Session 4: Using in vitro Enzyme and Transporter Data in the Prediction of Drug-drug Interactions
Chairs: Lei Zhang, US Food and Drug Administration, Silver Spring, Maryland, USA and Yurong Lai, Gilead Sciences, Foster City, California, USA
Approaches and Recommendations by the IQ Induction Working Group for the in vitro Conduct and Analysis of Cytochrome P450 (CYP) Induction for the Assessment of Human Drug-drug Interaction
Niresh Hariparsad, AstraZeneca, Boston, Massachusetts, USA
Industry Perspectives on Regulatory Guidance on Drug-drug Interactions 
Venkatesh Pilla Reddy, AstraZeneca, Cambridge, United Kingdom
EMA Perspectives on Regulatory Guidance on Drug-drug Interactions
Elin Lindhagen, Medical Products Agency, Uppsala, Sweden
FDA Perspectives on Regulatory Guidance on Drug-drug Interactions 
Xinning Yang, US Food and Drug Administration, Silver Spring, Maryland, USA
Application of Transporter Biomarkers to DDI Risk Assessment
Hiroyuki Kusuhara, University of Tokyo, Tokyo, Japan
PBPK Modeling of Concomitant CYP3A Auto-induction and Time-dependent Inhibition of the Pharmacokinetics of the CYP3A Substrate Aprepitant 
Tamara Cabalu, Merck & Co., Inc., Philadelphia, Pennsylvania, USA
Predictive in vitro in vivo Extrapolation for Time Dependent Inhibition Of CYP1A2, CYP2C8, CYP2C9, CYP2C19 AND CYP2D6 Using Pooled Human Hepatocytes and a Simple Mechanistic Static Model
Diane Ramsden, Takeda, Cambridge, Massachusetts, USA
Panel Discussion with All Speakers
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Industry Perspectives on Regulatory Guidance on Drug-drug Interactions | Dr. Venkatesh Pilla Reddy
Recorded 03/05/2021
Recorded 03/05/2021
Drug-Drug Interaction (DDI) represents one of the major factors to be evaluated in a clinical program. The potential for an investigational new drug to cause DDIs is normally investigated in a stepwise manner during drug development by following regional guidelines. Recently FDA and PMDA have updated in vitro and clinical DDI guidances. In addition, they have issued PBPK guidances for pharmaceutical industry because of increasing numbers of marketing authorization applications containing PBPK aspects (including EMA). This talk will focus on aspects of DDI where harmonization exists between regulatory agencies and parts that require the harmonization. The speaker will share the successful use of various modelling approaches (static, dynamic PBPK and PopPK M&S) for supporting DDI investigations in drug development programs across industry.
EMA Perspectives on Regulatory Guidance on Drug-drug Interactions | Dr. Elin Lindhagen
Recorded 03/05/2021
Recorded 03/05/2021
This presentation will give a brief overview of the current EMA DDI guideline. A concept paper was out for public consultation some time ago, with plans for an update of the guideline. The aim of the update was to incorporate new scientific data and increase clarity, this will be briefly discussed. In addition, some guideline differences between the geographic regions will be discussed, some of which will be covered in the ongoing ICH M12 guideline work.
FDA Perspectives on Regulatory Guidance on Drug-drug Interactions | Dr. Xinning Yang
Recorded 03/05/2021
Recorded 03/05/2021
Assessing drug-drug interaction (DDI) risk is an important aspect of the risk assessment of new drugs prior to market approval and during the post-marketing period. DDIs can occur when a co-administered drug alters the pharmacokinetics of another drug by modulating the activity and/or expression of drug transporters or metabolic enzymes including Cytochrome P450 (CYP) enzymes. DDIs may increase side effects or reduce efficacy of affected drugs, and can lead to clinical intervention such as close monitoring, dose adjustment, avoiding concomitant use, and in some circumstances contraindicating co-administration of drugs. DDI assessment during drug development usually starts with in vitro evaluation which provide mechanistic understanding, help define the potential for significant interactions in humans, and determine when and which clinical DDI assessment is needed. The US FDA issued final in vitro and clinical DDI guidance in 2020. While significant advances have been made in past decades greatly improving our understanding of ADME of drugs, there remain certain areas which need further research to address. This presentation will discuss some of those areas including time-dependent inhibition of CYPs, induction of CYPs, and transporter-mediated interactions.
Application of Transporter Biomarkers to DDI Risk Assessment | Dr. Hiroyuki Kusuhara
Recorded 03/05/2021
Recorded 03/05/2021
Inhibition of drug transporters is one of the mechanisms underlying the pharmacokinetic drug-drug interactions (DDI). Following guidelines, the DDI potential of drug candidates against drug transporters is routinely assessed during the drug development. Recently, some endogenous substrates of drug transporters are considered as biomarkers to assess the DDI risk clinically. Leverage of the endogenous biomarkers offers a new strategy to manage the DDI risk of drug candidates, for instance, risk assessment in early phase of clinical development, and multiplexed DDI risk analysis. We have conducted clinical studies to identify the endogenous biomarkers for drug transporters such as OATP1B, OATs and MATE1/2-K using their in vivo inhibitors. The pharmacokinetic parameters, such as area under the plasma concentration time curves or renal clearance, of the endogenous substrates were related to the degree of inhibition of the drug transporters, supporting that they serve as quantitative biomarkers. In addition, physiologically based pharmacokinetic model based analysis of the endogenous biomarkers can offer a middle out approach for the DDI prediction.
PBPK Modeling of Concomitant CYP3A Auto-induction and Time-dependent Inhibition of the Pharmacokinetics of the CYP3A Substrate Aprepitant | Dr. Tamara Cabalu
Recorded 04/25/2022
Recorded 04/25/2022
Aprepitant is a reversible and time-dependent inhibitor of CYP3A4, as well as an inducer of CYP3A4 in vitro. The compound is also a CYP3A4 substrate and the effects of autoinhibition and subsequent autoinduction are apparent in its multiple dose clinical PK profile. PBPK modeling reasonably predicted the increased exposure of aprepitant on day 7 relative to day 1, however, it was unable to capture the decrease in aprepitant exposure on day 28 or 56. Likewise, the clinical DDI with midazolam was predicted well on day 7, but was overpredicted on day 56 (using CYP3A4 kdeg value of 0.019 h−1), likely due to the inability of the model to simulate the observed aprepitant exposure on day 56. In addition to aprepitant, other examples will be presented for which PBPK modeling resulted in an over prediction of the midazolam DDI, suggesting the need to better understand system components and compound specific parameters to improve predictions with mixed inducers and time-dependent inhibitors.
Predictive in vitro in vivo Extrapolation for Time Dependent Inhibition Of CYP1A2, CYP2C8, CYP2C9, CYP2C19 AND CYP2D6 Using Pooled Human Hepatocytes and a Simple Mechanistic Static Model | Dr. Diane Ramsden
Recorded 04/25/2022
Recorded 04/25/2022
Background: As part of drug development it is necessary to characterize the likelihood for a new chemical entity to perpetrate drug-drug interactions and as such regulatory guidance documents recommend methods for modeling in vitro data to conduct clinical risk assessment. Inactivation of CYP450 enzymes can lead to significant and time-dependent increases in exposure of co-medicants. Human liver microsomes (HLM) are the conventional test system for evaluation of CYP450 inactivation (1); however they tend to over-predict clinical drug-drug interactions (DDIs) (2). Improvements in the predictive performance could be realized by using hepatocytes which include active transporters which may act to modulate intracellular concentrations. Use of hepatocytes may also minimize the potential for false-negatives from non-CYP pathways. The majority of available in vitro to in vivo extrapolation (IVIVE) data are focused on CYP3A4 and the translation to other CYP enzymes is unclear. To this end we sought to investigate the utility of human hepatocytes to predict the potential for clinically relevant DDIs with a focus on CYP1A2, CYP2C8, CYP2C9, CYP2C19 and CYP2D6. Since there was only weak clinical inhibition of CYP2B6 reported, evaluation of IVIVE for CYP2B6 was not conducted. Methods: A search of the University of Washington Drug-Drug Interaction Database identified clinically relevant weak, moderate and strong time-dependent inhibitors for selective substrates of CYP1A2, CYP2C8, CYP2C9, CYP2C19 and CYP2D6. In total, 115 clinical studies and 15 weak, moderate and strong clinical CYP inhibitors were identified for IVIVE evaluation. Pooled human hepatocytes from three donors were pre-incubated with increasing concentrations of inhibitors for designated timepoints. After each preincubation time, saturating substrate concentrations were added and formation of metabolite was determined by LC-MS/MS. Metabolite formation data were used to determine the first order inactivation rate constant (kobs), which was subsequently fit to non-linear regression models in GraphPad Prism (v. 8) to determine the kinetic parameters kinact and KI. Clinical risk assessment was conducted by incorporating the in vitro derived kinetic parameters into the recommended regulatory equations and mechanistic models. Results: Of the 15 selected inhibitors, 14 showed time-dependent inhibition towards CYP1A2, 2C8, C9, C19 or 2D6 and kinetic parameters could be confidently determined for 13. IVIVE evaluation resulted in no false negatives, however significant overprediction was observed when applying the basic models recommended by regulatory agencies. Mechanistic models, which consider the fraction of metabolism through the impacted enzyme, vastly improved the overall prediction accuracy. The best predictions were achieved when using the unbound portal blood concentration and compound dependent input parameters for Fa, Fg, Ka and RB, where 89.7% of studies predicted within 2-fold and 40% within bioequivalence. When compound specific parameters are unknown and default values of 0.03 min-1, 0.55 and 1 are used for Ka, RB and Fa:FG, respectively, 76.7% were predicted within 2-fold and 45% within bioequivalence. Conclusion: Collectively, the data demonstrate that coupling time-dependent inactivation parameters derived from pooled human hepatocytes with a mechanistic static model provides an easy and quantitatively accurate means to determine clinical DDI risk from in vitro data.
References
(1) Grimm SW, Einolf HJ, Hall SD, He K, Lim HK, Ling KH, et al. The conduct of in vitro studies to address time-dependent inhibition of drug-metabolizing enzymes: a perspective of the pharmaceutical research and manufacturers of America. Drug Metab Dispos 2009 Jul;37(7):1355-70.
(2) Chen Y, Liu L, Monshouwer M, Fretland AJ. Determination of Time-dependent Inactivation of CYP3A4 in Cryopreserved Human Hepatocyte and Assessment of Human Drug-Drug Interactions. Drug Metab Dispos 2011 Aug 11.
Approaches and Recommendations by the IQ Induction Working Group for the in vitro Conduct and Analysis of Cytochrome P450 (CYP) Induction for the Assessment of Human Drug-drug Interaction | Dr. Niresh Hariparsad
Recorded 04/25/2022
Recorded 04/25/2022
Enzyme induction can lead to decreased systemic exposure of co-administered drugs metabolized by the induced enzyme and can result in increased formation of active or toxic metabolites that change the pharmacologic and toxicologic outcomes in the induced state compared with the noninduced state. Hepatocytes contain the full complement of transcription factors, metabolic enzymes, and transporters, as well as coactivators and corepressors, and are now recognized as the most relevant and practical in vitro model for induction studies. Therefore, the use of plated human hepatocytes is considered the “gold standard” in vitro assay for induction-risk assessment. During this presentation key findings from the Translational and ADME Sciences Leadership Induction Working Group (IWG) will be reviewed. The mission of the IWG was to develop a better understanding of the overall regulatory guidelines on induction-based DDIs and provide data driven recommendations to conducting clinical risk assessment for in vitro induction. The IWG has published recommendations related to CYP downregulation, in vitro assessment of CYP2C and CYP2B6 induction, as well as time course and model fitting of CYP induction data. In addition, the IWG provided an extensive analysis of CYP3A4 induction response, thresholds, and variability and has made key recommendations related to the number of donors, criteria for characterizing positive and negative in vitro induction, including the 2-fold cutoff, the value of negative controls, and indexing response to prototypical inducers. Furthermore, the IWG sought to identify contributors to variable outcomes in clinical DDI induction data and methods for understanding how these factors impact characterization of induction.