmfPHvTr18+uTCUBM6aHU/VfSdSydK5S9z1ajrEQPvqQm5fBTJ2szz8jTJ1KSGSjr8ad74iL7FmiyVxUgZUMwCSOolE3QqTUgq/JOpNlap9Oljfroxzxy8kiLJ5B+EXr1BdZklmwbt26+OCDD7w9uJIfvkpKQWQ0eMsttyApLck+V2t6MFX/k3RScvadO9qcXMLNNxH27+OmZTDRYNAfvxOefNLOkJeUyrP6wXYyFa1k5wyJuZ/7DKHP2fbs3Soy08PbDoM1WFYi/6VOfUUeJMdxm2tuL98f/prbpMHYVLY+5WuUmRGOs88+GwcOHPD24Ep++EopJr799ls0atwIJEk0nLy3F0hJhzqOVZ9nUjx779KZ8DLPek3GGWySLQaZ1Z89nZDBRlKJP+Pp/JkvfNFsPKqy0UXcqY+/jgdgHb3Z6+qxRnvbYsH2GYyaxZI8HQmE6lUIKy/WoLyjSQLuZNWjYoU8Ky25YoavlmJCZvNz5861akXTKO8DqPqfpFOVc79ZPJMKZzPkWdV27oyCZf+9qPptL+G++wi1axMS0ggdhtl7wCYDUvlXkrSo32xCenl71Yi6sdyYz+JInfoUQmoyYeJwwt5PuT0ajC6U9dVbhHYtCF27dsXvv//u7bmVgvDVUo7Gtm3bkJySbB/F8Vcxi2CUnH1vac+k6tYlPPQg4dCf3JQMJukGycBFkvfIQCYrm5Bd2TabhU/YM0uTIal8J0lUNHw5oWoTQoSYO7c5K3uiW2bvJslnk5S4UqeenzPrTP0qLV97RDKLv+kSQmJCOB5++GFvj62Y4CumHI0///wTY8aMAUnVKnngTA9jKEk6HglsqsodTwRhMM/k333HfbP3o0ly7K/ijrZrV7uUaO1W9nE7zZbnH8nWyOjLCSf0JETJufcclszeQ2n7TD6rfGbugzLTCVcv1Vm96KePCV3a2cfmNE/9seErphwNWbJ/5plnkJ2dbQWWGR/CUJGsZEj1rgxCEnc4i/jfTj77LrrzDkKH9ma1Zw0YYKe3Nb32aPr7L8K3PJuS3y0V7RJS7Gx5E2/QKHxfSVZHZt9PaMbmbgXWyd57Z5YEppUk97y8TmJrpMJkQUmpY9kHN73OKZIz9Wewcrm9JRBGDiJsf4vbYwHjCyWtu88+NnfllVfi77//9vbYigm+Ysqx+PXXXzFs2DCEpYWBzs734IWKZPYuZ9/r2bP3FicSXuQZlskAnaZHHiacwZ1jQXXpwjPDKEI7ngl8uc382qLo55+IOxm7Fn4sz+zr8u8bezVhyXNm81IdW5JvXo7EteLBV2witznJO9+aVdrnTjIvyu+TI7ENCqgNy4lZ8UyS0wOSiZMHPZn8WZ7lgdDv0n4NJuhmHfzKTn7TqlUrfPbZZ96eWjkafNWU4yF7PikZKaCT+AFz8z5gQcmsiU1RSmXGcccyYgRh6yfcZAyGF0y64Xp7NeIc7jRLW+ZWtio+3kJYuIBQrZo982zehzBsGWGplrEtkpa/TJjA96TjMEIWtzVJ92oliJGoeV9UjROTly23Vvm+FqySVNtypj6NrxUbvdRP/4Hbn8kM3ar1DxDysqNx7rnn4vDhw95eWjkafNWU4yGRm23btrXMzlHFLvwp6Uzk7DvPgFK5Q7n1FsKvv3BzMRhdMOnHHwh9+xJycwmvsLmYfqYkkhz4YvZnTePfncfXLItQpTGhPw8k5j9qz1JNBheqEmM/92keOF5CqNeeB0d8vcITuM1JUJ3EffiyboSbTF4kA5+JrAqEmDhC+5aEbW+ERlDe/i8IY04nVKpUAVu3bvX20Mqx4CunFIUHH3wQETER9ijazbN5ScohnWIdQnQMoWsXu/66ydiCUc8+axej6XGyf6rhycx+x1eEK6+wa+an8QApmWdcnYbbS/mLnzGbXqhIVjem3EzoNYWQXYUHkdHc1vh+UFvWBJY/nq0jJi/78nI0TerKS+lZOSVi+vlgkRS66cDigXjl8oQbltsmaDJHt+jFhwgVyxFmzpzp7ZmV48FXTikKu3fvRosWLayCEq6tTicdbH9WNs+qIgnz5tln301mFoySY36yrB4WRrjrLvPP+FLffE148AGeeYxho09mZfKstR1h8CJ7dh8yefH5cy5eQxh1qR0pL0WAwqK4ndVmSSEjCYzz58D5yJ48t2nigStFsGRLQALveFYY1IN2CYiVfBUSEJtg16nfI8GkBoMMdv25gzD1TG4/mRl47733vD2zcjz46ilFQSI4b775ZiQkJYD65XvI3CDp5CS4qZW9ZFq7FuH++wgH/uDmYTCvYNX3uwk1ath757t2mn/G15KZ/e/7CVs+IpzP19mqdJdDSOGBVB2evQ7ggdSMVfYM3y3R+ZL+9/x1dtnXYTy7PKEHIY1n6zGpPHOXYDoJHjuyJF8WBis13HlwYWVnnMGS47ASXyOJrkQSdxLMRi/vfTKrASEijtC6mR2Ud9hly/cfviCD5XBMnz5dk98UA756SlH55ptvrOxKnvIe5x+7KapkeX4kS+INPIRTeGb1wWZuFgbDCnbJrNrDn3HObHsP3fQz/pbEBNx9N2Eszy7jZTZJdiR57daE7hPs2e6Cx4MzyY7M1qXcax820sbdeGbJs8vwcP6MYSyevVv55SWmxQmGKu9BZsDy3hqx3HD2XjLldWHxNU/mNnXHVYQ/XBJ9f4hn8WeeJoP0Gnj99de9PbJSFPgKKkXlSOGa6KRod+zNy1Kft+57bGwsYmJirKVlOQtuMqhg1j9/E/r1I2TzDPrppwOfwEeC/uS9dO5KmHcuoWlzQlQMIZFnulXYdJrzfenLZjn5RsJ5PCuWJW+TsQZSF6wnTL+LMJDNRYr41GxBSM0hREYRqtckTOQZ9Nlz+TNJfXc5qmZqg4GUnD+X5XsZgAT7/vwR9ZWttnAkJCQgJ9OD+XwPfviI25zBOINJr/HAt0rFcEyePFkL0RQTvoJKcTh48KC9Ny8dgywDmh40p0sGJ9Kp1SXEJcbh5JNPxqZNmzBv3jxkZUZh5Qr3pal9/XVC+fKE3jw4+2GP+WfKSjLgkKX7dB5c3cwmuXsf4eufCBs3Ey67htCJjb9mHUK5CjwbTrVn+uVrE1ryIKXfHNv4Z99HmP+Yvcy/9HnfL/VL9LuYuGTzk5UFWXqXM+xDeEDS7nQ7xWw8v7cENvCccrapn9iSsJDb1/P8+u18jb/7jbCNO+gaUgK2Ostpq18SpS6Jdvg6B73JyzN9Jht8ajgGDBiADz/8EJMmTURmZhqaNya8vcbe0zYZqNMlGf5kFp+Xl6Pn4ksAX0WluNx7772IiY+xlx9ND5yTJbMXfmBkkJKTk4NFixbh+++/tz6X/Ldbt26oVYuw4SX3pKuVKPrzuBOU4Lerrzb/TFlKYgN69SJUqkL4kv//hz/+q117Ce9+Qnj4KcJFVxImsQH17EOoW59Nnz+DVPyT4DWpd9+wM6EFm3/H4fwzUwgDziEMvZAw7lrCtNsIs+7lAcFqwtwHCfMe5oHBI3bZ3LkPsXHz12fxYGH6nfY59eEX2Wl6e/Pf6zyK0HoAofFJbNInEnLZpKPjZcWHDb0GoUt3wugJPFhZTriHf+9r79kDlYKfRSQ/R5KWdgTL1CYDpaEsWa6XpDgSqW76mWCRbINUlQFVDbz77rvW8yz71hJHJLXWq1UmXLcsOFPiyrl4qbMv5WSV4sNXUSkuO3fuRJcuXeyMXGKapofOiZKZi5x9T/KgYsWKWLNmDfbv3+/9VDZvvPEGKlQoh44duUP4lZuHwaSCTXKkTcrgZnFH8dV288+UpV7nmW5l7nTFuE2mWFBi+p/wLOz19wnPvEi49ibC+MmE7jxQaMCztOwcO4NfRCTx4JOQmGYvm8tAIIc7/txqLDbmPJ5Rl2PJf/P432LcOfw9SUAjgXGyVSCrBjKIiIiw8yPUqsuG340wjGeJF/OA47F1hJffJny4jfDNz4Q9v5vfc36tfoLbnYclwW8SA2Jqm/6SPJ99WXJsToxcZryydy3/llz4MviQEyXyddPrg0HyXLeQ/P5RuOOOO6xtxSMcOnTIikSXPB+pKR6MGkz4WRJaGczUifp3F7c9vj/SX2lEfcngK6kUF3mIVq9ejYyMDDvQpaw7ruLqyNl37tiTk5MxevRo7Nixw/tp/oucIpCViqysLIzi2dxPP3ITMRhVsEhWIx7mWavMQMeNM/9MWUriHS671D7G9/izZlMsiWQgsOkzwnOvEFbx55Vl/7k8K5d98bGTCKP4s4tRn8Gz6WF8X0dyexjDM+wJUwkzePa/7HLCbffwIOIFwtsf2bPyohh4UbTlS0K9+tz+eJBlGaypjfpLYvJyBv/IETo5uidH6GSZPpsV7AYvn+9kQlxcnLUqd7T9aknPvXTpUpQvXw4N6hAevY1wQAa8BmN1kl54kBAdHY3zzjvP2ipVig9fSaUk/Pzzz+jfv7/dcUn2KdMD6ATJKF+O/PH7zMnNwXXXXWe992Oxb98+LFmyBKmpkVi6hLDvN24mBsMKBonJv/G6baxOODXwB5veSTwzzi1nm7LJFN2mHTxQHCPPiCyNl3X9BzFw+ZuyNC9BpmyI1jabzOTlfL7pNcEiGbwP5AFjchiGDBlinf45FmKSjz76KBo2bIi8HMLSOYRft3K7NJirE/TjFkKfkwiNGjXCli1bvJ9CKS58NZWSIkvb0bHRdhENiVQ3PYiBknRuM1ltCBFJEWjTpg2ee+45a/muKIjRDx06FBkZ4bj5ZjtYzGRaquJJtgtkVUFm0mJ+JlN0m2RF4MY7yaoXYK18mdqrqvgaSfBkeNC4SWN8/fXX3if32Pzzzz9WkK0MCjIz4jC4j33+/B/JG2Ew2kBJ3s/tV5K1xXDttddqpblSwFdUKQ3jx4+HJ8FjnzU3PYiBkMze5f1UIKSlp1nJI/bs2eN9x0Vn165d6Nu3L5KTI7BurRq9L3T1VbbJr7jNbIhulZwcaCJJcCSSXdqnqd2qiq6pkljIY83KS7JXLbP6q6++GpUrV0aVih7cez1h3+fcRg2GGwhJLv4WTbjNNGmCP//80/uulZLAV1QpDW+++SaqV69u58V2wt6eZBHraXcANWvWxE033VQouK6oSOyBjPpPPPFENG5sL3u7JeI+EJKKdyefTKhbj/DiRrMZulU7fyWcNozbpWSYk/gQU9tVFU2y/VCbUKFCBTz22GP466+/vE9s8ZAKbs888wzat2+P7MwwzJ1C2PU+t1WD6Za1LllISElJwKpVq7zvVikpfEWV0iCjTAkKiU6MDmwe7CN7j2wgsUmx6N69u7WPVdpSjLK89/7776NcuRy0a2tXWjMZmOr4evcdQpWqhP6DCbt/M5uhm3XL3fYxPF2yL4Vms3iGK/kt7rnnHp8839999x0mTZqEzIwktDzBg3fXEQ59zW3WYL5loe1vEsrn2XEGP/zwg/edKiWFr6pSWrZv347mzZuDqvADKLmxTQ+nPyXxAFJdK4/YjMtZg47jBdcVF6nCV758efTqSdjzPTcbg4mpji5ZAZGl+pQUwlU3mE3Q7fr0G/u4nwxE6RyWqS2rji6JpG8tGQQTrVrq+Y/KlRaJyr/99tvRoEEDVCrvwbUXBOao3e9f2EVoKlYsZ8UQKaWHr6xSWuRhu+222+zjORK9W5Z7jvLgt2cl2XmdZfnNH8Ub/vjjD1xxxRVIT0/CrJmE3/Zy0zGYmcosqcU/bKht8p99azbBUFBvOekhx9nGs0ztWWWWRNKfQohMjMTYsWP9MsOVVQFJpCM5QFK4Pxk+kPD9B9x+DWbsD8mZ+LX38Cw+lzBx4kQ9Mucj+OoqvkCMVYLUPCkeuy626UH1peShl6paNQnJqckYNWoUtm3b5tPRfUEkMl9mEKmpUTiXBxf793HzMRiaqrDef8+uftexq9n8QkW3rPIWrZEENaZ2rSosedZPtZNYDRw4ED/++KP3ifQPMoCQI7SSFKtJfcLjd5TNmfpdmwhd2hKqV69mBf0qvoGvruILxFxffPFFK1qVGvJDKTNs0wPrC8nv7sPKtINvrr/+euzdu9f7TvzLb7/9hmHDhiEtLco6e/7nQW5CBlNT/Ve332ZnkZOjZCbzKwt9vpNw76OEi68iLL2YcM2NhKeetxPpmH7eH9rIM0OJS7Ci7E1tW1VYshWXQmjZsiW+/PJL75PoX2RW/8QTT6Bp06bIyfLg/Nn2uXWTOftKVy6RZF2JWLlypR6Z8yF8dRVfIUvas2bNQlRylJ0f3vTAllaSmrMNISwhzCqUI6lpSxpdWxJkMCMdzamnnors7HDccIP7itn4QwMHynFGwoa3zebnb73DHXTX7oTsXLvEbUICK5FQnwekkqLW9Bp/6ItdfC3k2ZDZvD8Hwm6QBNMOZ3G7kaNkr732mhUoV1bIsy7H8wYNGoTkpEic1o/w6Sv2srrJpEujz14j1KzmQa9evaxAQMV38BVWfImcR5cjZ1SeH86p3ofVFzpy9p076fT0dGswIakqA4WsHEiynJQUOyveH79zUzKYm4rw80+EmBjCkKF2VTaT+flLkojmpTcJDZsQGjUl3H4f4esfCd/vJ3z0JeGu1Wy8Zfyerl7BAw0eZARlgaeykizRD2AlE1q3bo2tW7d6n7yyRyYRMruuVKkSyuWEYdV1hN98eKZesu7J/n/lyhWtBGOKb+GrrPgaiUS3qtR14IfUF0F4EonMHaInzWNVlJKa9k6oqSxpNGWUn5YWaVV306V7s1bfT4iKJiy/3Gx6/pQY+pQZ9qz9yefMP1PWeuENb/nZ2iwn5JZwmuSanMHPe4rHOrXz1ltveZ+4wCFGv3btWisoLyM9HHMmc9t6m9u3wbSLI8ls98CNhMx0skpdl+VKRajAV1rxNTLDPuOMM6wldSs4zvQgF0XysEtq2vqEyIRIdDupGzZv3lzqs7G+Qpbz5PigGH1uboRl9Dqj/6+kzO0Z3GFLWdmn15tNz596n2dJNdlM23Wy0+h+tceeuUuJ22+LWEXO1/r2F0KP3tyuM1hOrvsQCMkM/nQ2+EyPtSL4+uuvO8b45H3IwF7O1KemxKLNiR5sfp7b+Lfc1guYd1El0fsnNCQ0btwYX331lfcvKb6Er7TiDySBjDRcqswP7ax8D3FRJWffB7Oy7eA6qSAlQW9ORJbuBw8ebM3olyzRPfr8+pA7sXr1CN162EvkJtPzp9ZusCveSa78hXxv6tS1l8rlvLok5XmSO2nT6/yt627mti3V4KT8rKn9h6JkUC9L9ClkGXwgl+iPhSQAk0x00r/lZntwNberkgTlScT+tNGEnJwcPPLIIzqL9xN8tRV/IMtbklI2JinGzvBVnCAjSU3bjpVIaNCwAZ5++mlHLM8fCxmFy4xe9uhvuF6X7kWSAOeuuyRimHDuBWaz87fueoDbERGqVCNUq06Ys4Bw1Qq7/GxGFg9AGhBeecf8Wn9qC3fwsoVBTb3t3fQchJK8QXae5P/N4J2MRL+/88476NmzJ8/qwzCUByffcDsymblJErz32O2EvJwITJ061bETGDfAV1zxF2LMcq7VOjs/osBDbZIs1UmSkFqEpNQkjBgxAp988olfz777CnmPspQn2xQSdX/5ZXqOXkr0TphAiIgkbP3GbHb+1q33cHtik5ez6Xc/aOeQl6/L0v18NhbZqxfD3833quBr/SnZJmjfid8bD4B8GqAajJLnXo7JpdtBdm+//XbQzGolEn7x4sUoXz4XzRsTnuJBbVHO1G99me9/C57ENKivZ+L9DF9xxZ989NFHqFOnjn0u+FipPHmGZdW7TpMo08pWNKs/Mtf5G4lHkON1co7+3HNDOzPeF58T6tYhNGoSmL1v0UNPc5tik6/Ks3jZC8//vVffJZSvaJutRNrn/15Z6NJrCGFylM5fx02DQWLwkhgoxT4m59Ql+mMhs3o5yitn6iUob/HMY2fK+/tbHvwOl1NCaVaBHcW/8FVX/IlkiZN6yMnJyaBu/DDLslzBB1327FvzbCs+3KoIJQ9MMCeDkHP09vG6aMzizxaque6fepIQybP4ZQGIqj+il98mxMYRmjb73yz+iDZ/xgPKqoSWbQjvffLf75WFnuXZXF45bvtSwbHgMxEKkrgbqRiZ6EG7du0cv0R/LGQlT+KQ5LlPTo62ztR/8IL5TP3zqwnJSRGYPHky9u3b5/0Nir/gq674G9lvOvPMM+1l+zP4wT5i9HK8bhSLO7q0jDTMnDnTSikZ7AEo8sDLZ5YUuJLrvlcvu3pdqJWpHTlCZiuEZ140m1xZaAvP0Fu1JWRmET4tkDP/8bV2AJ5Eun/OnXH+75WFtn5NOEVy2fMgxFrJKmiCbpYM7FsQohOirS2ur7/+Oii25Y6FvH9JCCarkJISt3KFMNx7w//q1P/L2voKoVkjsgY1n3/+ufeVij/hq6+UBTK7rVatGqgSP9xnsSRz3cmEsOQwNGzY0KoAJbN+NyGfR4raSPW6tjxblHr0f//1XyN0q6QgTYUKhJN5gLPlK7PJlYUkZe0Fl9hZ7mbPt2MDZOvgnY8JA4fYme8uvjIw2wly2mDxhYQYSYzTn2UyQzdKnv+GPJtNSbaOo/m6YmSgkVXI9evXW+Wu01IjMGsiYdtGu6rduKGE1NQErFu3zvvTir/hHkkpK8TIE5ITQI35IW/AiiGcdNJJ1jKXU86++xoZ2dtlanPRmD/3Op49/vN3YVN0m9Y8Q0hMJMxiYw3E0bn8kkh2SSWbnEJo0Zpn7qfYGfDieAZ96mA71azpdWWhJ54l5El2SH4/RkN0myazavDgKikBF154IX766Sfvk+IuZDVSTtzIICYhPgLtWhAWnc1tMNGDGTNmaG76MoR7JKWskEC6BQsWIDY2FhUrVsTy5cuxf/9+73fdzaZNm3Diic2RkhKBW252d+S9JMAZP45Qjs3r8XVmcytryUBDiuMMGUbofBJh6Eg7pe03P5t/vqy08xdCW8kMmcua4jVCN0q25uSIXKrHCqx96KGHgn55vijIat4DDzxgBRVGRkagf//+mpu+jOFeSSlLduzYgSlTplh13yWpRKggHZoYvZTjzcgIt/LdS053k0kGu7Z9IRXDCCecWDiiXVVYsmRPUayBXkN0myTArj8bfJrHikCXvBdlWVQq0MisXgrdSL/36quver+qlBXcKyllzS+//BISo/iCyGfeuXOnFYGbmhqJUaMIe391X0De448TD2QIk6ebTU31X8lRPinDK9UVXVeZTj5PV0J4UjhObHEiPvjgg5Ay+PxIMG6ofvZAwr2SopQtcmxmyZIlyMrKQseOhA0vuScV7gE2LTk2KKb11kdmU1P9V1//RGglBp/CkloNJrMMNskJGtl+aCJBj/FWYisZ4CpKWcM9k6KUPWL09957LypWLI9atQgrbnBH5L3kBGjWjFCDP9N2/n+Tqan+q92/2dn3yMMawzKZZrBpGKsiG3xivBV7o/vQSqDgnklRAoNE2Er96G7duiEzMxrTp/OsbkdwR9+/+oqdAEdM6zs2L5OpqQrrkWcIWdlsjMEeZT+P1YMQnhKO+g3qWydLQin2RnEe3DMpSmD5/vvvMX/+fKSnp6J9e8K6dcE7q5810z6qtvpxs5mpzPrwC0InKeSUxgrWxDhSNrcBISouyoo72bJlS0jG3ijOgnsmRQk8cp5eqvZJwqBq1QjXXhN8ee+lln6jRoQ2PFB5b6vZzFRmSYGcs2YRIhPZKCUrpMlEnSo5Hif598sR0jPSrWOye/bs8bZsRQks3DspijOQyFupuidnadPT43HKKTzD+9A+d24yVafpuWcJmZmEidMCV5AmmPXoGknvzGbZgmWq8eA0yXucweL3G5kUiZYtW+KFF17Q5XnFUXDvpCjOYu/evVi2bJmVNKRePQ9uv53wy8+FTdVJku2FefNsk7/nYbOJqY6tr/YQ6jdk06zsNU+TsTpFcjROguuq8sAkLQ0TJkywMrwpitPgHkpRnIfU4n/xxRet5CFS5GXQIDvJjMlgnaBdOwndTyJUqW7XajeZmOr4klUQSfdsGajJXJ0gCa5ryUom5Obl4u6777ZyXyiKE+EeSlGci3SeF1xwAWrWrImsLA8uWOpMs3/2WUJ2NmHUOLN5qYqmtRsIkZL9riNLaq2bTDZQmsOSqnkZUr0vG+PGjbNm7xpcpzgZ7qEUxdnIXr0ctRs4cCBSUuLRoQPhiSfsQDeT4Za1JGZg2YWE8HDCU+vN5qUqmj7ZYRfPoUyWzJhNZlvWksHGWFY9QkRcBNq3b4/HH3/cqkWhKE6HeylFCQ4kYvmOO+5A7dq1kZcXjnHc8W7/MvDn6qXYjhz9q1iZ8OE2s3mpiiYpmDNlOhtqGEuW7k2mW1aSwDopCd2Z4MnwICcnB0uXLrXKRitKsMC9lKIEF1Ke8+yzz7Yq+ZUvH4brryfs/DZwOfA/+tBOYzttJuHbAFd1c4NWPURWHIa1720y37KQrCIMYeXKe0nHoEGD8Omnn3pboKIED9xLKUrwIYF5zz77LPr06YPExDD07El4dl1gjttdegkhLp5w011m01IVT5LzXyr4kdSZlzPoJhP2l2T2LkltGrP4njZr3gx33XUXfv75Z2/LU5TggnspRQlOJOBJlvCvvfZa1K5di2f1kRg6lPDG62W3X3/wgL1U37gp4fVNZtNSFU+SDlgCGMOS2WhHsExm7GvJYEIKyrThv5sehtxyuZg1axa2b99upV9WlGCFeypFCX62bdtmdcq1atVCUlIEJvJs7MUXCL/vL2zMvpTkqq9QgTB0pCbA8aXue4QQl8Cm24Hlz8Q4Yu4TWJ0IniQP8vLyMHz4cGzcuFHNXXEF3FMpijuQTGPSOc+bN8/Kg1+pEuHMMwmbeYbtj/16+Z2yVC+56q+/xWxWqpLpi+8IeeXYfGuxZrNMBl1aSVnbtqwsQlRMFEaNGoU1a9ZYdc8VxS1wb6Uo7kLMXoqDTJkyBdWqVUG5cuEYO4bwzju+XcaXsrL9+tnV0yQq3GRWqpJrGA/QKJY1mmUy6ZJIZu4StS8z9zQPsnOzMWDAAGzYsMGqn6Bn3hW3wb2VoriTf/75B++++y7Gjx/Ps/pKyM31YPYse4n98KHCpl1cvfWmvVTfq4/ZpFSlk0TZy6kFOtlr0KWRLPlPZnVlZdoR82Lua9euxcGDB70tRlHcB/dWiuJu9u/fj9dee83KUJaQEGNVuRs/nrDxjdKZ/coVdgKcux80m5SqdJIo+xqyXJ/nNWmTeR9P8rqprPasbJ69R3rQs2dPPPnkk/jhhx+8LURR3Av3VooSGkjmvM2bN1sz+1q1aiIpKQbNmxPuvIPw2ad2pLzJzE2SBDwdO/BMvhIPFjabTUpVOn31A2H0BDZmSYxTnH15yVAnBW6kZG0NHojFhaNCxQpWdcN169ZZ2zm6LK+ECtxjKUpoIVHT7733HpYvX47OnTsjPj4KjRsTZs0ivPZq0fbtpSBNTAxh+JmE7XvMJqUqpfg+XHcL8WCMzboby2To+SXmLkvynVlVCGFRYWjSpAlmz56N9evX67K8EpJwj6Uoocnhw4fx7bff4qGHHkLv3r2RkpKEqlXDrGC6Rx8hHGCjOVrK3DtuJ0SzyV9ylcGcVD7Ty28Tatdl0+YZubFgjSzHSzDdGFZTnvVneRCTEIMWLVpgxYoV+OKLL6zESYoSqnCPpSiKLN9+/vnn1qyvWbNmSExMQF02lwsvtJPr/PjD/wxfzL93bzafOrYJmcxJ5Rvt3kcYdDobeCpLou2PmPt81gQW3wepPy9H4OrWrYuRI0daxYxka0ZRFDV5RfkPspQvs79bb70VvXr1QlJSknXevv+phIsvImx6n/D2W2zwtQk9T7FNyGROKt/pxjsIYZFs5hJlfxarF6sRK5MQHRuNli1b4rLLLrNOUsh+u6Io/0NNXlEMyPE7ib5+++23sWjRIjRo0AA5OSmoUiXCis6PjiZcerXZlFS+1ec7CQmS/S7RTjmbmJ7I96EKJkyYgOeffx47d+7UmbuiHAU1eUUpApIo5ZVXXrES7GRkZFgFaXb8YDYlle/Vqy8hKioKQ4YMwRNPPKEFYxSliKjJK0ox2Lp1K6pWrYp+A81mpPKPrl4pqydR1hE4RVGKjpq8ohSD++67D7GxYVY2NpMZqfyjl94kVKtO1kqKoihFR01eUYrBwIED0agp4c0PzGak8o++2EUYMISsVRTZOlEUpWioyStKEfnll1+Qk5NtJcDR/fiy1ff7CRdcQkhLi8XLL7/svSOKohwPNXlFKSL33nsvsrOjsfJ2sxGp/KsXNvJMvnoYpk+frrXeFaWIqMkrShE4dOiQVW+8Zu0wvLPFbEIq/+rbXwhduhPatm1rZSpUFOX4qMkrShH49NNPrUx4pw7SBDiB1LLLCHl5eVizZo33ziiKcizU5BXlOEjK23vuuQeJiYlaVjbA2vQZIS6OsGDBAmt1RVGUY6MmryjHQaK5p02bhvQMwic7zOajKht98zOhXSdC+/btsWfPHu8dUhTlaKjJK8pxkLSp9evXx6kDCTt/MZuPqmwkWyVLL5HZfCzeeecd7x1SFOVoqMkrynFYu3YtYmI8WHEbYc/vZvNRlZ3WvEgoX5Fw7rnneu+QoihHQ01eUY6DZFmrUYuw/nWz6ajKVlu2E7p1JzRv3lz35RXlOKjJK8ox+PXXX1GzZg0r29qXu82moypbyWrK/PMkyj4dr7/+uvdOKYpiQk1eUY7Bk08+idy8FFyiZWUdJVmyL1c+Cuedd55VFlhRFDNq8opyFCSr2syZM1G+QgReecdsNqrA6OsfCY1PIPTu3duq+68oihk1eUU5Ctu2bbOyq3XtbudON5mNKnBacD6hQoUKeOmll7x3TFGUgqjJK8pReOaZZ5Ceno7LrzObjCqwev51Qlx8OC6//HL89ddf3rumKEp+1OQVxcDhw4et/d6kZA82bjabjCqw+vQbwgknEnr16oX9+/d775yiKPlRk1cUAxJV36hRI/Tup1H1TtWuvYR5iyUxTgw+++wz751TFCU/avKKYuDdd99FbGwkLrxUC9I4Wfc9SsjMIlx//fXeO6coSn7U5BXFgCzVZ+UQbr+XsOMHNXqnSQIhv/mJ8NyrhEaNCSeddJLuyyuKATV5RSmAHJ0bPXq0FXSXmOhBl5MIF1xCeGQN4aMvNdI+UJIkOJ/tJKx5iXDVCsKAwYQMnsUnJCSgR48eWmNeUQyoyStKASS5yvbt2/HYY49Z+dHbtGmDuLho5OQSOnUlnDWTcNs9dtlTNXz/67NvCfc/Rpi/mNCrD6FKVUJMTJhVNOiss86yygBv2bJFU9wqigE1eUU5BmIcP/74I95//33rqFbr1q1RsWIeypVPQrkKYWjWgjB5OuG+RwhvfUjY+rVdDlUL2RRfu34lfM4z9fe2Ep54jnDOIkLHLoSKlTx8veNRoUI2GjZsgPnz52PDhg3YvXs3Dhw4YNX7VxTFjJq8ohQDMf2PPvoIq1atsmaRcnyrbt26VunT1DRCuw6EKWcTrllJeGo94cNtup9/NO3Zb8/Sn3+NcNOdhDkLCSf3IuSWI0RHR6Bq1aro0qULxo4di5UrV+KNN97A77//7r0TiqIUBTV5RSkhEuj1/fff47333sMjjzyCJUuWoHv37khLS0NMDKFKNUKrtoQhQwlXXE945gXCJ1+F9ixfjiO+8IZt6qMn8Ey9M6FWbUJiol0jvlWrVpg1a5a1BL9x40Z88803+PPPP71XXFGU4qImryg+QJaMxfRl+ViW96U62oUXXmgFhNWvXxcVKmYjMzMByclhqFyVrGC+idPsALLH1hFefddepv5kR/Au9+/81Z6ZS6zC6+/bRWRuvIMw4xxC776EOvUIySkepKfHoly5DNSuXRMdO3bEnDlzrJr93333Hf744w8rEZEuwSuKb1CTVxQ/IpH6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\" style=\"width: 188.5pt; height: 179.5pt;\" alt=\"image\"\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:justify;text-indent:.5in;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:150%;font-size:15px;font-family:\"Calibri\",sans-serif;text-align:justify;text-indent:.5in;'\u003e\u003cspan style='font-size:13px;line-height:150%;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4- Conclusion","content":"\u003cp\u003eThe HPTLC analytical technique outlined for identifying pioglitazone HCl and alogliptin benzoate in bulk and tablets. Linearity, accuracy, specificity and robustness, were all evaluated for the analytical procedure. With regression coefficients (r\u003csup\u003e2\u003c/sup\u003e) of 0.9998 and 0.9990 for alogliptin benzoate and pioglitazone HCl, respectively, within the working concentration range of 50.0% to 150.0%, the results demonstrated the linearity of the approach. Additionally, it was determined to be accurate within the 98.0% to 102.0% acceptable range. The method's validity as a stability-indicating approach was confirmed when it was examined for specificity and demonstrating selectivity for the active constituent without interference from excipients. Alogliptin benzoate and pioglitazone HCl in bulk, tablets, and human plasma can be determined using the HPTLC method outlined, and also content uniformity, which is also a reliable stability indicator. Finally, the environmental impact of this method was assessed using various assessment tools, including Analytical eco-scale, AGREE, AGREEprep, ComplexMoGAPI, and BAGI, which highlight its eco-friendly profile.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of interest\u003c/h2\u003e\u003cp\u003eAll authors declare that they have no conflicts of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding sources\u003c/h2\u003e\u003cp\u003eThis study did not receive funding from any public, commercial, or non-profit agencies.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors whose names appear on the submission1- made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data; or the creation of new software used in the work;2- drafted the work or revised it critically for important intellectual content; 3- approved the version to be published; and4- agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.\u003c/p\u003e\u003ch2\u003eAcknowledgment\u003c/h2\u003e\u003cp\u003eI would like to thank Pharmaceutical Service and Bioavailability Center, Faculty of Pharmacy, Assiut University where I did my research work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGalal, A.M., B. 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Samanidou, Blue applicability grade index (BAGI) and software: a new tool for the evaluation of method practicality. Green chemistry, 2023. 25(19): p. 7598\u0026ndash;7604.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Alogliptin, Pioglitazone, HPTLC, Method validation, T2DM, Green Chemistry","lastPublishedDoi":"10.21203/rs.3.rs-7804109/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7804109/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA high-performance thin-layer chromatography (HPTLC) technique that indicates stability was created and verified. to simultaneously quantify the active pharmaceutical ingredients pioglitazone, alogliptin, and their fixed-dose combination. This method is characterized by its clarity, directness, precision, and accuracy.\u003c/p\u003e\u003cp\u003eMethods: Densitograms of alogliptin (ALO) and pioglitazone (PIO) were created using n-butanol: water: glacial acetic acid: ammonia (8.0:0.5:0.5:1.0v/v) as the mobile phase and HPTLC plates (silica gel 60 F254) as the stationary phase. Densitometric quantification was implemented at a wavelength of 254 nm.\u003c/p\u003e\u003cp\u003eResults: The values of RF for Alogliptin and Pioglitazone were determined to be 0.28 and 0.75, respectively. The method's robustness, specificity, accuracy, and precision were all verified. With correlation coefficients of 0.9998 and 0.9990 for Alogliptin and Pioglitazone, respectively, the linearity was obtained in the concentration range of 125.0-2000.0 ng/band for Alogliptin and 250.0-4000.0 ng/band for Pioglitazone. The lowest quantity that could be quantified by the suggested method was found to be 0.31 and 2.75 ng /band for ALO and PIO, respectively. The detection limit for Alogliptin and Pioglitazone was found to be 0.1 and 0.9 ng/band, respectively. The environmental impact of the method was assessed using various greenness evaluation tools, confirming its eco-friendly nature.\u003c/p\u003e\u003cp\u003eConclusion: After validation, the method was determined to be specific, selective, and appropriate for analyzing these medications, whether in bulk form or fixed-dose combinations.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Green Method for Simultaneous Determination of Alogliptin and Pioglitazone in Bulk, Combined Tablet Dosage Forms, and Spiked Human Plasma by High-Performance Thin-Layer Chromatography","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-28 16:46:59","doi":"10.21203/rs.3.rs-7804109/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d2bbface-b4b9-44f4-bc4f-6256d41a51e5","owner":[],"postedDate":"October 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-25T13:09:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-28 16:46:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7804109","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7804109","identity":"rs-7804109","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}