Skip to main content
SpringerLink
Log in

Prediction of drug–drug interaction potential using physiologically based pharmacokinetic modeling

  • Review
  • Published:
Archives of Pharmacal Research Aims and scope Submit manuscript

Abstract

The occurrence of drug–drug interactions (DDIs) can significantly affect the safety of a patient, and thus assessing DDI risk is important. Recently, physiologically based pharmacokinetic (PBPK) modeling has been increasingly used to predict DDI potential. Here, we present a PBPK modeling concept and strategy. We also surveyed PBPK-related articles about the prediction of DDI potential in humans published up to October 10, 2017. We identified 107 articles, including 105 drugs that fit our criteria, with a gradual increase in the number of articles per year. Studies on antineoplastic and immunomodulatory drugs (26.7%) contributed the most to published PBPK models, followed by cardiovascular (20.0%) and anti-infective (17.1%) drugs. Models for specific products such as herbal products, therapeutic protein drugs, and antibody–drug conjugates were also described. Most PBPK models were used to simulate cytochrome P450 (CYP)-mediated DDIs (74 drugs, of which 85.1% were CYP3A4-mediated), whereas some focused on transporter-mediated DDIs (15 drugs) or a combination of CYP and transporter-mediated DDIs (16 drugs). Full PBPK, first-order absorption modules and Simcyp® software were predominantly used for modeling. Recently, DDI predictions associated with genetic polymorphisms, special populations, or both have increased. The 107 published articles reasonably predicted the DDI potentials, but further studies of physiological properties and harmonization of in vitro experimental designs are required to extend the application scope, and improvement of DDI predictions using PBPK modeling will be possible in the future.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Abduljalil K, Cain T, Humphries H, Rostami-Hodjegan A (2014) Deciding on success criteria for predictability of pharmacokinetic parameters from in vitro studies: an analysis based on in vivo observations. Drug Metab Dispos 42:1478–1484

    Article  PubMed  CAS  Google Scholar 

  • Aitken AE, Richardson TA, Morgan ET (2006) Regulation of drug-metabolizing enzymes and transporters in inflammation. Annu Rev Pharmacol Toxicol 46:123–149

    Article  CAS  PubMed  Google Scholar 

  • Almond LM, Mukadam S, Gardner I, Okialda K, Wong S, Hatley O, Tay S, Rowland-Yeo K, Jamei M, Rostami-Hodjegan A, Kenny JR (2016) Prediction of drug-drug interactions arising from CYP3A induction using a physiologically based dynamic model. Drug Metab Dispos 44:821–832

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bae SH, Park WS, Han S, Park GJ, Lee J, Hong T, Jeon S, Yim DS (2017) Physiologically based pharmacokinetic predictions of intestinal BCRP-mediated effect of telmisartan on the pharmacokinetics of rosuvastatin in humans. Biopharm Drug Dispos 38:363

    Article  CAS  PubMed  Google Scholar 

  • Ball K, Jamier T, Parmentier Y, Denizot C, Mallier A, Chenel M (2017) Prediction of renal transporter-mediated drug-drug interactions for a drug which is an OAT substrate and inhibitor using PBPK modelling. Eur J Pharm Sci 106:122–132

    Article  CAS  PubMed  Google Scholar 

  • Baneyx G, Fukushima Y, Parrott N (2012) Use of physiologically based pharmacokinetic modeling for assessment of drug-drug interactions. Future Med Chem 4:681–693

    Article  CAS  PubMed  Google Scholar 

  • Baneyx G, Parrott N, Meille C, Iliadis A, Lave T (2014) Physiologically based pharmacokinetic modeling of CYP3A4 induction by rifampicin in human: influence of time between substrate and inducer administration. Eur J Pharm Sci 56:1–15

    Article  CAS  PubMed  Google Scholar 

  • Bohnert T, Patel A, Templeton I, Chen Y, Lu C, Lai G, Leung L, Tse S, Einolf HJ, Wang YH, Sinz M, Stearns R, Walsky R, Geng W, Sudsakorn S, Moore D, He L, Wahlstrom J, Keirns J, Narayanan R, Lang D, Yang X, International Consortium for Innovation and Quality in Pharmaceutical Development Victim Drug-Drug Interactions Working Group (2016) Evaluation of a new molecular entity as a victim of metabolic drug-drug interactions-an industry perspective. Drug Metab Dispo 44:1399–1423

    Article  CAS  Google Scholar 

  • Boulenc X, Barberan O (2011) Metabolic-based drug-drug interactions prediction, recent approaches for risk assessment along drug development. Drug Metab Drug Interact 26:147–168

    Article  CAS  Google Scholar 

  • Bouzom F, Ball K, Perdaems N, Walther B (2012) Physiologically based pharmacokinetic (PBPK) modelling tools: how to fit with our needs? Biopharm Drug Dispos 33:55–71

    Article  CAS  PubMed  Google Scholar 

  • Brantley SJ, Gufford BT, Dua R, Fediuk DJ, Graf TN, Scarlett YV, Frederick KS, Fisher MB, Oberlies NH, Paine MF (2014) Physiologically based pharmacokinetic modeling framework for quantitative prediction of an herb-drug interaction. CPT Pharm Syst Pharmacol 3:e107

    Article  CAS  Google Scholar 

  • Budha NR, Ji T, Musib L, Eppler S, Dresser M, Chen Y, Jin JY (2016) Evaluation of cytochrome P450 3A4-mediated drug-drug interaction potential for cobimetinib using physiologically based pharmacokinetic modeling and simulation. Clin Pharmacokinet 55:1435–1445

    Article  CAS  PubMed  Google Scholar 

  • Bui KH, Zhou D, Agbo F, Guo J (2015) Effect of multiple intravenous doses of lanicemine (AZD6765) on the pharmacokinetics of midazolam in healthy subjects. J Clin Pharmacol 55:1024–1030

    Article  CAS  PubMed  Google Scholar 

  • Burt HJ, Neuhoff S, Almond L, Gaohua L, Harwood MD, Jamei M, Rostami-Hodjegan A, Tucker GT, Rowland-Yeo K (2016) Metformin and cimetidine: physiologically based pharmacokinetic modelling to investigate transporter mediated drug-drug interactions. Eur J Pharm Sci 88:70–82

    Article  CAS  PubMed  Google Scholar 

  • Chabner BA, Roberts TG Jr (2005) Timeline: chemotherapy and the war on cancer. Nat Rev Cancer 5:65–72

    Article  CAS  PubMed  Google Scholar 

  • Chen Y, Jin JY, Mukadam S, Malhi V, Kenny JR (2012) Application of IVIVE and PBPK modeling in prospective prediction of clinical pharmacokinetics: strategy and approach during the drug discovery phase with four case studies. Biopharm Drug Dispos 33:85–98

    Article  PubMed  CAS  Google Scholar 

  • Chen J, Liu D, Zheng X, Zhao Q, Jiang J, Hu P (2015a) 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 11:857–868

    Article  CAS  PubMed  Google Scholar 

  • Chen Y, Mao J, Hop CE (2015b) Physiologically based pharmacokinetic modeling to predict drug-drug interactions involving inhibitory metabolite: a case study of amiodarone. Drug Metab Dispos 43:182–189

    Article  PubMed  CAS  Google Scholar 

  • Chen Y, Samineni D, Mukadam S, Wong H, Shen BQ, Lu D, Girish S, Hop C, Jin JY, Li C (2015c) Physiologically based pharmacokinetic modeling as a tool to predict drug interactions for antibody-drug conjugates. Clin Pharmacokinet 54:81–93

    Article  CAS  PubMed  Google Scholar 

  • Chen Y, Ma F, Lu T, Budha N, Jin JY, Kenny JR, Wong H, Hop CE, Mao J (2016) Development of a physiologically based pharmacokinetic model for itraconazole pharmacokinetics and drug-drug interaction prediction. Clin Pharmacokinet 55:735–749

    Article  CAS  PubMed  Google Scholar 

  • Chenel M, Bouzom F, Aarons L, Ogungbenro K (2008) Drug-drug interaction predictions with PBPK models and optimal multiresponse sampling time designs: application to midazolam and a phase I compound. Part 1: comparison of uniresponse and multiresponse designs using PopDes. J Pharmacokinet Pharmacodyn 35:635–659

    Article  CAS  PubMed  Google Scholar 

  • Cherkaoui-Rbati MH, Paine SW, Littlewood P, Rauch C (2017) A quantitative systems pharmacology approach, incorporating a novel liver model, for predicting pharmacokinetic drug–drug interactions. PLoS ONE 12:e0183794

    Article  PubMed  PubMed Central  Google Scholar 

  • Cho DY, Bae SH, Lee JK, Kim YW, Kim BT, Bae SK (2014) Selective inhibition of cytochrome P450 2D6 by Sarpogrelate and its active metabolite, M-1, in human liver microsomes. Drug Metab Dispos 42:33–39

    Article  PubMed  CAS  Google Scholar 

  • Damle B, Varma MV, Wood N (2011) Pharmacokinetics of voriconazole administered concomitantly with fluconazole and population-based simulation for sequential use. Antimicrob Agents Chemother 55:5172–5177

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • de Kanter R, Sidharta PN, Delahaye S, Gnerre C, Segrestaa J, Buchmann S, Kohl C, Treiber A (2016) Physiologically-based pharmacokinetic modeling of macitentan: prediction of drug-drug interactions. Clin Pharmacokinet 55:369–380

    Article  PubMed  CAS  Google Scholar 

  • 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 100:548–557

    Article  PubMed  CAS  Google Scholar 

  • Dhuria S, Einolf H, Mangold J, Sen S, Gu H, Wang L, Cameron S (2013) Time-dependent inhibition and induction of human cytochrome P4503A4/5 by an oral IAP antagonist, LCL161, in vitro and in vivo in healthy subjects. J Clin Pharmacol 53:642–653

    Article  CAS  PubMed  Google Scholar 

  • Djebli N, Fabre D, Boulenc X, Fabre G, Sultan E, Hurbin F (2015) Physiologically based pharmacokinetic modeling for sequential metabolism: effect of CYP2C19 genetic polymorphism on clopidogrel and clopidogrel active metabolite pharmacokinetics. Drug Metab Dispos 43:510–522

    Article  CAS  PubMed  Google Scholar 

  • Duan P, Zhao P, Zhang L (2017) Physiologically based pharmacokinetic (PBPK) modeling of pitavastatin and atorvastatin to predict drug-drug interactions (DDIs). Eur J Drug Metab Pharmacokinet 42:689–705

    Article  CAS  PubMed  Google Scholar 

  • Einolf HJ (2007) Comparison of different approaches to predict metabolic drug-drug interactions. Xenobiotica 37:1257–1294

    CAS  PubMed  Google Scholar 

  • Einolf HJ, Zhou J, Won C, Wang L, Rebello S (2017a) A physiologically-based pharmacokinetic modeling approach to predict drug-drug interactions of Sonidegib (LDE225) with perpetrators of CYP3A in cancer patients. Drug Metab Dispos 45:361–374

    Article  CAS  PubMed  Google Scholar 

  • Einolf HJ, Lin W, Won CS, Wang L, Gu H, Chun DY, He H, Mangold JB (2017b) Physiologically-based pharmacokinetic model predictions of panobinostat (LBH589) as a victim and perpetrator of drug-drug interactions. Drug Metab Dispos

  • 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 Pharm Syst Pharmacol 2:e59

    Article  CAS  Google Scholar 

  • Fahmi OA, Boldt S, Kish M, Obach RS, Tremaine LM (2008) Prediction of drug-drug interactions from in vitro induction data: application of the relative induction score approach using cryopreserved human hepatocytes. Drug Metab Dispos 36:1971–1974

    Article  CAS  PubMed  Google Scholar 

  • Fenneteau F, Poulin P, Nekka F (2010) Physiologically based predictions of the impact of inhibition of intestinal and hepatic metabolism on human pharmacokinetics of CYP3A substrates. J Pharm Sci 99:486–514

    Article  CAS  PubMed  Google Scholar 

  • Fowler S, Morcos PN, Cleary Y, Martin-Facklam M, Parrott N, Gertz M, Yu L (2017) Progress in prediction and interpretation of clinically relevant metabolic drug-drug interactions: a minireview illustrating recent developments and current opportunities. Curr Pharmacol Rep 3:36–49

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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 57:796–804

    Article  CAS  PubMed  Google Scholar 

  • Gertz M, Cartwright CM, Hobbs MJ, Kenworthy KE, Rowland M, Houston JB, Galetin A (2013) Cyclosporine inhibition of hepatic and intestinal CYP3A4, uptake and efflux transporters: application of PBPK modeling in the assessment of drug-drug interaction potential. Pharm Res 30:761–780

    Article  CAS  PubMed  Google Scholar 

  • Gidal BE, Maganti R, Laurenza A, Yang H, Verbel DA, Schuck E, Ferry J (2017) Effect of enzyme inhibition on perampanel pharmacokinetics: why study design matters. Epilepsy Res 134:41–48

    Article  CAS  PubMed  Google Scholar 

  • Greupink R, Schreurs M, Benne MS, Huisman MT, Russel FG (2013) Semi-mechanistic physiologically-based pharmacokinetic modeling of clinical glibenclamide pharmacokinetics and drug–drug-interactions. Eur J Pharm Sci 49:819–828

    Article  CAS  PubMed  Google Scholar 

  • Grillo JA, Zhao P, Bullock J, Booth BP, Lu M, Robie-Suh K, Berglund EG, Pang KS, Rahman A, Zhang L, Lesko LJ, Huang SM (2012) Utility of a physiologically-based pharmacokinetic (PBPK) modeling approach to quantitatively predict a complex drug-drug-disease interaction scenario for rivaroxaban during the drug review process: implications for clinical practice. Biopharm Drug Dispos 33:99–110

    Article  CAS  PubMed  Google Scholar 

  • Guest EJ, Aarons L, Houston JB, Rostami-Hodjegan A, Galetin A (2011) Critique of the two-fold measure of prediction success for ratios: application for the assessment of drug-drug interactions. Drug Metab Dispos 39:170–173

    Article  CAS  PubMed  Google Scholar 

  • Gufford BT, Barr JT, Gonzalez-Perez V, Layton ME, White JR Jr, Oberlies NH, Paine MF (2015) Quantitative prediction and clinical evaluation of an unexplored herb-drug interaction mechanism in healthy volunteers. CPT Pharm Syst Pharmacol 4:701–710

    Article  CAS  Google Scholar 

  • Guo J, Zhou D, Li Y, Khanh BH (2015) Physiologically based pharmacokinetic modeling to predict complex drug-drug interactions: a case study of AZD2327 and its metabolite, competitive and time-dependent CYP3A inhibitors. Biopharm Drug Dispos 36:507–519

    Article  CAS  PubMed  Google Scholar 

  • 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, Xia C, Zhang X, Esseltine DL, Nemunaitis J (2017) 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

  • Hajjar ER, Cafiero AC, Hanlon JT (2007) Polypharmacy in elderly patients. Am J Geriatr Pharmacother 5:345e51

  • Han B, Mao J, Chien JY, Hall SD (2013) Optimization of drug-drug interaction study design: comparison of minimal physiologically based pharmacokinetic models on prediction of CYP3A inhibition by ketoconazole. Drug Metab Dispos 41:1329–1338

    Article  CAS  PubMed  Google Scholar 

  • Hartmanshenn C, Scherholz M, Androulakis IP (2016) Physiologically-based pharmacokinetic models: approaches for enabling personalized medicine. J Pharmacokinet Pharmacodyn 43:481–504

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hsu V, de LTVM, Zhao P, Zhang L, Zheng JH, Nordmark A, Berglund EG, Giacomini KM, Huang SM (2014) Towards quantitation of the effects of renal impairment and probenecid inhibition on kidney uptake and efflux transporters, using physiologically based pharmacokinetic modelling and simulations. Clin Pharmacokinet 53:283–293

    Article  CAS  PubMed  Google Scholar 

  • Huang SM, Abernethy DR, Wang Y, Zhao P, Zineh I (2013) The utility of modeling and simulation in drug development and regulatory review. J Pharm Sci 102:2912–2923

    Article  CAS  PubMed  Google Scholar 

  • Huang W, Nakano M, Sager JE, Ragueneau-Majlessi I, Isoherranen N (2017) Physiologically based pharmacokinetic (PBPK) model of the CYP2D6 Probe atomoxetine: extrapolation to special populations and drug-drug interactions. Drug Metab Dispos 45:1156–1165

    Article  PubMed  Google Scholar 

  • Hyland R, Dickins M, Collins C, Jones H, Jones B (2008) Maraviroc: in vitro assessment of drug-drug interaction potential. Br J Clin Pharmacol 66:498–507

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hynes SM, Wickremsinhe E, Zhang W, Decker R, Ott J, Chandler J, Mitchell M (2015) Evaluation of the likelihood of a selective CHK1 inhibitor (LY2603618) to inhibit CYP2D6 with desipramine as a probe substrate in cancer patients. Biopharm Drug Dispos 36:49–63

    Article  CAS  PubMed  Google Scholar 

  • Jamei M, Dickinson GL, Rostami-Hodjegan A (2009a) A framework for assessing inter-individual variability in pharmacokinetics using virtual human populations and integrating general knowledge of physical chemistry, biology, anatomy, physiology and genetics: a tale of ‘bottom-up’ vs ‘top-down’ recognition of covariates. Drug Metab Pharmacokinet 24:53–75

    Article  CAS  PubMed  Google Scholar 

  • Jamei M, Turner D, Yang J, Neuhoff S, Polak S, Rostami-Hodjegan A, Tucker G (2009b) Population-based mechanistic prediction of oral drug absorption. AAPS J 11:225–237

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jamei M, Bajot F, Neuhoff S, Barter Z, Yang J, Rostami-Hodjegan A, Rowland-Yeo K (2014) A mechanistic framework for in vitro-in vivo extrapolation of liver membrane transporters: prediction of drug-drug interaction between rosuvastatin and cyclosporine. Clin Pharmacokinet 53:73–87

    Article  CAS  PubMed  Google Scholar 

  • Jiang X, Zhuang Y, Xu Z, Wang W, Zhou H (2016) Development of a physiologically based pharmacokinetic model to predict disease-mediated therapeutic protein-drug interactions: modulation of multiple cytochrome p450 enzymes by interleukin-6. AAPS J 18:767–776

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jing J, Nelson C, Paik J, Shirasaka Y, Amory JK, Isoherranen N (2017) Physiologically based pharmacokinetic model of all-trans-retinoic acid with application to cancer populations and drug interactions. J Pharmacol Exp Ther 361:246–258

    Article  CAS  PubMed  Google Scholar 

  • Johnson TN, Rostami-Hodjegan A (2011) Resurgence in the use of physiologically based pharmacokinetic models in pediatric clinical pharmacology: parallel shift in incorporating the knowledge of biological elements and increased applicability to drug development and clinical practice. Paediatr Anaesth 21:291–301

    Article  PubMed  Google Scholar 

  • Jones HM, Parrott N, Jorga K, Lave T (2006) A novel strategy for physiologically based predictions of human pharmacokinetics. Clin Pharmacokinet 45:511–542

    Article  CAS  PubMed  Google Scholar 

  • Jones HM, Mayawala K, Poulin P (2013) Dose selection based on physiologically based pharmacokinetic (PBPK) approaches. AAPS J 15:377–387

    Article  CAS  PubMed  Google Scholar 

  • Jones HM, Chen Y, Gibson C, Heimbach T, Parrott N, Peters SA, Snoeys J, Upreti VV, Zheng M, Hall SD (2015) Physiologically based pharmacokinetic modeling in drug discovery and development: a pharmaceutical industry perspective. Clin Pharmacol Ther 97:247–262

    Article  CAS  PubMed  Google Scholar 

  • Ke A, Barter Z, Rowland-Yeo K, Almond L (2016) Towards a best practice approach in PBPK Modeling: case example of developing a unified efavirenz model accounting for induction of CYPs 3A4 and 2B6. CPT Pharmacometrics Syst Pharmacol 5:367–376

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Khalil F, Laer S (2011) Physiologically based pharmacokinetic modeling: methodology, applications, and limitations with a focus on its role in pediatric drug development. J Biomed Biotechnol 907461

  • Kim SJ, Toshimoto K, Yao Y, Yoshikado T, Sugiyama Y (2017) Quantitative analysis of complex drug-drug interactions between repaglinide and cyclosporin A/gemfibrozil using physiologically based pharmacokinetic models with in vitro transporter/enzyme inhibition data. J Pharm Sci

  • Koenen A, Kroemer HK, Grube M, Meyer zu Schwabedissen HE (2011) Current understanding of hepatic and intestinal OATP-mediated drug-drug interactions. Expert Rev Clin Pharmacol 4:729–742

    Article  CAS  PubMed  Google Scholar 

  • Kudo T, Hisaka A, Sugiyama Y, Ito K (2013) Analysis of the repaglinide concentration increase produced by gemfibrozil and itraconazole based on the inhibition of the hepatic uptake transporter and metabolic enzymes. Drug Metab Dispos 41:362–371

    Article  CAS  PubMed  Google Scholar 

  • Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D (2016) Applied concepts in PBPK modeling: how to build a PBPK/PD Model. CPT Pharmacometrics Syst Pharmacol 5:516–531

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Li J, Kim S, Sha X, Wiegand R, Wu J, LoRusso P (2014) Complex disease-, gene-, and drug-drug interactions: impacts of renal function, CYP2D6 phenotype, and OCT2 activity on veliparib pharmacokinetics. Clin Cancer Res 20:3931–3944

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lin W, Ji T, Einolf H, Ayalasomayajula S, Lin TH, Hanna I, Heimbach T, Breen C, Jarugula V, He H (2017) Evaluation of drug-drug interaction potential between sacubitril/valsartan (LCZ696) and statins using a physiologically based pharmacokinetic model. J Pharm Sci 106:1439–1451

    Article  CAS  PubMed  Google Scholar 

  • Lu C, Suri A, Shyu WC, Prakash S (2014) Assessment of cytochrome P450-mediated drug-drug interaction potential of orteronel and exposure changes in patients with renal impairment using physiologically based pharmacokinetic modeling and simulation. Biopharm Drug Dispos 35:543–552

    Article  CAS  PubMed  Google Scholar 

  • Lynch T, Price A (2007) The effect of cytochrome P450 metabolism on drug response, interactions, and adverse effects. Am Fam Physician 76:391–396

    PubMed  Google Scholar 

  • Mamidi R, Dallas S, Sensenhauser C, Lim HK, Scheers E, Verboven P, Cuyckens F, Leclercq L, Evans DC, Kelley MF, Johnson MD, Snoeys J (2017) In vitro and physiologically-based pharmacokinetic based assessment of drug-drug interaction potential of canagliflozin. Br J Clin Pharmacol 83:1082–1096

    Article  CAS  PubMed  Google Scholar 

  • Mano Y, Sugiyama Y, Ito K (2015) Use of a physiologically based pharmacokinetic model for quantitative prediction of drug-drug interactions via CYP3A4 and estimation of the intestinal availability of CYP3A4 substrates. J Pharm Sci 104:3183–3193

    Article  CAS  PubMed  Google Scholar 

  • Mao J, Fan P, Wong S, Wang J, Ismaili MHA, Dean B, Hop CECA, Wright M, Chen Y (2017) Strategy for CYP3A induction risk assessment from preclinical signal to human: a Case example of a late-stage discovery compound. Pharm Res

  • Marcantonio EE, Ballard J, Gibson CR, Kassahun K, Palamanda J, Tang C, Evers R, Liu C, Zajic S, Mahon C, Mostoller K, Hreniuk D, Mehta A, Morris D, Wagner JA, Stoch SA (2014) Prednisone has no effect on the pharmacokinetics of CYP3A4 metabolized drugs—midazolam and odanacatib. J Clin Pharmacol 54:1280–1289

    Article  CAS  PubMed  Google Scholar 

  • Marsousi N, Daali Y, Rudaz S, Almond L, Humphries H, Desmeules J, Samer CF (2014) Prediction of metabolic interactions with oxycodone via CYP2D6 and CYP3A inhibition using a physiologically based pharmacokinetic model. CPT Pharm Syst Pharmacol 3:e152

    Article  CAS  Google Scholar 

  • Marsousi N, Desmeules JA, Rudaz S, Daali Y (2017) Prediction of drug-drug interactions using physiologically-based pharmacokinetic models of CYP450 modulators included in Simcyp software. Biopharm Drug Dispos

  • Marzolini C, Rajoli R, Battegay M, Elzi L, Back D, Siccardi M (2017) Physiologically based pharmacokinetic modeling to predict drug-drug interactions with efavirenz involving simultaneous inducing and inhibitory effects on cytochromes. Clin Pharmacokinet 56:409–420

    Article  CAS  PubMed  Google Scholar 

  • Min JS, Kim D, Park JB, Heo H, Bae SH, Seo JH, Oh E, Bae SK (2016) Application of physiologically based pharmacokinetic modeling in predicting drug-drug interactions for sarpogrelate hydrochloride in humans. Drug Des Dev Ther 10:2959–2972

    Article  Google Scholar 

  • Moj D, Hanke N, Britz H, Frechen S, Kanacher T, Wendl T, Haefeli WE, Lehr T (2017) Clarithromycin, midazolam, and digoxin: application of PBPK modeling to gain new insights into drug-drug interactions and co-medication regimens. AAPS J 19:298–312

    Article  CAS  PubMed  Google Scholar 

  • 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 72:805–811

    PubMed  Google Scholar 

  • Nakamaru Y, Emoto C, Shimizu M, Yamazaki H (2015) Human pharmacokinetic profiling of the dipeptidyl peptidase-IV inhibitor teneligliptin using physiologically based pharmacokinetic modeling. Biopharm Drug Dispos 36:148–162

    Article  CAS  PubMed  Google Scholar 

  • Navid A, Ng DM, Wong SE, Lightstone FC (2016) Application of a physiologically based pharmacokinetic model to study theophylline metabolism and its interactions with ciprofloxacin and caffeine. CPT Pharm Syst Pharmacol 5:74–81

    Article  CAS  Google Scholar 

  • Neuhoff S, Yeo KR, Barter Z, Jamei M, Turner DB, Rostami-Hodjegan A (2013) Application of permeability-limited physiologically-based pharmacokinetic models: part II-prediction of P-glycoprotein mediated drug-drug interactions with digoxin. J Pharm Sci 102:3161–3173

    Article  CAS  PubMed  Google Scholar 

  • Nordmark A, Andersson A, Baranczewski P, Wanag E, Stahle L (2014) Assessment of interaction potential of AZD2066 using in vitro metabolism tools, physiologically based pharmacokinetic modelling and in vivo cocktail data. Eur J Clin Pharmacol 70:167–178

    Article  CAS  PubMed  Google Scholar 

  • Olafuyi O, Coleman M, Badhan RKS (2017a) Development of a paediatric physiologically based pharmacokinetic model to assess the impact of drug-drug interactions in tuberculosis co-infected malaria subjects: a case study with artemether-lumefantrine and the CYP3A4-inducer rifampicin. Eur J Pharm Sci 106:20–33

    Article  CAS  PubMed  Google Scholar 

  • Olafuyi O, Coleman M, Badhan RKS (2017b) The application of physiologically-based pharmacokinetic modelling to assess the impact of antiretroviral-mediated drug-drug interactions on piperaquine antimalarial therapy during pregnancy. Biopharm Drug Dispos

  • 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 45:390–398

    Article  CAS  PubMed  Google Scholar 

  • 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 106:2123–2135

    Article  CAS  PubMed  Google Scholar 

  • Park MH, Shin SH, Byeon JJ, Lee GH, Yu BY, Shin YG (2017) Prediction of pharmacokinetics and drug-drug interaction potential using physiologically based pharmacokinetic (PBPK) modeling approach: a case study of caffeine and ciprofloxacin. Korean J Physiol Pharmacol 2:107–115

    Article  Google Scholar 

  • Perdaems N, Blasco H, Vinson C, Chenel M, Whalley S, Cazade F, Bouzom F (2010) Predictions of metabolic drug-drug interactions using physiologically based modelling two cytochrome P450 3A4 Substrates coadministered with ketoconazole or verapamil. Clin Pharmacokinet 49:239–258

    Article  CAS  PubMed  Google Scholar 

  • Pfeifer ND, Goss SL, Swift B, Ghibellini G, Ivanovic M, Heizer WD, Gangarosa LM, Brouwer KL (2013) Effect of ritonavir on (99m)Technetium-mebrofenin disposition in humans: a semi-PBPK modeling and in vitro approach to predict transporter-mediated DDIs. CPT Pharm Syst Pharmacol 2:e20

    Article  CAS  Google Scholar 

  • Posada MM, Bacon JA, Schneck KB, Tirona RG, Kim RB, Higgins JW, Pak YA, Hall SD, Hillgren KM (2015) Prediction of renal transporter mediated drug-drug interactions for pemetrexed using physiologically based pharmacokinetic modeling. Drug Metab Dispos 43:325–334

    Article  PubMed  CAS  Google Scholar 

  • Posada MM, Cannady EA, Payne CD, Zhang X, Bacon JA, Pak YA, Higgins JW, Shahri N, Hall SD, Hillgren KM (2017) Prediction of transporter-mediated drug-drug interactions for baricitinib. Clin Transl Sci

  • Qi F, Zhu L, Li N, Ge T, Xu G, Liao S (2017) Influence of different proton pump inhibitors on the pharmacokinetics of voriconazole. Int J Antimicrob Agents 49:403–409

    Article  CAS  PubMed  Google Scholar 

  • Rangaraj N, Matthew H, Gondi K, Sekhar S (2016) Application of a “fit for purpose” PBPK model to investigate the CYP3A4 induction potential of enzalutamide. Drug Metab Lett 10:172–179

    Article  CAS  Google Scholar 

  • Rekić D, Röshammar D, Mukonzo J, Ashton M (2011) In silico prediction of efavirenz and rifampicin drug-drug interaction considering weight and CYP2B6 phenotype. Br J Clin Pharmacol 71:536–543

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Rougee LRA, Mohutsky MA, Bedwell DW, Ruterbories KJ, Hall SD (2017) The impact of the hepatocyte-to-plasma pH gradient on the prediction of hepatic clearance and drug-drug interactions for CYP2C9 and CYP3A4 substrates. Drug Metab Dispos 45:1008–1018

    Article  PubMed  Google Scholar 

  • Rowland M, Peck C, Tucker G (2011) Physiologically-based pharmacokinetics in drug development and regulatory science. Annu Rev Pharmacol Toxicol 51:45–73

    Article  CAS  PubMed  Google Scholar 

  • Rowland A, Mangoni AA, Hopkins A, Sorich MJ, Rowland A (2016) Optimized cocktail phenotyping study protocol using physiological based pharmacokinetic modeling and in silico assessment of metabolic drug-drug interactions involving modafinil. Front Pharmacol 27:517

    Google Scholar 

  • Rowland-Yeo K, Jamei M, Yang J, Tucker GT, Rostami-Hodjegan A (2010) Physiologically based mechanistic modelling to predict complex drug-drug interactions involving simultaneous competitive and time-dependent enzyme inhibition by parent compound and its metabolite in both liver and gut—the effect of diltiazem on the time-course of exposure to triazolam. Eur J Pharm Sci 39:298–309

    Article  CAS  PubMed  Google Scholar 

  • Rowland-Yeo K, Jamei M, Rostami-Hodjegan A (2013) Predicting drug-drug interactions: application of physiologically based pharmacokinetic models under a systems biology approach. Expert Rev Clin Pharmacol 6:143–157

    Article  CAS  Google Scholar 

  • Sager JE, Yu J, Ragueneau-Majlessi I, Isoherranen N (2015) Physiologically based pharmacokinetic (PBPK) modeling and simulation approaches: a systematic review of published models, applications, and model verification. Drug Metab Dispos 43:1823–1837

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Shebley M, Fu W, Badri P, Bow D, Fischer V (2017a) Physiologically based pharmacokinetic modeling suggests limited drug-drug interaction between clopidogrel and dasabuvir. Clin Pharmacol Ther 102:679–687

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Shebley M, Liu J, Kavetskaia O, Sydor J, De Morais SM, Fischer V, Nijsen M, Bo DAJ (2017b) Mechanisms and predictions of drug-drug interactions of the hepatitis C virus three direct-acting antiviral regimen: paritaprevir/ritonavir, ombitasvir, and dasabuvir. Drug Metab Dispos 45:755–764

    Article  CAS  PubMed  Google Scholar 

  • 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 97:177–185

    Article  CAS  PubMed  Google Scholar 

  • Shitara Y, Sugiyama Y (2017) Preincubation-dependent and long-lasting inhibition of organic anion transporting polypeptide (OATP) and its impact on drug-drug interactions. Pharmacol Ther 177:67–80

    Article  CAS  PubMed  Google Scholar 

  • Shugarts S, Benet LZ (2008) The role of transporters in the pharmacokinetics of orally administered drugs. Pharm Res 26:2039–2054

    Article  CAS  Google Scholar 

  • Siccardi M, Marzolini C, Seden K, Almond L, Kirov A, Khoo S, Owen A, Back D (2013a) Prediction of drug-drug interactions between various antidepressants and efavirenz or boosted protease inhibitors using a physiologically based pharmacokinetic modelling approach. Clinical Pharmacokinetics 52:583–592

    Article  CAS  PubMed  Google Scholar 

  • Siccardi M, Olagunju A, Seden K, Ebrahimjee F, Rannard S, Back D, Owen A (2013b) Use of a physiologically-based pharmacokinetic model to simulate artemether dose adjustment for overcoming the drug-drug interaction with efavirenz. In Silico Pharmacol 1:4

    Article  PubMed  PubMed Central  Google Scholar 

  • Sissung TM, Baum CE, Kirkland CT, Gao R, Gardner ER, Figg WD (2010) Pharmacogenetics of membrane transporters: an update on current approaches. Mol Biotechnol 44:152–167

    Article  CAS  PubMed  Google Scholar 

  • Snoeys J, Beumont M, Monshouwer M, Ouwerkerk-Mahadevan S (2016) Mechanistic understanding of the nonlinear pharmacokinetics and intersubject variability of simeprevir: a PBPK-guided drug development approach. Clin Pharmacol Ther 99:224–234

    Article  CAS  PubMed  Google Scholar 

  • Templeton I, Ravenstijn P, Sensenhauser C, Snoeys J (2016) A physiologically based pharmacokinetic modeling approach to predict drug-drug interactions between domperidone and inhibitors of CYP3A4. Biopharm Drug Dispos 37:15–27

    Article  CAS  PubMed  Google Scholar 

  • Teorell T (1937) Studies on the diffusion effect upon ionic distribution: II. Experiments on ionic accumulation. J Gen Physiol 21:107–122

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Tornio A, Filppula AM, Kailari O, Neuvonen M, Nyronen TH, Tapaninen T, Neuvonen PJ, Niemi M, Backman JT (2014) Glucuronidation converts clopidogrel to a strong time-dependent inhibitor of CYP2C8: a phase II metabolite as a perpetrator of drug-drug interactions. Clin Pharmacol Ther 96:498–507

    Article  CAS  PubMed  Google Scholar 

  • Tsamandouras N, Rostami-Hodjegan A, Aarons L (2015) Combining the ‘bottom up’ and ‘top down’ approaches in pharmacokinetic modelling: fitting PBPK models to observed clinical data. Br J Clin Pharmacol 79:48–55

    Article  CAS  PubMed  Google Scholar 

  • Varma MV, Lai Y, Feng B, Litchfield J, Goosen TC, Bergman A (2012) Physiologically based modeling of pravastatin transporter-mediated hepatobiliary disposition and drug-drug interactions. Pharm Res 29:2860–2873

    Article  CAS  PubMed  Google Scholar 

  • Varma MV, Lai Y, Kimoto E, Goosen TC, El-Kattan AF, Kumar V (2013) Mechanistic modeling to predict the transporter- and enzyme-mediated drug-drug interactions of repaglinide. Pharm Res 30:1188–1199

    Article  CAS  PubMed  Google Scholar 

  • Varma MV, Scialis RJ, Lin J, Bi YA, Rotter CJ, Goosen TC, Yang X (2014) Mechanism-based pharmacokinetic modeling to evaluate transporter-enzyme interplay in drug interactions and pharmacogenetics of glyburide. AAPS J 16:736–748

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Varma MV, Lin J, Bi YA, Kimoto E, Rodrigues AD (2015a) Quantitative rationalization of gemfibrozil drug interactions: consideration of transporters-enzyme interplay and the role of circulating metabolite gemfibrozil 1-o-beta-glucuronide. Drug Metab Dispos 43:1108–1118

    Article  CAS  PubMed  Google Scholar 

  • Varma MV, Pang KS, Isoherranen N, Zhao P (2015b) Dealing with the complex drug-drug interactions: towards mechanistic models. Biopharm Drug Dispos 36:71–92

    Article  CAS  PubMed  Google Scholar 

  • Varma MV, Kimoto E, Scialis R, Bi Y, Lin J, Eng H, Kalgutkar AS, El-Kattan AF, Rodrigues AD, Tremaine LM (2017) Transporter-mediated hepatic uptake plays an important role in the pharmacokinetics and drug-drug interactions of montelukast. Clin Pharmacol Ther 101:406–415

    Article  CAS  PubMed  Google Scholar 

  • Vieira ML, Zhao P, Berglund EG, Reynolds KS, Zhang L, Lesko LJ, Huang SM (2012) Predicting drug interaction potential with a physiologically based pharmacokinetic model: a case study of telithromycin, a time-dependent CYP3A inhibitor. Clin Pharmacol Ther 91:700–708

    Article  CAS  PubMed  Google Scholar 

  • Vieira MD, Kim MJ, Apparaju S, Sinha V, Zineh I, Huang SM, Zhao P (2014) PBPK model describes the effects of comedication and genetic polymorphism on systemic exposure of drugs that undergo multiple clearance pathways. Clin Pharmacol Ther 95:550–557

    Article  CAS  PubMed  Google Scholar 

  • Von Moltke LL, Greenblatt DJ, Schmider J, Wright CE, Harmatz JS, Shader RI (1998) In vitro approaches to predicting drug interactions in vivo. Biochem Pharmacol 55:113–122

    Article  Google Scholar 

  • Wagner C, Zhao P, Pan Y, Hsu V, Grillo J, Huang SM, Sinha V (2015) Application of physiologically based pharmacokinetic (PBPK) modeling to support dose selection: report of an FDA Public Workshop on PBPK. CPT Pharm Syst Pharmacol 4:226–230

    Article  CAS  Google Scholar 

  • Wagner C, Zhao P, Arya V, Mullick C, Struble K, Au S (2017) Physiologically based pharmacokinetic modeling for predicting the effect of intrinsic and extrinsic factors on darunavir or lopinavir exposure coadministered with ritonavir. J Clin Pharmacol

  • Wang J, Xia S, Xue W, Wang D, Sai Y, Liu L, Liu X (2013) A semi-physiologically-based pharmacokinetic model characterizing mechanism-based auto-inhibition to predict stereoselective pharmacokinetics of verapamil and its metabolite norverapamil in human. Eur J Pharm Sci 50:290–302

    Article  CAS  PubMed  Google Scholar 

  • Wang Q, Zheng M, Leil T (2017) Investigating transporter-mediated drug-drug interactions using a physiologically based pharmacokinetic model of rosuvastatin. CPT Pharmacometrics Syst Pharmacol 6:228–238

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wisniowska B, Polak S (2016) Virtual Clinical Trial Toward polytherapy safety assessment: combination of physiologically based pharmacokinetic/pharmacodynamic-based modeling and simulation approach with drug-drug interactions involving terfenadine as an example. J Pharm Sci 105:3415–3424

    Article  CAS  PubMed  Google Scholar 

  • World Health Organization (WHO) Collaborating centre for drug statistics methodology. http://www.whocc.no. Accessed March 1, 2017

  • Xia B, Barve A, Heimbach T, Zhang T, Gu H, Wang L, Einolf H, Alexander N, Hanna I, Ke J, Mangold JB, He H, Sunkara G (2014) Physiologically based pharmacokinetic modeling for assessing the clinical drug-drug interaction of alisporivir. Eur J Pharm Sci 63:103–112

    Article  CAS  PubMed  Google Scholar 

  • Xu Y, Hijazi Y, Wolf A, Wu B, Sun YN, Zhu M (2015) Physiologically based pharmacokinetic model to assess the influence of blinatumomab-mediated cytokine elevations on cytochrome P450 enzyme activity. CPT Pharm Syst Pharmacol 4:507–515

    Article  CAS  Google Scholar 

  • Yamashita F, Sasa Y, Yoshida S, Hisaka A, Asai Y, Kitano H, Hashida M, Suzuki H (2013) Modeling of rifampicin-induced CYP3A4 activation dynamics for the prediction of clinical drug-drug interactions from in vitro data. PLoS ONE 8:e70330

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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 43:1417–1429

    Article  CAS  PubMed  Google Scholar 

  • Yoshikado T, Yoshida K, Kotani N, Nakada T, Asaumi R, Toshimoto K, Maeda K, Kusuhara H, Sugiyama Y (2016) Quantitative analyses of hepatic OATP-mediated interactions between statins and inhibitors using PBPK modeling with a parameter optimization method. Clin Pharmacol Ther 100:513–523

    Article  CAS  PubMed  Google Scholar 

  • Yoshikado T, Kazuya M, Kusuhara H, Furihata KI, Sugiyama Y (2017) Quantitative analyses of the influence of parameters governing rate-determining process of hepatic elimination of drugs on the magnitudes of drug-drug interactions via hepatic OATPs and CYP3A using physiologically-based pharmacokinetic models. J Pharm Sci 106:2739–2750

    Article  CAS  PubMed  Google Scholar 

  • Yu Y, Loi CM, Hoffman J, Wang D (2016) Physiologically based pharmacokinetic modeling of palbociclib. J Clin Pharmacol 57:173–184

    Article  PubMed  CAS  Google Scholar 

  • Zhang T (2015) Physiologically based pharmacokinetic modeling of disposition and drug-drug interactions for atorvastatin and its metabolites. Eur J Pharm Sci 77:216–229

    Article  CAS  PubMed  Google Scholar 

  • Zhang L, Zhang YD, Zhao P, Huang SM (2009) Predicting drug-drug interactions: an FDA perspective. AAPS J 11:300–306

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhang H, Bu F, Li L, Jiao Z, Ma G, Cai W, Zhuang X, Lin HS, Shin JG, Xiang X. (2017) Prediction of drug-drug interaction between tacrolimus and principal ingredients of Wuzhi Capsule in Chinese healthy volunteers using physiologically-based pharmacokinetic modelling. Basic Clin Pharmacol Toxicol

  • Zhao Y, Hu ZY (2014) Physiologically based pharmacokinetic modelling and in vivo [I]/K(i) accurately predict P-glycoprotein-mediated drug-drug interactions with dabigatran etexilate. Br J Pharmacol 171:1043–1053

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhou SF, Liu JP, Chowbay B (2009) Polymorphism of human cytochrome P450 enzymes and its clinical impact. Drug Metab Rev 41:89–295

    Article  CAS  PubMed  Google Scholar 

  • Zhou D, Bui K, Sostek M, Al-Huniti N (2016) Simulation and prediction of the drug-drug interaction potential of naloxegol by physiologically based pharmacokinetic modeling. CPT Pharm Syst Pharmacol 5:250–257

    Article  CAS  Google Scholar 

  • Zhuang X, Lu C (2016) PBPK modeling and simulation in drug research and development. Acta Pharm Sin B 6:430–440

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This research was supported by the Bio & Medical Technology Development Program (No. 2013M3A9B5075838) and the Basic Research Laboratory (BRL) Program (2015R1A4A1042350) through the National Research Foundation of Korea grant funded by the Ministry of Education, Korea.

Author information

Authors and Affiliations

  1. College of Pharmacy and Integrated Research Institute of Pharmaceutical Sciences, The Catholic University of Korea, Bucheon, 14662, Republic of Korea

    Jee Sun Min & Soo Kyung Bae

Authors
  1. Jee Sun Min
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Soo Kyung Bae
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Soo Kyung Bae.

Ethics declarations

Conflict of interest

The authors have declared no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Min, J.S., Bae, S.K. Prediction of drug–drug interaction potential using physiologically based pharmacokinetic modeling. Arch. Pharm. Res. 40, 1356–1379 (2017). https://doi.org/10.1007/s12272-017-0976-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12272-017-0976-0

Keywords

Search

Navigation