Application of a physiologically based pharmacokinetic model to assess propofol hepatic and renal glucuronidation in isolation: utility of in vitro and in vivo data

Altitude effect on Propofol Pharmacokinetics in Rats

BACKGROUND

Propofol is an intravenous agent for clinical anesthesia. As the influence of the hypobaric-hypoxic environment (Qinghai-Tibetan region, altitude: 2800-4300 m, PaO2: 15.1-12.4 kPa) on the metabolism of Propofol is complex, the research results on the metabolic characteristics of Propofol in high-altitude areas remain unclear. This study aimed to investigate the pharmacokinetic characteristics of Propofol in a high-altitude hypoxic environment using animal experiments.

METHODS

Rats were randomly divided into three groups: high-altitude, medium-altitude, and plain groups. The time of disappearance and recovery of the rat righting reflex was recorded as the time of anesthesia induction and awakening, respectively. The plasma concentration of Propofol was determined by gas chromatography-mass spectrometry. A pharmacokinetic analysis software was used to analyze the blood-drug concentrations and obtain the pharmacokinetic parameters.

RESULTS

We observed that when Propofol anesthetizes rats, the anesthesia induction time was shortened, and the recovery time was prolonged with increased altitude. Compared with the plain group, the clearance of Propofol decreased, whereas the half-life, area under the concentration-time curve, peak plasma concentration, and average residence time extension increased.

CONCLUSION

The pharmacokinetic characteristics of Propofol are significantly altered in high-altitude hypoxic environments.

Physiologically Based Pharmacokinetic Modeling for Quantitative Prediction of Exposure to a Human Disproportionate Metabolite of the Selective NaV1.7 Inhibitor DS-1971a, a Mixed Substrate of Cytochrome P450 and Aldehyde Oxidase, Using Chimeric Mice With Humanized Liver

In a previous study on the human mass balance of DS-1971a, a selective Na_V1.7 inhibitor, its CYP2C8-dependent metabolite M1 was identified as a human disproportionate metabolite. The present study assessed the usefulness of pharmacokinetic evaluation in chimeric mice grafted with human hepatocytes (PXB-mice) and physiologically based pharmacokinetic (PBPK) simulation of M1. After oral administration of radiolabeled DS-1971a, the most abundant metabolite in the plasma, urine, and feces of PXB-mice was M1, while those of control SCID mice were aldehyde oxidase-related metabolites including M4, suggesting a drastic difference in the metabolism between these mouse strains. From a qualitative perspective, the metabolite profile observed in PXB-mice was remarkably similar to that in humans, but the quantitative evaluation indicated that the area under the plasma concentration-time curve (AUC) ratio of M1 to DS-1971a (M1/P ratio) was approximately only half of that in humans. A PXB-mouse-derived PBPK model was then constructed to achieve a more accurate prediction, giving an M1/P ratio (1.3) closer to that in humans (1.6) than the observed value in PXB-mice (0.69). In addition, simulated maximum plasma concentration and AUC values of M1 (3429 ng/ml and 17,116 ng·h/ml, respectively) were similar to those in humans (3180 ng/ml and 18,400 ng·h/ml, respectively). These results suggest that PBPK modeling incorporating pharmacokinetic parameters obtained with PXB-mice is useful for quantitatively predicting exposure to human disproportionate metabolites. SIGNIFICANCE STATEMENT: The quantitative prediction of human disproportionate metabolites remains challenging. This paper reports on a successful case study on the practical estimation of exposure (C _max and AUC) to DS-1971a and its CYP2C8-dependent, human disproportionate metabolite M1, by PBPK simulation utilizing pharmacokinetic parameters obtained from PXB-mice and in vitro kinetics in human liver fractions. This work adds to the growing knowledge regarding metabolite exposure estimation by static and dynamic models.

Feasibility of a Pragmatic PBPK Modeling Approach: Towards Model-Informed Dosing in Pediatric Clinical Care

BACKGROUND AND OBJECTIVE

More than half of all drugs are still prescribed off-label to children. Pharmacokinetic (PK) data are needed to support off-label dosing, however for many drugs such data are either sparse or not representative. Physiologically-based pharmacokinetic (PBPK) models are increasingly used to study PK and guide dosing decisions. Building compound models to study PK requires expertise and is time-consuming. Therefore, in this paper, we studied the feasibility of predicting pediatric exposure by pragmatically combining existing compound models, developed e.g. for studies in adults, with a pediatric and preterm physiology model.

METHODS

Seven drugs, with various PK characteristics, were selected (meropenem, ceftazidime, azithromycin, propofol, midazolam, lorazepam, and caffeine) as a proof of concept. Simcyp® v20 was used to predict exposure in adults, children, and (pre)term neonates, by combining an existing compound model with relevant virtual physiology models. Predictive performance was evaluated by calculating the ratios of predicted-to-observed PK parameter values (0.5- to 2-fold acceptance range) and by visual predictive checks with prediction error values.

RESULTS

Overall, model predicted PK in infants, children and adolescents capture clinical data. Confidence in PBPK model performance was therefore considered high. Predictive performance tends to decrease when predicting PK in the (pre)term neonatal population.

CONCLUSION

Pragmatic PBPK modeling in pediatrics, based on compound models verified with adult data, is feasible. A thorough understanding of the model assumptions and limitations is required, before model-informed doses can be recommended for clinical use.

Impact of route-dependent phase-II gut metabolism and enterohepatic circulation on the bioavailability and systemic disposition of resveratrol in rats and humans: A comprehensive whole body physiologically-based pharmacokinetic modeling

Resveratrol, a natural polyphenolic phytoalexin, is a dietary supplement that improves the outcomes of metabolic, cardiovascular, and other age-related diseases due to its diverse pharmacological activities. Although there have been several preclinical and clinical investigations of resveratrol, the contributions of gut phase-II metabolism and enterohepatic circulation to the oral bioavailability and pharmacokinetics of resveratrol remain unclear. Furthermore, a physiologically-based pharmacokinetic (PBPK) model that accurately describes and predicts the systemic exposure profiles of resveratrol in clinical settings has not been developed. Experimental data were acquired from several perspectives, including in vitro protein binding and blood distribution, in vitro tissue S9 metabolism, in situ intestinal perfusion, and in vivo pharmacokinetics and excretion studies. Using these datasets, an in-house whole-body PBPK model incorporating route-dependent phase-II (glucuronidation and sulfation) gut metabolism and enterohepatic circulation processes was constructed and optimized for chemical-specific parameters. The developed PBPK model aligned with the observed systemic exposure profiles of resveratrol in single and multiple dosing regimens with an acceptable accuracy of 0.538-0.999-fold errors. Furthermore, the model simulations elucidated the substantial contribution of gut first-pass metabolism to the oral bioavailability of resveratrol and suggested differential effects of enterohepatic circulation on the systemic exposure of resveratrol between rats and humans. After partial modification and verification, our proposed PBPK model would be valuable to optimize dosage regimens and predict food-drug interactions with resveratrol-based natural products in various clinical scenarios.

Development and application of a physiologically based pharmacokinetic model for entrectinib in rats and scale-up to humans: Route-dependent gut wall metabolism

Entrectinib (Rozlytrek®) is an oral antineoplastic agent approved by the U.S. Food and Drug Administration in 2019 for the treatment of c-ros oncogene 1 (ROS1)-positive non-small cell lung cancer and neurotrophic tyrosine receptor kinase (NTRK) fusion-positive solid tumors. Although there have been a few studies on the pharmacokinetics of entrectinib, the relative contributions of several kinetic factors determining the oral bioavailability and systemic exposure of entrectinib are still worthy of investigation. Experimental data on the intestinal absorption and disposition of entrectinib in rats were acquired from studies on in vitro protein binding/tissue S9 metabolism, in situ intestinal perfusion, and in vivo dose-escalation/hepatic extraction. Using these datasets, an in-house whole-body physiologically based pharmacokinetic (PBPK) model incorporating the Q_Gut model concepts and segregated blood flow in the gut was constructed and optimized with respect to drug-specific parameters. The established rat PBPK model was further extrapolated to humans through relevant physiological scale-up and parameter optimization processes. The optimized rat and human PBPK models adequately captured the impact of route-dependent gut metabolism on the systemic exposure to entrectinib and closely mirrored various preclinical and clinical observations. Our proposed PBPK model could be useful in optimizing dosage regimens and predicting drug interaction potential in various clinical conditions, after partial modification and validation.

Variability in Human In Vitro Enzyme Kinetics

There are many factors which are known to cause variability in human in vitro enzyme kinetic data. Factors such as the source of enzyme and how it was prepared, the genetics and background of the donor, how the in vitro studies are designed, and how the data are analyzed contribute to variability in the resulting kinetic parameters. It is important to consider not only the factors which cause variability within an experiment, such as selection of a probe substrate, but also those that cause variability when comparing kinetic data across studies and laboratories. For example, the artificial nature of the microsomal lipid membrane and microenvironment in some recombinantly expressed enzymes, relative to those found in native tissue microsomes, has been shown to influence enzyme activity and thus can be a source of variability when comparing across the two different systems. All of these factors, and several others, are discussed in detail in the chapter below. In addition, approaches which can be used to visualize the uncertainty arising from the use of enzyme kinetic data within the context of predicting human pharmacokinetics are discussed.

Quantitative Proteomic Map of Enzymes and Transporters in the Human Kidney: Stepping Closer to Mechanistic Kidney Models to Define Local Kinetics

The applications of translational modeling of local drug concentrations in various organs had a sharp increase over the last decade. These are part of the model-informed drug development initiative, adopted by the pharmaceutical industry and promoted by drug regulatory agencies. With respect to the kidney, the models serve as a bridge for understanding animal vs. human observations related to renal drug disposition and any consequential adverse effects. However, quantitative data on key drug-metabolizing enzymes and transporters relevant for predicting renal drug disposition are limited. Using targeted and global quantitative proteomics, we determined the abundance of multiple enzymes and transporters in 20 human kidney cortex samples. Nine enzymes and 22 transporters were quantified (8 for the first time in the kidneys). In addition, > 4,000 proteins were identified and used to form an open database. CYP2B6, CYP3A5, and CYP4F2 showed comparable, but generally low expression, whereas UGT1A9 and UGT2B7 levels were the highest. Significant correlation between abundance and activity (measured by mycophenolic acid clearance) was observed for UGT1A9 (Rs = 0.65, P = 0.004) and UGT2B7 (Rs = 0.70, P = 0.023). Expression of P-gp ≈ MATE-1 and OATP4C1 transporters were high. Strong intercorrelations were observed between several transporters (P-gp/MRP4, MRP2/OAT3, and OAT3/OAT4); no correlation in expression was apparent for functionally related transporters (OCT2/MATEs). This study extends our knowledge of pharmacologically relevant proteins in the kidney cortex, with implications on more prudent use of mechanistic kidney models under the general framework of quantitative systems pharmacology and toxicology.

Enzyme Kinetics of Uridine Diphosphate Glucuronosyltransferases (UGTs)

Glucuronidation, catalyzed by uridine diphosphate glucuronosyltransferases (UGTs), is an important process for the metabolism and clearance of many lipophilic chemicals, including drugs, environmental chemicals, and endogenous compounds. Glucuronidation is a bisubstrate reaction that requires the aglycone and the cofactor, UDP-GlcUA. Accumulating evidence suggests that the bisubstrate reaction follows a compulsory-order ternary mechanism. To simplify the kinetic modeling of glucuronidation reactions in vitro, UDP-GlcUA is usually added to incubations in large excess. Many factors have been shown to influence UGT activity and kinetics in vitro, and these must be accounted for during experimental design and data interpretation. While the assessment of drug-drug interactions resulting from UGT inhibition has been challenging in the past, the increasing availability of UGT enzyme-selective substrate and inhibitor "probes" provides the prospect for more reliable reaction phenotyping and assessment of drug-drug interaction potential. Although extrapolation of the in vitro intrinsic clearance of a glucuronidated drug often underpredicts in vivo clearance, careful selection of in vitro experimental conditions and inclusion of extrahepatic glucuronidation may improve the predictivity of in vitro-in vivo extrapolation. Physiologically based pharmacokinetic (PBPK) modeling has also shown to be of value for predicting PK of drugs eliminated by glucuronidation.

Evidence-based strategies for the characterisation of human drug and chemical glucuronidation in vitro and UDP-glucuronosyltransferase reaction phenotyping

Enzymes of the UDP-glucuronosyltransferase (UGT) superfamily contribute to the elimination of drugs from almost all therapeutic classes. Awareness of the importance of glucuronidation as a drug clearance mechanism along with increased knowledge of the enzymology of drug and chemical metabolism has stimulated interest in the development and application of approaches for the characterisation of human drug glucuronidation in vitro, in particular reaction phenotyping (the fractional contribution of the individual UGT enzymes responsible for the glucuronidation of a given drug), assessment of metabolic stability, and UGT enzyme inhibition by drugs and other xenobiotics. In turn, this has permitted the implementation of in vitro - in vivo extrapolation approaches for the prediction of drug metabolic clearance, intestinal availability, and drug-drug interaction liability, all of which are of considerable importance in pre-clinical drug development. Indeed, regulatory agencies (FDA and EMA) require UGT reaction phenotyping for new chemical entities if glucuronidation accounts for ≥25% of total metabolism. In vitro studies are most commonly performed with recombinant UGT enzymes and human liver microsomes (HLM) as the enzyme sources. Despite the widespread use of in vitro approaches for the characterisation of drug and chemical glucuronidation by HLM and recombinant enzymes, evidence-based guidelines relating to experimental approaches are lacking. Here we present evidence-based strategies for the characterisation of drug and chemical glucuronidation in vitro, and for UGT reaction phenotyping. We anticipate that the strategies will inform practice, encourage development of standardised experimental procedures where feasible, and guide ongoing research in the field.

Mechanistic Assessment of Extrahepatic Contributions to Glucuronidation of Integrase Strand Transfer Inhibitors

Integrase strand transfer inhibitor (INSTI)-based regimens dominate initial human immunodeficiency virus treatment. Most INSTIs are metabolized predominantly via UDP-glucuronosyltransferases (UGTs). For drugs predominantly metabolized by UGTs, including INSTIs, in vitro data recovered from human liver microsomes (HLMs) alone often underpredict human oral clearance. While several factors may contribute, extrahepatic glucuronidation may contribute to this underprediction. Thus, we comprehensively characterized the kinetics for the glucuronidation of INSTIs (cabotegravir, dolutegravir, and raltegravir) using pooled human microsomal preparations from liver (HLMs), intestine (HIMs), and kidney (HKMs) tissues; human embryonic kidney 293 cells expressing individual UGTs; and recombinant UGTs. In vitro glucuronidation of cabotegravir (HLMs≈HKMs>>>HIMs), dolutegravir (HLMs>HIMs>>HKMs), and raltegravir (HLMs>HKMs>> HIMs) occurred in hepatic and extrahepatic tissues. The kinetic data from expression systems suggested the major enzymes in each tissue: hepatic UGT1A9 > UGT1A1 (dolutegravir and raltegravir) and UGT1A1 (cabotegravir), intestinal UGT1A3 > UGT1A8 > UGT1A1 (dolutegravir) and UGT1A8 > UGT1A1 (raltegravir), and renal UGT1A9 (dolutegravir and raltegravir). Enzymes catalyzing cabotegravir glucuronidation in the kidney and intestine could not be identified unequivocally. Using data from dolutegravir glucuronidation as a prototype, a "bottom-up" physiologically based pharmacokinetic model was developed in a stepwise approach and predicted dolutegravir oral clearance within 4.5-fold (hepatic data only), 2-fold (hepatic and intestinal data), and 32% (hepatic, intestinal, and renal data). These results suggest clinically meaningful glucuronidation of dolutegravir in tissues other than the liver. Incorporation of additional novel mechanistic and physiologic underpinnings of dolutegravir metabolism along with in silico approaches appears to be a powerful tool to accurately predict the clearance of dolutegravir from in vitro data.

The use of PBPK modeling across the pediatric age range using propofol as a case

The project SAFEPEDRUG aims to provide guidelines for drug research in children, based on bottom-up and top-down approaches. Propofol, one of the studied model compounds, was selected because it is extensively metabolized in liver and kidney, with an important role for the glucuronidation pathway. Besides, being a lipophilic molecule, it is distributed into fat tissues, from where it redistributes into the systemic circulation. In the past, both bottom-up (Physiologically based pharmacokinetic, PBPK) and top-down approaches (population pharmacokinetic, popPK) were applied to describe its pharmacokinetics (PK). In this work, a combination of the two was used to check their performance to describe PK in children and neonates (both term and preterm) using propofol as a case compound. First, in vitro data was generated in human liver microsomes and recombinant enzymes and used to develop an adult PBPK model in Simcyp^®. Activity adjustment factors (AAFs) were calculated to account for differences between in vitro and in vivo enzyme activity. Clinical data were analyzed using a 3-compartment model in NONMEM. These data were used to construct a retrograde PBPK model and for qualification of the PBPK models. Once an accurate in vivo clearance was obtained accounting for the contribution of the different metabolic pathways, the resulting PBPK models were challenged with new data for qualification. After that, the constructed adult PPBK model for propofol was extrapolated to the pediatric population. Both the default built-in and in vivo derived ontogeny functions were used to do so. The models were qualified by comparing their predicted PK parameters to published values, and by comparison of predicted concentration-time profiles to available clinical data. Clearance values were predicted well, especially when compared with values obtained from trials where long-term sampling was applied, whereas volume of distribution was lower compared to the most common popPK model predictions. Concentration-time profiles were predicted well up until and including the preterm neonatal population. In this work, it was thus shown that PBPK can be used to predict the PK up to and including the preterm neonatal population without the use of pediatric in vivo data. This work adds weight to the need for further development of PBPK models, especially regarding distribution modeling and the use of in vivo derived ontogeny functions.

Particle Size Distribution Equivalency as Novel Predictors for Bioequivalence

The use of particle size distribution (PSD) similarity metrics and the development and incorporation of drug release predictions based on PSD properties into PBPK models for various drug administration routes may provide a holistic approach for evaluating the effect of PSD differences on in vitro drug release and bioavailability of disperse systems. The objectives of this study were to provide a rational approach for evaluating the utility of in vitro PSD comparators for predicting bioequivalence for subcutaneously administered test and reference drug emulsions. Two types of in vitro comparators for test and reference emulsion products were evaluated: PSD characterization comparators (overlap metrics, median, and span ratios) and release profile comparators (f₂ and various fractional time ratios). A subcutaneous-input PBPK disposition model was developed to simulate blood concentration-time profiles of reference and test emulsion products and pharmacokinetic responses (e.g., AUC, C_max, and T_max) were used to determine bioequivalence. A pool of 10,440 pairs of test and reference products was simulated using Monte Carlo experiments. The PSD and release profile comparators were correlated to pass/fail bioequivalence metrics using logistical regression. Based on the use of single in vitro comparators, the f₂ method was the best predictor of bioequivalence prediction. The use of combinations of f₂ and PSD overlap comparators (e.g., OVL or PROB) improved bioequivalence prediction to about 90%. Simulation procedures used in this study demonstrated a process for developing reliable in vitro BE predictors.

Propofol Breath Monitoring as a Potential Tool to Improve the Prediction of Intraoperative Plasma Concentrations

INTRODUCTION

Monitoring of drug concentrations in breathing gas is routinely being used to individualize drug dosing for the inhalation anesthetics. For intravenous anesthetics however, no decisive evidence in favor of breath concentration monitoring has been presented up until now. At the same time, questions remain with respect to the performance of currently used plasma pharmacokinetic models implemented in target-controlled infusion systems. In this study, we investigate whether breath monitoring of propofol could improve the predictive performance of currently applied, target-controlled infusion models.

METHODS

Based on data from a healthy volunteer study, we developed an addition to the current state-of-the-art pharmacokinetic model for propofol, to accommodate breath concentration measurements. The potential of using this pharmacokinetic (PK) model in a Bayesian forecasting setting was studied using a simulation study. Finally, by introducing bispectral index monitor (BIS) measurements and the accompanying BIS models into our PK model, we investigated the relationship between BIS and predicted breath concentrations.

RESULTS AND DISCUSSION

We show that the current state-of-the-art pharmacokinetic model is easily extended to reliably describe propofol kinetics in exhaled breath. Furthermore, we show that the predictive performance of the a priori model is improved by Bayesian adaptation based on the measured breath concentrations, thereby allowing further treatment individualization and a more stringent control on the targeted plasma concentrations during general anesthesia. Finally, we demonstrated concordance between currently advocated BIS models, relying on predicted effect-site concentrations, and our new approach in which BIS measurements are derived from predicted breath concentrations.

Novel minimal physiologically-based model for the prediction of passive tubular reabsorption and renal excretion clearance

PURPOSE

Develop a minimal mechanistic model based on in vitro-in vivo extrapolation (IVIVE) principles to predict extent of passive tubular reabsorption. Assess the ability of the model developed to predict extent of passive tubular reabsorption (Freab) and renal excretion clearance (CLR) from in vitro permeability data and tubular physiological parameters.

METHODS

Model system parameters were informed by physiological data collated following extensive literature analysis. A database of clinical CLR was collated for 157 drugs. A subset of 45 drugs was selected for model validation; for those, Caco-2 permeability (Papp) data were measured under pH6.5-7.4 gradient conditions and used to predict Freab and subsequently CLR. An empirical calibration approach was proposed to account for the effect of inter-assay/laboratory variation in Papp on the IVIVE of Freab.

RESULTS

The 5-compartmental model accounted for regional differences in tubular surface area and flow rates and successfully predicted the extent of tubular reabsorption of 45 drugs for which filtration and reabsorption were contributing to renal excretion. Subsequently, predicted CLR was within 3-fold of the observed values for 87% of drugs in this dataset, with an overall gmfe of 1.96. Consideration of the empirical calibration method improved overall prediction of CLR (gmfe=1.73 for 34 drugs in the internal validation dataset), in particular for basic drugs and drugs with low extent of tubular reabsorption.

CONCLUSIONS

The novel 5-compartment model represents an important addition to the IVIVE toolbox for physiologically-based prediction of renal tubular reabsorption and CLR. Physiological basis of the model proposed allows its application in future mechanistic kidney models in preclinical species and human.

Key to Opening Kidney for In Vitro-In Vivo Extrapolation Entrance in Health and Disease: Part II: Mechanistic Models and In Vitro-In Vivo Extrapolation

It is envisaged that application of mechanistic models will improve prediction of changes in renal disposition due to drug-drug interactions, genetic polymorphism in enzymes and transporters and/or renal impairment. However, developing and validating mechanistic kidney models is challenging due to the number of processes that may occur (filtration, secretion, reabsorption and metabolism) in this complex organ. Prediction of human renal drug disposition from preclinical species may be hampered by species differences in the expression and activity of drug metabolising enzymes and transporters. A proposed solution is bottom-up prediction of pharmacokinetic parameters based on in vitro-in vivo extrapolation (IVIVE), mediated by recent advances in in vitro experimental techniques and application of relevant scaling factors. This review is a follow-up to the Part I of the report from the 2015 AAPS Annual Meeting and Exhibition (Orlando, FL; 25th-29th October 2015) which focuses on IVIVE and mechanistic prediction of renal drug disposition. It describes the various mechanistic kidney models that may be used to investigate renal drug disposition. Particular attention is given to efforts that have attempted to incorporate elements of IVIVE. In addition, the use of mechanistic models in prediction of renal drug-drug interactions and potential for application in determining suitable adjustment of dose in kidney disease are discussed. The need for suitable clinical pharmacokinetics data for the purposes of delineating mechanistic aspects of kidney models in various scenarios is highlighted.

Requirements for the formal representation of pathophysiology mechanisms by clinicians

Knowledge of multiscale mechanisms in pathophysiology is the bedrock of clinical practice. If quantitative methods, predicting patient-specific behaviour of these pathophysiology mechanisms, are to be brought to bear on clinical decision-making, the Human Physiome community and Clinical community must share a common computational blueprint for pathophysiology mechanisms. A number of obstacles stand in the way of this sharing-not least the technical and operational challenges that must be overcome to ensure that (i) the explicit biological meanings of the Physiome's quantitative methods to represent mechanisms are open to articulation, verification and study by clinicians, and that (ii) clinicians are given the tools and training to explicitly express disease manifestations in direct contribution to modelling. To this end, the Physiome and Clinical communities must co-develop a common computational toolkit, based on this blueprint, to bridge the representation of knowledge of pathophysiology mechanisms (a) that is implicitly depicted in electronic health records and the literature, with (b) that found in mathematical models explicitly describing mechanisms. In particular, this paper makes use of a step-wise description of a specific disease mechanism as a means to elicit the requirements of representing pathophysiological meaning explicitly. The computational blueprint developed from these requirements addresses the Clinical community goals to (i) organize and manage healthcare resources in terms of relevant disease-related knowledge of mechanisms and (ii) train the next generation of physicians in the application of quantitative methods relevant to their research and practice.

Stereoselective Glucuronidation of Bupropion Metabolites In Vitro and In Vivo

Bupropion is a widely used antidepressant and smoking cessation aid in addition to being one of two US Food and Drug Administration-recommended probe substrates for evaluation of cytochrome P450 2B6 activity. Racemic bupropion undergoes oxidative and reductive metabolism, producing a complex profile of pharmacologically active metabolites with relatively little known about the mechanisms underlying their elimination. A liquid chromatography-tandem mass spectrometry assay was developed to simultaneously separate and detect glucuronide metabolites of (R,R)- and (S,S)-hydroxybupropion, (R,R)- and (S,S)-hydrobupropion (threo) and (S,R)- and (R,S)-hydrobupropion (erythro), in human urine and liver subcellular fractions to begin exploring mechanisms underlying enantioselective metabolism and elimination of bupropion metabolites. Human liver microsomal data revealed marked glucuronidation stereoselectivity [Cl(int), 11.4 versus 4.3 µl/min per milligram for the formation of (R,R)- and (S,S)-hydroxybupropion glucuronide; and Cl(max), 7.7 versus 1.1 µl/min per milligram for the formation of (R,R)- and (S,S)-hydrobupropion glucuronide], in concurrence with observed enantioselective urinary elimination of bupropion glucuronide conjugates. Approximately 10% of the administered bupropion dose was recovered in the urine as metabolites with glucuronide metabolites, accounting for approximately 40%, 15%, and 7% of the total excreted hydroxybupropion, erythro-hydrobupropion, and threo-hydrobupropion, respectively. Elimination pathways were further characterized using an expressed UDP-glucuronosyl transferase (UGT) panel with bupropion enantiomers (both individual and racemic) as substrates. UGT2B7 catalyzed the stereoselective formation of glucuronides of hydroxybupropion, (S,S)-hydrobupropion, (S,R)- and (R,S)-hydrobupropion; UGT1A9 catalyzed the formation of (R,R)-hydrobupropion glucuronide. These data systematically describe the metabolic pathways underlying bupropion metabolite disposition and significantly expand our knowledge of potential contributors to the interindividual and intraindividual variability in therapeutic and toxic effects of bupropion in humans.

Combining the 'bottom up' and 'top down' approaches in pharmacokinetic modelling: fitting PBPK models to observed clinical data

Pharmacokinetic models range from being entirely exploratory and empirical, to semi-mechanistic and ultimately complex physiologically based pharmacokinetic (PBPK) models. This choice is conditional on the modelling purpose as well as the amount and quality of the available data. The main advantage of PBPK models is that they can be used to extrapolate outside the studied population and experimental conditions. The trade-off for this advantage is a complex system of differential equations with a considerable number of model parameters. When these parameters cannot be informed from in vitro or in silico experiments they are usually optimized with respect to observed clinical data. Parameter estimation in complex models is a challenging task associated with many methodological issues which are discussed here with specific recommendations. Concepts such as structural and practical identifiability are described with regards to PBPK modelling and the value of experimental design and sensitivity analyses is sketched out. Parameter estimation approaches are discussed, while we also highlight the importance of not neglecting the covariance structure between model parameters and the uncertainty and population variability that is associated with them. Finally the possibility of using model order reduction techniques and minimal semi-mechanistic models that retain the physiological-mechanistic nature only in the parts of the model which are relevant to the desired modelling purpose is emphasized. Careful attention to all the above issues allows us to integrate successfully information from in vitro or in silico experiments together with information deriving from observed clinical data and develop mechanistically sound models with clinical relevance.

Rationalizing underprediction of drug clearance from enzyme and transporter kinetic data: from in vitro tools to mechanistic modeling

Over the years, there has been an increase in the number and quality of available in vitro tools for the assessment of clearance. Complexity of data analysis and modelling of corresponding in vitro data has increased in an analogous manner, in particular for the simultaneous characterization of transporter and metabolism kinetics, together with intracellular binding and passive diffusion. In the current chapter, the impact of different factors on the in vitro-in vivo extrapolation of clearance will be addressed in a stepwise manner, from the selection of the most adequate in vitro system and experimental design/condition to the corresponding modelling of data generated. The application of static or physiologically based pharmacokinetic models in the prediction of clearance will be discussed, highlighting limitations and current challenges of some of the approaches. Particular focus will be on the ability of in vitro and in silico predictive tools to overcome the trend of clearance underprediction. Improvements made as a result of inclusion of extrahepatic metabolism and consideration of transporter-metabolism interplay across different organs will be discussed.

The role of drug metabolizing enzymes in clearance

INTRODUCTION

Metabolism is one of the most important clearance pathways representing the major clearance route of 75% drugs. The four most common drug metabolizing enzymes (DME) that contribute significantly to elimination pathways of new chemical entities are cytochrome P450s, UDP-glucuronosyltransferases, aldehyde oxidase and sulfotransferases. Accurate prediction of human in vivo clearance by these enzymes, using both in vitro and in vivo tools, is critical for the success of drug candidates in human translation.

AREAS COVERED

Important recent advances of key DME are reviewed and highlighted in the following areas: major isoforms, tissue distribution, generic polymorphism, substrate specificity, species differences, mechanism of catalysis, in vitro-in vivo extrapolation and the importance of using optimal assay conditions and relevant animal models.

EXPERT OPINION

Understanding the clearance mechanism of a compound is the first step toward successful prediction of human clearance. It is critical to apply appropriate in vitro and in vivo methodologies and physiologically based models in human translation. While high-confidence prediction for P450-mediated clearance has been achieved, the accuracy of human clearance prediction is significantly lower for other enzyme classes. More accurate predictive methods and models are being developed to address these challenges.