Developing Superior Amoxicillin Delivery Systems: AI-Driven Optimization of LNPs for H. pylori Treatment

Developing Superior Amoxicillin Delivery Systems: AI-Driven Optimization of LNPs for H. pylori Treatment | 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 Developing Superior Amoxicillin Delivery Systems: AI-Driven Optimization of LNPs for H. pylori Treatment Kumari Kajal, MUTHU KUMAR SAMPATH, Hare Ram Singh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4251223/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Nov, 2024 Read the published version in Biologia → Version 1 posted 6 You are reading this latest preprint version Abstract The development of nano delivery systems, particularly lipid nanoparticles (LNP), for both hydrophobic and hydrophilic drugs has seen significant advancements in recent years. Fine tuning LNP formulations is crucial due to the impact of various parameters on their quality of efficacy. The study investigated the influence of formulation variables on amoxicillin-loaded LNPs designed for anti- Helicobacter pylori activity. Size, polydispersity index (PDI), Zeta potential and entrapment efficiency were evaluated across diverse formulations. The impact of particle size on drug release and encapsulation was explored. Artificial intelligence AI based design of experiments generated formulations to minimize the particle size, PDI and Zeta potential while maximizing the EE, accounting for factor interactions. Additionally, the user friendliness of QbD (Quality by Design), Machine Learning (ML), and DOE were compared. Methods and results : A Box-Behnken design with 27 formulations was chosen for amoxicillin (amox) LNP optimization. Particle size distribution, zetapotential, PDI, and entrapment efficiency were measured for each formulation. LNP ranged in size from 200–600 nm, zeta potential ranged from − 5 - -40 mV, PDI from 0.1- 1 and EE from 5-100%. Characterization included DLS, FESEM, FTIR and SEM. Obtained results were statistically analysed. Discussion : This study demonstrates the potential of AI- driven DOE for optimizing LNP formulations. We explained effect of different parameters lipid concentration, surfactant concentration, sonication time and sonication speed on nanoparticles and derived formula for further prediction. The identified formulations exhibited desired antibiotic efficiency with minimum chemical usage, suggesting the effectiveness of this approach. Further research explored it as a drug with more bioavailability, stability and cheap alternative over traditional drugs in market with more side effects and less bioavailability. Solid Lipid nanoparticles design of experiment Nano biotics Artificial intelligence Machine Learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction In the development of robust protocols and the establishment of products from the bedrock upon which scientific progress thrives across diverse fields of inquiry. This holds true for nanomedicines, where the creation of nanoparticles-based drug delivery systems involves intricate experimental conditions that significantly impact outcomes. These conditions are closely tied to the specific materials under study. In recent work, the application of design of experiment (DoE) has gained prominence. DoE has gained prominence. DoE is a powerful statistical technique that simultaneously varies multiple factors to identify optimal parameter configurations while minimizing the number of experimental runs. Despite its potential for innovation and process optimization, DoE is a powerful statistical technique that simultaneously varies multiple factors to identify optimal parameter configurations while minimizing the number of experimental runs. Despite its potential for innovation and process optimization, DoE remains underutilized in nanomedicine. Now here the question arises why Design of experiment why not ML ? the simple answer to this question is the Design of Experiments (DOE) and Machine Learning (ML) are distinct approaches used for different purposes. DOE focuses on controlled experiments, causalities and process optimization while ML excels at pattern recognition and preduction on large datasets. Moreover ML requires much more advanced learning of coding language like python or oracle which can be time consuming process. So moving on excluding ML for optimization. In this study we explore DoE applications in formulating nano vectors for drug delivery, emphasizing process variables, as drug Amoxicillin was taken and its efficacy was tested against Helicobacter pylori . H. pylori is a gram-negative microorganism characterized by its short, helical, S-shaped structure, measuring 0.5–1.5µm in width and 2–4 µm in length. Predominantly located in region of the stomach, H. pylori is responsible for chronic gastric infections, affecting over half of the global population (Marshall & Adams,2008) . While the precise mode of transmission and infections, affecting over half of the global population. while the precise mode of transmission and infections, affecting over half of the global population. The mode of transmission and infection remains unclear, it is commonly believed to occur through faecal- oral and oral- oral routes, often facilitated by water or food consumption ( Brown, 2000 ) . The increasing prevalence of antibiotic resistance in H. pylori necessitates the development of more effective drugs to combat this widespread infection (“United Nations Meeting on Antimicrobial Resistance,2019) . Current treatment regimens often involving multiple antibiotics and medications, can be complex, expensive, and have potential side effects, leading to decreased patient compliance and treatment failure. This highlights the urgent need for simpler and more effective therapies. Furthermore, H. pylori infections pose a significant public health burden, being linked to various gastrointestinal issues and incurring substantial healthcare costs. Eradicating the bacteria not only reduces this burden but also offers long term benefits for individuals including a decreased risk of peptic ulcers and potentially even stomach cancer ( Chen et al., 2021 ). H. pylori plays a role in the development of various extra- gastric conditions, including mucosa-associated lymphoid tissue lymphoma (MALT), idiopathic thrombocytopenic purpura, as well as vitamin B12 and iron deficiencies (Oztekin et al., 2021). Recently the nanomaterials have been evaluated as an important agent in medicine as carriers for various soluble and insoluble drugs ( Li & Mooney, 2016 ). SLNs are tiny, spherical artificial vesicles that can be made from cholesterol and natural phospholipids. The hydrophobic and hydrophilic properties of SLNs, in addition to their biocompatibility, make them ideal drug delivery platforms (Akbarzadeh et al., 2013 ). Solid lipid nanoparticles now these days are attracting many researchers as a safe drug delivery system. Lipids are naturally present in our body so when they are inside our body, they are not considered foreign substances which reduces their chance of rejection from our body these nanoparticles have shown the least immunogenicity, unlike other drug delivery systems ( Mukherjee et al., 2009 ). There are large numbers of factors that influence the process of manufacturing different formulations, if they are carried out by hit & a trial method of screening these could be a lethargic task. This will not only be time-consuming but also an expensive process and there are very less chances that they will give accurate as well as verified results. For this purpose, Box- Behnken Design can be one of the reliable options to optimize the effect of variables on different formulations and drug delivery systems like Liposomes ( Harbi et al., 2016 ), transferosomes ( Ahmed, 2014 ) , niosomes (Vanaja & Shobha Rani, 2007) , Alginate- reinforcement Chitosan nanoparticles (Ahmed & El-Say, 2014) , Transdermal film (matrix Type) ( El-Say et al., 2015 ), and protein loaded PLGA nanoparticles, Eudragit microspheres, and control. Amoxicillin-loaded solid lipid nanoparticles (SLNs) were created in this study to protect amoxicillin from acidic degradation, enhance local release, and improve bioavailability ( Gilta, 2022 ). In this research we have developed alternative to these current systems for eradication of H. pylori. As reported by many researchers’ Solid lipid nanoparticles (SLNs), which have been produced, are unique among all drug delivery systems due to their low toxicity and the technological and financial practicality of mass manufacture They are typically 1nm to 1000 nm ( Kshirsagar & Saudagar, 2016 ) ( Almeida & Souto, 2007 ) . The linear model, placket-Burman design was used to optimise the SLNs loading capacity, polydispersity index, and particle size. The upgraded nanoparticles were described and found to have a mean size width of 280 nm and a zeta potential of approximately about − 20 mV. The SLNs likewise showed a low polydispersity file of 0.21. The SLNs were also characterized which showed a spherical shape (Ceballos et al., 2005). Materials and Methodology In the context of our scientific study, palmitic acid and Tween 80 were procured from Pallav Chemicals & solvents Pvt. Ltd. As supplied. We employed 99% Ethanol, acquired from Changsu Hongsheng Fine Chemical Co. Ltd., as the solvent. Additionally, purified water for dispersion purposes was sourced from the Nanopure UltraTM water distillation unit. Notably, all materials were utilized in their original analytical grade form without any modifications. 2.1 Experimental Methodology 2.1.1 Preparation of Palmitic acid-based SLN The purpose of our study was to investigate the formulation and characterization of palmitic acid-based lipid nanoparticles. Specifically, we explore their preparation using a combination of solvent evaporation and probe sonication methods. Our Focus lies in understanding the behavior of these nanoparticles and their potential applications in various fields. In the formulation of palmitic acid-based lipid nanoparticles, we employed a combination of solvent evaporation and probe sonication techniques (Camellia Sinensis et al., 2018) . Initially, Palmitic acid was completely dissolved in 5 ml of ethanol through magnetic stirring at 60 o C ± 5 o C, yielding the organic solvent. Simultaneously, in a separate container, Tween 80 was dissolved in 30 ml of Milli Q water (Type I) as the aqueous medium, utilizing a magnetic stirrer at 70 o C and a speed of 1200 rpm. Subsequently, the lipid-containing organic phase was gradually added to the aqueous phase under continuous stirring. The resulting mixture was concentrated to a final volume of 7 ml over an evaporation period of approximately 3 hrs. The resulting white, translucent liquid was then combined with chilled Milli Q water to maintain a total volume of 15 ml (Zhang et al., 2007). 2.1.2 Experimental Optimization Design Pharmacological formulation development can be time-consuming so we explored an efficient approach using the Design of Experiments (DOE) ( Barot et al., 2011 ). Unlike traditional methods, DOE allows us to simultaneously vary multiple factors, making it cost effective and time efficient specifically, we employed a 27 run, four factor Box- Behnken design, which is suitable for quadratic models (Jankovic et al., 2021) . Independent variables: X1 (Lipid Concentration): We use palmitic acid as a lipid the concentration was measured in mg/ml. X2 (Surfactant Concentration): Tween80 serves as the surfactant, with its concentration measured in mg. X3 (sonication time): The sonication time is measured in minutes (min). X4(Pulse Frequency): we analyze the pulse frequency, expressed as a percentage. Our goal was to understand the impact of these variables on particle size, polydispersity index (PDI), and Zeta potential in order get formulation with max EE and sustained drug release, using dynamic light scattering (DLS) measurements with a Zetasizer instrument (Ana-Maria et al., 2018) . By systematically exploring these factors, we aim to optimize solid lipid nanoparticle formulations for various applications. As demonstrated in Table 1 , this design was made to create 27 alternative Solid lipid nanoparticle formulations. Table 1 Twenty seven experiment series designed by Box-Behnken model Exp No Exp Name Run Order Incl/Excl lipid concentration Surfactant concentration sonication time pulse frequency Particle Size PDI EE Zeta potential 1 N1 2 Incl 2 150 20 60 275 0.33 67 -20.44 2 N2 18 Incl 30 150 20 60 380 0.5 89 -17 3 N3 21 Incl 2 300 20 60 250 0.358 75 -18.89 4 N4 20 Incl 30 300 20 60 430 0.28 68 -17.98 5 N5 4 Incl 16 225 10 30 430 0.45 95.8 -21.03 6 N6 10 Incl 16 225 30 30 274 0.33 58 -22 7 N7 15 Incl 16 225 10 90 345 0.76 64.9 -26.6 8 N8 13 Incl 16 225 30 90 260 0.45 87 -26.8 9 N9 6 Incl 2 225 20 30 245 0.33 81.5 -25.7 10 N10 14 Incl 30 225 20 30 400 0.31 85 -23 11 N11 23 Incl 2 225 20 90 216 0.52 67 -28.9 12 N12 11 Incl 30 225 20 90 389 0.7 78 -22.1 13 N13 19 Incl 16 150 10 60 378 0.67 87 -20.13 14 N14 5 Incl 16 300 10 60 420 0.59 78 -14.4 15 N15 17 Incl 16 150 30 60 300 0.42 78 -19.13 16 N16 12 Incl 16 300 30 60 367 0.35 66 -15.19 17 N17 1 Incl 2 225 10 60 286 0.72 79 -17.8 18 N18 25 Incl 30 225 10 60 490 0.85 84 -19.56 19 N19 22 Incl 2 225 30 60 265 0.45 69 -24.3 20 N20 8 Incl 30 225 30 60 387 0.47 79 -17.9 21 N21 7 Incl 16 150 20 30 390 0.48 82 -19.9 22 N22 26 Incl 16 300 20 30 350 0.17 72 -18.88 23 N23 9 Incl 16 150 20 90 289 0.39 67.8 -23.67 24 N24 24 Incl 16 300 20 90 315 0.65 72 -25.87 25 N25 27 Incl 16 225 20 60 310 0.84 90 -30.67 26 N26 16 Incl 16 225 20 60 345 0.85 89 -28.8 27 N27 3 Incl 16 225 20 60 305 0.75 87 -28.03 2.2 Characterization of the lipid nanoparticles: 2.2.1 Particle size Distribution Zeta potential and polydispersity index A comprehensive characterization study was conducted on 27 distinct nanoparticles formulations to assess key parameters including particle size, zetapotential, and polydispersity index (PDI). Dynamic light scattering (DLS), employing a zetasizer (Malvern Instruments Ltd., UK), was the technique of choice for these analyses (Mehnert & Mader, 2012). Specifically, DLS provided insights into the Z-Average diameter (Particle size), PDI (size distribution homogeneity), and zeta potential (surface charge). Prior to measurement, each formulation was diluted 10- fold with Milli Q water. 2.2.2 Scanning Electron Microscopy Lyophilized and liquid state Amoxicillin Solid lipid nanoparticles were scanned under SEM JSM 6390 LV manufactured by Jeol in Japan for determination of the morphology of prepared nanoparticles ( Tenorio et al., 2010 ). 2.2.3 Fourier transform Infra-Red In this study we used FTIR to analyze amoxicillin, amoxicillin- loaded solid lipid nanoparticles (SLNs), and palmitic acid using 60 MHz varian EM 360 instrument manufactured by PerkinElmer in the US. The obtained peaks were evaluated to specify any noticeable changes in the prepared sample. By comparing the FTIR spectra, changes within the SLN was identified that indicated successful encapsulation of amoxicillin within the SLNs using palmitic acid. This analysis helps to ensure the formulation’s integrity 2.2.4 Entrapment efficiency The entrapment efficiency (EE) of Amoxicillin was measured by centrifugation technique. To prepare for the experiment, the prepared nanoparticle containing amoxicillin was centrifuged at 15000 rpm using REMI CPR 24 -PLUS centrifuge for 2 hrs. The supernatant was collected and analysed spectrophotometrically at 220 nm. ( Ataklti et al., 2016 ) ( Rohit & Pal 2013 ). The entrapment efficiency was evaluated using the equation: EE%= \(\frac{Drug\left(Total\right)-Drug\left(Supernatant\right)}{Drug\left(Total\right)}\times 100\) 2.2.5 In-Vitro Drug Release In-Vitro drug release study was performed using the dialysis sack method. The dialysis membrane of 7kDa was used for the study. The dialysis bag was soaked overnight before experimenting. The prepared Amoxicillin nanoparticles were centrifuged using 15000rpm for 2 hrs. The supernatant was discarded and the pellet was kept in a dialysis bag and tied from both ends. The dialysis bag was kept in a suitable buffer. The release was evaluated at three distinct pH levels mimicking the physiological condition found in our stomach lumen, gastrointestinal mucus layer, and epithelium layer ( Celli et al., 2009 ). Three different release media was used respectively mimicking the conditions namely NaCl-HCl solution at pH -1.5 with bile salt and lecithin, Acetate buffer at pH 5, and PBS buffer at pH 7.4. 2.2.6 Stability study For the determination of the stability of amoxicillin nanoparticles, the prepared nanoparticles were placed at 4 o C for 45 days in an airtight dark tainted bottle in a light-protected environment. After regular predetermined time intervals, the samples were withdrawn to analyse for particle size, zeta potential, and PDI. 2.2.7 FESEM analysis For FESEM analysis Sigma 300 model manufactured by carl Zeiss, Germany was used. The prepared nanoparticles were lyophilised to get powder form of the particles. Then these samples were coated with metal for imaging. 2.2.8 Antibiotic efficacy This study utilised a resistant strain of the bacterial pathogen H. pylori obtained from the Department of Bioengineering and Biotechnology at Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India (PIN: 835215). To ensure its viability, the strain was maintained on Muller Hinton agar slants stored at 4 o C.The experiment employed a spread plate technique to inoculate diluted H. pylori cultures onto Muller Hinton agar plates. Sterile L-shaped disposable rods facilitated the spreading process. Subsequently, wells were created in the agar and filled with varying concentrations (0.1%, 0.5%, and1%, ) of amoxicillin nanoparticles. The plates were then incubated at 30 o C for 24 hours. To establish negative controls, deionized water and hollow solid lipid nanoparticles (SLNs) were utilized and as a positive control commercially available clarithromycin tablet powdered and diluted in Distilled water (.3mg/ml) same as the concentration of Amoxicillin added in nanoparticle suspension (Abdelghany et al., 2021). Results 3.1.1 Particle size distribution, Zeta potential, Polydispersity index The particle size of nanoparticles plays a pivotal role in influencing their interaction with bacterial cells, particularly in the context of combating H. pylori (Azhar Shekoufeh Bahari & Hamishehkar,2016). The prepared nanoparticle formulations containing amoxicillin 0.3mg/ml showed uniform particle size distribution with sizes ranging between 100–600 nm of different baches as shown in Fig. 1 ; A and B . Moving ahead all of them carried a negative charge due to a negative charge on the lipid with a zeta-potential range of -16 - -20 mV Zeta potential is acritical characteristic of nanoparticles, serving as gauge for their surface charge. By influencing these electrostatic dynamics, the Zeta potential contributes significantly to the overall effectiveness of nanoparticles in their interactions with bacterial entities. The polydispersity index of different batches of prepared nanoparticles showed uniform dispersion below the value of 0.5. 3.1.2 SEM and FESEM analysis Scanning Electron Microscopy and Field Emission Scanning Electron Microscopy was done to check the morphology of prepared nanoparticles. The samples were evaluated at 1000X and 50.0KX magnification The prepared nanoparticles were nearly spherical they were present as individual entities which showed their stability as shown in Fig. 2 B. In the lyophilized form there was deformity of shape due to dehydration of prepared nanoparticles as shown in Fig. 2 A. The lyophilized sample was analysed at 30X magnification. Similar type of results was shown by other researchers where they prepared different types of NP and hydrogels ( Daniel da Silva et al., 2012 ). 3.2 FTIR spectra In order evaluate interaction between amoxicillin and palmitic acid in prepared solid lipid nanoparticles FTIR was done. There was no major interaction seen between the palmitic acid and amoxicillin which could change the chemical nature of prepared nanoparticles. The spectra were obtained at 4000 cm − 1 to 400 cm − 1 . Figure 3 A-C, show the FTIR study of amoxicillin, palmitic acid and amoxicillin palmitic acid nanoparticles. Two wide and strong signals were visible in FTIR spectra of crude Amoxicillin at 1678 and 1468 Cm − 1 , which can be attributed to hydroxyl group (Zha et al., 2013) shown in Fig. 3 A. The researchers have seen related peaks at 1666 cm − 1 and 1390 cm − 1 , (Junejo et al., 2014). The presence of peaks of OH at 3400cm − 1 and NH at 3166 cm − 1 represents COOH and NH 2 groups as reported by ( Jerzsele& Nagy, 2009 ) The NH and OH stretching frequencies are re-presented in the wide band at 2872 cm − 1 and NH 3357 cm − 1 as shown in Fig. 3 B. 3.3 Stability studies Amoxicillin solid lipid nanoparticles were stored in a light-protected tainted glassware at 4 o C for 45 days. At weeks 0, 1, and 4 and after 45 days of study conclusion, the particle size, the zeta potential, and the PDI were assessed. Figure 4 . suggest that there was little change in the size of the prepared nanoparticle it could be to aggregation the size of the particle tend to increase. The zeta potential remained the same. The PDI value increased a bit around 0.1. Different researchers have worked on amoxicillin for the irradiation of Helicobacter pylori (Asgari et al., 2022). But their stability remains a concern. This prepared amoxicillin SLN showed impressive stability results. 3.4 Drug Release Utilizing the dialysis diffusion technique in the sink circumstances, in vitro drug release was evaluated. LNP suspensions were added to a dialysis bag and kept in a dissolving media that had been heated at 37 o C, protected from light, and stirred on the magnetic rotor (100 ml). The drug release profile all the formulation was recorded as given in Fig. 5 (A) The drug release profile of SLN formulation number 9 due to its high EE and low particle size and desired stability was evaluated against traditional Clarithromycin formulation and Amoxicillin formulation used as given in Fig. 5 (B)( Sethi et al., 2014 ). The entire release assay experiment was carried at 27 o C with stirring speed 150 rpm. 3.5 Entrapment Efficiency The entrapment efficiency of prepared nanoparticles varied with the size and PDI of different nanoparticles prepared as given in Table 1 . The entrapment efficiency measured using UV spectroscopy and centrifugation method and absorbance was taken at 220 nm. 3.6 Effect of different parameters on particle size The statistical analysis of variance ANOVA as given in Table 2 illustrates that amongst all the most significant variables influencing the size of the nanoparticle were the surfactant concentration X2 with a p-value of 0.00021, the sonication time X3 with a p-value equal to 0.007 and the lipid concentration X1 with p-value 0.028. Table 2 Annova table Note: X1: Lipid Concentration, X2: Surfactant concentration, X3: Sonication time, X4: PF% Variable Particle Size (nm) PDI Zeta potential (mV) EE% Estimated P-Value Estimated P-Value Estimated P-Value Estimated P-Value X1 26.8328 0.028569 0.00223607 0.728566 -0.45504 0.00166258 1.18512 0.000164551 X2 74.0139 0.000217257 0.0201246 0.0169675 0.598148 0.000392264 1.09567 0.000254312 X3 -37.1187 0.00741006 0.0581378 7.96209e-05 -0.656286 0.000235856 -3.68951 2.19755e-07 X4 2.45967 0.801544 0.0290689 0.00323163 0.0346592 0.695093 0.603739 0.00547619 Lack of Fit 0.415 0.340 0.210 0.548 R 2 0.851 0.878 0.945 0.926 Adj. R 2 0.824 0.823 0.904 0.887 Q 2 0.767 0.697 0.804 0.810 The other investigated variables did not have much influence on the particle size of the nanoparticle. The residual plot shows the residuals vs. independent variables which helps to know whether a linear model is appropriate for a given set of data or not, In Fig. 6 (A) all the residuals are randomly distributed around zero which shows the model is fit for this set. Summary of the fit plot of SLN particle size as given in Fig. 6 : (B) suggests R 2 = 0.851 (green bar), which can be considered a good model. R 2 illustrates whether the model is fit or not. R 2 value equal to 0.5 indicates that the model is of low significance. For the good model it should be greater than 0.5 and for the excellent model it should be equal to 1. R 2 cannot be greater than 1. The blue bar represents Q 2 in Fig. 6 (B) it illustrates an estimate of the future prediction precision. Q 2 should be greater than 0.1 for a significant model and greater than 0.5 for a good model. The yellow bar in Fig. 6 (B) represents model validity. Model validity is a test for diverse model problems. A value less than 0.25 indicate statically significant model problems, such as the presence of outliers, an incorrect model, or a transformation problem. The model validity for particle size is 0.62 which shows that this model for particle size is significant. The turquoise colour bar in Fig. 6 (B) indicates reproducibility. Reproducibility is the variation of replicates compared to overall variability. The reproducibility of SLN particle size in this model is high. The replicate graph given in Fig. 6 : (C) implies that there is very less difference between the size of replicates N9-N11. Figure 6 :( D). coefficient Plot showed the effect of various variables like concentration of lipids, the concentration of surfactant (tween 80), sonication time, and sonication frequency on the size of nanoparticles from the above graph it can be depicted that the positive value in the coefficient graph represents a direct relationship between the specified variable and a negative value indicates an inverse relationship between the specified variable and particle size. From the given graph in F ig:6 (D ), it can be conferred that the concentration of lipid is a direct relation to the size of the lipid nanoparticle, following concentration of surfactant has a high peak value which means that it is directly proportional to the size of lipid nanoparticle, a negative value for sonication time means it is sharing inverse relation with the size of lipid nanoparticles and relatively very low value of PF% shows that its effect on the size of solid lipid nanoparticle is relatively insignificant or of very low significance. The interaction between the sonication time and PF% shows a p-value < 0.05. So there a synergy effect exists between these 2 variables-their combinations is more powerful than the sum of their effects. Statistical analysis of the response contour plot given in Fig. 6 : (E ) of particle size suggested that how the size of lipid nanoparticle varies along with lipid concentration and surfactant concentration. This plot gives the idea about what concentration of lipid and surfactant should be used to get a particular size range of lipid nanoparticles the gradient of colours shows the range from small to large. Blue denotes the smallest particle size while red colour denotes the largest particle size. The lowest size nanoparticle around 250 nm was obtained at lipid concentration 2–10 mg and surfactant concentration 150–180 mg keeping sonication time and PF% constant at 20 min and 60 respectively similar results were found by (Siddique et.al ., 2013). The general formula derived for calculating particle size varying these indepent factors and their interaction are : Particle size = 325.9-78.25X 1− 41.33X 3 -22.9X 4 + 24.2X 3 2 3.7 Effect of different parameters on zeta potential of solid lipid nanoparticles Zeta potential is property of macromolecule. It is basically the electrical potential at its surface or any interface which separates the two phases. Zeta potential quantifies the charge on the surface of nanoparticle. The statistical analysis of variance (ANOVA) of the response in Table 2 illustrates that lipid concentration, surfactant concentration and sonication time are most significant variables with p-value 0.001, 0.0003 and 0.0002 respectively that affect the zeta potential of nanoparticles. Residual normal probability plot given in Fig. 7 : ( A) Illustrates that most of the residuals are randomly distributed around zero which suggests that the linear model fits the given set of data. The summary of fit plot of zeta potential given in F ig: 7(B) gives the value of R 2 equals to 0.97. For a significant model R 2 should be greater than 0.5. The R 2 value greater than 0.5 reflects that this model for the zeta potential is good and significant. The blue bar Fig. 2 gives the value of Q 2 equals to 0.618 which suggests that this model is valid and can be used for future prediction. The model validity for zeta potential is 0.5 which is decent and which gives the idea about the model chosen for screening is valid or good. From the graph we can infer that this model for screening of zeta potential for lipid nanoparticle is valid. The green bar of the graph for zeta potential reflects the reproducibility of the model. In this graph we found that reproducibility of this graph is high. The residual plot given in F ig: 7(C) suggests that there is very less difference between the zeta potential of replicates N9-N11. The coefficient plot of SLN zeta potential given in Fig. 7 : ( D) Showed that concentration of lipid led to significant level of increase with increase in lipid concentration while surfactant up to certain amount didn’t impact the ZP but on doubling the surfactant concentration considerable amount change was observed in ZP and sonication time showed negative or inverse proportionality to the zeta potential of prepared lipid nanoparticle formulation. The response contour plot given in F ig: 7(E) illustrates the variance of zeta potential with respect to concentration of tween 80 and lipid. It was observed on varying the concentration of tween 80 and lipid the zeta potential range varied from min − 19mV to maximum − 15mV. From response surface plot it can inferred that in lipid range 150–180 with tween 80 as surfactant in concentration 28 mg -30 mg zeta potential obtained can be in range of -19 mV keeping sonication time 30 min and PF % 60. Blue colour range in the plot indicates minimum zeta potential and green colour gradient denotes the centre point and red colour gradient denotes the maximum range of zeta potential of solid lipid nanoparticles. From the Regression analysis the general formula for Zeta potential is: ZP= -28.7-1.2X 1 -2.22X 4 + 4.19X 1 2 + 6.4X 2 2 + 4.8X 3 2 + 2.04X 1 X 2 + 1.85X 1 X 4 3.8 Effect of different parameters on PDI of lipid nanoparticle Polydispersity index is used to measure the macromolecules based on their size. It gives idea about the macromolecule, whether the molecules are monodispersed or polydisperse. Polydispersity in a given sample can occur when the macromolecules in the sample agglomerate or aggregate during analysis or experiment. The PI of sample can be measured by instrument that utilise dynamic light scattering (DLS) or via electron micrographs. PI values greater than 0.7 are highly polydisperse and PI values less than 0.5 are monodisperse (Clayton et al., 2016 ). The statistical analysis of variance given in Table 2 illustrates that X1 (lipid concentration) X2 (surfactant concentration) X3 (Sonication time), X4 (PF %) and their interactions has most significant impact on the PDI of prepared nanoparticles with p-value equal to 7.96e-05 and 0.003 respectively. Fig: 8 (A) Residual normal probability plot illustrates that all the residuals are uniformly distributed around zero which suggest the linearity in the given set of data. The summary of fit plot for PDI given in Fig: 8(B) gives R 2 equal to 0.878 which shows this model is significant. Q 2 of the plot is 0.697 which illustrates that this model is valid for PDI and can be used for future prediction. Model validity for PDI is also high. Reproducibility of the given chart is also high. So, on basis of this response counter plot can be referred for further prediction. The F ig: 8(C) Replicate plot indicated the difference in PDI values of replicates which suggests the accuracy of the experiment so here in plot replicates showed very less difference. Q 2 in the model suggests model predictability in the given plot is 0.69 which suggest that model is fit for prediction. Yellow bar in the model suggests model validity, model validity bar above 0.5 suggest that model is valid. Turquoise green bar in the plot gives the model reproducibility any value in this model above 0.5 gives good reproducibility. This modelling established that all variables had a linear effect on PDI and showed an interaction between sonication time and pulse frequency. The coefficient plot of PDI Fig. 8 : (D) Suggests that from the given independent factor sonication time has greatest influence on PDI of lipid nanoparticles. The response contour plot of PDI given in F ig:8(E) suggests that at high concentration of lipids and surfactant, 100% of the particles PDI may come in range 0.345 at sonication time fixed at 20 min and PF% equal to 60 and the gradient of colours indicates their respective percentage and their PDI. Figure 8 (F) observed vs. predicted plot for Zeta potential suggests that observed values are close to predicted values they are unbiased which shows the good fit of the model and there are no outliners.From the regression ananlysis the general formula for calculating PDI : PDI = 0.74-0.13X3 + 0.11X4-0.13X 1 2 -0.21X 2 2 -0.17X 4 2 + 0.14X 2 X 4 3.9 Effect of different parameters on Entrapment efficiency of lipid nanoparticles The statistical analysis of variance (ANOVA) of the response in Table 2 illustrates that lipid concentration, surfactant concentration and sonication time are most significant variables with p-value 0.001, 0.0002 and 2.19755e-07 respectively that affect the entrapment efficiency of nanoparticles.The effect of four independent factors X1, X2, X3 and X4 on entrapment efficiency was studied, and the applicable Residual normal probability plot, summary of fit plot, coefficient plot, replicate plot, observed vs predicted plot and response surface plot was studied. From the plots it was detected that Fig. 9 (A) gives residual normal probability graph which suggest that the residuals lie in between the normal lines as shown in Fig. 4 (A). It can be illustrated that the residuals are scattered normally. The in between spaces observed in response defines that non-linear relationship exists between the factors and the response ( Yassin et al., 2010 ). The deviation of response could be due to the reason that some irrelevant term is seen in the regression coefficient. Figure 9 (B) the summary of fit plot for Entrapment efficiency gives R 2 equal to 0.986 which shows this model is significant. Q 2 of the plot is 0.804 which illustrates that this model is valid for Entrapment efficiency and can be used for future prediction. Model validity for entrapment efficiency is also high. Reproducibility of the given chart is also high. So, on basis of this response counter plot can be referred for further prediction ( Shah et al., 2012 ). The replicate plot given in Fig. 9 (C) that how much difference is there between the replicates as per the performed experiments analysis the given plot suggests that there was very less difference between the replicated which provides the clearance of accuracy of experiments performed. Investigation of results suggested that which features has significant effect on the entrapment efficiency Fig. 9 (D) suggested that concentration of lipid (X1) and concentration of surfactant (X2) has significant role on entrapment efficiency of amoxicillin lipid nanoparticles increasing the concentration of lipid and concentration of surfactant increased the particle size of lipid nanoparticles while sonication time whose highest value was chosen 30 min and lowest 10 min has significant effect in reducing the size of lipid nanoparticle which negatively influence the entrapment efficacy of lipid nanoparticle. Figure (9 E) of response surface plot suggest that with increasing the lipid concentration entrapment efficacy increased similar results were found with the concentration of surfactant. Observed vs predicted graph of amoxicillin nanoparticles suggest that there is no biasness which conclude it’s a good fit for the model ( Rohit & Pal 2013 ). The general formula for finding EE derived from regression plot is : EE = 88.6-3.7X 1 -3.3X 2 -4.3X 3 -3.1X 4 -5.3X 1 2 -7.8X 2 2 -4.8X 3 2 -6.7X 4 2 -7.2X 1 X 2 + 3.5X 2 X 4 + 14.9X 3 X 4 3.10 Antibiotic efficacy The antibiotic efficacy of prepared amoxicillin nanoparticles and traditional were tested against H.pylori , hallow zone was seen around the antibiotic well which confirmed its inhibition against H. pylori . The highest hallow zone was recorded at 1% concentration of amoxicillin nanoparticles as shown in Fig. 10 . As the negative control hollow nanoparticle and distilled was taken which did not showed zone of inhibition and as positive control Amoxicillin crude drug and Clarithromycin commercial drug was taken. Discussion In our study on lipid nanoparticle as a superior drug delivery agent we investigated the impact of lipid concentration, surfactant concentration, sonication time and sonication speed on various parametres of lipid nanoparticles like particle size, Zeta potential, Poly Dispersity index and Entrapment Efficiency of nanoparticles and results indicated that the lipid concentration significantly influeces the entrapment efficiency of lipid nanoparticles as the lipid content increases, more drug molecules can be encapsulated within the lipid matrix but the values can be oppsite and may fluctuate based on what type of drug are being encapsulated ( Schober et al., 2024 ). The sonication time in solid lipid nanoparticles plays an important role in breaking the macromolecules uniformly. The more time the sample is sonicated the chances of getting these macromolecules aggregated is high ( Laserra et al., 2015 ). The PF% is also directly related to PDI of lipid nanoparticles the reason behind it can be, more PF% provide more power to sonicator which can sonicate the molecules more effectively with high power with can lead to more aggregation and agglomeration ( Jain et al., 2004) . Concentration of surfactant also positively regulates the PDI of lipid, high surfactant concentration led to adsorption of the surfactant on lipid nanoparticles which can lead to broader particle size and weight. Other variable lipid concentration has insignificant influence on PDI of lipid nanoparticles. The interaction plot between 2 variables lipid concentration and sonication time showed significant and inverse relation with PDI of lipid nanoparticles. Which means that their synergic effect on PDI value is significant than their individual effect on PDI of lipid nanoparticles ( Shah et al., 2022). The concentration of surfactant showed direct relation with the zeta potential of prepared nanoparticle the reason behind it could be SLN is completely masked by a non-ionic surfactant like tween 80 ( Asasutjarit et al., 2007 ) and it doubles the electric double layer covering the nanoparticle and thus increase the zeta potential. PF % showed negative relation and it has significant effect on SLN. The interaction plot of sonication time and lipid concentration showed that synergic effect of these two variables has significant effect on the zeta potential of lipid nanoparticles. Zeta potential play an important role in defining the stability of nanoparticles. The zeta potential of nanoparticle can be influenced by various factors. In this experiment we have studied the effect of various parameters like lipid concentration, surfactant concentration, sonication time and PF% on zeta potential of lipid nanoparticles. All the nanoparticle formulation showed negative zeta potential due to presence of negatively charge on the surface of lipid nanoparticles ( Patel et al., 2018 ). Conclusions In this study, palmitic acid lipid nanoparticle screening was done for four different variables influencing the formulation of solid lipid nanoparticles. For this study, Box-Behnken design was used to differentiate the most significant variable on the quality attributes of synthesized palmitic acid-based lipid nanoparticles by solvent evaporation followed by probe sonication. From the performed study it was concluded that lipid concentration, surfactant concentration, sonication time and PF% have the significant effect on the particle size, zeta potential, PDI and EE from which standard formula was derived. Therefore, palmitic acid can be used as a lipid to prepare lipid-based nanocarrier, low amount of lipid as well as surfactant and medium sonication time is preferred to obtain lipid nanoparticle of smaller size. To obtain the nanoparticle with zetapotential in range − 15mV - -30 mV it is preferred to keep high lipid, sonication time and low surfactant concentration. Furthermore, to obtain the mono-dispersed solution of lipid nanoparticle it is preferred to take sonication time 20 min, PF% 60 less amount of surfactant. Antibiotic efficacy assay suggested that the formulated drug formulations show better and sustained antibiotic delivery to kill pathogens. To conclude out of all formulations in the range of 275–350 showed better drug entrapment efficacy and sustained release up to 36 hours and with the formula we can directly reach to desired Particle size, zeta potential, PDI and EE cutting off the lethargic “Hit n Trial”. Declarations Ethics approval and consent to participate Not Applicable Consent for publication Not Applicable Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This study was conducted without the support of a dedicated grant from any funding agency, whether in the public, commercial, or not-for-profit sectors Authors' contributions H.R.S and M.K.S were involved in planning and supervised the work, K.K processed the experimental data, performed the analysis, drafted the manuscript and designed the figures. All authors discussed the results and commented on the manuscript. Acknowledgement The authors acknowledge central instrumentation facility (CIF), Birla Institute of Technology, Mesra, Jharkhand for providing the characterization facilities. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4251223","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":293226031,"identity":"e9cfcde9-a8d9-43fb-a70a-827b59b66291","order_by":0,"name":"Kumari Kajal","email":"","orcid":"","institution":"Birla Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Kumari","middleName":"","lastName":"Kajal","suffix":""},{"id":293226032,"identity":"3a63452a-9316-4878-81d0-5998d5718be3","order_by":1,"name":"MUTHU KUMAR SAMPATH","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYHACNgbGBgkGfgnGBiRBA3w6mCFaJGcwNiLpIawFqOYGA4o1uIHujPxjj3l3WMgZ325uf3Sjoi6fv4H54QeGgjs4tZjdSGY35j0jYWx252Bjc86Zw5YzDrAZSzAYPMOnhU2at00icduNxMbm3LYDBgwHGMyA7jxMWMvmGSAt/+oM5A+wfyNOywYJkJYGZgODAzwEbDnz2ExyLtAvEkC/zM45dtjA8DBPsUQCPi3HE59JvN1RJ8c/u/3B55yaOgO54+0bP3z4g1sLFsAMxAmkaBgFo2AUjIJRgAEAvL1UPEAEVfoAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-3812-9574","institution":"Birla Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"MUTHU","middleName":"KUMAR","lastName":"SAMPATH","suffix":""},{"id":293226033,"identity":"afe18033-0f65-43a8-ae08-e8dd7cbdb041","order_by":2,"name":"Hare Ram Singh","email":"","orcid":"","institution":"Birla Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Hare","middleName":"Ram","lastName":"Singh","suffix":""}],"badges":[],"createdAt":"2024-04-11 08:43:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4251223/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4251223/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11756-024-01825-z","type":"published","date":"2024-11-19T15:57:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55138821,"identity":"21c415bd-bccb-4183-934c-620e8210db8c","added_by":"auto","created_at":"2024-04-23 07:14:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":201778,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eParticle size distribution and zeta potential for batch 9 of SLN\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4251223/v1/553119d427563f77dd37d342.png"},{"id":55138819,"identity":"11f851d9-54fa-4a7d-8274-3ee9d40722e6","added_by":"auto","created_at":"2024-04-23 07:14:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":741107,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) SEM result of prepared amoxicillin nanoparticles Lyophilised sample (B) FESEM image of Lyophilised Batch no 5\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4251223/v1/c0db3b012c32666666c1de71.png"},{"id":55138820,"identity":"f0a2f98c-6350-4c9b-8502-494e67b32cf1","added_by":"auto","created_at":"2024-04-23 07:14:30","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":155427,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFTIR spectra of (A) amoxicillin, (B) Palmitic acid (C) amoxicillin SLN .\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4251223/v1/ca7c61bdd6af7eb7fbd17bae.jpg"},{"id":55139223,"identity":"cf724fb4-a294-4565-a3af-01a804281ead","added_by":"auto","created_at":"2024-04-23 07:22:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":136105,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e45-day stability study of Amoxicillin nanoparticle A. Particle size change in 45 days. B. Zetapotential change in 45 days. C. PDI change in 45 days.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4251223/v1/75d9ee257defccc0eb5d74be.png"},{"id":55138828,"identity":"60f065d1-7d9a-4ea6-8012-c148dfcb6ca4","added_by":"auto","created_at":"2024-04-23 07:14:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":283024,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A): Drug release profile of 24 formulations(Excluding replicates) (B)Drug release profile of Clarithromycin(Commercial) , Amoxicillin(Crude) and F9 formulation.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4251223/v1/101a738f338d6fbeb88c3bb3.png"},{"id":55138825,"identity":"904ed602-91c3-41bb-b4a8-9bb4fbf715c7","added_by":"auto","created_at":"2024-04-23 07:14:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":965793,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. Residual normal probability plot for particle size B. Summary of fit plot for particle size C. Replicate plot for particle size D. coefficient plot for particle size E. Response contour plot of particle size F. observed vs. predicted plot for particle size .\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4251223/v1/5cdc78a4de9426d1275b1530.png"},{"id":55138822,"identity":"40032be6-e8e2-47f7-b37d-f48c50115ced","added_by":"auto","created_at":"2024-04-23 07:14:30","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1036813,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. Residual normal probability plot for Zeta potential B. Summary of fit plot for Zeta potential C. Replicate plot for zeta potential D. coefficient plot for Zeta potential E. Response contour plot of Zeta potential F. observed vs. predicted plot for Zeta potential.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4251223/v1/fcfc96d99ffe19fdcc886693.png"},{"id":55139224,"identity":"a3998ef5-63b9-4c4c-a17b-71c84d1bb601","added_by":"auto","created_at":"2024-04-23 07:22:30","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":984699,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. Residual normal probability plot for PDI B. summary of fit plot for PDI C. replicate plot for PDI D. Coefficient plot for PDI E. Response surface plot for PDI F. observed vs Predicted plot for PDI.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4251223/v1/06c6e46d626c0ab9e75d0f52.png"},{"id":55138826,"identity":"9c1ca4b2-eb99-4e8e-99c8-792ba8a7f891","added_by":"auto","created_at":"2024-04-23 07:14:30","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":958630,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. Residual normal probability plot for EE%. B. summary of fit plot for EE%. C. replicate plot for EE%. D. Coefficient plot for EE% E. Response surface plot for EE%. F. observed vs Predicted plot for EE%.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4251223/v1/d67802bdaa1a5dcb857c415b.png"},{"id":55138827,"identity":"fbd438e8-919d-4e2d-a780-5ee9e1571a9f","added_by":"auto","created_at":"2024-04-23 07:14:30","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":640789,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eZone of inhibition at different concentration of Amoxicillin SLN (A.) negative control (Distilled water/ LNP) (B) 0.1% SLN-Amoxicillin (C.) 0.5% SLN-Amoxicillin (D.) 1% SLN-Amoxicillin (E.) Positive control Clarithromycin (Commercial formulation). (F.) Positive control Amoxicillin (Commercial formulation).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4251223/v1/01233e834d846d27affb91cd.png"},{"id":69834924,"identity":"aefab531-1eca-42e4-9cf3-2ca9863ad183","added_by":"auto","created_at":"2024-11-25 16:10:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7390562,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4251223/v1/56e33df0-6361-4424-bdbd-1fcfe3349bd8.pdf"}],"financialInterests":"","formattedTitle":"Developing Superior Amoxicillin Delivery Systems: AI-Driven Optimization of LNPs for H. pylori Treatment","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn the development of robust protocols and the establishment of products from the bedrock upon which scientific progress thrives across diverse fields of inquiry. This holds true for nanomedicines, where the creation of nanoparticles-based drug delivery systems involves intricate experimental conditions that significantly impact outcomes. These conditions are closely tied to the specific materials under study.\u003c/p\u003e \u003cp\u003eIn recent work, the application of design of experiment (DoE) has gained prominence. DoE has gained prominence. DoE is a powerful statistical technique that simultaneously varies multiple factors to identify optimal parameter configurations while minimizing the number of experimental runs. Despite its potential for innovation and process optimization, DoE is a powerful statistical technique that simultaneously varies multiple factors to identify optimal parameter configurations while minimizing the number of experimental runs. Despite its potential for innovation and process optimization, DoE remains underutilized in nanomedicine.\u003cb\u003eNow here the question arises why Design of experiment why not ML\u003c/b\u003e ? the simple answer to this question is the Design of Experiments (DOE) and Machine Learning (ML) are distinct approaches used for different purposes. DOE focuses on controlled experiments, causalities and process optimization while ML excels at pattern recognition and preduction on large datasets. Moreover ML requires much more advanced learning of coding language like python or oracle which can be time consuming process. So moving on excluding ML for optimization.\u003c/p\u003e \u003cp\u003eIn this study we explore DoE applications in formulating nano vectors for drug delivery, emphasizing process variables, as drug Amoxicillin was taken and its efficacy was tested against \u003cem\u003eHelicobacter pylori\u003c/em\u003e. \u003cem\u003eH. pylori\u003c/em\u003e is a gram-negative microorganism characterized by its short, helical, S-shaped structure, measuring 0.5\u0026ndash;1.5\u0026micro;m in width and 2\u0026ndash;4 \u0026micro;m in length. Predominantly located in region of the stomach, \u003cem\u003eH. pylori\u003c/em\u003e is responsible for chronic gastric infections, affecting over half of the global population \u003cb\u003e(Marshall \u0026amp; Adams,2008)\u003c/b\u003e. While the precise mode of transmission and infections, affecting over half of the global population. while the precise mode of transmission and infections, affecting over half of the global population. The mode of transmission and infection remains unclear, it is commonly believed to occur through faecal- oral and oral- oral routes, often facilitated by water or food consumption \u003cb\u003e(\u003c/b\u003eBrown, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2000\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThe increasing prevalence of antibiotic resistance in H. pylori necessitates the development of more effective drugs to combat this widespread infection \u003cb\u003e(\u0026ldquo;United Nations Meeting on Antimicrobial Resistance,2019)\u003c/b\u003e. Current treatment regimens often involving multiple antibiotics and medications, can be complex, expensive, and have potential side effects, leading to decreased patient compliance and treatment failure. This highlights the urgent need for simpler and more effective therapies. Furthermore, H. pylori infections pose a significant public health burden, being linked to various gastrointestinal issues and incurring substantial healthcare costs. Eradicating the bacteria not only reduces this burden but also offers long term benefits for individuals including a decreased risk of peptic ulcers and potentially even stomach cancer \u003cb\u003e(\u003c/b\u003eChen et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). \u003cem\u003eH. pylori\u003c/em\u003e plays a role in the development of various extra- gastric conditions, including mucosa-associated lymphoid tissue lymphoma (MALT), idiopathic thrombocytopenic purpura, as well as vitamin B12 and iron deficiencies \u003cb\u003e(Oztekin et al., 2021).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eRecently the nanomaterials have been evaluated as an important agent in medicine as carriers for various soluble and insoluble drugs \u003cb\u003e(\u003c/b\u003eLi \u0026amp; Mooney, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e SLNs are tiny, spherical artificial vesicles that can be made from cholesterol and natural phospholipids. The hydrophobic and hydrophilic properties of SLNs, in addition to their biocompatibility, make them ideal drug delivery platforms (Akbarzadeh et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Solid lipid nanoparticles now these days are attracting many researchers as a safe drug delivery system. Lipids are naturally present in our body so when they are inside our body, they are not considered foreign substances which reduces their chance of rejection from our body these nanoparticles have shown the least immunogenicity, unlike other drug delivery systems \u003cb\u003e(\u003c/b\u003eMukherjee et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere are large numbers of factors that influence the process of manufacturing different formulations, if they are carried out by hit \u0026amp; a trial method of screening these could be a lethargic task. This will not only be time-consuming but also an expensive process and there are very less chances that they will give accurate as well as verified results. For this purpose, Box- Behnken Design can be one of the reliable options to optimize the effect of variables on different formulations and drug delivery systems like Liposomes \u003cb\u003e(\u003c/b\u003eHarbi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), transferosomes \u003cb\u003e(\u003c/b\u003eAhmed, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, niosomes \u003cb\u003e(Vanaja \u0026amp; Shobha Rani, 2007)\u003c/b\u003e, Alginate- reinforcement Chitosan nanoparticles \u003cb\u003e(Ahmed \u0026amp; El-Say, 2014)\u003c/b\u003e, Transdermal film (matrix Type) \u003cb\u003e(\u003c/b\u003eEl-Say et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and protein loaded PLGA nanoparticles, Eudragit microspheres, and control. Amoxicillin-loaded solid lipid nanoparticles (SLNs) were created in this study to protect amoxicillin from acidic degradation, enhance local release, and improve bioavailability \u003cb\u003e(\u003c/b\u003eGilta, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn this research we have developed alternative to these current systems for eradication of \u003cem\u003eH. pylori.\u003c/em\u003e As reported by many researchers\u0026rsquo; Solid lipid nanoparticles (SLNs), which have been produced, are unique among all drug delivery systems due to their low toxicity and the technological and financial practicality of mass manufacture They are typically 1nm to 1000 nm \u003cb\u003e(\u003c/b\u003eKshirsagar \u0026amp; Saudagar, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e\u003cb\u003e) (\u003c/b\u003eAlmeida \u0026amp; Souto, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The linear model, placket-Burman design was used to optimise the SLNs loading capacity, polydispersity index, and particle size. The upgraded nanoparticles were described and found to have a mean size width of 280 nm and a zeta potential of approximately about \u0026minus;\u0026thinsp;20 mV. The SLNs likewise showed a low polydispersity file of 0.21. The SLNs were also characterized which showed a spherical shape \u003cb\u003e(Ceballos et al., 2005).\u003c/b\u003e\u003c/p\u003e"},{"header":"Materials and Methodology","content":"\u003cp\u003eIn the context of our scientific study, palmitic acid and Tween 80 were procured from Pallav Chemicals \u0026amp; solvents Pvt. Ltd. As supplied. We employed 99% Ethanol, acquired from Changsu Hongsheng Fine Chemical Co. Ltd., as the solvent. Additionally, purified water for dispersion purposes was sourced from the Nanopure UltraTM water distillation unit. Notably, all materials were utilized in their original analytical grade form without any modifications.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Experimental Methodology\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Preparation of Palmitic acid-based SLN\u003c/h2\u003e \u003cp\u003eThe purpose of our study was to investigate the formulation and characterization of palmitic acid-based lipid nanoparticles. Specifically, we explore their preparation using a combination of solvent evaporation and probe sonication methods. Our Focus lies in understanding the behavior of these nanoparticles and their potential applications in various fields. In the formulation of palmitic acid-based lipid nanoparticles, we employed a combination of solvent evaporation and probe sonication techniques \u003cb\u003e(Camellia Sinensis et al., 2018)\u003c/b\u003e. Initially, Palmitic acid was completely dissolved in 5 ml of ethanol through magnetic stirring at 60\u003csup\u003eo\u003c/sup\u003e C\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003csup\u003eo\u003c/sup\u003eC, yielding the organic solvent. Simultaneously, in a separate container, Tween 80 was dissolved in 30 ml of Milli Q water (Type I) as the aqueous medium, utilizing a magnetic stirrer at 70\u003csup\u003eo\u003c/sup\u003eC and a speed of 1200 rpm.\u003c/p\u003e \u003cp\u003eSubsequently, the lipid-containing organic phase was gradually added to the aqueous phase under continuous stirring. The resulting mixture was concentrated to a final volume of 7 ml over an evaporation period of approximately 3 hrs. The resulting white, translucent liquid was then combined with chilled Milli Q water to maintain a total volume of 15 ml \u003cb\u003e(Zhang et al., 2007).\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.1.2 Experimental Optimization Design\u003c/h2\u003e \u003cp\u003ePharmacological formulation development can be time-consuming so we explored an efficient approach using the Design of Experiments (DOE) \u003cb\u003e(\u003c/b\u003eBarot et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Unlike traditional methods, DOE allows us to simultaneously vary multiple factors, making it cost effective and time efficient specifically, we employed a 27 run, four factor Box- Behnken design, which is suitable for quadratic models \u003cb\u003e(Jankovic et al., 2021)\u003c/b\u003e. Independent variables: X1 (Lipid Concentration): We use palmitic acid as a lipid the concentration was measured in mg/ml. X2 (Surfactant Concentration): Tween80 serves as the surfactant, with its concentration measured in mg. X3 (sonication time): The sonication time is measured in minutes (min). X4(Pulse Frequency): we analyze the pulse frequency, expressed as a percentage. Our goal was to understand the impact of these variables on particle size, polydispersity index (PDI), and Zeta potential in order get formulation with max EE and sustained drug release, using dynamic light scattering (DLS) measurements with a Zetasizer instrument \u003cb\u003e(Ana-Maria et al., 2018)\u003c/b\u003e. By systematically exploring these factors, we aim to optimize solid lipid nanoparticle formulations for various applications. As demonstrated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, this design was made to create 27 alternative Solid lipid nanoparticle formulations.\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\u003eTwenty seven experiment series designed by Box-Behnken model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExp\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExp\u003c/p\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRun\u003c/p\u003e \u003cp\u003eOrder\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncl/Excl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003elipid\u003c/p\u003e \u003cp\u003econcentration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSurfactant\u003c/p\u003e \u003cp\u003econcentration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003esonication\u003c/p\u003e \u003cp\u003etime\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003epulse\u003c/p\u003e \u003cp\u003efrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eParticle\u003c/p\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/th\u003e \u003cth 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colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-20.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-18.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-17.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e95.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-21.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e64.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-26.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-26.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e81.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-25.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-28.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-22.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-20.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-14.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-19.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-15.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-17.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-19.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-24.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-17.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-19.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-18.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e67.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-23.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-25.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-30.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-28.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-28.03\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=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Characterization of the lipid nanoparticles:\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Particle size Distribution Zeta potential and polydispersity index\u003c/h2\u003e \u003cp\u003eA comprehensive characterization study was conducted on 27 distinct nanoparticles formulations to assess key parameters including particle size, zetapotential, and polydispersity index (PDI). Dynamic light scattering (DLS), employing a zetasizer (Malvern Instruments Ltd., UK), was the technique of choice for these analyses (Mehnert \u0026amp; Mader, 2012). Specifically, DLS provided insights into the Z-Average diameter (Particle size), PDI (size distribution homogeneity), and zeta potential (surface charge). Prior to measurement, each formulation was diluted 10- fold with Milli Q water.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.2.2 Scanning Electron Microscopy\u003c/h2\u003e \u003cp\u003eLyophilized and liquid state Amoxicillin Solid lipid nanoparticles were scanned under SEM JSM 6390 LV manufactured by Jeol in Japan for determination of the morphology of prepared nanoparticles \u003cb\u003e(\u003c/b\u003eTenorio et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.2.3 Fourier transform Infra-Red\u003c/h2\u003e \u003cp\u003eIn this study we used FTIR to analyze amoxicillin, amoxicillin- loaded solid lipid nanoparticles (SLNs), and palmitic acid using 60 MHz varian EM 360 instrument manufactured by PerkinElmer in the US. The obtained peaks were evaluated to specify any noticeable changes in the prepared sample. By comparing the FTIR spectra, changes within the SLN was identified that indicated successful encapsulation of amoxicillin within the SLNs using palmitic acid. This analysis helps to ensure the formulation\u0026rsquo;s integrity\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.2.4 Entrapment efficiency\u003c/h2\u003e \u003cp\u003eThe entrapment efficiency (EE) of Amoxicillin was measured by centrifugation technique. To prepare for the experiment, the prepared nanoparticle containing amoxicillin was centrifuged at 15000 rpm using REMI CPR 24 -PLUS centrifuge for 2 hrs. The supernatant was collected and analysed spectrophotometrically at 220 nm. \u003cb\u003e(\u003c/b\u003eAtaklti et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) \u003cb\u003e(\u003c/b\u003eRohit \u0026amp; Pal \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe entrapment efficiency was evaluated using the equation:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEE%=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{Drug\\left(Total\\right)-Drug\\left(Supernatant\\right)}{Drug\\left(Total\\right)}\\times 100\\)\u003c/span\u003e\u003c/span\u003e\u003c/h2\u003e \u003cp\u003e \u003cb\u003e2.2.5\u003c/b\u003e \u003cb\u003eIn-Vitro\u003c/b\u003e \u003cb\u003eDrug Release\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eIn-Vitro\u003c/em\u003e drug release study was performed using the dialysis sack method. The dialysis membrane of 7kDa was used for the study. The dialysis bag was soaked overnight before experimenting. The prepared Amoxicillin nanoparticles were centrifuged using 15000rpm for 2 hrs. The supernatant was discarded and the pellet was kept in a dialysis bag and tied from both ends. The dialysis bag was kept in a suitable buffer. The release was evaluated at three distinct pH levels mimicking the physiological condition found in our stomach lumen, gastrointestinal mucus layer, and epithelium layer \u003cb\u003e(\u003c/b\u003eCelli et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Three different release media was used respectively mimicking the conditions namely NaCl-HCl solution at pH -1.5 with bile salt and lecithin, Acetate buffer at pH 5, and PBS buffer at pH 7.4.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.2.6 Stability study\u003c/h2\u003e \u003cp\u003eFor the determination of the stability of amoxicillin nanoparticles, the prepared nanoparticles were placed at 4\u003csup\u003eo\u003c/sup\u003eC for 45 days in an airtight dark tainted bottle in a light-protected environment. After regular predetermined time intervals, the samples were withdrawn to analyse for particle size, zeta potential, and PDI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.2.7 FESEM analysis\u003c/h2\u003e \u003cp\u003eFor FESEM analysis Sigma 300 model manufactured by carl Zeiss, Germany was used. The prepared nanoparticles were lyophilised to get powder form of the particles. Then these samples were coated with metal for imaging.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.2.8 Antibiotic efficacy\u003c/h2\u003e \u003cp\u003eThis study utilised a resistant strain of the bacterial pathogen H. pylori obtained from the Department of Bioengineering and Biotechnology at Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India (PIN: 835215). To ensure its viability, the strain was maintained on Muller Hinton agar slants stored at 4\u003csup\u003eo\u003c/sup\u003eC.The experiment employed a spread plate technique to inoculate diluted H. pylori cultures onto Muller Hinton agar plates. Sterile L-shaped disposable rods facilitated the spreading process. Subsequently, wells were created in the agar and filled with varying concentrations (0.1%, 0.5%, and1%, ) of amoxicillin nanoparticles. The plates were then incubated at 30\u003csup\u003eo\u003c/sup\u003e C for 24 hours. To establish negative controls, deionized water and hollow solid lipid nanoparticles (SLNs) were utilized and as a positive control commercially available clarithromycin tablet powdered and diluted in Distilled water (.3mg/ml) same as the concentration of Amoxicillin added in nanoparticle suspension (Abdelghany et al., 2021).\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":" \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Particle size distribution, Zeta potential, Polydispersity index\u003c/h2\u003e \u003cp\u003eThe particle size of nanoparticles plays a pivotal role in influencing their interaction with bacterial cells, particularly in the context of combating \u003cem\u003eH. pylori\u003c/em\u003e \u003cb\u003e(Azhar Shekoufeh Bahari \u0026amp; Hamishehkar,2016).\u003c/b\u003eThe prepared nanoparticle formulations containing amoxicillin 0.3mg/ml showed uniform particle size distribution with sizes ranging between 100\u0026ndash;600 nm of different baches as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; \u003cb\u003eA and B\u003c/b\u003e. Moving ahead all of them carried a negative charge due to a negative charge on the lipid with a zeta-potential range of -16 - -20 mV Zeta potential is acritical characteristic of nanoparticles, serving as gauge for their surface charge. By influencing these electrostatic dynamics, the Zeta potential contributes significantly to the overall effectiveness of nanoparticles in their interactions with bacterial entities. The polydispersity index of different batches of prepared nanoparticles showed uniform dispersion below the value of 0.5.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.1.2 SEM and FESEM analysis\u003c/h2\u003e \u003cp\u003eScanning Electron Microscopy and Field Emission Scanning Electron Microscopy was done to check the morphology of prepared nanoparticles. The samples were evaluated at 1000X and 50.0KX magnification The prepared nanoparticles were nearly spherical they were present as individual entities which showed their stability as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB. In the lyophilized form there was deformity of shape due to dehydration of prepared nanoparticles as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA. The lyophilized sample was analysed at 30X magnification. Similar type of results was shown by other researchers where they prepared different types of NP and hydrogels \u003cb\u003e(\u003c/b\u003eDaniel da Silva et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.2 FTIR spectra\u003c/h2\u003e \u003cp\u003eIn order evaluate interaction between amoxicillin and palmitic acid in prepared solid lipid nanoparticles FTIR was done. There was no major interaction seen between the palmitic acid and amoxicillin which could change the chemical nature of prepared nanoparticles. The spectra were obtained at 4000 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e to 400 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-C, show the FTIR study of amoxicillin, palmitic acid and amoxicillin palmitic acid nanoparticles. Two wide and strong signals were visible in FTIR spectra of crude Amoxicillin at 1678 and 1468 Cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, which can be attributed to hydroxyl group \u003cb\u003e(Zha et al., 2013)\u003c/b\u003e shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA. The researchers have seen related peaks at 1666 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 1390 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, \u003cb\u003e(Junejo et al., 2014).\u003c/b\u003e The presence of peaks of OH at 3400cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and NH at 3166 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e represents COOH and NH\u003csub\u003e2\u003c/sub\u003e groups as reported by \u003cb\u003e(\u003c/b\u003eJerzsele\u0026amp; Nagy, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e The NH and OH stretching frequencies are re-presented in the wide band at 2872 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eand NH 3357 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eas shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Stability studies\u003c/h2\u003e \u003cp\u003eAmoxicillin solid lipid nanoparticles were stored in a light-protected tainted glassware at 4\u003csup\u003eo\u003c/sup\u003e C for 45 days. At weeks 0, 1, and 4 and after 45 days of study conclusion, the particle size, the zeta potential, and the PDI were assessed. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. suggest that there was little change in the size of the prepared nanoparticle it could be to aggregation the size of the particle tend to increase. The zeta potential remained the same. The PDI value increased a bit around 0.1. Different researchers have worked on amoxicillin for the irradiation of \u003cem\u003eHelicobacter pylori\u003c/em\u003e \u003cb\u003e(Asgari et al., 2022).\u003c/b\u003e But their stability remains a concern. This prepared amoxicillin SLN showed impressive stability results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Drug Release\u003c/h2\u003e \u003cp\u003eUtilizing the dialysis diffusion technique in the sink circumstances, \u003cem\u003ein vitro\u003c/em\u003e drug release was evaluated. LNP suspensions were added to a dialysis bag and kept in a dissolving media that had been heated at 37\u003csup\u003eo\u003c/sup\u003eC, protected from light, and stirred on the magnetic rotor (100 ml). The drug release profile all the formulation was recorded as given in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(A)\u003c/p\u003e \u003cp\u003eThe drug release profile of SLN formulation number 9 due to its high EE and low particle size and desired stability was evaluated against traditional Clarithromycin formulation and Amoxicillin formulation used as given in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cb\u003e(B)(\u003c/b\u003eSethi et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The entire release assay experiment was carried at 27\u003csup\u003eo\u003c/sup\u003e C with stirring speed 150 rpm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Entrapment Efficiency\u003c/h2\u003e \u003cp\u003eThe entrapment efficiency of prepared nanoparticles varied with the size and PDI of different nanoparticles prepared as given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The entrapment efficiency measured using UV spectroscopy and centrifugation method and absorbance was taken at 220 nm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Effect of different parameters on particle size\u003c/h2\u003e \u003cp\u003eThe statistical analysis of variance ANOVA as given in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates that amongst all the most significant variables influencing the size of the nanoparticle were the surfactant concentration X2 with a p-value of 0.00021, the sonication time X3 with a p-value equal to 0.007 and the lipid concentration X1 with p-value 0.028.\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\u003e\u003cb\u003eAnnova table\u003c/b\u003e Note: X1: Lipid Concentration, X2: Surfactant concentration, X3: Sonication time, X4: PF%\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eParticle Size (nm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePDI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eZeta potential (mV)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eEE%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEstimated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEstimated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEstimated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.8328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.028569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00223607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.728566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.45504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00166258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.18512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.000164551\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.0139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000217257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0201246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0169675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.598148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000392264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.09567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.000254312\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-37.1187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00741006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0581378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.96209e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.656286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000235856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-3.68951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.19755e-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.45967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.801544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0290689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00323163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0346592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.695093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.603739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00547619\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLack of Fit\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdj. R\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.810\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\u003eThe other investigated variables did not have much influence on the particle size of the nanoparticle. The residual plot shows the residuals vs. independent variables which helps to know whether a linear model is appropriate for a given set of data or not, In Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u003cb\u003e(A)\u003c/b\u003e all the residuals are randomly distributed around zero which shows the model is fit for this set. Summary of the fit plot of SLN particle size as given in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e: \u003cb\u003e(B)\u003c/b\u003e suggests R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.851 (green bar), which can be considered a good model. R\u003csup\u003e2\u003c/sup\u003eillustrates whether the model is fit or not. R\u003csup\u003e2\u003c/sup\u003e value equal to 0.5 indicates that the model is of low significance. For the good model it should be greater than 0.5 and for the excellent model it should be equal to 1. R\u003csup\u003e2\u003c/sup\u003e cannot be greater than 1. The blue bar represents Q\u003csup\u003e2\u003c/sup\u003e in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e(B)\u003c/b\u003e it illustrates an estimate of the future prediction precision. Q\u003csup\u003e2\u003c/sup\u003e should be greater than 0.1 for a significant model and greater than 0.5 for a good model. The yellow bar in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u003cb\u003e(B)\u003c/b\u003e represents model validity. Model validity is a test for diverse model problems. A value less than 0.25 indicate statically significant model problems, such as the presence of outliers, an incorrect model, or a transformation problem. The model validity for particle size is 0.62 which shows that this model for particle size is significant. The turquoise colour bar in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e(B)\u003c/b\u003e indicates reproducibility. Reproducibility is the variation of replicates compared to overall variability. The reproducibility of SLN particle size in this model is high. The replicate graph given in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e:\u003cb\u003e(C)\u003c/b\u003e implies that there is very less difference between the size of replicates N9-N11. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e :(\u003cb\u003eD).\u003c/b\u003e coefficient Plot showed the effect of various variables like concentration of lipids, the concentration of surfactant (tween 80), sonication time, and sonication frequency on the size of nanoparticles from the above graph it can be depicted that the positive value in the coefficient graph represents a direct relationship between the specified variable and a negative value indicates an inverse relationship between the specified variable and particle size. From the given graph in F\u003cb\u003eig:6 (D\u003c/b\u003e), it can be conferred that the concentration of lipid is a direct relation to the size of the lipid nanoparticle, following concentration of surfactant has a high peak value which means that it is directly proportional to the size of lipid nanoparticle, a negative value for sonication time means it is sharing inverse relation with the size of lipid nanoparticles and relatively very low value of PF% shows that its effect on the size of solid lipid nanoparticle is relatively insignificant or of very low significance. The interaction between the sonication time and PF% shows a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. So there a synergy effect exists between these 2 variables-their combinations is more powerful than the sum of their effects.\u003c/p\u003e \u003cp\u003eStatistical analysis of the response contour plot given in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e: \u003cb\u003e(E\u003c/b\u003e) of particle size suggested that how the size of lipid nanoparticle varies along with lipid concentration and surfactant concentration. This plot gives the idea about what concentration of lipid and surfactant should be used to get a particular size range of lipid nanoparticles the gradient of colours shows the range from small to large. Blue denotes the smallest particle size while red colour denotes the largest particle size. The lowest size nanoparticle around 250 nm was obtained at lipid concentration 2\u0026ndash;10 mg and surfactant concentration 150\u0026ndash;180 mg keeping sonication time and PF% constant at 20 min and 60 respectively similar results were found by \u003cb\u003e(Siddique et.al ., 2013). The general formula derived for calculating particle size varying these indepent factors and their interaction are\u003c/b\u003e :\u003c/p\u003e \u003cp\u003eParticle size\u0026thinsp;=\u0026thinsp;325.9-78.25X\u003csub\u003e1\u0026minus;\u003c/sub\u003e41.33X\u003csub\u003e3\u003c/sub\u003e-22.9X\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;24.2X\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section4\"\u003e \u003ch2\u003e3.7 Effect of different parameters on zeta potential of solid lipid nanoparticles\u003c/h2\u003e \u003cp\u003eZeta potential is property of macromolecule. It is basically the electrical potential at its surface or any interface which separates the two phases. Zeta potential quantifies the charge on the surface of nanoparticle.\u003c/p\u003e \u003cp\u003eThe statistical analysis of variance (ANOVA) of the response in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates that lipid concentration, surfactant concentration and sonication time are most significant variables with p-value 0.001, 0.0003 and 0.0002 respectively that affect the zeta potential of nanoparticles. Residual normal probability plot given in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e :\u003cb\u003e( A)\u003c/b\u003e Illustrates that most of the residuals are randomly distributed around zero which suggests that the linear model fits the given set of data. The summary of fit plot of zeta potential given in F\u003cb\u003eig: 7(B)\u003c/b\u003e gives the value of R\u003csup\u003e2\u003c/sup\u003e equals to 0.97. For a significant model R\u003csup\u003e2\u003c/sup\u003e should be greater than 0.5. The R\u003csup\u003e2\u003c/sup\u003e value greater than 0.5 reflects that this model for the zeta potential is good and significant. The blue bar Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e gives the value of Q\u003csup\u003e2\u003c/sup\u003e equals to 0.618 which suggests that this model is valid and can be used for future prediction. The model validity for zeta potential is 0.5 which is decent and which gives the idea about the model chosen for screening is valid or good. From the graph we can infer that this model for screening of zeta potential for lipid nanoparticle is valid. The green bar of the graph for zeta potential reflects the reproducibility of the model. In this graph we found that reproducibility of this graph is high. The residual plot given in F\u003cb\u003eig: 7(C)\u003c/b\u003e suggests that there is very less difference between the zeta potential of replicates N9-N11. The coefficient plot of SLN zeta potential given in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e:\u003cb\u003e( D)\u003c/b\u003e Showed that concentration of lipid led to significant level of increase with increase in lipid concentration while surfactant up to certain amount didn\u0026rsquo;t impact the ZP but on doubling the surfactant concentration considerable amount change was observed in ZP and sonication time showed negative or inverse proportionality to the zeta potential of prepared lipid nanoparticle formulation.\u003c/p\u003e \u003cp\u003eThe response contour plot given in F\u003cb\u003eig: 7(E)\u003c/b\u003e illustrates the variance of zeta potential with respect to concentration of tween 80 and lipid. It was observed on varying the concentration of tween 80 and lipid the zeta potential range varied from min \u0026minus;\u0026thinsp;19mV to maximum \u0026minus;\u0026thinsp;15mV. From response surface plot it can inferred that in lipid range 150\u0026ndash;180 with tween 80 as surfactant in concentration 28 mg -30 mg zeta potential obtained can be in range of -19 mV keeping sonication time 30 min and PF % 60. Blue colour range in the plot indicates minimum zeta potential and green colour gradient denotes the centre point and red colour gradient denotes the maximum range of zeta potential of solid lipid nanoparticles. From the Regression analysis the general formula for Zeta potential is:\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cp\u003eZP= -28.7-1.2X\u003csub\u003e1\u003c/sub\u003e-2.22X\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;4.19X\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;6.4X\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;4.8X\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;2.04X\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;1.85X\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section4\"\u003e \u003ch2\u003e3.8 Effect of different parameters on PDI of lipid nanoparticle\u003c/h2\u003e \u003cp\u003ePolydispersity index is used to measure the macromolecules based on their size. It gives idea about the macromolecule, whether the molecules are monodispersed or polydisperse. Polydispersity in a given sample can occur when the macromolecules in the sample agglomerate or aggregate during analysis or experiment. The PI of sample can be measured by instrument that utilise dynamic light scattering (DLS) or via electron micrographs. PI values greater than 0.7 are highly polydisperse and PI values less than 0.5 are monodisperse (Clayton et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe statistical analysis of variance given in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates that X1 (lipid concentration) X2 (surfactant concentration) X3 (Sonication time), X4 (PF %) and their interactions has most significant impact on the PDI of prepared nanoparticles with p-value equal to 7.96e-05 and 0.003 respectively. \u003cb\u003eFig: 8 (A)\u003c/b\u003e Residual normal probability plot illustrates that all the residuals are uniformly distributed around zero which suggest the linearity in the given set of data.\u003c/p\u003e \u003cp\u003eThe summary of fit plot for PDI given in \u003cb\u003eFig: 8(B)\u003c/b\u003e gives R\u003csup\u003e2\u003c/sup\u003e equal to 0.878 which shows this model is significant. Q\u003csup\u003e2\u003c/sup\u003e of the plot is 0.697 which illustrates that this model is valid for PDI and can be used for future prediction. Model validity for PDI is also high. Reproducibility of the given chart is also high. So, on basis of this response counter plot can be referred for further prediction. The F\u003cb\u003eig: 8(C)\u003c/b\u003e Replicate plot indicated the difference in PDI values of replicates which suggests the accuracy of the experiment so here in plot replicates showed very less difference.\u003c/p\u003e \u003cp\u003eQ\u003csup\u003e2\u003c/sup\u003e in the model suggests model predictability in the given plot is 0.69 which suggest that model is fit for prediction. Yellow bar in the model suggests model validity, model validity bar above 0.5 suggest that model is valid. Turquoise green bar in the plot gives the model reproducibility any value in this model above 0.5 gives good reproducibility. This modelling established that all variables had a linear effect on PDI and showed an interaction between sonication time and pulse frequency.\u003c/p\u003e \u003cp\u003eThe coefficient plot of PDI Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e :\u003cb\u003e(D)\u003c/b\u003e Suggests that from the given independent factor sonication time has greatest influence on PDI of lipid nanoparticles.\u003c/p\u003e \u003cp\u003eThe response contour plot of PDI given in F\u003cb\u003eig:8(E)\u003c/b\u003e suggests that at high concentration of lipids and surfactant, 100% of the particles PDI may come in range 0.345 at sonication time fixed at 20 min and PF% equal to 60 and the gradient of colours indicates their respective percentage and their PDI. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e\u003cb\u003e(F)\u003c/b\u003e observed vs. predicted plot for Zeta potential suggests that observed values are close to predicted values they are unbiased which shows the good fit of the model and there are no outliners.From the regression ananlysis the general formula for calculating PDI :\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cp\u003ePDI\u0026thinsp;=\u0026thinsp;0.74-0.13X3\u0026thinsp;+\u0026thinsp;0.11X4-0.13X\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e-0.21X\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e-0.17X\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;0.14X\u003csub\u003e2\u003c/sub\u003eX\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003cdiv id=\"Sec28\" class=\"Section4\"\u003e \u003ch2\u003e3.9 Effect of different parameters on Entrapment efficiency of lipid nanoparticles\u003c/h2\u003e \u003cp\u003eThe statistical analysis of variance (ANOVA) of the response in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates that lipid concentration, surfactant concentration and sonication time are most significant variables with p-value 0.001, 0.0002 and 2.19755e-07 respectively that affect the entrapment efficiency of nanoparticles.The effect of four independent factors X1, X2, X3 and X4 on entrapment efficiency was studied, and the applicable Residual normal probability plot, summary of fit plot, coefficient plot, replicate plot, observed vs predicted plot and response surface plot was studied. From the plots it was detected that Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e\u003cb\u003e(A)\u003c/b\u003e gives residual normal probability graph which suggest that the residuals lie in between the normal lines as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(A). It can be illustrated that the residuals are scattered normally. The in between spaces observed in response defines that non-linear relationship exists between the factors and the response \u003cb\u003e(\u003c/b\u003eYassin et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The deviation of response could be due to the reason that some irrelevant term is seen in the regression coefficient. Figure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e\u003cb\u003e(B)\u003c/b\u003e the summary of fit plot for Entrapment efficiency gives R\u003csup\u003e2\u003c/sup\u003e equal to 0.986 which shows this model is significant. Q\u003csup\u003e2\u003c/sup\u003e of the plot is 0.804 which illustrates that this model is valid for Entrapment efficiency and can be used for future prediction. Model validity for entrapment efficiency is also high. Reproducibility of the given chart is also high. So, on basis of this response counter plot can be referred for further prediction \u003cb\u003e(\u003c/b\u003eShah et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The replicate plot given in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e\u003cb\u003e(C)\u003c/b\u003e that how much difference is there between the replicates as per the performed experiments analysis the given plot suggests that there was very less difference between the replicated which provides the clearance of accuracy of experiments performed. Investigation of results suggested that which features has significant effect on the entrapment efficiency Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e\u003cb\u003e(D)\u003c/b\u003e suggested that concentration of lipid (X1) and concentration of surfactant (X2) has significant role on entrapment efficiency of amoxicillin lipid nanoparticles increasing the concentration of lipid and concentration of surfactant increased the particle size of lipid nanoparticles while sonication time whose highest value was chosen 30 min and lowest 10 min has significant effect in reducing the size of lipid nanoparticle which negatively influence the entrapment efficacy of lipid nanoparticle. Figure\u0026nbsp;(9\u003cb\u003eE)\u003c/b\u003e of response surface plot suggest that with increasing the lipid concentration entrapment efficacy increased similar results were found with the concentration of surfactant. Observed vs predicted graph of amoxicillin nanoparticles suggest that there is no biasness which conclude it\u0026rsquo;s a good fit for the model \u003cb\u003e(\u003c/b\u003eRohit \u0026amp; Pal \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e\u003cb\u003e). The general formula for finding EE derived from regression plot is\u003c/b\u003e:\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003cp\u003eEE\u0026thinsp;=\u0026thinsp;88.6-3.7X\u003csub\u003e1\u003c/sub\u003e-3.3X\u003csub\u003e2\u003c/sub\u003e-4.3X\u003csub\u003e3\u003c/sub\u003e-3.1X\u003csub\u003e4\u003c/sub\u003e-5.3X\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e-7.8X\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e-4.8X\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e-6.7X\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e-7.2X\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;3.5X\u003csub\u003e2\u003c/sub\u003eX\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;14.9X\u003csub\u003e3\u003c/sub\u003eX\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e3.10 Antibiotic efficacy\u003c/h2\u003e \u003cp\u003eThe antibiotic efficacy of prepared amoxicillin nanoparticles and traditional were tested against \u003cem\u003eH.pylori\u003c/em\u003e, hallow zone was seen around the antibiotic well which confirmed its inhibition \u003cem\u003eagainst H. pylori\u003c/em\u003e. The highest hallow zone was recorded at 1% concentration of amoxicillin nanoparticles as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. As the negative control hollow nanoparticle and distilled was taken which did not showed zone of inhibition and as positive control Amoxicillin crude drug and Clarithromycin commercial drug was taken.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our study on lipid nanoparticle as a superior drug delivery agent we investigated the impact of lipid concentration, surfactant concentration, sonication time and sonication speed on various parametres of lipid nanoparticles like particle size, Zeta potential, Poly Dispersity index and Entrapment Efficiency of nanoparticles and results indicated that the lipid concentration significantly influeces the entrapment efficiency of lipid nanoparticles as the lipid content increases, more drug molecules can be encapsulated within the lipid matrix but the values can be oppsite and may fluctuate based on what type of drug are being encapsulated \u003cb\u003e(\u003c/b\u003eSchober et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe sonication time in solid lipid nanoparticles plays an important role in breaking the macromolecules uniformly. The more time the sample is sonicated the chances of getting these macromolecules aggregated is high \u003cb\u003e(\u003c/b\u003eLaserra et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The PF% is also directly related to PDI of lipid nanoparticles the reason behind it can be, more PF% provide more power to sonicator which can sonicate the molecules more effectively with high power with can lead to more aggregation and agglomeration (\u003cb\u003eJain et al., 2004)\u003c/b\u003e. Concentration of surfactant also positively regulates the PDI of lipid, high surfactant concentration led to adsorption of the surfactant on lipid nanoparticles which can lead to broader particle size and weight. Other variable lipid concentration has insignificant influence on PDI of lipid nanoparticles. The interaction plot between 2 variables lipid concentration and sonication time showed significant and inverse relation with PDI of lipid nanoparticles. Which means that their synergic effect on PDI value is significant than their individual effect on PDI of lipid nanoparticles (\u003cb\u003eShah et al., 2022).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe concentration of surfactant showed direct relation with the zeta potential of prepared nanoparticle the reason behind it could be SLN is completely masked by a non-ionic surfactant like tween 80 \u003cb\u003e(\u003c/b\u003eAsasutjarit et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and it doubles the electric double layer covering the nanoparticle and thus increase the zeta potential. PF % showed negative relation and it has significant effect on SLN. The interaction plot of sonication time and lipid concentration showed that synergic effect of these two variables has significant effect on the zeta potential of lipid nanoparticles.\u003c/p\u003e \u003cp\u003eZeta potential play an important role in defining the stability of nanoparticles. The zeta potential of nanoparticle can be influenced by various factors. In this experiment we have studied the effect of various parameters like lipid concentration, surfactant concentration, sonication time and PF% on zeta potential of lipid nanoparticles. All the nanoparticle formulation showed negative zeta potential due to presence of negatively charge on the surface of lipid nanoparticles \u003cb\u003e(\u003c/b\u003ePatel et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, palmitic acid lipid nanoparticle screening was done for four different variables influencing the formulation of solid lipid nanoparticles. For this study, Box-Behnken design was used to differentiate the most significant variable on the quality attributes of synthesized palmitic acid-based lipid nanoparticles by solvent evaporation followed by probe sonication. From the performed study it was concluded that lipid concentration, surfactant concentration, sonication time and PF% have the significant effect on the particle size, zeta potential, PDI and EE from which standard formula was derived. Therefore, palmitic acid can be used as a lipid to prepare lipid-based nanocarrier, low amount of lipid as well as surfactant and medium sonication time is preferred to obtain lipid nanoparticle of smaller size. To obtain the nanoparticle with zetapotential in range \u0026minus;\u0026thinsp;15mV - -30 mV it is preferred to keep high lipid, sonication time and low surfactant concentration. Furthermore, to obtain the mono-dispersed solution of lipid nanoparticle it is preferred to take sonication time 20 min, PF% 60 less amount of surfactant. Antibiotic efficacy assay suggested that the formulated drug formulations show better and sustained antibiotic delivery to kill pathogens. To conclude out of all formulations in the range of 275\u0026ndash;350 showed better drug entrapment efficacy and sustained release up to 36 hours and with the formula we can directly reach to desired Particle size, zeta potential, PDI and EE cutting off the lethargic \u0026ldquo;Hit n Trial\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;\u0026nbsp;\u003cbr\u003e\u0026nbsp;The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted without the support of a dedicated grant from any funding agency, whether in the public, commercial, or not-for-profit sectors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.R.S and M.K.S were involved in planning and supervised the work, K.K processed the experimental data, performed the analysis, drafted the manuscript and designed the figures. All authors discussed the results and commented on the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge central instrumentation facility (CIF), Birla Institute of Technology, Mesra, Jharkhand for providing the characterization facilities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that supports the findings of this study are available within the article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMarshall B, Adams PC (2008) Helicobacter Pylori: A Nobel Pursuit? 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Talanta Open 6:100129. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.talo.2022.100129\u003c/span\u003e\u003cspan address=\"10.1016/j.talo.2022.100129\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\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":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"biologia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"biol","sideBox":"Learn more about [Biologia](http://link.springer.com/journal/11756)","snPcode":"11756","submissionUrl":"https://www.editorialmanager.com/biol/default2.aspx","title":"Biologia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Solid Lipid nanoparticles, design of experiment, Nano biotics, Artificial intelligence, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-4251223/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4251223/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe development of nano delivery systems, particularly lipid nanoparticles (LNP), for both hydrophobic and hydrophilic drugs has seen significant advancements in recent years. Fine tuning LNP formulations is crucial due to the impact of various parameters on their quality of efficacy. The study investigated the influence of formulation variables on amoxicillin-loaded LNPs designed for anti-\u003cem\u003eHelicobacter pylori\u003c/em\u003e activity. Size, polydispersity index (PDI), Zeta potential and entrapment efficiency were evaluated across diverse formulations. The impact of particle size on drug release and encapsulation was explored. Artificial intelligence AI based design of experiments generated formulations to minimize the particle size, PDI and Zeta potential while maximizing the EE, accounting for factor interactions. Additionally, the user friendliness of QbD (Quality by Design), Machine Learning (ML), and DOE were compared.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods and results\u003c/b\u003e: A Box-Behnken design with 27 formulations was chosen for amoxicillin (amox) LNP optimization. Particle size distribution, zetapotential, PDI, and entrapment efficiency were measured for each formulation. LNP ranged in size from 200\u0026ndash;600 nm, zeta potential ranged from \u0026minus;\u0026thinsp;5 - -40 mV, PDI from 0.1- 1 and EE from 5-100%. Characterization included DLS, FESEM, FTIR and SEM. Obtained results were statistically analysed.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDiscussion\u003c/b\u003e: This study demonstrates the potential of AI- driven DOE for optimizing LNP formulations. We explained effect of different parameters lipid concentration, surfactant concentration, sonication time and sonication speed on nanoparticles and derived formula for further prediction. The identified formulations exhibited desired antibiotic efficiency with minimum chemical usage, suggesting the effectiveness of this approach. Further research explored it as a drug with more bioavailability, stability and cheap alternative over traditional drugs in market with more side effects and less bioavailability.\u003c/p\u003e","manuscriptTitle":"Developing Superior Amoxicillin Delivery Systems: AI-Driven Optimization of LNPs for H. pylori Treatment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-23 07:14:25","doi":"10.21203/rs.3.rs-4251223/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2024-08-28T04:47:02+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-05-13T05:10:23+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-19T14:52:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Biologia","date":"2024-04-19T14:43:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-16T02:45:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biologia","date":"2024-04-11T23:16:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biologia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"biol","sideBox":"Learn more about [Biologia](http://link.springer.com/journal/11756)","snPcode":"11756","submissionUrl":"https://www.editorialmanager.com/biol/default2.aspx","title":"Biologia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"42f01233-a3e5-4f6f-a8bd-949b534b4183","owner":[],"postedDate":"April 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-25T16:03:02+00:00","versionOfRecord":{"articleIdentity":"rs-4251223","link":"https://doi.org/10.1007/s11756-024-01825-z","journal":{"identity":"biologia","isVorOnly":false,"title":"Biologia"},"publishedOn":"2024-11-19 15:57:47","publishedOnDateReadable":"November 19th, 2024"},"versionCreatedAt":"2024-04-23 07:14:25","video":"","vorDoi":"10.1007/s11756-024-01825-z","vorDoiUrl":"https://doi.org/10.1007/s11756-024-01825-z","workflowStages":[]},"version":"v1","identity":"rs-4251223","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4251223","identity":"rs-4251223","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}