The use of mechanistic static and dynamic physiologically based pharmacokinetic (PBPK) models by incorporating CYP3A4/5-mediated time-dependent inhibition (TDI) parameters from human liver microsomes (HLM) can potentially give rise to significant overprediction of drug-drug interactions (DDI) caused by TDI, which may result in conducting unnecessary clinical DDI trials. This work aimed to evaluate the predictive performance of mechanistic static and dynamic PBPK models employed to predict the likelihood and the magnitude of clinical DDI caused by drugs with in vitro CYP3A4/5 TDI parameters measured in HLM and human hepatocytes (HHEPs). We examined the effect of differences in in vitro CYP3A4/5 TDI parameters such as the inhibition constant (total or unbound) in experimental conditions (supplementation of glutathione in HLM incubations or plasma in HHEP incubations) on the magnitude of predicted DDI risk in comparison to clinical results. In mechanistic static models, the average unbound organ exit concentrations and the maximum organ entry concentrations were compared for projecting DDI risks. Model performance was assessed using false-negative rates and negative predictive errors for a cutoff value of either 1.25- or 2-fold change in midazolam exposure. DDI caused by CYP3A4/5-mediated TDI was reliably predicted using mechanistic static model with average unbound organ exit concentrations or dynamic PBPK modeling, yielding less marked overpredictions of DDI. Models using in vitro inhibition constant corrected for incubation unbound fraction generated in either HLM or HHEP buffer incubations showed best statistical performance while maintaining high prediction accuracy and precision. SIGNIFICANCE STATEMENT: CYP3A4/5 time-dependent inhibition can lead to drug-drug interactions. CYP3A4 time-dependent inhibition parameters for 15 drugs, known as in vitro time-dependent inhibitors, were measured using various experimental conditions. These data were used in mechanistic static and physiologically based pharmacokinetic models to predict drug-drug interactions and identify false positives from in vitro experiments.

Midostaurin and its active metabolites are substrates, mixed inhibitors/inducers of cytochrome P450 (CYP)3A4. The main objective of this study was to develop/refine a physiologically based pharmacokinetic (PBPK) model that incorporated recent clinical drug-drug interaction (DDI) data with midazolam after multiple dosing, to qualify the pharmacokinetic (PK) model simulations of midostaurin and its metabolites, and to apply it to predict untested clinical DDI scenarios with potential comedications. In this study, Simcyp PBPK model of midostaurin and its 2 metabolites was refined from a previously published model associated with endogenous biomarker 4β-hydroxycholesterol data through further optimization of CYP3A4 inhibition/induction potency and was qualified to simulate midostaurin steady-state PK. The incorporation of these parameters enabled DDI predictions of high midostaurin doses on the PK of midazolam and oral contraceptives containing ethinyl estradiol. Additionally, scaling factors for in vitro breast cancer resistance protein and the organic anion transporting polypeptide (OATP1B) inhibition were applied to account for the observed single-dose DDI with rosuvastatin and further extrapolated to predict steady-state DDI with other OATP1B drug substrates. The overall prediction results showed minimal impact of midostaurin at high doses on CYP3A substrates or an effect on the exposure of OATP1B substrates. In summary, the midostaurin PBPK model was retrospectively refined, requalified, and used to simulate the steady-state perpetrator DDI of midostaurin and its metabolites. This PBPK modeling approach and the resulting model predictions were implemented into the midostaurin product label (up to 100 mg twice a day) without the need for confirmatory clinical studies. SIGNIFICANCE STATEMENT: The manuscript describes how a midostaurin PBPK model was updated, verified, and applied to untested scenarios by a predict-learn-confirm cycle as new clinical data become available. It also provides a learning experience of prospective prediction by utilizing endogenous biomarker 4β-hydroxycholesterol to evaluate a complex CYP3A4-mediated drug interaction.

Background: Asciminib (Scemblix®) is approved for the first-line treatment of adult patients with chronic myeloid leukemia in the chronic phase at 40 mg twice daily (BID) and 80 mg once daily (QD) or 200 mg BID for patients harboring the T315I mutation. Objectives: (1) Extrapolate the DDI magnitude as the perpetrator or victim of other drugs and the effect of organ impairment to untested doses; (2) Predict clinically untested DDI scenarios. Methods: Asciminib is primarily cleared by cytochrome P450 (CYP)3A4, UDP-glucuronosyltransferases (UGT)2B7, UGT2B17, UGT1A3/4, and the breast-cancer-resistance protein (BCRP). In vitro asciminib is an inhibitor of several CYP, UGT enzymes, and transporters and is an inducer of CYP1A2 and CYP3A4. Clinical DDI studies assessed asciminib 40 mg BID as a perpetrator on CYP-sensitive substrates. Additional studies evaluated the impact of strong CYP3A4 perpetrators and imatinib on a single 40 mg dose of asciminib. Hepatic and renal impairment studies were also conducted at the 40 mg dose. A nonlinear whole-body physiologically based pharmacokinetic (PBPK) model was developed and verified for asciminib as a CYP3A4, UGT, and BCRP substrate and a perpetrator of several CYP and UGT enzymes. Results: This PBPK model was applied in lieu of clinical pharmacology studies to support the new drug application of Scemblix® and to bridge data from 40 mg BID to the 80 mg QD and 200 mg BID dose regimens. Conclusions: The PBPK predictions informed the drug product label and are estimated to have replaced at least 10 clinical studies.

The ICH M12 Guideline on Drug Interaction Studies is the result of a harmonization process led by global regulatory and industry experts with experience in drug-drug interaction (DDI) assessments and interpretation. The Expert Working Group (EWG) built on areas of regional consensus and identified solutions to topics lacking initial consensus. This article describes the topics addressed in the guideline, with emphasis on areas that required extensive discussion. It mentions topics that were the subject of comments during the public consultation period. The scope of the guideline is pharmacokinetic DDIs mediated by metabolic enzymes and drug transporters. It describes in vitro and clinical DDI studies and predictive modeling evaluations conducted during drug development. The understanding of DDI liability, in the context of the intended patient population, guides the development of risk management strategies. In the in vitro area, this article describes the considerations that support the use of experimentally measured fraction unbound for drugs with > 99% protein binding, modification of several in vitro criteria used to recommend a clinical DDI study and modification of DDI assessment for metabolites. Areas of close attention by the EWG for clinical evaluation included the use of endogenous biomarker studies, the use of nested DDI studies, and the establishment of no-effect boundaries. The article indicates the value of describing a general process for evaluating UGT-mediated DDIs, although specific criteria are not available. The guideline describes the current understanding of the role of predictive modeling in DDI evaluation. The topics described in this article can stimulate further growth in the science of DDI assessments.

Evidence-based recommendations for malaria treatment in patients weighing < 5 kg are lacking as a consequence of differences in pharmacokinetics due to age and/or body weight (BW), and recruitment challenges in conducting trials in this population. A physiologically based pharmacokinetic (PBPK) model was developed and validated to predict artemether and lumefantrine concentrations in patients < 5 kg BW aged 1–28 days. The model predictions supplemented data from a trial (CALINA; NCT04300309) with an optimized dose of artemether–lumefantrine (5 mg artemether: 60 mg lumefantrine) in patients < 5 kg with Plasmodium falciparum malaria. PBPK models of artemether and lumefantrine were developed using Simcyp (Version 22) and validation was performed using historical data from adults and paediatric patients. To compare model-predicted and observed values, populations were matched to clinical trial populations (ranging from adults to infants) for patient numbers and demographics. The models were applied to predict artemether maximal concentration (Cmax) and lumefantrine Cmax and Day 7 concentration (C168h) in neonates of < 5 kg BW aged 1–28 days, and for subgroups aged 1–7, 8–14, and 15–28 days. Validated models for artemether and lumefantrine were used to predict plasma concentrations in neonates and young infants with BW < 5 kg after 3-day administration of 5 mg artemether and 60 mg lumefantrine twice daily with high confidence. The PBPK model using Upreti hepatic cytochrome P450 (CYP)3A4 ontogeny predicted observed artemether and lumefantrine exposure in infants and neonates better than Salem ontogeny. The predicted variability in neonates was comparable to or larger than the variability of observed concentrations in infants and older neonates in the CALINA study. Based on the success of the PBPK models for artemether and lumefantrine in predicting drug concentrations in adults and children, including neonates, modelling and simulation results can be used with confidence to supplement the limited available data for neonates (1–28 days old) < 5 kg BW obtained from the CALINA study for this rarer and more difficult to recruit patient population.

Introduction: Asciminib, a first-in-class BCR::ABL1 inhibitor that works by specifically targeting the ABL myristoyl pocket (STAMP), has been recently approved by the FDA for adult patients with Philadelphia chromosome-positive chronic myeloid leukemia in chronic phase (Ph+ CML-CP) treated with ≥2 prior tyrosine kinase inhibitors (TKIs), as well as adult patients with Ph+ CML-CP with the T315I mutation. The recommended asciminib doses for patients with Ph+ CML-CP without the T315I mutation are 40 mg twice daily (BID), which is the dose tested in the pivotal phase III study (ASCEMBL), and a more patient-centric dose of 80 mg once daily (QD). For patients with the T315I mutation, the approved dose of asciminib is 200 mg BID. All asciminib drug-drug interaction (DDI) and hepatic and renal impairment studies were conducted at single or multiple doses of 40 mg. Here, we aim to assess the clinical impact of hepatic and renal impairment and of DDIs on the pharmacokinetics (PK) of asciminib and provide recommendations for eventual necessary dose adjustments. Methods: Organ impairment effect was quantified using population pharmacokinetics (PopPK), while DDIs were predicted using physiologically-based pharmacokinetics (PBPK). A 2-compartment PopPK model with a delayed first-order absorption and first-order elimination was developed for asciminib (Li et al. Clin Pharmacokinet, 2022). The original analysis included pooled PK data from patients receiving asciminib at 10−200 mg BID (in the phase I dose-finding study, NCT02081378) or 40 mg BID (in the phase III study ASCEMBL, NCT03106779), and identified baseline absolute glomerular filtration rate (aGFR), nominal total daily dose, body weight and formulation as significant covariates affecting asciminib PK. The model has been further extended to describe patients with renal (based on aGFR) or hepatic (based on Child-Pugh class) impairment, using data from two phase I PK studies in patients with renal (NCT03605277) or hepatic (NCT02857868) impairment. A PBPK model was developed and validated for asciminib using SimCYP v19.1. A stepwise "middle out” approach was used leveraging in vitro, in silico and in vivo data. The PBPK model was validated using single and multiple dose PK at 20‒400 mg daily dose as well as clinical DDI and PK data in patients with organ impairment. Comprehensive DDI scenarios and PK in patients with hepatic and renal impairment, which had not been clinically tested, were simulated at the different recommended doses of asciminib (40 mg BID, 80 mg QD, 200 mg BID). Results: There was no significant impact of hepatic function on asciminib exposure; a non-significant trend to lower clearance was observed in patients with severe hepatic impairment. The PopPK derived steady-state exposures (measured as area under the curve [AUC]0-24h) for patients with severe hepatic impairment were almost identical for both dosing regimens (40 mg BID: 13,018 ng*h/mL, 80 mg QD: 13,020 ng*h/mL), and only marginally higher than those of patients with normal hepatic function (12,638 ng*h/mL and 12,646 ng*h/mL, respectively), well within the therapeutic window of asciminib. Renal function assessment showed that clearance was slightly decreased in patients with renal impairment, although this was not clinically relevant. For both 80 mg QD and 40 mg BID, the median predicted AUC0-24h and maximum plasma concentration (Cmax) for severe renal impairment were 60% and 40% higher, respectively, than those of a typical individual with normal renal function. However, this difference is not considered to be clinically relevant given the relatively flat exposure-safety relationship over a 5-fold difference in exposure. The PBPK model predicted no drug interaction that would significantly impact the PK of asciminib (as a victim) or of other substrates (as a perpetrator). Overall, asciminib exposure was weakly affected by interactions with inducers and inhibitors as a victim (Figure 1B) and was predicted to be a weak inhibitor of CYP3A4 and P-gp at all tested doses and a weak to moderate inhibitor of CYP2C9 as a perpetrator (Figure 1A). Conclusions: Hepatic or renal impairment does not have a clinically significant impact on the PK of asciminib. Hence, dose adaptations for patients with hepatic or renal impairment are not warranted. Overall, asciminib has a manageable DDI risk at all recommended doses. Sponsor: Novartis. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Dofetilide is a rapid delayed rectifier potassium current inhibitor widely used to prevent the recurrence of atrial fibrillation and flutter. The clinical use of this drug is associated with increases in QTc interval, which predispose patients to ventricular cardiac arrhythmias. The mechanisms involved in the disposition of dofetilide, including its movement in and out of cardiomyocytes, remain unknown. Using a xenobiotic transporter screen, we identified MATE1 (SLC47A1) as a transporter of dofetilide and found that genetic knockout or pharmacological inhibition of MATE1 in mice was associated with enhanced retention of dofetilide in cardiomyocytes and increased QTc prolongation. The urinary excretion of dofetilide was also dependent on the MATE1 genotype, and we found that this transport mechanism provides a mechanistic basis for previously recorded drug-drug interactions of dofetilide with various contraindicated drugs, including bictegravir, cimetidine, ketoconazole, and verapamil. The translational significance of these observations was examined with a physiologically-based pharmacokinetic model that adequately predicted the drug-drug interaction liabilities in humans. These findings support the thesis that MATE1 serves a conserved cardioprotective role by restricting excessive cellular accumulation and warrant caution against the concurrent administration of potent MATE1 inhibitors and cardiotoxic substrates with a narrow therapeutic window.

Tropifexor (NVP-LJN452) is a highly potent, selective, nonsteroidal, non-bile acid farnesoid X receptor agonist for the treatment of nonalcoholic steatohepatitis. Its absorption, metabolism, and excretion were studied after a 1-mg oral dose of [14C]tropifexor was given to four healthy male subjects. Mass balance was achieved with ∼94% of the administered dose recovered in excreta through a 312-hour collection period. Fecal excretion of tropifexor-related radioactivity played a major role (∼65% of the total dose). Tropifexor reached a maximum blood concentration (Cmax) of 33.5 ng/ml with a median time to reach Cmax of 4 hours and was eliminated with a plasma elimination half-life of 13.5 hours. Unchanged tropifexor was the principal drug-related component found in plasma (∼92% of total radioactivity). Two minor oxidative metabolites, M11.6 and M22.4, were observed in circulation. Tropifexor was eliminated predominantly via metabolism with >68% of the dose recovered as metabolites in excreta. Oxidative metabolism appeared to be the major clearance pathway of tropifexor. Metabolites containing multiple oxidative modifications and combined oxidation and glucuronidation were also observed in human excreta. The involvement of direct glucuronidation could not be ruled out based on previous in vitro and nonclinical in vivo studies indicating its contribution to tropifexor clearance. The relative contribution of the oxidation and glucuronidation pathways appeared to be dose-dependent upon further in vitro investigation. Because of these complexities and the instability of glucuronide metabolites in the gastrointestinal tract, the contribution of glucuronidation remained undefined in this study. SIGNIFICANCE STATEMENT: Tropifexor was found to be primarily cleared from the human body via oxidative metabolism. In vitro metabolism experiments revealed that the relative contribution of oxidation and glucuronidation was concentration-dependent, with glucuronidation as the predominant pathway at higher concentrations and the oxidative process becoming more important at lower concentrations near clinical exposure range. The body of work demonstrated the importance of carefully designed in vivo and in vitro experiments for better understanding of disposition processes during drug development.

Translational and ADME Sciences Leadership Group Induction Working Group (IWG) presents an analysis on the time course for cytochrome P450 induction in primary human hepatocytes. Induction of CYP1A2, CYP2B6, and CYP3A4 was evaluated by seven IWG laboratories after incubation with prototypical inducers (omeprazole, phenobarbital, rifampicin, or efavirenz) for 6-72 hours. The effect of incubation duration and model-fitting approaches on induction parameters (Emax and EC50) and drug-drug interaction (DDI) risk assessment was determined. Despite variability in induction response across hepatocyte donors, the following recommendations are proposed: 1) 48 hours should be the primary time point for in vitro assessment of induction based on mRNA level or activity, with no further benefit from 72 hours; 2) when using mRNA, 24-hour incubations provide reliable assessment of induction and DDI risk; 3) if validated using prototypical inducers (>10-fold induction), 12-hour incubations may provide an estimate of induction potential, including characterization as negative if <2-fold induction of mRNA and no concentration dependence; 4) atypical dose-response ("bell-shaped") curves can be addressed by removing points outside an established confidence interval and %CV; 5) when maximum fold induction is well defined, the choice of nonlinear regression model has limited impact on estimated induction parameters; 6) when the maximum fold induction is not well defined, conservative DDI risk assessment can be obtained using sigmoidal three-parameter fit or constraining logistic three- or four-parameter fits to the maximum observed fold induction; 7) preliminary data suggest initial slope of the fold induction curve can be used to estimate Emax/EC50 and for induction risk assessment. SIGNIFICANCE STATEMENT: Regulatory agencies provide inconsistent guidance on the optimum length of time to evaluate cytochrome P450 induction in human hepatocytes, with EMA recommending 72 hours and FDA suggesting 48-72 hours. The Induction Working Group analyzed a large data set generated by seven member companies and determined that induction response and drug-drug risk assessment determined after 48-hour incubations were representative of 72-hour incubations. Additional recommendations are provided on model-fitting techniques for induction parameter estimation and addressing atypical concentration-response curves.

Asciminib is a potent, specific BCR-ABL1 inhibitor being developed for the treatment of patients with chronic myelogenous leukemia (CML) and Philadelphia chromosome positive acute lymphoblastic leukemia (Ph + ALL). Here, we present the results of human oral absorption, distribution, metabolism, excretion (ADME) and in vitro studies that together provide an overall understanding of the metabolism, distribution and clearance of asciminib in humans. Asciminib was rapidly absorbed with a maximum plasma concentration at two hours post-dose. Total radioactivity and asciminib showed similar terminal half-lives in plasma. Oral asciminib absorption ranged between a minimum of 33%, and a maximum of 57% based on the metabolite profiles of late time-point feces collections. Asciminib was eliminated mainly through feces via unchanged asciminib excretion and metabolism. Direct glucuronidation and oxidation were major metabolic pathways in human that were catalyzed predominantly by UDP-glucuronosyltransferase (UGT)2B7 and cytochrome P450 (CYP)3A4, respectively. The relative contribution of the glucuronidation pathway to the total clearance of asciminib via metabolism is estimated to range similar to 28-58%, whereas the relative contribution of the oxidative pathway is estimated to range similar to 37-64%, based upon the maximum oral absorption in humans.

Everolimus is currently approved in Europe as an adjunctive therapy for patients aged ≥ 2 years with tuberous sclerosis complex (TSC)–associated treatment‐refractory partial‐onset seizures, based on the EXIST‐3 study (NCT01713946) results. As TSC‐associated seizures can also affect children aged between 6 months and 2 years, a modeling and simulation (M&S) approach was undertaken to extrapolate exposure (trough plasma concentration (Cmin)) after a dose of 6 mg/m2 and reduction in seizure frequency (RSF). A physiologically based pharmacokinetic model using Simcyp was developed to predict Cmin in adult and pediatric patients, which was then used by a population pharmacodynamic model and a linear mixed effect model to predict short‐term and long‐term efficacy in adults (for validation) and in children, respectively. Based on the results of the M&S study, everolimus at the dose of 6 mg/m2 is anticipated to be an efficacious treatment in children 6 months to 2 years of age (up to 77.8% RSF) with concentrations within the recommended target range.

Reliable approaches to predict clinical outcome during drug development are essential. Characterization of the potential of a drug to cause interactions relies initially on preclinical data, followed by clinical studies. Physiologically-based pharmacokinetic (PBPK) modeling and simulation (M&S) emerged as a powerful translational tool that aids in planning of clinical drug-drug interaction (DDI) studies and is often used to bridge gaps during regulatory filings. 1 Practical applications of PBPK in drug development will be discussed.

Regulatory agencies currently recommend itraconazole (ITZ) as a strong cytochrome P450 3A (CYP3A) inhibitor for clinical drug-drug interaction (DDI) studies. This work by an International Consortium for Innovation and Quality in Pharmaceutical Development working group (WG) is to develop and verify a mechanistic ITZ physiologically-based pharmacokinetic model and provide recommendations for optimal DDI study design based on model simulations. To support model development and verification, in vitro and clinical PK data for ITZ and its metabolites were collected from WG member companies. The model predictions of ITZ DDIs with seven different CYP3A substrates were within the guest criteria for 92% of area under the concentration-time curve ratios and 95% of maximum plasma concentration ratios, thus verifying the model for DDI predictions. The verified model was used to simulate various clinical DDI study scenarios considering formulation, duration of dosing, dose regimen, and food status to recommend the optimal design for maximal inhibitory effect by ITZ.

This work provides a perspective on the qualification and verification of physiologically based pharmacokinetic (PBPK) platforms/models intended for regulatory submission based on the collective experience of the Simcyp Consortium members. Examples of regulatory submission of PBPK analyses across various intended applications are presented and discussed. European Medicines Agency (EMA) and US Food and Drug Administration (FDA) recent draft guidelines regarding PBPK analyses and reporting are encouraging, and to advance the use and acceptability of PBPK analyses, more clarity and flexibility are warranted.

The 2016 World Health Organization treatment recommendations for drug-resistant tuberculosis (DR-TB) positioned clofazimine as a core second-line drug. Being identified as a cytochrome P450 (P450) inhibitor in vitro, a P450-mediated drug interaction may be likely when clofazimine is coadministered with substrates of these enzymes. The P450-mediated drug interaction potential of clofazimine was evaluated using both static [estimation of the R1 and area under the plasma concentration-time curve ratio (AUCR) values] and dynamic [physiologically based pharmacokinetics (PBPK)] modeling approaches. For static and dynamic predictions, midazolam, repaglinide, and desipramine were used as probe substrates for CYP3A4/5, CYP2C8, and CYP2D6, respectively. The AUCR static model estimations for clofazimine with the substrates midazolam, repaglinide, and desipramine were 5.59, 1.34, and 1.69, respectively. The fold increases in the area under the curve (AUC) predicted for midazolam, repaglinide, and desipramine with clofazimine (based on PBPK modeling) were 2.69, 1.60, and 1.47, respectively. Clofazimine was predicted to be a moderate-to-strong CYP3A4/5 inhibitor and weak CYP2C8 and CYP2D6 inhibitor based on the calculated AUCR by static and PBPK modeling. Additionally, for selected antiretroviral, antitubercular, antihypertensive, antidiabetic, antileprotics, and antihyperlipidemic CYP3A4/5 substrate drugs, approximately 2- to 6-fold increases in the AUC were predicted with static modeling when coadministered with 100 mg of clofazimine. Therefore, the possibility of an increase in the AUC of CYP3A4/5 substrates when coadministered with clofazimine cannot be ignored.

Midostaurin (PKC412) is being investigated for the treatment of acute myeloid leukemia (AML) and advanced systemic mastocytosis (advSM). It is extensively metabolized by CYP3A4 to form two major active metabolites, CGP52421 and CGP62221. In vitro and clinical drug-drug interaction (DDI) studies indicated that midostaurin and its metabolites are substrates, reversible and time-dependent inhibitors, and inducers of CYP3A4. A simultaneous pharmacokinetic model of parent and active metabolites was initially developed by incorporating data from in vitro, preclinical, and clinical pharmacokinetic studies in healthy volunteers and in patients with AML or advSM. The model reasonably predicted changes in midostaurin exposure after single-dose administration with ketoconazole (a 5.8-fold predicted versus 6.1-fold observed increase) and rifampicin (90% predicted versus 94% observed reduction) as well as changes in midazolam exposure (1.0 predicted versus 1.2 observed ratio) after daily dosing of midostaurin for 4 days. The qualified model was then applied to predict the DDI effect with other CYP3A4 inhibitors or inducers and the DDI potential with midazolam under steady-state conditions. The simulated midazolam area under the curve ratio of 0.54 and an accompanying observed 1.9-fold increase in the CYP3A4 activity of biomarker 4β-hydroxycholesterol indicated a weak-to-moderate CYP3A4 induction by midostaurin and its metabolites at steady state in patients with advSM. In conclusion, a simultaneous parent-and-active-metabolite modeling approach allowed predictions under steady-state conditions that were not possible to achieve in healthy subjects. Furthermore, endogenous biomarker data enabled evaluation of the net effect of midostaurin and its metabolites on CYP3A4 activity at steady state and increased confidence in DDI predictions.

The Innovation and Quality Induction Working Group presents an assessment of best practice for data interpretation of in vitro induction, specifically, response thresholds, variability, application of controls, and translation to clinical risk assessment with focus on CYP3A4 mRNA. Single concentration control data and Emax/EC50 data for prototypical CYP3A4 inducers were compiled from many human hepatocyte donors in different laboratories. Clinical CYP3A induction and in vitro data were gathered for 51 compounds, 16 of which were proprietary. A large degree of variability was observed in both the clinical and in vitro induction responses; however, analysis confirmed in vitro data are able to predict clinical induction risk. Following extensive examination of this large data set, the following recommendations are proposed. a) Cytochrome P450 induction should continue to be evaluated in three separate human donors in vitro. b) In light of empirically divergent responses in rifampicin control and most test inducers, normalization of data to percent positive control appears to be of limited benefit. c) With concentration dependence, 2-fold induction is an acceptable threshold for positive identification of in vitro CYP3A4 mRNA induction. d) To reduce the risk of false positives, in the absence of a concentration-dependent response, induction ≥ 2-fold should be observed in more than one donor to classify a compound as an in vitro inducer. e) If qualifying a compound as negative for CYP3A4 mRNA induction, the magnitude of maximal rifampicin response in that donor should be ≥ 10-fold. f) Inclusion of a negative control adds no value beyond that of the vehicle control.

The European Medicines Agency (EMA), the Pharmaceutical and Medical Devices Agency (PMDA), and the Food and Drug Administration (FDA) have issued guidelines for the conduct of drug-drug interaction 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 (IWG). A team of 19 scientists, from 16 of the 39 pharmaceutical companies that are members of the IQ Consortium and two Contract Research Organizations reviewed the recommendations, focusing initially on the current EMA guidelines. Questions were collated from IQ member companies as to which aspects of the guidelines require further evaluation. The EMA was then approached to provide insights into their recommendations on the following: 1) evaluation of down-regulation, 2) in vitro assessment of CYP2C induction, 3) the use of CITCO as the positive control for CYP2B6 induction by CAR, 4) data interpretation (a 2-fold increase inmRNAas evidence of induction), and 5) the duration of incubation of hepatocytes with test article. The IWG conducted an anonymous survey among IQ member companies to query current practices, focusing specifically on the aforementioned key points. Responses were received from 19 companies. All data and informationwere blinded before being shared with the IWG. The results of the survey are presented, together with consensus recommendations on downregulation, CYP2C induction, and CYP2B6 positive control. Results and recommendations related to data interpretation and induction time course will be reported in subsequent articles.