Determination and Validation of Tiaprofenic Acid in Human Plasma: A Detailed LC-MS/MS-Based Analysis Following ICH M10 Guidelines and the Accuracy Profile Approach

Determination and Validation of Tiaprofenic Acid in Human Plasma: A Detailed LC-MS/MS-Based Analysis Following ICH M10 Guidelines and the Accuracy Profile Approach | 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 Determination and Validation of Tiaprofenic Acid in Human Plasma: A Detailed LC-MS/MS-Based Analysis Following ICH M10 Guidelines and the Accuracy Profile Approach Houda Bouchafra, Aimen El Orche, Choukri El Khabbaz, Amine Cheikh, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3848449/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Dec, 2023 Read the published version in Current Chemistry Letters → Version 1 posted You are reading this latest preprint version Abstract The validation of bioanalytical methods holds critical importance for regulatory agencies and organizations dedicated to ensuring the safety, efficacy, and quality of pharmaceuticals. In this context, the recent release of the ICH M10 guideline in May 2022 represents a significant milestone in standardizing bioanalytical method validation globally. However, this guideline lacks explicit experimental protocols for implementation. In this study, we address the practical implementation of the newly released ICH M10 guideline by providing a detailed validation protocol for a bioanalytical method. Our method specifically targets tiaprofenic acid, a widely used nonsteroidal anti-inflammatory drug. Tiaprofenic acid is a critical component in bioequivalence studies, underscoring the necessity for precise and accurate quantification within complex biological matrices. The integration of the accuracy profile approach, a statistical tool, enhances the significance of this work. This approach aids in assessing the accuracy and precision of bioanalytical methods, establishing confidence intervals around measured concentrations and quantifying the level of accuracy and precision expected when using the validated method. Overall, this study contributes to advancing bioanalytical science, aligning with the latest ICH guidelines, and emphasizing the importance of validated methods supported by modern statistical approaches like the accuracy profile. It underscores the commitment to maintaining the highest standards of quality and reliability in bioanalysis, which is crucial for ensuring public health and advancing drug therapies. Bioanalytical method validation ICH M10 guideline accuracy profile tiaprofenic acid LC-MS/MS analysis pharmaceutical research Figures Figure 1 Figure 2 1. Introduction Bioanalysis has always been a subject of paramount interest to numerous regulatory agencies and organizations committed to safeguarding public health by ensuring the quality, efficacy, and safety of pharmaceutical drugs [ 1 ]. Among these regulatory bodies, the United States Food and Drug Administration (FDA) emerged as one of the pioneers in developing guidelines for the validation of bioanalytical methods. Over time, these guidelines have evolved in response to the dynamic landscape of bioanalysis [ 2 ]. Concurrently, the European Medicines Agency (EMA) has formulated its own guide on bioanalytical method validation, albeit with certain nuances compared to the FDA's standards [ 3 ]. A significant milestone in the realm of bioanalytical method validation was reached in May 2022 when the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) released the latest iteration of the ICH-M10 guideline. This guideline represents a significant stride towards standardizing the validation of bioanalytical methods on a global scale [ 3 ]. While the ICH-M10 guideline meticulously outlines the parameters for validating bioanalytical methods and establishes acceptance criteria, it does not provide explicit experimental protocols for their application. In response to this need, this work aims to fill the gap by presenting a comprehensive experimental protocol for validating a bioanalytical method in accordance with the ICH-M10 standard. For our experimental validation, we have selected tiaprofenic acid as the target analyte. Tiaprofenic acid, a nonsteroidal anti-inflammatory drug (NSAID) belonging to the propionic acid group, derived from benzoic acid, is widely used as an analgesic and antipyretic agent [ 4 ]. Its inclusion in bioequivalence studies underscores the importance of accurate quantification in biological matrices, as this is pivotal for making critical determinations regarding drug safety and efficacy [ 5 ]. In the context of bioequivalence studies, the precise measurement of pharmaceutical dosage in biological matrices assumes paramount significance[ 1 – 6 ]. Consequently, the development and validation of bioanalytical methods are imperative to establish the credibility and robustness of study outcomes [ 7 , 8 ]. The ICH-M10 guideline serves as a compass, offering recommendations and validation criteria for the rigorous evaluation of bioanalytical methods [ 3 ]. However, it is the integration of the accuracy profile approach that elevates the significance of this work. The accuracy profile approach, a statistical tool, plays a pivotal role in ensuring the precision and accuracy of bioanalytical methods [ 9 – 10 ]. It provides a systematic means to assess the reliability of analytical results by establishing confidence intervals around the measured concentrations [ 11 ]. This approach helps quantify the degree of accuracy and precision that can be expected when using the validated method [ 12 ]. In summary, this research endeavor bridges the gap between the ICH-M10 guideline and practical implementation, ultimately facilitating the development of validated bioanalytical methods essential for conducting rigorous bioequivalence studies and ensuring the safety and efficacy of pharmaceutical interventions. The incorporation of the accuracy profile approach underscores our commitment to delivering not just validated methods but also robust and reliable tools for drug evaluation in the pursuit of better public health and patient outcomes. 2. Materials and methods 2.1. Chemical Reagents and Equipment Tiaprofenic acid was procured from Erregierre (San Paolo d'Argon, Italy), while the internal standard (IS), Ibuprofen, was sourced from Sigma Aldrich (Saint Louis, USA). Acetonitrile (LC/MS grade and HPLC grade) was acquired from VWR (Fontenay-Sous-Bois, France), while Methanol (LC/MS grade and HPLC grade) was obtained from Merck (Darmstadt, Germany). Formic acid (ACS grade) was purchased from Scharlau (Barcelona, Spain), and ammonium formate was procured from HIMEDIA (India). Ultrapure water, with a resistance of > 18.0 Ω/cm, was employed. Drug-free fresh human plasma, collected from healthy subjects with lithium heparin as an anticoagulant, was sourced from BD-Vacutainer (UK) and stored at freezing temperature (~-25°C) until required. 2.2. LC-MS/MS Conditions The UHPLC system comprised an Agilent 1290 Infinity II quaternary pump (Germany), Agilent 1290 Infinity II autosampler (Germany), and Agilent 1290 Infinity II column thermostat (Germany). Chromatography was conducted on a Phenomenex C18 analytical column (150 mm × 2 mm, 4 µm particle size; Phenomenex, USA) at a flow rate of 0.2 mL/min. Mobile phases consisted of 2-mM ammonium formate in water with 0.1% formic acid (A) and acetonitrile (LC/MS grade) (B). Optimal chromatographic separation was achieved with a 50% A and 50% B composition. The column temperature was maintained at 40°C, and tiaprofenic acid and Ibuprofen (IS) eluted at approximately 4.87 and 7.12 minutes, respectively. Mass spectrometric analysis was performed using an Agilent 6420 triple quadrupole mass spectrometer (Agilent, Germany) controlled by MassHunter B.09.00 software. The mass spectrometer operated in the positive Multiple Reaction Monitoring (MRM) mode with a dwell time of 200 ms per transition and utilized Electrospray Ionization (ESI). MRM transitions were 261 > > 105 and 183 for tiaprofenic acid and 207 > > 161 for IS, ensuring the widest resolution for all analytes. Fragmenter settings were optimized at 130 V for tiaprofenic acid and 132 V for IS, with collision energy set at 18 eV for tiaprofenic acid and 10 eV for IS. 2.3. Preparation of Calibration Standards and Quality Control Samples Calibration samples were prepared by mixing human plasma (475 µL) with an aliquot (25 µL) of Tiaprofenic acid stock solutions in methanol (2, 10, 20, 100, 200, 250, 500, and 1000 ng/µL) to achieve nominal concentrations of 100, 500, 1000, 5000, 10000, 12500, 25000, and 50000 ng/mL for tiaprofenic acid. Quality control (QC) samples were prepared by mixing human plasma (475 mL) with an aliquot (25 µL) of tiaprofenic acid stock solutions in methanol (2, 4, 400, 800 ng/µL) to provide LLOQ, low, medium, and high QC samples with nominal concentrations of 100, 200, 20000, and 40000 ng/mL for tiaprofenic acid. Sample Preparation: Sample preparation involved a protein precipitation process. Specifically, 25 µL of IS stock solution in methanol (500 ng/µL) was added to 500 µL of plasma containing tiaprofenic acid. After agitation, 500 µL of methanol and 1000 µL of acetonitrile were introduced, followed by 30 seconds of agitation. The samples were then centrifuged for 10 minutes at 5000 rpm. The extracted samples were filtered and diluted by half with A (2mM ammonium format with 0.1% formic acid). Finally, an aliquot (5 µl) was injected into the UHPLC system. 2.4. Bioanalytical Method Validation The work conducted in this study adhered rigorously to the principles of Good Laboratory Practice, as stipulated by the Organisation for Economic Cooperation and Development (OECD). Additionally, all validation experiments were executed in accordance with the ICH M10 guideline on Bioanalytical Method Validation and Study Sample Analysis [ 3 ]. However, it is noteworthy that the statistical evaluation of accuracy and precision of the method diverged from the ICH M10 guideline and instead followed the guidelines set forth by the SFSTP [ 13 ] 2.4.1. Selectivity Selectivity, as defined by ICH M10, signifies the capacity of an analytical method to differentiate and quantify the target analyte amidst potential interfering substances in the blank biological matrix. In this context, chromatograms were generated for blank plasma obtained from six individual sources, blank plasma spiked with tiaprofenic acid at the Lower Limit of Quantification (LLOQ) concentration, and the Internal Standard (IS) at the concentration utilized in the study. 2.4.2. Specificity Specificity, as per ICH M10, denotes the ability of a bioanalytical method to detect and distinguish the target analyte from other substances, including its related substances. In this vein, chromatograms were acquired for blank plasma, blank plasma spiked with tiaprofenic acid at the LLOQ concentration, and blank plasma spiked with IS at the concentration employed in the study. 2.4.3. Matrix Effect, Extraction Recovery & Process Efficiency: Evaluation of the matrix effect encompassed an analysis of low, medium, and high Calibration Quality Control samples (CQs) (n = 4 for each level) prepared in the mobile phase (designated as A), those same CQ levels prepared in blank plasma before extraction from six different individual sources (designated as B), and those same CQ levels prepared in extracted blank plasma from six different individual sources (designated as C). Three parameters were scrutinized: Matrix Effect (ME) on the compound's LC-MS/MS response: ME = (average of peak areas (C) / average of peak areas (A)) * 100. Extraction Recovery (ER) , accounting for the matrix effect: ER = (mean of peak areas (B) / mean of peak areas (A)) * 100. Process Efficiency (PE) , representing the extraction yield of the sample preparation method, disregarding the matrix effect: PE = (mean of peak areas (B) / mean of peak areas (C)) * 100. Any ratio falling below 85% or exceeding 115% indicated the presence of an endogenous matrix effect. 2.4.4. Calibration and Response Function The LC-MS/MS system employed a variety of regression models chosen based on the calibration range's accuracy and the quality controls in use. The calibration curve was established using eight concentration levels, and the evaluation was based on three independent runs conducted on different days. 2.4.5. Accuracy and Precision Accuracy and precision were assessed using four concentration levels as Quality Controls (QCs) within the calibration curve range, including the Lower Limit of Quantification (LLOQ), low, medium, and high QCs. These assessments were conducted both within-run and between-run using the same dataset. Within-run accuracy and precision were determined using six replicate samples for each of the four QC levels. Between-run accuracy and precision were derived from five independent runs on different days. The acceptance criterion for accuracy at each concentration level was set within ± 15% of the nominal concentration, except at the LLOQ, where it was ± 20%. For precision (%CV), the criterion was that it should not exceed 15%, except at the LLOQ, where it was ± 20%. Compliance with this criterion necessitated that at least 2/3 of the total QCs and at least 50% at each concentration level met these requirements. To statistically demonstrate the accuracy and precision of the method, an accuracy profile approach was applied. This approach utilizes tolerance intervals to calculate upper and lower limits at each concentration level, resulting in a visually comprehensible graph that aids in the assessment of method performance. 2.4.6. Stability The study encompassed an investigation into the stability of tiaprofenic acid under various storage conditions using low and high-quality control concentrations. 2.4.7. Hemolysis Effect The hemolysis effect test was conducted on hemolyzed plasma loaded with low and high-quality controls in triplicate. The mean accuracy of QCs was expected to fall within ± 15% of the nominal concentration, with the precision (%CV) not exceeding 15%. 2.4.8. Dilution Integrity Test The dilution integrity test was carried out at twice the concentration of the high-quality control, with five replicates. Here, the mean accuracy of the dilution QCs was required to be within ± 15% of the nominal concentration, and the precision (%CV) should not surpass 15%. 2.4.9. Carryover Test To evaluate the auto sampler's carryover, a specific sequence of injections was followed, including blank plasma, Lower Limit of Quantification (LLOQ), Upper Limit of Quantification (ULOQ), and blank plasma. The criterion for carryover in the blank plasma following the ULOQ was that it should not exceed 20% of the analyte response at the LLOQ and 5% of the response for the IS. 2.4.10. Accuracy Profile Approach To robustly demonstrate the accuracy and precision of the method, an accuracy profile approach was applied. This approach utilizes tolerance intervals to calculate upper and lower limits at each concentration level, resulting in a visually comprehensible graph that aids in the assessment of method performance. The accuracy profile is a powerful tool based on total error, providing a guarantee and prediction of future results when employing an approved approach. 3. Results and discussion 3.1. Selectivity The selectivity of the method was assessed by examining the absence of any interfering peaks that co-eluted with tiaprofenic acid and the Internal Standard (IS) at their respective retention times. The results revealed that the interference percentage (%) for tiaprofenic acid at the Lower Limit of Quantification (LLOQ) was below 20%, while for the IS, it remained below 5%. These findings confirm the method's selectivity and are summarized in Table 1 for reference. Table 1 selectivity result Human plasma Batch-1 Batch − 2 Batch − 3 Batch − 4 Batch − 5 Batch − 6 Interference percentage (%) of tiaprofenic acid 7.38 3.79 1.50 0.98 0.77 0.72 Interference percentage (%) of IS 0.02 0.04 0.02 0.01 0.17 0.01 3.2. Specificity The specificity of our method was rigorously evaluated through a meticulous examination of chromatograms representing different scenarios. Figure 1 displays these chromatograms, which include those for blank plasma, blank plasma spiked with tiaprofenic acid at the Lower Limit of Quantification (LLOQ) concentration, and blank plasma spiked with the Internal Standard (IS) at the concentration utilized in this study. The key criterion for assessing specificity was the determination of retention times, which were found to be approximately 5 minutes for tiaprofenic acid and 7.4 minutes for IS. Significantly, the examination of these chromatograms revealed the complete absence of any interfering peaks in the blank plasma at the specific retention times corresponding to tiaprofenic acid and IS. This unequivocal absence of interferences reinforces the method's exceptional specificity, affirming its suitability for precise and accurate analytical purposes. 3.3. Matrix effect In order to comprehensively assess the matrix effect, extraction recovery, and process efficiency of tiaprofenic acid, we present the findings in Table 2 . This analysis is crucial for understanding the impact of the biological matrix on the analytical method's performance. Table 2 matrix effect result Tiaprofenic acid LQC (200 ng/ml) HQC (40000ng/ml) Tiprofenic acid preparing in mobile phase Tiaprofenic Acid preparing in extracted blank plasma Tiaprofenic acid preparing in blank plasma before extraction Tiprofenic acid preparing in mobile phase Tiaprofenic Acid preparing in extracted blank plasma Tiaprofenic acid preparing in blank plasma before extraction Plasma control-1 Area Mean 18145.01 18703.82 18934.87 6052813.13 5469410.50 5465336.64 STD 1128.68 193.90 249.21 844062.99 10848.04 351220.47 CV % 6.22 1.04 1.32 13.94 0.20 6.43 ME 103.08 90.36 ER 104.35 90.29 PE 101.24 99.93 Plasma control-2 Area Mean 18145.01 19359.94 18053.78 6052813.13 6465180.12 5689189.69 Standard deviation 1128.68 160.03 250.29 844062.99 89070.50 118996.97 CV % 6.22 0.83 1.39 13.94 1.38 2.09 ME 106.70 106.81 ER 99.50 93.99 PE 93.25 88.00 Plasma control-3 Area Mean 18145.01 19658.51 18567.70 6052813.13 6465180.12 6255489.38 Standard deviation 1128.68 173.92 244.13 844062.99 89070.50 197894.89 CV % 6.22 0.88 1.31 13.94 1.38 3.16 ME 108.34 106.81 ER 102.33 103.35 PE 94.45 96.76 Plasma control-4 Area Mean 18145.01 18289.31 17839.32 6052813.13 5469410.50 6368561.23 Standard deviation 1128.68 141.66 251.53 844062.99 10848.04 129947.42 CV % 6.22 0.77 1.41 13.94 0.20 2.04 ME 100.80 90.36 ER 98.32 105.22 PE 97.54 116.44 Plasma control-5 Area Mean 18145.01 19907.54 18573.06 6052813.13 5703717.02 5749054.35 Standard deviation 1128.68 113.04 296.31 844062.99 29775.83 127751.15 CV % 6.22 0.57 1.60 13.94 0.52 2.22 ME 109.71 94.23 ER 102.36 94.98 PE 93.30 100.79 Plasma control-6 Area Mean 18145.01 18572.06 19203.24 6052813.13 5693190.39 5023272.32 Standard deviation 1128.68 270.37 210.87 844062.99 237057.05 132605.99 CV % 6.22 1.46 1.10 13.94 4.16 2.64 ME 102.35 94.06 ER 105.83 82.99 PE 103.40 88.23 Remarkably, regardless of the concentration levels tested for tiaprofenic acid, no significant matrix effect was observed. The matrix effect (ME) values fell within the range of [90% − 109%], suggesting minimal interference from the matrix in the analysis. The process efficiency (PE) remained within the range of [87% − 116%], indicating the method's effectiveness in extracting the analyte from the matrix. Furthermore, the extraction recovery (ER) values obtained for both low and high concentrations of tiaprofenic acid were between [82% − 105%], affirming the method's reliability in recovering the analyte from the biological matrix. These findings collectively reinforce the robustness and reliability of our analytical method in the presence of diverse biological matrices, highlighting its suitability for accurately quantifying tiaprofenic acid. 3.4. Calibration and response function Our calibration process for tiaprofenic acid meticulously evaluated various regression models to determine the most suitable model for calculating sample concentrations accurately. The assessment revealed that a quadratic model with a weighting of 1/concentration^2 provided the most robust and accurate results across the calibration curve. This model selection is crucial, as it directly impacts the accuracy and reliability of concentration calculations. The nominal percentages of the three distinct calibration ranges are presented in Table 3 . These ranges cover a wide spectrum of concentrations, ensuring the method's applicability across a broad analytical range. It is noteworthy that the accuracy of the back-calculated concentrations for each calibration standard falls comfortably within the stringent criterion of ± 15%. This indicates the method's precision and reliability in accurately quantifying tiaprofenic acid concentrations, regardless of the specific concentration levels. Table 3 calibration result Nominal concentration (ng/mL) 100 500 1000 5000 10000 12500 25000 50000 Accuracy (%) 1 97.19 102.36 102.22 98.67 101.54 90.86 105.80 100.16 2 97.95 100.56 100.14 100.80 100.18 97.72 100.72 104.51 3 104.46 90.25 88.71 106.94 105.93 105.52 103.77 98.58 Table 4 accuracy and precision result Analyte Nominal Conc. (ng/mL) Within-run (n = 6) Between-run (n = 30) Calculated Conc. (ng/mL) Mean ± SD Accuracy (%) Precision Calculated Conc. (ng/mL) Mean ± SD Accuracy (%) Precision Tiaprofenic acid LLOQ (100 ng/mL) 94.8 ± 2.17 94 2.28 96.2 ± 11.5 96.2 11.9 QCL(200 ng/mL) 207 ± 19.1 103 2.24 197 ± 14.8 98.3 7.51 QCM(20000 ng/mL) 20000 ± 900 100 4.49 20600 ± 1490 103 7.26 QCH(40000 ng/mL) 37300 ± 2500 93.3 6.69 40700 ± 2190 102 5.39 These findings collectively validate the robustness and accuracy of our calibration process and demonstrate the method's suitability for precise quantification of tiaprofenic acid concentrations across a wide range of values. This level of accuracy is essential for various bioanalytical applications, where reliable concentration determination is paramount. 3.5. Accuracy and precision The accuracy of our method was calculated by comparing the determined concentrations to the nominal values. Across all concentration levels, ranging from LLOQ to CQH, the accuracy consistently ranged from 80.9–109% of the nominal values. These accuracy percentages indicate that our method provides reliable and consistent measurements, as they are well within acceptable limits. Precision, often expressed as the coefficient of variation (%CV), provides insights into the repeatability and reproducibility of the method. The precision values, expressed as %CV, ranged from 0.45–11.9%. These precision values are indicative of excellent repeatability and reproducibility, as they are substantially below the acceptable limit of 20% at the LLOQ and 15% at all other concentration levels. The combined assessment of accuracy and precision underscores the robustness and reliability of our method for tiaprofenic acid quantification. Notably, at all concentration levels, the accuracy and precision values remained well within the predefined acceptance criteria. Specifically, accuracy values were < 20% at the LLOQ and < 15% at all other levels. Consequently, our method meets the stringent criteria for accuracy and precision, reinforcing its suitability for accurate tiaprofenic acid determination in diverse analytical applications. These results unequivocally establish the robustness and reliability of our method for tiaprofenic acid quantification. The method's consistent accuracy and precision, well within stringent acceptance criteria, demonstrate its suitability for precise and reliable tiaprofenic acid analysis across a wide range of concentrations. This level of analytical performance is fundamental to various bioanalytical applications where accuracy and precision are paramount. The accuracy profile, depicted in Fig. 2 , plays a critical role in assessing the method's reliability and its ability to consistently produce accurate results. This assessment incorporates tolerance intervals within the acceptance limits, emphasizing a crucial aspect of method validation. The inclusion of tolerance intervals within the acceptance limits signifies a statistical assurance that the method can reliably deliver results meeting predefined criteria. Specifically, this means that the probability of the difference between the calculated concentrations and the reference values remaining below the acceptance limit exceeds the chosen β value, set as a proportion of future results at 90%. In practical terms, this indicates that across a concentration range spanning from 100 ng/ml to 40000 ng/ml, the analyst can have confidence that the method consistently maintains an average β probability of producing results falling within the acceptable limits. This statistical assurance provides a high level of confidence in the method's ability to yield accurate and precise results consistently over a wide range of tiaprofenic acid concentrations. This utilization of tolerance intervals and the β value underscores the method's robustness and its suitability for bioanalytical applications where maintaining consistent accuracy and precision is essential. It offers a valuable tool for quality control and assurance, enhancing the method's reliability and trustworthiness in various analytical settings. 3.6. Stability Assessment Ensuring the stability of tiaprofenic acid under various conditions is integral to establishing the reliability and applicability of our method. The stability tests conducted under different scenarios yielded encouraging results, reaffirming the method's robustness. 3.6.1. Freeze-Thaw Stability Tiaprofenic acid exhibited remarkable stability during three consecutive freeze-thaw cycles. The mean calculated concentrations for both low (QCL) and high (QCH) quality controls, as shown in Table 5 , remained consistent, with accuracy percentages ranging from 101.6–111.7%. This outcome underscores the method's suitability for handling samples subject to multiple freeze-thaw cycles, a condition often encountered in real-world sample processing. Table 5 stability result Stability experiments Mean calculated Conc. (n = 3) Accuracy (%) QCL (200 ng/mL) QCH (40000 ng/mL) QCL (200 ng/mL) QCH (40000 ng/mL) Freeze-thaw matrix stability (3rd cycles) 203.2 44668 101.6 111.7 Bench top (short-term) matrix stability (8 h at 22°C) 225.5 42619 112.7 106.5 Auto-sampler matrix stability (72 h at 10°C) 201.7 43463 100.8 108.6 Long-term matrix stability (164j at -25°C) 229.1 43585 114.5 105.11 3.6.2. Bench Top (Short-Term) Stability Short-term benchtop stability, conducted over 8 hours at 22°C, demonstrated the resilience of tiaprofenic acid. The calculated concentrations for QCL and QCH quality controls remained stable, with accuracy percentages of 112.7% and 106.5%, respectively. This result indicates that the method is well-suited for short-term sample storage at ambient conditions without compromising accuracy. 3.6.3. Auto-Sampler Stability The method also exhibited commendable stability during auto-sampler storage for an extended period of 72 hours at 10°C. The calculated concentrations for both QCL and QCH quality controls remained consistent, with accuracy percentages of 100.8% and 108.6%, respectively. This finding is essential for analytical workflows that involve automated sample handling and storage. 3.6.4. Long-Term Stability Perhaps most impressively, tiaprofenic acid demonstrated exceptional long-term stability during a 164-day storage period at -25°C. The calculated concentrations for QCL and QCH quality controls remained virtually unchanged, with accuracy percentages of 114.5% and 105.11%, respectively. This remarkable stability over an extended duration highlights the method's reliability for long-term sample storage, a critical aspect of bioanalytical studies. 3.7. Hemolysis Effect The assessment of the hemolysis effect on tiaprofenic acid concentrations at both QCL and QCH using hemolyzed plasma produced encouraging results. The accuracy values obtained for each concentration were 97.2% and 99.7%, respectively, both well below the accepted threshold of 15%. This outcome firmly establishes the absence of a hemolysis effect, affirming the method's suitability for analyzing plasma samples that may be subject to hemolysis. 3.8. Dilution Integrity Test The dilution integrity test, conducted on a plasma sample loaded with twice the concentration of CQH (40000 ng/mL), yielded accuracy and precision results of 96.08% and 8.21%, respectively. These results indicate that tiaprofenic acid samples can be reliably diluted by a factor of 2, extending the range of the calibration curve. This flexibility in sample dilution is valuable in situations where samples with high concentrations need to be analyzed accurately. 3.9. Auto-Sampler Carryover Test To investigate the potential carryover effect of the auto-sampler, a sample of blank plasma was injected immediately after a sample from the upper limit of quantification of tiaprofenic acid. The interference percentages for tiaprofenic acid and IS were found to be 0.62% and 0.015%, respectively, both comfortably below the 20% threshold for tiaprofenic acid and 5% for IS. This outcome provides strong evidence of the absence of auto-sampler carryover for tiaprofenic acid and IS, ensuring the integrity of subsequent sample analyses. 4. Conclusion In this study, we have undertaken the validation of a bioanalytical method for the determination of tiaprofenic acid in human plasma by LC-MS/MS, guided by the principles of the ICH M10 guideline. Our comprehensive approach not only adheres to the conventional validation parameters but also integrates the powerful accuracy profile methodology. Through rigorous assessment, we have demonstrated the reliability and robustness of this method for its intended application in bioequivalence studies. The integration of the accuracy profile approach significantly enhances our confidence in the method's performance. By evaluating accuracy and precision across a wide dynamic range, we have gained a deeper understanding of the method's capabilities. This is crucial, especially in the context of bioequivalence studies, where minute differences in drug concentrations can influence clinical outcomes. The accuracy profile's statistical rigor and predictive power make it an invaluable tool for ensuring the accuracy and reliability of analytical results. Furthermore, our work aligns with the latest ICH M10 guideline, providing a practical and experimental protocol for bioanalytical method validation. Tiaprofenic acid, a nonsteroidal anti-inflammatory drug, served as our model compound, illustrating the method's potential application in drug analysis. As we move forward in the realm of pharmaceutical and clinical research, the importance of validated bioanalytical methods cannot be overstated. They form the bedrock upon which critical decisions regarding drug safety and efficacy are made. This study not only contributes to the advancement of bioanalytical science but also highlights the importance of integrating modern statistical approaches like the accuracy profile. By doing so, we bolster our ability to generate accurate and precise data, ensuring that the conclusions drawn from our analyses are founded on solid scientific principles. Ultimately, this work underscores our commitment to upholding the highest standards of quality, reliability, and integrity in bioanalysis. In the ever-evolving landscape of pharmaceutical research and development, the validation of bioanalytical methods remains an indispensable step. As we continue to pursue innovative solutions for improving human health, validated methods supported by rigorous assessments like the accuracy profile will play a pivotal role in safeguarding public health and advancing our understanding of drug therapies. Declarations Author Contribution Houda Bouchafra: Methodology, Validation, Investigation, Writing – original draft, Visualization. Aimen Elorche: Methodology, Validation, Investigation, Writing – original draft, Visualization. Choukri Elkhabbaz: Investigation, review . Amine Cheikh: Formal analysis, Software, Writing – review & editing. My El Abbes Faouzi: Methodology, Writing – review & editing. Yahia Cherrah: Writing – review & editing. Aziz Hirri: Writing – review & editing. Lhoussaine Zarayby: Investigation. References S. Pandey, P. Pandey, G. Tiwari, R. Tiwari. 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Chemometric Approach Based on Accuracy Profile and Data Chronological Distribution as a Tool to Detect Performance Degradation and Improve the Analytical Quality Control for Aflatoxins’ Analysis in Almonds Using UPLC–MS/MS. ACS Omega. 18 mai 2021;6(19):12746‑54. P. Hubert, J.J. Nguyen-Huu, B. Boulanger, E. Chapuzet, P. Chiap, N. Cohen, et al. Harmonization of strategies for the validation of quantitative analytical procedures A SFSTP proposal--Part I (2004) J Pharm Biomed Anal;36(3):579‑86. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 31 Dec, 2023 Read the published version in Current Chemistry Letters → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Abbes","lastName":"Faouzi","suffix":""},{"id":266719582,"identity":"05256327-c3ae-4c4a-9610-22c0c35024f8","order_by":5,"name":"Yahia Cherrah","email":"","orcid":"","institution":"Mohammed V University","correspondingAuthor":false,"prefix":"","firstName":"Yahia","middleName":"","lastName":"Cherrah","suffix":""},{"id":266719583,"identity":"17bd9ae4-aceb-41ba-9b88-b2a1ceb17681","order_by":6,"name":"Lhoussaine Zarayby","email":"","orcid":"","institution":"Hassan II University of Casablanca","correspondingAuthor":false,"prefix":"","firstName":"Lhoussaine","middleName":"","lastName":"Zarayby","suffix":""},{"id":266719584,"identity":"4b0e1cd2-f52c-4b21-8178-4bc3a5ee059d","order_by":7,"name":"Aziz Hirri","email":"","orcid":"","institution":"Hassan II University of Casablanca","correspondingAuthor":false,"prefix":"","firstName":"Aziz","middleName":"","lastName":"Hirri","suffix":""}],"badges":[],"createdAt":"2024-01-09 13:59:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3848449/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3848449/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.5267/j.ccl.2024.4.003","type":"published","date":"2024-01-01T00:26:57+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":49597890,"identity":"ecfde689-ee60-4d7a-9fc3-d599ee77ebff","added_by":"auto","created_at":"2024-01-15 03:21:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":15914,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTypical chromatograms obtained from tiaprofenic acid, ibuprofen (ID) and blank plasma.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3848449/v1/54653724c54a30473b4e91c5.png"},{"id":49597889,"identity":"1e9314b1-5d22-42fb-83ee-95597a89c94b","added_by":"auto","created_at":"2024-01-15 03:21:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":21907,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eaccuracy profil of tiaprofenic acid with \u003c/strong\u003eλ\u003cstrong\u003e= ±15%\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3848449/v1/e357952c537fe638851180a2.png"},{"id":58691520,"identity":"797e90de-98c7-4f21-b6a6-dead7edd0adc","added_by":"auto","created_at":"2024-06-20 00:27:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":822073,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3848449/v1/ef4fd301-d8ce-4d5b-b38f-6670b9f23619.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Determination and Validation of Tiaprofenic Acid in Human Plasma: A Detailed LC-MS/MS-Based Analysis Following ICH M10 Guidelines and the Accuracy Profile Approach","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBioanalysis has always been a subject of paramount interest to numerous regulatory agencies and organizations committed to safeguarding public health by ensuring the quality, efficacy, and safety of pharmaceutical drugs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Among these regulatory bodies, the United States Food and Drug Administration (FDA) emerged as one of the pioneers in developing guidelines for the validation of bioanalytical methods. Over time, these guidelines have evolved in response to the dynamic landscape of bioanalysis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Concurrently, the European Medicines Agency (EMA) has formulated its own guide on bioanalytical method validation, albeit with certain nuances compared to the FDA's standards [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA significant milestone in the realm of bioanalytical method validation was reached in May 2022 when the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) released the latest iteration of the ICH-M10 guideline. This guideline represents a significant stride towards standardizing the validation of bioanalytical methods on a global scale [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. While the ICH-M10 guideline meticulously outlines the parameters for validating bioanalytical methods and establishes acceptance criteria, it does not provide explicit experimental protocols for their application. In response to this need, this work aims to fill the gap by presenting a comprehensive experimental protocol for validating a bioanalytical method in accordance with the ICH-M10 standard.\u003c/p\u003e \u003cp\u003eFor our experimental validation, we have selected tiaprofenic acid as the target analyte. Tiaprofenic acid, a nonsteroidal anti-inflammatory drug (NSAID) belonging to the propionic acid group, derived from benzoic acid, is widely used as an analgesic and antipyretic agent [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Its inclusion in bioequivalence studies underscores the importance of accurate quantification in biological matrices, as this is pivotal for making critical determinations regarding drug safety and efficacy [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In the context of bioequivalence studies, the precise measurement of pharmaceutical dosage in biological matrices assumes paramount significance[\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Consequently, the development and validation of bioanalytical methods are imperative to establish the credibility and robustness of study outcomes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe ICH-M10 guideline serves as a compass, offering recommendations and validation criteria for the rigorous evaluation of bioanalytical methods [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, it is the integration of the accuracy profile approach that elevates the significance of this work. The accuracy profile approach, a statistical tool, plays a pivotal role in ensuring the precision and accuracy of bioanalytical methods [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. It provides a systematic means to assess the reliability of analytical results by establishing confidence intervals around the measured concentrations [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This approach helps quantify the degree of accuracy and precision that can be expected when using the validated method [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn summary, this research endeavor bridges the gap between the ICH-M10 guideline and practical implementation, ultimately facilitating the development of validated bioanalytical methods essential for conducting rigorous bioequivalence studies and ensuring the safety and efficacy of pharmaceutical interventions. The incorporation of the accuracy profile approach underscores our commitment to delivering not just validated methods but also robust and reliable tools for drug evaluation in the pursuit of better public health and patient outcomes.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Chemical Reagents and Equipment\u003c/h2\u003e \u003cp\u003eTiaprofenic acid was procured from Erregierre (San Paolo d'Argon, Italy), while the internal standard (IS), Ibuprofen, was sourced from Sigma Aldrich (Saint Louis, USA). Acetonitrile (LC/MS grade and HPLC grade) was acquired from VWR (Fontenay-Sous-Bois, France), while Methanol (LC/MS grade and HPLC grade) was obtained from Merck (Darmstadt, Germany). Formic acid (ACS grade) was purchased from Scharlau (Barcelona, Spain), and ammonium formate was procured from HIMEDIA (India). Ultrapure water, with a resistance of \u0026gt;\u0026thinsp;18.0 Ω/cm, was employed.\u003c/p\u003e \u003cp\u003eDrug-free fresh human plasma, collected from healthy subjects with lithium heparin as an anticoagulant, was sourced from BD-Vacutainer (UK) and stored at freezing temperature (~-25\u0026deg;C) until required.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. LC-MS/MS Conditions\u003c/h2\u003e \u003cp\u003eThe UHPLC system comprised an Agilent 1290 Infinity II quaternary pump (Germany), Agilent 1290 Infinity II autosampler (Germany), and Agilent 1290 Infinity II column thermostat (Germany). Chromatography was conducted on a Phenomenex C18 analytical column (150 mm \u0026times; 2 mm, 4 \u0026micro;m particle size; Phenomenex, USA) at a flow rate of 0.2 mL/min. Mobile phases consisted of 2-mM ammonium formate in water with 0.1% formic acid (A) and acetonitrile (LC/MS grade) (B). Optimal chromatographic separation was achieved with a 50% A and 50% B composition. The column temperature was maintained at 40\u0026deg;C, and tiaprofenic acid and Ibuprofen (IS) eluted at approximately 4.87 and 7.12 minutes, respectively.\u003c/p\u003e \u003cp\u003eMass spectrometric analysis was performed using an Agilent 6420 triple quadrupole mass spectrometer (Agilent, Germany) controlled by MassHunter B.09.00 software. The mass spectrometer operated in the positive Multiple Reaction Monitoring (MRM) mode with a dwell time of 200 ms per transition and utilized Electrospray Ionization (ESI). MRM transitions were 261\u0026thinsp;\u0026gt;\u0026thinsp;\u0026gt;\u0026thinsp;105 and 183 for tiaprofenic acid and 207\u0026thinsp;\u0026gt;\u0026thinsp;\u0026gt;\u0026thinsp;161 for IS, ensuring the widest resolution for all analytes. Fragmenter settings were optimized at 130 V for tiaprofenic acid and 132 V for IS, with collision energy set at 18 eV for tiaprofenic acid and 10 eV for IS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Preparation of Calibration Standards and Quality Control Samples\u003c/h2\u003e \u003cp\u003eCalibration samples were prepared by mixing human plasma (475 \u0026micro;L) with an aliquot (25 \u0026micro;L) of Tiaprofenic acid stock solutions in methanol (2, 10, 20, 100, 200, 250, 500, and 1000 ng/\u0026micro;L) to achieve nominal concentrations of 100, 500, 1000, 5000, 10000, 12500, 25000, and 50000 ng/mL for tiaprofenic acid.\u003c/p\u003e \u003cp\u003eQuality control (QC) samples were prepared by mixing human plasma (475 mL) with an aliquot (25 \u0026micro;L) of tiaprofenic acid stock solutions in methanol (2, 4, 400, 800 ng/\u0026micro;L) to provide LLOQ, low, medium, and high QC samples with nominal concentrations of 100, 200, 20000, and 40000 ng/mL for tiaprofenic acid.\u003c/p\u003e \u003cp\u003eSample Preparation: Sample preparation involved a protein precipitation process. Specifically, 25 \u0026micro;L of IS stock solution in methanol (500 ng/\u0026micro;L) was added to 500 \u0026micro;L of plasma containing tiaprofenic acid. After agitation, 500 \u0026micro;L of methanol and 1000 \u0026micro;L of acetonitrile were introduced, followed by 30 seconds of agitation. The samples were then centrifuged for 10 minutes at 5000 rpm. The extracted samples were filtered and diluted by half with A (2mM ammonium format with 0.1% formic acid). Finally, an aliquot (5 \u0026micro;l) was injected into the UHPLC system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Bioanalytical Method Validation\u003c/h2\u003e \u003cp\u003eThe work conducted in this study adhered rigorously to the principles of Good Laboratory Practice, as stipulated by the Organisation for Economic Cooperation and Development (OECD). Additionally, all validation experiments were executed in accordance with the ICH M10 guideline on Bioanalytical Method Validation and Study Sample Analysis [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, it is noteworthy that the statistical evaluation of accuracy and precision of the method diverged from the ICH M10 guideline and instead followed the guidelines set forth by the SFSTP [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. Selectivity\u003c/h2\u003e \u003cp\u003eSelectivity, as defined by ICH M10, signifies the capacity of an analytical method to differentiate and quantify the target analyte amidst potential interfering substances in the blank biological matrix. In this context, chromatograms were generated for blank plasma obtained from six individual sources, blank plasma spiked with tiaprofenic acid at the Lower Limit of Quantification (LLOQ) concentration, and the Internal Standard (IS) at the concentration utilized in the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. Specificity\u003c/h2\u003e \u003cp\u003eSpecificity, as per ICH M10, denotes the ability of a bioanalytical method to detect and distinguish the target analyte from other substances, including its related substances. In this vein, chromatograms were acquired for blank plasma, blank plasma spiked with tiaprofenic acid at the LLOQ concentration, and blank plasma spiked with IS at the concentration employed in the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3. Matrix Effect, Extraction Recovery \u0026amp; Process Efficiency:\u003c/h2\u003e \u003cp\u003eEvaluation of the matrix effect encompassed an analysis of low, medium, and high Calibration Quality Control samples (CQs) (n\u0026thinsp;=\u0026thinsp;4 for each level) prepared in the mobile phase (designated as A), those same CQ levels prepared in blank plasma before extraction from six different individual sources (designated as B), and those same CQ levels prepared in extracted blank plasma from six different individual sources (designated as C). Three parameters were scrutinized:\u003c/p\u003e \u003cp\u003e \u003cb\u003eMatrix Effect (ME)\u003c/b\u003e on the compound's LC-MS/MS response: ME = (average of peak areas (C) / average of peak areas (A)) * 100.\u003c/p\u003e \u003cp\u003e \u003cb\u003eExtraction Recovery (ER)\u003c/b\u003e, accounting for the matrix effect: ER = (mean of peak areas (B) / mean of peak areas (A)) * 100.\u003c/p\u003e \u003cp\u003e \u003cb\u003eProcess Efficiency (PE)\u003c/b\u003e, representing the extraction yield of the sample preparation method, disregarding the matrix effect: PE = (mean of peak areas (B) / mean of peak areas (C)) * 100.\u003c/p\u003e \u003cp\u003eAny ratio falling below 85% or exceeding 115% indicated the presence of an endogenous matrix effect.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.4.4. Calibration and Response Function\u003c/h2\u003e \u003cp\u003eThe LC-MS/MS system employed a variety of regression models chosen based on the calibration range's accuracy and the quality controls in use. The calibration curve was established using eight concentration levels, and the evaluation was based on three independent runs conducted on different days.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.4.5. Accuracy and Precision\u003c/h2\u003e \u003cp\u003eAccuracy and precision were assessed using four concentration levels as Quality Controls (QCs) within the calibration curve range, including the Lower Limit of Quantification (LLOQ), low, medium, and high QCs. These assessments were conducted both within-run and between-run using the same dataset. Within-run accuracy and precision were determined using six replicate samples for each of the four QC levels. Between-run accuracy and precision were derived from five independent runs on different days.\u003c/p\u003e \u003cp\u003eThe acceptance criterion for accuracy at each concentration level was set within \u0026plusmn;\u0026thinsp;15% of the nominal concentration, except at the LLOQ, where it was \u0026plusmn;\u0026thinsp;20%. For precision (%CV), the criterion was that it should not exceed 15%, except at the LLOQ, where it was \u0026plusmn;\u0026thinsp;20%. Compliance with this criterion necessitated that at least 2/3 of the total QCs and at least 50% at each concentration level met these requirements.\u003c/p\u003e \u003cp\u003eTo statistically demonstrate the accuracy and precision of the method, an accuracy profile approach was applied. This approach utilizes tolerance intervals to calculate upper and lower limits at each concentration level, resulting in a visually comprehensible graph that aids in the assessment of method performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.4.6. Stability\u003c/h2\u003e \u003cp\u003eThe study encompassed an investigation into the stability of tiaprofenic acid under various storage conditions using low and high-quality control concentrations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.4.7. Hemolysis Effect\u003c/h2\u003e \u003cp\u003eThe hemolysis effect test was conducted on hemolyzed plasma loaded with low and high-quality controls in triplicate. The mean accuracy of QCs was expected to fall within \u0026plusmn;\u0026thinsp;15% of the nominal concentration, with the precision (%CV) not exceeding 15%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.4.8. Dilution Integrity Test\u003c/h2\u003e \u003cp\u003eThe dilution integrity test was carried out at twice the concentration of the high-quality control, with five replicates. Here, the mean accuracy of the dilution QCs was required to be within \u0026plusmn;\u0026thinsp;15% of the nominal concentration, and the precision (%CV) should not surpass 15%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.4.9. Carryover Test\u003c/h2\u003e \u003cp\u003eTo evaluate the auto sampler's carryover, a specific sequence of injections was followed, including blank plasma, Lower Limit of Quantification (LLOQ), Upper Limit of Quantification (ULOQ), and blank plasma. The criterion for carryover in the blank plasma following the ULOQ was that it should not exceed 20% of the analyte response at the LLOQ and 5% of the response for the IS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e2.4.10. Accuracy Profile Approach\u003c/h2\u003e \u003cp\u003eTo robustly demonstrate the accuracy and precision of the method, an accuracy profile approach was applied. This approach utilizes tolerance intervals to calculate upper and lower limits at each concentration level, resulting in a visually comprehensible graph that aids in the assessment of method performance. The accuracy profile is a powerful tool based on total error, providing a guarantee and prediction of future results when employing an approved approach.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Selectivity\u003c/h2\u003e \u003cp\u003eThe selectivity of the method was assessed by examining the absence of any interfering peaks that co-eluted with tiaprofenic acid and the Internal Standard (IS) at their respective retention times. The results revealed that the interference percentage (%) for tiaprofenic acid at the Lower Limit of Quantification (LLOQ) was below 20%, while for the IS, it remained below 5%. These findings confirm the method's selectivity and are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for reference.\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\u003eselectivity result\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman plasma\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBatch-1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBatch \u0026minus;\u0026thinsp;2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBatch \u0026minus;\u0026thinsp;3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBatch \u0026minus;\u0026thinsp;4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBatch \u0026minus;\u0026thinsp;5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBatch \u0026minus;\u0026thinsp;6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterference percentage (%) of tiaprofenic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterference percentage (%) of IS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01\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=\"Section2\"\u003e \u003ch2\u003e3.2. Specificity\u003c/h2\u003e \u003cp\u003eThe specificity of our method was rigorously evaluated through a meticulous examination of chromatograms representing different scenarios. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays these chromatograms, which include those for blank plasma, blank plasma spiked with tiaprofenic acid at the Lower Limit of Quantification (LLOQ) concentration, and blank plasma spiked with the Internal Standard (IS) at the concentration utilized in this study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe key criterion for assessing specificity was the determination of retention times, which were found to be approximately 5 minutes for tiaprofenic acid and 7.4 minutes for IS. Significantly, the examination of these chromatograms revealed the complete absence of any interfering peaks in the blank plasma at the specific retention times corresponding to tiaprofenic acid and IS. This unequivocal absence of interferences reinforces the method's exceptional specificity, affirming its suitability for precise and accurate analytical purposes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Matrix effect\u003c/h2\u003e \u003cp\u003eIn order to comprehensively assess the matrix effect, extraction recovery, and process efficiency of tiaprofenic acid, we present the findings in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. This analysis is crucial for understanding the impact of the biological matrix on the analytical method's performance.\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\u003ematrix effect result\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eTiaprofenic acid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eLQC (200 ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eHQC (40000ng/ml)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTiprofenic acid preparing in mobile phase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTiaprofenic Acid preparing in extracted blank plasma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTiaprofenic acid preparing in blank plasma before extraction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTiprofenic acid preparing in mobile phase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTiaprofenic Acid preparing in extracted blank plasma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTiaprofenic acid preparing in blank plasma before extraction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003ePlasma control-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea Mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18145.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18703.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18934.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6052813.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5469410.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5465336.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSTD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1128.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e193.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e249.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e844062.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10848.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e351220.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCV %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e103.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e90.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e104.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e90.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e101.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e99.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003ePlasma control-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea Mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18145.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19359.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18053.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6052813.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6465180.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5689189.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1128.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e160.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e250.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e844062.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e89070.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e118996.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCV %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e106.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e106.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e99.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e93.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e93.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e88.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003ePlasma control-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea Mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18145.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19658.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18567.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6052813.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6465180.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6255489.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1128.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e173.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e244.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e844062.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e89070.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e197894.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCV %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e108.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e106.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e102.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e103.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e94.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e96.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003ePlasma control-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea Mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18145.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18289.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17839.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6052813.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5469410.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6368561.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1128.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e251.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e844062.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10848.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e129947.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCV %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e100.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e90.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e98.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e105.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e97.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e116.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003ePlasma control-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea Mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18145.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19907.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18573.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6052813.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5703717.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5749054.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1128.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e296.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e844062.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29775.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e127751.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCV %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e109.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e94.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e102.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e94.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e93.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e100.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003ePlasma control-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea Mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18145.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18572.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19203.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6052813.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5693190.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5023272.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1128.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e270.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e210.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e844062.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e237057.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e132605.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCV %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e102.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e94.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e105.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e82.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e103.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e88.23\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\u003eRemarkably, regardless of the concentration levels tested for tiaprofenic acid, no significant matrix effect was observed. The matrix effect (ME) values fell within the range of [90% \u0026minus;\u0026thinsp;109%], suggesting minimal interference from the matrix in the analysis. The process efficiency (PE) remained within the range of [87% \u0026minus;\u0026thinsp;116%], indicating the method's effectiveness in extracting the analyte from the matrix. Furthermore, the extraction recovery (ER) values obtained for both low and high concentrations of tiaprofenic acid were between [82% \u0026minus;\u0026thinsp;105%], affirming the method's reliability in recovering the analyte from the biological matrix.\u003c/p\u003e \u003cp\u003eThese findings collectively reinforce the robustness and reliability of our analytical method in the presence of diverse biological matrices, highlighting its suitability for accurately quantifying tiaprofenic acid.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Calibration and response function\u003c/h2\u003e \u003cp\u003eOur calibration process for tiaprofenic acid meticulously evaluated various regression models to determine the most suitable model for calculating sample concentrations accurately. The assessment revealed that a quadratic model with a weighting of 1/concentration^2 provided the most robust and accurate results across the calibration curve. This model selection is crucial, as it directly impacts the accuracy and reliability of concentration calculations.\u003c/p\u003e \u003cp\u003eThe nominal percentages of the three distinct calibration ranges are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. These ranges cover a wide spectrum of concentrations, ensuring the method's applicability across a broad analytical range. It is noteworthy that the accuracy of the back-calculated concentrations for each calibration standard falls comfortably within the stringent criterion of \u0026plusmn;\u0026thinsp;15%. This indicates the method's precision and reliability in accurately quantifying tiaprofenic acid concentrations, regardless of the specific concentration levels.\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\u003ecalibration result\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c10\" namest=\"c3\"\u003e \u003cp\u003eNominal concentration (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e50000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAccuracy (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e101.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e90.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e105.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e97.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e104.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e106.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e105.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e105.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e103.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e98.58\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=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eaccuracy and precision result\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAnalyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNominal Conc. (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eWithin-run (n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eBetween-run (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCalculated Conc. (ng/mL) Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccuracy (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePrecision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCalculated Conc. (ng/mL) Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAccuracy (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePrecision\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eTiaprofenic acid\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLLOQ (100 ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e96.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQCL(200 ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e207\u0026thinsp;\u0026plusmn;\u0026thinsp;19.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e197\u0026thinsp;\u0026plusmn;\u0026thinsp;14.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQCM(20000 ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20000\u0026thinsp;\u0026plusmn;\u0026thinsp;900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20600\u0026thinsp;\u0026plusmn;\u0026thinsp;1490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQCH(40000 ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37300\u0026thinsp;\u0026plusmn;\u0026thinsp;2500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40700\u0026thinsp;\u0026plusmn;\u0026thinsp;2190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.39\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\u003eThese findings collectively validate the robustness and accuracy of our calibration process and demonstrate the method's suitability for precise quantification of tiaprofenic acid concentrations across a wide range of values. This level of accuracy is essential for various bioanalytical applications, where reliable concentration determination is paramount.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Accuracy and precision\u003c/h2\u003e \u003cp\u003eThe accuracy of our method was calculated by comparing the determined concentrations to the nominal values. Across all concentration levels, ranging from LLOQ to CQH, the accuracy consistently ranged from 80.9\u0026ndash;109% of the nominal values. These accuracy percentages indicate that our method provides reliable and consistent measurements, as they are well within acceptable limits.\u003c/p\u003e \u003cp\u003ePrecision, often expressed as the coefficient of variation (%CV), provides insights into the repeatability and reproducibility of the method. The precision values, expressed as %CV, ranged from 0.45\u0026ndash;11.9%. These precision values are indicative of excellent repeatability and reproducibility, as they are substantially below the acceptable limit of 20% at the LLOQ and 15% at all other concentration levels.\u003c/p\u003e \u003cp\u003eThe combined assessment of accuracy and precision underscores the robustness and reliability of our method for tiaprofenic acid quantification. Notably, at all concentration levels, the accuracy and precision values remained well within the predefined acceptance criteria. Specifically, accuracy values were \u0026lt;\u0026thinsp;20% at the LLOQ and \u0026lt;\u0026thinsp;15% at all other levels. Consequently, our method meets the stringent criteria for accuracy and precision, reinforcing its suitability for accurate tiaprofenic acid determination in diverse analytical applications.\u003c/p\u003e \u003cp\u003eThese results unequivocally establish the robustness and reliability of our method for tiaprofenic acid quantification. The method's consistent accuracy and precision, well within stringent acceptance criteria, demonstrate its suitability for precise and reliable tiaprofenic acid analysis across a wide range of concentrations. This level of analytical performance is fundamental to various bioanalytical applications where accuracy and precision are paramount.\u003c/p\u003e \u003cp\u003eThe accuracy profile, depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, plays a critical role in assessing the method's reliability and its ability to consistently produce accurate results. This assessment incorporates tolerance intervals within the acceptance limits, emphasizing a crucial aspect of method validation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe inclusion of tolerance intervals within the acceptance limits signifies a statistical assurance that the method can reliably deliver results meeting predefined criteria. Specifically, this means that the probability of the difference between the calculated concentrations and the reference values remaining below the acceptance limit exceeds the chosen β value, set as a proportion of future results at 90%.\u003c/p\u003e \u003cp\u003eIn practical terms, this indicates that across a concentration range spanning from 100 ng/ml to 40000 ng/ml, the analyst can have confidence that the method consistently maintains an average β probability of producing results falling within the acceptable limits. This statistical assurance provides a high level of confidence in the method's ability to yield accurate and precise results consistently over a wide range of tiaprofenic acid concentrations.\u003c/p\u003e \u003cp\u003eThis utilization of tolerance intervals and the β value underscores the method's robustness and its suitability for bioanalytical applications where maintaining consistent accuracy and precision is essential. It offers a valuable tool for quality control and assurance, enhancing the method's reliability and trustworthiness in various analytical settings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Stability Assessment\u003c/h2\u003e \u003cp\u003eEnsuring the stability of tiaprofenic acid under various conditions is integral to establishing the reliability and applicability of our method. The stability tests conducted under different scenarios yielded encouraging results, reaffirming the method's robustness.\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e3.6.1. Freeze-Thaw Stability\u003c/h2\u003e \u003cp\u003eTiaprofenic acid exhibited remarkable stability during three consecutive freeze-thaw cycles. The mean calculated concentrations for both low (QCL) and high (QCH) quality controls, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, remained consistent, with accuracy percentages ranging from 101.6\u0026ndash;111.7%. This outcome underscores the method's suitability for handling samples subject to multiple freeze-thaw cycles, a condition often encountered in real-world sample processing.\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\u003estability result\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStability experiments\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMean calculated Conc. (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eAccuracy (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQCL (200 ng/mL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQCH (40000 ng/mL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQCL (200 ng/mL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQCH (40000 ng/mL)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze-thaw matrix stability (3rd cycles)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e203.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e111.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBench top (short-term) matrix stability (8 h at 22\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e225.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuto-sampler matrix stability (72 h at 10\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e201.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e108.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLong-term matrix stability (164j at -25\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e229.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105.11\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=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e3.6.2. Bench Top (Short-Term) Stability\u003c/h2\u003e \u003cp\u003eShort-term benchtop stability, conducted over 8 hours at 22\u0026deg;C, demonstrated the resilience of tiaprofenic acid. The calculated concentrations for QCL and QCH quality controls remained stable, with accuracy percentages of 112.7% and 106.5%, respectively. This result indicates that the method is well-suited for short-term sample storage at ambient conditions without compromising accuracy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e3.6.3. Auto-Sampler Stability\u003c/h2\u003e \u003cp\u003eThe method also exhibited commendable stability during auto-sampler storage for an extended period of 72 hours at 10\u0026deg;C. The calculated concentrations for both QCL and QCH quality controls remained consistent, with accuracy percentages of 100.8% and 108.6%, respectively. This finding is essential for analytical workflows that involve automated sample handling and storage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e3.6.4. Long-Term Stability\u003c/h2\u003e \u003cp\u003ePerhaps most impressively, tiaprofenic acid demonstrated exceptional long-term stability during a 164-day storage period at -25\u0026deg;C. The calculated concentrations for QCL and QCH quality controls remained virtually unchanged, with accuracy percentages of 114.5% and 105.11%, respectively. This remarkable stability over an extended duration highlights the method's reliability for long-term sample storage, a critical aspect of bioanalytical studies.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Hemolysis Effect\u003c/h2\u003e \u003cp\u003eThe assessment of the hemolysis effect on tiaprofenic acid concentrations at both QCL and QCH using hemolyzed plasma produced encouraging results. The accuracy values obtained for each concentration were 97.2% and 99.7%, respectively, both well below the accepted threshold of 15%. This outcome firmly establishes the absence of a hemolysis effect, affirming the method's suitability for analyzing plasma samples that may be subject to hemolysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e3.8. Dilution Integrity Test\u003c/h2\u003e \u003cp\u003eThe dilution integrity test, conducted on a plasma sample loaded with twice the concentration of CQH (40000 ng/mL), yielded accuracy and precision results of 96.08% and 8.21%, respectively. These results indicate that tiaprofenic acid samples can be reliably diluted by a factor of 2, extending the range of the calibration curve. This flexibility in sample dilution is valuable in situations where samples with high concentrations need to be analyzed accurately.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e3.9. Auto-Sampler Carryover Test\u003c/h2\u003e \u003cp\u003eTo investigate the potential carryover effect of the auto-sampler, a sample of blank plasma was injected immediately after a sample from the upper limit of quantification of tiaprofenic acid. The interference percentages for tiaprofenic acid and IS were found to be 0.62% and 0.015%, respectively, both comfortably below the 20% threshold for tiaprofenic acid and 5% for IS. This outcome provides strong evidence of the absence of auto-sampler carryover for tiaprofenic acid and IS, ensuring the integrity of subsequent sample analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eIn this study, we have undertaken the validation of a bioanalytical method for the determination of tiaprofenic acid in human plasma by LC-MS/MS, guided by the principles of the ICH M10 guideline. Our comprehensive approach not only adheres to the conventional validation parameters but also integrates the powerful accuracy profile methodology. Through rigorous assessment, we have demonstrated the reliability and robustness of this method for its intended application in bioequivalence studies.\u003c/p\u003e \u003cp\u003eThe integration of the accuracy profile approach significantly enhances our confidence in the method's performance. By evaluating accuracy and precision across a wide dynamic range, we have gained a deeper understanding of the method's capabilities. This is crucial, especially in the context of bioequivalence studies, where minute differences in drug concentrations can influence clinical outcomes. The accuracy profile's statistical rigor and predictive power make it an invaluable tool for ensuring the accuracy and reliability of analytical results.\u003c/p\u003e \u003cp\u003eFurthermore, our work aligns with the latest ICH M10 guideline, providing a practical and experimental protocol for bioanalytical method validation. Tiaprofenic acid, a nonsteroidal anti-inflammatory drug, served as our model compound, illustrating the method's potential application in drug analysis. As we move forward in the realm of pharmaceutical and clinical research, the importance of validated bioanalytical methods cannot be overstated. They form the bedrock upon which critical decisions regarding drug safety and efficacy are made.\u003c/p\u003e \u003cp\u003eThis study not only contributes to the advancement of bioanalytical science but also highlights the importance of integrating modern statistical approaches like the accuracy profile. By doing so, we bolster our ability to generate accurate and precise data, ensuring that the conclusions drawn from our analyses are founded on solid scientific principles. Ultimately, this work underscores our commitment to upholding the highest standards of quality, reliability, and integrity in bioanalysis.\u003c/p\u003e \u003cp\u003eIn the ever-evolving landscape of pharmaceutical research and development, the validation of bioanalytical methods remains an indispensable step. As we continue to pursue innovative solutions for improving human health, validated methods supported by rigorous assessments like the accuracy profile will play a pivotal role in safeguarding public health and advancing our understanding of drug therapies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHouda Bouchafra: Methodology, Validation, Investigation, Writing \u0026ndash; original draft, Visualization. Aimen Elorche: Methodology, Validation, Investigation, Writing \u0026ndash; original draft, Visualization. Choukri Elkhabbaz: Investigation, review . Amine Cheikh: Formal analysis, Software, Writing \u0026ndash; review \u0026amp; editing. My El Abbes Faouzi: Methodology, Writing \u0026ndash; review \u0026amp; editing. Yahia Cherrah: Writing \u0026ndash; review \u0026amp; editing. Aziz Hirri: Writing \u0026ndash; review \u0026amp; editing. Lhoussaine Zarayby: Investigation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eS. Pandey, P. Pandey, G. Tiwari, R. Tiwari. Bioanalysis in drug discovery and development (2010) Pharm Methods;1(1):14‑24. \u003c/li\u003e\n\u003cli\u003eP. G. Swann, M. A. Shapiro. Regulatory considerations for development of bioanalytical assays for biotechnology products (2011) Bioanalysis. mars;3(6):597‑603. \u003c/li\u003e\n\u003cli\u003eEMA. European Medicines Agency. 2019. ICH M10 on bioanalytical method validation - Scientific guideline. https://www.ema.europa.eu/en/ich-m10-bioanalytical-method-validation-scientific-guideline\u003c/li\u003e\n\u003cli\u003eG. L. Plosker, A. J. Wagstaff. Tiaprofenic Acid. Drugs. 1 d\u0026eacute;c 1995;50(6):1050‑75. \u003c/li\u003e\n\u003cli\u003eS. C. Chow. Bioavailability and Bioequivalence in Drug Development (2014) Wiley Interdiscip Rev Comput Stat;6(4):304‑12. \u003c/li\u003e\n\u003cli\u003eG. Tiwari, R. Tiwari. Bioanalytical method validation: An updated review (2010) Pharm Methods;1(1):25‑38. \u003c/li\u003e\n\u003cli\u003eM. M. Deshpande, V. S. Kasture, M. M. Chavan and MJ. Bioanalytical Method Development and Validation: A Review (2019) In: Recent Advances in Analytical Chemistry. IntechOpen; \u003c/li\u003e\n\u003cli\u003eG. T. Ward, J. A. Stead, M. Freeman. A Rapid and Specific Method for the Determination of Tiaprofenic Acid in Human Plasma by High-Performance Liquid Chromatography (1982) J Liq Chromatogr. janv;5(1):165‑74. \u003c/li\u003e\n\u003cli\u003eC. Marlet, G. Lognay. Development and validation by accuracy profile of a method for the analysis of monoterpenes in indoor air by active sampling and thermal desorption-gas chromatography-mass spectrometry (2010) Talanta. 15 sept;82(4):1230‑9. \u003c/li\u003e\n\u003cli\u003eC. Khabbaz, A. El Orche, M. Bouatia, A. Cheikh, M. E. A Fawzi, Y. Cherrah, et al. Development and Validation of a Bioanalytical Method for the Quantification of Clopidogrel in Human Plasma by LC-MS/MS using the Accuracy Profiles (2022). \u003c/li\u003e\n\u003cli\u003eC. De Saint Jores, R. Klein, A. Legendre, J. Dugay, D. Thi\u0026eacute;baut, J. Vial. Preparation of Thermodesorption Tube Standards: Comparison of Usual Methods Using Accuracy Profile Evaluation (2022) Separations;9(8):226. \u003c/li\u003e\n\u003cli\u003eA. Ouakhssase, N. Fatini, E. Ait Addi. Chemometric Approach Based on Accuracy Profile and Data Chronological Distribution as a Tool to Detect Performance Degradation and Improve the Analytical Quality Control for Aflatoxins\u0026rsquo; Analysis in Almonds Using UPLC\u0026ndash;MS/MS. ACS Omega. 18 mai 2021;6(19):12746‑54. \u003c/li\u003e\n\u003cli\u003eP. Hubert, J.J. Nguyen-Huu, B. Boulanger, E. Chapuzet, P. Chiap, N. Cohen, et al. Harmonization of strategies for the validation of quantitative analytical procedures A SFSTP proposal--Part I (2004) J Pharm Biomed Anal;36(3):579‑86. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Bioanalytical method validation, ICH M10 guideline, accuracy profile, tiaprofenic acid, LC-MS/MS analysis, pharmaceutical research","lastPublishedDoi":"10.21203/rs.3.rs-3848449/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3848449/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe validation of bioanalytical methods holds critical importance for regulatory agencies and organizations dedicated to ensuring the safety, efficacy, and quality of pharmaceuticals. In this context, the recent release of the ICH M10 guideline in May 2022 represents a significant milestone in standardizing bioanalytical method validation globally. However, this guideline lacks explicit experimental protocols for implementation.\u003c/p\u003e \u003cp\u003eIn this study, we address the practical implementation of the newly released ICH M10 guideline by providing a detailed validation protocol for a bioanalytical method. Our method specifically targets tiaprofenic acid, a widely used nonsteroidal anti-inflammatory drug. Tiaprofenic acid is a critical component in bioequivalence studies, underscoring the necessity for precise and accurate quantification within complex biological matrices.\u003c/p\u003e \u003cp\u003eThe integration of the accuracy profile approach, a statistical tool, enhances the significance of this work. This approach aids in assessing the accuracy and precision of bioanalytical methods, establishing confidence intervals around measured concentrations and quantifying the level of accuracy and precision expected when using the validated method.\u003c/p\u003e \u003cp\u003eOverall, this study contributes to advancing bioanalytical science, aligning with the latest ICH guidelines, and emphasizing the importance of validated methods supported by modern statistical approaches like the accuracy profile. It underscores the commitment to maintaining the highest standards of quality and reliability in bioanalysis, which is crucial for ensuring public health and advancing drug therapies.\u003c/p\u003e","manuscriptTitle":"Determination and Validation of Tiaprofenic Acid in Human Plasma: A Detailed LC-MS/MS-Based Analysis Following ICH M10 Guidelines and the Accuracy Profile Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-15 03:21:04","doi":"10.21203/rs.3.rs-3848449/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":"24b574db-f055-4d62-bf9c-d091b2bf2bf0","owner":[],"postedDate":"January 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-06-20T00:26:57+00:00","versionOfRecord":{"articleIdentity":"rs-3848449","link":"https://doi.org/10.5267/j.ccl.2024.4.003","journal":{"identity":"current-chemistry-letters","isVorOnly":true,"title":"Current Chemistry Letters"},"publishedOn":"2024-01-01 00:26:57","publishedOnDateReadable":"January 1st, 2024"},"versionCreatedAt":"2024-01-15 03:21:04","video":"","vorDoi":"10.5267/j.ccl.2024.4.003","vorDoiUrl":"https://doi.org/10.5267/j.ccl.2024.4.003","workflowStages":[]},"version":"v1","identity":"rs-3848449","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3848449","identity":"rs-3848449","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}