Mechanistic insights on clearance and inhibition discordance between liver microsomes and hepatocytes when clearance in liver microsomes is higher than in hepatocytes

Discovery of First Branched-Chain Ketoacid Dehydrogenase Kinase (BDK) Inhibitor Clinical Candidate PF-07328948

Inhibition of branched-chain ketoacid dehydrogenase kinase (BDK or BCKDK), a negative regulator of branched-chain amino acid (BCAA) metabolism, is hypothesized to treat cardio-metabolic diseases. From a starting point with potential idiosyncratic toxicity risk, modification to a benzothiophene core and discovery of a cryptic pocket allowed for improved potency with 3-aryl substitution to arrive at PF-07328948, which was largely devoid of protein covalent binding liability. This BDK inhibitor was shown also to be a BDK degrader in cells and in vivo rodent studies. Plasma biomarkers, including BCAAs and branched-chain ketoacids (BCKAs), were lowered in vivo with enhanced pharmacodynamic effect upon chronic dosing due to BDK degradation. This molecule improves metabolic and heart failure end points in rodent models. PF-07328948 is the first known selective BDK inhibitor candidate to be examined in clinical studies, with Phase 1 single ascending dose data showing good tolerability and a pharmacokinetic profile commensurate with once-daily dosing.

Design and application of synthetic 17B-HSD13 substrates reveals preserved catalytic activity of protective human variants

Several hydroxysteroid dehydrogenase 17-beta 13 variants have previously been identified as protective against metabolic dysfunction-associated steatohepatitis (MASH) fibrosis, ballooning and inflammation, and as such this target holds significant therapeutic potential. However, over 5 years later, the function of 17B-HSD13 remains unknown. Structure-aided design enables the development of potent and selective sulfonamide-based 17B-HSD13 inhibitors. In order to probe their inhibitory potency in endogenous expression systems like primary human hepatocytes, inhibitors are transformed into synthetic surrogate substrates with distinct selectivity advantages over substrates previously published. Their application to cells endogenously expressing 17B-HSD13 enables quantitative measures of enzymatic inhibition in primary human hepatocytes which has never been reported to date. Application to multiple cellular systems expressing the protective human variants reveals that the most prevalent IsoD variant maintains NAD-dependent catalytic activity towards some but not all substrates, contradicting reports that the truncation results in loss-of-function.

How predictive are isolated perfused liver data of in vivo hepatic clearance? A meta-analysis of isolated perfused rat liver data

Isolated perfused rat liver (IPRL) experiments have been used to answer clearance-related questions, including evaluating the impact of pathological and physiological processes on hepatic clearance (CLH). However, to date, IPRL data has not been evaluated for in vivo CLH prediction accuracy.In addition to a detailed overview of available IPRL literature, we present an in-depth analysis of the performance of IPRL in CLH prediction.While the entire dataset poorly predicted CLH (GAFE = 3.2; 64% within 3-fold), IPRL conducted under optimal experimental conditions, such as in the presence of plasma proteins and with a perfusion rate within 2-fold of physiological liver blood flow and corrected for unbound fraction in the presence of red blood cells, can accurately predict rat CLH (GAFE = 2.0; 78% within 3-fold). Careful consideration of experimental conditions is needed to allow proper data analysis.Further, isolated perfused liver experiments in other species, including human livers, may allow us to address the current in vitro-in vivo disconnects of hepatic metabolic clearance and improve our methodology for CLH predictions.

Characterization and Applications of Permeabilized Hepatocytes in Drug Discovery

Hepatocytes are one of the most physiologically relevant in vitro liver systems for human translation of clearance and drug-drug interactions (DDI). However, the cell membranes of hepatocytes can limit the entry of certain compounds into the cells for metabolism and DDI. Passive permeability through hepatocytes can be different in vitro and in vivo, which complicates the human translation. Permeabilized hepatocytes offer a useful tool to probe mechanistic understanding of permeability-limited metabolism and DDI. Incubation with saponin of 0.01% at 0.5 million cells/mL and 0.05% at 5 million cells/mL for 5 min at 37°C completely permeabilized the plasma membrane of hepatocytes, while leaving the membranes of subcellular organelles intact. Permeabilized hepatocytes maintained similar enzymatic activity as intact unpermeabilized hepatocytes and can be stored at -80°C for at least 7 months. This approach reduces costs by preserving leftover hepatocytes. The relatively low levels of saponin in permeabilized hepatocytes had no significant impact on the enzymatic activity. As the cytosolic contents leak out from permeabilized hepatocytes, cofactors need to be added to enable metabolic reactions. Cytosolic enzymes will no longer be present if the media are removed after cells are permeabilized. Hence permeabilized hepatocytes with and without media removal may potentially enable reaction phenotyping of cytosolic enzymes. Although permeabilized hepatocytes work similarly as human liver microsomes and S9 fractions experimentally requiring addition of cofactors, they behave more like hepatocytes maintaining enzymatic activities for over 4 h. Permeabilized hepatocytes are a great addition to the drug metabolism toolbox to provide mechanistic insights.

Prediction of Human Clearance Using In Silico Models with Reduced Bias

Predicting human clearance with high accuracy from in silico-derived parameters alone is highly desirable, as it is fast, saves in vitro resources, and is animal-sparing. We derived random forest (RF) models from 1340 compounds with human intravenous pharmacokinetic (PK) data, the largest data set publicly available today. To assess the general applicability of the RF models, we systematically removed structural-therapeutic class analogues and other compounds with structural similarity from the training sets. For a quasi-prospective test set of 343 compounds, we show that RF models devoid of structurally similar compounds in the training set predict human clearance with a geometric mean fold error (GMFE) of 3.3. While the observed GMFE illustrates how difficult it is to generate a useful model that is broadly applicable, we posit that our RF models yield a more realistic assessment of how well human clearance can be predicted prospectively. We deployed the conformal prediction formalism to assess the model applicability and to determine the prediction confidence intervals for each prediction. We observed that clearance can be predicted better for renally cleared compounds than for other clearance mechanisms. We show that applying a classification model for predicting renal clearance identifies a subset of compounds for which clearance can be predicted with higher accuracy, yielding a GMFE of 2.3. In addition, our in silico RF human clearance models compared well to models derived from scaling human hepatocytes or preclinical in vivo data.

Comparison of Tumor Binding Across Tumor Types and Cell Lines to Support Free Drug Considerations for Oncology Drug Discovery

Tumor binding is an important parameter to derive unbound tumor concentration to explore pharmacokinetics (PK) and pharmacodynamics (PD) relationships for oncology disease targets. Tumor binding was evaluated using eleven matrices, including various commonly used ex vivo human and mouse xenograft and syngeneic tumors, tumor cell lines and liver as a surrogate tissue. The results showed that tumor binding is highly correlated among the different tumors and tumor cell lines except for the mouse melanoma (B16F10) tumor type. Liver fraction unbound (fu) has a good correlation with B16F10 tumor binding. Liver also demonstrates a two-fold equivalency, on average, with binding of other tumor types when a scaling factor is applied. Predictive models were developed for tumor binding, with correlations established with LogD (acids), predicted muscle fu (neutrals) and measured plasma protein binding (bases) to estimate tumor fu when experimental data are not available. Many approaches can be applied to obtain and estimate tumor binding values. One strategy proposed is to use a surrogate tumor tissue, such as mouse xenograft ovarian cancer (OVCAR3) tumor, as a surrogate for tumor binding (except for B16F10) to provide an early assessment of unbound tumor concentrations for development of PK/PD relationships.

Predicting the in vivo developmental toxicity of fenarimol from in vitro toxicity data using PBTK modelling-facilitated reverse dosimetry approach

In vitro methods are widely used in modern toxicological testing; however, the data cannot be directly employed for risk assessment. In vivo toxicity of chemicals can be predicted from in vitro data using physiologically based toxicokinetic (PBTK) modelling-facilitated reverse dosimetry (PBTK-RD). In this study, a minimal-PBTK model was constructed to predict the in-vivo kinetic profile of fenarimol (FNL) in rats and humans. The model was verified by comparing the observed and predicted pharmacokinetics of FNL for rats (calibrator) and further applied to humans. Using the PBTK-RD approach, the reported in vitro developmental toxicity data for FNL was translated to in vivo dose-response data to predict the assay equivalent oral dose in rats and humans. The predicted assay equivalent rat oral dose (36.46 mg/kg) was comparable to the literature reported in vivo BMD10 value (22.8 mg/kg). The model was also employed to derive the chemical-specific adjustment factor (CSAF) for interspecies toxicokinetics variability of FNL. Further, Monte Carlo simulations were performed to predict the population variability in the plasma concentration of FNL and to derive CSAF for intersubject human kinetic differences. The comparison of CSAF values for interspecies and intersubject toxicokinetic variability with their respective default values revealed that the applied uncertainty factors were adequately protective.

The Effect of Deuteration and Homologation of the Lactam Ring of Nirmatrelvir on Its Biochemical Properties and Oxidative Metabolism

This study explores the relationship between structural alterations of nirmatrelvir, such as homologation and deuteration, and metabolic stability of newly synthesized derivatives. We developed a reliable synthetic protocol toward dideutero-nirmatrelvir and its homologated analogues with high isotopic incorporation. Deuteration of the primary metabolic site of nirmatrelvir provides a 3-fold improvement of its human microsomal stability but is accompanied by an increased metabolism rate at secondary sites. Homologation of the lactam ring allows the capping group modification to decrease and delocalize the molecule's lipophilicity, reducing the metabolic rate at secondary sites. The effect of deuteration was less pronounced for the 6-membered lactam than for its 5-membered analogue in human microsomes, but the trend is reversed in the case of mouse microsomes. X-ray data revealed that the homologation of the lactam ring favors the orientation of the drug's nitrile warhead for interaction with the catalytic sulfur of the SARS-CoV-2 Mpro, improving its binding. Comparable potency against SARS-CoV-2 Mpro from several variants of concern and selectivity over human cysteine proteases cathepsin B, L, and S was observed for the novel deuterated/homologated derivative and nirmatrelvir. Synthesized compounds displayed a large interspecies variability in hamster, rat, and human hepatocyte stability assays. Overall, we aimed to apply a rational approach in changing the physicochemical properties of the drug to refine its biochemical and biological parameters.

The Comparison of Machine Learning and Mechanistic In Vitro-In Vivo Extrapolation Models for the Prediction of Human Intrinsic Clearance

Accurate prediction of human pharmacokinetics (PK) remains one of the key objectives of drug metabolism and PK (DMPK) scientists in drug discovery projects. This is typically performed by using in vitro-in vivo extrapolation (IVIVE) based on mechanistic PK models. In recent years, machine learning (ML), with its ability to harness patterns from previous outcomes to predict future events, has gained increased popularity in application to absorption, distribution, metabolism, and excretion (ADME) sciences. This study compares the performance of various ML and mechanistic models for the prediction of human IV clearance for a large (645) set of diverse compounds with literature human IV PK data, as well as measured relevant in vitro end points. ML models were built using multiple approaches for the descriptors: (1) calculated physical properties and structural descriptors based on chemical structure alone (classical QSAR/QSPR); (2) in vitro measured inputs only with no structure-based descriptors (ML IVIVE); and (3) in silico ML IVIVE using in silico model predictions for the in vitro inputs. For the mechanistic models, well-stirred and parallel-tube liver models were considered with and without the use of empirical scaling factors and with and without renal clearance. The best ML model for the prediction of in vivo human intrinsic clearance (CLint) was an in vitro ML IVIVE model using only six in vitro inputs with an average absolute fold error (AAFE) of 2.5. The best mechanistic model used the parallel-tube liver model, with empirical scaling factors resulting in an AAFE of 2.8. The corresponding mechanistic model with full in silico inputs achieved an AAFE of 3.3. These relative performances of the models were confirmed with the prediction of 16 Pfizer drug candidates that were not part of the original data set. Results show that ML IVIVE models are comparable to or superior to their best mechanistic counterparts. We also show that ML IVIVE models can be used to derive insights into factors for the improvement of mechanistic PK prediction.

Design of Next-Generation DGAT2 Inhibitor PF-07202954 with Longer Predicted Half-Life

Diacylglycerol O-acyltransferase 2 (DGAT2) inhibitors have been shown to lower liver triglyceride content and are being explored clinically as a treatment for non-alcoholic steatohepatitis (NASH). This work details efforts to find an extended-half-life DGAT2 inhibitor. A basic moiety was added to a known inhibitor template, and the basicity and lipophilicity were fine-tuned by the addition of electrophilic fluorines. A weakly basic profile was required to find an appropriate balance of potency, clearance, and permeability. This work culminated in the discovery of PF-07202954 (12), a weakly basic DGAT2 inhibitor that has advanced to clinical studies. This molecule displays a higher volume of distribution and longer half-life in preclinical species, in keeping with its physicochemical profile, and lowers liver triglyceride content in a Western-diet-fed rat model.

Quantitative Translation of Substrate Intrinsic Clearance from Recombinant CYP1A1 to Humans

CYP1A1 is a cytochrome P450 family 1 enzyme that is mostly expressed in the extrahepatic tissues. To understand the CYP1A1 contribution to drug clearance in humans, we examined the in vitro-in vivo extrapolation (IVIVE) of intrinsic clearance (CLint) for a set of drugs that are in vitro CYP1A1 substrates. Despite being strong in vitro CYP1A1 substrates, 82% of drugs gave good IVIVE with predicted CLint within 2-3-fold of the observed values using human liver microsomes and hepatocytes, suggesting they were not in vivo CYP1A1 substrates due to the lack of extrahepatic contribution to CLint. Only three drugs (riluzole, melatonin and ramelteon) that are CYP1A2 substrates yielded significant underprediction of in vivo CLint up to 11-fold. The fold of CLint underprediction was linearly proportional to human recombinant CYP1A1 (rCYP1A1) CLint, indicating they were likely to be in vivo CYP1A1 substrates. Using these three substrates, a calibration curve can be developed to enable direct translation from in vitro rCYP1A1 CLint to in vivo extrahepatic contributions in humans. In vivo CYP1A1 substrates are planar and small, which is consistent with the structure of the active site. This is in contrast to the in vitro substrates, which include large and nonplanar molecules, suggesting rCYP1A1 is more accessible than what is in vivo. The impact of CYP1A1 on first-pass intestinal metabolism was also evaluated and shown to be minimal. This is the first study providing new insights on in vivo translation of CYP1A1 contributions to human clearance using in vitro rCYP1A1 data.

The Disconnect in Intrinsic Clearance Determined in Human Hepatocytes and Liver Microsomes Results from Divergent Cytochrome P450 Activities

Candidate drugs may exhibit higher unbound intrinsic clearances (CLint,u) in human liver microsomes (HLMs) relative to human hepatocytes (HHs), posing a challenge as to which value is more predictive of in vivo clearance (CL). This work was aimed at better understanding the mechanism(s) underlying this 'HLM:HH disconnect' via examination of previous explanations, including passive permeability limited CL or cofactor exhaustion in hepatocytes. A series of structurally related, passively permeable (Papps > 5 × 10-6 cm/s), 5-azaquinazolines were studied in different liver fractions, and metabolic rates and routes were determined. A subset of these compounds demonstrated a significant HLM:HH (CLint,u ratio 2-26) disconnect. Compounds were metabolized via combinations of liver cytosol aldehyde oxidase (AO), microsomal cytochrome P450 (CYP) and flavin monooxygenase (FMO). For this series, the lack of concordance between CLint,u determined in HLM and HH contrasted with an excellent correlation of AO dependent CLint,u determined in human liver cytosol[Formula: see text], r2 = 0.95, P < 0.0001). The HLM:HH disconnect for both 5-azaquinazolines and midazolam was as a result of significantly higher CYP activity in HLM and lysed HH fortified with exogenous NADPH relative to intact HH. Moreover, for the 5-azaquinazolines, the maintenance of cytosolic AO and NADPH-dependent FMO activity in HH, relative to CYP, supports the conclusion that neither substrate permeability nor intracellular NADPH for hepatocytes were limiting CLint,u Further studies are required to identify the underlying cause of the lower CYP activities in HH relative to HLM and lysed hepatocytes in the presence of exogenous NADPH. SIGNIFICANCE STATEMENT: Candidate drugs may exhibit higher intrinsic clearance in human liver microsomes relative to human hepatocytes, posing a challenge as to which value is predictive of in vivo clearance. This work demonstrates that the difference in activity determined in liver fractions results from divergent cytochrome P450 but not aldehyde oxidase or flavin monooxygenase activity. This is inconsistent with explanations including substrate permeability limitations or cofactor exhaustion and should inform the focus of further studies to understand this cytochrome P450 specific disconnect phenomenon.

Recent advances in measurement of metabolic clearance, metabolite profile and reaction phenotyping of low clearance compounds

INTRODUCTION

Low metabolic clearance is usually a highly desirable property of drug candidates in order to reduce dose and dosing frequency. However, measurement of low clearance can be challenging in drug discovery. A number of new tools have recently been developed to address the gaps in the measurement of intrinsic clearance, identification of metabolites, and reaction phenotyping of low clearance compounds.

AREAS COVERED

The new methodologies of low clearance measurements are discussed, including the hepatocyte relay, HepatoPac®, HμREL®, and spheroid systems. In addition, metabolite formation rate determination and in vivo allometric scaling approaches are covered as alternative methods for low clearance measurements. With these new methods, measurement of ~ 20-fold lower limit of intrinsic clearance can be achieved. The advantages and limitations of each approach are highlighted.

EXPERT OPINION

Although several novel methods have been developed in recent years to address the challenges of low clearance, these assays tend to be time and labor intensive and costly. Future innovations focusing on developing systems with high enzymatic activities, ultra-sensitive universal quantifiable detectors, and artificial intelligence will further enhance our ability to explore the low clearance space.

In Vitro-In Vivo Extrapolation and Scaling Factors for Clearance of Human and Preclinical Species with Liver Microsomes and Hepatocytes

In vitro-in vivo extrapolation ((IVIVE) and empirical scaling factors (SF) of human intrinsic clearance (CL_int) were developed using one of the largest dataset of 455 compounds with data from human liver microsomes (HLM) and human hepatocytes (HHEP). For extended clearance classification system (ECCS) class 2/4 compounds, linear SFs (SF_lin) are approximately 1, suggesting enzyme activities in HLM and HHEP are similar to those in vivo under physiological conditions. For ECCS class 1A/1B compounds, a unified set of SFs was developed for CL_int. These SFs contain both SF_lin and an exponential SF (SF_β) of fraction unbound in plasma (f_u,p). The unified SFs for class 1A/1B eliminate the need to identify the transporters involved prior to clearance prediction. The underlying mechanisms of these SFs are not entirely clear at this point, but they serve practical purposes to reduce biases and increase prediction accuracy. Similar SFs have also been developed for preclinical species. For HLM-HHEP disconnect (HLM > HHEP) ECCS class 2/4 compounds that are mainly metabolized by cytochrome P450s/FMO, HLM significantly overpredicted in vivo CL_int, while HHEP slightly underpredicted and geometric mean of HLM and HHEP slightly overpredicted in vivo CL_int. This observation is different than in rats, where rat liver microsomal CL_int correlates well with in vivo CL_int for compounds demonstrating permeability-limited metabolism. The good CL_int IVIVE developed using HLM and HHEP helps build confidence for prospective predictions of human clearance and supports the continued utilization of these assays to guide structure-activity relationships to improve metabolic stability.

Discovery of the Potent and Selective MC4R Antagonist PF-07258669 for the Potential Treatment of Appetite Loss

The melanocortin-4 receptor (MC4R) is a centrally expressed, class A GPCR that plays a key role in the regulation of appetite and food intake. Deficiencies in MC4R signaling result in hyperphagia and increased body mass in humans. Antagonism of MC4R signaling has the potential to mitigate decreased appetite and body weight loss in the setting of anorexia or cachexia due to underlying disease. Herein, we report on the identification of a series of orally bioavailable, small-molecule MC4R antagonists using a focused hit identification effort and the optimization of these antagonists to provide clinical candidate 23. Introduction of a spirocyclic conformational constraint allowed for simultaneous optimization of MC4R potency and ADME attributes while avoiding the production of hERG active metabolites observed in early series leads. Compound 23 is a potent and selective MC4R antagonist with robust efficacy in an aged rat model of cachexia and has progressed into clinical trials.

Study on In Vitro Metabolism and In Vivo Pharmacokinetics of Beauvericin

Beauvericin (BEA) is a well-known mycotoxin produced by many fungi, including Beaveria bassiana. The purpose of this study was to evaluate the in vitro distribution and metabolism characteristics as well as the in vivo pharmacokinetic (PK) profile of BEA. The in vitro metabolism studies of BEA were performed using rat, dog, mouse, monkey and human liver microsomes, cryopreserved hepatocytes and plasma under conditions of linear kinetics to estimate the respective elimination rates. Additionally, LC-UV-MSn (n = 1~2) was used to identify metabolites in human, rat, mouse, dog and monkey liver microsomes. Furthermore, cytochrome P450 (CYP) reaction phenotyping was carried out. Finally, the absolute bioavailability of BEA was evaluated by intravenous and oral administration in rats. BEA was metabolically stable in the liver microsomes and hepatocytes of humans and rats; however, it was a strong inhibitor of midazolam 1′-hydroxylase (CYP3A4) and mephenytoin 4′-hydroxylase (CYP2C19) activities in human liver microsomes. The protein binding fraction values of BEA were >90% and the half-life (T1/2) values of BEA were approximately 5 h in the plasma of the five species. The absolute bioavailability was calculated to be 29.5%. Altogether, these data indicate that BEA has great potential for further development as a drug candidate. Metabolic studies of different species can provide important reference values for further safety evaluation.

Recent advances in the translation of drug metabolism and pharmacokinetics science for drug discovery and development

Drug metabolism and pharmacokinetics (DMPK) is an important branch of pharmaceutical sciences. The nature of ADME (absorption, distribution, metabolism, excretion) and PK (pharmacokinetics) inquiries during drug discovery and development has evolved in recent years from being largely descriptive to seeking a more quantitative and mechanistic understanding of the fate of drug candidates in biological systems. Tremendous progress has been made in the past decade, not only in the characterization of physiochemical properties of drugs that influence their ADME, target organ exposure, and toxicity, but also in the identification of design principles that can minimize drug-drug interaction (DDI) potentials and reduce the attritions. The importance of membrane transporters in drug disposition, efficacy, and safety, as well as the interplay with metabolic processes, has been increasingly recognized. Dramatic increases in investments on new modalities beyond traditional small and large molecule drugs, such as peptides, oligonucleotides, and antibody-drug conjugates, necessitated further innovations in bioanalytical and experimental tools for the characterization of their ADME properties. In this review, we highlight some of the most notable advances in the last decade, and provide future perspectives on potential major breakthroughs and innovations in the translation of DMPK science in various stages of drug discovery and development.

In Vitro - in Vivo Extrapolation of Hepatic Clearance in Preclinical Species

Accurate prediction of human clearance is of critical importance in drug discovery. In this study, in vitro - in vivo extrapolation (IVIVE) of hepatic clearance was established using large sets of compounds for four preclinical species (mouse, rat, dog, and non-human primate) to enable better understanding of clearance mechanisms and human translation. In vitro intrinsic clearances were obtained using pooled liver microsomes (LMs) or hepatocytes (HEPs) and scaled to hepatic clearance using the parallel-tube and well-stirred models. Subsequently, IVIVE scaling factors (SFs) were derived to best predict in vivo clearance. The SFs for extended clearance classification system (ECCS) class 2/4 compounds, involving metabolic clearance, were generally small (≤ 2.6) using both LMs and HEPs with parallel-tube model, with the exception of the rodents (~ 2.4-4.6), suggesting in vitro reagents represent in vivo reasonably well. SFs for ECCS class 1A and 1B are generally higher than class 2/4 across the species, likely due to the contribution of transporter-mediated clearance that is under-represented with in vitro reagents. The parallel-tube model offered lower variability in clearance predictions over the well-stirred model. For compounds that likely demonstrate passive permeability-limited clearance in vitro, rat LM predicted in vivo clearance more accurately than HEP. This comprehensive analysis demonstrated reliable IVIVE can be achieved using LMs and HEPs. Evaluation of clearance IVIVE in preclinical species helps to better understand clearance mechanisms, establish more reliable IVIVE in human, and enhance our confidence in human clearance and PK prediction, while considering species differences in drug metabolizing enzymes and transporters.

Permeabilized Cryopreserved Human Hepatocytes as an Exogenous Metabolic System in a Novel Metabolism-Dependent Cytotoxicity Assay for the Evaluation of Metabolic Activation and Detoxification of Drugs Associated with Drug-Induced Liver Injuries: Results with Acetaminophen, Amiodarone, Cyclophosphamide, Ketoconazole, Nefazodone, and Troglitazone

We report here a novel in vitro experimental system, the metabolism-dependent cytotoxicity assay (MDCA), for the definition of the roles of hepatic drug metabolism in toxicity. MDCA employs permeabilized cofactor-supplemented cryopreserved human hepatocytes (MetMax Human Hepatocytes, MMHH), as an exogenous metabolic activating system, and human embryonic kidney 293 (HEK293) cells, a cell line devoid of drug-metabolizing enzyme activity, as target cells for the quantification of drug toxicity. The assay was performed in the presence and absence of cofactors for key drug metabolism pathways known to play key roles in drug toxicity: NADPH/NAD+ for phase 1 oxidation, uridine 5'-diphosphoglucuronic acid (UDPGA) for uridine 5'-diphospho-glucuronosyltransferase (UGT) mediated glucuronidation, 3'-phosphoadenosine-5'-phosphosulfate (PAPS) for cytosolic sulfotransferase (SULT) mediated sulfation, and glutathione (GSH) for glutathione S-transferase (GST) mediated GSH conjugation. Six drugs with clinically significant hepatoxicity, resulting in liver failure or a need for liver transplantation: acetaminophen, amiodarone, cyclophosphamide, ketoconazole, nefazodone, and troglitazone were evaluated. All six drugs exhibited cytotoxicity enhancement by NADPH/NAD+, suggesting metabolic activation via phase 1 oxidation. Attenuation of cytotoxicity by UDPGA was observed for acetaminophen, ketoconazole, and troglitazone, by PAPS for acetaminophen, ketoconazole, and troglitazone, and by GSH for all six drugs. Our results suggest that MDCA can be applied toward the elucidation of metabolic activation and detoxification pathways, providing information that can be applied in drug development to guide structure optimization to reduce toxicity and to aid the assessment of metabolism-based risk factors for drug toxicity. GSH detoxification represents an endpoint for the identification of drugs forming cytotoxic reactive metabolites, a key property of drugs with idiosyncratic hepatotoxicity. SIGNIFICANCE STATEMENT: Application of the metabolism-dependent cytotoxicity assay (MDCA) for the elucidation of the roles of metabolic activation and detoxification pathways in drug toxicity may provide information to guide structure optimization in drug development to reduce hepatotoxic potential and to aid the assessment of metabolism-based risk factors. Glutathione (GSH) detoxification represents an endpoint for the identification of drugs forming cytotoxic reactive metabolites that may be applied toward the evaluation of idiosyncratic hepatotoxicity.

Predictive In Vitro-In Vivo Extrapolation for Time Dependent Inhibition of CYP1A2, CYP2C8, CYP2C9, CYP2C19, and CYP2D6 Using Pooled Human Hepatocytes, Human Liver Microsomes, and a Simple Mechanistic Static Model

Inactivation of Cytochrome P450 (CYP450) enzymes can lead to significant increases in exposure of comedicants. The majority of reported in vitro to in vivo extrapolation (IVIVE) data have historically focused on CYP3A, leaving the assessment of other CYP isoforms insubstantial. To this end, the utility of human hepatocytes (HHEP) and human liver microsomes (HLM) to predict clinically relevant drug-drug interactions was investigated with a focus on CYP1A2, CYP2C8, CYP2C9, CYP2C19, and CYP2D6. Evaluation of IVIVE for CYP2B6 was limited to only weak inhibition. A search of the University of Washington Drug-Drug Interaction Database was conducted to identify a clinically relevant weak, moderate, and strong inhibitor for selective substrates of CYP1A2, CYP2C8, CYP2C9, CYP2C19, and CYP2D6, resulting in 18 inhibitors for in vitro characterization against 119 clinical interaction studies. Pooled human hepatocytes and HLM were preincubated with increasing concentrations of inhibitors for designated timepoints. Time dependent inhibition was detected in HLM for four moderate/strong inhibitors, suggesting that some optimization of incubation conditions (i.e., lower protein concentrations) is needed to capture weak inhibition. Clinical risk assessment was conducted by incorporating the in vitro derived kinetic parameters maximal rate of enzyme inactivation (min-1) (kinact) and concentration of inhibitor resulting in 50% of the maximum enzyme inactivation (KI) into static equations recommended by regulatory authorities. Significant overprediction was observed when applying the basic models recommended by regulatory agencies. Mechanistic static models, which consider the fraction of metabolism through the impacted enzyme, using the unbound hepatic inlet concentration lead to the best overall prediction accuracy with 92% and 85% of data from HHEPs and HLM, respectively, within twofold of the observed value. SIGNIFICANCE STATEMENT: Coupling time-dependent inactivation parameters derived from pooled human hepatocytes and human liver microsomes (HLM) with a mechanistic static model provides an easy and quantitatively accurate means to determine clinical drug-drug interaction risk from in vitro data. Optimization is needed to evaluate time-dependent inhibition (TDI) for weak and moderate inhibitors using HLM. Recommendations are made with respect to input parameters for in vitro to in vivo extrapolation (IVIVE) of TDI with non-CYP3A enzymes using available data from HLM and human hepatocytes.

Evolving approaches on measurements and applications of intracellular free drug concentration and Kpuu in drug discovery

Introduction: Intracellular-free drug concentration (C_u,cell) and unbound partition coefficient (Kp_uu) are two important parameters to develop pharmacokinetic and pharmacodynamic relationships, predict drug-drug interaction potentials and estimate therapeutic indices.Area covered: Methods on measurements of C_u,cell, Kp_uu, partition coefficient (Kp) and fraction unbound of cells (f_uc) are discussed. Advantages and limitations of several f_uc methods are reviewed. Applications highlighted here are bridging the potency gaps between biochemical and cell-based assays, in vitro hepatocyte assay to predict in vivo liver-to-plasma Kp_uu, the role of Kp_uu in prediction of hepatic clearance for enzyme- and transporter-mediated mechanisms using extended clearance equation, and structural attributes governing tissue Kp_uu.Expert opinion: C_u,cell and Kp_uu are of growing applications in drug discovery. Methods for measurements of these properties continue to evolve in order to achieve higher precision/accuracy and obtain more detailed information at the subcellular levels. Future directions of the field include the development of in vitro and in silico models to predict tissue Kp_uu, direct measurement of free drug concentration in subcellular organelles, and further investigations into the critical elements governing cell and tissue Kp_uu. Significant innovation is needed to advance this complex, but highly impactful and exciting area of science.

Recent developments in in vitro and in vivo models for improved translation of preclinical pharmacokinetics and pharmacodynamics data

Improved pharmacokinetics/pharmacodynamics (PK/PD) prediction in the early stages of drug development is essential to inform lead optimization strategies and reduce attrition rates. Recently, there have been significant advancements in the development of new in vitro and in vivo strategies to better characterize pharmacokinetic properties and efficacy of drug leads. Herein, we review advances in experimental and mathematical models for clearance predictions, advancements in developing novel tools to capture slowly metabolized drugs, in vivo model developments to capture human etiology for supporting drug development, limitations and gaps in these efforts, and a perspective on the future in the field.

Designing QSARs for Parameters of High-Throughput Toxicokinetic Models Using Open-Source Descriptors

The intrinsic metabolic clearance rate (Cl_int) and the fraction of the chemical unbound in plasma (f_up) serve as important parameters for high-throughput toxicokinetic (TK) models, but experimental data are limited for many chemicals. Open-source quantitative structure-activity relationship (QSAR) models for both parameters were developed to offer reliable in silico predictions for a diverse set of chemicals regulated under the U.S. law, including pharmaceuticals, pesticides, and industrial chemicals. As a case study to demonstrate their utility, model predictions served as inputs to the TK component of a risk-based prioritization approach based on bioactivity/exposure ratios (BERs), in which a BER < 1 indicates that exposures are predicted to exceed a biological activity threshold. When applied to a subset of the Tox21 screening library (6484 chemicals), we found that the proportion of chemicals with BER <1 was similar using either in silico (1133/6484; 17.5%) or in vitro (148/848; 17.5%) parameters. Further, when considering only the chemicals in the Tox21 set with in vitro data, there was a high concordance of chemicals classified with either BER <1 or >1 using either in silico or in vitro parameters (767/848, 90.4%). Thus, the presented QSARs may be suitable for prioritizing the risk posed by many chemicals for which measured in vitro TK data are lacking.

Influence of Proteome Profiles and Intracellular Drug Exposure on Differences in CYP Activity in Donor-Matched Human Liver Microsomes and Hepatocytes

Human liver microsomes (HLM) and human hepatocytes (HH) are important in vitro systems for studies of intrinsic drug clearance (CLint) in the liver. However, the CLint values are often in disagreement for these two systems. Here, we investigated these differences in a side-by-side comparison of drug metabolism in HLM and HH prepared from 15 matched donors. Protein expression and intracellular unbound drug concentration (Kpuu) effects on the CLint were investigated for five prototypical probe substrates (bupropion-CYP2B6, diclofenac-CYP2C9, omeprazole-CYP2C19, bufuralol-CYP2D6, and midazolam-CYP3A4). The samples were donor-matched to compensate for inter-individual variability but still showed systematic differences in CLint. Global proteomics analysis outlined differences in HLM from HH and homogenates of human liver (HL), indicating variable enrichment of ER-localized cytochrome P450 (CYP) enzymes in the HLM preparation. This suggests that the HLM may not equally and accurately capture metabolic capacity for all CYPs. Scaling CLint with CYP amounts and Kpuu could only partly explain the discordance in absolute values of CLint for the five substrates. Nevertheless, scaling with CYP amounts improved the agreement in rank order for the majority of the substrates. Other factors, such as contribution of additional enzymes and variability in the proportions of active and inactive CYP enzymes in HLM and HH, may have to be considered to avoid the use of empirical scaling factors for prediction of drug metabolism.