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Recent Advances in Development and Application of Physiologically-Based Pharmacokinetic (PBPK) Models: a Transition from Academic Curiosity to Regulatory Acceptance
Open accessAbstract
There is a renewed surge of interest in applications of physiologically-based pharmacokinetic (PBPK) models by the pharmaceutical industry and regulatory agencies. Developing PBPK models within a systems pharmacology context allows separation of the parameters pertaining to the animal or human body (the system) from that of the drug and the study design which is essential to develop generic drug-independent models used to extrapolate PK/PD properties in various healthy and patient populations. This has expanded the classical paradigm to a 'predict-learn-confirm-apply' concept. Recently, a number of drug labels are informed by simulation results generated using PBPK models. These cases show that either the simulations are used in lieu of conducting clinical studies or have informed the drug label that otherwise would have been silent in some specific situations. It will not be surprising to see applications of these models in implementing precision dosing at the point of care in the near future.
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![Fig. 2 The distribution of area of applications of 136 regulatory submissions to FDA (until Oct 2014) where PBPK modelling has been applied, updated by Grillo [10] after [14]](/paper-figures/d1046e2f4094474aa604ab0684e7ec80/recent-advances-in-development-and-application-of-physiologically-based-figure-2.jpg)
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Cites
Rodgers, T., & Rowland, M. (2006) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.20502 · Cited by 949
Physiologically-Based Pharmacokinetics in Drug Development and Regulatory Science
Rowland, M., Peck, C., & Tucker, G. (2011) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010510-100540 · Cited by 662
Sager, J. E., Yu, J., Ragueneau, I., & Isoherranen, N. (2015) · Drug Metabolism and Disposition · doi:10.1124/dmd.115.065920 · Cited by 541
Simulation and prediction of in vivo drug metabolism in human populations from in vitro data
Rostami‐Hodjegan, A., & Tucker, G. T. (2007) · Nature Reviews Drug Discovery · doi:10.1038/nrd2173 · Cited by 533
Zhao, P., Zhang, L., Grillo, J. A., Liu, Q., Bullock, J. M., Moon, Y. J., Song, P., Brar, S. S., Madabushi, R., Wu, T. C., et al. (2010) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2010.298 · Cited by 524
Jones, H., Chen, Y., Gibson, C., Heimbach, T., Parrott, N., Peters, S., Snoeys, J., Upreti, V., Zheng, M., & Hall, S. (2015) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.37 · Cited by 488
Jamei, M., Dickinson, G. L., & Rostami‐Hodjegan, A. (2009) · Drug Metabolism and Pharmacokinetics · doi:10.2133/dmpk.24.53 · Cited by 377
Self-citationRostami-Hodjegan, A. (2012) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2012.65 · Cited by 331
Poulin, P., & Theil, F. (2002) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.10128 · Cited by 303
Wagner, C., Zhao, P., Pan, Y., Hsu, V, Grillo, J., Huang, S., & Sinha, V (2015) · CPT: Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.33 · Cited by 276
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Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913
Darwich, A. S., Ogungbenro, K., Vinks, A. A. T. M. M., Powell, J. R., Reny, J., Marsousi, N., Daali, Y., Fairman, D., Cook, J., Lesko, L. J., et al. (2017) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.659 · Cited by 287
Cardiovascular disease models: A game changing paradigm in drug discovery and screening
Savoji, H., Mohammadi, M. H., Rafatian, N., Toroghi, M. K., Wang, E. Y., Zhao, Y., Korolj, A., Ahadian, S., & Radisic, M. (2019) · Biomaterials · doi:10.1016/j.biomaterials.2018.09.036 · Cited by 223
Physiologically-based pharmacokinetic models for children: Starting to reach maturation?
Verscheijden, L. F. M., Koenderink, J. B., Johnson, T. N., de Wildt, S. N., & Rüssel, F. G. M. (2020) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2020.107541 · Cited by 153
An in vitro toolbox to accelerate anti-malarial drug discovery and development
Charman, S. A., Andreu, A., Barker, H., Blundell, S. A., Campbell, A., Campbell, M., Chen, G., Chiu, F. C. K., Crighton, E., Katneni, K., et al. (2020) · Malaria Journal · doi:10.1186/s12936-019-3075-5 · Cited by 154
Miller, N. A., Reddy, M. B., Heikkinen, A. T., Lukacova, V., & Parrott, N. (2019) · Clinical Pharmacokinetics · doi:10.1007/s40262-019-00741-9 · Cited by 142
Lin, W., Chen, Y., Unadkat, J. D., Zhang, X., Wu, D., & Heimbach, T. (2022) · Pharmaceutical Research · doi:10.1007/s11095-022-03274-2 · Cited by 137
Physiologically-based pharmacokinetic models: approaches for enabling personalized medicine
Hartmanshenn, C., Scherholz, M. L., & Androulakis, I. P. (2016) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1007/s10928-016-9492-y · Cited by 142
Couto, N. A. S., Al‐Majdoub, Z. M., Gibson, S., Davies, P. J., Achour, B., Harwood, M. D., Carlson, G., Barber, J., Rostami‐Hodjegan, A., & Warhurst, G. (2020) · Drug Metabolism and Disposition · doi:10.1124/dmd.119.089656 · Cited by 103
Pathak, S. M., Ruff, A., Kostewicz, E. S., Patel, N. K., Turner, D. B., & Jamei, M. (2017) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.7b00406 · Cited by 97
Self-citationCouto, N. A. S., Al‐Majdoub, Z. M., Achour, B., Wright, P. C., Rostami‐Hodjegan, A., & Barber, J. (2019) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.8b00941 · Cited by 94
Prasad, B., Bhatt, D. K., Johnson, K., Chapa, R., Chu, X., Salphati, L., Xiao, G., Lee, C. G. L., Hop, C. E. C. A., Mathias, A. A., et al. (2018) · Drug Metabolism and Disposition · doi:10.1124/dmd.118.080523 · Cited by 91
Zhang, Q., Li, J., Middleton, A., Bhattacharya, S., & Conolly, R. B. (2018) · Frontiers in Public Health · doi:10.3389/fpubh.2018.00261 · Cited by 99
Germovsek, E., Barker, C. I. S., Sharland, M., & Standing, J. F. (2018) · Clinical Pharmacokinetics · doi:10.1007/s40262-018-0659-0 · Cited by 89
Hubal, E. A. C., Wetmore, B. A., Wambaugh, J. F., El-Masri, H. A., Sobus, J. R., & Bahadori, T. (2018) · Journal of Exposure Science & Environmental Epidemiology · doi:10.1038/s41370-018-0046-9 · Cited by 82
Usefulness of PBPK Modeling in Incorporation of Clinical Conditions in Personalized Medicine
Marsousi, N., Desmeules, J. A., Rudaz, S. L., & Daali, Y. (2017) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2017.04.035 · Cited by 76
Perry, C., Davis, G., Conner, T. M., & Zhang, T. (2020) · Current Pharmacology Reports · doi:10.1007/s40495-020-00212-x · Cited by 75
Ke, A. B., Greupink, R., & Abduljalil, K. (2018) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12274 · Cited by 74
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Grimm, F. A. (2018) · ALTEX · doi:10.14573/altex.1805301 · Cited by 72
Polasek, T. M., Tucker, G. T., Sorich, M. J., Wiese, M. D., Mohan, T. S., Rostami‐Hodjegan, A., Korprasertthaworn, P., Perera, V., & Rowland, A. (2017) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.13480 · Cited by 67
Utembe, W., M.A., H. J. C. I., Sanabria, N. M., Doganis, P., & Gulumian, M. (2020) · Nanomaterials · doi:10.3390/nano10071267 · Cited by 69
Yoshida, K. J., Budha, N. R., & Jin, J. (2017) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.622 · Cited by 65
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Rekić, D., Reynolds, K. S., Zhao, P., Zhang, L., Yoshida, K. J., Sachar, M., Piquette‐Miller, M., Huang, S. M., & Zineh, I. (2017) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2017.04.016 · Cited by 65
An integrated pathway based on in vitro data for the human hazard assessment of nanomaterials
Romeo, D., Salieri, B., Hischier, R., Nowack, B., & Wick, P. (2020) · Environment International · doi:10.1016/j.envint.2020.105505 · Cited by 62
PBPK model reporting template for chemical risk assessment applications
Tan, Y., Chan, M. P. L., Chukwudebe, A. C., Domoradzki, J. Y., Fisher, J. W., Hack, C. E., Hinderliter, P. M., Hirasawa, K., Leonard, J. A., Lumen, A., et al. (2020) · Regulatory Toxicology and Pharmacology · doi:10.1016/j.yrtph.2020.104691 · Cited by 62
New approach methodologies (NAMs) for human-relevant biokinetics predictions
Punt, A. (2020) · ALTEX · doi:10.14573/altex.2003242 · Cited by 61
Abouir, K., Samer, C. F., Gloor, Y. S., Desmeules, J. A., & Daali, Y. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2021.708299 · Cited by 60
Physiologically Based Pharmacokinetic Models Are Effective Support for Pediatric Drug Development
Wang, K., Jiang, K., Wei, X., Li, Y., Wang, T., & Song, Y. (2021) · AAPS PharmSciTech · doi:10.1208/s12249-021-02076-w · Cited by 57
Paini, A., Leonard, J. A., Kliment, T., Tan, Y., & Worth, A. P. (2017) · Regulatory Toxicology and Pharmacology · doi:10.1016/j.yrtph.2017.08.019 · Cited by 54
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Cicali, E. J., Smith, D. M., Duong, B. Q., Kovar, L., Cavallari, L. H., & Johnson, J. A. (2020) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1768 · Cited by 53
Mitra, A., Parrott, N. J., Miller, N. A., Lloyd, R. S., Tistaert, C., Heimbach, T., Ji, Y., & Kesisoglou, F. (2019) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2019.11.017 · Cited by 54
Badée, J., Fowler, S., de Wildt, S. N., Collier, A., Schmidt, S., & Parrott, N. (2018) · Clinical Pharmacokinetics · doi:10.1007/s40262-018-0681-2 · Cited by 43
Achour, B., Feteisi, H. A., Lanucara, F., Rostami‐Hodjegan, A., & Barber, J. (2017) · Drug Metabolism and Disposition · doi:10.1124/dmd.116.074732 · Cited by 50
Najjar, A., Punt, A., Wambaugh, J. F., Paini, A., Ellison, C. A., Fragki, S., Bianchi, E., Zhang, F., Westerhout, J., Mueller, D., et al. (2022) · Archives of Toxicology · doi:10.1007/s00204-022-03356-5 · Cited by 50
Bardhi, K., Coates, S., Watson, C. J. W., & Lazarus, P. J. (2022) · Expert Review of Clinical Pharmacology · doi:10.1080/17512433.2022.2148655 · Cited by 54
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Vezina, H. E., Cotreau, M. M., Han, T. H., & Gupta, M. (2017) · The Journal of Clinical Pharmacology · doi:10.1002/jcph.981 · Cited by 50
Türk, D., Hanke, N., Wolf, S., Frechen, S., Eißing, T., Wendl, T., Schwab, M. G., & Lehr, T. (2019) · Clinical Pharmacokinetics · doi:10.1007/s40262-019-00777-x · Cited by 48
Anand, O., Pepin, X. J. H., Kolhatkar, V., & Seo, P. (2022) · Pharmaceutical Research · doi:10.1007/s11095-022-03280-4 · Cited by 50
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Thiele, I., Clancy, C. M., Heinken, A., & Fleming, R. M. T. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.06.001 · Cited by 45
Polasek, T. M., Rostami‐Hodjegan, A., Yim, D., Jamei, M., Lee, H., Kimko, H. H. C., Kim, J. K., Phương, N. T. T., Darwich, A. S., & Shin, J. (2019) · The AAPS Journal · doi:10.1208/s12248-018-0286-6 · Cited by 46
Self-citationVerscheijden, L. F. M., Koenderink, J. B., de Wildt, S. N., & Rüssel, F. G. M. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007117 · Cited by 45
Fowler, S. M., Morcos, P. N., Cleary, Y., Martin‐Facklam, M., Parrott, N. J., Gertz, M., & Yu, L. J. (2017) · Current Pharmacology Reports · doi:10.1007/s40495-017-0082-5 · Cited by 44
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Stader, F., Penny, M. A., Siccardi, M., & Marzolini, C. (2019) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12399 · Cited by 45
Foti, R. (2024) · Drug Metabolism and Disposition · doi:10.1124/dmd.123.001384 · Cited by 43
An updated overview with simple and practical approach for developing in vitro–in vivo correlation
Jacob, S., & Nair, A. B. (2018) · Drug Development Research · doi:10.1002/ddr.21427 · Cited by 42
Dong, Z., Li, J., Wu, F. P., Zhao, P., Lee, S., Zhang, L., Seo, P., & Zhang, L. (2020) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12541 · Cited by 42
Marsousi, N., Desmeules, J. A., Rudaz, S. L., & Daali, Y. (2017) · Biopharmaceutics & Drug Disposition · doi:10.1002/bdd.2107 · Cited by 40
Patel, N. K., Clarke, J. F., Salem, F., Abdulla, T., Martins, F. S., Arora, S., Tsakalozou, E., Hodgkinson, A., Arjmandi‐Tash, O., Cristea, S., et al. (2022) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12814 · Cited by 40
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Darwich, A. S., Ogungbenro, K., Hatley, O. J. D., & Rostami‐Hodjegan, A. (2017) · Translational Cancer Research · doi:10.21037/tcr.2017.09.14 · Cited by 39
Polasek, T. M., Rayner, C. R., Peck, R., Rowland, A., Kimko, H. H. C., & Rostami‐Hodjegan, A. (2018) · Clinical Pharmacology in Drug Development · doi:10.1002/cpdd.638 · Cited by 37
Guimarães, M., Statelova, M., Holm, R., Reppas, C. I., Symillides, M., Vertzoni, M., & Fotaki, N. (2018) · Journal of Pharmacy and Pharmacology · doi:10.1111/jphp.12955 · Cited by 37
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Louisse, J., Alewijn, M., Peijnenburg, A., Cnubben, N. H. P., Heringa, M. B., Coecke, S., & Punt, A. (2019) · Toxicology in Vitro · doi:10.1016/j.tiv.2019.104722 · Cited by 37
Armani, S., Ting, L. S. L., Sauter, N. P., Darstein, C., Tripathi, A. P., Wang, L., Zhu, B. Y., Gu, H., Chun, D. Y., Einolf, H. J., et al. (2017) · Clinical Drug Investigation · doi:10.1007/s40261-017-0497-0 · Cited by 37
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Huang, C. Y., Nicholson, M. W., Wang, J. Y., Ting, C. Y., Tsai, M. H., Cheng, Y. C., Liu, C. L., Chan, D. Z. H., Lee, Y. C., Hsu, C. C., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110643 · Cited by 36
Cheong, E. J. Y., Teo, D. W. X., Chua, D. X. Y., & Chan, E. C. Y. (2019) · Drug Metabolism and Disposition · doi:10.1124/dmd.119.086918 · Cited by 36
Zhang, H., Wolford, C., Basit, A., Li, A. P., Fan, P. W., Murray, B. P., Takahashi, R., Khojasteh, S. C., Smith, B. J., Thummel, K. E., et al. (2020) · Drug Metabolism and Disposition · doi:10.1124/dmd.120.090738 · Cited by 36
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Willmann, S., Coboeken, K., Kapsa, S., Thelen, K., Mundhenke, M., Fischer, K., Hügl, B. J., & Mück, W. M. (2020) · The Journal of Clinical Pharmacology · doi:10.1002/jcph.1784 · Cited by 39
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Berg, E. L. (2017) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2017.01.001 · Cited by 33
Einolf, H. J., Lin, W., Won, C. S., Wang, L., Gu, H., Chun, D. Y., He, H., & Mangold, J. B. (2017) · Drug Metabolism and Disposition · doi:10.1124/dmd.117.076851 · Cited by 34
Paglialunga, S., Offman, E., Ichhpurani, N., Marbury, T., & Morimoto, B. (2017) · Expert Review of Clinical Pharmacology · doi:10.1080/17512433.2017.1274651 · Cited by 27
El‐Khateeb, E., Vasilogianni, A., Alrubia, S., Al‐Majdoub, Z. M., Couto, N. A. S., Howard, M., Barber, J., Rostami‐Hodjegan, A., & Achour, B. (2019) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2019.107397 · Cited by 32
Einolf, H. J., Zhou, J., Won, C. S., Wang, L., & Rebello, S. S. (2017) · Drug Metabolism and Disposition · doi:10.1124/dmd.116.073585 · Cited by 32
Rösslein, M., Liptrott, N. J., Owen, A., Boisseau, P., Wick, P., & Herrmann, I. K. (2017) · Nanotoxicology · doi:10.1080/17435390.2017.1279361 · Cited by 32
Rose, R. H., Turner, D. B., Neuhoff, S., & Jamei, M. (2017) · The AAPS Journal · doi:10.1208/s12248-017-0099-z · Cited by 31
Self-citationPatel, N., Wiśniowska, B., Jamei, M., & Polak, S. (2017) · AAPS Journal · doi:10.1208/s12248-017-0155-8 · Cited by 35
Self-citationCleary, Y., Gertz, M., Morcos, P. N., Yu, L. J., Youdim, K. A., Phipps, A., Fowler, S. M., & Parrott, N. J. (2017) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.956 · Cited by 31
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Loisios-Konstantinidis, I., & Dressman, J. (2020) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.0c00903 · Cited by 30
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Adiwidjaja, J., Boddy, A. V., & McLachlan, A. J. (2019) · Clinical Pharmacokinetics · doi:10.1007/s40262-019-00736-6 · Cited by 30
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Kohlmann, P., Stillhart, C., Kuentz, M., & Parrott, N. J. (2017) · The AAPS Journal · doi:10.1208/s12248-017-0149-6 · Cited by 30
Yau, E., Olivares‐Morales, A., Gertz, M., Parrott, N. J., Darwich, A. S., Aarons, L. J., & Ogungbenro, K. (2020) · The AAPS Journal · doi:10.1208/s12248-020-0418-7 · Cited by 28
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Thakur, A., Parvez, M. M., Leeder, J. S., & Prasad, B. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1554-6_18 · Cited by 28
Pappalardo, F., Russo, G. L., Corsini, E., Paini, A., & Worth, A. P. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.03.024 · Cited by 26
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Wedagedera, J. R., Onasanwo, A., Chirumamilla, S. K., Momiji, H., Leary, R. H., Dunlavey, M., Matthews, R., Abduljalil, K., Jamei, M., & Bois, F. Y. (2022) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12787 · Cited by 26
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Tabosa, M. A. M., Hoppel, M., Bunge, A. L., Guy, R. H., & Delgado-Charro, M. B. (2020) · Drug Delivery and Translational Research · doi:10.1007/s13346-020-00864-8 · Cited by 26
Vizirianakis, I. S., Miliotou, Α. N., Mystridis, G. A., Andriotis, E. G., Andreadis, I. I., Papadopoulou, L. C., & Fatouros, D. G. (2019) · Expert Review of Precision Medicine and Drug Development · doi:10.1080/23808993.2019.1605828 · Cited by 25
Johnson, T. N., & Ke, A. B. (2021) · The Journal of Clinical Pharmacology · doi:10.1002/jcph.1834 · Cited by 25
Sugiyama, Y., & Aoki, Y. (2023) · Drug Metabolism and Disposition · doi:10.1124/dmd.123.001344 · Cited by 25
Stader, F., Kinvig, H., Battegay, M., Khoo, S., Owen, A., Siccardi, M., & Marzolini, C. (2018) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.00717-18 · Cited by 25
Stader, F., Courlet, P., Kinvig, H., Battegay, M., Decosterd, L. A., Penny, M. A., Siccardi, M., & Marzolini, C. (2020) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.14402 · Cited by 24
Wu, F. P., Cristofoletti, R., Zhao, L. Y., & Rostami‐Hodjegan, A. (2021) · Biopharmaceutics & Drug Disposition · doi:10.1002/bdd.2274 · Cited by 25
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Metadata
- Authors
- Jamei, M.
- Affiliations
- Simcyp (United Kingdom) (GB)
- Corresponding author
- Jamei, Masoud
- ORCID
- Jamei, Masoud
- Venue
- Current Pharmacology Reports
- Published
- 2016-04-14
- Volume
- 2
- Issue
- 3
- Pages
- 161-169
- ISSN
- 2198-641X (ISSN-L)
- PMC
- PMC4856711
- OpenAlex
- W2341647963
- Cited by
- 244
- References
- 41
- Citations by year
- 2026: 10 · 2025: 21 · 2024: 17 · 2023: 29 · 2022: 20 · 2021: 25 · 2020: 31 · 2019: 24 · 2018: 24 · 2017: 37 · 2016: 6
- FWCI
- 4.70
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Current Pharmacology Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Simcyp Limited (A Certara Company)
- Topics
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Pharmaceutical studies and practices
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
- Keywords
- physiologically based pharmacokinetic modeling, regulatory acceptance, systems pharmacology, precision dosing
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