Physiologically based pharmacokinetic modeling for assessing the clinical drug–drug interaction of alisporivir

Assessing Dose-Exposure-Response Relationships of Miltefosine in Adults and Children using Physiologically-Based Pharmacokinetic Modeling Approach

OBJECTIVES

Miltefosine is the first and only oral medication to be successfully utilized as an antileishmanial agent. However, the drug is associated with differences in exposure patterns and cure rates among different population groups e.g. ethnicity and age (i.e., children v adults) in clinical trials. In this work, mechanistic population physiologically-based pharmacokinetic (PBPK) models have been developed to study the dose-exposure-response relationship of miltefosine in in silico clinical trials and evaluate the differences in population groups, particularly children and adults.

METHODS

The Simcyp population pharmacokinetics platform was employed to predict miltefosine exposure in plasma and peripheral blood mononuclear cells (PBMCs) in a virtual population under different dosing regimens. The cure rate of a simulation was based on the percentage of number of the individual virtual subjects with AUCd0-28 > 535 µg⋅day/mL in the virtual population.

RESULTS

It is shown that both adult and paediatric PBPK models of miltefosine can be developed to predict the PK data of the clinical trials accurately. There was no significant difference in the predicted dose-exposure-response of the miltefosine treatment for different simulated ethnicities under the same dose regime and the dose-selection strategies determined the clinical outcome of the miltefosine treatment. A lower cure rate of the miltefosine treatment in paediatrics was predicted because a lower exposure of miltefosine was simulated in virtual paediatric in comparison with adult virtual populations when they received the same dose of the treatment.

CONCLUSIONS

The mechanistic PBPK model suggested that the higher fraction of unbound miltefosine in plasma was responsible for a higher probability of failure in paediatrics because of the difference in the distribution of plasma proteins between adults and paediatrics. The developed PBPK models could be used to determine an optimal miltefosine dose regime in future clinical trials.

Model-based comparative analysis of rifampicin and rifabutin drug-drug interaction profile

Rifamycins are widely used for treating mycobacterial and staphylococcal infections. Drug-drug interactions (DDI) caused by rifampicin (RIF) is a major issue. We used a model-based approach to predict the magnitude of DDI with RIF and rifabutin (RBT) for 217 cytochrome P450 (CYP) substrates. On average, DDI caused by low-dose RIF were twice more potent than those caused by RBT. Contrary to RIF, RBT appears unlikely to cause severe DDI, even with sensitive CYP substrates.

Physiologically based pharmacokinetic modeling of altered tizanidine systemic exposure by CYP1A2 modulation: Impact of drug-drug interactions and cigarette consumption

Tizanidine is an alpha2-adrenergic agonist, used to treat spasticity associated with multiple sclerosis and spinal injury. Tizanidine is primarily metabolized by CYP1A2 and is considered a sensitive index substrate for this enzyme. The physiologically based pharmacokinetic (PBPK) modeling platform Simcyp® was used to evaluate the impact of CYP1A2 modulation on tizanidine exposure through drug-drug interactions (DDIs) and host-dependent habits (cigarette smoking). A PBPK model was developed to predict tizanidine disposition in healthy volunteers following oral administration. The model was verified based on agreement between model-simulated and clinically observed systemic exposure metrics (C_max, AUC). The model was then used to carry-out DDI simulations to predict alterations in tizanidine systemic exposure when co-administered with various CYP1A2 perpetrators including competitive inhibitors (fluvoxamine, ciprofloxacin), a mechanism-based inhibitor (rofecoxib), and an inducer (rifampin). Additional simulations were performed to evaluate the impact of cigarette smoking on systemic exposure. Under each scenario, the PBPK model was able to capture the observed fold changes in tizanidine C_max and AUC of tizanidine when coadministered with CYP1A2 inhibitors or inducers. These results add to the available research findings in the literature on PBPK predictions of drug-drug interactions and illustrate the potential application in drug development, specifically to support product labeling.

Physiologically Based Pharmacokinetic Modeling in Lead Optimization. 1. Evaluation and Adaptation of GastroPlus To Predict Bioavailability of Medchem Series

When medicinal chemists need to improve bioavailability (%F) within a chemical series during lead optimization, they synthesize new series members with systematically modified properties mainly by following experience and general rules of thumb. More quantitative models that predict %F of proposed compounds from chemical structure alone have proven elusive. Global empirical %F quantitative structure-property (QSPR) models perform poorly, and projects have too little data to train local %F QSPR models. Mechanistic oral absorption and physiologically based pharmacokinetic (PBPK) models simulate the dissolution, absorption, systemic distribution, and clearance of a drug in preclinical species and humans. Attempts to build global PBPK models based purely on calculated inputs have not achieved the <2-fold average error needed to guide lead optimization. In this work, local GastroPlus PBPK models are instead customized for individual medchem series. The key innovation was building a local QSPR for a numerically fitted effective intrinsic clearance (CL_loc). All inputs are subsequently computed from structure alone, so the models can be applied in advance of synthesis. Training CL_loc on the first 15-18 rat %F measurements gave adequate predictions, with clear improvements up to about 30 measurements, and incremental improvements beyond that.

Prediction of Drug-Drug Interactions Arising from CYP3A induction Using a Physiologically Based Dynamic Model

Using physiologically based pharmacokinetic modeling, we predicted the magnitude of drug-drug interactions (DDIs) for studies with rifampicin and seven CYP3A4 probe substrates administered i.v. (10 studies) or orally (19 studies). The results showed a tendency to underpredict the DDI magnitude when the victim drug was administered orally. Possible sources of inaccuracy were investigated systematically to determine the most appropriate model refinement. When the maximal fold induction (Ind_max) for rifampicin was increased (from 8 to 16) in both the liver and the gut, or when the Ind_max was increased in the gut but not in liver, there was a decrease in bias and increased precision compared with the base model (Ind_max = 8) [geometric mean fold error (GMFE) 2.12 vs. 1.48 and 1.77, respectively]. Induction parameters (mRNA and activity), determined for rifampicin, carbamazepine, phenytoin, and phenobarbital in hepatocytes from four donors, were then used to evaluate use of the refined rifampicin model for calibration. Calibration of mRNA and activity data for other inducers using the refined rifampicin model led to more accurate DDI predictions compared with the initial model (activity GMFE 1.49 vs. 1.68; mRNA GMFE 1.35 vs. 1.46), suggesting that robust in vivo reference values can be used to overcome interdonor and laboratory-to-laboratory variability. Use of uncalibrated data also performed well (GMFE 1.39 and 1.44 for activity and mRNA). As a result of experimental variability (i.e., in donors and protocols), it is prudent to fully characterize in vitro induction with prototypical inducers to give an understanding of how that particular system extrapolates to the in vivo situation when using an uncalibrated approach.