Prediction of pharmacokinetic profile of valsartan in humans based on in vitro uptake-transport data

Prediction of Drug Clearance from Enzyme and Transporter Kinetics

Accurate estimation of in vivo clearance in human is pivotal to determine the dose and dosing regimen for drug development. In vitro-in vivo extrapolation (IVIVE) has been performed to predict drug clearance using empirical and physiological scalars. Multiple in vitro systems and mathematical modeling techniques have been employed to estimate in vivo clearance. The models for predicting clearance have significantly improved and have evolved to become more complex by integrating multiple processes such as drug metabolism and transport as well as passive diffusion. This chapter covers the use of conventional as well as recently developed methods to predict metabolic and transporter-mediated clearance along with the advantages and disadvantages of using these methods and the associated experimental considerations. The general approaches to improve IVIVE by use of appropriate scalars, incorporation of extrahepatic metabolism and transport and application of physiologically based pharmacokinetic (PBPK) models with proteomics data are also discussed. The chapter also provides an overview of the advantages of using such dynamic mechanistic models over static models for clearance predictions to improve IVIVE.

A Study of the Effect of Cyclosporine on Fevipiprant Pharmacokinetics and its Absolute Bioavailability Using an Intravenous Microdose Approach

This drug-drug interaction study determined the effect of cyclosporine, an inhibitor of organic anion transporting polypeptide (OATP) 1B3 and P-gp, on the pharmacokinetics (PK) of fevipiprant, an oral, highly selective, competitive antagonist of the prostaglandin D₂ receptor 2 and a substrate of the two transporters. The concomitant administration of an intravenous microdose of stable isotope-labeled fevipiprant provided the absolute bioavailability of fevipiprant as well as mechanistic insights into its PK and sensitivity to drug interactions. Liquid chromatography-mass spectrometry/mass spectrometry was used to measure plasma and urine concentrations. Geometric mean ratios [90% confidence interval (CI)] for oral fevipiprant with or without cyclosporine were 3.02 (2.38, 3.82) for C _max, 2.50 (2.17, 2.88) for AUC_last, and 2.35 (1.99, 2.77) for AUC_inf The geometric mean ratios (90% CI) for fevipiprant intravenous microdose with or without cyclosporine were 1.04 (0.86, 1.25) for C _max, 2.04 (1.83, 2.28) for AUC_last, and 1.95 (1.76, 2.16) for AUC_inf The absolute bioavailability for fevipiprant was approximately 0.3 to 0.4 in the absence and 0.5 in the presence of cyclosporine. The intravenous microdose allowed differentiation between systemic and presystemic effects of cyclosporine on fevipiprant, demonstrating a small (approximately 1.2-fold) presystemic effect of cyclosporine and a larger (approximately twofold) effect on systemic elimination of fevipiprant. Uptake by OATP1B3 appears to be the rate-limiting step in the hepatic elimination of fevipiprant, whereas P-gp does not have a relevant effect on oral absorption. SIGNIFICANCE STATEMENT: The drug interaction investigated here with cyclosporine, an inhibitor of several drug transporters, provides a refined quantitative understanding of the role of active transport processes in liver and intestine for the absorption and elimination of fevipiprant as well as the basis to assess the need for dose adjustment in the presence of transporter inhibitors. The applied intravenous microdose approach presents a strategy to maximize learnings from a trial, limit the number and duration of clinical trials, and enhance mechanistic drug-drug interaction understanding.

Cell cultures in drug discovery and development: The need of reliable in vitro-in vivo extrapolation for pharmacodynamics and pharmacokinetics assessment

For ethical and cost-related reasons, use of animals for the assessment of mode of action, metabolism and/or toxicity of new drug candidates has been increasingly scrutinized in research and industrial applications. Implementation of the 3 "Rs"¹; rule (Reduction, Replacement, Refinement) through development of in silico or in vitro assays has become an essential element of risk assessment. Physiologically based pharmacokinetic (PBPK²) modeling is the most potent in silico tool used for extrapolation of pharmacokinetic parameters to animal or human models from results obtained in vitro. Although, many types of in vitro assays are conducted during drug development, use of cell cultures is the most reliable one. Two-dimensional (2D) cell cultures have been a part of drug development for many years. Nowadays, their role is decreasing in favor of three-dimensional (3D) cell cultures and co-cultures. 3D cultures exhibit protein expression patterns and intercellular junctions that are closer to in vivo states in comparison to classical monolayer cultures. Co-cultures allow for examinations of the mutual influence of different cell lines. However, the complexity and high costs of co-cultures and 3D equipment exclude such methods from high-throughput screening (HTS).³In vitro absorption, distribution, metabolism, and excretion assessment, as well as drug-drug interaction (DDI), are usually performed with the use of various cell culture based assays. Progress in in silico and in vitro methods can lead to better in vitro-in vivo extrapolation (IVIVE⁴) outcomes and have a potential to contribute towards a significant reduction in the number of laboratory animals needed for drug research. As such, concentrated efforts need to be spent towards the development of an HTS in vitro platform with satisfactory IVIVE features.

Evaluation of the pharmacokinetic and pharmacodynamic drug interactions between cilnidipine and valsartan, in healthy volunteers

PURPOSE

Although cilnidipine and valsartan are widely coadministered to patients with hypertension, their drug-drug interaction potential has not been investigated. This study compared the pharmacokinetic (PK), pharmacodynamic (PD), and tolerability profiles of cilnidipine and valsartan, both alone and in combination, in healthy male subjects.

PATIENTS AND METHODS

Fifty-four subjects, enrolled into an open-label, single-dose, three-treatment, three-period crossover study, randomly received cilnidipine (10 mg), valsartan (160 mg), or both according to one of six sequences. Blood samples were collected at baseline and up to 24 hours after drug administration in each period. Plasma concentrations of cilnidipine and valsartan were determined by liquid chromatography with tandem mass spectrometry. Maximum plasma concentration (Cmax) and area under the concentration-time curve from 0 to the last measurable time (AUC(last)) were estimated using a noncompartmental method. Tolerability was evaluated by assessing adverse events (AEs), vital signs, electrocardiograms, and clinical laboratory tests. Blood pressure was also measured for PD assessment.

RESULTS

A total of 51 subjects completed the study. The PK profile of cilnidipine was not significantly affected by coadministered valsartan; the geometric mean ratio and 90% confidence interval (90% CI) of AUC(last) for cilnidipine with and without valsartan was 1.04 (0.98-1.10). Likewise, cilnidipine did not affect the PK of valsartan; the geometric mean ratio (90% CI) of AUC(last) for valsartan with and without cilnidipine was 0.94 (0.83-1.07). Coadministration of cilnidipine and valsartan reduced blood pressure in an additive way. No serious AEs were reported, and both cilnidipine and valsartan were well tolerated.

CONCLUSION

Coadministered cilnidipine and valsartan do not cause a significant PK or PD interaction, and they are well tolerated.

A "middle-out" approach to human pharmacokinetic predictions for OATP substrates using physiologically-based pharmacokinetic modeling

Physiologically based pharmacokinetic (PBPK) models provide a framework useful for generating credible human pharmacokinetic predictions from data available at the earliest, preclinical stages of pharmaceutical research. With this approach, the pharmacokinetic implications of in vitro data are contextualized via scaling according to independent physiological information. However, in many cases these models also require model-based estimation of additional empirical scaling factors (SFs) in order to accurately recapitulate known human pharmacokinetic behavior. While this practice clearly improves data characterization, the introduction of empirically derived SFs may belie the extrapolative power commonly attributed to PBPK. This is particularly true when such SFs are compound dependent and/or when there are issues with regard to identifiability. As such, when empirically-derived SFs are necessary, a critical evaluation of parameter estimation and model structure are prudent. In this study, we applied a global optimization method to support model-based estimation of a single set of empirical SFs from intravenous clinical data on seven OATP substrates within the context of a previously published PBPK model as well as a revised PBPK model. The revised model with experimentally measured unbound fraction in liver, permeability between liver compartments, and permeability limited distribution to selected tissues improved data characterization. We utilized large-sample approximation and resampling approaches to estimate confidence intervals for the revised model in support of forward predictions that reflect the derived uncertainty. This work illustrates an objective approach to estimating empirically-derived SFs, systematically refining PBPK model performance and conveying the associated confidence in subsequent forward predictions.

Dose selection based on physiologically based pharmacokinetic (PBPK) approaches

Physiologically based pharmacokinetic (PBPK) models are built using differential equations to describe the physiology/anatomy of different biological systems. Readily available in vitro and in vivo preclinical data can be incorporated into these models to not only estimate pharmacokinetic (PK) parameters and plasma concentration-time profiles, but also to gain mechanistic insight into compound properties. They provide a mechanistic framework to understand and extrapolate PK and dose across in vitro and in vivo systems and across different species, populations and disease states. Using small molecule and large molecule examples from the literature and our own company, we have shown how PBPK techniques can be utilised for human PK and dose prediction. Such approaches have the potential to increase efficiency, reduce the need for animal studies, replace clinical trials and increase PK understanding. Given the mechanistic nature of these models, the future use of PBPK modelling in drug discovery and development is promising, however some limitations need to be addressed to realise its application and utility more broadly.

Use of mechanistic modeling to assess interindividual variability and interspecies differences in active uptake in human and rat hepatocytes

Interindividual variability in activity of uptake transporters is evident in vivo, yet limited data exist in vitro, confounding in vitro-in vivo extrapolation. The uptake kinetics of seven organic anion-transporting polypeptide substrates was investigated over a concentration range in plated cryopreserved human hepatocytes. Active uptake clearance (CL(active, u)), bidirectional passive diffusion (P(diff)), intracellular binding, and metabolism were estimated for bosentan, pitavastatin, pravastatin, repaglinide, rosuvastatin, telmisartan, and valsartan in HU4122 donor using a mechanistic two-compartment model in Matlab. Full uptake kinetics of rosuvastatin and repaglinide were also characterized in two additional donors, whereas for the remaining drugs CL(active, u) was estimated at a single concentration. The unbound affinity constant (K(m, u)) and P(diff) values were consistent across donors, whereas V(max) was on average up to 2.8-fold greater in donor HU4122. Consistency in K(m, u) values allowed extrapolation of single concentration uptake activity data and assessment of interindividual variability in CL(active) across donors. The maximal contribution of active transport to total uptake differed among donors, for example, 85 to 96% and 68 to 87% for rosuvastatin and repaglinide, respectively; however, in all cases the active process was the major contributor. In vitro-in vivo extrapolation indicated a general underprediction of hepatic intrinsic clearance, an average empirical scaling factor of 17.1 was estimated on the basis of seven drugs investigated in three hepatocyte donors, and donor-specific differences in empirical factors are discussed. Uptake K(m, u) and CL(active, u) were on average 4.3- and 7.1-fold lower in human hepatocytes compared with our previously published rat data. A strategy for the use of rat uptake data to facilitate the experimental design in human hepatocytes is discussed.

Membrane transporters in drug development

Membrane transporters have wide, but specific tissue distributions. They can impact on multiple endogenous and xenobiotic processes. Knowledge and awareness within the pharmaceutical industry of their impact on drug absorption, distribution, metabolism and elimination (ADME) and drug safety is growing rapidly. Clinically important transporter-mediated drug-drug interactions (DDIs) have been observed. Up to nine diverse transporters are implicated in the DDIs of a number of widely prescribed drugs, posing a significant challenge to the pharmaceutical industry. There is a complex interplay between multiple transporters and/or enzymes in the ADME and pharmacogenomics of drugs. Integrating these different mechanisms to understand their relative contributions to ADME is a key challenge. Many different factors complicate the study of membrane transporters in drug development. These include a lack of specific substrates and inhibitors, non-standard in vitro tools, and competing/complementary mechanisms (e.g. passive permeability and metabolism). Discovering and contextualizing the contribution of membrane transporters to drug toxicity is a significant new challenge. Drug interactions with key membrane transporters are routinely assessed for central nervous system (CNS) drug discovery therapies, but are not generally considered across the wider drug discovery. But, there is interest in utilizing membrane transporters as drug delivery agents. Computational modeling approaches, notably physiology-based/pharmacokinetic (PB/PK) modeling are increasingly applied to transporter interactions, and permit integration of multiple ADME mechanisms. Because of the range of tissues and transporters of interest, robust transporter, in vitro to in vivo, scaling factors are required. Empirical factors have been applied, but absolute protein quantitation will probably be required.

Simultaneous assessment of uptake and metabolism in rat hepatocytes: a comprehensive mechanistic model

Kinetic parameters describing hepatic uptake in hepatocytes are frequently estimated without appropriate incorporation of bidirectional passive diffusion, intracellular binding, and metabolism. A mechanistic two-compartment model was developed to describe all of the processes occurring during the in vitro uptake experiments performed in freshly isolated rat hepatocytes plated for 2 h. Uptake of rosuvastatin, pravastatin, pitavastatin, valsartan, bosentan, telmisartan, and repaglinide was investigated over a 0.1 to 300 μM concentration range at 37°C for 2 or 45-90 min; nonspecific binding was taken into account. All concentration-time points were analyzed simultaneously by using a mechanistic two-compartment model describing uptake kinetics [unbound affinity constant (K(m,u)), maximum uptake rate (V(max)), unbound active uptake clearance (CL(active,u))], passive diffusion [unbound passive diffusion clearance (P(diff,u))], and intracellular binding [intracellular unbound fraction (fu(cell))]. When required (telmisartan and repaglinide), the model was extended to account for the metabolism [unbound metabolic clearance (CL(met,u))]. The CL(active,u) ranged 8-fold, reflecting a 11-fold range in uptake K(m,u), with telmisartan and valsartan showing the highest affinity for uptake transporters (K(m,u) <10 μM). Both P(diff,u) and fu(cell) span over two orders of magnitude and reflected the lipophilicity of the drugs in the dataset. An extended incubation time allowed steady state to be reached between media and intracellular compartment concentrations and reduced the error in certain parameter estimates observed with shorter incubation times. Active transport accounted for >70% of total uptake for all drugs investigated and was 4- and 112-fold greater than CL(met,u) for telmisartan and repaglinide, respectively. Modeling of uptake kinetics in conjunction with metabolism improved the precision of the uptake parameter estimates for repaglinide and telmisartan. Recommendations are made for uptake experimental design and modeling strategies.

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.