Interlaboratory Variability in Human Hepatocyte Intrinsic Clearance Values and Trends with Physicochemical Properties

An Integrated Hepatocyte Stability Assay for Simultaneous Metabolic Stability Assessment and Metabolite Profiling

The determination of metabolic stability is critical for drug discovery programs, allowing for the optimization of chemical entities and compound prioritization. As such, it is common to perform high-volume in vitro metabolic stability experiments early in the lead optimization process to understand metabolic liabilities. Additional metabolite identification experiments are subsequently performed for a more comprehensive understanding of the metabolic clearance routes to aid medicinal chemists in the structural design of compounds. Collectively, these experiments require extensive sample preparation and a substantial amount of time and resources. To overcome the challenges, a high-throughput integrated assay for simultaneous hepatocyte metabolic stability assessment and metabolite profiling was developed. This assay platform consists of four parts: 1) an automated liquid-handling system for sample preparation and incubation, 2) a liquid chromatography and high-resolution mass spectrometry-based system to simultaneously monitor the parent compound depletion and metabolite formation, 3) an automated data analysis and report system for hepatic clearance assessment; and 4) streamlined autobatch processing for software-based metabolite profiling. The assay platform was evaluated using eight control compounds with various metabolic rates and biotransformation routes in hepatocytes across three species. Multiple sample preparation and data analysis steps were evaluated and validated for accuracy, repeatability, and metabolite coverage. The combined utility of an automated liquid-handling instrument, a high-resolution mass spectrometer, and multiple streamlined data processing software improves the process of these highly demanding screening assays and allows for simultaneous determination of metabolic stability and metabolite profiles for more efficient lead optimization during early drug discovery. SIGNIFICANCE STATEMENT: Metabolic stability assessment and metabolite profiling are pivotal in drug discovery to fully comprehend metabolic liabilities for chemical entity optimization and lead selection. Process of these assays can be repetitive and resource demanding. Here, we developed an integrated hepatocyte stability assay that combines automation, high-resolution mass spectrometers, and batch-processing software to improve and combine the workflow of these assays. The integrated approach allows simultaneous metabolic stability assessment and metabolite profiling, significantly accelerating screening and lead optimization in a resource-effective manner.

Identifying xenobiotic metabolites with in silico prediction tools and LCMS suspect screening analysis

Understanding the metabolic fate of a xenobiotic substance can help inform its potential health risks and allow for the identification of signature metabolites associated with exposure. The need to characterize metabolites of poorly studied or novel substances has shifted exposure studies towards non-targeted analysis (NTA), which often aims to profile many compounds within a sample using high-resolution liquid-chromatography mass-spectrometry (LCMS). Here we evaluate the suitability of suspect screening analysis (SSA) liquid-chromatography mass-spectrometry to inform xenobiotic chemical metabolism. Given a lack of knowledge of true metabolites for most chemicals, predictive tools were used to generate potential metabolites as suspect screening lists to guide the identification of selected xenobiotic substances and their associated metabolites. Thirty-three substances were selected to represent a diverse array of pharmaceutical, agrochemical, and industrial chemicals from Environmental Protection Agency's ToxCast chemical library. The compounds were incubated in a metabolically-active in vitro assay using primary hepatocytes and the resulting supernatant and lysate fractions were analyzed with high-resolution LCMS. Metabolites were simulated for each compound structure using software and then combined to serve as the suspect screening list. The exact masses of the predicted metabolites were then used to select LCMS features for fragmentation via tandem mass spectrometry (MS/MS). Of the starting chemicals, 12 were measured in at least one sample in either positive or negative ion mode and a subset of these were used to develop the analysis workflow. We implemented a screening level workflow for background subtraction and the incorporation of time-varying kinetics into the identification of likely metabolites. We used haloperidol as a case study to perform an in-depth analysis, which resulted in identifying five known metabolites and five molecular features that represent potential novel metabolites, two of which were assigned discrete structures based on in silico predictions. This workflow was applied to five additional test chemicals, and 15 molecular features were selected as either reported metabolites, predicted metabolites, or potential metabolites without a structural assignment. This study demonstrates that in some-but not all-cases, suspect screening analysis methods provide a means to rapidly identify and characterize metabolites of xenobiotic chemicals.

Multispecies Machine Learning Predictions of In Vitro Intrinsic Clearance with Uncertainty Quantification Analyses

In pharmaceutical research, compounds are optimized for metabolic stability to avoid a too fast elimination of the drug. Intrinsic clearance (CL_int) measured in liver microsomes or hepatocytes is an important parameter during lead optimization. In this work, machine learning models were developed to relate the compound structure to microsomal metabolic stability and predict CL_int for new compounds. A multitask (MT) learning architecture was introduced to model the CL_int of six species simultaneously, giving as a result a multispecies machine learning model. MT graph neural network (MT-GNN) regression was identified as the top-performing method, and an ensemble of 10 MT-GNN models was evaluated prospectively. Geometric mean fold errors were consistently smaller than 2-fold. Moreover, high precision values were obtained in the prediction of "high" (>300 μL/min/mg) and "low" (<100 μL/min/mg) CL_int compounds. Precision values ranged from 80 to 94% for low CL_int predictions and from 75 to 97% for high CL_int predictions, depending on the species. Uncertainty on experimental values and model predictions was systematically quantified. Experimental variability (aleatoric uncertainty) of all historical Novartis in vitro clearance experiments was analyzed. Interestingly, MT-GNN models' performance approached assays' experimental variability. Moreover, uncertainty estimation in predictions (epistemic uncertainty) enabled identifying predictions associated with lower and higher error. Taken together, our manuscript combines a multispecies deep learning model and large-scale uncertainty analyses to improve CL_int predictions and facilitate early informed decisions for compound prioritization.

Physiologically based pharmacokinetic (PBPK) modelling of tamsulosin related to CYP2D6*10 allele

Tamsulosin, a selective [Formula: see text]-adrenoceptor blocker, is commonly used for alleviation of lower urinary tract symptoms related to benign prostatic hyperplasia. Tamsulosin is predominantly metabolized by CYP3A4 and CYP2D6 enzymes, and several studies reported the effects of CYP2D6 genetic polymorphism on the pharmacokinetics of tamsulosin. This study aims to develop and validate the physiologically based pharmacokinetic (PBPK) model of tamsulosin in CYP2D6*wt/*wt, CYP2D6*wt/*10, and CYP2D6*10/*10 genotypes, using Simcyp® simulator. Physicochemical, and formulation properties and data for absorption, distribution, metabolism and excretion were collected from previous publications, predicted in the simulator, or optimized in different CYP2D6 genotypes. The tamsulosin PBPK model in CYP2D6*wt/*wt and CYP2D6*wt/*10 genotypes were developed based on the clinical pharmacokinetic study where a single oral dose of 0.2 mg tamsulosin was administered to 25 healthy Korean male volunteers with CYP2D6*wt/*wt and CYP2D6*wt/*10 genotypes. A previous pharmacokinetic study was used to develop the model in CYP2D6*10/*10 genotype. The developed model was validated using other clinical pharmacokinetic studies not used in development. The predicted exposures via the PBPK model in CYP2D6*wt/*10 and CYP2D6*10/*10 genotype was 1.23- and 1.76-fold higher than CYP2D6*wt/*wt genotype, respectively. The simulation profiles were visually similar to the observed profiles, and fold errors of all development and validation datasets were included within the criteria. Therefore, the tamsulosin PBPK model in different CYP2D6 genotypes with regards to CYP2D6*10 alleles was appropriately established. Our model can contribute to the implementation of personalized pharmacotherapy of patients, appropriately predicting the pharmacokinetics of tamsulosin reflecting their demographic and CYP2D6 genotype characteristics without unnecessary drug exposure.

Prediction of hepatic drug clearance with a human microfluidic four-cell liver acinus microphysiology system

Predicting human hepatic clearance remains a fundamental challenge in both pharmaceutical drug development and toxicological assessments of environmental chemicals, with concerns about both accuracy and precision of in vitro-derived estimates. Suggested sources of these issues have included differences in experimental protocols, differences in cell sourcing, and use of a single cell type, liver parenchymal cells (hepatocytes). Here we investigate the ability of human microfluidic four-cell liver acinus microphysiology system (LAMPS) to make predictions as to hepatic clearance for seven representative compounds: Caffeine, Pioglitazone, Rosiglitazone, Terfenadine, Tolcapone, Troglitazone, and Trovafloxacin. The model, whose reproducibility was recently confirmed in an inter-lab comparison, was constructed using primary human hepatocytes or human induced pluripotent stem cell (iPSC)-derived hepatocytes and 3 human cell lines for the endothelial, Kupffer and stellate cells. We calculated hepatic clearance estimates derived from experiments using LAMPS or traditional 2D cultures and compared the outcomes with both in vivo human clinical study-derived and in vitro human hepatocyte suspension culture-derived values reported in the literature. We found that, compared to in vivo clinically-derived values, the LAMPS model with iPSC-derived hepatocytes had higher precision as compared to primary cells in suspension or 2D culture, but, consistent with previous studies in other microphysiological systems, tended to underestimate in vivo clearance. Overall, these results suggest that use of LAMPS and iPSC-derived hepatocytes together with an empirical scaling factor warrants additional study with a larger set of compounds, as it has the potential to provide more accurate and precise estimates of hepatic clearance.

Successful and Unsuccessful Prediction of Human Hepatic Clearance for Lead Optimization

Development of new chemical entities is costly, time-consuming, and has a low success rate. Accurate prediction of pharmacokinetic properties is critical to progress compounds with favorable drug-like characteristics in lead optimization. Of particular importance is the prediction of hepatic clearance, which determines drug exposure and contributes to projection of dose, half-life, and bioavailability. The most commonly employed methodology to predict hepatic clearance is termed in vitro to in vivo extrapolation (IVIVE) that involves measuring drug metabolism in vitro, scaling-up this in vitro intrinsic clearance to a prediction of in vivo intrinsic clearance by reconciling the enzymatic content between the incubation and an average human liver, and applying a model of hepatic disposition to account for limitations of protein binding and blood flow to predict in vivo clearance. This manuscript reviews common in vitro techniques used to predict hepatic clearance as well as current challenges and recent theoretical advancements in IVIVE.

Primary Human Hepatocyte Spheroid Model as a 3D In Vitro Platform for Metabolism Studies

3D cultures of primary human hepatocytes (PHH) are emerging as a more in vivo-like culture system than previously available hepatic models. This work describes the characterisation of drug metabolism in 3D PHH spheroids. Spheroids were formed from three different donors of PHH and the expression and activities of important cytochrome P450 enzymes (CYP1A2, 2B6, 2C9, 2D6, and 3A4) were maintained for up to 21 days after seeding. The activity of CYP2B6 and 3A4 decreased, while the activity of CYP2C9 and 2D6 increased over time (P < 0.05). For six test compounds, that are metabolised by multiple enzymes, intrinsic clearance (CL_int) values were comparable to standard in vitro hepatic models and successfully predicted in vivo CL_int within 3-fold from observed values for low clearance compounds. Remarkably, the metabolic turnover of these low clearance compounds was reproducibly measured using only 1-3 spheroids, each composed of 2000 cells. Importantly, metabolites identified in the spheroid cultures reproduced the major metabolites observed in vivo, both primary and secondary metabolites were captured. In summary, the 3D PHH spheroid model shows promise to be used in drug discovery projects to study drug metabolism, including unknown mechanisms, over an extended period of time.

Investigating the Theoretical Basis for In Vitro-In Vivo Extrapolation (IVIVE) in Predicting Drug Metabolic Clearance and Proposing Future Experimental Pathways

Extensive studies have been conducted to predict in vivo metabolic clearance from in vitro human liver metabolism parameters (i.e., in vitro-in vivo extrapolation (IVIVE)) with little success. Here, deriving IVIVE from first principles, we show that the product of fraction unbound in the blood and the predicted in vivo intrinsic clearance determined from hepatocyte or microsomal incubations is the lower boundary condition for in vivo hepatic clearance and the prerequisite for IVIVE predictions to be valid, regardless of extraction ratio. For 60-80% of drugs evaluated here, this product is markedly less than the in vivo measured clearance, a result that violates the lower boundary of the predictive relationship. This can only be explained by (a) suboptimal in vitro metabolic stability assay conditions, (b) significant error in the assumption that in vitro intrinsic clearance determinations will predict in vivo intrinsic clearance simply by scaling-up the amount of enzyme (in vitro incubation to in vivo liver), and/or (c) the methods of determining fraction unbound are incorrect. We further suggest that widely employed organ blood flow values underpredict the effective blood flow within the organ by approximately 2.5-fold, thus impacting IVIVE of high clearance compounds. We propose future pathways that should be investigated in terms of the relationship to experimentally measured clearance values, rather than model-dependent intrinsic clearance. IVIVE outcome can be improved by estimating the ratio of unbound drug concentration in the liver tissue to the liver plasma, examining the assumption of the free drug theory (i.e., there are no transporter effects at the blood cell membrane) and the finding that the upper limit of organ clearance may be greater than blood flow entering the organ.

AMPKα2 Overexpression Reduces Cardiomyocyte Ischemia-Reperfusion Injury Through Normalization of Mitochondrial Dynamics

Cardiac ischemia-reperfusion (I/R) injury is associated with mitochondrial dysfunction. Recent studies have reported that mitochondrial function is determined by mitochondrial dynamics. Here, we hypothesized that AMPKα2 functions as an upstream mediator that sustains mitochondrial dynamics in cardiac I/R injury and cardiomyocyte hypoxia-reoxygenation (H/R) in vitro. To test this, we analyzed cardiomyocyte viability and survival along with mitochondrial dynamics and function using western blots, qPCR, immunofluorescence, and ELISA. Our results indicated that both AMPKα2 transcription and translation were reduced by H/R injury in cardiomyocytes. Decreased AMPKα2 levels were associated with cardiomyocyte dysfunction and apoptosis. Adenovirus-mediated AMPKα2 overexpression dramatically inhibited H/R-mediated cardiomyocyte damage, possibly by increasing mitochondrial membrane potential, inhibiting cardiomyocyte oxidative stress, attenuating intracellular calcium overload, and inhibiting mitochondrial apoptosis. At the molecular level, AMPKα2 overexpression alleviated abnormal mitochondrial division and improved mitochondrial fusion through activation of the Sirt3/PGC1α pathway. This suggests AMPKα2 contributes to maintaining normal mitochondrial dynamics. Indeed, induction of mitochondrial dynamics disorder abolished the cardioprotective effects afforded by AMPKα2 overexpression. Thus, cardiac I/R-related mitochondrial dynamics disorder can be reversed by AMPKα2 overexpression in a manner dependent on the activation of Sirt3/PGC1α signaling.

SirT3 activates AMPK-related mitochondrial biogenesis and ameliorates sepsis-induced myocardial injury

Sirtuin-3 (SirT3) and AMPK stimulate mitochondrial biogenesis, which increases mitochondrial turnover and cardiomyocyte regeneration. We studied the effects of SirT3, AMPK, and mitochondrial biogenesis on sepsis-induced myocardial injury. Our data showed that after treating cardiomyocytes with lipopolysaccharide, SirT3 and AMPK levels decreased, and this was followed by mitochondrial dysfunction and cardiomyocyte death. Overexpression of SirT3 activated the AMPK pathway and improved mitochondrial biogenesis, which is required to sustain mitochondrial redox balance, maintain mitochondrial respiration, and suppress mitochondrial apoptosis. Inhibition of mitochondrial biogenesis abolished SirT3/AMPK-induced cardioprotection by causing mitochondrial damage. These findings indicate that SirT3 reduces sepsis-induced myocardial injury by activating AMPK-related mitochondrial biogenesis.