ABSTRACT:Cytochrome P450 3A4 (CYP3A4) is the most important enzyme in drug metabolism and because it is the most frequent target for pharmacokinetic drug-drug interactions (DDIs) it is highly desirable to be able to predict CYP3A4-based DDIs from in vitro data. In this study, the prediction of clinical DDIs for 30 drugs on the pharmacokinetics of midazolam, a probe substrate for CYP3A4, was done using in vitro inhibition, inactivation, and induction data. Two DDI prediction approaches were used, which account for effects at both the liver and intestine. The first was a model that simultaneously combines reversible inhibition, time-dependent inactivation, and induction data with static estimates of relevant in vivo concentrations of the precipitant drug to provide point estimates of the average magnitude of change in midazolam exposure. This model yielded a success rate of 88% in discerning DDIs with a mean -fold error of 1.74. The second model was a computational physiologically based pharmacokinetic model that uses dynamic estimates of in vivo concentrations of the precipitant drug and accounts for interindividual variability among the population (Simcyp). This model yielded success rates of 88 and 90% (for "steady-state" and "time-based" approaches, respectively) and mean -fold errors of 1.59 and 1.47. From these findings it can be concluded that in vivo DDIs for CYP3A4 can be predicted from in vitro data, even when more than one biochemical phenomenon occurs simultaneously.

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT• Numerous retrospective analyses have shown the utility of in vitro systems for predicting potential drug-drug interactions (DDIs).• Prediction of DDIs from in vitro data is commonly obtained using estimates of enzyme Ki, inhibitor and substrate concentrations and absorption rate for substrate and inhibitor. WHAT THIS STUDY ADDS• Using a generic approach for all test compounds, the findings from the current study showed the use of recombinant P450s provide a more robust in vitro measure of P450 contribution (fraction metabolized, fm) than that achieved when using chemical inhibitors in combination with human liver microsomes, for the prediction of potential CYP3A4 drug-drug interactions prior to clinical investigation.• The current study supported the use of SIMCYP®, a modelling and simulation software in utilizing the in vitro measures in the prediction of potential drug-drug interactions. AIMSThe aim of this study was to explore and optimize the in vitro and in silico approaches used for predicting clinical DDIs. A data set containing clinical information on the interaction of 20 Pfizer compounds with ketoconazole was used to assess the success of the techniques. METHODSThe study calculated the fraction and the rate of metabolism of 20 Pfizer compounds via each cytochrome P450. Two approaches were used to determine fraction metabolized (fm); 1) by measuring substrate loss in human liver microsomes (HLM) in the presence and absence of specific chemical inhibitors and 2) by measuring substrate loss in individual cDNA expressed P450s (also referred to as recombinant P450s (rhCYP)) The fractions metabolized via each CYP were used to predict the drug-drug interaction due to CYP3A4 inhibition by ketoconazole using the modelling and simulation software SIMCYP ® . RESULTSWhen in vitro data were generated using Gentest supersomes, 85% of predictions were within two-fold of the observed clinical interaction. Using PanVera baculosomes, 70% of predictions were predicted within two-fold. In contrast using chemical inhibitors the accuracy was lower, predicting only 37% of compounds within two-fold of the clinical value. Poorly predicted compounds were found to either be metabolically stable and/or have high microsomal protein binding. The use of equilibrium dialysis to generate accurate protein binding measurements was especially important for highly bound drugs. CONCLUSIONSThe current study demonstrated that the use of rhCYPs with SIMCYP ® provides a robust in vitro system for predicting the likelihood and magnitude of changes in clinical exposure of compounds as a consequence of CYP3A4 inhibition by a concomitantly administered drug.

1. Alectinib is a highly selective, central nervous system-active small molecule anaplastic lymphoma kinase inhibitor. 2. The absolute bioavailability, metabolism, excretion and pharmacokinetics of alectinib were studied in a two-period single-sequence crossover study. A 50 μg radiolabelled intravenous microdose of alectinib was co-administered with a single 600 mg oral dose of alectinib in the first period, and a single 600 mg/67 μCi oral dose of radiolabelled alectinib was administered in the second period to six healthy male subjects. 3. The absolute bioavailability of alectinib was moderate at 36.9%. Geometric mean clearance was 34.5 L/h, volume of distribution was 475 L and the hepatic extraction ratio was low (0.14). 4. Near-complete recovery of administered radioactivity was achieved within 168 h post-dose (98.2%) with excretion predominantly in faeces (97.8%) and negligible excretion in urine (0.456%). Alectinib and its major active metabolite, M4, were the main components in plasma, accounting for 76% of total plasma radioactivity. In faeces, 84% of dose was excreted as unchanged alectinib with metabolites M4, M1a/b and M6 contributing to 5.8%, 7.2% and 0.2% of dose, respectively. 5. This novel study design characterised the full absorption, distribution, metabolism and excretion properties in each subject, providing insight into alectinib absorption and disposition in humans.

The lack of standardization in the way that quantitative and systems pharmacology (QSP) models are developed, tested, and documented hinders their reproducibility, reusability, and expansion or reduction to alternative contexts. This in turn undermines the potential impact of QSP in academic, industrial, and regulatory frameworks. This article presents a minimum set of recommendations from the UK Quantitative and Systems Pharmacology Network (UK QSP Network) to guide QSP practitioners seeking to maximize their impact, and stakeholders considering the use of QSP models in their environment.

The efficacy and safety of alectinib, a central nervous system-active and selective anaplastic lymphoma kinase (ALK) inhibitor, has been demonstrated in patients with ALK-positive (ALK+) non-small cell lung cancer (NSCLC) progressing on crizotinib. Alectinib is mainly metabolized by cytochrome P450 3A (CYP3A) to a major similarly active metabolite, M4. Alectinib and M4 show evidence of weak time-dependent inhibition and small induction of CYP3A in vitro. We present results from 3 fixed-sequence studies evaluating drug-drug interactions for alectinib through CYP3A. Studies NP28990 and NP29042 enrolled 17 and 24 healthy subjects, respectively, and investigated potent CYP3A inhibition with posaconazole and potent CYP3A induction through rifampin, respectively, on the single oral dose pharmacokinetics (PK) of alectinib. A substudy of the global phase 2 NP28673 study enrolled 15 patients with ALK+ NSCLC to determine the effect of multiple doses of alectinib on the single oral dose PK of midazolam, a sensitive substrate of CYP3A. Potent CYP3A inhibition or induction resulted in only minor effects on the combined exposure of alectinib and M4. Multiple doses of alectinib did not influence midazolam exposure. These results suggest that dose adjustments may not be needed when alectinib is coadministered with CYP3A inhibitors or inducers or for coadministered CYP3A substrates.