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

A pregnancy physiologically based pharmacokinetic (p-PBPK) model for disposition of drugs metabolized by CYP1A2, CYP2D6 and CYP3A4

AIMS

Pregnant women are usually not part of the traditional drug development programme. Pregnancy is associated with major biological and physiological changes that alter the pharmacokinetics (PK) of drugs. Prediction of the changes to drug exposure in this group of patients may help to prevent under- or overtreatment. We have used a pregnancy physiologically based pharmacokinetic (p-PBPK) model to assess the likely impact of pregnancy on three model compounds, namely caffeine, metoprolol and midazolam, based on the knowledge of their disposition in nonpregnant women and information from in vitro studies.

METHODS

A perfusion-limited form of a 13-compartment full-PBPK model (Simcyp® Simulator) was used for the nonpregnant women, and this was extended to the pregnant state by applying known changes to all model components (including the gestational related activity of specific cytochrome P450 enzymes) and through the addition of an extra compartment to represent the fetoplacental unit. The uterus and the mammary glands were grouped into the muscle compartment. The model was implemented in Matlab Simulink and validated using clinical observations.

RESULTS

The p-PBPK model predicted the PK changes of three model compounds (namely caffeine, metoprolol and midazolam) for CYP1A2, CYP2D6 and CYP3A4 during pregnancy within twofold of observed values. The changes during the third trimester were predicted to be a 100% increase, a 30% decrease and a 35% decrease in the exposure of caffeine, metoprolol and midazolam, respectively, compared with the nonpregnant women.

CONCLUSIONS

In the absence of clinical data, the in silico prediction of PK behaviour during pregnancy can provide a valuable aid to dose adjustment in pregnant women. The performance of the model for drugs metabolized by a single enzyme to different degrees (high and low extraction) and for drugs that are eliminated by several different routes warrants further study.

Cytochrome P450 enzymes in drug metabolism: regulation of gene expression, enzyme activities, and impact of genetic variation

Cytochromes P450 (CYP) are a major source of variability in drug pharmacokinetics and response. Of 57 putatively functional human CYPs only about a dozen enzymes, belonging to the CYP1, 2, and 3 families, are responsible for the biotransformation of most foreign substances including 70-80% of all drugs in clinical use. The highest expressed forms in liver are CYPs 3A4, 2C9, 2C8, 2E1, and 1A2, while 2A6, 2D6, 2B6, 2C19 and 3A5 are less abundant and CYPs 2J2, 1A1, and 1B1 are mainly expressed extrahepatically. Expression of each CYP is influenced by a unique combination of mechanisms and factors including genetic polymorphisms, induction by xenobiotics, regulation by cytokines, hormones and during disease states, as well as sex, age, and others. Multiallelic genetic polymorphisms, which strongly depend on ethnicity, play a major role for the function of CYPs 2D6, 2C19, 2C9, 2B6, 3A5 and 2A6, and lead to distinct pharmacogenetic phenotypes termed as poor, intermediate, extensive, and ultrarapid metabolizers. For these CYPs, the evidence for clinical significance regarding adverse drug reactions (ADRs), drug efficacy and dose requirement is rapidly growing. Polymorphisms in CYPs 1A1, 1A2, 2C8, 2E1, 2J2, and 3A4 are generally less predictive, but new data on CYP3A4 show that predictive variants exist and that additional variants in regulatory genes or in NADPH:cytochrome P450 oxidoreductase (POR) can have an influence. Here we review the recent progress on drug metabolism activity profiles, interindividual variability and regulation of expression, and the functional and clinical impact of genetic variation in drug metabolizing P450s.

In vivo quantitative prediction of the effect of gene polymorphisms and drug interactions on drug exposure for CYP2C19 substrates

We present a unified quantitative approach to predict the in vivo alteration in drug exposure caused by either cytochrome P450 (CYP) gene polymorphisms or CYP-mediated drug-drug interactions (DDI). An application to drugs metabolized by CYP2C19 is presented. The metrics used is the ratio of altered drug area under the curve (AUC) to the AUC in extensive metabolizers with no mutation or no interaction. Data from 42 pharmacokinetic studies performed in CYP2C19 genetic subgroups and 18 DDI studies were used to estimate model parameters and predicted AUC ratios by using Bayesian approach. Pharmacogenetic information was used to estimate a parameter of the model which was then used to predict DDI. The method adequately predicted the AUC ratios published in the literature, with mean errors of -0.15 and -0.62 and mean absolute errors of 0.62 and 1.05 for genotype and DDI data, respectively. The approach provides quantitative prediction of the effect of five genotype variants and 10 inhibitors on the exposure to 25 CYP2C19 substrates, including a number of unobserved cases. A quantitative approach for predicting the effect of gene polymorphisms and drug interactions on drug exposure has been successfully applied for CYP2C19 substrates. This study shows that pharmacogenetic information can be used to predict DDI. This may have important implications for the development of personalized medicine and drug development.

Novel in vitro and mathematical models for the prediction of chemical toxicity

The focus of much scientific and medical research is directed towards understanding the disease process and defining therapeutic intervention strategies. The scientific basis of drug safety is very complex and currently remains poorly understood, despite the fact that adverse drug reactions (ADRs) are a major health concern and a serious impediment to development of new medicines. Toxicity issues account for ∼21% drug attrition during drug development and safety testing strategies require considerable animal use. Mechanistic relationships between drug plasma levels and molecular/cellular events that culminate in whole organ toxicity underpins development of novel safety assessment strategies. Current in vitro test systems are poorly predictive of toxicity of chemicals entering the systemic circulation, particularly to the liver. Such systems fall short because of (1) the physiological gap between cells currently used and human hepatocytes existing in their native state, (2) the lack of physiological integration with other cells/systems within organs, required to amplify the initial toxicological lesion into overt toxicity, (3) the inability to assess how low level cell damage induced by chemicals may develop into overt organ toxicity in a minority of patients, (4) lack of consideration of systemic effects. Reproduction of centrilobular and periportal hepatocyte phenotypes in in vitro culture is crucial for sensitive detection of cellular stress. Hepatocyte metabolism/phenotype is dependent on cell position along the liver lobule, with corresponding differences in exposure to substrate, oxygen and hormone gradients. Application of bioartificial liver (BAL) technology can encompass in vitro predictive toxicity testing with enhanced sensitivity and improved mechanistic understanding. Combining this technology with mechanistic mathematical models describing intracellular metabolism, fluid-flow, substrate, hormone and nutrient distribution provides the opportunity to design the BAL specifically to mimic the in vivo scenario. Such mathematical models enable theoretical hypothesis testing, will inform the design of in vitro experiments, and will enable both refinement and reduction of in vivo animal trials. In this way, development of novel mathematical modelling tools will help to focus and direct in vitro and in vivo research, and can be used as a framework for other areas of drug safety science.

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.

A pilot randomized, placebo controlled, double blind phase I trial of the novel SIRT1 activator SRT2104 in elderly volunteers

BACKGROUND

SRT2104 has been developed as a selective small molecule activator of SIRT1, a NAD(+)-dependent deacetylase involved in the regulation of energy homeostasis and the modulation of various metabolic pathways, including glucose metabolism, oxidative stress and lipid metabolism. SIRT1 has been suggested as putative therapeutic target in multiple age-related diseases including type 2 diabetes and dyslipidemias. We report the first clinical trial of SRT2104 in elderly volunteers.

METHODS

Oral doses of 0.5 or 2.0 g SRT2104 or matching placebo were administered once daily for 28 days. Pharmacokinetic samples were collected through 24 hours post-dose on days 1 and 28. Multiple pharmacodynamic endpoints were explored with oral glucose tolerance tests (OGTT), serum lipid profiles, magnetic resonance imaging (MRI) for assessment of whole body visceral and subcutaneous fat, maximal aerobic capacity test and muscle 31P magnetic resonance spectroscopy (MRS) for estimation of mitochondrial oxidative capacity.

RESULTS

SRT2104 was generally safe and well tolerated. Pharmacokinetic exposure increased less than dose-proportionally. Mean Tmax was 2-4 hours with elimination half-life of 15-20 hours. Serum cholesterol, LDL levels and triglycerides decreased with treatment. No significant changes in OGTT responses were observed. 31P MRS showed trends for more rapid calculated adenosine diphosphate (ADP) and phosphocreatine (PCr) recoveries after exercise, consistent with increased mitochondrial oxidative phosphorylation.

CONCLUSIONS

SRT2104 can be safely administered in elderly individuals and has biological effects in humans that are consistent with SIRT1 activation. The results of this study support further development of SRT2104 and may be useful in dose selection for future clinical trials in patients.

TRIAL REGISTRATION

ClinicalTrials.gov NCT00964340.

The promiscuous binding of pharmaceutical drugs and their transporter-mediated uptake into cells: what we (need to) know and how we can do so

A recent paper in this journal sought to counter evidence for the role of transport proteins in effecting drug uptake into cells, and questions that transporters can recognize drug molecules in addition to their endogenous substrates. However, there is abundant evidence that both drugs and proteins are highly promiscuous. Most proteins bind to many drugs and most drugs bind to multiple proteins (on average more than six), including transporters (mutations in these can determine resistance); most drugs are known to recognise at least one transporter. In this response, we alert readers to the relevant evidence that exists or is required. This needs to be acquired in cells that contain the relevant proteins, and we highlight an experimental system for simultaneous genome-wide assessment of carrier-mediated uptake in a eukaryotic cell (yeast).

Omics and drug response

A new generation of technologies commonly named omics permits assessment of the entirety of the components of biological systems and produces an explosion of data and a major shift in our concepts of disease. These technologies will likely shape the future of health care. One aspect of these advances is that the data generated document the uniqueness of each human being in regard to disease risk and treatment response. These developments have reemphasized the concept of personalized medicine. Here we review the impact of omics technologies on one key aspect of personalized medicine: the individual drug response. We describe how knowledge of different omics may affect treatment decisions, namely drug choice and drug dose, and how it can be used to improve clinical outcomes.

Electron attachment to antipyretics: possible implications of their metabolic pathways

The empty-level structures and formation of negative ion states via resonance attachment of low-energy (0-15 eV) electrons into vacant molecular orbitals in a series of non-steroidal anti-inflammatory drugs (NSAIDs), namely aspirin, paracetamol, phenacetin, and ibuprofen, were investigated in vacuo by electron transmission and dissociative electron attachment (DEA) spectroscopies, with the aim to model the behavior of these antipyretic agents under reductive conditions in vivo. The experimental findings are interpreted with the support of density functional theory calculations. The negative and neutral fragments formed by DEA in the gas phase display similarities with the main metabolites of these commonly used NSAIDs generated in vivo by the action of cytochrome P450 enzymes, as well as with several known active agents. It is concluded that xenobiotic molecules which possess pronounced electron-accepting properties could in principle follow metabolic pathways which parallel the gas-phase dissociative decay channels observed in the DEA spectra at incident electron energies below 1 eV. Unwanted side effects as, e.g., hepatoxicity or carcinogenicity produced by the NSAIDs under study in human organism are discussed within the "free radical model" framework, reported earlier to describe the toxic action of the well-known model toxicant carbon tetrachloride.

Interactions between CYP2E1 and CYP2B4: effects on affinity for NADPH-cytochrome P450 reductase and substrate metabolism

Studies in microsomal and reconstituted systems have shown that the presence of one cytochrome P450 isoform can significantly influence the catalytic activity of another isoform. In this study, we assessed whether CYP2E1 could influence the catalytic activity of CYP2B4 under steady-state turnover conditions. The results show that CYP2E1 inhibits CYP2B4-mediated metabolism of benzphetamine (BNZ) with a K(i) of 0.04 µM. However, CYP2B4 is not an inhibitor of CYP2E1-mediated p-nitrophenol hydroxylation. When these inhibition studies were performed with the artificial oxidant tert-butyl hydroperoxide, CYP2E1 did not significantly inhibit CYP2B4 activity. Determinations of the apparent K(M) and k(cat) of CYP2B4 for CPR in the presence of increasing concentrations of CYP2E1 revealed a mixed inhibition of CYP2B4 by CYP2E1. At low concentrations of CYP2E1, the apparent K(M) of CYP2B4 for CPR increased up to 23-fold with virtually no change in the k(cat) for the reaction, however, at higher concentrations of CYP2E1, the apparent K(M) of CYP2B4 for CPR decreased to levels similar to those observed in the absence of CYP2E1 and the k(cat) also decreased by 11-fold. Additionally, CYP2E1 increased the apparent K(M) of CYP2B4 for BNZ by 8-fold and the apparent K(M) did not decrease to its original value when saturating concentrations of CPR were used. While the individual apparent K(M) values of CYP2B4 and CYP2E1 for CPR are similar, the apparent K(M) of CYP2E1 for CPR in the presence of CYP2B4 decreased significantly, thus suggesting that CYP2B4 enhances the affinity of CYP2E1 for CPR and this may allow CYP2E1 to out-compete CYP2B4 for CPR.

Role of carbonyl reducing enzymes in the phase I biotransformation of the non-steroidal anti-inflammatory drug nabumetone in vitro

1. Nabumetone is a clinically used non-steroidal anti-inflammatory drug, its biotransformation includes major active metabolite 6-methoxy-2-naphtylacetic acid and another three phase I as well as corresponding phase II metabolites which are regarded as inactive. One important biotransformation pathway is carbonyl reduction, which leads to the phase I metabolite, reduced nabumetone. 2. The aim of this study is the determination of the role of a particular human liver subcellular fraction in the nabumetone reduction and the identification of participating carbonyl reducing enzymes along with their stereospecificities. 3. Both subcellular fractions take part in the carbonyl reduction of nabumetone and the reduction is at least in vitro the main biotransformation pathway. The activities of eight cytosolic carbonyl reducing enzymes--CBR1, CBR3, AKR1B1, AKR1B10, AKR1C1-4--toward nabumetone were tested. Except for CBR3, all tested reductases transform nabumetone to its reduced metabolite. AKR1C4 and AKR1C3 have the highest intrinsic clearances. 4. The stereospecificity of the majority of the tested enzymes is shifted to the production of an (+)-enantiomer of reduced nabumetone; only AKR1C1 and AKR1C4 produce predominantly an (-)-enantiomer. This project provides for the first time evidence that seven specific carbonyl reducing enzymes participate in nabumetone metabolism.

Impact of the pharmaceutical sciences on health care: a reflection over the past 50 years

During the last century, particularly the latter half, spectacular progress has been made in improving the health and longevity of people. The reasons are many, but the development of medicines has played a critical role. This report documents and reflects on the impressive contribution that those working in the pharmaceutical sciences have made to healthcare over the past 50 years. It is divided into six sections (drug discovery; absorption, distribution, metabolism, and excretion; pharmacokinetics and pharmacodynamics; drug formulation; drug regulation; and drug utilization), each describing key contributions that have been made in the progression of medicines, from conception to use. A common thread throughout is the application of translational science to the improvement of drug discovery, development, and therapeutic application. Each section has been coordinated by a leading scientist who was asked, after consulting widely with many colleagues across the globe, to identify "The five most influential ideas/concepts/developments introduced by 'pharmaceutical scientists' (in their field) over the past 50 years?" Although one cannot predict where the important breakthroughs will come in the future to meet the unmet medical needs, the evidence presented in this report should leave no doubt that those engaged in the pharmaceutical sciences will continue to make their contributions heavily felt.

Use of physiologically based pharmacokinetic modeling for assessment of drug-drug interactions

Interactions between co-administered medicines can reduce efficacy or lead to adverse effects. Understanding and managing such interactions is essential in bringing safe and effective medicines to the market. Ideally, interaction potential should be recognized early and minimized in compounds that reach late stages of drug development. Physiologically based pharmacokinetic models combine knowledge of physiological factors with compound-specific properties to simulate how a drug behaves in the human body. These software tools are increasingly used during drug discovery and development and, when integrating relevant in vitro data, can simulate drug interaction potential. This article provides some background and presents illustrative examples. Physiologically based models are an integral tool in the discovery and development of drugs, and can significantly aid our understanding and prediction of drug interactions.

Inter-individual variability in the pre-clinical drug cardiotoxic safety assessment--analysis of the age-cardiomyocytes electric capacitance dependence

Electrical phenomena located within the plasma membrane of the mammalian cardiac cells are connected with the cells' main physiological functions--signals processing and contractility. They were extensively studied and described mathematically in so-called Hodgkin-Huxley paradigm. One of the physiological parameters, namely cell electric capacitance, has not been analyzed in-depth. The aim of the study was to validate the mechanistic model describing the capacitive properties of cells, based on a collected experimental dataset which describes the electric capacitance of human ventricular myocytes. The gathered data was further utilized for developing an empirical correlation between a healthy individual's age and cardiomyocyte electric capacitance.

Virtual population generator for human cardiomyocytes parameters: in silico drug cardiotoxicity assessment

BACKGROUND

The anatomical and histological parameters of the human ventricle depend on many factors including age and sex. Myocyte volume and electric capacitance are significant physiological parameters of left ventricle cardiomyocyte mathematical models. They allow the assessment of inter-individual variability during in vitro-in vivo extrapolation of the drug cardiotoxic effect.

OBJECTIVE

The current research was carried out to analyze the relationship between age, human left ventricle cardiomyocyte volume, and electric capacitance in a healthy population.

METHODS

In order to collect data describing cardiomyocyte volume and membrane area, literature searches were performed. It was assumed that the cardiomyocyte volume (VOL) and area (AREA) distribution have non-negative support and are skewed to the right. A log-linear model with constant variance was used. A simulation study was run to assess the influence of physiological parameters on action potential duration.

RESULTS

The coefficient of determination for the proposed model R(2) = 0.95, that is, 95% of the variability observed in log cardiomyocyte volume can be explained by the estimated regression equation. To allow simple calculation and model performance validation, a simple Excel file was developed (Supplementary material).

CONCLUSIONS

To the best of our knowledge, there is no other model available, combining age, cardiomyocyte volume, and area. The main limitations of the proposed models result from the assumptions made at the data analysis stage. The limited amount of information available in the literature and the lack of differentiation between sexes results in one common equation. The developed model is a part of the computational system for drug cardiotoxicity assessment.

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.

Evaluation of exposure change of nonrenally eliminated drugs in patients with chronic kidney disease using physiologically based pharmacokinetic modeling and simulation

Chronic kidney disease, or renal impairment (RI) can increase plasma levels for drugs that are primarily renally cleared and for some drugs whose renal elimination is not a major pathway. We constructed physiologically based pharmacokinetic (PBPK) models for 3 nonrenally eliminated drugs (sildenafil, repaglinide, and telithromycin). These models integrate drug-dependent parameters derived from in vitro, in silico, and in vivo data, and system-dependent parameters that are independent of the test drugs. Plasma pharmacokinetic profiles of test drugs were simulated in subjects with severe RI and normal renal function, respectively. The simulated versus observed areas under the concentration versus time curve changes (AUCR, severe RI/normal) were comparable for sildenafil (2.2 vs 2.0) and telithromycin (1.6 vs 1.9). For repaglinide, the initial, simulated AUCR was lower than that observed (1.2 vs 3.0). The underestimation was corrected once the estimated changes in transporter activity were incorporated into the model. The simulated AUCR values were confirmed using a static, clearance concept model. The PBPK models were further used to evaluate the changes in pharmacokinetic profiles of sildenafil metabolite by RI and of telithromycin by RI and co-administration with ketoconazole. The simulations demonstrate the utility and challenges of the PBPK approach in evaluating the pharmacokinetics of nonrenally cleared drugs in subjects with RI.

An overview of the evidence and mechanisms of herb-drug interactions

Despite the lack of sufficient information on the safety of herbal products, their use as alternative and/or complementary medicine is globally popular. There is also an increasing interest in medicinal herbs as precursor for pharmacological actives. Of serious concern is the concurrent consumption of herbal products and conventional drugs. Herb-drug interaction (HDI) is the single most important clinical consequence of this practice. Using a structured assessment procedure, the evidence of HDI presents with varying degree of clinical significance. While the potential for HDI for a number of herbal products is inferred from non-human studies, certain HDIs are well established through human studies and documented case reports. Various mechanisms of pharmacokinetic HDI have been identified and include the alteration in the gastrointestinal functions with consequent effects on drug absorption; induction and inhibition of metabolic enzymes and transport proteins; and alteration of renal excretion of drugs and their metabolites. Due to the intrinsic pharmacologic properties of phytochemicals, pharmacodynamic HDIs are also known to occur. The effects could be synergistic, additive, and/or antagonistic. Poor reporting on the part of patients and the inability to promptly identify HDI by health providers are identified as major factors limiting the extensive compilation of clinically relevant HDIs. A general overview and the significance of pharmacokinetic and pharmacodynamic HDI are provided, detailing basic mechanism, and nature of evidence available. An increased level of awareness of HDI is necessary among health professionals and drug discovery scientists. With the increasing number of plant-sourced pharmacological actives, the potential for HDI should always be assessed in the non-clinical safety assessment phase of drug development process. More clinically relevant research is also required in this area as current information on HDI is insufficient for clinical applications.

Humanized transgenic mouse models for drug metabolism and pharmacokinetic research

Extrapolation of the metabolic, pharmacokinetic and toxicological data obtained from animals to humans is not always straightforward, given the remarkable species difference in drug metabolism that is due in large part to the differences in drug-metabolizing enzymes between animals and humans. Furthermore, genetic variations in drug-metabolizing enzymes may significantly alter pharmacokinetics, drug efficacy and safety. Thus, humanized transgenic mouse lines, in which the human drug-metabolizing enzymes are expressed in mouse tissues in the presence or absence of mouse orthologues, have been developed to address such challenges. These humanized transgenic mice are valuable animal models in understanding the significance of specific human drug-metabolizing enzymes in drug clearance and pharmacokinetics, as well as in predicting potential drug-drug interactions and chemical toxicity in humans. This review, therefore, aims to summarize the development and application of some humanized transgenic mouse models expressing human drug-metabolizing enzymes. The limitations of these genetically modified mouse models are also discussed.

Predicting drug interaction potential with a physiologically based pharmacokinetic model: a case study of telithromycin, a time-dependent CYP3A inhibitor

Telithromycin is a substrate and an inhibitor of cytochrome P450 3A (CYP3A4), with dose- and time-dependent nonlinear pharmacokinetics (PK). We hypothesized that the time-dependent inhibition (TDI) of CYP3A4 was responsible for the nonlinear PK of telithromycin and then used physiologically based PK (PBPK) modeling and simulation to verify this mechanism. Telithromycin PBPK models integrating in vitro, in silico, and in vivo PK data ruled out the contribution of enzyme/transporter saturation and suggested that TDI is a plausible mechanism for PK nonlinearity. The model successfully predicted the clinical interaction with the CYP3A4 substrate midazolam, as verified by external data not used for the model-building (intravenous (i.v.) and oral (p.o.) midazolam area under the concentration-time curve (AUC) ratio with/without concurrent telithromycin administration: 3.26 and 6.72 predicted vs. 2.20 and 6.11 observed, respectively). Models assuming reversible inhibition failed to predict such strong CYP3A4 inhibition. In the absence of in vitro TDI data, a PBPK model can be used to incorporate TDI mechanisms based on nonlinear PK data to predict clinical drug-drug interactions.

Using Simcyp to project human oral pharmacokinetic variability in early drug research to mitigate mechanism-based adverse events

Positive allosteric modulators ('potentiators') of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) have been shown to display a mechanism-based exposure-response continuum in preclinical species with procognitive electrophysiological and behavioral effects ('efficacy') at low exposures and motor coordination disruptions at progressively higher exposures. Due to the dose-capping nature of such motor coordination deficits, an exposure threshold-mediated adverse event (C(AE) ), the adequacy of separation between the maximal total plasma compound concentration (C(max) ) at a predicted clinically efficacious oral dose and this adverse event (AE) was explored in early drug research with three AMPAR potentiators considered potential candidates for clinical trials. In vitro metabolism studies in human liver microsomes and human hepatocytes demonstrated the metabolic clearance for each compound was predominately due to cytochromes P450 (CYP). Thus, for each compound's anticipated clinically efficacious dose, human C(max) variability following oral administration was assessed using Simcyp software, which combines its virtual human populations database using extensive demographic, physiological and genomic information with routinely collected compound-specific in vitro biochemical data to simulate and predict drug disposition. Using a combination of experimentally determined recombinant human CYP intrinsic clearances for CYP1A2, CYP2C8, CYP2C9, CYP2C19, CYP2D6 and CYP3A4, human binding factors, expected fraction absorbed and estimated steady-state volume of distribution, Simcyp simulations demonstrated that two of the three potentiators had acceptable projected C(max) variability (i.e. the 95th percentile C(max) did not breach C(AE) ). This evaluation aided in the selection of compounds for preclinical progression, and represents a novel application of pharmacologically based pharmacokinetic (PBPK) software approaches to predict interpatient variability.

Preclinical in vivo ADME studies in drug development: a critical review

INTRODUCTION

The last two decades have brought many fundamental changes to the drug development process. One such change is the importance of preclinical pharmacokinetics, which has become an essential part of early drug discovery. Furthermore, bioanalytical methods have become more sensitive and the identification and quantitation of metabolites can now be carried out on limited amount of biological material. There has also been a change in regulatory expectations, which are now particularly focused on the safety of human metabolites.

AREAS COVERED

The focus of this paper is on some 'traditional' in vivo ADME studies: excretion balance, metabolic profile and WBA in the toxicological species. These studies, performed with radiolabeled material, have a long history: and are a regular presence in submission dossiers. This paper reviews their value in the perspective of the contemporary drug development process.

EXPERT OPINION

These experiments may sometimes still be relevant to explain toxicological findings or for other special purposes but should not be considered required pieces of the registration dossiers. An appropriate investigation of samples coming from safety evaluation and human Phase I studies and the knowledge generated during the lead optimization phase provide, in most instances, all the DMPK information needed to take decisions in the drug development process.

A personalized life: biomarker monitoring from cradle to grave

Considering the holy grail of future medical treatment being personalized medicines, biomarker research will become more and more the focus for attention not only to develop new medical treatment regimes, based on changes in biomarker patterns, but also for nutritional advice to guarantee a lifelong optimized health condition. The current review gives an outline of how personalized medicine can become established for actual medical treatment using new biomarker concepts. Starting from the development of biomarker research using mainly immunological techniques, the review gives an overview about biomarkers of prediction evolved and focuses on new methodology for the identification of biomarkers using hyphenated analytical techniques like metabolomics and lipidomics. The actual use of multivariate statistical methods in combination with metabolomics and lipidomics is discussed not only for medical treatment but also for precautionary risk identification in human biomonitoring studies.

Peptide and protein drugs: the study of their metabolism and catabolism by mass spectrometry

Peptide and protein drugs have evolved in recent years into mainstream therapeutics, representing a significant portion of the pharmaceutical market. Peptides and proteins exhibit highly diverse structures, broad biological activities as hormones, neurotransmitters, structural proteins, metabolic modulators and therefore have a significant role as both therapeutics and biomarkers. Understanding the metabolism of synthetic or biotechnologically derived peptide and protein drugs is critical for pharmaceutical development as metabolism has a significant impact on drug efficacy and safety. Although the same principles of pharmacokinetics and metabolism of small molecule drugs apply to peptide and protein drugs, there are few notable differences. Moreover, the study of peptide and protein drug metabolism is a rather complicated process which requires sophisticated analytical techniques, and mass spectrometry based approaches have provided the capabilities for efficient and reliable quantification, characterization, and metabolite identification. This review article will focus on the current use of mass spectrometry for the study of the metabolism of peptide and protein drugs.

The evolution of the OATP hepatic uptake transport protein family in DMPK sciences: from obscure liver transporters to key determinants of hepatobiliary clearance

Over the last two decades the impact on drug pharmacokinetics of the organic anion transporting polypeptides (OATPs: OATP-1B1, 1B3 and 2B1), expressed on the sinusoidal membrane of the hepatocyte, has been increasingly recognized. OATP-mediated uptake into the hepatocyte coupled with subsequent excretion into bile via efflux proteins, such as MRP2, is often referred to as hepatobiliary excretion. OATP transporter proteins can impact some drugs in several ways including pharmacokinetic variability, pharmacodynamic response and drug-drug interactions (DDIs). The impact of transporter mediated hepatic clearance is illustrated with case examples, from the literature and also from the Pfizer portfolio. The currently available in vitro techniques to study the hepatic transporter proteins involved in the hepatobiliary clearance of drugs are reviewed herein along with recent advances in using these in vitro data to predict the human clearance of compounds recognized by hepatic uptake transporters.

MEGen: A Physiologically Based Pharmacokinetic Model Generator

Physiologically based pharmacokinetic models are being used in an increasing number of different areas. However, they are perceived as complex, data hungry, resource intensive, and time consuming. In addition, model validation and verification are hindered by the relative complexity of the equations. To begin to address these issues a web application called MEGen for the rapid construction and documentation of bespoke deterministic PBPK model code is under development. MEGen comprises a parameter database and a model code generator that produces code for use in several commercial software packages and one that is freely available. Here we present an overview of the current capabilities of MEGen, and discuss future developments.

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.

In vivo-in vitro-in silico pharmacokinetic modelling in drug development: current status and future directions

Although clinical drug trials are indispensable in providing an appropriate background for dosage recommendations, they can provide mechanistic pharmacokinetic information only indirectly with the help of certain biomarkers for pathological, physiological and pharmacological determinants. Thus, to provide such mechanistic information of clinical value, various in vitro and in silico tests and approaches are increasingly employed in drug discovery and development. Integration of the results of these primarily preclinical studies has been made possible by various computational models, such as in vitro-in vivo extrapolation of hepatic clearance or physiologically based pharmacokinetic modelling. In this article, the current status of these modelling approaches is surveyed and some examples are given, highlighting advantages and disadvantages in applying them at various phases of drug development. A new paradigm of model-based drug development is briefly described, and the importance of the approach of integrating all of the information coming from different investigations at all levels--be it in vivo, in vitro or in silico--is emphasized.

Case studies addressing human pharmacokinetic uncertainty using a combination of pharmacokinetic simulation and alternative first in human paradigms

PF-184298 ((S)-2,3-dichloro-N-isobutyl-N-pyrrolidin-3-ylbenzamide) and PF-4776548 ((3-(4-fluoro-2-methoxy-benzyl)-7-hydroxy-8,9-dihydro-3H,7H-pyrrolo[2,3-c][1,7]naphthyridin-6-one)) are novel compounds which were selected to progress to human studies. Discordant human pharmacokinetic predictions arose from pre-clinical in vivo studies in rat and dog, and from human in vitro studies, resulting in a clearance prediction range of 3 to >20 mL min⁻¹ kg⁻¹ for PF-184298, and 5 to >20 mL min⁻¹ kg⁻¹ for PF-4776548. A package of work to investigate the discordance for PF-184298 is described. Although ultimately complementary to the human pharmacokinetic data in characterising the disposition of PF-184298 in humans, these data did not provide any further confidence in pharmacokinetic prediction. A fit for purpose human pharmacokinetic study was conducted for each compound, with an oral pharmacologically active dose for PF-184298, and an intravenous and oral microdose for PF-4776548. This provided a relatively low cost, clear decision making approach, resulting in the termination of PF-4776548 and further progression of PF-184298. A retrospective analysis of the data showed that, if the tools had been available at the time, the pharmacokinetics of PF-184298 in human could have been predicted from a population based simulation tool in combination with physicochemical properties and in vitro human intrinsic clearance.