Box-Behnken Modeling Served for the Development and Optimization of...

[Full title: Box-Behnken Modeling Served for the Development and Optimization of Nanoparticles Loaded with Perindopril and Erbumine]

Asian Journal of Pharmaceutic s • Jul-Sep 2023 • 17 (3) | 579 Box-Behnken Modeling Served for the Development and Optimization of Nanoparticles Loaded with Perindopril and Erbumine Gaurav Mude 1 , Vedanshu Malviya 2 , Sanjay Nagdev 3 , Mona Gajbhiye 4 , Shantilal Singune 5 , Vijay Lambole 1 1 Department of Pharmacology, Datta Meghe College of Pharmacy DMIHER (DU), Wardha, Maharashtra, India, 2 Department of Pharmaceutics, P.R. Pote Patil College of Pharmacy, Amravati, Maharashtra, India, 3 Department of Quality Assurance, Shri. Prakashchand Jain College of Pharmacy, Jalgaon, Maharashtra, India, 4 Department of Pharmacology, G. H. Raisoni University Saikheda, Madhya Pradesh, India, 5 Department of Pharmacology, Institute of Pharmaceutical Sciences, SAGE University, Indore, Madhya Pradesh, India Abstract Background: The study aimed to optimize and validate a nano-particulate technology for the sustained release of perindopril erbumine, an angiotensin-converting enzyme (ACE) inhibitor, using a box-behnken experimental methodology. Methods: The researcher used a Box-behnken experimental methodology to optimize the formulation and assess various characteristics such as particle size, zeta potential, surface shape, encapsulation efficiency and in vitro drug release. The nanoparticles characterization findings were recorded included the size, polydispersity index, zeta potential and encapsulation efficiency. Results: The nanoparticles had a smooth surface and their size was determined to be 122.38 ± 0.75 nm. The polydispersity index was 0.298, the zeta potential was 38.79 ± 0.05 mv and the encapsulation efficiency was 61.73 ± 0.06%. In vitro release was restricted for up to two hours, but at a pH of 7.4, the rate of drug release increased and was maintained Conclusion: The study concluded that the nano-particulate technology for the potential to improve therapeutic efficacy and decrease dosage frequency for drug that need repeated doses such as perindopril erbumine. Keywords: Angiotensin-converting enzyme inhibitor, Box-Behnken design, Nanoprecipitation, Perindopril erbumine Address for correspondence: Vedanshu Malviya, Department of Pharmaceutics, P.R. Pote Patil College of Pharmacy, Amravati - 444 602, Maharashtra, India. E-mail: vedanshumlv 56@gmail.com Received: 04-07-2023 Revised: 14-09-2023 Accepted: 24-09-2023 INTRODUCTION P olymeric nanoparticles (NPs) are one of the drug encapsulation techniques that have received the greatest research attention in contemporary medicine. The fundamental goal of the study is to create a formulation that can deliver medicine precisely where it needs to go. Specifically, we highlight stimulus-responsive NPs due to their superior intracellular drug delivery, lengthy half-lives, and ability to go to the disease location [1] Enzymes such as pepsin and the stomach’s low pH make protein digestion difficult. Intestinal brush-border enzymes and pancreatic enzymes released into the lumen of the gut both play important roles in decreasing drug action. To enter the circulation, a medication must overcome the physical barrier created by gut cells [2] To sum up the preceding points, a novel NP-based medication delivery technology is now available Perindopril erbumine (C 23 H 43 N 3 O 5) is a medication that is a medication for treating high blood pressure, congestion, and hypertension. Because perindopril is not well absorbed after oral administration, its availability is quite limited [3] The immediate oral administration of perindopril is recommended. Due to the short half-life (0.8–1 h) when taken orally, multiple dosing is necessary. Second, excellent absorption and first-pass metabolism in the liver contribute to the drug’s ORIGINAL AR TICLE

Mude, et al .: Development and optimization of Perindopril and Erbumine by Box-Behnken Model Asian Journal of Pharmaceutic s • Jul-Sep 2023 • 17 (3) | 580 effectiveness [4,5] This research sets out to answer the feasibility of developing a controlled-release formulation of perindopril erbumine using a novel polymer to assure a sustained release throughout the course of a lengthier time frame The design of experiments method is a systematic analytical methodology that may be used to investigate the impact of response variables and the interplay of independent factors. Response surface approaches, such as Box-Behnken, D-optimal, and central composite, are often used in experimental design. The formulation of polymer NPs was optimized in this work using a Box-Behnken experimental strategy. Studies of stability and in vitro drug release profiles were conducted after physicochemical properties were characterized in the NPs METHODOLOGY Samples of perindopril erbumine were generously provided by Lara Drugs Pvt. Ltd. in Hyderabad, India. The Eudragit S 100 sample was generously provided by Evonik India, Mumbai. S.D. Fine Chem in Mumbai was where we stocked up on polyvinyl alcohol. No further purification of the materials collected from their different sources was performed. All reagents and substances utilized were of a high enough purity for analytical usage Analyzing the convergence of drugs and their excipients Fourier transform infrared spectroscopy (FTIR) The manufactured formulation is analyzed by FTIR for signature moieties. Perindopril erbumine and excipients were analyzed for their chemical makeup. FTIR was used to identify and verify the presence of functional moieties in the medication and excipient’s physical combination. Weighing and correctly mixing the samples with potassium bromide, a 1:1 ratio of medication and polymer was achieved. A little amount of the powder was squeezed together to form a pellet. To investigate the likelihood of interference, the infrared spectra of the beads were collected from 400 to 4000 cm -1 and compared to the reference spectrum [6] Thermal study The drug and excipient heat stability were evaluated using differential scanning calorimetry (DSC). Five mg of pure drug and the physical the aluminum pan mixing of the medication and polymer were scanned at 10°C/min between 50 and 400°C. Nitrogen was sucked out of the sample bottle at a rate of 20 mL per minute [7,8] Standard calibration curve In a standard flask, we mixed 100 mg of perindopril erbumine with 100 mL of phosphate buffer, pH 6.8. Aliquots of 100 g/mL solution were pipetted into 10 mL volumetric flasks at concentrations of 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, and 5 mL. Concentrations of 5, 10, 15, 20, 25, 30, 35, 40, 45, and 50 g/mL were attained by bringing the right quantity of phosphate buffer 6.8 up to the correct level. The absorbance at 215 nm was measured for each concentration [9,10] Preparation of perindopril erbumine nanoparticles (PE-ES-NPs) Perindopril erbumine-laden NPs were synthesized using the nanoprecipitation technique as per the design represented in Table 1. Drug and Eudragit S 100 dissolution were accomplished using acetone. After injecting this solution into a warm aqueous PVA solution, we agitated it constantly for 3 h to enable the organic solvent to fully evaporate. After many washing, the fluid containing the NPs was centrifuged at 10,000 rpm for 20 min at 40°C (ELTEK, Refrigerated Centrifuge RC 800 S) to collect them [8,11] Optimization of PE-ES-NPs Researchers used a Box-Behnken design (BBD) experimental design using Design-Expert® Software 11’s 3-factor, 3-level hierarchy to determine the optimal procedure for producing perindopril erbumine-loaded NPs as shown in Table 2. The polynomial equations and three-dimensional response surface plots for the factor interaction analysis were created using Design-Expert® Software 11. This equation is a representation of the BBDs resulting polynomial: Y= A 0 + A 1 * X 1 * X 2 * X 3 * X 4 * X 5 * X 1 * X 2 * X 4 * X 6 * X 1 * X 3 * X 7 * X 1 * X 4 * X 8 * X 2 * X 3 * X 9 * X 2 * X 4 * X 10 * X 3 * X 4 * X 11 * X 12 * X 12 * XaXb (where a and b are 1, 2, 3, 4) = interaction terms and Xi 2 (where i is 1, 2, 3, 4) = interaction terms; X 1 to X 4 = coded values of independent variables; Y = Response value of dependent variables; A 0 = intercept; A 1 to A 14 = regression coefficients; and XaXb Xi 2 = interaction terms. The independent variable’s additive and subtractive effects on particle size and entrapment efficiency are represented by the positive and negative coefficients, respectively, in the polynomial equation. The ideal batch was determined by maximizing encapsulation efficiency and minimizing particle size Characterization of PE-ES-NPs Drug entrapment Ten mL of phosphate buffer at pH 7.4 were mixed with 10 mg of PE-ES-NPs. After being centrifuged at 8000 rpm for 5 min, the solvent containing the NPs was removed. The centrifuged and filtered supernatant was then disposed of with great care. Using a UV Spectrophotometer set at 215 nm and room temperature, the drug concentration in the supernatant was calculated [12]

Mude, et al .: Development and optimization of Perindopril and Erbumine by Box-Behnken Model Asian Journal of Pharmaceutic s • Jul-Sep 2023 • 17 (3) | 581 Determination of particle size PE-ES-NPs with the desired scattering intensity were produced by dispersing the dried NPs in water. The Malvern zeta size analyzer was used to quantify the particle size [13] Determination of zeta potential The zeta potential was determined by measuring the electrostatic attraction between two gold-plated electrodes in a polycarbonate cell and a sample prepared in water using a zeta sizer manufactured by Malvern Instruments. NPs’ stability is linked to their surface potential, which is described by the zeta potential [13] Scanning electron microscopy PE-ES-NPs surface morphology was analyzed using scanning electron microscopy. On create the PE-ES-NPs, the powder was sprinkled over a double-sided sticky tape and then applied on a wooden stub. Then, in an ultra-high vacuum evaporator with a gold sputter module and an argon atmosphere, the tips were coated with platinum. The samples were guaranteed to be moisture-free In-vitro drug release study PE-ES-NPs in vitro release kinetics were measured using the dialysis bag diffusion technique. Dialysis membranes-50 (Hi-media) was used with a molecular weight cutoff between 12,000 and 14,000. To prepare the dialysis membrane for use, it was immersed in a 7.4-pH phosphate buffer solution overnight. Dialysis membranes are hermetically sealed on both ends, and the produced NPs were inserted within. After that, we filled the beaker to the top with 100 mL of 7.4-pH phosphate buffer. At maintain a steady sink, the beaker was placed on a magnetic stirrer and the rpm was adjusted at 100. Phosphate buffer at pH 7.4 was used, and 2 mL samples were obtained at regular intervals [14-16] Following appropriate dilution, materials were examined at a wavelength of 215 nm using a UV-visible spectrophotometer Release kinetics The kinetics of drug release from NPs was evaluated using data from in vitro drug release investigations using zero order, first order, Higuchi’s model, and the Korsmeyer- Peppas equations [17,18] RESULTS AND DISCUSSION Drug-excipients interaction study FTIR The FTIR data were compared to the standard as depicted in Figures 1 and 2, and it was found that the pure medication had the same peaks as the standard. Next, it was made sure that no new peaks arose, vanished, or were mismatched between the optimized formulation and the pure medication by comparing their peaks. C-H stretching at 2931.91 cm -1 , C=O stretching at 1736.51 cm -1 , N-H bending at 1643.01 cm -1 , C=C aromatic at 1566.96 cm -1 , and C-H scissoring and bending at 1404.61 cm -1 were all seen in the FTIR spectra of the optimized formulation, just as they were in the spectra of the pure medication. That the API and excipients used were chemically and physically compatible with one another was proven here Table 1: Box‑Behnken design variables and their scales Variables Levels Units −1 (Low) 0 (Medium) +1 (High) Independent variables X 1 Volume of organic phase mL 2.5 5 7.5 X 2 Drug loading Percentage 10 20 30 X 3 Concentration of surfactant Percentage 0.5 1 1.5 Constraints Dependent variables Y 1 Particle size nm Minimize Y 2 Entrapment efficiency Percentage Maximize

Mude, et al .: Development and optimization of Perindopril and Erbumine by Box-Behnken Model Asian Journal of Pharmaceutic s • Jul-Sep 2023 • 17 (3) | 582 Thermal study DSC thermogram of Perindopril erbumine and its improved formulation shown in Figures 3 and 4 both exhibit an endothermic peak at 161.680°C, although the latter’s peak moves somewhat lower, to 159.340°C, suggesting a change in melting point. Chemical and physical stability in the presence of the excipients was demonstrated by the absence of a significant temperature difference between the pure medication and the optimized formulation Standard calibration curve The concentration range of the standard curve from 5 to 50 ng/mL was determined to be linear, with a regression value of R 2 = 0.997. Hence, the sample perindopril erbumine at a concentration between 5 and 50 μ g/mL obeys the Beer- Lamberts law. The equation can be seen in Figure 5 Optimization of PE-ES-NPs by experimental design Table 2 summarizes the results of 17 different NP formulations created using Design Expert® software 11 and the accompanying response factors. Design Expert® Software 11 was used to fit mathematical models to the data seen from 17 different formulations, including linear, firstorder, cubic, and quadratic models, to learn more about the interplay between the variables. Based on the data collected, a quadratic model was found to be the most appropriate for analyzing PE-ES-NPs. Each answer was analyzed by plotting it on a three-dimensional graph Particle size (Y 1) The quadratic equation generated for the Y 1 response for PE-ES-NPs is as follows: Y 1 = +122.59+19.789 X 1 +1.166 X 2 -2.594 X 3 -8.42 X 1 X 2 -3.923 X 1 X 3 +11.35 X 2 X 3 +21.68 X 1 2 +5.303 X 2 2 + 19.463 X 3 2 Based on the results of the ANOVA, we can deduce that the model terms relating to response Y 1 are significantly impacted by the independent variables and their interaction effects. Table 3 shows that P -value for the Y 1 response is <0.0001. Table 4 shows the lack of fit, model F value, P -value, modified R 2, and projected R 2 for particle size (response Y 1) and encapsulation efficiency (response Y 2) According to the quadratic equation, particle size is positively influenced by both the volume of the organic phase (X 1) and the drug loading (X 2). Particle size grows in response to a rise in acetone concentration when the medium capacity is big. As the drug loading in the PE-ES-NPs was raised, so was the particle size. However, the NP particle size was shown to decrease with increasing concentration of surfactant (X 3) due to the generation of tiny droplets. As shown in Figure 6 a-c, the three-dimensional response graphs look like this: Table 2: Experimental runs and calculated responses ( n =3) Formulation code Independent variables Dependent variables Volume of organic phase: X 1 (mL) Drug loading: X 2 (%) Concentration of surfactant: X 3 (%) Particle size (Y 1 ) (nm±SD)* Entrapment efficiency (Y 2 ) (%±SD)* PENP 1 1 1 0 166.14±0.57 59.78±1.45 PENP 2 1 0 −1 188.92±1.23 60.87±2.17 PENP 3 0 0 0 122.46±0.87 64.52±0.15 PENP 4 0 1 −1 136.85±0.42 56.77±1.72 PENP 5 0 0 0 120.68±0.22 62.56±0.14 PENP 6 0 0 0 118.67±0.65 58.91±1.66 PENP 7 −1 −1 0 116.17±0.9 40.84±0.90 PENP 8 −1 0 −1 144.34±0.28 54.92±0.75 PENP 9 0 0 0 128.68±0.31 66.85±1.52 PENP 10 −1 0 1 146.39±0.67 54.21±0.25 PENP 11 −1 1 0 140.56±0.77 56.82±1.62 PENP 12 1 0 1 175.28±0.94 60.35±0.49 PENP 13 0 0 0 122.46±1.21 62.46±0.09 PENP 14 1 −1 0 175.42±1.51 47.76±0.36 PENP 15 0 −1 1 135.16±0.16 38.62±0.06 PENP 16 0 1 1 154.97±0.40 54.72±0.24 PENP 17 0 −1 −1 162.44±1.33 43.25±0.15 SD: Standard deviation, *Utilization of nano‑particulate technology

Mude, et al .: Development and optimization of Perindopril and Erbumine by Box-Behnken Model Asian Journal of Pharmaceutic s • Jul-Sep 2023 • 17 (3) | 583 Table 4: Summary of various quadratic parameters Response Adjusted R 2 Predicted R 2 Lack of fit F Model F Particle size (Y 1 ) 0.9644 0.8500 1.70 49.22 Encapsulation efficiency (Y 2 ) 0.9195 0.8843 0.18 21.30 Table 3: ANOVA for quadratic model for particle size (response Y 1 ) Source Sum of squares df Mean square F P Model 8072.59 9 896.95 49.22 <0.0001 Significant A‑Volume of organic phase 3132.36 1 3132.36 171.90 <0.0001 B‑drug loading 10.88 1 10.88 0.5971 0.4650 C‑concentration of surfactant 53.82 1 53.82 2.95 0.1294 AB 283.42 1 283.42 15.55 0.0056 AC 61.54 1 61.54 3.38 0.1087 BC 515.29 1 515.29 28.28 0.0011 A² 1979.04 1 1979.04 108.61 <0.0001 B² 118.39 1 118.39 6.50 0.0382 C² 1594.90 1 1594.90 87.53 <0.0001 Residual 127.55 7 18.22 Lack of fit 71.42 3 23.81 1.70 0.3045 Not significant Pure error 56.14 4 14.03 Cor total 8200.14 16 ANOVA: Analysis of variance Figure 1: Fourier transform infrared spectroscopy of pure drug Entrapment efficiency (Y 2 ) The quadratic equation generated for the Y 2 response for PE-ES-NPs is as follows: Y 2 = +63.06+2.75 X 1 +7.203 X 2 -0.9888 X 3 -0.99 X 1 X 2 +0.0475 X 1 X 3 +0.645 X 2 X 3 -1.256 X 1 2 -10.504 X 22-4.216 X 3 2 According to the results of the analysis of variance, there are statistically significant model terms provided by the independent variables and their interaction effects with respect to the Y 2 response. A Y 2 response had P = 0.0003 ( Table 5 ) As can be seen from the aforementioned quadratic equation, the reaction Y 2 is jointly influenced by the organic phase’s volume (X 1) and the drug loading (X 2). However, encapsulation efficiency decreases as surfactant concentration increases. The three-dimensional Y 2 response graphs are shown in Figure 7 a-c.

Mude, et al .: Development and optimization of Perindopril and Erbumine by Box-Behnken Model Asian Journal of Pharmaceutic s • Jul-Sep 2023 • 17 (3) | 584 Figure 2: Fourier transform infrared spectroscopy of drug with excipients Figure 3: Differential scanning calorimetry of pure drug Figure 4: Differential scanning calorimetry of drug with excipients

Mude, et al .: Development and optimization of Perindopril and Erbumine by Box-Behnken Model Asian Journal of Pharmaceutic s • Jul-Sep 2023 • 17 (3) | 585 Figure 5: Standard calibration curve of perindopril erbumine Table 5: ANOVA for quadratic model for encapsulation efficiency (response Y 2 ) Source Sum of squares df Mean square F P Model 1067.55 9 118.62 21.30 0.0003 Significant A‑volume of organic phase 60.34 1 60.34 10.84 0.0133 B‑drug loading 415.01 1 415.01 74.53 <0.0001 C‑concentration of surfactant 7.82 1 7.82 1.40 0.2746 AB 3.92 1 3.92 0.7040 0.4292 AC 0.0090 1 0.0090 0.0016 0.9690 BC 1.66 1 1.66 0.2988 0.6016 A² 6.64 1 6.64 1.19 0.3108 B² 464.54 1 464.54 83.42 <0.0001 C² 74.85 1 74.85 13.44 0.0080 Residual 38.98 7 5.57 Lack of fit 4.65 3 1.55 0.1807 0.9043 Not significant Pure error 34.33 4 8.58 Cor total 1106.53 16 ANOVA: Analysis of variance Optimization and validation The best formulation of PE-ES-NPs was selected using the Design Expert software’s numeric point prediction method, with the goals of minimal particle size and high encapsulation efficiency in mind. With a desire of 0.956, the optimal formulation for PE-ES-NPs included a volume of 4.8 mL of acetone, a drug loading of 20.2%, and a surfactant content of 0.94 weight percent. Particle size (128.68 nm) and entrapment efficiency (64.85%) of PE-ES-NPs were found to be consistent with those predicted by Design Expert Figure 6: Effect of drug loading, surfactant concentration, and organic phase volume on particle size in polyethersulfone nanoparticles (a), nanoparticles (b), and nanoparticles (c) in three‑dimensional response surface plots a c b

Mude, et al .: Development and optimization of Perindopril and Erbumine by Box-Behnken Model Asian Journal of Pharmaceutic s • Jul-Sep 2023 • 17 (3) | 586 software® (120.43 nm and 63.65%, respectively). Therefore, the PENP 9 batch of formulation was selected as the best possible option Determination of particle size One of the most crucial factors is the particle size defining features of NPs. NPs of perindopril erbumine in the best formulation were on average 128.68 nm in size. NPs with PDI values of 0.336 and intercepts of 0.963 were found in the particle size study. PE-ES-NPs particle size distribution as a percentage of intensity is shown in Figure 8 Determination of zeta potential PE-ES-NPs zeta potential may be used as a stability indicator and mentioned in Figure 9. The zeta potential of perindopril erbumine PENP 9 NPs was measured to be −28 mV 0.05 mV, and their polydispersity index was reported to be 0.220. The peak area of the observed zeta potential is 100% intense. Low polydispersity index and negative zeta potential demonstrate uniform particle distribution and physical stability of the delivery method In vitro drug release study The in vitro drug release from optimized PE-ES-NPs was studied at a pH of 7.4 [Figure 10]. Drug release from NPs was somewhat modest for the first 2 h, but beyond that time, Figure 8: Percentage intensity of particle size distribution of perindopril erbumine nanoparticles Figure 9: Zeta potential distribution of perindopril erbumine nanoparticles it was steady and increased, suggesting that drug loss was significantly reduced. Figure 10 shows that the percentage of medication release varied between 15.35% and 89.28% The in vitro dialysis bag diffusion technique release data were used to test hypotheses about a range of mathematical models. Figure 7: Three‑dimensional response surface for PE‑ES‑NPs showing effect of (a) drug loading and volume of the organic phase, (b) concentration of surfactant and volume of the organic phase, and (c) concentration of surfactant and drug loading on encapsulation efficiency e c b

Mude, et al .: Development and optimization of Perindopril and Erbumine by Box-Behnken Model Asian Journal of Pharmaceutic s • Jul-Sep 2023 • 17 (3) | 587 Table 6: Kinetic release models for the optimized perindopril erbumine nanoparticles Formulation Zero order ( R 2 ) First order ( R 2 ) Higuchi ( R 2 ) Korsmeyer-Peppas kinetics R 2 n Perindopril erbumine nanoparticles 0.7723 0.5168 1.000 0.9999 0.156 Figure 10: In vitro drug release profile of optimized formulation PENP 9 The Higuchi equation best described the rate of release of NPs of perindopril erbumine. Diffusion of the medication from both homogeneous and granular matrices is described by the Higuchi equation. The Korsmeyer-Peppas model predicts Fickian diffusion kinetics, and a diffusion exponent (n) of 0.156 supports this idea. Table 6 displays the R 2 and n 2 exponential values. According to the published kinetic data, the Higuchi formulation of PE-ES-NPs is the most accurate, followed by the Korsmeyer-Peppas, Zero Order, and First Order formulations Surface morphology To get a deeper understanding of morphology, scanning electron microscopy is important. SEM research from Figure 11 revealed that PE-ES-NPs are round to oval in form, with a polymeric surface that has been assembled CONCLUSION The study concluded that the nano-particulate technology for the potential to improve therapeutic efficacy and decrease dosage frequency for drug that need repeated doses such as perindopril erbumine REFERENCES 1. Jawahar N, Meyyanathan SN. Polymeric nanoparticles for drug delivery and targeting: A comprehensive review. Int J Health Allied Sci 2012;1:217-33 2. Silk DB. Progress report. Peptide absorption in man. Gut 1974;15:494 3. Bojarska J, Maniukiewicz W, Sieroń L, Fruziński A, Kopczacki P, Walczyński K, et al . Novel pseudopolymorph of the active metabolite of perindopril. Acta Crystallogr C 2012;68:o 341-3 4. Todd PA, Fitton A. Perindopril. Drugs 1991;42:90-114 5. Doyle AE. ACE inhibition: Benefits beyond blood pressure control. Am J Med 1992;92:1 S-2 6. Canbay HS, Polat M, Doğantürk M. Study of stability and drug-excipient compatibility of estriol. Bilge Int J Sci Technol Res 2019;3:102-7 7. Sipos E, Kósa N, Kazsoki A, Szabó ZI, Zelkó R. Formulation and characterization of aceclofenac-loaded nanofiber based orally dissolving webs. Pharmaceutics 2019;11:417 8. Swathi P, Sailaja AK. Formulation of ibuprofen loaded ethyl cellulose nanoparticles by nanoprecipitation technique. Asian J Pharm Clin Res 2014;7:44-8 9. Laxmi MV, Krishna V. Formulation and evaluation of aceclofenac matrix tablets using ethyl cellulose and cellulose acetate phthalate. J Glob Trends Pharm Sci 2014;5:1804-10 10. Rajitha K, Prasanna NL, Vasundhara G, Kumar RN, Kumar AA. UV spectrophotometric method development and validation for the simultaneous quantitative estimation of mebeverine hydrochloride and chlordiazepoxide in capsules. Int J Pharm Pharm Sci 2011;6:345-9 11. Aisha AF, Abdulmajid AM, Ismail Z, Alrokayan SA, Abu-Salah KM. Development of polymeric nanoparticles of Garcinia mangostana xanthones in eudragit RL 100/ RS 100 for anti-colon cancer drug delivery. J Nanomater 2015;2015:701979 12. Betala S, Varma MM, Abbulu K. Formulation and evaluation of polymeric nanoparticles of an antihypetensive drug for gastroretention. J Drug Deliv Ther 2018;8:82-6 13. Pal SL, Jana U, Manna PK, Mohanta GP, Manavalan R. Nanoparticle: An overview of preparation and characterization. J Appl Pharm Sci 2011;1:228-34 14. Shid RL, Dhole SN, Kulkarni N, Shid SL. Formulation and evaluation of nanosuspension formulation for drug delivery of simvastatin. Int J Pharm Sci Nanotechnol 2014;7:2650-65 Figure 11: SEM of prepared nanoparticles (a) magnification at 10 K and (b) Magnification at 25 K a b

Mude, et al .: Development and optimization of Perindopril and Erbumine by Box-Behnken Model Asian Journal of Pharmaceutic s • Jul-Sep 2023 • 17 (3) | 588 15. Sreelola VU, Sailaja AK, Pharmacy M. Preparation and characterisation of ibuprofen loaded polymeric nanoparticles by solvent evaporation technique. Int J Pharm Pharm Sci 2014;6:416-21 16. Yu M, Yuan W, Li D, Schwendeman A, Schwendeman SP. Predicting drug release kinetics from nanocarriers inside dialysis bags. J Controlled Release 2019;315: 23-30 17. Bose A, Wong TW, Singh N. Formulation development and optimization of sustained release matrix tablet of Itopride HCl by response surface methodology and its evaluation of release kinetics. Saudi Pharm J 2013;21:201-13 18. Barzegar-Jalali M. Kinetic analysis of drug release from nanoparticles. J Pharm Pharm Sci 2008;11:167-77 Source of Support: Nil. Conflicts of Interest: None declared.

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Shem, Smooth surface, High blood pressure, Experimental design, Oral administration, Therapeutic efficacy, Particle size, Physicochemical properties, Particle size distribution, Sustained release, ANOVA, Repeated dose, Scanning Electron Microscopy, Nanoparticle, Fourier transform infrared spectroscopy, Angiotensin Converting Enzyme inhibitor, Differential scanning calorimetry, Chemical stability, Chemical makeup, Surface morphology, Standard deviation, Infrared spectra, First Pass Metabolism, Zeta potential, ACE inhibitor, FTIR, Encapsulation efficiency, Design Expert software, Drug Loading, UV-visible spectrophotometer, Entrapment efficiency, In-vitro release, Organic solvent, Polynomial equation, UV Spectrophotometer, Korsmeyer-Peppas model, In vitro drug release, Korsmeyer-Peppas equation, Phosphate buffer, Regression coefficient, Standard calibration curve, Drug release profile, Dosage frequency, Response surface plot, Pharmaceutical science, In-Vitro Drug Release Study, Drug delivery, Drug delivery technology, Drug entrapment, Polydispersity Index, In vitro Drug Release Profile, Surfactant Concentration, Magnetic stirrer, Higuchi's model, Experimental Methodology, Physical stability, Polymeric nanoparticle, Electrostatic attraction, Eudragit-S 100, Polyvinyl alcohol, Zero order, First order, Design of Experiment, DSC thermogram, Dialysis membrane, Perindopril Erbumine, Box Behnken design, Experimental runs, Higuchi Equation, Drug-excipients interaction, Mathematical Model, DSC, Perindopril, Nanoprecipitation Technique, Controlled release formulation, Organic phase, Potassium bromide, Nanoprecipitation, Volume of organic phase, Acetone, Quadratic Model, In vitro release kinetics, Drug-excipients interaction study, Nanoparticles loaded, Intracellular drug delivery, Dependent variable, Independent variable, Optimization and validation, Thermal Study, Entrapment Efficiency Percentage, Kinetic release models, FTIR data, Release kinetic, Drug encapsulation techniques, Zeta sizer, Asian Journal, Determination of Particle size, Chemical and physical stability, Independent factor, Response variable, Organic phase volume, Regression value, Kinetic data, Molecular weight cutoff, Quadratic equation, Functional moieties, Model term, Aqueous PVA solution, Surface shape, Acetone concentration, Experimental strategy, Stability indicator, Response value.