The use of mechanistic DM-PK-PD modelling to assess the power of pharmacogenetic studies -CYP2C9 and warfarin as an example

Modeled Hepatic/Plasma Exposures of Fluvastatin Prescribed Alone in Subjects with Impaired Cytochrome P450 2C9*3 as One of Possible Determinant Factors Likely Associated with Hepatic Toxicity Reported in a Japanese Adverse Event Database

Fluvastatin is a 3-hydroxy-3-methylglutaryl CoA reductase inhibitor that competitively inhibits human cytochrome P450 (P450) 2C9 in vitro. Drug interactions between a variety of P450 2C9 substrates/inhibitors and fluvastatin can increase the incidence of fluvastatin-related hepatic or skeletal muscle toxicity in vivo. In this survey, the prescribed dosage of fluvastatin was reduced or discontinued in 133 of 164 patients receiving fluvastatin alone, as recorded in the Japanese Adverse Drug Event Report database of spontaneously reported events. The median days to onset of fluvastatin-related disorders were in the range 30-35 d in the 87 patients. Therefore, we aimed to focus on fluvastatin and, using the pharmacokinetic modeling technique, estimated the virtual plasma and hepatic exposures in subjects harboring the impaired CYP2C9*3 allele. The plasma concentrations of fluvastatin modeled after a virtual oral 20-mg dose increased in homozygotes with CYP2C9*3; the area under the plasma concentration curve was 4.9-fold higher than that in Japanese homozygotes for wild-type CYP2C9*1. The modeled hepatic concentrations of fluvastatin in patients with CYP2C9*3/*3 after virtual daily 20-mg doses for 7 d were 31-fold higher than those in subjects with CYP2C9*1/*1. However, heterozygous Chinese patients with CYP2C9*1/*3 reportedly have a limited elevation (1.2-fold) in plasma maximum concentrations. Virtual hepatic/plasma exposures in subjects harboring the impaired CYP2C9*3 allele estimated using pharmacokinetic modeling indicate that such exposure could be a causal factor for hepatic disorders induced by fluvastatin prescribed alone in a manner similar to that for interactions with a variety of co-administered drugs.

Pharmacometrics-Based Considerations for the Design of a Pharmacogenomic Clinical Trial Assessing Irinotecan Safety

PURPOSE

Pharmacometric models provide useful tools to aid the rational design of clinical trials. This study evaluates study design-, drug-, and patient-related features as well as analysis methods for their influence on the power to demonstrate a benefit of pharmacogenomics (PGx)-based dosing regarding myelotoxicity.

METHODS

Two pharmacokinetic and one myelosuppression model were assembled to predict concentrations of irinotecan and its metabolite SN-38 given different UGT1A1 genotypes (poor metabolizers: CLSN-38: -36%) and neutropenia following conventional versus PGx-based dosing (350 versus 245 mg/m2 (-30%)). Study power was assessed given diverse scenarios (n = 50-400 patients/arm, parallel/crossover, varying magnitude of CLSN-38, exposure-response relationship, inter-individual variability) and using model-based data analysis versus conventional statistical testing.

RESULTS

The magnitude of CLSN-38 reduction in poor metabolizers and the myelosuppressive potency of SN-38 markedly influenced the power to show a difference in grade 4 neutropenia (<0.5·109 cells/L) after PGx-based versus standard dosing. To achieve >80% power with traditional statistical analysis (χ2/McNemar's test, α = 0.05), 220/100 patients per treatment arm/sequence (parallel/crossover study) were required. The model-based analysis resulted in considerably smaller total sample sizes (n = 100/15 given parallel/crossover design) to obtain the same statistical power.

CONCLUSIONS

The presented findings may help to avoid unfeasible trials and to rationalize the design of pharmacogenetic studies.

Physiologically based pharmacokinetic modeling to assess metabolic drug-drug interaction risks and inform the drug label for fedratinib

Purpose

Fedratinib (INREBIC^®), a Janus kinase 2 inhibitor, is approved in the United States to treat patients with myelofibrosis. Fedratinib is not only a substrate of cytochrome P450 (CYP) enzymes, but also exhibits complex auto-inhibition, time-dependent inhibition, or mixed inhibition/induction of CYP enzymes including CYP3A. Therefore, a mechanistic modeling approach was used to characterize pharmacokinetic (PK) properties and assess drug–drug interaction (DDI) potentials for fedratinib under clinical scenarios.

Methods

The physiologically based pharmacokinetic (PBPK) model of fedratinib was constructed in Simcyp^® (V17R1) by integrating available in vitro and in vivo information and was further parameterized and validated by using clinical PK data.

Results

The validated PBPK model was applied to predict DDIs between fedratinib and CYP modulators or substrates. The model simulations indicated that the fedratinib-as-victim DDI extent in terms of geometric mean area under curve (AUC) at steady state is about twofold or 1.2-fold when strong or moderate CYP3A4 inhibitors, respectively, are co-administered with repeated doses of fedratinib. In addition, the PBPK model successfully captured the perpetrator DDI effect of fedratinib on a sensitive CY3A4 substrate midazolam and predicted minor effects of fedratinib on CYP2C8/9 substrates.

Conclusions

The PBPK-DDI model of fedratinib facilitated drug development by identifying DDI potential, optimizing clinical study designs, supporting waivers for clinical studies, and informing drug label claims. Fedratinib dose should be reduced to 200 mg QD when a strong CYP3A4 inhibitor is co-administered and then re-escalated to 400 mg in a stepwise manner as tolerated after the strong CYP3A4 inhibitor is discontinued.

Electronic supplementary material

The online version of this article (10.1007/s00280-020-04131-y) contains supplementary material, which is available to authorized users.

Potential Underprediction of Warfarin Drug Interaction From Conventional Interaction Studies and Risk Mitigation: A Case Study With Epacadostat, an IDO1 Inhibitor

Drug-drug interaction (DDI) studies involving warfarin are typically conducted with subtherapeutic doses of warfarin to ensure the safety of volunteers. However, this approach may potentially have a systemic bias of underestimating pharmacodynamic (PD) DDI effect on warfarin at therapeutic levels of anticoagulation. We demonstrate here the utility of model-based DDI prediction for a clinically relevant warfarin regimen, using the example of epacadostat (INCB024360), the first-in-class indoleamine 2,3-dioxygenase 1 inhibitor in clinical development as a novel orally active immuno-oncological therapy. Observed data from a dedicated clinical DDI study using subtherapeutic warfarin suggested warfarin pharmacokinetics (PK), but not PD (anticoagulation), was significantly affected by concomitant epacadostat. However, subsequent PK/PD modeling and simulations indicated a clinically important DDI effect on warfarin PD at a higher baseline of the international normalization ratio (INR) and enabled recommendation of warfarin dose adjustment that is dependent on epacadostat dosing regimen and target INR.

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.

Assessment of drug metabolism enzyme and transporter pharmacogenetics in drug discovery and early development: perspectives of the I-PWG

Genetic variants of drug metabolism enzymes and transporters can result in high pharmacokinetic and pharmacodynamic variability, unwanted characteristics of efficacious and safe drugs. Ideally, the contributions of these enzymes and transporters to drug disposition can be predicted from in vitro experiments and in silico modeling in discovery or early development, and then be utilized during clinical development. Recently, regulatory agencies have provided guidance on the preclinical investigation of pharmacogenetics, for application to clinical drug development. This white paper summarizes the results of an industry survey conducted by the Industry Pharmacogenomics Working Group on current practice and challenges with using in vitro systems and in silico models to understand pharmacogenetic causes of variability in drug disposition.

Prediction of in vivo clearance and associated variability of CYP2C19 substrates by genotypes in populations utilizing a pharmacogenetics-based mechanistic model

It is important to examine the cytochrome P450 2C19 (CYP2C19) genetic contribution to drug disposition and responses of CYP2C19 substrates during drug development. Design of such clinical trials requires projection of genotype-dependent in vivo clearance and associated variabilities of the investigational drug, which is not generally available during early stages of drug development, but is essential for CYP2C19 substrates with multiple clearance pathways. This study evaluated the utility of pharmacogenetics-based mechanistic modeling in predicting such parameters. Hepatic CYP2C19 activity and variability within genotypes were derived from in vitro S-mephenytoin metabolic activity in genotyped human liver microsomes (N = 128). These data were then used in mechanistic models to predict genotype-dependent disposition of CYP2C19 substrates (i.e., S-mephenytoin, citalopram, pantoprazole, and voriconazole) by incorporating in vivo clearance or pharmacokinetics of wild-type subjects and parameters of other clearance pathways. Relative to the wild-type, the CYP2C19 abundance (coefficient of variation percentage) in CYP2C19*17/*17, *1/*17, *1/*1, *17/null, *1/null, and null/null microsomes was estimated as 1.85 (117%), 1.79 (155%), 1.00 (138%), 0.83 (80%), 0.38 (130%), and 0 (0%), respectively. The subsequent modeling and simulations predicted, within 2-fold of the observed, the means and variabilities of urinary S/R-mephenytoin ratio (36 of 37 genetic groups), the oral clearance of citalopram (9 of 9 genetic groups) and pantoprazole (6 of 6 genetic groups), and voriconazole oral clearance (4 of 4 genetic groups). Thus, relative CYP2C19 genotype-dependent hepatic activity and variability were quantified in vitro and used in a mechanistic model to predict pharmacokinetic variability, thus allowing the design of pharmacogenetics and drug-drug interaction trials for CYP2C19 substrates.

Mechanistic insights into the effect of CYP2C9*2 and CYP2C9*3 variants on the 7-hydroxylation of warfarin

AIM

To evaluate the impact of CYP2C9*2 and CYP2C9*3 variants on binding and hydroxylation of warfarin.

MATERIALS & METHODS

Multiple linear regression model of warfarin pharmacokinetics was developed from the dataset of patients (n = 199). Pymol based in silico models were developed for the genetic variants.

RESULTS

CYP2C9*2 and CYP2C9*3 variants exhibited high warfarin/7-hydroxywarfarin (multiple linear regression model), dose-dependent disruption of hydrogen bonds with warfarin, dose-dependent increase in the distance between C7 of S-warfarin and Fe-O of CYP2C9, dose-dependent decrease in the glide scores (in silico).

CONCLUSION

CYP2C9*2 and CYP2C9*3 variants result in disruption of hydrogen bonding interactions with warfarin and longer distance between C7 and Fe-O thus impairing warfarin 7-hydroxylation due to lower binding affinity of warfarin. Original submitted 7 May 2014; Revision submitted 30 October 2014.

Computer-based prediction of the drug proarrhythmic effect: problems, issues, known and suspected challenges

It is likely that computer modelling and simulations will become an element of comprehensive cardiac safety testing. Their role would be primarily the integration and the interpretation of previously gathered data. There are still unanswered questions and issues which we list and describe below. They include sources of data used for the development of the models as well as data utilized as input information, which can come from the in vitro studies and the quantitative structure-activity relationship models. The pharmacokinetics of the drugs in question play a crucial role as their active concentration should be considered, yet the question remains where is the right place to assess it. The pharmacodynamic angle includes complications coming from multiple drugs (i.e. active metabolites) acting in parallel as well as the type of interaction with (potentially) multiple affected channels. Once established, the model and the methodology of its use should be further validated, optimistically against individual data reported at the clinical level as the physiological, anatomical, and genetic parameters play a crucial role in the drug-triggered arrhythmia induction. All the abovementioned issues should be at least considered and-hopefully-resolved, to properly utilize the mathematical models for a cardiac safety assessment.

Mechanism-based pharmacokinetic modeling to evaluate transporter-enzyme interplay in drug interactions and pharmacogenetics of glyburide

The purpose of this study is to characterize the involvement of hepato-biliary transport and cytochrome-P450 (CYP)-mediated metabolism in the disposition of glyburide and predict its pharmacokinetic variability due to drug interactions and genetic variations. Comprehensive in vitro studies suggested that glyburide is a highly permeable drug with substrate affinity to multiple efflux pumps and to organic anion transporting polypeptide (OATP)1B1 and OATP2B1. Active hepatic uptake was found to be significantly higher than the passive uptake clearance (15.8 versus 5.3 μL/min/10(6)-hepatocytes), using the sandwich-cultured hepatocyte model. In vitro, glyburide is metabolized (intrinsic clearance, 52.9 μL/min/mg-microsomal protein) by CYP3A4, CYP2C9, and CYP2C8 with fraction metabolism of 0.53, 0.36, and 0.11, respectively. Using these in vitro data, physiologically based pharmacokinetic models, assuming rapid-equilibrium between blood and liver compartments or permeability-limited hepatic disposition, were built to describe pharmacokinetics and evaluate drug interactions. Permeability-limited model successfully predicted glyburide interactions with rifampicin and other perpetrator drugs. Conversely, model assuming rapid-equilibrium mispredicted glyburide interactions, overall, suggesting hepatic uptake as the primary rate-determining process in the systemic clearance of glyburide. Further modeling and simulations indicated that the impairment of CYP2C9 function has a minimal effect on the systemic exposure, implying discrepancy in the contribution of CYP2C9 to glyburide clearance.

Implications of pharmacogenetics for the therapeutic use of antiepileptic drugs

INTRODUCTION

Epilepsy is a chronic neurological disease manifesting as recurrent seizures. Despite the availability of numerous antiepileptic drugs (AEDs), one-third of the patients are not responsive to treatment. Such inter-individual variability in the response to AEDs may be partly explained by genetic differences. This review summarizes the pharmacogenetics (PGx) of AEDs. In addition, a model-based approach is presented that enables the integration of PGx data with other relevant sources of variability, such as demographic characteristics and co-medications.

AREAS COVERED

A comprehensive overview is provided of the data available in the literature on the evidence for correlations between genetic mutations and pharmacokinetic (PK) and/or pharmacodynamics (PD) of AEDs. This information is then used in an integrated manner in the second part, where PGx differences are parameterized as covariates in PK and PKPD models.

EXPERT OPINION

Polymorphisms are profuse in the PK and PD of AEDs. However, understanding of their clinical implication remains limited due to the lack of methodologies that discriminate the contribution of other sources of variability in CNS exposure to drugs. A model-based approach, in which other intrinsic (e.g., demographic covariates) and extrinsic (e.g., drug-drug interactions) factors are evaluated concurrently is needed to ensure optimization and individualization of treatment in epileptic patients.

Prediction of warfarin maintenance dose in Han Chinese patients using a mechanistic model based on genetic and non-genetic factors

BACKGROUND AND OBJECTIVES

Many attempts have been made to predict the warfarin maintenance dose in patients beginning warfarin therapy using a descriptive model based on multiple linear regression. Here we report the first attempt to develop a comprehensive mechanistic model integrating in vitro-in vivo extrapolation (IVIVE) with a pharmacokinetic-pharmacodynamic model to predict the warfarin maintenance dose in Han Chinese patients. The model incorporates demographic factors [sex, age, body weight (BW)] and the genetic polymorphisms of cytochrome P450 (CYP) 2C9 (CYP2C9) and vitamin K epoxide reductase complex subunit 1 (VKORC1).

METHODS

Information on the various factors, mean warfarin daily dose and International Normalized Ratio (INR) was available for a cohort of 197 Han Chinese patients. Based on in vitro enzyme kinetic parameters for S-warfarin metabolism, demographic data for Han Chinese and some scaling factors, the S-warfarin clearance (CL) was predicted for patients in the cohort with different CYP2C9 genotypes using IVIVE. The plasma concentration of S-warfarin after a single oral dose was simulated using a one-compartment pharmacokinetic model with first-order absorption and a lag time and was combined with a mechanistic coagulation model to simulate the INR response. The warfarin maintenance dose was then predicted based on the demographic data and genotypes of CYP2C9 and VKORC1 for each patient and using the observed steady-state INR (INRss) as a target value. Finally, sensitivity analysis was carried out to determine which factor(s) affect the warfarin maintenance dose most strongly.

RESULTS

The predictive performance of this mechanistic model is not inferior to that of our previous descriptive model. There were significant differences in the mean warfarin daily dose in patients with different CYP2C9 and VKORC1 genotypes. Using IVIVE, the predicted mean CL of S-warfarin for patients with CYP2C9*1/*3 (0.092 l/h, n = 11) was 57 % less than for those with wild-type *1/*1 (0.215 l/h, n = 186). In addition, *1/*1 patients needed about 1 week to reach steady state, whereas *1/*3 patients needed about 2 weeks. In terms of the predicted INRss values, only ten patients had INRss values outside the expected therapeutic range (1.5-2.8). To evaluate our mechanistic model, we predicted the warfarin maintenance dose for 183 patients and explained 42 % of its variation, which is comparable to our previous prediction using a descriptive model based on multiple linear regression. The mean predicted/observed warfarin doses (mg/day) for different combinations of CYP2C9 and VKORC1 genotypes were 1.54/3.75 (n = 1) for *1/*1 and GG, 3.33/3.66 (n = 36) for *1/*1 and AG, 2.31/2.41 (n = 136) for *1/*1 and AA, and 1.56/1.69 (n = 10) for *1/*3 and AA, respectively. Sensitivity analysis indicated BW and genetic polymorphisms of CYP2C9 and VKORC1 were important factors affecting the warfarin maintenance dose in the study population.

CONCLUSION

The mechanistic model reported is the first to integrate IVIVE with a pharmacokinetic-pharmacodynamic model to describe the association of the warfarin maintenance dose with sex, age, BW and the genotypes of CYP2C9 and VKORC1. The model was effective in predicting S-warfarin clearance and in simulating its plasma concentration-time curve in a cohort of Han Chinese patients. In addition, the model accurately predicted the INR response and warfarin maintenance dose in a cohort of Han Chinese patients.

CYP2B6 pharmacogenetics-based in vitro-in vivo extrapolation of efavirenz clearance by physiologically based pharmacokinetic modeling

Efavirenz is mainly cleared by CYP2B6. The CYP2B6*6 allele is associated with lower efavirenz clearance. Efavirenz clearance was predictable using in vitro data for carriers of the CYP2B6*1/*1 genotype, but the prediction in carriers of the CYP2B6*6 allele was poor. To test the hypothesis that incorporation of mechanism of reduced efavirenz metabolism by the CYP2B6*6 allele can predict the genetic effect on efavirenz pharmacokinetics, in vitro-in vivo extrapolation of efavirenz clearance was performed by physiologically based pharmacokinetic modeling (Simcyp Simulator; Simcyp Ltd., Sheffield, UK) using data obtained from expressed CYP2B6.1 and CYP2B6.6 as well as human liver microsomes (HLMs) with CYP2B6*1/*1, *1/*6, and *6/*6 genotypes. Simulated pharmacokinetics of a single 600-mg oral dose of efavirenz for individuals with each genotype was compared with data observed in healthy subjects genotyped for the CYP2B6*6 allele (n = 20). Efavirenz clearance for carriers of the CYP2B6*1/*1 genotype was predicted reasonably well using HLM data, but the clearance in carriers of the CYP2B6*6 allele was underpredicted using both expressed and HLM systems. Improved prediction of efavirenz clearance was obtained from expressed CYP2B6 after recalculating intersystem extrapolation factors for CYP2B6.1 and CYP2B6.6 based on in vitro intrinsic clearance of bupropion 4-hydroxylation. These findings suggest that genetic effect on both CYP2B6 protein expression and catalytic efficiency needs to be taken into account for the prediction of pharmacokinetics in individuals carrying the CYP2B6*6/*6 genotype. Expressed CYP2B6 proteins may be a reliable in vitro system to predict effect of the CYP2B6*6 allele on the metabolism of CYP2B6 substrates.

A systems approach to designing effective clinical trials using simulations

BACKGROUND

Pharmacogenetics in warfarin clinical trials have failed to show a significant benefit in comparison with standard clinical therapy. This study demonstrates a computational framework to systematically evaluate preclinical trial design of target population, pharmacogenetic algorithms, and dosing protocols to optimize primary outcomes.

METHODS AND RESULTS

We programmatically created an end-to-end framework that systematically evaluates warfarin clinical trial designs. The framework includes options to create a patient population, multiple dosing strategies including genetic-based and nongenetic clinical-based, multiple-dose adjustment protocols, pharmacokinetic/pharmacodynamics modeling and international normalization ratio prediction, and various types of outcome measures. We validated the framework by conducting 1000 simulations of the applying pharmacogenetic algorithms to individualize dosing of warfarin (CoumaGen) clinical trial primary end points. The simulation predicted a mean time in therapeutic range of 70.6% and 72.2% (P=0.47) in the standard and pharmacogenetic arms, respectively. Then, we evaluated another dosing protocol under the same original conditions and found a significant difference in the time in therapeutic range between the pharmacogenetic and standard arm (78.8% versus 73.8%; P=0.0065), respectively.

CONCLUSIONS

We demonstrate that this simulation framework is useful in the preclinical assessment phase to study and evaluate design options and provide evidence to optimize the clinical trial for patient efficacy and reduced risk.

Bottom-up modeling and simulation of tacrolimus clearance: prospective investigation of blood cell distribution, sex and CYP3A5 expression as covariates and assessment of study power

The objectives were to investigate the ability of population-based in vitro-in vivo extrapolation (IVIVE) to reproduce the influence of haematocrit on the clearance of tacrolimus, observed previously, and to assess the power of clinical studies to detect the effects of covariates on the clearance of tacrolimus. A population-based pharmacokinetic simulator (Simcyp) was used to simulate tacrolimus clearance from in vitro metabolism data and demographic characteristics of Japanese liver transplant patients (JLTs). The relationship between haematocrit and dose-to-concentration (D/C) ratio was validated using seven JLTs, whose highly variable haematocrit and D/C ratio were previously analysed. This validation was used as a surrogate for establishing 'interindividual' variability and to assess the power of clinical studies to discern the effect of haematocrit, sex and CYP3A5 genotype on tacrolimus clearance in a virtual JLT population. The relationship between haematocrit and D/C ratio was reproducible by Simcyp and corresponded well to those observed in seven JLTs. The number of JLTs required to detect the influence of CYP3A5 genotype and sex were estimated to be about 50 and > 600, respectively, which was consistent with the results of previous population pharmacokinetic studies for tacrolimus. In conclusion, population-based IVIVE is considered to be a useful approach to assess the influence of covariates a priori before conducting clinical studies. This is also helpful with study design and assessment of the statistical power of clinical studies involving population-based pharmacokinetics to detect the effects of covariates.

Physiologically-based pharmacokinetics in drug development and regulatory science

The application of physiologically-based pharmacokinetic (PBPK) modeling is coming of age in drug development and regulation, reflecting significant advances over the past 10 years in the predictability of key pharmacokinetic (PK) parameters from human in vitro data and in the availability of dedicated software platforms and associated databases. Specific advances and contemporary challenges with respect to predicting the processes of drug clearance, distribution, and absorption are reviewed, together with the ability to anticipate the quantitative extent of PK-based drug-drug interactions and the impact of age, genetics, disease, and formulation. The value of this capability in selecting and designing appropriate clinical studies, its implications for resource-sparing techniques, and a more holistic view of the application of PK across the preclinical/clinical divide are considered. Finally, some attention is given to the positioning of PBPK within the drug development and approval paradigm and its future application in truly personalized medicine.

The Simcyp population-based ADME simulator

The Simcyp population-based absorption, distribution, metabolism and excretion simulator is a platform and database for 'bottom-up' mechanistic modelling and simulation of the processes of oral absorption, tissue distribution, metabolism and excretion of drugs and drug candidates in healthy and disease populations. It combines experimental data generated routinely during preclinical drug discovery and development from in vitro enzyme and cellular systems and relevant physicochemical attributes of compound and dosage form with demographic, physiological and genetic information on different patient populations. The mechanistic approach implemented in the Simcyp Simulator allows simulation of complex absorption, distribution, metabolism and excretion outcomes, particularly those involving multiple drug interactions, parent drug and metabolite profiles and time- and dose-dependent phenomena such as auto-induction and auto-inhibition.This review describes the framework and organisation of the simulator and how it combines the different categories of information.

Novel variants of major drug-metabolising enzyme genes in diverse African populations and their predicted functional effects

Pharmacogenetics enables personalised therapy based on genetic profiling and is increasingly applied in drug discovery. Medicines are developed and used together with pharmacodiagnostic tools to achieve desired drug efficacy and safety margins. Genetic polymorphism of drug-metabolising enzymes such as cytochrome P450s (CYPs) and N-acetyltransferases (NATs) has been widely studied in Caucasian and Asian populations, yet studies on African variants have been less extensive. The aim of the present study was to search for novel variants of CYP2C9, CYP2C19, CYP2D6 and NAT2 genes in Africans, with a particular focus on their prevalence in different populations, their relevance to enzyme functionality and their potential for personalised therapy. Blood samples from various ethnic groups were obtained from the AiBST Biobank of African Populations. The nine exons and exon-intron junctions of the CYP genes and exon 2 of NAT2 were analysed by direct DNA sequencing. Computational tools were used for the identification, haplotype analysis and prediction of functional effects of novel single nucleotide polymorphisms (SNPs). Novel SNPs were discovered in all four genes, grouped to existing haplotypes or assigned new allele names, if possible. The functional effects of non-synonymous SNPs were predicted and known African-specific variants were confirmed, but no significant differences were found in the frequencies of SNPs between African ethnicities. The low prevalence of our novel variants and most known functional alleles is consistent with the generally high level of diversity in gene loci of African populations. This indicates that profiles of rare variants reflecting interindividual variability might become the most relevant pharmacodiagnostic tools explaining Africans' diversity in drug response.

Simulation and prediction of in vivo drug metabolism in human populations from in vitro data

The perceived failure of new drug development has been blamed on deficiencies in in vivo studies of drug efficacy and safety. Prior simulation of the potential exposure of different individuals to a given dose might help to improve the design of such studies. This should also help researchers to focus on the characteristics of individuals who present with extreme reactions to therapy. An effort to build virtual populations using extensive demographic, physiological, genomic and in vitro biochemical data to simulate and predict drug disposition from routinely collected in vitro data is outlined.