Population pharmacokinetics-pharmacodynamics of oral everolimus in patients with seizures associated with tuberous sclerosis complex.

Everolimus is approved in Europe and in the USA for the adjunctive treatment of patients aged 2 years and older whose refractory partial-onset seizures, with or without secondary generalization, are associated with tuberous sclerosis complex. The objective of this analysis was to establish a population pharmacokinetic (PK)/pharmacodynamic model describing the relationship between seizure frequency and everolimus exposure to confirm the recommended target concentration range of 5-15 ng/mL. The PK model was a two-compartment model with first order absorption and clearance. CYP3A and P-gp inducers and body-surface area were shown to impact everolimus exposure, justifying dose adjustments. A Poisson distribution was found to adequately describe the random nature of daily seizure counts during the screening phase. A placebo effect on the Poisson seizure mean was implemented as an asymptotic exponential function of time leading to a new steady-state seizure mean. The everolimus effect was implemented as an inhibitory Emax function of Cmin on the seizure mean, where Emax exhibited an asymptotic exponential increase over time to a higher steady-state value. Increasing age was found to decrease the baseline seizure mean and to prolong the half-life of the increase in Emax. The dependence of seizure frequencies on Cmin was explored by simulation. The responder rate increased with increasing Cmin. As Cmin decreased below 5 ng/mL, variability in response became larger and responder rates decreased more rapidly. The results supported the recommended target concentration range for everolimus of 5-15 ng/mL to ensure treatment efficacy.

Pazopanib Exposure Relationship with Clinical Efficacy and Safety in the Adjuvant Treatment of Advanced Renal Cell Carcinoma.

Purpose: PROTECT, a phase III, randomized, placebo-controlled study, evaluated pazopanib efficacy and safety in the adjuvant renal cell carcinoma setting. The relationship between pazopanib exposure (Ctrough) and efficacy and safety was evaluated.Patients and Methods: Evaluable steady-state blood trough concentrations were collected from 311 patients at week 3 or 5 (early Ctrough) and 250 patients at week 16 or 20 (late Ctrough). Pazopanib pharmacokinetic (PK) data were analyzed via a population model approach. Relationship between Ctrough or dose intensity and disease-free survival (DFS) was explored via Kaplan-Meier and multivariate analysis. Adverse events (AE) and AE-related treatment discontinuation proportions were summarized by Ctrough quartiles.Results: Most (>90%) patients with early or late Ctrough data started on 600 mg. Mean early and late Ctrough overlapped across dose levels. Patients with higher early Ctrough quartiles achieved longer DFS (adjusted HR, 0.58; 95% confidence interval, 0.42-0.82; P = 0.002). Patients achieving early or late Ctrough >20.5 µg/mL had significantly longer DFS: not estimable (NE) versus 29.5 months, P = 0.006, and NE versus 29.9 months, P = 0.008, respectively. Dose intensity up to week 8 did not correlate with DFS, consistent with population PK model-based simulations showing overlapping pazopanib exposure with 600 and 800 mg doses. The proportion of AE-related treatment discontinuation and grade 3/4 AEs, with the exception of hypertension, was not correlated to CtroughConclusions: In the adjuvant setting, higher pazopanib Ctrough was associated with improved DFS and did not increase treatment discontinuations or grade 3/4 AEs, with the exception of hypertension. Clin Cancer Res; 24(13); 3005-13. ©2018 AACRSee related commentary by Rini, p. 2979.

Physiologically based pharmacokinetic modeling of CYP3A4 induction by rifampicin in human: influence of time between substrate and inducer administration.

The induction of cytochrome P450 enzymes (CYPs) is an important source of drug-drug interaction (DDI) and can result in pronounced changes in pharmacokinetics (PK). Rifampicin (RIF) is a potent inducer of CYP3A4 and also acts as a competitive inhibitor which can partially mask the induction. The objective of this study was to determine a clinical DDI study design for RIF resulting in maximum CYP3A4 induction. A physiologically based pharmacokinetic (PBPK) model was developed to project the dynamics and magnitude of CYP3A4 induction in vivo from in vitro data generated with primary human hepatocytes. The interaction model included both inductive and inhibitory effects of RIF on CYP3A4 in gut and liver and accounting for the observed RIF auto-induction. The model has been verified for 4 CYP3A4 substrates: midazolam, triazolam, alfentanil and nifedipine using plasma concentration data from 20 clinical study designs with intravenous (n=7) and oral (n=13) administrations. Finally, the influence of the time between RIF and substrate administration was explored for the interaction between midazolam and RIF. The model integrating in vitro induction parameters correctly predicted intravenous induction but underestimated oral induction with 30% of simulated concentrations more than 2-fold higher than of observed data. The use of a 1.6-fold higher value for the maximum induction effect (Emax) improved significantly the accuracy and precision of oral induction with 82% of simulated concentrations and all predicted PK parameters within 2-fold of observed data. Our simulations suggested that a concomitant administration of RIF and midazolam resulted in significant competitive inhibition limited to intestinal enzyme. Accordingly, a maximum induction effect could be achieved with a RIF pretreatment of 600 mg/day during 5 days and a substrate administration at least 2 h after the last RIF dose. A period of 2 weeks after RIF removal was found sufficient to allow return to baseline levels of enzyme.

Application of PBPK modeling to predict human intestinal metabolism of CYP3A substrates - an evaluation and case study using GastroPlus.

First pass metabolism in the intestinal mucosa is a determinant of oral bioavailability of CYP3A substrates and so the prediction of intestinal availability (Fg) of potential drug candidates is important. Although intestinal metabolism can be modeled in commercial physiologically based pharmacokinetic (PBPK) software tools, a thorough evaluation of prediction performance is lacking. The current study evaluates the accuracy and precision of GastroPlus Fg predictions for 20 CYP3A substrates using in vitro and in silico input data for metabolic clearance and membrane permeation, and illustrates a potential impact of intestinal metabolism modeling on decision making in a drug Research and Development project. This analysis supports that CYP3A mediated metabolic clearance measured in human liver microsomes can be used to predict gut wall metabolism. Using values scaled from in vitro cell permeability as input for effective jejunal permeability resulted in good Fg prediction accuracy (no significant bias and ∼95% of predictions within 2 fold from in vivo estimated Fg), whereas simulations with in silico predicted permeability tended to overestimate gut metabolism (40% of Fg predictions under predicted more than 2 fold) ±2 fold range as an estimate of imprecision in metabolic clearance and permeability inputs propagated to >5 and <2 fold ranges of predicted Fg for compounds with <30% and >75% in vivo Fg, respectively, suggesting lower precision of predictions for high extraction compounds. Furthermore, parameter sensitivity analysis suggests that limitations in solubility or dissolution may either decrease Fg by preventing saturation of metabolism or increase Fg by shifting the site of absorption towards the colon where expression of CYP3A is low. The case example illustrates how, when accounting for the associated uncertainty in predicted pharmacokinetics and linking to predictive models for efficacy, PBPK modeling of intestinally metabolized compounds can support decision making in drug Research and Development.

Use of physiologically based pharmacokinetic modeling for assessment of drug-drug interactions.

Interactions between co-administered medicines can reduce efficacy or lead to adverse effects. Understanding and managing such interactions is essential in bringing safe and effective medicines to the market. Ideally, interaction potential should be recognized early and minimized in compounds that reach late stages of drug development. Physiologically based pharmacokinetic models combine knowledge of physiological factors with compound-specific properties to simulate how a drug behaves in the human body. These software tools are increasingly used during drug discovery and development and, when integrating relevant in vitro data, can simulate drug interaction potential. This article provides some background and presents illustrative examples. Physiologically based models are an integral tool in the discovery and development of drugs, and can significantly aid our understanding and prediction of drug interactions.