pharmjournalРазработка и регистрация лекарственных средствDrug development & registration2305-20662658-5049LLC «CPHA»10.33380/2305-2066-2025-14-4-2183pharmjournal-2190Research ArticleДОКЛИНИЧЕСКИЕ И КЛИНИЧЕСКИЕ ИССЛЕДОВАНИЯPRECLINICAL AND CLINICAL STUDIESФизиологически обоснованное моделирование фармакокинетики: прошлое, настоящее, будущее (обзор)Physiologically-based pharmacokinetic modeling: past, present and future (review)https://orcid.org/0000-0001-7496-8186БагаеваН. С.BagaevaN. S.

117638, г. Москва, Симферопольский бульвар, д. 8

8, Simferopolsky bulvar, Moscow, 117246

n.bagaeva@cpha.ruhttps://orcid.org/0009-0007-3274-7845ЕвтюхинаЕ. И.EvtyukhinaE. I.

117638, г. Москва, Симферопольский бульвар, д. 8

8, Simferopolsky bulvar, Moscow, 117246

https://orcid.org/0000-0003-4894-7001ЩелгачеваД. С.ShchelgachevaD. S.

117638, г. Москва, Симферопольский бульвар, д. 8

8, Simferopolsky bulvar, Moscow, 117246

https://orcid.org/0009-0008-1688-1902ПоповаМ. О.PopovaM. O.

117638, г. Москва, Симферопольский бульвар, д. 8

8, Simferopolsky bulvar, Moscow, 117246

https://orcid.org/0000-0001-6621-1060АрчаковаО. А.ArchakovaO. A.

117638, г. Москва, Симферопольский бульвар, д. 8

8, Simferopolsky bulvar, Moscow, 117246

https://orcid.org/0000-0001-8354-7877КомаровТ. Н.KomarovT. N.

117638, г. Москва, Симферопольский бульвар, д. 8; 197022, г. Санкт-Петербург, ул. Профессора Попова, 14, литера А

8, Simferopolsky bulvar, Moscow, 117246; 14A, Prof. Popova str., Saint-Petersburg, 197022

https://orcid.org/0009-0008-6963-6919МарченкоА. Н.MarchenkoA. N.

119991, г. Москва, ул. Трубецкая, д. 8, стр. 2

8/2, Trubetskaya str., Mosсow, 119991

https://orcid.org/0000-0002-4183-7822МалашенкоЕ. А.MalashenkoE. A.

119991, г. Москва, ул. Трубецкая, д. 8, стр. 2

8/2, Trubetskaya str., Mosсow, 119991

https://orcid.org/0000-0002-1185-8630ШохинИ. Е.ShohinI. E.

117638, г. Москва, Симферопольский бульвар, д. 8; 115409, г. Москва, Каширское шоссе, д. 31

8, Simferopolsky bulvar, Moscow, 117246; 31, Kashirskoe shosse, Moscow, 115409

Общество с ограниченной ответственностью «Центр фармацевтической аналитики» (ООО «ЦФА»)РоссияLimited Liability Company "Center of Pharmaceutical Analytics" (LLC "CPHA")Russian FederationОбщество с ограниченной ответственностью «Центр фармацевтической аналитики» (ООО «ЦФА»); Федеральное государственное бюджетное образовательное учреждение высшего образования «Санкт-Петербургский государственный химико-фармацевтический университет» Министерства здравоохранения Российской Федерации (ФГБОУ ВО СПХФУ Минздрава России)РоссияLimited Liability Company "Center of Pharmaceutical Analytics" (LLC "CPHA"); Saint-Petersburg State Chemical and Pharmaceutical UniversityRussian FederationФедеральное государственное автономное образовательное учреждение высшего образования «Первый Московский государственный медицинский университет имени И. М. Сеченова» Министерства здравоохранения Российской Федерации (Сеченовский Университет)РоссияI. M. Sechenov First MSMU of the Ministry of Health of the Russian Federation (Sechenov University)Russian FederationОбщество с ограниченной ответственностью «Центр фармацевтической аналитики» (ООО «ЦФА»); Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский ядерный университет «МИФИ» (НИЯУ МИФИ)РоссияLimited Liability Company "Center of Pharmaceutical Analytics" (LLC "CPHA"); National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)Russian Federation202503122025144183194Copyright © Багаева Н.С., Евтюхина Е.И., Щелгачева Д.С., Попова М.О., Арчакова О.А., Комаров Т.Н., Марченко А.Н., Малашенко Е.А., Шохин И.Е., 20252025Багаева Н.С., Евтюхина Е.И., Щелгачева Д.С., Попова М.О., Арчакова О.А., Комаров Т.Н., Марченко А.Н., Малашенко Е.А., Шохин И.Е.Bagaeva N.S., Evtyukhina E.I., Shchelgacheva D.S., Popova M.O., Archakova O.A., Komarov T.N., Marchenko A.N., Malashenko E.A., Shohin I.E.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.pharmjournal.ru/jour/article/view/2190Введение

Введение. Физиологически обоснованное моделирование фармакокинетики представляет собой метод, позволяющий предсказывать распределение лекарственных веществ в организме на основе анатомических и физиологических параметров. Данное направление получило широкое распространение лишь с развитием вычислительных технологий. Сегодня PBPK активно используется регуляторными агентствами для оптимизации клинических исследований и сокращения числа экспериментов на животных.

Текст

Текст. PBPK-модели представляют организм как систему взаимосвязанных компартментов, соответствующих органам и тканям. Описываются три основных типа моделей: полноценная (Full PBPK), обеспечивающая максимальную точность за счет детализации; усеченная (Reduced PBPK), снижающая вычислительную сложность; и гибридная (Hybrid PBPK), сочетающая оба подхода для баланса между точностью и эффективностью. Подробно рассмотрены ключевые параметры для построения моделей: физико-химические свойства веществ (LogP, pKa, растворимость), физиологические параметры (объемы органов, скорость кровотока, активность ферментов и мембранных транспортных белков) и фармакокинетические параметры (объем распределения, клиренс). Особое внимание уделяется модели всасывания и транзита Гордона Амидона (CAT/ACAT) и её интеграции в PBPK-моделирование. Приведены процедуры обеспечения надежности моделей: калибровка (настройка параметров), валидация (оценка предсказательной способности), квалификация (подтверждение пригодности для конкретной цели) и верификация (проверка математической корректности). Описаны статистические метрики для оценки точности. Представлен обзор популярного программного обеспечения для PBPK-моделирования, такого как GastroPlus, Simcyp, PK-Sim, SimBiology и Mrgsolve, с указанием их основных преимуществ и областей применения в фармацевтической индустрии и академических исследованиях.

Заключение

Заключение. PBPK-моделирование находится на пороге новой эры, где его применение выйдет за рамки традиционной фармакокинетики, став неотъемлемой частью цифровой медицины, биотехнологий и прецизионной терапии. В перспективе такие технологии смогут не только предсказывать поведение лекарств в организме, но и стать основой для виртуальных клинических испытаний, что коренным образом изменит подход к разработке и применению лекарственных средств.

Introduction

Introduction. Physiologically-based pharmacokinetic modelling is a method that allows predicting the distribution of drugs in the body based on anatomical and physiological parameters. This approach has become widespread only with the development of computing technologies. Today PBPK is actively used by regulatory agencies to optimize clinical trials and reduce the number of animal experiments.

Text

Text. PBPK models represent the body as a system of interconnected compartments corresponding to organs and tissues. Three main types of models are described: Full PBPK, which maximizes accuracy at the expense of detail; Reduced PBPK, which reduces computational complexity; Hybrid PBPK, which combines both approaches to balance accuracy and efficiency. Key parameters for model building are discussed in detail: physicochemical properties of substances (LogP, pKa, solubility), physiological parameters (organ volumes, blood flow, enzyme activity and membrane transport proteins) and pharmacokinetic parameters (volume of distribution, clearance). Special attention is given to the Gordon Amidon absorption and transit model (CAT/ACAT) and its integration into PBPK modeling. Procedures for model reliability are given calibration (parameter tuning), validation (assessment of predictive ability), qualification (confirmation of fitness for purpose), and verification (verification of mathematical correctness). Statistical metrics for assessing accuracy are described. An overview of popular PBPK modeling software such as GastroPlus, Simcyp, PK-Sim, SimBiology, and Mrgsolve is presented, highlighting their main advantages and applications in the pharmaceutical industry and academic research.

Conclusion

Conclusion. PBPK modeling is on the threshold of a new era where its application will go beyond traditional pharmacokinetics, becoming an integral part of digital medicine, biotechnology and precision therapeutics. In the future, such technologies will not only be able to predict the behavior of drugs in the body, but also become the basis for virtual clinical trials, which will fundamentally change the approach to drug development and application.

физиологически обоснованное моделирование фармакокинетикиPBPKабсорбцияраспределениеphysiologically-based pharmacokinetic modelingPBPKabsorptiondistributionReferencesКолбин А. С., Радаева К. С. Дозирование лекарственных средств в педиатрии: возможные подходы. Вопросы современной педиатрии. 2023;22(4):289–297. DOI: 10.15690/vsp.v22i4.2593.Kolbin A. S., Radaeva K. S. Drug Dosing in Pediatrics: Possible Approaches. Current Pediatrics. 2023;22(4):289–297. (In Russ.). DOI: 10.15690/vsp.v22i4.2593.Teorell T. Kinetics of distribution of substances administered to the body. Arch Intern Pharmacodyn. 1937;57:205–240.Teorell T. Kinetics of distribution of substances administered to the body. Arch Intern Pharmacodyn. 1937;57:205–240.Himmelstein K. J., Lutz R. J. A review of the applications of physiologically based pharmacokinetic modeling. Journal of Pharmacokinetics and Biopharmaceutics. 1979;7(2):127–145. DOI: 10.1007/BF01059734.Himmelstein K. J., Lutz R. J. A review of the applications of physiologically based pharmacokinetic modeling. Journal of Pharmacokinetics and Biopharmaceutics. 1979;7(2):127–145. DOI: 10.1007/BF01059734.Bischoff K. B., Dedrick R. L. , Zaharko D. S., Longstreth J. A. Methotrexate pharmacokinetics. Journal of Pharmaceutical Sciences. 1971;60(8):1128–1133. DOI: 10.1002/jps.2600600803.Bischoff K. B., Dedrick R. L. , Zaharko D. S., Longstreth J. A. Methotrexate pharmacokinetics. Journal of Pharmaceutical Sciences. 1971;60(8):1128–1133. DOI: 10.1002/jps.2600600803.Andersen M. E., Clewell H. J. 3rd, Gargas M. L., Smith F. A., Reitz R. H. Physiologically based pharmacokinetics and the risk assessment process for methylene chloride. Toxicology and Applied Pharmacology. 1987;87(2):185–205. DOI: 10.1016/0041-008x(87)90281-x.Andersen M. E., Clewell H. J. 3rd, Gargas M. L., Smith F. A., Reitz R. H. Physiologically based pharmacokinetics and the risk assessment process for methylene chloride. Toxicology and Applied Pharmacology. 1987;87(2):185–205. DOI: 10.1016/0041-008x(87)90281-x.Davis N. R., Mapleson W. W. A physiological model for the distribution of injected agents, with special reference to pethidine. British Journal of Anaesthesia. 1993;70(3):248–258. DOI: 10.1093/bja/70.3.248.Davis N. R., Mapleson W. W. A physiological model for the distribution of injected agents, with special reference to pethidine. British Journal of Anaesthesia. 1993;70(3):248–258. DOI: 10.1093/bja/70.3.248.Benowitz N., Forsyth R. P., Melmon K. L., Rowland M. Lidocaine disposition kinetics in monkey and man; I. Prediction by a perfusion model. Clinical Pharmacology & Therapeutics. 1974;16(1):87–98. DOI: 10.1002/cpt1974161part187.Benowitz N., Forsyth R. P., Melmon K. L., Rowland M. Lidocaine disposition kinetics in monkey and man; I. Prediction by a perfusion model. Clinical Pharmacology & Therapeutics. 1974;16(1):87–98. DOI: 10.1002/cpt1974161part187.Liu F., Zhuang X., Yang C., Li Z., Xiong S., Zhang Z., Li J., Lu C., Zhang Z. Characterization of preclinical in vitro and in vivo ADME properties and prediction of human PK using a physiologically based pharmacokinetic model for YQA-14, a new dopamine D 3 receptor antagonist candidate for treatment of drug addiction. Biopharmaceutics & Drug Disposition. 2014;35(5):296–307. DOI: 10.1002/bdd.1897.Liu F., Zhuang X., Yang C., Li Z., Xiong S., Zhang Z., Li J., Lu C., Zhang Z. Characterization of preclinical in vitro and in vivo ADME properties and prediction of human PK using a physiologically based pharmacokinetic model for YQA-14, a new dopamine D 3 receptor antagonist candidate for treatment of drug addiction. Biopharmaceutics & Drug Disposition. 2014;35(5):296–307. DOI: 10.1002/bdd.1897.Mörk A.-K., Johanson G. Chemical-specific adjustment factors for intraspecies variability of acetone toxicokinetics using a probabilistic approach. Toxicological Sciences. 2010;116(2):336–348. DOI: 10.1093/toxsci/kfq116.Mörk A.-K., Johanson G. Chemical-specific adjustment factors for intraspecies variability of acetone toxicokinetics using a probabilistic approach. Toxicological Sciences. 2010;116(2):336–348. DOI: 10.1093/toxsci/kfq116.Chetty M., Johnson T. N., Polak S., Salem F., Doki K., Rostami-Hodjegan A. Physiologically based pharmacokinetic modelling to guide drug delivery in older people. Advanced Drug Delivery Reviews. 2018;135:85–96. DOI: 10.1016/j.addr.2018.08.013.Chetty M., Johnson T. N., Polak S., Salem F., Doki K., Rostami-Hodjegan A. Physiologically based pharmacokinetic modelling to guide drug delivery in older people. Advanced Drug Delivery Reviews. 2018;135:85–96. DOI: 10.1016/j.addr.2018.08.013.Small B. G., Hatley O., Jamei M., Gardner I., Johnson T. N. Incorporation and performance verification of hepatic portal blood flow shunting in minimal and full PBPK models of liver cirrhosis. Clinical Pharmacology & Therapeutics. 2023;114(6):1264–1273. DOI: 10.1002/cpt.3032.Small B. G., Hatley O., Jamei M., Gardner I., Johnson T. N. Incorporation and performance verification of hepatic portal blood flow shunting in minimal and full PBPK models of liver cirrhosis. Clinical Pharmacology & Therapeutics. 2023;114(6):1264–1273. DOI: 10.1002/cpt.3032.Heimbach T., Chen Y., Chen J., Dixit V., Parrott N., Peters S. A., Poggesi I., Sharma P., Snoeys J., Shebley M., Tai G., Tse S., Upreti V. V., Wang Y. H., Tsai A., Xia B., Zheng M., Zhu A. Z. X., Hall S. Physiologically-Based Pharmacokinetic Modeling in Renal and Hepatic Impairment Populations: A Pharmaceutical Industry Perspective. Clinical Pharmacology & Therapeutics. 2021;110(2):297–310. DOI: 10.1002/cpt.2125.Heimbach T., Chen Y., Chen J., Dixit V., Parrott N., Peters S. A., Poggesi I., Sharma P., Snoeys J., Shebley M., Tai G., Tse S., Upreti V. V., Wang Y. H., Tsai A., Xia B., Zheng M., Zhu A. Z. X., Hall S. Physiologically-Based Pharmacokinetic Modeling in Renal and Hepatic Impairment Populations: A Pharmaceutical Industry Perspective. Clinical Pharmacology & Therapeutics. 2021;110(2):297–310. DOI: 10.1002/cpt.2125.Yang C.-G., Chen T., Si W.-T., Wang A.-H., Ren H.-C., Wang L. High-performance PBPK model for predicting CYP3A4 induction-mediated drug interactions: a refined and validated approach. Frontiers in Pharmacology. 2025;16:1521068. DOI: 10.3389/fphar.2025.1521068.Yang C.-G., Chen T., Si W.-T., Wang A.-H., Ren H.-C., Wang L. High-performance PBPK model for predicting CYP3A4 induction-mediated drug interactions: a refined and validated approach. Frontiers in Pharmacology. 2025;16:1521068. DOI: 10.3389/fphar.2025.1521068.Lang J., Vincent L., Chenel M., Ogungbenro K., Galetin A. Reduced physiologically-based pharmacokinetic model of dabigatran etexilate-dabigatran and its application for prediction of intestinal P-gp-mediated drug-drug interactions. European Journal of Pharmaceutical Sciences. 2021;165:105932. DOI: 10.1016/j.ejps.2021.105932.Lang J., Vincent L., Chenel M., Ogungbenro K., Galetin A. Reduced physiologically-based pharmacokinetic model of dabigatran etexilate-dabigatran and its application for prediction of intestinal P-gp-mediated drug-drug interactions. European Journal of Pharmaceutical Sciences. 2021;165:105932. DOI: 10.1016/j.ejps.2021.105932.Zhuang X., Lu C. PBPK modeling and simulation in drug research and development. Acta Pharmaceutica Sinica B. 2016;6(5):430–440. DOI: 10.1016/j.apsb.2016.04.004.Zhuang X., Lu C. PBPK modeling and simulation in drug research and development. Acta Pharmaceutica Sinica B. 2016;6(5):430–440. DOI: 10.1016/j.apsb.2016.04.004.Scotcher D., Melillo N., Tadimalla S., Darwich A. S., Ziemian S., Ogungbenro K., Schütz G., Sourbron S., Galetin A. Physiologically Based Pharmacokinetic Modeling of Transporter-Mediated Hepatic Disposition of Imaging Biomarker Gadoxetate in Rats. Molecular Pharmaceutics. 2021;18(8):2997–3009. DOI: 10.1021/acs.molpharmaceut.1c00206.Scotcher D., Melillo N., Tadimalla S., Darwich A. S., Ziemian S., Ogungbenro K., Schütz G., Sourbron S., Galetin A. Physiologically Based Pharmacokinetic Modeling of Transporter-Mediated Hepatic Disposition of Imaging Biomarker Gadoxetate in Rats. Molecular Pharmaceutics. 2021;18(8):2997–3009. DOI: 10.1021/acs.molpharmaceut.1c00206.Mu R.-J., Liu T.-L., Liu X.-D., Liu L. PBPK-PD model for predicting morphine pharmacokinetics, CNS effects and naloxone antagonism in humans. Acta Pharmacologica Sinica. 2024;45(8):1752–1764. DOI: 10.1038/s41401-024-01255-2.Mu R.-J., Liu T.-L., Liu X.-D., Liu L. PBPK-PD model for predicting morphine pharmacokinetics, CNS effects and naloxone antagonism in humans. Acta Pharmacologica Sinica. 2024;45(8):1752–1764. DOI: 10.1038/s41401-024-01255-2.Bloomingdale P., Bumbaca-Yadav D., Sugam J., Grauer S., Smith B., Antonenko S., Judo M., Azadi G., Yee K. L. PBPK-PD modeling for the preclinical development and clinical translation of tau antibodies for Alzheimer’s disease. Frontiers in Pharmacology. 2022;13:867457. DOI: 10.3389/fphar.2022.867457.Bloomingdale P., Bumbaca-Yadav D., Sugam J., Grauer S., Smith B., Antonenko S., Judo M., Azadi G., Yee K. L. PBPK-PD modeling for the preclinical development and clinical translation of tau antibodies for Alzheimer’s disease. Frontiers in Pharmacology. 2022;13:867457. DOI: 10.3389/fphar.2022.867457.Liu X., Wang W., Chen J., Chen D., Tao Y., Ouyang D. PBPK/PD modeling of nifedipine for precision medicine in pregnant women: enhancing clinical decision-making for optimal drug therapy. Pharmaceutical Research. 2024;41(1):63–75. DOI: 10.1007/s11095-023-03638-2.Liu X., Wang W., Chen J., Chen D., Tao Y., Ouyang D. PBPK/PD modeling of nifedipine for precision medicine in pregnant women: enhancing clinical decision-making for optimal drug therapy. Pharmaceutical Research. 2024;41(1):63–75. DOI: 10.1007/s11095-023-03638-2.Toshimoto K. Beyond the basics: A deep dive into parameter estimation for advanced PBPK and QSP models. Drug Metabolism and Pharmacokinetics. 2024;56:101011. DOI: 10.1016/j.dmpk.2024.101011.Toshimoto K. Beyond the basics: A deep dive into parameter estimation for advanced PBPK and QSP models. Drug Metabolism and Pharmacokinetics. 2024;56:101011. DOI: 10.1016/j.dmpk.2024.101011.Geerts H., Walker M., Rose R., Bergeler S., van der Graaf P. H., Schuck E., Koyama A., Yasuda S., Hussein Z., Reyderman L., Swanson C., Cabal A. A combined physiologically-based pharmacokinetic and quantitative systems pharmacology model for modeling amyloid aggregation in Alzheimer’s disease. CPT: Pharmacometrics & Systems Pharmacology. 2023;12(4):444–461. DOI: 10.1002/psp4.12912.Geerts H., Walker M., Rose R., Bergeler S., van der Graaf P. H., Schuck E., Koyama A., Yasuda S., Hussein Z., Reyderman L., Swanson C., Cabal A. A combined physiologically-based pharmacokinetic and quantitative systems pharmacology model for modeling amyloid aggregation in Alzheimer’s disease. CPT: Pharmacometrics & Systems Pharmacology. 2023;12(4):444–461. DOI: 10.1002/psp4.12912.Jones H. M., Gardner I. B., Collard W. T., Stanley P. J., Oxley P., Hosea N. A., Plowchalk D., Gernhardt S., Lin J., Dickins M., Rahavendran S. R., Jones B. C., Watson K. J., Pertinez H., Kumar V., Cole S. Simulation of human intravenous and oral pharmacokinetics of 21 diverse compounds using physiologically based pharmacokinetic modelling. Clinical Pharmacokinetics. 2011;50(5):331–347. DOI: 10.2165/11539680-000000000-00000.Jones H. M., Gardner I. B., Collard W. T., Stanley P. J., Oxley P., Hosea N. A., Plowchalk D., Gernhardt S., Lin J., Dickins M., Rahavendran S. R., Jones B. C., Watson K. J., Pertinez H., Kumar V., Cole S. Simulation of human intravenous and oral pharmacokinetics of 21 diverse compounds using physiologically based pharmacokinetic modelling. Clinical Pharmacokinetics. 2011;50(5):331–347. DOI: 10.2165/11539680-000000000-00000.Jones H. M., Rowland‐Yeo K. Basic concepts in physiologically based pharmacokinetic modeling in drug discovery and development. CPT: Pharmacometrics & Systems Pharmacology. 2013;2(8):1–12. DOI: 10.1038/psp.2013.41.Jones H. M., Rowland‐Yeo K. Basic concepts in physiologically based pharmacokinetic modeling in drug discovery and development. CPT: Pharmacometrics & Systems Pharmacology. 2013;2(8):1–12. DOI: 10.1038/psp.2013.41.Maza D. D.-A, Olukotun S. F., Akinlade G. O. A Human Physiologically-based Bio-kinetic Model for Cadmium. American Journal of Mathematical and Computer Modelling. 2021;6(1):9–13. DOI: 10.11648/j.ajmcm.20210601.12.Maza D. D.-A, Olukotun S. F., Akinlade G. O. A Human Physiologically-based Bio-kinetic Model for Cadmium. American Journal of Mathematical and Computer Modelling. 2021;6(1):9–13. DOI: 10.11648/j.ajmcm.20210601.12.Jamei M., Turner D., Yang J., Neuhoff S., Polak S., Rostami-Hodjegan A., Tucker G. Population-based mechanistic prediction of oral drug absorption. The AAPS Journal. 2009;11(2):225–237. DOI: 10.1208/s12248-009-9099-y.Jamei M., Turner D., Yang J., Neuhoff S., Polak S., Rostami-Hodjegan A., Tucker G. Population-based mechanistic prediction of oral drug absorption. The AAPS Journal. 2009;11(2):225–237. DOI: 10.1208/s12248-009-9099-y.Willmann S., Schmitt W., Keldenich J., Lippert J., Dressman J. B. A physiological model for the estimation of the fraction dose absorbed in humans. Journal of Medicinal Chemistry. 2004;47(16):4022–4031. DOI: 10.1021/jm030999b.Willmann S., Schmitt W., Keldenich J., Lippert J., Dressman J. B. A physiological model for the estimation of the fraction dose absorbed in humans. Journal of Medicinal Chemistry. 2004;47(16):4022–4031. DOI: 10.1021/jm030999b.Amidon G. L., Lennernäs H., Shah V. P., Crison J. R. A theoretical basis for a biopharmaceutic drug classification: the correlation of in vitro drug product dissolution and in vivo bioavailability. Pharmaceutical Research. 1995;12(3):413–420. DOI: 10.1023/a:1016212804288.Amidon G. L., Lennernäs H., Shah V. P., Crison J. R. A theoretical basis for a biopharmaceutic drug classification: the correlation of in vitro drug product dissolution and in vivo bioavailability. Pharmaceutical Research. 1995;12(3):413–420. DOI: 10.1023/a:1016212804288.Lawrence X. Y., Lipka E., Crison J. R., Amidon G. L. Transport approaches to the biopharmaceutical design of oral drug delivery systems: prediction of intestinal absorption. Advanced Drug Delivery Reviews. 1996;19(3):359–376. DOI: 10.1016/0169-409x(96)00009-9.Lawrence X. Y., Lipka E., Crison J. R., Amidon G. L. Transport approaches to the biopharmaceutical design of oral drug delivery systems: prediction of intestinal absorption. Advanced Drug Delivery Reviews. 1996;19(3):359–376. DOI: 10.1016/0169-409x(96)00009-9.Lawrence X. Y., Amidon G. L. A compartmental absorption and transit model for estimating oral drug absorption. International Journal of Pharmaceutics. 1999;186(2):119–125. DOI: 10.1016/s0378-5173(99)00147-7.Lawrence X. Y., Amidon G. L. A compartmental absorption and transit model for estimating oral drug absorption. International Journal of Pharmaceutics. 1999;186(2):119–125. DOI: 10.1016/s0378-5173(99)00147-7.Noyes A. A., Whitney W. R. The rate of solution of solid substances in their own solutions. Journal of the American Chemical Society. 1897;19(12):930–934. DOI: 10.1021/ja02086a003.Noyes A. A., Whitney W. R. The rate of solution of solid substances in their own solutions. Journal of the American Chemical Society. 1897;19(12):930–934. DOI: 10.1021/ja02086a003.Elmokadem A., Zhang Y., Knab T., Jordie E., Gillespie W. R. Bayesian PBPK modeling using r/stan/torsten and julia/ sciml/turing.jl. CPT: Pharmacometrics & Systems Pharmacology. 2023;12(3):300–310. DOI: 10.1002/psp4.12926.Elmokadem A., Zhang Y., Knab T., Jordie E., Gillespie W. R. Bayesian PBPK modeling using r/stan/torsten and julia/ sciml/turing.jl. CPT: Pharmacometrics & Systems Pharmacology. 2023;12(3):300–310. DOI: 10.1002/psp4.12926.Dadashova K., Smith R. C., Haider M. A., Reich B. J. Bayesian inference informed by parameter subset selection for a minimal PBPK brain model. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2025;383(2292):20240219. DOI: 10.1098/rsta.2024.0219.Dadashova K., Smith R. C., Haider M. A., Reich B. J. Bayesian inference informed by parameter subset selection for a minimal PBPK brain model. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2025;383(2292):20240219. DOI: 10.1098/rsta.2024.0219.Najjar A., Hamadeh A., Krause S., Schepky A., Edginton A. Global sensitivity analysis of Open Systems Pharmacology Suite physiologically based pharmacokinetic models. CPT: Pharmacometrics & Systems Pharmacology. 2024;13(12):2052–2067. DOI: 10.1002/psp4.13256.Najjar A., Hamadeh A., Krause S., Schepky A., Edginton A. Global sensitivity analysis of Open Systems Pharmacology Suite physiologically based pharmacokinetic models. CPT: Pharmacometrics & Systems Pharmacology. 2024;13(12):2052–2067. DOI: 10.1002/psp4.13256.

The authors declare that there are no conflicts of interest present.