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Modelling Based Approaches to Support Generic Drug Regulatory Submissions-Practical Considerations and Case Studies

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Abstract

Model informed drug development (MiDD) is useful to predict in vivo exposure of drugs during various stages of the drug development process. This approach employs a variety of quantitative tools to assess the risks during the drug development process. One important tool in the MiDD tool kit is the Physiologically Based Pharmacokinetic Modelling (PBPK). This tool is extensively used to reduce the development cost and to accelerate the access of medicines to the patients. In this work, we provide an overview of PBPK modelling approaches in the generic drug development process, with a special emphasis on the bio-waiver applications. We describe herein approaches and common pitfalls while submitting model based justifications as a response to the regulatory deficiencies during the generic drug development process. With some in-house case studies, we have attempted to provide a clear path for PBPK model based justifications for bio-waivers. With this review, the gap between theoretical knowledge and practical application of modelling and simulation tools for generic drug product development could be potentially reduced.

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References

  1. Zhao P, Zhang L, Grillo JA, Liu Q, Bullock JM, Moon YJ, Song P, Brar SS, Madabushi R, Wu TC, Booth BP. Applications of physiologically based pharmacokinetic (PBPK) modeling and simulation during regulatory review. Clin Pharm Ther. 2011;89(2):259–67. https://doi.org/10.1038/clpt.2010.298.

    Article  CAS  Google Scholar 

  2. Sager JE, Yu J, Ragueneau-Majlessi I, Isoherranen N. Physiologically based pharmacokinetic (PBPK) modeling and simulation approaches: a systematic review of published models, applications, and model verification. Drug Metab Dispos. 2015;43(11):1823–37. https://doi.org/10.1124/dmd.115.065920.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. El-Khateeb E, Burkhill S, Murby S, Amirat H, Rostami-Hodjegan A, Ahmad A. Physiological-based pharmacokinetic modeling trends in pharmaceutical drug development over the last 20-years; in-depth analysis of applications, organizations, and platforms. Biopharm Drug Dispos. 2021;42(4):107–17. https://doi.org/10.1002/bdd.2257.

    Article  CAS  PubMed  Google Scholar 

  4. Guideline on the reporting of physiologically based pharmacokinetic (PBPK) modelling and simulation. Committee for Medicinal Products for Human Use (CHMP). 2018; Available from:https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-reporting-physiologically-based-pharmacokinetic-pbpk-modelling-simulation_en.pdf. Accessed on 15 Dec 2022

  5. Physiologically based pharmacokinetic analyses — format and content. U.S. Department of Health and Human Services Food and Drug Administration Center for Drug Evaluation and Research (CDER) 2018. https://www.fda.gov/media/101469/download. Accessed on 15 Dec 2022

  6. Laisney M, Heimbach T, Mueller-Zsigmondy M, Blumenstein L, Costa R, Ji Y. Physiologically based biopharmaceutics modeling to demonstrate virtual bioequivalence and bioequivalence safe-space for ribociclib which has permeation rate-controlled absorption. J Pharm Sci. 2022 111(1):274-284. https://doi.org/10.1016/j.xphs.2021.10.017.

  7. Wu D, Sanghavi M, Kollipara S, Ahmed T, Saini AK, Heimbach T. Physiologically based pharmacokinetics modeling in biopharmaceutics: case studies for establishing the bioequivalence safe space for innovator and generic drugs. Pharm Res. 2023;40(2):337–57. https://doi.org/10.1007/s11095-022-03319-6.

    Article  CAS  PubMed  Google Scholar 

  8. Shebley M, Sandhu P, Emami Riedmaier A, Jamei M, Narayanan R, Patel A, Peters SA, Reddy VP, Zheng M, de Zwart L, Beneton M. Physiologically based pharmacokinetic model qualification and reporting procedures for regulatory submissions: a consortium perspective. Clin Pharm Ther. 2018;104(1):88–110. https://doi.org/10.1002/cpt.1013.

    Article  Google Scholar 

  9. Naga D, Parrott N, Ecker GF, Olivares-Morales A. Evaluation of the success of high-throughput physiologically based pharmacokinetic (HT-PBPK) modeling predictions to inform early drug discovery. Mol Pharm. 2022;19(7):2203–16. https://doi.org/10.1021/acs.molpharmaceut.2c00040.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Kilford PJ, Chen KF, Crewe K, Gardner I, Hatley O, Ke AB, Neuhoff S, Zhang M, Rowland YK. Prediction of CYP-mediated DDIs involving inhibition: Approaches to address the requirements for system qualification of the Simcyp Simulator. CPT: Pharmacometrics & Systems. Pharmacol. 2022;11(7):822–32. https://doi.org/10.1002/psp4.12794.

    Article  CAS  Google Scholar 

  11. Hanke N, Frechen S, Moj D, Britz H, Eissing T, Wendl T, Lehr T. PBPK models for CYP3A4 and P-gp DDI prediction: a modeling network of rifampicin, itraconazole, clarithromycin, midazolam, alfentanil, and digoxin. CPT: Pharm Syst Pharm. 2018;7(10):647–59. https://doi.org/10.1002/psp4.12343.

    Article  CAS  Google Scholar 

  12. Frechen S, Solodenko J, Wendl T, Dallmann A, Ince I, Lehr T, Lippert J, Burghaus R. A generic framework for the physiologically-based pharmacokinetic platform qualification of PK-Sim and its application to predicting cytochrome P450 3A4–mediated drug–drug interactions. CPT: Pharm Syst Pharmacol. 2021;10(6):633–44. https://doi.org/10.1002/psp4.12636.

    Article  CAS  Google Scholar 

  13. Loisios-Konstantinidis I, Dressman J. Physiologically based pharmacokinetic/pharmacodynamic modeling to support waivers of in vivo clinical studies: current status, challenges, and opportunities. Mol Pharm. 2020;18(1):1–7. https://doi.org/10.1021/acs.molpharmaceut.0c00903.

    Article  CAS  PubMed  Google Scholar 

  14. Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D. Applied concepts in PBPK modeling: how to build a PBPK/PD model. CPT: Pharm Syst Pharmacol. 2016;5(10):516–31. https://doi.org/10.1002/psp4.12134.

    Article  CAS  Google Scholar 

  15. Advancing biopharmaceutics & drug formulation using in silico modeling model-informed formulation development (MIFD) increases speed and certainty for new and generic drugs; Available from: https://www.certara.com/app/uploads/2023/03/WP_Model-Informed-Formulation-Development_final.pdf. Accessed on 30th march 2023

  16. CDER Conversation: Model Informed Drug Development 2018; Available from: https://www.fda.gov/drugs/news-events-human-drugs/cder-conversation-model-informed-drug-development. Accessed on 15 Dec 2022

  17. Wu F, Shah H, Li M, Duan P, Zhao P, Suarez S, Raines K, Zhao Y, Wang M, Lin HP, Duan J. Biopharmaceutics applications of physiologically based pharmacokinetic absorption modeling and simulation in regulatory submissions to the US food and drug administration for new drugs. The AAPS J. 2021;23:1–4. https://doi.org/10.1208/s12248-021-00564-2.

    Article  Google Scholar 

  18. Anand O, Pepin XJ, Kolhatkar V, Seo P. The use of physiologically based pharmacokinetic analyses—in biopharmaceutics applications-regulatory and industry perspectives. Pharm Res. 2022;39(8):1681–700. https://doi.org/10.1007/s11095-022-03280-4.

    Article  CAS  PubMed  Google Scholar 

  19. The use of physiologically based pharmacokinetic analyses — biopharmaceutics applications for oral drug product development, manufacturing changes, and controls guidance for industry. U.S. Department of Health and Human Services Food and Drug Administration Center for Drug Evaluation and Research (CDER) 2020; Available from: https://www.fda.gov/media/142500/download. Accessed on 15 Dec 2022

  20. Loisios-Konstantinidis I, Hens B, Mitra A, Kim S, Chiann C, Cristofoletti R. Using physiologically based pharmacokinetic modeling to assess the risks of failing bioequivalence criteria: a tale of two ibuprofen products. The AAPS J. 2020;22:1–9. https://doi.org/10.1208/s12248-020-00495-4.

    Article  CAS  Google Scholar 

  21. Jaiswal S, Ahmed T, Kollipara S, Bhargava M, Chachad S. Development, validation and application of physiologically based biopharmaceutics model to justify the change in dissolution specifications for DRL ABC extended release tablets. Drug Dev Indust Pharma. 2021;47(5):778–89. https://doi.org/10.1080/03639045.2021.1934870.

    Article  CAS  Google Scholar 

  22. Willmann S, Thelen K, Lippert J. Integration of dissolution into physiologically-based pharmacokinetic models III: PK-Sim®. J Pharma Pharmacol. 2012;64(7):997–1007. https://doi.org/10.1111/j.2042-7158.2012.01534.x.

    Article  CAS  Google Scholar 

  23. Dong Z, Li J, Wu F, Zhao P, Lee SC, Zhang L, Seo P, Zhang L. Application of physiologically-based pharmacokinetic modeling to predict gastric pH-dependent drug–drug interactions for weak base drugs. CPT: Pharmacom Syst Pharmacol. 2020;9(8):456–65. https://doi.org/10.1002/psp4.12541.

    Article  CAS  Google Scholar 

  24. Chirumamilla SK, Banala VT, Jamei M, Turner DB. Mechanistic PBPK modelling to predict the advantage of the salt form of a drug when dosed with acid reducing agents. Pharm. 2021;13(8):1169. https://doi.org/10.3390/pharmaceutics13081169.

    Article  CAS  Google Scholar 

  25. Gray VA, Mann JC, Barker R, Pepin XJ. The case for physiologically based biopharmaceutics modelling (PBBM): what do dissolution scientists need to know. Dev. 2020;12:14. https://doi.org/10.14227/DT270320P6.

    Article  Google Scholar 

  26. Mitra A, Parrott N, Miller N, Lloyd R, Tistaert C, Heimbach T, Ji Y, Kesisoglou F. Prediction of pH-dependent drug-drug interactions for basic drugs using physiologically based biopharmaceutics modeling: industry case studies. J Pharm Sci. 2020;109(3):1380–94. https://doi.org/10.1016/j.xphs.2019.11.017.

    Article  CAS  PubMed  Google Scholar 

  27. Evaluation of gastric pH dependent drug interactions with acid-reducing agents: study design, data analysis, and clinical implications. U.S. Department of Health and Human Services Food and Drug Administration Center for Drug Evaluation and Research (CDER) 2020; Available from: https://www.fda.gov/media/144026/download. Accessed on 15 Dec 2022

  28. Cheng L, Wong H. Food effects on oral drug absorption: application of physiologically-based pharmacokinetic modeling as a predictive tool. Pharm. 2020;12(7):672. https://doi.org/10.3390/pharmaceutics12070672.

    Article  CAS  Google Scholar 

  29. Li M, Zhao P, Pan Y, Wagner C. Predictive performance of physiologically based pharmacokinetic models for the effect of food on oral drug absorption: current status. CPT: Pharm Syst Pharmacol. 2018;7(2):82–9. https://doi.org/10.1002/psp4.12260.

    Article  CAS  Google Scholar 

  30. Riedmaier AE, Lindley DJ, Hall JA, Castleberry S, Slade RT, Stuart P, Carr RA, Borchardt TB, Bow DA, Nijsen M. Mechanistic physiologically based pharmacokinetic modeling of the dissolution and food effect of a biopharmaceutics classification system IV compound—the venetoclax story. J Pharm Sci. 2018;107(1):495–502. https://doi.org/10.1016/j.xphs.2017.09.027.

    Article  CAS  Google Scholar 

  31. Andreas CJ, Pepin X, Markopoulos C, Vertzoni M, Reppas C, Dressman JB. Mechanistic investigation of the negative food effect of modified release zolpidem. Eur J Pharm Sci. 2017;1(102):284–98. https://doi.org/10.1016/j.ejps.2017.03.011.

    Article  CAS  Google Scholar 

  32. Rebeka J, Jerneja O, Igor L, Boštjan P, Aleksander B, Simon Ž, Albin K. PBPK absorption modeling of food effect and bioequivalence in fed state for two formulations with crystalline and amorphous forms of BCS 2 class drug in generic drug development. AAPS PharmSciTech. 2019 20:1-0. https://doi.org/10.1208/s12249-018-1285-8.

  33. Dodd S, Kollipara S, Sanchez-Felix M, Kim H, Meng Q, Beato S, Heimbach T. Prediction of ARA/PPI drug-drug interactions at the drug discovery and development interface. J Pharm Sci. 2019;108(1):87–101. https://doi.org/10.1016/j.xphs.2018.10.032.

    Article  CAS  PubMed  Google Scholar 

  34. Zhang L, Wu F, Lee SC, Zhao H, Zhang L. pH-dependent drug–drug interactions for weak base drugs: potential implications for new drug development. Clin Pharm Ther. 2014;96(2):266–77. https://doi.org/10.1038/clpt.2014.87.

    Article  CAS  Google Scholar 

  35. Bhattachar SN, Perkins EJ, Tan JS, Burns LJ. Effect of gastric pH on the pharmacokinetics of a bcs class II compound in dogs: utilization of an artificial stomach and duodenum dissolution model and gastroplus,™ simulations to predict absorption. J Pharm Sci. 2011;100(11):4756–65. https://doi.org/10.1002/jps.22669.

    Article  CAS  PubMed  Google Scholar 

  36. Gray VA, Diaz DA, Dressman J, Tsume Y, Fotaki N. Highlights from the 2020 AAPS 360 Annual Meeting. Dissolution Technol. 2021;28(2):36–41. https://doi.org/10.14227/DT280221P36.

    Article  Google Scholar 

  37. Mittapelly N, Polak S. Modelling and simulation approaches to support formulation optimization, clinical development and regulatory assessment of the topically applied formulations–Nimesulide solution gel case study. Eur J Pharm Biopharm. 2022;1(178):140–9. https://doi.org/10.1016/j.ejpb.2022.08.005.

    Article  Google Scholar 

  38. Tsakalozou E, Babiskin A, Zhao L. Physiologically-based pharmacokinetic modeling to support bioequivalence and approval of generic products: a case for diclofenac sodium topical gel, 1%. CPT: Pharmacometrics & Systems. Pharmacol. 2021;10(5):399–411. https://doi.org/10.1002/psp4.12600.

    Article  CAS  Google Scholar 

  39. Jamei M. Recent advances in development and application of physiologically-based pharmacokinetic (PBPK) models: a transition from academic curiosity to regulatory acceptance. Current Pharmacol Rep. 2016;2:161–9. https://doi.org/10.1007/s40495-016-0059-9.

    Article  CAS  Google Scholar 

  40. Wilson CG, Aarons L, Augustijns P, Brouwers J, Darwich AS, De Waal T, Garbacz G, Hansmann S, Hoc D, Ivanova A, Koziolek M. Integration of advanced methods and models to study drug absorption and related processes: An UNGAP perspective. Eur J Pharm Sci. 2022;1(172):106100. https://doi.org/10.1016/j.ejps.2021.106100.

    Article  CAS  Google Scholar 

  41. Frechen S, Rostami-Hodjegan A. Quality assurance of PBPK modeling platforms and guidance on building, evaluating, verifying and applying PBPK models prudently under the umbrella of qualification: why, when, what, how and by whom? Pharm Res. 2022;39(8):1733–48. https://doi.org/10.1007/s11095-022-03250-w.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Varma MV, Steyn SJ, Allerton C, El-Kattan AF. Predicting clearance mechanism in drug discovery: extended clearance classification system (ECCS). Pharm Res. 2015;32:3785–802. https://doi.org/10.1007/s11095-015-1749-4.

    Article  CAS  PubMed  Google Scholar 

  43. Guideline on quality of oral modified release products. Committee for Medicinal Products for Human Use (CHMP) 2012; Available from: https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-quality-oral-modified-release-products_en.pdf. Accessed on 15 Dec 2022

  44. Extended release oral dosage forms: development, evaluation, and application of in vitro/in vivo correlations. U.S. Department of Health and Human Services Food and Drug Administration Center for Drug Evaluation and Research (CDER) 1997. https://www.fda.gov/media/70939/download. Accessed on 15 Dec 2022

  45. Guideline On the Investigation of Bioequivalence. Committee for Medicinal Products For Human Use (CHMP) 2010. https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-investigation-bioequivalence-rev1_en.pdf. Accessed on 15 Dec 2022

  46. Bioavailability and bioequivalence studies submitted in NDAs or INDs — general considerations. U.S. Department of Health and Human Services Food and Drug Administration Center for Drug Evaluation and Research (CDER) 2014. https://www.fda.gov/files/drugs/published/Bioavailability-and-Bioequivalence-Studies-Submitted-in-NDAs-or-INDs-%E2%80%94-General-Considerations.pdf. Accessed on 15 Dec 2022

  47. Mitra A, Suarez-Sharp S, Pepin XJ, Flanagan T, Zhao Y, Kotzagiorgis E, Parrott N, Sharan S, Tistaert C, Heimbach T, Zolnik B. Applications of physiologically based biopharmaceutics modeling (PBBM) to support drug product quality: a workshop summary report. J Pharm Sci. 2021;110(2):594–609. https://doi.org/10.1016/j.xphs.2020.10.059.

    Article  CAS  PubMed  Google Scholar 

  48. Ahmad A, Pepin X, Aarons L, Wang Y, Darwich AS, Wood JM, Tannergren C, Karlsson E, Patterson C, Thörn H, Ruston L. IMI–Oral biopharmaceutics tools project–Evaluation of bottom-up PBPK prediction success part 4: Prediction accuracy and software comparisons with improved data and modelling strategies. Eur J Pharm Biopharm. 2020;1(156):50–63. https://doi.org/10.1016/j.ejpb.2020.08.006.

    Article  CAS  Google Scholar 

  49. Jereb R, Kristl A, Mitra A. Prediction of fasted and fed bioequivalence for immediate release drug products using physiologically based biopharmaceutics modeling (PBBM). Eur J Pharm Sci. 2020;1(155):105554. https://doi.org/10.1016/j.ejps.2020.105554.

    Article  CAS  Google Scholar 

  50. Yuvaneshwari K, Kollipara S, Ahmed T, Chachad S. Applications of PBPK/PBBM modeling in generic product development: an industry perspective. J Drug Deliv Sci Technol. 2022;2:103152. https://doi.org/10.1016/j.jddst.2022.103152.

    Article  CAS  Google Scholar 

  51. Bhattiprolu AK, Kollipara S, Ahmed T, Boddu R, Chachad S. Utility of physiologically based biopharmaceutics modeling (PBBM) in regulatory perspective: application to supersede f2, enabling biowaivers & creation of dissolution safe space. J Pharm Sci. 2022;111(12):3397–410. https://doi.org/10.1016/j.xphs.2022.09.003.

    Article  CAS  PubMed  Google Scholar 

  52. Safety Reporting Requirements for INDs and BA/BE Studies. U.S. Department of Health and Human Services Food and Drug Administration Center for Drug Evaluation and Research (CDER) Center for Biologics Evaluation and Research (CBER) 2012; Available from: https://www.fda.gov/files/drugs/published/Safety-Reporting-Requirements-for-INDs-%28Investigational-New-Drug-Applications%29-and-BA-BE-%28Bioavailability-Bioequivalence%29-Studies.pdf. Accessed on 15 Dec 2022

  53. SUPAC-IR: Immediate-release solid oral dosage forms: scale-up and post-approval changes: chemistry, manufacturing and controls, in vitro dissolution testing, and in vivo bioequivalence documentation. Center for Drug Evaluation and Research 1995; Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/supac-ir-immediate-release-solid-oral-dosage-forms-scale-and-post-approval-changes-chemistry. Accessed on 15 Dec 2022

  54. Loisios-Konstantinidis I, Cristofoletti R, Fotaki N, Turner DB, Dressman J. Establishing virtual bioequivalence and clinically relevant specifications using in vitro biorelevant dissolution testing and physiologically-based population pharmacokinetic modeling. case example: Naproxen. Eur J Pharm Sci. 2020;15(143):105170. https://doi.org/10.1016/j.ejps.2019.105170.

    Article  CAS  Google Scholar 

  55. Clinically relevant dissolution specifications: a biopharmaceutics’ risk based approach: an FDA perspective. USFDA 2021; Available from: https://www.apsgb.co.uk/wp-content/uploads/2021/05/Clinically-Relevant-Dissolution-Specifications-an-FDA-Perspective-__Om-Anand.pdf. Accessed on 15 Dec 2022

  56. Kollipara S, Bhattiprolu AK, Boddu R, Ahmed T, Chachad S. Best practices for integration of dissolution data into physiologically based biopharmaceutics models (PBBM): a biopharmaceutics modeling scientist perspective. AAPS PharmSciTech. 2023;24(2):59. https://doi.org/10.1208/s12249-023-02521-y.

    Article  PubMed  Google Scholar 

  57. Sugano K. Lost in modelling and simulation? ADMET DMPK. 2021;9(2):75–109. https://doi.org/10.5599/admet.923.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Jereb R, Opara J, Legen I, Petek B, Grabnar-Peklar D. In vitro–in vivo relationship and bioequivalence prediction for modified-release capsules based on a PBPK absorption model. AAPS PharmSciTech. 2020;21:1–1. https://doi.org/10.1208/s12249-019-1566-x.2020.

    Article  Google Scholar 

  59. Aishwarya R, Murthy A, Ahmed T, Chachad S. A novel approach to justify dissolution differences in an extended release drug product using physiologically based biopharmaceutics modeling and simulation. Journal of Pharmaceutical Sciences. 2022;111(6):1820–32. https://doi.org/10.1016/j.xphs.2022.02.007.

    Article  CAS  PubMed  Google Scholar 

  60. GastroPlus: mechanistic deconvolution and the future role of physiological modeling in IVIVC; Available from: https://pqri.org/wp-content/uploads/2015/08/pdf/Bolger.pdf. Accessed on 10th march 2023

  61. Simcyp PBPK for drug-drug interactions (DDIs): a regulatory imperative; Available from: https://www.certara.com/app/uploads/2021/12/WP_DDI-v2.pdf. Accessed on 20th jan 2023

  62. Wang W, Ouyang D. Opportunities and challenges of physiologically based pharmacokinetic modeling in drug delivery. Drug Dis Today. 2022. https://doi.org/10.1016/j.drudis.2022.04.015

  63. Krstevska A, Đuriš J, Ibrić S, Cvijić S. In-depth analysis of physiologically based pharmacokinetic (PBPK) modeling utilization in different application fields using text mining tools. Pharm. 2022;15(1):107. https://doi.org/10.3390/pharmaceutics15010107.

    Article  CAS  Google Scholar 

  64. Flanagan T, Van Peer A, Lindahl A. Use of physiologically relevant biopharmaceutics tools within the pharmaceutical industry and in regulatory sciences: where are we now and what are the gaps? Eur J Pharm Sci. 2016;25(91):84–90. https://doi.org/10.1016/j.ejps.2016.06.006.

    Article  CAS  Google Scholar 

  65. Jamei M, Abrahamsson B, Brown J, Bevernage J, Bolger MB, Heimbach T, Karlsson E, Kotzagiorgis E, Lindahl A, McAllister M, Mullin JM. Current status and future opportunities for incorporation of dissolution data in PBPK modeling for pharmaceutical development and regulatory applications: OrBiTo consortium commentary. Eur J Pharm Biopharm. 2020;1(155):55–68. https://doi.org/10.1016/j.ejpb.2020.08.005.

    Article  CAS  Google Scholar 

  66. Tang C, Ou-Yang CX, Chen WJ, Zou C, Huang J, Cui C, Yang S, Guo C, Yang XY, Lin Y, Pei Q. Prediction of pharmacokinetic parameters of inhaled indacaterol formulation in healthy volunteers using physiologically-based pharmacokinetic (PBPK) model. Eur J Pharm Sci. 2022;1(168):106055. https://doi.org/10.1016/j.ejps.2021.106055.

    Article  CAS  Google Scholar 

  67. Mueller-Zsigmondy M, Limoncini FM. White paper-biopharmaceutics modelling as a fundamental tool to support accelerated access. efpia Website. 2020(a):n-an. https://oak.novartis.com/id/eprint/42879

  68. Zhang T, Wells E. A review of current methods for food effect prediction during drug development. Curr Pharm Rep. 2020;6(5):267–79. https://doi.org/10.1007/s40495-020-00230-9.

    Article  Google Scholar 

  69. Kambayashi A, Kiyota T, Fujiwara M, Dressman JB. PBPK modeling coupled with biorelevant dissolution to forecast the oral performance of amorphous solid dispersion formulations. Eur J Pharm Sci. 2019;1(135):83–90. https://doi.org/10.1016/j.ejps.2019.05.013.

    Article  CAS  Google Scholar 

  70. Tannergren C, Jadhav H, Eckernäs E, Fagerberg J, Augustijns P, Sjögren E. Physiologically based biopharmaceutics modeling of regional and colon absorption in humans. Eur J Pharm Biopharm. 2023;1(186):144–59. https://doi.org/10.1016/j.ejpb.2023.03.013.

    Article  CAS  Google Scholar 

  71. Stamatopoulos K, Ferrini P, Nguyen D, Zhang Y, Butler JM, Hall J, Mistry N. Integrating in vitro biopharmaceutics into physiologically based biopharmaceutic model (PBBM) to predict food effect of BCS IV zwitterionic drug (GSK3640254). Pharm. 2023;15(2):521. https://doi.org/10.3390/pharmaceutics15020521.

    Article  CAS  Google Scholar 

  72. Lee JB, Zgair A, Taha DA, Zang X, Kagan L, Kim TH, Kim MG, Yun HY, Fischer PM, Gershkovich P. Quantitative analysis of lab-to-lab variability in Caco-2 permeability assays. Eur J Pharm Biopharm. 2017;1(114):38–42. https://doi.org/10.1016/j.ejpb.2016.12.027.

    Article  CAS  Google Scholar 

  73. Madabushi R, Seo P, Zhao L, Tegenge M, Zhu H. Role of model-informed drug development approaches in the lifecycle of drug development and regulatory decision-making. Pharm Res. 2022;39(8):1669–80. https://doi.org/10.1007/s11095-022-03288-w.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Zhao L, Kim MJ, Zhang L, Lionberger R. Generating model integrated evidence for generic drug development and assessment. Clin Pharmacol Ther. 2019;105(2):338–49. https://doi.org/10.1002/cpt.1282.

    Article  PubMed  Google Scholar 

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Authors would like to thank Dr Reddy’s Laboratories Ltd. for providing an opportunity to publish this work.

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  1. Biopharmaceutics Department, Global Clinical Management, Dr. Reddy’s Laboratories Ltd., Hyderabad, India

    Prajwala Karnati, Aditya Murthy, Manoj Gundeti & Tausif Ahmed

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PK—concept and design, writing manuscript; MG—concept and design, writing manuscript; AM—concept and design, writing manuscript, manuscript review; and TA—concept and design, manuscript review, approval for version to be published.

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Correspondence to Tausif Ahmed.

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Karnati, P., Murthy, A., Gundeti, M. et al. Modelling Based Approaches to Support Generic Drug Regulatory Submissions-Practical Considerations and Case Studies. AAPS J 25, 63 (2023). https://doi.org/10.1208/s12248-023-00831-4

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  • DOI: https://doi.org/10.1208/s12248-023-00831-4

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