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Application of physiologically based pharmacokinetics modeling in the research of small-molecule targeted anti-cancer drugs

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Abstract

Introduction

Physiologically based pharmacokinetics (PBPK) models are increasingly used in the drug research and development, especially in anti-cancer drugs. Between 2001 and 2020, a total of 89 small-molecule targeted antitumor drugs were approved in China and the United States, some of which already included PBPK modeling in their application or approval packages. This article intended to review the prevalence and application of PBPK model in these drugs.

Method

Article search was performed in the PubMed to collect English research articles on small-molecule targeted anti-cancer drugs using PBPK modeling. The selected articles were classified into nine categorizes according to the application areas and further analyzed.

Result

From 2001 to 2020, more than 60% of small-molecule targeted anti-cancer drugs (54/89) were studied using PBPK model with a wide range of application. Ninety research articles were included, of which 48 involved enzyme-mediated drug-drug interaction (DDI). Of these retrieved articles, Simcyp, GastroPlus, and PK-Sim were the most widely model building platforms, which account for 63.8%, 15.2%, and 8.6%, respectively.

Conclusion

PBPK modeling is commonly and widely used to research small-molecule targeted anti-cancer drugs.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Zhong L, Li Y, **ong L, Wang W, Wu M, Yuan T, Yang W, Tian C, Miao Z, Wang T, Yang S (2021) Small molecules in targeted cancer therapy: advances, challenges, and future perspectives. Signal Transduct Target Ther 6(1):201. https://doi.org/10.1038/s41392-021-00572-w

    Article  PubMed  PubMed Central  Google Scholar 

  2. Zhang X, Yang Y, Grimstein M, Fan J, Grillo JA, Huang SM, Zhu H, Wang Y (2020) Application of PBPK Modeling and Simulation for Regulatory Decision Making and Its Impact on US Prescribing Information: An Update on the 2018–2019 Submissions to the US FDA’s Office of Clinical Pharmacology. J Clin Pharmacol 60(Suppl 1):S160–S178. https://doi.org/10.1002/jcph.1767

    Article  CAS  PubMed  Google Scholar 

  3. Perry C, Davis G, Conner TM, Zhang T (2020) Utilization of Physiologically Based Pharmacokinetic Modeling in Clinical Pharmacology and Therapeutics: an Overview. Curr Pharmacol Rep 6(3):71–84. https://doi.org/10.1007/s40495-020-00212-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Yamazaki S, Johnson TR, Smith BJ (2015) Prediction of Drug-Drug Interactions with Crizotinib as the CYP3A Substrate Using a Physiologically Based Pharmacokinetic Model. Drug Metab Dispos 43(10):1417–1429. https://doi.org/10.1124/dmd.115.064618

    Article  CAS  PubMed  Google Scholar 

  5. Yamazaki S, Skaptason J, Romero D, Vekich S, Jones HM, Tan W, Wilner KD, Koudriakova T (2011) Prediction of oral pharmacokinetics of cMet kinase inhibitors in humans: physiologically based pharmacokinetic model versus traditional one-compartment model. Drug Metab Dispos 39(3):383–393. https://doi.org/10.1124/dmd.110.035857

    Article  CAS  PubMed  Google Scholar 

  6. Sharma J, Lv H, Gallo JM (2013) Intratumoral modeling of gefitinib pharmacokinetics and pharmacodynamics in an orthotopic mouse model of glioblastoma. Cancer Res 73(16):5242–5252. https://doi.org/10.1158/0008-5472.Can-13-0690

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Bi Y, Deng J, Murry DJ, An G (2016) A Whole-Body Physiologically Based Pharmacokinetic Model of Gefitinib in Mice and Scale-Up to Humans. Aaps j 18(1):228–238. https://doi.org/10.1208/s12248-015-9836-3

    Article  CAS  PubMed  Google Scholar 

  8. Moltó J, Rajoli R, Back D, Valle M, Miranda C, Owen A, Clotet B, Siccardi M (2017) Use of a physiologically based pharmacokinetic model to simulate drug-drug interactions between antineoplastic and antiretroviral drugs. J Antimicrob Chemother 72(3):805–811. https://doi.org/10.1093/jac/dkw485

    Article  CAS  PubMed  Google Scholar 

  9. Chen Y, Zhou D, Tang W, Zhou W, Al-Huniti N, Masson E (2018) Physiologically Based Pharmacokinetic Modeling to Evaluate the Systemic Exposure of Gefitinib in CYP2D6 Ultrarapid Metabolizers and Extensive Metabolizers. J Clin Pharmacol 58(4):485–493. https://doi.org/10.1002/jcph.1036

    Article  CAS  PubMed  Google Scholar 

  10. Jakubiak P, Wagner B, Grimm HP, Petrig-Schaffland J, Schuler F, Alvarez-Sánchez R (2016) Development of a Unified Dissolution and Precipitation Model and Its Use for the Prediction of Oral Drug Absorption. Mol Pharm 13(2):586–598. https://doi.org/10.1021/acs.molpharmaceut.5b00808

    Article  CAS  PubMed  Google Scholar 

  11. Gruber A, Czejka M, Buchner P, Kitzmueller M, Kirchbaumer Baroian N, Dittrich C, Sahmanovic Hrgovcic A (2018) Monitoring of erlotinib in pancreatic cancer patients during long-time administration and comparison to a physiologically based pharmacokinetic model. Cancer Chemother Pharmacol 81(4):763–771. https://doi.org/10.1007/s00280-018-3545-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Dong Z, Li J, Wu F, Zhao P, Lee SC, Zhang L, Seo P, Zhang L (2020) Application of Physiologically-Based Pharmacokinetic Modeling to Predict Gastric pH-Dependent Drug-Drug Interactions for Weak Base Drugs. CPT Pharmacometrics Syst Pharmacol 9(8):456–465. https://doi.org/10.1002/psp4.12541

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Cheong EJY, Ng DZW, Chin SY, Wang Z, Chan ECY (2022) Application of a physiologically based pharmacokinetic model of rivaroxaban to prospective simulations of drug-drug-disease interactions with protein kinase inhibitors in cancer-associated venous thromboembolism. Br J Clin Pharmacol 88(5):2267–2283. https://doi.org/10.1111/bcp.15158

    Article  CAS  PubMed  Google Scholar 

  14. Hudachek SF, Gustafson DL (2013) Physiologically based pharmacokinetic model of lapatinib developed in mice and scaled to humans. J Pharmacokinet Pharmacodyn 40(2):157–176. https://doi.org/10.1007/s10928-012-9295-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Chen J, Liu D, Zheng X, Zhao Q, Jiang J, Hu P (2015) Relative contributions of the major human CYP450 to the metabolism of icotinib and its implication in prediction of drug–drug interaction between icotinib and CYP3A4 inhibitors/inducers using physiologically based pharmacokinetic modeling. Expert Opin Drug Metab Toxicol 11(6):857–868. https://doi.org/10.1517/17425255.2015.1034688

    Article  CAS  PubMed  Google Scholar 

  16. Yu Y, DuBois SG, Wetmore C, Khosravan R (2020) Physiologically Based Pharmacokinetic Modeling and Simulation of Sunitinib in Pediatrics. Aaps j 22(2):31. https://doi.org/10.1208/s12248-020-0423-x

    Article  CAS  PubMed  Google Scholar 

  17. Pawaskar DK, Straubinger RM, Fetterly GJ, Hylander BH, Repasky EA, Ma WW, Jusko WJ (2013) Physiologically based pharmacokinetic models for everolimus and sorafenib in mice. Cancer Chemother Pharmacol 71(5):1219–1229. https://doi.org/10.1007/s00280-013-2116-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Edginton AN, Zimmerman EI, Vasilyeva A, Baker SD, Panetta JC (2016) Sorafenib metabolism, transport, and enterohepatic recycling: physiologically based modeling and simulation in mice. Cancer Chemother Pharmacol 77(5):1039–1052. https://doi.org/10.1007/s00280-016-3018-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Ruanglertboon W, Sorich MJ, Hopkins AM, Rowland A (2021) Mechanistic Modelling Identifies and Addresses the Risks of Empiric Concentration-Guided Sorafenib Dosing. Pharmaceuticals (Basel). https://doi.org/10.3390/ph14050389

    Article  PubMed  Google Scholar 

  20. Wang Z, **ang X, Liu S, Tang Z, Sun H, Parvez M, Ghim JL, Shin JG, Cai W (2021) A physiologically based pharmacokinetic/pharmacodynamic modeling approach for drug-drug interaction evaluation of warfarin enantiomers with sorafenib. Drug Metab Pharmacokinet. https://doi.org/10.1016/j.dmpk.2020.10.001

    Article  PubMed  Google Scholar 

  21. Mendes MS, Hatley O, Gill KL, Yeo KR, Ke AB (2020) A physiologically based pharmacokinetic - pharmacodynamic modelling approach to predict incidence of neutropenia as a result of drug-drug interactions of paclitaxel in cancer patients. Eur J Pharm Sci. https://doi.org/10.1016/j.ejps.2020.105355

    Article  PubMed  Google Scholar 

  22. Wagner C, Kesisoglou F, Pepin XJH, Parrott N, Emami Riedmaier A (2021) Use of Physiologically Based Pharmacokinetic Modeling for Predicting Drug-Food Interactions: Recommendations for Improving Predictive Performance of Low Confidence Food Effect Models. Aaps j 23(4):85. https://doi.org/10.1208/s12248-021-00601-0

    Article  CAS  PubMed  Google Scholar 

  23. Sorich MJ, Mutlib F, van Dyk M, Hopkins AM, Polasek TM, Marshall JC, Rodrigues AD, Rowland A (2019) Use of Physiologically Based Pharmacokinetic Modeling to Identify Physiological and Molecular Characteristics Driving Variability in Axitinib Exposure: A Fresh Approach to Precision Dosing in Oncology. J Clin Pharmacol 59(6):872–879. https://doi.org/10.1002/jcph.1377

    Article  CAS  PubMed  Google Scholar 

  24. Gerner B, Scherf-Clavel O (2021) Physiologically Based Pharmacokinetic Modelling of Cabozantinib to Simulate Enterohepatic Recirculation. Pharmaceutics, Drug-Drug Interaction with Rifampin and Liver Impairment. https://doi.org/10.3390/pharmaceutics13060778

    Book  Google Scholar 

  25. Liu H, Yu Y, Guo N, Wang X, Han B, **ang X (2021) Application of Physiologically Based Pharmacokinetic Modeling to Evaluate the Drug-Drug and Drug-Disease Interactions of Apatinib. Front Pharmacol. https://doi.org/10.3389/fphar.2021.780937

    Article  PubMed  PubMed Central  Google Scholar 

  26. Adiwidjaja J, Boddy AV, McLachlan AJ (2019) Implementation of a Physiologically Based Pharmacokinetic Modeling Approach to Guide Optimal Dosing Regimens for Imatinib and Potential Drug Interactions in Paediatrics. Front Pharmacol 10:1672. https://doi.org/10.3389/fphar.2019.01672

    Article  CAS  PubMed  Google Scholar 

  27. Adiwidjaja J, Boddy AV, McLachlan AJ (2019) Physiologically Based Pharmacokinetic Modelling of Hyperforin to Predict Drug Interactions with St John’s Wort. Clin Pharmacokinet 58(7):911–926. https://doi.org/10.1007/s40262-019-00736-6

    Article  CAS  PubMed  Google Scholar 

  28. Adiwidjaja J, Boddy AV, McLachlan AJ (2020) Physiologically-Based Pharmacokinetic Predictions of the Effect of Curcumin on Metabolism of Imatinib and Bosutinib. Pharm Res, In Vitro and In Vivo Disconnect. https://doi.org/10.1007/s11095-020-02834-8

    Book  Google Scholar 

  29. Adiwidjaja J, Gross AS, Boddy AV, McLachlan AJ (2022) Physiologically-based pharmacokinetic model predictions of inter-ethnic differences in imatinib pharmacokinetics and dosing regimens. Br J Clin Pharmacol 88(4):1735–1750. https://doi.org/10.1111/bcp.15084

    Article  CAS  PubMed  Google Scholar 

  30. Adiwidjaja J, Boddy AV, McLachlan AJ (2022) Physiologically based pharmacokinetic model predictions of natural product-drug interactions between goldenseal, berberine, imatinib and bosutinib. Eur J Clin Pharmacol 78(4):597–611. https://doi.org/10.1007/s00228-021-03266-y

    Article  CAS  PubMed  Google Scholar 

  31. Chang M, Bathena S, Christopher LJ, Shen H, Roy A (2022) Prediction of drug-drug interaction potential mediated by transporters between dasatinib and metformin, pravastatin, and rosuvastatin using physiologically based pharmacokinetic modeling. Cancer Chemother Pharmacol 89(3):383–392. https://doi.org/10.1007/s00280-021-04394-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Pahwa S, Alam K, Crowe A, Farasyn T, Neuhoff S, Hatley O, Ding K, Yue W (2017) Pretreatment With Rifampicin and Tyrosine Kinase Inhibitor Dasatinib Potentiates the Inhibitory Effects Toward OATP1B1- and OATP1B3-Mediated Transport. J Pharm Sci 106(8):2123–2135. https://doi.org/10.1016/j.xphs.2017.03.022

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Vaidhyanathan S, Wang X, Crison J, Varia S, Gao JZH, Saxena A, Good D (2019) Bioequivalence Comparison of Pediatric Dasatinib Formulations and Elucidation of Absorption Mechanisms Through Integrated PBPK Modeling. J Pharm Sci 108(1):741–749. https://doi.org/10.1016/j.xphs.2018.11.005

    Article  CAS  PubMed  Google Scholar 

  34. Stader F, Battegay M, Marzolini C (2021) Physiologically-Based Pharmacokinetic Modeling to Support the Clinical Management of Drug-Drug Interactions With Bictegravir. Clin Pharmacol Ther 110(5):1231–1239. https://doi.org/10.1002/cpt.2221

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Heimbach T, Lin W, Hourcade-Potelleret F, Tian X, Combes FP, Horvath N, He H (2019) Physiologically Based Pharmacokinetic Modeling to Supplement Nilotinib Pharmacokinetics and Confirm Dose Selection in Pediatric Patients. J Pharm Sci 108(6):2191–2198. https://doi.org/10.1016/j.xphs.2019.01.028

    Article  CAS  PubMed  Google Scholar 

  36. Ono C, Hsyu PH, Abbas R, Loi CM, Yamazaki S (2017) Application of Physiologically Based Pharmacokinetic Modeling to the Understanding of Bosutinib Pharmacokinetics: Prediction of Drug-Drug and Drug-Disease Interactions. Drug Metab Dispos 45(4):390–398. https://doi.org/10.1124/dmd.116.074450

    Article  CAS  PubMed  Google Scholar 

  37. Yamazaki S, Loi CM, Kimoto E, Costales C, Varma MV (2018) Application of Physiologically Based Pharmacokinetic Modeling in Understanding Bosutinib Drug-Drug Interactions: Importance of Intestinal P-Glycoprotein. Drug Metab Dispos 46(8):1200–1211. https://doi.org/10.1124/dmd.118.080424

    Article  CAS  PubMed  Google Scholar 

  38. Adiwidjaja J, Boddy AV, McLachlan AJ (2020) Potential for pharmacokinetic interactions between Schisandra sphenanthera and bosutinib, but not imatinib: in vitro metabolism study combined with a physiologically-based pharmacokinetic modelling approach. Br J Clin Pharmacol 86(10):2080–2094. https://doi.org/10.1111/bcp.14303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Shi JG, Fraczkiewicz G, Williams WV, Yeleswaram S (2015) Predicting drug-drug interactions involving multiple mechanisms using physiologically based pharmacokinetic modeling: a case study with ruxolitinib. Clin Pharmacol Ther 97(2):177–185. https://doi.org/10.1002/cpt.30

    Article  CAS  PubMed  Google Scholar 

  40. Umehara K, Huth F, ** Y, Schiller H, Aslanis V, Heimbach T, He H (2019) Drug-drug interaction (DDI) assessments of ruxolitinib, a dual substrate of CYP3A4 and CYP2C9, using a verified physiologically based pharmacokinetic (PBPK) model to support regulatory submissions. Drug Metab Pers Ther. https://doi.org/10.1515/dmpt-2018-0042

    Article  PubMed  Google Scholar 

  41. Alsmadi MM, Al-Daoud NM, Jaradat MM, Alzughoul SB, Abu Kwiak AD, Abu Laila SS, Abu Shameh AJ, Alhazabreh MK, Jaber SA, Abu Kassab HT (2021) Physiologically-based pharmacokinetic model for alectinib, ruxolitinib, and panobinostat in the presence of cancer, renal impairment, and hepatic impairment. Biopharm Drug Dispos 42(6):263–284. https://doi.org/10.1002/bdd.2282

    Article  CAS  PubMed  Google Scholar 

  42. Kayesh R, Farasyn T, Crowe A, Liu Q, Pahwa S, Alam K, Neuhoff S, Hatley O, Ding K, Yue W (2021) Assessing OATP1B1- and OATP1B3-Mediated Drug-Drug Interaction Potential of Vemurafenib Using R-Value and Physiologically-Based Pharmacokinetic Models. J Pharm Sci 110(1):314–324. https://doi.org/10.1016/j.xphs.2020.06.016

    Article  CAS  PubMed  Google Scholar 

  43. Cohen-Rabbie S, Zhou L, Vishwanathan K, Wild M, Xu S, Freshwater T, Jain L, Schalkwijk S, Tomkinson H, Zhou D (2021) Physiologically Based Pharmacokinetic Modeling for to Evaluate Drug-Drug Interactions and Pediatric Dose Regimens. J Clin Pharmacol 61(11):1493–1504. https://doi.org/10.1002/jcph.1935

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Pepin XJH, Hammarberg M, Mattinson A, Moir A (2023) Physiologically Based Biopharmaceutics Model for Selumetinib Food Effect Investigation and Capsule Dissolution Safe Space - Part I: Adults. Pharm Res 40(2):387–403. https://doi.org/10.1007/s11095-022-03339-2

    Article  CAS  PubMed  Google Scholar 

  45. Yu Y, Loi CM, Hoffman J, Wang D (2017) Physiologically Based Pharmacokinetic Modeling of Palbociclib. J Clin Pharmacol 57(2):173–184. https://doi.org/10.1002/jcph.792

    Article  CAS  PubMed  Google Scholar 

  46. Li S, Yu Y, ** Z, Dai Y, Lin H, Jiao Z, Ma G, Cai W, Han B, **ang X (2019) Prediction of pharmacokinetic drug-drug interactions causing atorvastatin-induced rhabdomyolysis using physiologically based pharmacokinetic modelling. Biomed Pharmacother. https://doi.org/10.1016/j.biopha.2019.109416

    Article  PubMed  Google Scholar 

  47. Li J, Jiang J, Wu J, Bao X, Sanai N (2021) Physiologically Based Pharmacokinetic Modeling of Central Nervous System Pharmacokinetics of CDK4/6 Inhibitors to Guide Selection of Drug and Dosing Regimen for Brain Cancer Treatment. Clin Pharmacol Ther 109(2):494–506. https://doi.org/10.1002/cpt.2021

    Article  CAS  PubMed  Google Scholar 

  48. Farasyn T, Crowe A, Hatley O, Neuhoff S, Alam K, Kanyo J, Lam TT, Ding K, Yue W (2019) Preincubation With Everolimus and Sirolimus Reduces Organic Anion-Transporting Polypeptide (OATP)1B1- and 1B3-Mediated Transport Independently of mTOR Kinase Inhibition: Implication in Assessing OATP1B1- and OATP1B3-Mediated Drug-Drug Interactions. J Pharm Sci 108(10):3443–3456. https://doi.org/10.1016/j.xphs.2019.04.019

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Combes FP, Einolf HJ, Coello N, Heimbach T, He H, Grosch K (2020) Model-Informed Drug Development for Everolimus Dosing Selection in Pediatric Infant Patients. CPT Pharmacometrics Syst Pharmacol 9(4):230–237. https://doi.org/10.1002/psp4.12502

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Emoto C, Fukuda T, Cox S, Christians U, Vinks AA (2013) Development of a Physiologically-Based Pharmacokinetic Model for Sirolimus: Predicting Bioavailability Based on Intestinal CYP3A Content. CPT Pharmacometrics Syst Pharmacol. https://doi.org/10.1038/psp.2013.33

    Article  PubMed  PubMed Central  Google Scholar 

  51. Emoto C, Fukuda T, Johnson TN, Adams DM, Vinks AA (2015) Development of a Pediatric Physiologically Based Pharmacokinetic Model for Sirolimus: Applying Principles of Growth and Maturation in Neonates and Infants. CPT Pharmacometrics Syst Pharmacol. https://doi.org/10.1002/psp4.17

    Article  PubMed  PubMed Central  Google Scholar 

  52. Emoto C, Fukuda T, Venkatasubramanian R, Vinks AA (2015) The impact of CYP3A5*3 polymorphism on sirolimus pharmacokinetics: insights from predictions with a physiologically-based pharmacokinetic model. Br J Clin Pharmacol 80(6):1438–1446. https://doi.org/10.1111/bcp.12743

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Rioux N, Waters NJ (2016) Physiologically Based Pharmacokinetic Modeling in Pediatric Oncology Drug Development. Drug Metab Dispos 44(7):934–943. https://doi.org/10.1124/dmd.115.068031

    Article  CAS  PubMed  Google Scholar 

  54. Moj D, Britz H, Burhenne J, Stewart CF, Egerer G, Haefeli WE, Lehr T (2017) A physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model of the histone deacetylase (HDAC) inhibitor vorinostat for pediatric and adult patients and its application for dose specification. Cancer Chemother Pharmacol 80(5):1013–1026. https://doi.org/10.1007/s00280-017-3447-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Freise KJ, Shebley M, Salem AH (2017) Quantitative Prediction of the Effect of CYP3A Inhibitors and Inducers on Venetoclax Pharmacokinetics Using a Physiologically Based Pharmacokinetic Model. J Clin Pharmacol 57(6):796–804. https://doi.org/10.1002/jcph.858

    Article  CAS  PubMed  Google Scholar 

  56. Emami Riedmaier A, Lindley DJ, Hall JA, Castleberry S, Slade RT, Stuart P, Carr RA, Borchardt TB, Bow DAJ, Nijsen M (2018) Mechanistic Physiologically Based Pharmacokinetic Modeling of the Dissolution and Food Effect of a Biopharmaceutics Classification System IV Compound-The Venetoclax Story. J Pharm Sci 107(1):495–502. https://doi.org/10.1016/j.xphs.2017.09.027

    Article  CAS  PubMed  Google Scholar 

  57. Bhatnagar S, Mukherjee D, Salem AH, Miles D, Menon RM, Gibbs JP (2021) Dose adjustment of venetoclax when co-administered with posaconazole: clinical drug-drug interaction predictions using a PBPK approach. Cancer Chemother Pharmacol 87(4):465–474. https://doi.org/10.1007/s00280-020-04179-w

    Article  CAS  PubMed  Google Scholar 

  58. Henze LJ, Koehl NJ, O’shea JP, Holm R, Vertzoni M, Griffin BT (2021) Combining species specific in vitro & in silico models to predict in vivo food effect in a preclinical stage - case study of Venetoclax. Eur J Pharm Sci. https://doi.org/10.1016/j.ejps.2021.105840

    Article  PubMed  Google Scholar 

  59. Dong J, Liu SB, Rasheduzzaman JM, Huang CR, Miao LY (2022) Development of Physiology Based Pharmacokinetic Model to Predict the Drug Interactions of Voriconazole and Venetoclax. Pharm Res 39(8):1921–1933. https://doi.org/10.1007/s11095-022-03289-9

    Article  CAS  PubMed  Google Scholar 

  60. Mukherjee D, Brackman DJ, Suleiman AA, Zha J, Menon RM, Salem AH (2023) Impact of Multiple Concomitant CYP3A Inhibitors on Venetoclax Pharmacokinetics: A PBPK and Population PK-Informed Analysis. J Clin Pharmacol 63(1):119–125. https://doi.org/10.1002/jcph.2140

    Article  CAS  PubMed  Google Scholar 

  61. Dolton MJ, Chiang PC, Ma F, ** JY, Chen Y (2020) A Physiologically Based Pharmacokinetic Model of Vismodegib: Deconvoluting the Impact of Saturable Plasma Protein Binding, pH-Dependent Solubility and Nonsink Permeation. Aaps j 22(5):117. https://doi.org/10.1208/s12248-020-00503-7

    Article  CAS  PubMed  Google Scholar 

  62. Lin L, Wright MR, Hop C, Wong H (2022) Physiologically Based Pharmacokinetic Models Can Be Used to Predict the Unique Nonlinear Absorption Profiles of Vismodegib. Drug Metab Dispos 50(9):1170–1181. https://doi.org/10.1124/dmd.122.000885

    Article  CAS  PubMed  Google Scholar 

  63. Zhang L, Mager DE (2015) Physiologically-based pharmacokinetic modeling of target-mediated drug disposition of bortezomib in mice. J Pharmacokinet Pharmacodyn 42(5):541–552. https://doi.org/10.1007/s10928-015-9445-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Iwasaki S, Zhu A, Hanley M, Venkatakrishnan K, **a C (2020) A Translational Physiologically Based Pharmacokinetics/Pharmacodynamics Framework of Target-Mediated Disposition, Target Inhibition and Drug-Drug Interactions of Bortezomib. Aaps j 22(3):66. https://doi.org/10.1208/s12248-020-00448-x

    Article  CAS  PubMed  Google Scholar 

  65. Gupta N, Hanley MJ, Venkatakrishnan K, Bessudo A, Rasco DW, Sharma S, O’Neil BH, Wang B, Liu G, Ke A, Patel C, Rowland Yeo K, **a C, Zhang X, Esseltine DL, Nemunaitis J (2018) Effects of Strong CYP3A Inhibition and Induction on the Pharmacokinetics of Ixazomib, an Oral Proteasome Inhibitor: Results of Drug-Drug Interaction Studies in Patients With Advanced Solid Tumors or Lymphoma and a Physiologically Based Pharmacokinetic Analysis. J Clin Pharmacol 58(2):180–192. https://doi.org/10.1002/jcph.988

    Article  CAS  PubMed  Google Scholar 

  66. Parrott NJ, Yu LJ, Takano R, Nakamura M, Morcos PN (2016) Physiologically Based Absorption Modeling to Explore the Impact of Food and Gastric pH Changes on the Pharmacokinetics of Alectinib. Aaps j 18(6):1464–1474. https://doi.org/10.1208/s12248-016-9957-3

    Article  CAS  PubMed  Google Scholar 

  67. Cleary Y, Gertz M, Morcos PN, Yu L, Youdim K, Phipps A, Fowler S, Parrott N (2018) Model-Based Assessments of CYP-Mediated Drug-Drug Interaction Risk of Alectinib: Physiologically Based Pharmacokinetic Modeling Supported Clinical Development. Clin Pharmacol Ther 104(3):505–514. https://doi.org/10.1002/cpt.956

    Article  CAS  PubMed  Google Scholar 

  68. Morcos PN, Cleary Y, Sturm-Pellanda C, Guerini E, Abt M, Donzelli M, Vazvaei F, Balas B, Parrott N, Yu L (2018) Effect of Hepatic Impairment on the Pharmacokinetics of Alectinib. J Clin Pharmacol 58(12):1618–1628. https://doi.org/10.1002/jcph.1286

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Chen W, ** D, Shi Y, Zhang Y, Zhou H, Li G (2020) The underlying mechanisms of lorlatinib penetration across the blood-brain barrier and the distribution characteristics of lorlatinib in the brain. Cancer Med 9(12):4350–4359. https://doi.org/10.1002/cam4.3061

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Pilla Reddy V, Walker M, Sharma P, Ballard P, Vishwanathan K (2018) Development, Verification, and Prediction of Osimertinib Drug-Drug Interactions Using PBPK Modeling Approach to Inform Drug Label. CPT Pharmacometrics Syst Pharmacol 7(5):321–330. https://doi.org/10.1002/psp4.12289

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Gu H, Dutreix C, Rebello S, Ouatas T, Wang L, Chun DY, Einolf HJ, He H (2018) Simultaneous Physiologically Based Pharmacokinetic (PBPK) Modeling of Parent and Active Metabolites to Investigate Complex CYP3A4 Drug-Drug Interaction Potential: A Case Example of Midostaurin. Drug Metab Dispos 46(2):109–121. https://doi.org/10.1124/dmd.117.078006

    Article  CAS  PubMed  Google Scholar 

  72. De Zwart L, Snoeys J, Jacobs F, Li LY, Poggesi I, Verboven P, Goris I, Scheers E, Wynant I, Monshouwer M, Mamidi R (2021) Prediction of the drug-drug interaction potential of the α1-acid glycoprotein bound, CYP3A4/CYP2C9 metabolized oncology drug, erdafitinib. CPT Pharmacometrics Syst Pharmacol 10(9):1107–1118. https://doi.org/10.1002/psp4.12682

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Parrott N, Stillhart C, Lindenberg M, Wagner B, Kowalski K, Guerini E, Djebli N, Meneses-Lorente G (2020) Physiologically Based Absorption Modelling to Explore the Impact of Food and Gastric pH Changes on the Pharmacokinetics of Entrectinib. Aaps j 22(4):78. https://doi.org/10.1208/s12248-020-00463-y

    Article  CAS  PubMed  Google Scholar 

  74. Djebli N, Buchheit V, Parrott N, Guerini E, Cleary Y, Fowler S, Frey N, Yu L, Mercier F, Phipps A, Meneses-Lorente G (2021) Physiologically-Based Pharmacokinetic Modelling of Entrectinib Parent and Active Metabolite to Support Regulatory Decision-Making. Eur J Drug Metab Pharmacokinet 46(6):779–791. https://doi.org/10.1007/s13318-021-00714-z

    Article  CAS  PubMed  Google Scholar 

  75. Seo SW, Han DG, Choi E, Park T, Byun JH, Cho HJ, Jung IH, Yoon IS (2022) Development and application of a physiologically based pharmacokinetic model for entrectinib in rats and scale-up to humans: Route-dependent gut wall metabolism. Biomed Pharmacother. https://doi.org/10.1016/j.biopha.2021.112520

    Article  PubMed  Google Scholar 

  76. de Zwart L, Snoeys J, De Jong J, Sukbuntherng J, Mannaert E, Monshouwer M (2016) Ibrutinib Dosing Strategies Based on Interaction Potential of CYP3A4 Perpetrators Using Physiologically Based Pharmacokinetic Modeling. Clin Pharmacol Ther 100(5):548–557. https://doi.org/10.1002/cpt.419

    Article  CAS  PubMed  Google Scholar 

  77. Rose RH, Turner DB, Neuhoff S, Jamei M (2017) Incorporation of the Time-Varying Postprandial Increase in Splanchnic Blood Flow into a PBPK Model to Predict the Effect of Food on the Pharmacokinetics of Orally Administered High-Extraction Drugs. Aaps j 19(4):1205–1217. https://doi.org/10.1208/s12248-017-0099-z

    Article  CAS  PubMed  Google Scholar 

  78. Zhou D, Podoll T, Xu Y, Moorthy G, Vishwanathan K, Ware J, Slatter JG, Al-Huniti N (2019) Evaluation of the Drug-Drug Interaction Potential of Acalabrutinib and Its Active Metabolite, ACP-5862, Using a Physiologically-Based Pharmacokinetic Modeling Approach. CPT Pharmacometrics Syst Pharmacol 8(7):489–499. https://doi.org/10.1002/psp4.12408

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Pilla Reddy V, Jo H, Neuhoff S (2021) Food constituent- and herb-drug interactions in oncology: Influence of quantitative modelling on Drug labelling. Br J Clin Pharmacol 87(10):3988–4000. https://doi.org/10.1111/bcp.14822

    Article  CAS  PubMed  Google Scholar 

  80. Chen B, Zhou D, Wei H, Yotvat M, Zhou L, Cheung J, Sarvaria N, Lai R, Sharma S, Vishwanathan K, Ware J (2022) Acalabrutinib CYP3A mediated Drug-Drug Interactions: Clinical Evaluations and Physiologically-Based Pharmacokinetic Modeling to inform dose adjustment strategy. Br J Clin Pharmacol. https://doi.org/10.1111/bcp.15278

    Article  PubMed  Google Scholar 

  81. Wang K, Yao X, Zhang M, Liu D, Gao Y, Sahasranaman S, Ou YC (2021) Comprehensive PBPK model to predict drug interaction potential of Zanubrutinib as a victim or perpetrator. CPT Pharmacometrics Syst Pharmacol 10(5):441–454. https://doi.org/10.1002/psp4.12605

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Wu F, Krishna G, Surapaneni S (2020) Physiologically based pharmacokinetic modeling to assess metabolic drug-drug interaction risks and inform the drug label for fedratinib. Cancer Chemother Pharmacol 86(4):461–473. https://doi.org/10.1007/s00280-020-04131-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Budha NR, Ji T, Musib L, Eppler S, Dresser M, Chen Y, ** JY (2016) Evaluation of Cytochrome P450 3A4-Mediated Drug-Drug Interaction Potential for Cobimetinib Using Physiologically Based Pharmacokinetic Modeling and Simulation. Clin Pharmacokinet 55(11):1435–1445. https://doi.org/10.1007/s40262-016-0412-5

    Article  CAS  PubMed  Google Scholar 

  84. Takahashi RH, Choo EF, Ma S, Wong S, Halladay J, Deng Y, Rooney I, Gates M, Hop CE, Khojasteh SC, Dresser MJ, Musib L (2016) Absorption, Metabolism, Excretion, and the Contribution of Intestinal Metabolism to the Oral Disposition of [14C]Cobimetinib, a MEK Inhibitor. Humans Drug Metab Dispos 44(1):28–39. https://doi.org/10.1124/dmd.115.066282

    Article  CAS  PubMed  Google Scholar 

  85. Samant TS, Huth F, Umehara K, Schiller H, Dhuria SV, Elmeliegy M, Miller M, Chakraborty A, Heimbach T, He H, Ji Y (2020) Ribociclib Drug-Drug Interactions: Clinical Evaluations and Physiologically-Based Pharmacokinetic Modeling to Guide Drug Labeling. Clin Pharmacol Ther 108(3):575–585. https://doi.org/10.1002/cpt.1950

    Article  CAS  PubMed  Google Scholar 

  86. Laisney M, Heimbach T, Mueller-Zsigmondy M, Blumenstein L, Costa R, Ji Y (2022) Physiologically Based Biopharmaceutics Modeling to Demonstrate Virtual Bioequivalence and Bioequivalence Safe-space for Ribociclib which has Permeation Rate-controlled Absorption. J Pharm Sci 111(1):274–284. https://doi.org/10.1016/j.xphs.2021.10.017

    Article  CAS  PubMed  Google Scholar 

  87. Posada MM, Morse BL, Turner PK, Kulanthaivel P, Hall SD, Dickinson GL (2020) Predicting Clinical Effects of CYP3A4 Modulators on Abemaciclib and Active Metabolites Exposure Using Physiologically Based Pharmacokinetic Modeling. J Clin Pharmacol 60(7):915–930. https://doi.org/10.1002/jcph.1584

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Gajewska M, Blumenstein L, Kourentas A, Mueller-Zsigmondy M, Lorenzo S, Sinn A, Velinova M, Heimbach T (2020) Physiologically Based Pharmacokinetic Modeling of Oral Absorption, pH, and Food Effect in Healthy Volunteers to Drive Alpelisib Formulation Selection. Aaps j 22(6):134. https://doi.org/10.1208/s12248-020-00511-7

    Article  CAS  PubMed  Google Scholar 

  89. Einolf HJ, Lin W, Won CS, Wang L, Gu H, Chun DY, He H, Mangold JB (2017) Physiologically Based Pharmacokinetic Model Predictions of Panobinostat (LBH589) as a Victim and Perpetrator of Drug-Drug Interactions. Drug Metab Dispos 45(12):1304–1316. https://doi.org/10.1124/dmd.117.076851

    Article  CAS  PubMed  Google Scholar 

  90. Prakash C, Fan B, Ke A, Le K, Yang H (2020) Physiologically based pharmacokinetic modeling and simulation to predict drug-drug interactions of ivosidenib with CYP3A perpetrators in patients with acute myeloid leukemia. Cancer Chemother Pharmacol 86(5):619–632. https://doi.org/10.1007/s00280-020-04148-3

    Article  CAS  PubMed  Google Scholar 

  91. Bolleddula J, Ke A, Yang H, Prakash C (2021) PBPK modeling to predict drug-drug interactions of ivosidenib as a perpetrator in cancer patients and qualification of the Simcyp platform for CYP3A4 induction. CPT Pharmacometrics Syst Pharmacol 10(6):577–588. https://doi.org/10.1002/psp4.12619

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Einolf HJ, Zhou J, Won C, Wang L, Rebello S (2017) A Physiologically-Based Pharmacokinetic Modeling Approach To Predict Drug-Drug Interactions of Sonidegib (LDE225) with Perpetrators of CYP3A in Cancer Patients. Drug Metab Dispos 45(4):361–374. https://doi.org/10.1124/dmd.116.073585

    Article  CAS  PubMed  Google Scholar 

  93. Pilla Reddy V, Bui K, Scarfe G, Zhou D, Learoyd M (2019) Physiologically Based Pharmacokinetic Modeling for Olaparib Dosing Recommendations: Bridging Formulations, Drug Interactions, and Patient Populations. Clin Pharmacol Ther 105(1):229–241. https://doi.org/10.1002/cpt.1103

    Article  CAS  PubMed  Google Scholar 

  94. Physiologically Based Pharmacokinetic Analyses — Format and Content Guidance for Industry (2018). FDA Guidance Document https:wwwfdagov/regulatory-information/search-fda-guidance-documents/physiologically-based-pharmacokinetic-analyses-format-and-content-guidance-industry

  95. Zhuang X, Lu C (2016) PBPK modeling and simulation in drug research and development. Acta Pharm Sin B 6(5):430–440. https://doi.org/10.1016/j.apsb.2016.04.004

    Article  PubMed  PubMed Central  Google Scholar 

  96. Fahmi OA, Shebley M, Palamanda J, Sinz MW, Ramsden D, Einolf HJ, Chen L, Wang H (2016) Evaluation of CYP2B6 Induction and Prediction of Clinical Drug-Drug Interactions: Considerations from the IQ Consortium Induction Working Group—An Industry Perspective. Drug Metab Dispos 44(10):1720. https://doi.org/10.1124/dmd.116.071076

    Article  CAS  PubMed  Google Scholar 

  97. Drozdzik M, Busch D, Lapczuk J, Müller J, Ostrowski M, Kurzawski M, Oswald S (2019) Protein Abundance of Clinically Relevant Drug Transporters in the Human Liver and Intestine: A Comparative Analysis in Paired Tissue Specimens. Clin Pharmacol Ther 105(5):1204–1212. https://doi.org/10.1002/cpt.1301

    Article  CAS  PubMed  Google Scholar 

  98. Ocvirk S, O’Keefe SJD (2021) Dietary fat, bile acid metabolism and colorectal cancer. Semin Cancer Biol 73:347–355. https://doi.org/10.1016/j.semcancer.2020.10.003

    Article  CAS  PubMed  Google Scholar 

  99. Li M, Zhao P, Pan Y, Wagner C (2018) Predictive Performance of Physiologically Based Pharmacokinetic Models for the Effect of Food on Oral Drug Absorption: Current Status. CPT Pharmacometrics Syst Pharmacol 7(2):82–89. https://doi.org/10.1002/psp4.12260

    Article  CAS  PubMed  Google Scholar 

  100. Edginton AN, Willmann S (2008) Physiology-based simulations of a pathological condition: prediction of pharmacokinetics in patients with liver cirrhosis. Clin Pharmacokinet 47(11):743–752. https://doi.org/10.2165/00003088-200847110-00005

    Article  PubMed  Google Scholar 

  101. Vizirianakis IS, Mystridis GA, Avgoustakis K, Fatouros DG, Spanakis M (2016) Enabling personalized cancer medicine decisions: The challenging pharmacological approach of PBPK models for nanomedicine and pharmacogenomics (Review). Oncol Rep 35(4):1891–1904. https://doi.org/10.3892/or.2016.4575

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The authors thanks Qingfeng He, PhD, and **ao Zhu, PhD, for their writing support.

Funding

This project was supported by National Natural Science Foundation of China and National Research Foundation of Korea (No. 82011540409), Minhang District Science and Technology Commission Foundation (No. 2020MHZ035).

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XW, FC, ZG, NG, HL, XX, YS, and BH contributed to the data collection and analysis and review; wrote and reviewed the manuscript; and approved the final manuscript for publication.

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Correspondence to Yufei Shi or Bing Han.

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**aowen Wang, Fang Chen, Zhichun Gu, Nan Guo, Houwen Lin, **aoqiang **ang, Yufei Shi, and Bing Han have no conflicts of interest that are directly relevant to the content of this article.

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Wang, X., Chen, F., Guo, N. et al. Application of physiologically based pharmacokinetics modeling in the research of small-molecule targeted anti-cancer drugs. Cancer Chemother Pharmacol 92, 253–270 (2023). https://doi.org/10.1007/s00280-023-04566-z

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