An increased appreciation of the importance of transporter and enzyme interplay in drug clearance and a desire to delineate these mechanisms necessitates the utilization of models that contain a full complement of enzymes and transporters at physiologically relevant activities. Additionally, the development of drugs with longer half-lives requires in vitro systems with extended incubation times that allow characterization of metabolic pathways for low-clearance drugs. A recently developed coculture hepatocyte model, HepatoPac, has been applied to meet these challenges. Faldaprevir is a drug in late-stage development for the treatment of hepatitis C. Faldaprevir is a low-clearance drug with the somewhat unique characteristic of being slowly metabolized, producing two abundant hydroxylated metabolites (M2a and M2b) in feces (∼40% of the dose) without exhibiting significant levels of circulating metabolites in humans. The human HepatoPac model was investigated to characterize the metabolism and transport of faldaprevir. In human HepatoPac cultures, M2a and M2b were the predominant metabolites formed, with extents of formation comparable to in vivo. Direct glucuronidation of faldaprevir was shown to be a minor metabolic pathway. HepatoPac studies also demonstrated that faldaprevir is concentrated in liver with active uptake by multiple transporters (including OATP1B1 and Na(+)-dependent transporters). Overall, human HepatoPac cultures provided valuable insights into the metabolism and disposition of faldaprevir in humans and demonstrated the importance of enzyme and transporter interplay in the clearance of the drug.

Accurate determination of rates of de novo synthesis and degradation of cytochromes P450 (P450) has been challenging. There is a high degree of variability in the multiple published values of turnover for specific P450s which is likely exacerbated by differences in methodologies. For CYP3A4, half-life values range from 10 to 140h (Yang et al., 2008). An accurate value for k_deg has been identified as a major limitation for prediction of drug interactions involving mechanism based inhibition and/or induction. Estimation of P450 half-life from in vitro test systems, such as human hepatocytes, is complicated by differential decreased enzyme function over culture time, attenuation of the impact of enzyme loss through inclusion of glucocorticoids in media, and viability limitations over long-term culture times. HepatoPac overcomes some of these challenges providing an extended stability of enzymes (2.5 weeks in our hands). As such it is a unique tool for studying rates of enzyme degradation achieved through modulation of enzyme levels. CYP3A4 mRNA levels were rapidly depleted by >90% using either siRNA or addition of IL6 which allowed an estimation of the degradation rate constant for CYP3A protein over an incubation time of 96 hours. The degradation rate constant of 0.0240 ± 0.005 h^-1 was reproducible in hepatocytes from five different human donors. These donors also reflected the overall population with respect to CYP3A5 genotype. This methodology can be applied to additional enzymes and may provide a more accurate in vitro derived kdeg value for predicting clinical DDI outcomes.

Drug–drug interactions (DDIs) due to CYP2B6 induction have recently gained prominence and clinical induction risk assessment is recommended by regulatory agencies. This work aimed to evaluate the potency of CYP2B6 versus CYP3A4 induction in vitro and from clinical studies and to assess the predictability of efavirenz versus bupropion as clinical probe substrates of CYP2B6 induction. The analysis indicates that the magnitude of CYP3A4 induction was higher than CYP2B6 both in vitro and in vivo. The magnitude of DDIs caused by induction could not be predicted for bupropion with static or dynamic models. On the other hand, the relative induction score, net effect, and physiologically based pharmacokinetics SimCYP models using efavirenz resulted in improved DDI predictions. Although bupropion and efavirenz have been used and are recommended by regulatory agencies as clinical CYP2B6 probe substrates for DDI studies, CYP3A4 contributes to the metabolism of both probes and is induced by all reference CYP2B6 inducers. Therefore, caution must be taken when interpreting clinical induction results because of the lack of selectivity of these probes. Although in vitro–in vivo extrapolation for efavirenz performed better than bupropion, interpretation of the clinical change in exposure is confounded by the coinduction of CYP2B6 and CYP3A4, as well as the increased contribution of CYP3A4 to efavirenz metabolism under induced conditions. Current methods and probe substrates preclude accurate prediction of CYP2B6 induction. Identification of a sensitive and selective clinical substrate for CYP2B6 (fraction metabolized > 0.9) is needed to improve in vitro–in vivo extrapolation for characterizing the potential for CYP2B6-mediated DDIs. Alternative strategies and a framework for evaluating the CYP2B6 induction risk are proposed.

The European Medicines Agency (EMA), the Pharmaceutical and Medical Devices Agency and the Food and Drug Administration have issued guidances for the conduct of drug-drug interaction (DDI) studies. To examine the applicability of these regulatory recommendations specifically for induction, a group of scientists, under the auspices of the Drug Metabolism Leadership Group of the Innovation and Quality (IQ) Consortium, formed the Induction Working Group (WG). A team of 20 scientists, from 16 of the 39 pharmaceutical companies, which are members of the IQ Consortium, and three Contract Research Organizations, reviewed the recommendations, focusing initially on the current EMA guidance. Questions were collated from IQ member companies as to which aspects of the guidance required further evaluation. The EMA was then approached to provide insights into their recommendations on the following points; a) evaluation of down-regulation, b) in vitro assessment of CYP2C induction, c) the recommendation to use CITCO as the positive control for CYP2B6 induction, d) data interpretation (two-fold increase in mRNA as evidence of induction), and e) duration of incubation of hepatocytes with test article. The Induction WG conducted an anonymous survey among IQ member companies to query current practices, specifically focusing on how the aforementioned key points are evaluated. Responses were received from 19 companies. All data/information was blinded prior to being shared with the Induction WG. The results of the survey are presented together with consensus recommendations on down-regulation, CYP2C induction and CYP2B6 positive control. Data interpretation and incubation duration will be reported in subsequent manuscripts.

Small interfering RNAs (siRNAs) represent a new class of medicines that are smaller (~16,000 Da) than biological therapeutics (>150,000 Da) but much larger than small molecules (3000-fold above the therapeutic clinical C_max,ss and importantly no clinical inhibition was projected. Of four GalNAc siRNAs tested none were substrates for transporters and one caused inhibition of P-gp, calculated not to be clinically relevant. Pharmacological basis for DDI, including consideration of the target and/or off-target profile for GalNAc-siRNAs should be made as part of the overall DDI risk assessment. If modulation of the target protein doesn9t interfere with CYPs or transporters, then in vitro or clinical investigations into the DDI potential of the GalNAc-siRNAs are not warranted. SIGNIFICANCE STATEMENT Recommendations for evaluating DDI potential of small molecule drugs are well established, however guidance for novel modalities, particularly oligonucleotide-based therapeutics are lacking. Given the paucity of published data in this field in vitro DDI investigations are often conducted. The aggregate analysis of GalNAc-siRNA data reviewed herein demonstrates that like new biological entities (NBEs), these oligonucleotide-based therapeutics are unlikely to result in DDI, and therefore it is recommended that the need for in vitro or clinical investigations similarly be determined on a case-by-case basis. Given the mechanism of siRNA action, special consideration should be made in cases where there may be a pharmacological basis for DDI.

A recent publication from the IQ induction working group collated a large clinical dataset with the goal to evaluate the accuracy of drug-drug interaction prediction from in vitro data. Somewhat surprisingly, there was appreciable variability in the magnitude of outcome when mean or median reported AUCR were compared across studies. This commentary explores possible drivers of this range of outcomes observed in clinical induction studies. While recommendations on clinical study design are not being proposed, some key observations were informative during the aggregate analysis of clinical data. Although DDI data are often presented using median data, individual data would enable evaluation of how differences in study design, baseline expression and number of subjects contribute. Since variability in perpetrator PK could impact the overall DDI interpretation, should this be routinely captured? Maximal induction was typically observed after 5 to 7 days of dosing. Thus, when the half-life of the inducer is less than 30 hours, are there benefits to a more standardized study design? A large proportion of CYP3A4 inducers were also CYP3A4 inhibitors/inactivators, based on in vitro data. In these cases, using CYP3A selective substrates has limitations. More intensive monitoring of changes in AUC over time is warranted. With selective CYP3A substrates the net effect was often inhibition whereas less selective substrates could discern induction through mechanisms not susceptible to inhibition. The latter included oral contraceptives which raise concerns of reduced efficacy following induction. Alternative approaches for modeling induction such as applying biomarkers and PBPK are also considered. SIGNIFICANCE STATEMENT The goal of this commentary is to stimulate discussion on whether there are opportunities to optimize clinical drug-drug interaction study design. The overall aim is to reduce, understand and contextualize the variability observed in the magnitude of induction across reported clinical studies. A large clinical CYP3A induction dataset was collected and further analyzed to identify trends and gaps. Reporting individual victim PK data, characterizing perpetrator PK and including additional PK assessments for mixed mechanism perpetrators may provide insights into how these factors impact differences observed in clinical outcomes. The potential utility of biomarkers and PBPK modeling are discussed in considering future directions.