Considerations from the IQ Induction Working Group in Response to Drug-Drug Interaction Guidance from Regulatory Agencies: Focus on Downregulation, CYP2C Induction, and CYP2B6 Positive Control

Comprehensive Physiologically Based Pharmacokinetic Model to Assess Drug-Drug Interactions of Phenytoin

Regulatory agencies worldwide expect that clinical pharmacokinetic drug-drug interactions (DDIs) between an investigational new drug and other drugs should be conducted during drug development as part of an adequate assessment of the drug's safety and efficacy. However, it is neither time nor cost efficient to test all possible DDI scenarios clinically. Phenytoin is classified by the Food and Drug Administration as a strong clinical index inducer of CYP3A4, and a moderate sensitive substrate of CYP2C9. A physiologically based pharmacokinetic (PBPK) platform model was developed using GastroPlus® to assess DDIs with phenytoin acting as the victim (CYP2C9, CYP2C19) or perpetrator (CYP3A4). Pharmacokinetic data were obtained from 15 different studies in healthy subjects. The PBPK model of phenytoin explains the contribution of CYP2C9 and CYP2C19 to the formation of 5-(4'-hydroxyphenyl)-5-phenylhydantoin. Furthermore, it accurately recapitulated phenytoin exposure after single and multiple intravenous and oral doses/formulations ranging from 248 to 900 mg, the dose-dependent nonlinearity and the magnitude of the effect of food on phenytoin pharmacokinetics. Once developed and verified, the model was used to characterize and predict phenytoin DDIs with fluconazole, omeprazole and itraconazole, i.e., simulated/observed DDI AUC ratio ranging from 0.89 to 1.25. This study supports the utility of the PBPK approach in informing drug development.

Investigation of CYP3A induction by PF-05251749 in early clinical development: comparison of linear slope physiologically based pharmacokinetic prediction and biomarker response

PF-05251749 is a dual inhibitor of casein kinase 1 δ/ε under clinical development to treat disruption of circadian rhythm in Alzheimer's and Parkinson's diseases. In vitro, PF-05251749 (0.3-100 μM) induced CYP3A in cryopreserved human hepatocytes, demonstrating non-saturable, dose-dependent CYP3A mRNA increases, with induction slopes in the range 0.036-0.39 μM^-1 . In a multiple-dose study (B8001002) in healthy participants, CYP3A activity was explored by measuring changes in 4β-hydroxycholesterol/cholesterol ratio. Following repeated oral administration of PF-05251749, up to 400 mg q.d., no significant changes were observed in 4β-hydroxycholesterol/cholesterol ratio; this ratio increased significantly (~1.5-fold) following administration of PF-05251749 at 750 mg q.d., suggesting potential CYP3A induction at this dose. Physiologically based pharmacokinetic (PBPK) models were developed to characterize the observed clinical pharmacokinetics (PK) of PF-05251749 at 400 and 750 mg q.d.; the PBPK induction model was calibrated using the in vitro linear fit induction slope, with rifampin as reference compound (Ind_max  = 8, EC₅₀  = 0.32 μM). Clinical trial simulation following co-administration of PF-05251749, 400 mg q.d. with oral midazolam 2 mg, predicted no significant drug interaction risk. PBPK model predicted weak drug interaction following co-administration of PF-05251749, 750 mg q.d. with midazolam 2 mg. In conclusion, good agreement was obtained between CYP3A drug interaction risk predicted using linear-slope PBPK model and exploratory biomarker trends. This agreement between two orthogonal approaches enabled assessment of drug interaction risks of PF-05251749 in early clinical development, in the absence of a clinical drug-drug interaction study.

Isotretinoin and its Metabolites Alter mRNA of Multiple Enzyme and Transporter Genes In Vitro, but Downregulation of Organic Anion Transporting Polypeptide Does Not Translate to the Clinic

Isotretinoin [13-cis-retinoic acid (13cisRA)] is widely used for the treatment of neuroblastoma and acne. It acts via regulating gene transcription through binding to retinoic acid receptors. Yet, the potential for isotretinoin to cause transcriptionally mediated drug-drug interactions (DDIs) has not been fully explored. We hypothesized that isotretinoin and its active metabolites all-trans-retinoic acid (atRA) and 4-oxo-13cisRA would alter the transcription of enzymes and transporters in the human liver via binding to nuclear receptors. The goal of this study was to define the DDI potential of isotretinoin and its metabolites resulting from transcriptional regulation of cytochrome P450 and transporter mRNAs. In human hepatocytes (n = 3), 13cisRA, atRA, and 4-oxo-13cisRA decreased OATP1B1, CYP1A2, CYP2C9, and CYP2D6 mRNA and increased CYP2B6 and CYP3A4 mRNA in a concentration-dependent manner. The EC50 values for OATP1B1 mRNA downregulation ranged from 2 to 110 nM, with maximum effect (Emax ) ranging from 0.17- to 0.54-fold. Based on the EC50 and Emax values and the known circulating concentrations of 13cisRA and its metabolites after isotretinoin dosing, a 55% decrease in OATP1B1 activity was predicted in vivo. In vivo DDI potential was evaluated clinically in participants dosed with isotretinoin for up to 32 weeks using coproporphyrin-I (CP-I) as an OATP1B1 biomarker. CP-I steady-state serum concentrations were unaltered following 2, 8, or 16 weeks of isotretinoin treatment. These data show that isotretinoin and its metabolites alter transcription of multiple enzymes and transporters in vitro, but translation of these changes to in vivo drug-drug interactions requires clinical evaluation for each enzyme. SIGNIFICANCE STATEMENT: Isotretinoin and its metabolites alter the mRNA expression of multiple cytochrome P450s (CYPs) and transporters in human hepatocytes, suggesting that isotretinoin may cause clinically significant drug-drug interactions (DDIs). Despite the observed changes in organic anion transporting polypeptide 1B1 (OATP1B1) mRNA in human hepatocytes, no clinical DDI was observed when measuring a biomarker, coproporphyrin-I. Further work is needed to determine whether these findings can be extrapolated to a lack of a DDI with CYP1A2, CYP2B6, and CYP2C9 substrates.

Current Practices, Gap Analysis, and Proposed Workflows for PBPK Modeling of Cytochrome P450 Induction: An Industry Perspective

The International Consortium for Innovation and Quality (IQ) Physiologically Based Pharmacokinetic (PBPK) Modeling Induction Working Group (IWG) conducted a survey across participating companies around general strategies for PBPK modeling of induction, including experience with its utility to address various questions, regulatory interactions, and regulatory acceptance. The results highlight areas where PBPK modeling is used with high confidence and identifies opportunities where confidence is lower and further evaluation is needed. To enhance the survey results, the PBPK-IWG also collected case studies and analyzed recent literature examples where PBPK models were applied to predict CYP3A induction-mediated drug-drug interactions. PBPK modeling of induction has evolved and progressed significantly, proving to have great potential to accelerate drug discovery and development. With the aim of enabling optimal use for new molecular entities that are either substrates and/or inducers of CYP3A, the PBPK-IWG proposes initial workflows for PBPK application, discusses future trends, and identifies gaps that need to be addressed.

In vitro investigations into the roles of CYP450 enzymes and drug transporters in the drug interactions of zanubrutinib, a covalent Bruton's tyrosine kinase inhibitor

Zanubrutinib is a highly selective, potent, orally available, targeted covalent inhibitor (TCI) of Bruton's tyrosine kinase (BTK). This work investigated the in vitro drug metabolism and transport of zanubrutinib, and its potential for clinical drug-drug interactions (DDIs). Phenotyping studies indicated cytochrome P450 (CYP) 3A are the major CYP isoform responsible for zanubrutinib metabolism, which was confirmed by a clinical DDI study with itraconazole and rifampin. Zanubrutinib showed mild reversible inhibition with half maximal inhibitory concentration (IC50 ) of 4.03, 5.69, and 7.80 μM for CYP2C8, CYP2C9, and CYP2C19, respectively. Data in human hepatocytes disclosed induction potential for CYP3A4, CYP2B6, and CYP2C enzymes. Transport assays demonstrated that zanubrutinib is not a substrate of human breast cancer resistance protein (BCRP), organic anion transporting polypeptide (OATP)1B1/1B3, organic cation transporter (OCT)2, or organic anion transporter (OAT)1/3 but is a potential substrate of the efflux transporter P-glycoprotein (P-gp). Additionally, zanubrutinib is neither an inhibitor of P-gp at concentrations up to 10.0 μM nor an inhibitor of BCRP, OATP1B1, OATP1B3, OAT1, and OAT3 at concentrations up to 5.0 μM. The in vitro results with CYPs and transporters were correlated with the available clinical DDIs using basic models and mechanistic static models. Zanubrutinib is not likely to be involved in transporter-mediated DDIs. CYP3A inhibitors and inducers may impact systemic exposure of zanubrutinib. Dose adjustments may be warranted depending on the potency of CYP3A modulators.

Variation in Expression of Cytochrome P450 3A Isoforms and Toxicological Effects: Endo- and Exogenous Substances as Regulatory Factors and Substrates

The CYP3A subfamily, which includes isoforms CYP3A4, CYP3A5, and CYP3A7 in humans, plays important roles in the metabolism of various endogenous and exogenous substances. Gene and protein expression of CYP3A4, CYP3A5, and CYP3A7 show large inter-individual differences, which are caused by many endogenous and exogenous factors. Inter-individual differences can cause negative outcomes, such as adverse drug events and disease development. Therefore, it is important to understand the variations in CYP3A expression caused by endo- and exogenous factors, as well as the variation in the metabolism and kinetics of endo- and exogenous substrates. In this review, we summarize the factors regulating CYP3A expression, such as bile acids, hormones, microRNA, inflammatory cytokines, drugs, environmental chemicals, and dietary factors. In addition, variations in CYP3A expression under pathological conditions, such as coronavirus disease 2019 and liver diseases, are described as examples of the physiological effects of endogenous factors. We also summarize endogenous and exogenous substrates metabolized by CYP3A isoforms, such as cholesterol, bile acids, hormones, arachidonic acid, vitamin D, and drugs. The relationship between the changes in the kinetics of these substrates and the toxicological effects in our bodies are discussed. The usefulness of these substrates and metabolites as endogenous biomarkers for CYP3A activity is also discussed. Notably, we focused on discrimination between CYP3A4, CYP3A5, and CYP3A7 to understand inter-individual differences in CYP3A expression and function.

Utility of In Vitro Bioactivity as a Lower Bound Estimate of In Vivo Adverse Effect Levels and in Risk-Based Prioritization

Use of high-throughput, in vitro bioactivity data in setting a point-of-departure (POD) has the potential to accelerate the pace of human health safety evaluation by informing screening-level assessments. The primary objective of this work was to compare PODs based on high-throughput predictions of bioactivity, exposure predictions, and traditional hazard information for 448 chemicals. PODs derived from new approach methodologies (NAMs) were obtained for this comparison using the 50th (PODNAM, 50) and the 95th (PODNAM, 95) percentile credible interval estimates for the steady-state plasma concentration used in in vitro to in vivo extrapolation of administered equivalent doses. Of the 448 substances, 89% had a PODNAM, 95 that was less than the traditional POD (PODtraditional) value. For the 48 substances for which PODtraditional < PODNAM, 95, the PODNAM and PODtraditional were typically within a factor of 10 of each other, and there was an enrichment of chemical structural features associated with organophosphate and carbamate insecticides. When PODtraditional < PODNAM, 95, it did not appear to result from an enrichment of PODtraditional based on a particular study type (eg, developmental, reproductive, and chronic studies). Bioactivity:exposure ratios, useful for identification of substances with potential priority, demonstrated that high-throughput exposure predictions were greater than the PODNAM, 95 for 11 substances. When compared with threshold of toxicological concern (TTC) values, the PODNAM, 95 was greater than the corresponding TTC value 90% of the time. This work demonstrates the feasibility, and continuing challenges, of using in vitro bioactivity as a protective estimate of POD in screening-level assessments via a case study.

PBPK modeling to predict drug-drug interactions of ivosidenib as a perpetrator in cancer patients and qualification of the Simcyp platform for CYP3A4 induction

Ivosidenib is a potent, targeted, orally active, small-molecule inhibitor of mutant isocitrate dehydrogenase 1 (IDH1) that has been approved in the United States for the treatment of adults with newly diagnosed acute myeloid leukemia (AML) who are greater than or equal to 75 years of age or ineligible for intensive chemotherapy, and those with relapsed or refractory AML, with a susceptible IDH1 mutation. Ivosidenib is an inducer of the CYP2B6, CYP2C8, CYP2C9, and CYP3A4 and an inhibitor of P-glycoprotein (P-gp), organic anion transporting polypeptide-1B1/1B3 (OATP1B1/1B3), and organic anion transporter-3 (OAT3) in vitro. A physiologically-based pharmacokinetic (PK) model was developed to predict drug-drug interactions (DDIs) of ivosidenib in patients with AML. The in vivo CYP3A4 induction effect of ivosidenib was quantified using 4β-hydroxycholesterol and was subsequently verified with the PK data from an ivosidenib and venetoclax combination study. The verified model was prospectively applied to assess the effect of multiple doses of ivosidenib on a sensitive CYP3A4 substrate, midazolam. The simulated midazolam geometric mean area under the curve (AUC) and maximum plasma concentration (C_max ) ratios were 0.18 and 0.27, respectively, suggesting ivosidenib is a strong inducer. The model was also used to predict the DDIs of ivosidenib with CYP2B6, CYP2C8, CYP2C9, P-gp, OATP1B1/1B3, and OAT3 substrates. The AUC ratios following multiple doses of ivosidenib and a single dose of CYP2B6 (bupropion), CYP2C8 (repaglinide), CYP2C9 (warfarin), P-gp (digoxin), OATP1B1/1B3 (rosuvastatin), and OAT3 (methotrexate) substrates were 0.90, 0.52, 0.84, 1.01, 1.02, and 1.27, respectively. Finally, in accordance with regulatory guidelines, the Simcyp modeling platform was qualified to predict CYP3A4 induction using known inducers and sensitive substrates.

Glycolytic competence in gastric adenocarcinomas negatively impacts survival outcomes of patients treated with salvage paclitaxel-ramucirumab

INTRODUCTION

For energy production, cancer cells maintain a high rate of glycolysis instead of oxidative phosphorylation converting glucose into lactic acid. This metabolic shift is useful to survive in unfavorable microenvironments. We investigated whether a positive glycolytic profile (PGP) in gastric adenocarcinomas may be associated with unfavorable outcomes under an anticancer systemic therapy, including the anti-angiogenic ramucirumab.

MATERIALS AND METHODS

Normal mucosa (NM) and primary tumor (PT) of 40 metastatic gastric adenocarcinomas patients who received second-line paclitaxel-ramucirumab (PR) were analyzed for mRNA expression of the following genes: HK-1, HK-2, PKM-2, LDH-A, and GLUT-1. Patients were categorized with PGP when at least a doubling of mRNA expression (PT vs. NM) in all glycolytic core enzymes (HK-1 or HK-2, PKM-2, LDH-A) was observed. PGP was also related to TP53 mutational status.

RESULTS

Mean LDH-A, HK-2, PKM-2 mRNA expression levels were significantly higher in PT compared with NM. 18 patients were classified as PGP, which was associated with significantly worse progression-free and overall survival times. No significant association was observed between PGP and clinical-pathologic features, including TP53 positive mutational status, in 28 samples.

CONCLUSIONS

Glycolytic proficiency may negatively affect survival outcomes of metastatic gastric cancer patients treated with PR systemic therapy. TP53 mutational status alone does not seem to explain such a metabolic shift.

Application of Physiologically Based Pharmacokinetic Modeling to Predict Drug Exposure and Support Dosing Recommendations for Potential Drug-Drug Interactions or in Special Populations: An Example Using Tofacitinib

Tofacitinib is an oral Janus kinase inhibitor for the treatment of rheumatoid arthritis, psoriatic arthritis, and ulcerative colitis. It is eliminated via multiple pathways including oxidative metabolism (∼70%) and renal excretion (29%). This study aimed to predict the impact of drug‐drug interactions and renal or hepatic impairment on tofacitinib pharmacokinetics using a physiologically based pharmacokinetic (PBPK) model. The model was developed using Simcyp based on the physicochemical properties and in vitro and in vivo pharmacokinetics data for tofacitinib. The model was verified by comparing the predicted pharmacokinetic profiles with those observed in available clinical studies after single or multiple doses of tofacitinib, as well as with tofacitinib as a victim of drug‐drug interactions (because of inhibition of cytochrome P450 [CYP450] 3A4, CYP450 2C19, or CYP450 induction). In general, good agreement was observed between Simcyp predictions and clinical data. The results from this study provide confidence in using the PBPK modeling and simulation approach to predict the pharmacokinetics of tofacitinib under intrinsic (eg, renal or hepatic impairment) or extrinsic (eg, inhibition of CYP450 enzymes and/or renal transporters) conditions. This approach may also be useful in predicting pharmacokinetics under untested or complex situations (eg, when a combination of intrinsic and extrinsic factors may impact pharmacokinetics) when conducting clinical studies may be difficult, in response to health authority questions regarding dosing in special populations, or for labeling discussions.

Mertansine Inhibits mRNA Expression and Enzyme Activities of Cytochrome P450s and Uridine 5′-Diphospho-Glucuronosyltransferases in Human Hepatocytes and Liver Microsomes

Mertansine, a tubulin inhibitor, is used as the cytotoxic component of antibody–drug conjugates (ADCs) for cancer therapy. The effects of mertansine on uridine 5′-diphospho-glucuronosyltransferase (UGT) activities in human liver microsomes and its effects on the mRNA expression of cytochrome P450s (CYPs) and UGTs in human hepatocytes were evaluated to assess the potential for drug–drug interactions (DDIs). Mertansine potently inhibited UGT1A1-catalyzed SN-38 glucuronidation, UGT1A3-catalyzed chenodeoxycholic acid 24-acyl-β-glucuronidation, and UGT1A4-catalyzed trifluoperazine N-β-d-glucuronidation, with K_i values of 13.5 µM, 4.3 µM, and 21.2 µM, respectively, but no inhibition of UGT1A6, UGT1A9, and UGT2B7 enzyme activities was observed in human liver microsomes. A 48 h treatment of mertansine (1.25–2500 nM) in human hepatocytes resulted in the dose-dependent suppression of mRNA levels of CYP1A2, CYP2B6, CYP3A4, CYP2C8, CYP2C9, CYP2C19, UGT1A1, and UGT1A9, with IC₅₀ values of 93.7 ± 109.1, 36.8 ± 18.3, 160.6 ± 167.4, 32.1 ± 14.9, 578.4 ± 452.0, 539.5 ± 233.4, 856.7 ± 781.9, and 54.1 ± 29.1 nM, respectively, and decreased the activities of CYP1A2-mediated phenacetin O-deethylase, CYP2B6-mediated bupropion hydroxylase, and CYP3A4-mediated midazolam 1′-hydroxylase. These in vitro DDI potentials of mertansine with CYP1A2, CYP2B6, CYP2C8/9/19, CYP3A4, UGT1A1, and UGT1A9 substrates suggest that it is necessary to carefully characterize the DDI potentials of ADC candidates with mertansine as a payload in the clinic.

A summary of the current drug interaction guidance from the European Medicines Agency and considerations of future updates

The current EMA drug interaction guideline was published in 2012. This guideline gives important recommendations on the information required to elucidate the interaction potential of an investigational drug, both as effects of the investigational drug on the PK of other drugs and effects of other medicinal products on the PK of the investigational drug. Additional information on the use of PBPK modelling to inform drug interaction information, is also available in the guideline on the reporting of physiologically based modelling and simulation (2018). Some points of clarification on the drug interaction guideline, particularly in the area of enzyme induction screening, have been published as the EMA questions and answers (2014) and these points and further additional points, were proposed to be incorporated into a new update of the guideline, for which a concept paper was published in 2017. This update, which is still in progress, was to include new recommendations in line with relevant emerging scientific data (e.g. in the area of drug transporters). It is also intended to harmonise requirements on drug interactions with other Regulatory Agencies and this will be facilitated by the recently announced ICH initiative.

Perspectives from the Innovation and Quality Consortium Induction Working Group on Factors Impacting Clinical Drug-Drug Interactions Resulting from Induction: Focus on Cytochrome 3A Substrates

A recent publication from the Innovation and Quality Consortium Induction Working Group collated a large clinical data set with the goal of evaluating the accuracy of drug-drug interaction (DDI) prediction from in vitro data. Somewhat surprisingly, comparison across studies of the mean- or median-reported area under the curve ratio showed appreciable variability in the magnitude of outcome. This commentary explores the 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 the number of subjects contribute. Since variability in perpetrator pharmacokinetics (PK) could impact the overall DDI interpretation, should this be routinely captured? Maximal induction was typically observed after 5-7 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 and/or inactivators based on in vitro data. In these cases, using CYP3A selective substrates has limitations. More intensive monitoring of changes in area under the curve 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 physiologically based pharmacokinetic modeling (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.

[In silico prediction models of the induction of drug-metabolizing enzymes for drug discovery]

Drug metabolism in the liver is a major factor affecting pharmacokinetics of drugs, and cytochrome P450s (P450s) are major enzymes responsible for it. Since drug-drug interactions (DDIs) can affect the pharmacokinetics of concomitantly administrated drugs, it may limit the drug therapy such as dose adjustment and contraindications for co-administration and lead to dose adjustment and contraindications for co-administration. DDI is thus one of the risk factors to be reduced in the lead-optimization stage. Therefore, it is important to estimate DDI risk in the early drug discovery stage and develop candidates with low DDI risk. P450 induction is one of the important mechanisms causing DDIs and the activation of nuclear receptors is involved in this phenomenon. In this manuscript, the mechanism and evaluation methods of P450 induction are briefly reviewed, and then the new in silico methods for the prediction of P450 induction, which have been recently established by us, and its application to drug development are introduced.

Does In Vitro Cytochrome P450 Downregulation Translate to In Vivo Drug-Drug Interactions? Preclinical and Clinical Studies With 13-cis-Retinoic Acid

All‐trans‐retinoic acid (atRA) downregulates cytochrome P450 (CYP)2D6 in several model systems. The aim of this study was to determine whether all active retinoids downregulate CYP2D6 and whether in vitro downregulation translates to in vivo drug–drug interactions (DDIs). The retinoids atRA, 13cisRA, and 4‐oxo‐13cisRA all decreased CYP2D6 mRNA in human hepatocytes in a concentration‐dependent manner. The in vitro data predicted ~ 50% decrease in CYP2D6 activity in humans after dosing with 13cisRA. However, the geometric mean area under plasma concentration‐time curve (AUC) ratio for dextromethorphan between treatment and control was 0.822, indicating a weak induction of dextromethorphan clearance following 13cisRA treatment. Similarly, in mice treatment with 4‐oxo‐13cisRA–induced mRNA expression of multiple mouse Cyp2d genes. In comparison, a weak induction of CYP3A4 in human hepatocytes translated to a weak in vivo induction of CYP3A4. These data suggest that in vitro CYP downregulation may not translate to in vivo DDIs, and better understanding of the mechanisms of CYP downregulation is needed.

CYP Suppression in Human Hepatocytes by Monomethyl Auristatin E, the Payload in Brentuximab Vedotin (Adcetris®), is Associated with Microtubule Disruption

BACKGROUND AND OBJECTIVES

Monomethyl auristatin E (MMAE), the toxin linked to CD30-specific monoclonal antibody of Adcetris^® (brentuximab vedotin), is a potent anti-microtubule agent. Brentuximab vedotin has been approved for the treatment of relapsed or refractory Hodgkin lymphoma and anaplastic large cell lymphoma. Cytochrome P450 (CYP) induction assessment of MMAE was conducted in human hepatocytes to assess DDI potentials and its translation to clinic.

METHODS

MMAE was incubated at 1-1000 nM with cultured primary human hepatocytes for 72 h, and CYP1A2, CYP2B6, and CYP3A4 mRNA expression was assessed by quantitative reverse transcription-polymerase chain reaction and CYP-specific probe substrate by liquid chromatography coupled with mass spectrometry, along with microtubule disruption by immunofluorescence staining using anti-β-tubulin antibody and imaging.

RESULTS

MMAE up to 10 nM had no significant effect on CYP1A2, CYP2B6, and CYP3A4 mRNA expression and activity, whereas at higher concentrations of 100- and 1000-nM MMAE, the CYP mRNA expression and activity were diminished substantially. Further investigation showed that the degree of CYP suppression was paralleled by that of microtubule disruption by MMAE, as measured by increase in the number of β-tubulin-positive aggregates. At the clinical dose, the concentration of MMAE was 7 nM which did not show any significant CYP suppression or microtubule disruption in hepatocytes.

CONCLUSIONS

MMAE was not a CYP inducer in human hepatocytes. However, it caused a concentration-dependent CYP mRNA suppression and activity. The CYP suppression was associated with microtubule disruption, supporting the reports that intact microtubule architecture is required for CYP regulations. The absence of CYP suppression and microtubule disruption in vitro at the clinical plasma concentrations of MMAE (< 10 nM) explains the lack of pharmacokinetic drug interaction between brentuximab vedotin and midazolam, a sensitive CYP3A substrate, reported in patients.

Considerations from the Innovation and Quality Induction Working Group in Response to Drug-Drug Interaction Guidances from Regulatory Agencies: Focus on CYP3A4 mRNA In Vitro Response Thresholds, Variability, and Clinical Relevance

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.

Establishing Transcriptional Signatures to Differentiate PXR-, CAR-, and AhR-Mediated Regulation of Drug Metabolism and Transport Genes in Cryopreserved Human Hepatocytes

The potential for drug-drug interactions (DDIs) arising from transcriptional regulation of drug-disposition genes via activation of nuclear receptors (NRs), such as pregnane X receptor (PXR), constitutive androstane receptor (CAR), and aryl hydrocarbon receptor (AhR), remains largely unexplored, as highlighted in a recent guidance document from the European Medicines Agency. The goal of this research was to establish PXR-/CAR-/AhR-specific drug-metabolizing enzyme (DME) and transporter gene expression signatures in sandwich-cultured cryopreserved human hepatocytes using selective activators of PXR (rifampin), CAR (CITCO), and AhR (omeprazole). Dose response for ligand-induced changes to 38 major human DMEs and critical hepatobiliary transporters were assessed using a custom gene expression array card. We identified novel differentially expressed drug-disposition genes for PXR (↑ABCB1/MDR1, CYP2C9, CYP2C19, and EPHX1, ↓ABCB11), CAR [↑sulfotransferase (SULT) 1E1, uridine glucuronosyl transferase (UGT) 2B4], and AhR (↑SLC10A1/NTCP, SLCO1B1/OATP1B1], and coregulated genes (CYP1A1, CYP2B6, CYP2C8, CYP3A4, UGT1A1, UGT1A4). Subsequently, DME gene expression signatures were generated for known CYP3A4 inducers PF-06282999 and pazopanib. The former produced an induction signature almost identical to that of rifampin, suggesting activation of the PXR pathway, whereas the latter produced an expression signature distinct from those of PXR, CAR, or AhR, suggesting involvement of an alternate pathway(s). These results demonstrate that involvement of PXR/CAR/AhR can be identified via expression changes of signature DME/transporter genes. Inclusion of such signature genes could serve to simultaneously identify potential inducers and inhibitors, and the NRs involved in the transcriptional regulation, thus providing a more holistic and mechanism-based assessment of DDI risk for DMEs and transporters beyond conventional cytochrome P450 isoforms.