Insights into Praziquantel Metabolism and Potential Enantiomeric Cytochrome P450-Mediated Drug-Drug Interaction.

The active enantiomer R-Praziquantel (PZQ) shows a clinically lower relative exposure when administered enantiomerically pure compared with a racemic form. We investigated the hypothesis that enantiomer-enantiomer interactions on cytochrome P450 (P450) enzymes could explain this observation and aimed to further deepen the understanding of PZQ metabolism. First, in an in vitro metabolite profiling study, the formation of multiple metabolites per P450, together with an observed interconversion of cis-4'-OH-PZQ to trans-4'-OH-PZQ in human hepatocytes, pointed out the inadequacy of measuring metabolite formation in kinetic studies. Thus, a substrate depletion approach to study PZQ enantiomeric interactions was applied. Second, an abundant CYP3A4 metabolite found in previous studies was structurally characterized. Third, substrate depletion methodologies were applied to determine P450 enzyme kinetics of PZQ and to further estimate enantiomer-enantiomer inhibitory parameters. A competitive inhibition between PZQ enantiomers for CYP2C9, 2C19, 3A4, and 3A5 was revealed. Analyses considering the clearance of only one or both enantiomers provided comparable enantiomer-enantiomer inhibition estimates. To conclude, this paper provides new insights into PZQ metabolic profile to enable a better understanding of enantioselective pharmacokinetics using substrate depletion-based methods. SIGNIFICANCE STATEMENT: In this study, enantiomer-enantiomer interactions of praziquantel on cytochrome P450 metabolizing enzymes are investigated via substrate depletion measurement using modeling methods. Together with new insights into the praziquantel metabolism, this work provides a novel data set to understand its pharmacokinetics.

Extrapolation of praziquantel pharmacokinetics to a pediatric population: a cautionary tale.

L-praziquantel (PZQ) pharmacokinetic data were analyzed from two relative bioavailability Phase 1 studies in adult, healthy subjects with two new oral dispersion tablet (ODT) formulations of L-PZQ administered under various combinations of co-administration with food, water, and/or crushing. Linear mixed effects models adequately characterized the noncompartmental estimates of the pharmacokinetic profiles in both studies. Dose, food, and formulation were found to significantly affect L-PZQ exposure in both studies. The model for AUC was then extrapolated to children 2-5 years old accounting for enzyme maturation and weight. The predicted exposures were compared to an external Phase 1 study conducted by the Swiss Tropical and Public Health Institute using a currently marketed formulation (Cesol 600 mg immediate-release tablets) and found to be substantially lower than observed. A root cause analysis was completed to identify the reason for failure of the models. Various scenarios were proposed and tested. Two possible reasons for the failure were identified. One reason was that the model did not account for the reduced hepatic clearance seen in patients compared to the healthy volunteer population used to build the model. The second possible reason was that PZQ absorption appears sensitive to meal composition and the model did not account for differences in meals between a standardized Phase 1 unit and clinical sites in Africa. Further studies are needed to confirm our hypotheses.

Abundance of Hepatic Transporters in Caucasians: A Meta-Analysis.

This study aimed to derive quantitative abundance values for key hepatic transporters suitable for in vitro-in vivo extrapolation within a physiologically based pharmacokinetic modeling framework. A meta-analysis was performed whereby data on abundance measurements, sample preparation methods, and donor demography were collated from the literature. To define values for a healthy Caucasian population, a subdatabase was created whereby exclusion criteria were applied to remove samples from non-Caucasian individuals, those with underlying disease, or those with subcellular fractions other than crude membrane. Where a clinically relevant active genotype was known, only samples from individuals with an extensive transporter phenotype were included. Authors were contacted directly when additional information was required. After removing duplicated samples, the weighted mean, geometric mean, standard deviation, coefficient of variation, and between-study homogeneity of transporter abundances were determined. From the complete database containing 24 transporters, suitable abundance data were available for 11 hepatic transporters from nine studies after exclusion criteria were applied. Organic anion transporting polypeptides OATP1B1 and OATP1B3 showed the highest population abundance in healthy adult Caucasians. For several transporters, the variability in abundance was reduced significantly once the exclusion criteria were applied. The highest variability was observed for OATP1B3 > OATP1B1 > multidrug resistance protein 2 > multidrug resistance gene 1. No relationship was found between transporter expression and donor age. To our knowledge, this study provides the first in-depth analysis of current quantitative abundance data for a wide range of hepatic transporters, with the aim of using these data for in vitro-in vivo extrapolation, and highlights the significance of investigating the background of tissue(s) used in quantitative transporter proteomic studies. Similar studies are now warranted for other ethnicities.

Tipifarnib physiologically-based pharmacokinetic modeling to assess drug-drug interaction, organ impairment, and biopharmaceutics in healthy subjects and cancer patients.

A physiologically-based pharmacokinetic (PBPK) model for tipifarnib, which included mechanistic absorption, was built and verified by integrating in vitro data and several clinical data in healthy subjects and cancer patients. The final PBPK model was able to recover the clinically observed single and multiple-dose plasma concentrations of tipifarnib in healthy subjects and cancer patients under several dosing conditions, such as co-administration with a strong CYP3A4 inhibitor and inducer, an acid-reducing agent (proton pump inhibitor and H2 receptor antagonist), and with a high-fat meal. In addition, the model was able to accurately predict the effect of mild or moderate hepatic impairment on tipifarnib exposure. The appropriately verified model was applied to prospectively simulate the liability of tipifarnib as a victim of CYP3A4 enzyme-based drug-drug interactions (DDIs) with a moderate inhibitor and inducer as well as tipifarnib as a perpetrator of DDIs with sensitive substrates of CYP3A4, CYP2B6, CYP2D6, CYP2C9, and CYP2C19 in healthy subjects and cancer patients. The effect of a high-fat meal, acid-reducing agent, and formulation change at the therapeutic dose was simulated. Finally, the model was used to predict the effect of mild, moderate, or severe hepatic, and renal impairment on tipifarnib PK. This multipronged approach of combining the available clinical data with PBPK modeling-guided dosing recommendations for tipifarnib under several conditions. This example showcases the totality of the data approach to gain a more thorough understanding of clinical pharmacology and biopharmaceutic properties of oncology drugs in development.

Development and application of a PBPK modeling strategy to support antimalarial drug development.

As part of a collaboration between Medicines for Malaria Venture (MMV), Certara UK and Monash University, physiologically-based pharmacokinetic (PBPK) models were developed for 20 antimalarials, using data obtained from standardized in vitro assays and clinical studies within the literature. The models have been applied within antimalarial drug development at MMV for more than 5 years. During this time, a strategy for their impactful use has evolved. All models are described in the supplementary material and are available to researchers. Case studies are also presented, demonstrating real-world development and clinical applications, including the assessment of the drug-drug interaction liability between combination partners or with co-administered drugs. This work emphasizes the benefit of PBPK modeling for antimalarial drug development and decision making, and presents a strategy to integrate it into the research and development process. It also provides a repository of shared information to benefit the global health research community.

Progress curve mechanistic modeling approach for assessing time-dependent inhibition of CYP3A4.

A progress curve method for assessing time-dependent inhibition of CYP3A4 is based on simultaneous quantification of probe substrate metabolite and inhibitor concentrations during the experiment. Therefore, it may overcome some of the issues associated with the traditional two-step method and estimation of inactivation rate (k(inact)) and irreversible inhibition (K(I)) constants. In the current study, seven time-dependent inhibitors were investigated using a progress curve method and recombinant CYP3A4. A novel mechanistic modeling approach was applied to determine inhibition parameters using both inhibitor and probe metabolite data. Progress curves generated for clarithromycin, erythromycin, diltiazem, and N-desmethyldiltiazem were described well by the mechanistic mechanism-based inhibition (MBI) model. In contrast, mibefradil, ritonavir, and verapamil required extension of the model and inclusion of competitive inhibition term for the metabolite. In addition, this analysis indicated that verapamil itself causes minimal MBI, and the formation of inhibitory metabolites was responsible for the irreversible loss of CYP3A4 activity. The k(inact) and K(I) estimates determined in the current study were compared with literature data generated using the conventional two-step method. In the current study, the inactivation efficiency (k(inact)/K(I)) for clarithromycin, ritonavir, and erythromycin were up to 7-fold higher, whereas k(inact)/K(I) for mibefradil, N-desmethyldiltiazem, and diltiazem were, on average, 2- to 4.8-fold lower than previously reported estimates. Use of human liver microsomes instead of recombinant CYP3A4 resulted in 5-fold lower k(inact)/K(I) for erythromycin. In conclusion, the progress curve method has shown a greater mechanistic insight when determining kinetic parameters for MBI in addition to providing a more comprehensive experimental protocol.

Development and verification of an endogenous PBPK model to inform hydrocortisone replacement dosing in children and adults with cortisol deficiency.

The goal of hormone replacement is to mirror physiology. Hydrocortisone granules and modified release formulations are being developed to optimise cortisol replacement in the rare disease of adrenal insufficiency. To facilitate clinical development, we built and verified a physiologically based pharmacokinetic (PBPK) model for the endogenous hormone cortisol (hydrocortisone) in healthy adults, and children and adults with adrenal insufficiency. The model predicted immediate-release hydrocortisone pharmacokinetics in adults across the dose range 0.5 to 20 mg, with predicted/observed AUCs within 0.8 to 1.25-fold.  The model also tightly predicted pharmacokinetic parameters for modified-release formulations, with AUCs within 0.8 to 1.25-fold after single and multiple dosing.  Predicted modified-release formulation pharmacokinetics (PK) in 12 to 18-year olds showed PK to be similar to adults. This hydrocortisone PBPK model is a useful tool to predict adult and paediatric pharmacokinetics of both immediate- and modified-release hydrocortisone formulations, and develop clinical dosing regimens.

IC50-based approaches as an alternative method for assessment of time-dependent inhibition of CYP3A4.

The predictive utility of two in vitro methods (empirical IC(50)-based and mechanistic k(inact)/K(I)) for the assessment of time-dependent cytochrome P450 3A4 (CYP3A4) inhibition has been compared. IC(50) values were determined at multiple pre-incubation time points over 30 min for five CYP3A4 time-dependent inhibitors (verapamil, diltiazem, erythromycin, clarithromycin, and azithromycin). The ability of IC(50) data obtained following pre-incubation to predict k(inact)/K(I) parameters was investigated and its utility was assessed relative to the conventional k(inact)/K(I) model using 50 reported clinical drug-drug interactions (DDIs). Models with either hepatic or hepatic with intestinal components were explored. For low/medium potency time-dependent inhibitors, 81% of the predicted k(inact)/K(I(unbound)) from IC(50) data were within an order of magnitude of the actual values, in contrast to 50% of potent inhibitors. An underprediction trend and > 50% of false-negatives were observed when IC(50) data were used in the DDI hepatic prediction model; incorporation of the intestine improved the prediction accuracy. On the contrary, 86% of the DDI studies were predicted within twofold using k(inact)/K(I) mechanistic approach and the combined hepatic and intestinal model. Use of the empirical IC(50) approach as an alternative to the mechanistic k(inact)/K(I) model for in vivo DDI prediction is limited and is best restricted to preliminary investigations.

Physiologically-Based Pharmacokinetic Models for Evaluating Membrane Transporter Mediated Drug-Drug Interactions: Current Capabilities, Case Studies, Future Opportunities, and Recommendations.

Physiologically-based pharmacokinetic (PBPK) modeling has been extensively used to quantitatively translate in vitro data and evaluate temporal effects from drug-drug interactions (DDIs), arising due to reversible enzyme and transporter inhibition, irreversible time-dependent inhibition, enzyme induction, and/or suppression. PBPK modeling has now gained reasonable acceptance with the regulatory authorities for the cytochrome-P450-mediated DDIs and is routinely used. However, the application of PBPK for transporter-mediated DDIs (tDDI) in drug development is relatively uncommon. Because the predictive performance of PBPK models for tDDI is not well established, here, we represent and discuss examples of PBPK analyses included in regulatory submission (the US Food and Drug Administration (FDA), the European Medicines Agency (EMA), and the Pharmaceuticals and Medical Devices Agency (PMDA)) across various tDDIs. The goal of this collaborative effort (involving scientists representing 17 pharmaceutical companies in the Consortium and from academia) is to reflect on the use of current databases and models to address tDDIs. This challenges the common perceptions on applications of PBPK for tDDIs and further delves into the requirements to improve such PBPK predictions. This review provides a reflection on the current trends in PBPK modeling for tDDIs and provides a framework to promote continuous use, verification, and improvement in industrialization of the transporter PBPK modeling.

The nested enzyme-within-enterocyte (NEWE) turnover model for predicting dynamic drug and disease effects on the gut wall.

Physiologically-based pharmacokinetic (PBPK) models provide a framework for in vitro-in vivo extrapolation of metabolic drug clearance. Many of the concepts in PBPK can have consequential impact on more mechanistic systems pharmacology models. In the gut wall, turnover of enzymes and enterocytes are typically lumped into one rate constant that describes the time dependent enzyme activity. This assumption may influence predictability of any sustained and dynamic effects such as mechanism-based inhibition (MBI), particularly when considering translation from healthy to gut disease. A novel multi-level systems PBPK model was developed. This model comprised a 'nested enzyme-within enterocyte' (NEWE) turnover model to describe levels of drug-metabolising enzymes. The ability of the model to predict gut metabolism following MBI and gut disease was investigated and compared to the conventional modelling approach. For MBI, the default NEWE model performed comparably to the conventional model. However, when drug-specific spatial crypt-villous absorption was considered, up to approximately 50% lower impact of MBI was simulated for substrates highly metabolised by cytochrome P450 (CYP) 3A4, interacting with potent inhibitors. Further, the model showed potential in predicting the disease effect of gastrointestinal mucositis and untreated coeliac disease when compared to indirect clinical pharmacokinetic parameters. Considering the added complexity of the NEWE model, it does not provide an attractive solution for improving upon MBI predictions in healthy individuals. However, nesting turnover may enable extrapolation to gut disease-drug interactions. The principle detailed herein may be useful for modelling drug interactions with cellular targets where turnover is significant enough to affect this process.

Maximal inhibition of intestinal first-pass metabolism as a pragmatic indicator of intestinal contribution to the drug-drug interactions for CYP3A4 cleared drugs.

For certain CYP3A4 substrates intestinal first-pass metabolism makes a substantial contribution to low oral bioavailability and extent of drug-drug interactions (DDI). In order to include the contribution of enzyme inhibition in the gut wall in the assessment of DDI potential, the ratio of the intestinal wall availability in the presence and absence of an inhibitor (F(G)(') and F(G), respectively) has been incorporated into a prediction equation based on hepatic enzyme interactions. This approach has been applied for both reversible and irreversible DDIs, involving 36 different inhibitors and 11 CYP3A4 substrates. The aim was to investigate the use of maximal (complete) inhibition of intestinal CYP3A4 (F(G)(')=1) as a pragmatic measure of the intestinal enzyme interaction and to compare this approach with observed in vivo values (where available) and predicted F(G) ratios from an intestinal model. The latter was obtained from the decrease in the intestinal intrinsic clearance in the presence of an inhibitor, using an estimated inhibitor concentration in the intestinal wall during absorption phase (I(G)) and an in vitro obtained K(i). In addition, the impact of variability in the enterocytic blood flow on the estimated I(G) and subsequently the model predicted F(G) ratio was investigated. The maximal F(G) ratios for the 11 CYP3A4 substrates investigated ranged from 1.06-7.14 for alprazolam and tacrolimus, respectively. In 91% of the studies investigated the model predicted F(G) ratio was within 40% of the maximal value. Maximal F(G) ratio is proposed as an initial indicator of the magnitude of intestinal enzyme interaction; the implications for drug elimination involving substrates cleared either by metabolism or by a combination of metabolism and efflux transporters are discussed.

More Power to OATP1B1: An Evaluation of Sample Size in Pharmacogenetic Studies Using a Rosuvastatin PBPK Model for Intestinal, Hepatic, and Renal Transporter-Mediated Clearances.

Rosuvastatin is a substrate of choice in clinical studies of organic anion-transporting polypeptide (OATP)1B1- and OATP1B3-associated drug interactions; thus, understanding the effect of OATP1B1 polymorphisms on the pharmacokinetics of rosuvastatin is crucial. Here, physiologically based pharmacokinetic (PBPK) modeling was coupled with a power calculation algorithm to evaluate the influence of sample size on the ability to detect an effect (80% power) of OATP1B1 phenotype on pharmacokinetics of rosuvastatin. Intestinal, hepatic, and renal transporters were mechanistically incorporated into a rosuvastatin PBPK model using permeability-limited models for intestine, liver, and kidney, respectively, nested within a full PBPK model. Simulated plasma rosuvastatin concentrations in healthy volunteers were in agreement with previously reported clinical data. Power calculations were used to determine the influence of sample size on study power while accounting for OATP1B1 haplotype frequency and abundance in addition to its correlation with OATP1B3 abundance. It was determined that 10 poor-transporter and 45 intermediate-transporter individuals are required to achieve 80% power to discriminate the AUC0-48h of rosuvastatin from that of the extensive-transporter phenotype. This number was reduced to 7 poor-transporter and 40 intermediate-transporter individuals when the reported correlation between OATP1B1 and 1B3 abundance was taken into account. The current study represents the first example in which PBPK modeling in conjunction with power analysis has been used to investigate sample size in clinical studies of OATP1B1 polymorphisms. This approach highlights the influence of interindividual variability and correlation of transporter abundance on study power and should allow more informed decision making in pharmacogenomic study design.

Identification and optimization of an aminoalcohol-carbazole series with antimalarial properties.

Recent observations on the emergence of artemisinin resistant parasites have highlighted the need for new antimalarial treatments. An HTS campaign led to the identification of the 1-(1-aminopropan-2-ol)carbazole analogues as potent hits against Plasmodium falciparum K1 strain. The SAR study and optimization of early ADME and physicochemical properties direct us to the selection of a late lead compound that shows good efficacy when orally administrated in the in vivo P. berghei mouse model.

Prediction of time-dependent CYP3A4 drug-drug interactions: impact of enzyme degradation, parallel elimination pathways, and intestinal inhibition.

Time-dependent inhibition of CYP3A4 often results in clinically significant drug-drug interactions. In the current study, 37 in vivo cases of irreversible inhibition were collated, focusing on macrolides (erythromycin, clarithromycin, and azithromycin) and diltiazem as inhibitors. The interactions included 17 different CYP3A substrates showing up to a 7-fold increase in AUC (13.5% of studies were in the range of potent inhibition). A systematic analysis of the impact of CYP3A4 degradation half-life (mean t1/2deg = 3 days, ranging from 1 to 6 days) on the prediction of the extent of interaction for compounds with a differential contribution from CYP3A4 to the overall elimination (defined by fmCYP3A4) was performed. Although the prediction accuracy was very sensitive to the CYP3A4 degradation rate for substrates mainly eliminated by this enzyme fm(CYP3A4 >or= 0.9), minimal effects are observed when CYP3A4 contributes less than 50% to the overall elimination in cases when the parallel elimination pathway is not subject to inhibition. Use of the mean CYP3A4 t1/2deg (3 days), average unbound systemic plasma concentration of the inhibitor, and the corresponding fm(CYP3A4) resulted in 89% of studies predicted within 2-fold of the in vivo value. The impact of the interaction in the gut wall was assessed by assuming maximal intestinal inhibition of CYP3A4. Although a reduced number of false-negative predictions was observed, there was an increased number of overpredictions, and generally, a loss of prediction accuracy was observed. The impact of the possible interplay between CYP3A4 and efflux transporters on the intestinal interaction requires further evaluation.