Using human recombinant UDP-glucuronosyltransferase isoforms and a relative activity factor approach to model total body clearance of laropiprant (MK-0524) in humans.

A major pathway of elimination of the prostaglandin D2 receptor 1 antagonist laropiprant in humans is by uridine diphosphate-glucuronosyltransferase (UGT)-mediated biotransformation. In this study, liver and kidney relative activity factors were developed for UGT1A1, 1A9 and 2B7 to allow for in vitro-in vivo extrapolation of intrinsic clearance data to whole organ clearance using recombinant human UGT isoforms applying this to laropiprant as a model substrate. The total body metabolic clearance of laropiprant determined using this approach (5.0 L/hr) agreed well with the value determined in vivo following intravenous administration to healthy human volunteers (5.1 L/hr). The results suggest that approximately 36%, 36% and 28% of the hepatic metabolic clearance of laropiprant was mediated by UGT1A1, 1A9 and 2B7, respectively. Likewise, 80% and 20% of the renal metabolic clearance was mediated by UGT1A9 and 2B7, respectively. Furthermore, the data suggested that the contribution of the kidney to the overall total metabolic clearance was minor relative to the liver (≈ 12%).

Physiologically Based Pharmacokinetic Modeling for Maribavir to Inform Dosing in Drug-Drug Interaction Scenarios with CYP3A4 Inducers and Inhibitors.

Maribavir, an orally available antiviral agent, has been approved in multiple countries for the treatment of patients with refractory post-transplant cytomegalovirus (CMV) infection and/or disease. Maribavir is primarily metabolized by CYP3A4; coadministration with CYP3A4 inducers and inhibitors may significantly alter maribavir exposure, thereby affecting its efficacy and safety. The effect of CYP3A4 inducers and inhibitors on maribavir exposure was evaluated based on a drug-drug interaction (DDI) study and physiologically-based pharmacokinetic (PBPK) modeling. The effect of rifampin (a strong inducer of CYP3A4 and moderate inducer of CYP1A2), administered at a 600 mg dose once daily, on maribavir pharmacokinetics was assessed in a clinical phase 1 DDI study in healthy participants. A full PBPK model for maribavir was developed and verified using in vitro and clinical pharmacokinetic data from phase 1 studies. The verified PBPK model was then used to simulate maribavir DDI interactions with various CYP3A4 inducers and inhibitors. The DDI study results showed that coadministration with rifampin decreased the maribavir maximum plasma concentration (Cmax ), area under the plasma concentration-time curve (AUC), and trough concentration (Ctrough ) by 39%, 60%, and 82%, respectively. Based on the results from the clinical DDI study, the coadministration of maribavir with rifampin is not recommended. The PBPK model did not predict a clinically significant effect of CYP3A4 inhibitors on maribavir exposure; however, it predicted that strong or moderate CYP3A4 inducers, including carbamazepine, efavirenz, phenobarbital, and phenytoin, may reduce maribavir exposure to a clinically significant extent, and may prompt the consideration of a maribavir dosing increase, in accordance with local approved labels and/or regulations.

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.

Sources of interindividual variability in IVIVE of clearance: an investigation into the prediction of benzodiazepine clearance using a mechanistic population-based pharmacokinetic model.

Prediction of metabolic clearance in extreme individuals rather than the 'average human' is becoming an attractive tool within the pharmaceutical industry. The current study involved prediction of variability in metabolic clearance for alprazolam, triazolam and midazolam with emphasis on the following factors: first, evaluation of clearance prediction accuracy using intrinsic clearance (CL(int)) data from in vitro metabolic data and back-calculation from in vivo clearance data. Second, the sensitivity of predicted in vivo variability to changes in variability for physiological parameters (e.g. liver weight, haematocrit, CYP3A abundance). Finally, reported estimates of variability in hepatic CYP3A4 abundance (coefficient of variation (CV) 95%) were refined by separating experimental from interindividual variability using a repeat measurement protocol in 52 human liver samples. Using in vitro metabolic data, predicted clearances were within 2-fold of observed for triazolam and midazolam. Clearance of alprazolam was overpredicted by 2.0- to 3.7-fold. Use of in vivo CL(int) values improved prediction of intravenous clearance to within 2-fold of observed for all drugs. Initially, the variability in clearance was overestimated for all drugs (by 1.8- to 3.6-fold). Use of a reduced hepatic CYP3A4 CV of 41%, representative of interindividual variability alone improved predictions of variability in clearance for all drugs to within 2-fold of observed.

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.

Using physiologically-based pharmacokinetic modeling for predicting the effects of hepatic impairment on the pharmacokinetics of olanzapine and samidorphan given as a combination tablet.

A combination of olanzapine and samidorphan (OLZ/SAM) was recently approved by the US Food and Drug Administration for treatment of patients with schizophrenia or bipolar I disorder. The effects of moderate hepatic impairment on the pharmacokinetics (PKs) of olanzapine and samidorphan after a single dose of OLZ/SAM were characterized in a clinical study. Physiologically-based pharmacokinetic (PBPK) modeling was used to extend the clinical findings to predict the effects of varying degrees of hepatic impairment on the PKs of olanzapine and samidorphan. A previously developed PBPK model for OLZ/SAM was refined to recover the observed pharmacokinetic differences between individuals with moderate hepatic impairment and healthy controls. The optimized model was applied to predict changes in olanzapine and samidorphan PKs after multiple once-daily doses of OLZ/SAM in subjects with mild, moderate, and severe hepatic impairment relative to healthy controls. Modifications to model parameters, including absorption rate constant and fraction unbound to plasma protein, were made to recover the observed change in the PKs of olanzapine and samidorphan in individuals with moderate hepatic impairment. In applying the optimized model, mild, moderate, and severe hepatic impairment were predicted to increase steady-state total systemic exposures by 1.1-, 1.5-, and 1.6-fold, respectively, for olanzapine, and by 1.2-, 1.9-, and 2.3-fold, respectively, for samidorphan. PBPK modeling allowed for prediction of untested clinical scenarios of varying degrees of hepatic impairment in lieu of additional clinical studies.

Scaling factors for the extrapolation of in vivo metabolic drug clearance from in vitro data: reaching a consensus on values of human microsomal protein and hepatocellularity per gram of liver.

Reported predictions of human in vivo hepatic clearance from in vitro data have used a variety of values for the scaling factors human microsomal protein (MPPGL) and hepatocellularity (HPGL) per gram of liver, generally with no consideration of the extent of their inter-individual variability. We have collated and analysed data from a number of sources, to provide weighted meangeo values of human MPPGL and HPGL of 32 mg g-1 (95% Confidence Interval (CI); 29-34 mg.g-1) and 99x10(6) cells.g-1 (95% CI; 74-131 mg.g-1), respectively. Although inter-individual variability in values of MPPGL and HPGL was statistically significant, gender, smoking or alcohol consumption could not be detected as significant covariates by multiple linear regression. However, there was a weak but statistically significant inverse relationship between age and both MPPGL and HPGL. These findings indicate the importance of considering differences between study populations when forecasting in vivo pharmacokinetic behaviour. Typical clinical pharmacology studies, particularly in early drug development, use young, fit, healthy male subjects of around 30 years of age. In contrast, the average age of patients for many diseases is about 60 years of age. The relationship between age and MPPGL observed in this study estimates values of 40 mg.g-1 for a 30 year old individual and 31 mg.g-1 for a 60 year old individual. Investigators may wish to consider the reported covariates in the selection of scaling factors appropriate for the population in which estimates of clearance are being predicted. Further studies are required to clarify the influence of age (especially in paediatric subjects), donor source and ethnicity on values of MPPGL and HPGL. In the meantime, we recommend that the estimates (and their variances) from the current meta-analysis be used when predicting in vivo kinetic parameters from in vitro data.

Differences in cytochrome p450-mediated pharmacokinetics between chinese and caucasian populations predicted by mechanistic physiologically based pharmacokinetic modelling.

BACKGROUND: International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) guidelines emphasize the need for better understanding of the influence of ethnicity on drug response to minimize duplication of clinical studies, thereby expediting drug approval. OBJECTIVES: We have developed a Chinese database for the prediction of differences in the population kinetics of drugs mainly metabolized by cytochromes P450 (CYPs) relative to Caucasian populations. Such predictions should help to inform the need for duplication of in vivo pharmacokinetic studies in the two ethnic groups and the design of such studies. METHODS: Demographic and physiological data for Chinese, along with information on CYP abundances and the frequencies of associated genetic polymorphisms in Chinese, were collated from literature sources and incorporated within the Simcyp Population-based Simulator(®) (v11.1). Default Simcyp parameter values for a virtual Caucasian population and for model compounds metabolized principally by specific CYPs were used as the point of reference. The drugs and the main CYPs involved in their metabolism were phenacetin (CYP1A2), desipramine (CYP2D6), tolbutamide (CYP2C9), omeprazole (CYP2C19), and alprazolam and midazolam (CYP3A). Hydroxy bupropion formation was used as a more sensitive marker of CYP2B6 activity than bupropion kinetics. Observed plasma drug concentration-time profiles and pharmacokinetic parameters after oral and, where possible, intravenous dosing were obtained from published in vivo studies in both Chinese and Caucasian subjects. Virtual subjects generated within Simcyp were matched to the subjects used in the in vivo studies with respect to age, sex, dosage and, where possible, CYP phenotype frequency. Predicted and observed plasma drug concentrations and weight-normalized clearances were compared between the ethnic groups. RESULTS: Significant differences were identified between Chinese and Caucasian populations in the frequency of CYP2C19 poor metabolizers (PMs) [Chinese 13 %; Caucasian 2.4 %], CYP2D6 PMs and intermediate metabolizers (IMs) [Chinese PMs 0.3 %, IMs 39 %; Caucasian PMs 8 %, IMs <1 %], the hepatic abundance of CYP2C19 (mean values: Chinese 8 pmol/mg; Caucasian 14 pmol/mg) and liver weight (mean values: Chinese 1198 g; Caucasian 1603 g). The observed plasma drug concentration-time profiles and weight-normalized clearances were predicted with reasonable accuracy (100 % within twofold; 89 % within 1.5-fold) in both ethnic groups. The predicted phenacetin, tolbutamide, omeprazole, desipramine, midazolam (intravenous), midazolam (oral), alprazolam (intravenous) and alprazolam (oral) clearances were 36, 25, 51, 43, 24, 17, 21 and 22 % lower, respectively, in Chinese than in Caucasians; the observed clearances were 28, 2, 75, 42, 19, 62, 20 and 21 % lower, respectively. Predicted and observed formation of hydroxy bupropion was lower in Caucasians than in Chinese (6 and 20 %, respectively). Differences between ethnic groups were less after normalization for body weight. CONCLUSION: The results of this study indicate the value of simulation based on mechanistic physiologically based pharmacokinetic modelling (PBPK) in anticipating the likely extent of any differences in the kinetics of CYP substrates in Chinese and Caucasian populations arising from demographic, physiological and genetic differences.

Application of permeability-limited physiologically-based pharmacokinetic models: part I-digoxin pharmacokinetics incorporating P-glycoprotein-mediated efflux.

A prerequisite for the prediction of the magnitude of P-glycoprotein (P-gp)-mediated drug-drug interactions between digoxin and P-gp inhibitors (e.g. verapamil and its metabolite norverapamil) or P-gp inducers (e.g. rifampicin) is a predictive pharmacokinetic model for digoxin itself. Thus, relevant in vitro metabolic, transporter and inhibitory data incorporated into permeability-limited models, such as the "advanced dissolution, absorption and metabolism" (ADAM) module and the permeability-limited liver (PerL) module, integrated with a mechanistic physiologically-based pharmacokinetic (PBPK) model such as that of the Simcyp Simulator (version 12.2) are necessary. Simulated concentration-time profiles of digoxin generated using the developed model were consistent with observed data across 31 independent studies [13 intravenous single dose (SD), 12 per oral SD and six multiple dose studies]. The fact that predicted tmax (time of maximum plasma concentration observed) and Cmax (maximum plasma concentration observed) of oral digoxin were similar to observed values indicated that the relative contributions of permeation and P-gp-mediated efflux in the model were appropriate. There was no indication of departure from dose proportionality over the dose range studied (0.25-1.5 mg). All dose normalised area under the plasma concentration-time curve profiles (AUCs) for the 0.25, 0.5, 0.75 and 1 mg doses resembled each other. Thus, PBPK modelling in conjunction with mechanistic absorption and distribution models and reliable in vitro transporter data can be used to assess the impact of dose on P-gp-mediated efflux (or otherwise).

Age related changes in fractional elimination pathways for drugs: assessing the impact of variable ontogeny on metabolic drug-drug interactions.

The magnitude of any metabolic drug-drug interactions (DDIs) depends on fractional importance of inhibited pathway which may not necessarily be the same in young children when compared to adults. The ontogeny pattern of cytochrome P450 (CYP) enzymes (CYPs 1A2, 2B6, 2C8, 2C9, 2C18/19, 2D6, 2E1, 3A4) and renal function were analyzed systematically. Bootstrap methodology was used to account for variability, and to define the age range over which statistical differences existed between each pair of specific pathways. A number of DDIs were simulated (Simcyp Pediatric v12) for virtual compounds to highlight effects of age on fractional elimination and consequent magnitude of DDI. For a theoretical drug metabolized 50% by each of CYP2D6 and CYP3A4 pathways at birth, co-administration of ketoconazole (3 mg/kg) resulted in a 1.65-fold difference between inhibited versus uninhibited AUC compared to 2.4-fold in 1 year olds and 3.2-fold in adults. Conversely, neonates could be more sensitive to DDI than adults in certain scenarios. Thus, extrapolation from adult data may not be applicable across all pediatric age groups. The use of pediatric physiologically based pharmacokinetic (p-PBPK) models may offer an interim solution to uncovering potential periods of vulnerability to DDI where there are no existing clinical data derived from children.

Application of permeability-limited physiologically-based pharmacokinetic models: part II - prediction of P-glycoprotein mediated drug-drug interactions with digoxin.

Digoxin is the recommended substrate for assessment of P-glycoprotein (P-gp)-mediated drug-drug interactions (DDIs) in vivo. The overall aim of our study was to investigate the inhibitory potential of both verapamil and norverapamil on the P-gp-mediated efflux of digoxin in both gut and liver. Therefore, a physiologically-based pharmacokinetic (PBPK) model for verapamil and its primary metabolite was developed and validated through the recovery of observed clinical plasma concentration data for both moieties and the reported interaction with midazolam, albeit a cytochrome P450 3A4-mediated DDI. The validated inhibitor model was then used in conjunction with the model developed previously for digoxin. The range of values obtained for the 10 trials indicated that increases in area under the plasma concentration-time curve (AUC) profiles and maximum plasma concentration observed (Cmax ) values of digoxin following administration of verapamil were more comparable with in vivo observations, when P-gp inhibition by the metabolite, norverapamil, was considered as well. The predicted decrease in AUC and Cmax values of digoxin following administration of rifampicin because of P-gp induction was 1.57- (range: 1.42-1.77) and 1.62-fold (range: 1.53-1.70), which were reasonably consistent with observed values of 1.4- and 2.2-fold, respectively. This study demonstrates the application of permeability-limited models of absorption and distribution within a PBPK framework together with relevant in vitro data on transporters to assess the clinical impact of modulated P-gp-mediated efflux by drugs in development.