RESPONSE SURFACE METHODOLOGY (RSM) AS A TOOL IN PHARMACEUTICAL FORMULATION DEVELOPMENT

Authors

  • SRABANI PODDER Department of Pharmaceutics Eminent College of Pharmaceutical Technology, Barasat, West Bengal, India. https://orcid.org/0000-0002-6396-6115
  • SUDIPTA MUKHERJEE Department of Pharmaceutics Global College of Pharmaceutical Technology, Krishnagar, West Bengal, India

DOI:

https://doi.org/10.22159/ajpcr.2024v17i11.52149

Keywords:

Design of experiments, Central composite design, Box-Behnken design

Abstract

Response surface methodology (RSM) serves as a valuable tool in pharmaceutical formulation development, facilitating the optimization of drug formulations by systematically exploring the effects of multiple variables on desired responses. This methodology involves the design of experiments to generate mathematical models that predict the relationship between formulation parameters and critical quality attributes. By utilizing statistical techniques such as factorial design, central composite design, and Box-Behnken design, RSM enables the identification of optimal formulation conditions while minimizing the number of experimental trials. Across iterative experimentation and model refinement, RSM assists in understanding the complex interactions between formulation components, process variables, and product characteristics. In this review, we discuss the application of RSM in pharmaceutical formulation studies, highlighting its efficacy in optimizing drug delivery systems, enhancing product stability, and ensuring quality control. In addition, we explore recent advancements in RSM-driven approaches, including its integration with computational modeling and artificial intelligence techniques for enhanced formulation design and process optimization. Overall, RSM offers a systematic and efficient approach for developing robust pharmaceutical formulations, thereby accelerating the drug development process and improving therapeutic outcomes.

Downloads

Download data is not yet available.

References

Yolmeh M, Jafari SM. Applications of response surface methodology in the food industry processes. Food Bioproc Technol. 2017 Mar;10(3):413-33.

Franceschini G, Macchietto S. Model-based design of experiments for parameter precision: State of the art. Chem Eng Sci. 2008 Oct 01;63(19):4846-72.

Anderson-Cook CM, Borror CM, Montgomery DC. Response surface design evaluation and comparison. J Stat Plan Inference. 2009 Feb 01;139(2):629-41.

De Oliveira LG, de Paiva AP, Balestrassi PP, Ferreira JR, da Costa SC, da Silva Campos PH. Response surface methodology for advanced manufacturing technology optimization: Theoretical fundamentals, practical guidelines, and survey literature review. Int J Adv Manuf Technol. 2019 Oct;104:1785-837.

Sibanda W, Pretorius P. Comparative study of the application of Box Behnken Design (BBD) and binary logistic regression (BLR) to study the effect of demographic characteristics on HIV risk in South Africa. J Appl Med Sci. 2012;1(2):15-40.

Peng Y, Khaled U, Al-Rashed AA, Meer R, Goodarzi M, Sarafraz MM. Potential application of Response Surface Methodology (RSM) for the prediction and optimization of thermal conductivity of aqueous CuO (II) nanofluid: A statistical approach and experimental validation. Phys A Stat Mech Appl. 2020 Sep 15;554:124353.

Elfghi FM. A hybrid statistical approach for modeling and optimization of RON: A comparative study and combined application of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) based on Design of Experiment (DOE). Chem Eng Res Des. 2016 Sep 01;113:264-72.

Hlangwani E. Response Surface Methodology and Artificial Neural Networks Bioprocessing Approach for Umqombothi (South African Traditional Beverage) and Investigation of its Composition. South Africa: University of Johannesburg; 2021.

Durivage MA. Practical Design of Experiments (DOE). Australia: Quality Press; 2016 Feb 01.

Schiavon M. Multi-Objective Surrogate Optimization of a Surface Permanent Magnet Synchronous Machine. Padua Thesis and Dissertation Archive; ???.

Gilmour SG, Trinca LA. Response surface experiments on processes with high variation. In: Response Surface Methodology and Related Topics. Singapore: World Scientific; 2006. p. 19-46.

Bhoop BS, Raza K, Beg S. Developing “optimized” drug products employing “designed” experiments. Chem Ind Dig. 2013 Jun;23:70-6.

Tekade RK. The Future of Pharmaceutical Product Development and Research. United States: Academic Press; 2020 Sep 2.

Jain A, Hurkat P, Jain SK. Development of liposomes using formulation by design: Basics to recent advances. Chem Phys Lipids. 2019 Nov 1;224:104764.

Simpson T, Toropov V, Balabanov V, Viana F. Design and Analysis of Computer Experiments in Multidisciplinary Design Optimization: A Review of how Far we have Come-or Not. In: 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference; 2008 Sep 10. p. 5802.

Rampado R, Peer D. Design of experiments in the optimization of nanoparticle-based drug delivery systems. J Control Release. 2023 Jun 01;358:398-419.

Singh B, Kapil R, Nandi M, Ahuja N. Developing oral drug delivery systems using formulation by design: Vital precepts, retrospect and prospects. Expert Opin Drug Deliv. 2011 Oct 01;8(10):1341-60.

Grangeia HB, Silva C, Simões SP, Reis MS. Quality by design in pharmaceutical manufacturing: A systematic review of current status, challenges and future perspectives. Eur J Pharm Biopharm. 2020 Feb 01;147:19-37.

Mishra V, Thakur S, Patil A, Shukla A. Quality by design (QbD) approaches in current pharmaceutical set-up. Expert Opin Drug Deliv. 2018 Aug 03;15(8):737-58.

Wang S, Di J, Wang D, Dai X, Hua Y, Gao X, et al. State-of-the-art review of artificial neural networks to predict, characterize and optimize pharmaceutical formulation. Pharmaceutics. 2022 Jan 13;14(1):183.

Pereira LM, Milan TM, Tapia-Blácido DR. Using Response Surface Methodology (RSM) to optimize 2G bioethanol production: A review. Biomass Bioenergy. 2021 Aug 01;151:106166.

Czyrski A, Jarzębski H. Response surface methodology as a useful tool for evaluation of the recovery of the fluoroquinolones from plasma-The study on applicability of box-behnken design, central composite design and doehlert design. Processes. 2020 Apr 17;8(4):473.

Yu P, Low MY, Zhou W. Design of experiments and regression modelling in food flavour and sensory analysis: A review. Trends Food Sci Technol. 2018 Jan 01;71:202-15.

Usman AI, Aziz AA, Sodipo BK. Application of central composite design for optimization of biosynthesized gold nanoparticles via sonochemical method. SN Appl Sci. 2019 May;1:403.

Mior Zakuan Azmi M, Taip FS, Mustapa Kamal SM, Chin NL. Effects of temperature and time on the physical characteristics of moist cakes baked in air fryer. J Food Sci Technol. 2019 Oct;56:4616-24.

Vandervoort J, Ludwig A. Biocompatible stabilizers in the preparation of PLGA nanoparticles: A factorial design study. Int J Pharm. 2002 May 15;238(1-2):77-92.

Myers RH, Montgomery DC, Anderson-Cook CM. Response Surface Methodology: Process and Product Optimization Using Designed Experiments. United States: John Wiley & Sons; 2016 Jan 04.

Madsen JI, Shyy W, Haftka RT. Response surface techniques for diffuser shape optimization. AIAA J. 2000 Sep;38(9):1512-8.

Arshad HM, Akhtar M, Gilmour SG. Augmented box-behnken designs for fitting third-order response surfaces. Commun Stat Theory Methods. 2012 Dec 01;41(23):4225-39.

Pino JM, Dreiling JM, Figgatt C, Gaebler JP, Moses SA, Allman MS, et al. Demonstration of the trapped-ion quantum CCD computer architecture. Nature. 2021 Apr 08;592(7853):209-13.

Black P. Mechanical Technology for Higher Engineering Technicians. Netherlands: Elsevier; 2014 May 17.

Singh B, Dahiya M, Saharan V, Ahuja N. Optimizing drug delivery systems using systematic “design of experiments.” Part II: Retrospect and prospects. Crit Rev Ther Drug Carrier Syst. 2005;22(3):215-94.

Bezerra MA, Santelli RE, Oliveira EP, Villar LS, Escaleira LA. Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta. 2008;76:965-77.

MacGillivray LR, Atwood JL. Structural classification and general principles for the design of spherical molecular hosts. Angew Chem Int Ed Engl. 1999 Apr 19;38(8):1018-33.

Notaros BM. Higher order frequency-domain computational electromagnetics. IEEE Trans Antennas Propag. 2008 Aug 05;56(8):2251-76.

Aungst BJ. Optimizing oral bioavailability in drug discovery: An overview of design and testing strategies and formulation options. J Pharm Sci. 2017 Apr 01;106(4):921-9.

Zarmpi P, Flanagan T, Meehan E, Mann J, Fotaki N. Biopharmaceutical aspects and implications of excipient variability in drug product performance. Eur J Pharm Biopharm. 2017 Feb 01;111:1-15.

Saeio K, Pongpaibul Y, Viernstein H, Okonogi S. Factors influencing drug dissolution characteristic from hydrophilic polymer matrix tablet. Sci Pharm. 2007 Dec;75(4):147-64.

Singh B, Chakkal SK, Ahuja N. Formulation and optimization of controlled release mucoadhesive tablets of atenolol using response surface methodology. AAPS PharmSciTech. 2006 Mar;7:E19-28.

Tranmer M, Elliot M. Multiple linear regression. The Cathie Marsh Centre for Census and Survey Research (CCSR). Vol. 5. 2008. p. 1-5.

Majdi H, Esfahani JA, Mohebbi M. Optimization of convective drying by response surface methodology. Comput Electron Agric. 2019 Jan 01;156:574-84.

Atkinson A, Donev A, Tobias R. Optimum Experimental Designs, with SAS. Oxford: OUP; 2007 May 24.

Raissi S, Farsani RE. Statistical process optimization through multi-response surface methodology. Int J Math Comput Sci. 2009 Mar 25;3(3):197-201.

Li Z, Lu D, Gao X. Optimization of mixture proportions by statistical experimental design using response surface method-A review. J Build Eng. 2021 Apr 01;36:102101.

Haji SH, Abdulazeez AM. Comparison of optimization techniques based on gradient descent algorithm: A review. PalArchs J Archaeol Egypt Egyptol. 2021 Feb 18;18(4):2715-43.

Candioti LV, De Zan MM, Cámara MS, Goicoechea HC. Experimental design and multiple response optimization. Using the desirability function in analytical methods development. Talanta. 2014 Jun 15;124:123-38.

Fan SK, Liang YC, Zahara E. A genetic algorithm and a particle swarm optimizer hybridized with Nelder-Mead simplex search. Comput Ind Eng. 2006 Aug 01;50(4):401-25.

Biles WE. A response surface method for experimental optimization of multi-response processes. Ind Eng Chem Process Des Dev. 1975 Apr;14(2):152-8.

Pianosi F, Beven K, Freer J, Hall JW, Rougier J, Stephenson DB, et al. Sensitivity analysis of environmental models: A systematic review with practical workflow. Environ Model Softw. 2016 May 01;79:214-32.

Veza I, Spraggon M, Fattah IR, Idris M. Response surface methodology (RSM) for optimizing engine performance and emissions fueled with biofuel: Review of RSM for sustainability energy transition. Results Eng. 2023 Jun 02;18:101213.

Samson D, Terziovski M. The relationship between total quality management practices and operational performance. J Oper Manag. 1999 Jun 01;17(4):393-409.

Lamidi S, Olaleye N, Bankole Y, Obalola A, Aribike E, Adigun I. Applications of Response Surface Methodology (RSM) in Product Design, Development, and Process Optimization. London: IntechOpen; 2022 Sep 16.

Flannery A. Precision Drug Delivery for Vancomycin Efficacy and Safety in Critically Ill Patients. Theses and Dissertations--Clinical and Translational Science; ???.

Koenderink JJ, Van Doorn AJ. Surface shape and curvature scales. Image Vis Comput. 1992 Oct 01;10(8):557-64.

Hotza D, Greil P. Aqueous tape casting of ceramic powders. Mater Sci Eng A. 1995 Nov 01;202(1-2):206-17.

Rakić T, Kasagić-Vujanović I, Jovanović M, Jančić-Stojanović B, Ivanović D. Comparison of full factorial design, central composite design, and box-behnken design in chromatographic method development for the determination of fluconazole and its impurities. Anal Lett. 2014 May 24;47(8):1334-47.

Shi Y, Gao P, Gong Y, Ping H. Application of a biphasic test for characterization of in vitro drug release of immediate release formulations of celecoxib and its relevance to in vivo absorption. Mol Pharm. 2010 Oct 04;7(5):1458-65.

Van Boekel MA, Zwietering MH. Experimental design, data processing and model fitting in predictive microbiology. In: Modelling Microorganisms in Food. Cambridge: Woodhead Publishing; 2007 Jan 01. p. 22-43.

Vining GG, Kowalski SM, Montgomery DC. Response surface designs within a split-plot structure. J Qual Technol. 2005 Apr 01;37(2):115-29.

Box GE, Hunter JS. Multi-factor experimental designs for exploring response surfaces. Ann Math Stat. 1957 Mar 01;28:195-241.

Reji M, Kumar R. Response surface methodology (RSM): An overview to analyze multivariate data. Indian J Microbiol Res. 2022;9:241-8.

Alexopoulos EC. Introduction to multivariate regression analysis. Hippokratia. 2010 Dec;14(Suppl 1):23-8.

Nathans LL, Oswald FL, Nimon K. Interpreting multiple linear regression: A guidebook of variable importance. Pract Assess Res Eval. 2012 Apr;17(9):n9.

Mandel J. The partitioning of interaction in analysis of variance. J Res Natl Bur Stand Ser B. 1969 Oct;73:309-28.

Jaccard J. Interaction Effects in Factorial Analysis of Variance. United Kingdom: Sage; 1998.

Malekjani N, Jafari SM. Food process modeling and optimization by response surface methodology (RSM). In: Mathematical and Statistical Applications in Food Engineering. United States: CRC Press; 2020 Jan 30. p. 181-203.

Fernandez GC. Residual analysis and data transformations: Important tools in statistical analysis. HortScience. 1992 Apr 01;27(4):297-300.

Martin J, De Adana DD, Asuero AG. Fitting models to data: Residual analysis, a primer. In: Uncertainty Quantification and Model Calibration. London: IntechOpen; 2017 Jul 05. p. 133.

Hawkins DM, Basak SC, Mills D. Assessing model fit by cross-validation. J Chem Inf Comput Sci. 2003 Mar 24;43(2):579-86.

Beg S, Swain S, Rahman M, Hasnain MS, Imam SS. Application of design of experiments (DoE) in pharmaceutical product and process optimization. In: Pharmaceutical Quality by Design. United States: Academic Press; 2019 Jan 01. p. 43-64.

Li Y, Guan Q, Xu J, Zhang H, Liu S, Ding Z, et al. Comparative study of cyclosporine A liposomes and emulsions for ophthalmic drug delivery: Process optimization through response surface methodology (RSM) and biocompatibility evaluation. Colloids Surf B Biointerfaces. 2023 May 01;225:113267.

Hashemi SH, Montazer M, Naghdi N, Toliyat T. Formulation and characterization of alprazolam-loaded nanoliposomes: Screening of process variables and optimizing characteristics using RSM. Drug Dev Ind Pharm. 2018 Feb 01;44(2):296-305.

Luiz MT, Viegas JS, Abriata JP, Viegas F, de Carvalho Vicentini FT, Bentley MV, et al. Design of experiments (DoE) to develop and to optimize nanoparticles as drug delivery systems. Eur J Pharm Biopharm. 2021 Aug 01;165:127-48.

Mahapatra AP, Saraswat R, Botre M, Paul B, Prasad N. Application of response surface methodology (RSM) in statistical optimization and pharmaceutical characterization of a patient compliance effervescent tablet formulation of an antiepileptic drug levetiracetam. Future J Pharm Sci. 2020 Dec;6:82.

Ghaemmaghamian Z, Zarghami R, Walker G, O’Reilly E, Ziaee A. Stabilizing vaccines via drying: Quality by design considerations. Adv Drug Deliv Rev. 2022 Aug 01;187:114313.

Kasemiire A, Avohou HT, De Bleye C, Sacre PY, Dumont E, Hubert P, et al. Design of experiments and design space approaches in the pharmaceutical bioprocess optimization. Eur J Pharm Biopharm. 2021 Sep 01;166:144-54.

Jaswir I, Noviendri D, Taher M, Mohamed F, Octavianti F, Lestari W, et al. Optimization and formulation of fucoxanthin-loaded microsphere

Published

07-11-2024

How to Cite

SRABANI PODDER, and SUDIPTA MUKHERJEE. “RESPONSE SURFACE METHODOLOGY (RSM) AS A TOOL IN PHARMACEUTICAL FORMULATION DEVELOPMENT”. Asian Journal of Pharmaceutical and Clinical Research, vol. 17, no. 11, Nov. 2024, pp. 18-25, doi:10.22159/ajpcr.2024v17i11.52149.

Issue

Section

Review Article(s)