Prediction Of Human Clearance Research Articles

Data set of fraction unbound values in the in vitro incubations for metabolic studies for better prediction of human clearance.

In vitro-in vivo extrapolation is a commonly applied technique for liver clearance prediction. Various in vitro models are available such as hepatocytes, human liver microsomes, or recombinant cytochromes P450. According to the free drug theory, only the unbound fraction (fu) of a chemical can undergo metabolic changes. Therefore, to ensure the reliability of predictions, both specific and nonspecific binding in the model should be accounted. However, the fraction unbound in the experiment is often not reported. The study aimed to provide a detailed repository of the literature data on the compound's fu value in various in vitro systems used for drug metabolism evaluation and corresponding human plasma binding levels. Data on the free fraction in plasma and different in vitro models were supplemented with the following information: the experimental method used for the assessment of the degree of drug binding, protein or cell concentration in the incubation, and other experimental conditions, if different from the standard ones, species, reference to the source publication, and the author's name and date of publication. In total, we collected 129 literature studies on 1425 different compounds. The provided data set can be used as a reference for scientists involved in pharmacokinetic/physiologically based pharmacokinetic modelling as well as researchers interested in Quantitative Structure-Activity Relationship models for the prediction of fraction unbound based on compound structure. Database URL: https://data.mendeley.com/datasets/3bs5526htd/1.

Comparison of Human Long-Term Liver Models for Clearance Prediction of Slowly Metabolized Compounds.

The accurate prediction of human clearance is an important task during drug development. The proportion of low clearance compounds has increased in drug development pipelines across the industry since such compounds may be dosed in lower amounts and at lower frequency. These type of compounds present new challenges to in vitro systems used for clearance extrapolation. In this study, we compared the accuracy of clearance predictions of suspension culture to four different long-term stable in vitro liver models, including HepaRG sandwich culture, the Hµrel stochastic co-culture, the Hepatopac micropatterned co-culture (MPCC), and a micro-array spheroid culture. Hepatocytes in long-term stable systems remained viable and active over several days of incubation. Although intrinsic clearance values were generally high in suspension culture, clearance of low turnover compounds could frequently not be determined using this method. Metabolic activity and intrinsic clearance values from HepaRG cultures were low and, consequently, many compounds with low turnover did not show significant decline despite long incubation times. Similarly, stochastic co-cultures occasionally failed to show significant turnover for multiple low and medium turnover compounds. Among the different methods, MPCCs and spheroids provided the most consistent measurements. Notably, all culture methods resulted in underprediction of clearance; this could, however, be compensated for by regression correction. Combined, the results indicate that spheroid culture as well as the MPCC system provide adequate in vitro tools for human extrapolation for compounds with low metabolic turnover. SIGNIFICANCE STATEMENT: In this study, we compared suspension cultures, HepaRG sandwich cultures, the Hµrel liver stochastic co-cultures, the Hepatopac micropatterned co-cultures (MPCC), and micro-array spheroid cultures for low clearance determination and prediction. Overall, HepaRG and suspension cultures showed modest value for the low determination and prediction of clearance compounds. The micro-array spheroid culture resulted in the most robust clearance measurements, whereas using the MPCC resulted in the most accurate prediction for low clearance compounds.

Prediction of human clearance using human liver microsomes and hepatocytes with 455 drugs

Prediction of human clearance using human liver microsomes and hepatocytes with 455 drugs

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.

Scaling approaches for the prediction of human clearance of LNA-i-mir-221: A retrospective validation

LNA-i-miR-221 is a novel microRNA(miRNA)-221 inhibitor designed for the treatment of human malignancies. It has recently undergone phase 1 clinical trial (P1CT) and early pharmacokinetics (PKs) data in cancer patients are now available. We previously used multiple allometric interspecies scaling methods to draw inferences about LNA-i-miR-221 PKs in humans and estimated the patient dose based on the safe and pharmacodynamic (PD) active dose observed in mice, therefore providing a framework for the definition of safe starting and escalation doses for the P1CT. The preliminary data collected during the P1CT showed that the LNA-i-miR-221 anticipated doses, according to our human PK estimation approach, were indeed well tolerated and effective. PD data demonstrated concentration-dependent downregulation of miR-221 and upregulation of its CDKN1B/p27 and PTEN canonical targets as well as stable disease in 8 (50.0%) patients and partial response in 1 (6.3%) colorectal cancer case. Here, we detail the experimentally evaluated PK parameters of LNA-i-miR-221 in human, using both a non-compartmental and a population PKs approach. The population approach was adequately described by a three-compartments model with first-order elimination. The recorded age, sex and body weight of patients were evaluated as potential covariates. The estimated typical population parameter values were clearance (CL = 200 mL/h/kg), central volume of distribution (V1 = 45 mL/kg), peripheral volume of distribution (V2 = 200 mL/kg, volume of the second peripheral compartment V3 = 930 mL/h/kg) and inter-compartmental clearance (Q2 = 480 mL/h/kg and Q3 = 68 mL/h/kg). Age was found to be a predictor of Q3, with a statistically significant correlation. This work aimed also at retrospectively comparing the measured plasmatic clearance values with those predicted by different allometric scaling approaches. Our comparative analysis showed that the most accurate prediction was achieved by applying the single species allometric scaling approach and that the use of more than one species in allometric scaling to predict therapeutic oligonucleotides PKs would not necessarily generate the best prediction. Finally, our predictive approach was found accurate not only in predicting the main PK parameters in human but suggesting the range of effective and safe dose to be applied in the next clinic phase 2.

Quantum Mechanics Modeling of Oxetanes as Epoxide Hydrolase Substrates

Background: Epoxide hydrolases comprise an important class of enzymes that have critical functions in the detoxification of xenobiotics and regulation of signaling molecules. In addition to epoxides, oxetanes have recently been identified as novel substrates of microsomal epoxide hydrolase (mEH). Oxetanes are common scaffolds used in medicinal chemistry design to improve potency and drug-like properties. Metabolism of oxetanes by mEH can result in high uncertainties in the prediction of human clearance due to extrahepatic contribution and large interindividual variability. Therefore, reducing mEH-mediated oxetane metabolism is highly desirable to minimize its contribution to clearance. Objective: The aim of the study is to evaluate whether quantum mechanical parameters are able to predict the hydrolytic rate of mEH-mediated oxetane metabolism in order to guide medicinal chemistry design in order to minimize mEH contribution to clearance. Methods: Quantum mechanics modeling was used to evaluate the hydrolytic rate of twenty-three oxetanes by mEH. All modeling studies were performed with the Maestro software package. Results: The results show that LUMO energy is highly correlated with the diol formation rate of oxetane hydrolysis by mEH for compounds that are structurally similar, while other quantum mechanical parameters are less predictive. The data suggest that the intrinsic reactivity determines the hydrolytic rate of oxetanes. This occurs when the orientations of the molecules in the mEH active site are similar. Predictions of mEH substrate metabolic rates using LUMO are most accurate when comparing subtle structural changes without drastic changes in MW and chemotype. Conclusion: The study suggests that LUMO energy can be used to rank-order oxetanes for their hydrolytic rate by mEH for structurally similar compounds. This finding enables the medicinal chemistry design to reduce mEH-mediated oxetane metabolism based on the calculated LUMO energy.

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 (CLint) 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 (SFlin) 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 CLint. These SFs contain both SFlin and an exponential SF (SFβ) of fraction unbound in plasma (fu,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 CLint, while HHEP slightly underpredicted and geometric mean of HLM and HHEP slightly overpredicted in vivo CLint. This observation is different than in rats, where rat liver microsomal CLint correlates well with in vivo CLint for compounds demonstrating permeability-limited metabolism. The good CLint 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.

Current Approaches for Predicting Human PK for Small Molecule Development Candidates: Findings from the IQ Human PK Prediction Working Group Survey.

Accurate prediction of human clearance (CL) and volume of distribution at steady state (Vd,ss) for small molecule drug candidates is an essential component of assessing likely efficacious dose and clinical safety margins. In 2021, the IQ Consortium Human PK Prediction Working Group undertook a survey of IQ member companies to understand the current PK prediction methods being used to estimate these parameters across the pharmaceutical industry. The survey revealed a heterogeneity in approaches being used across the industry (e.g., the use of allometric approaches, differing incorporation of binding terms, and inconsistent use of empirical correction factors for in vitro-in vivo extrapolation, IVIVE), which could lead to different PK predictions with the same input data. Member companies expressed an interest in improving human PK predictions by identifying the most appropriate compound-class specific methods, as determined by physiochemical properties and knowledge of CL pathways. Furthermore, there was consensus that increased understanding of the uncertainty inherent to the compound class-dependent prediction would be invaluable in aiding communication of human PK and dose uncertainty at the time of candidate nomination for development. The human PK Prediction Working Group is utilizing these survey findings to help interrogate clinical IV datasets from across the IQ consortium member companies to understand PK prediction accuracy and uncertainty from preclinical datasets.

Application of empirical scalars to enable early prediction of human hepatic clearance using IVIVE in drug discovery: an evaluation of 173 drugs.

The utilization of in vitro data to predict drug pharmacokinetics (PK) in vivo has been a consistent practice in early drug discovery for decades. However, its success is hampered by mispredictions attributed to uncharacterized biological phenomena/experimental artifacts. Predicted drug clearance (CL) from experimental data (i.e. hepatocyte intrinsic clearance: CLint, fraction unbound in plasma: fu,p) is often systematically underpredicted using the well-stirred model (WSM). The objective of this study was to evaluate using empirical scalars in the WSM to correct for CL mispredictions. Drugs (N=28) were used to generate numerical scalars on CLint (α), and fu,p (β) to minimize the error (AAFE) for CL predictions. These scalars were validated using an additional dataset (N=28 drugs) and applied to a non-redundant AstraZeneca (AZ) dataset available in the literature (N=117 drugs) for a total of 173 compounds. CL predictions using the WSM were improved for most compounds using an α value of 3.66 (~64%<2-fold) compared to no scaling (~46%<2-fold). Similarly, using a β value of 0.55 or combination of α and β scalars (values of 1.74 and 0.66, respectively) resulted in a similar improvement in predictions (~64%<2-fold and ~65%<2-fold, respectively). For highly bound compounds (fu,p{less than or equal to}0.01), AAFE was substantially reduced across all scaling methods. Using the β scalar alone or a combination of α and β appeared optimal; and produce larger magnitude corrections for highly-bound compounds. Some drugs are still disproportionally mispredicted, however the improvements in prediction error and simplicity of applying these scalars suggests its utility for early-stage CL predictions. Significance Statement In early drug discovery, prediction of human clearance using in vitro experimental data plays an essential role in triaging compounds prior to in vivo studies. These predictions have been systematically underestimated. Here we introduce empirical scalars calibrated on the extent of plasma protein binding that appear to improve clearance prediction across multiple datasets. This approach can be used in early phases of drug discovery prior to the availability of pre-clinical data for early quantitative predictions of human clearance.

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.

Cynomolgus Monkey as an Emerging Animal Model to Study Drug Transporters: In Vitro, In Vivo, In Vitro-to-In Vivo Translation.

Membrane transporters have been recognized as one of the key determinants of pharmacokinetics and are also known to affect the efficacy and toxicity of drugs. Both qualitatively and quantitatively, however, transporter studies conducted using human in vitro systems have not always been predictive. Consequently, researchers have used cynomolgus monkeys as a model to study drug transporters and anticipate their effects in humans. Burgeoning reports of data in the last few years necessitates a comprehensive review on the topic of drug transporters in cynomolgus monkeys that includes cell-based tools, sequence homology, tissue expression, in vitro studies, in vivo studies, and in vitro-to-in vivo extrapolation. This review highlights the state-of-the-art applications of monkey transporter models to support the evaluation of transporter-mediated drug-drug interactions, clearance predictions, and endogenous transporter biomarker identification and validation. The data demonstrate that cynomolgus monkey transporter models, when used appropriately, can be an invaluable tool to support drug discovery and development processes. Most importantly, they enable an early in vitro-to-in vivo extrapolation assessment, which provides additional context to human in vitro data. Additionally, comprehending species similarities and differences in transporter tissue expression and activity is crucial when translating monkey data to humans. The challenges and limitations when applying such models to inform decision-making must also be considered. SIGNIFICANCE STATEMENT: This paper presents a comprehensive review of currently available published reports describing cynomolgus monkey transporter models. The data indicate that Cynomolgus monkeys provide mechanistic insight regarding the role of intestinal, hepatic, and renal transporters in drug and biomarker disposition and drug interactions. The data generated with cynomolgus monkey models provide mechanistic insight into transporter-mediated drug absorption and disposition. They are valuable to human clearance prediction, drug drug interaction assessment, and endogenous biomarker development related to drug transporters.

Can In Vitro-In Vivo Extrapolation Be Successful? Recognizing the Incorrect Clearance Assumptions.

For a number of years, our laboratory has been investigating the underlying reasons for the published poor in vitro-in vivo extrapolation (IVIVE) predictability of human clearance both from a theoretical and from an experimental perspective. Here, we critically examine clearance concepts and commonly employed IVIVE approaches, concluding that there is no theoretical reason that IVIVE should work, just as it does not. Our analysis, however, has identified 10 misconceptions and/or poorly understood aspects of clearance that are listed in the Conclusion section of this manuscript. Chief among these are that all published human drug clearance values are arterial clearances-clearance calculated as organ blood flow multiplied by the extraction ratio is the arterial clearance of the organ of elimination (and not the published drug clearance value)-and that the well-stirred model equation taught in all pharmacokinetic courses that relates organ blood flow, fraction unbound in blood, and intrinsic clearance has no validity. We further list 10 conclusions relating to the IVIVE process. The primary IVIVE-related conclusions are that the intrinsic clearance value determined from an in vitro incubation is an arterial intrinsic clearance, there is no theoretical basis upon which an arterial intrinsic clearance can be related to a whole-body arterial clearance to accomplish IVIVE, there are no published data demonstrating that in vitro intrinsic metabolic clearance can predict in vivo organ clearance as IVIVE assumes, and the scientific basis for the hypothesized albumin-mediated hepatic uptake phenomenon is invalid. We further propose three IVIVE process recommendations.

A Single Animal Species-Based Prediction of Human Clearance and First-in-Human Dose of Monoclonal Antibodies: Beyond Monkey.

These days, there is a lot of emphasis on the prediction of human clearance (CL) from a single species for monoclonal antibodies (mabs). Many studies indicate that monkey is the most suitable species for the prediction of human clearance for mabs. However, it is not well established if rodents (mouse or rat) can also be used to predict human CL for mabs. The objectives of this study were to predict and compare human CL as well as first-in-human dose of mabs from mouse or rat, ormonkey. Four methods were used for the prediction of human CL of mabs. These methods were: use of four allometric exponents (0.75, 0.80, 0.85, and 0.90), a minimal physiologically based pharmacokinetics method (mPBPK), lymph flow rate, and liver blood flow rate. Based on the predicted CL, first-in-human dose of mabs was projected using either exponent 1.0 (linear scaling) or exponent 0.85, and human-equivalent dose (HED) from each of these species. The results of the study indicated that rat or mouse could provide a reasonably accurate prediction of human CL as well as first-in-human dose of mabs. When exponent 0.85 was used for CL prediction, there were 78%, 95%, and 92% observations within a 2-fold prediction error for mouse, rat, and monkey, respectively. Predicted human dose fell within the observed human dose range (administered to humans) for 10 out of 13 mabs for mouse, 11 out of 12 mabs for rat, and 12 out of 15 mabs for monkey. Overall, the clearance and first-in-human dose of mabs were predicted reasonably well by all three species (a single species). On average, monkey may be the best species for the prediction of human clearance and human dose but mouse or rat especially; rat can be a very useful species for conducting the aforementioned studies.

Prediction of Total Drug Clearance in Humans Using Animal Data: Proposal of a Multimodal Learning Method Based on Deep Learning.

Research into pharmacokinetics plays an important role in the development process of new drugs. Accurately predicting human pharmacokinetic parameters from preclinical data can increase the success rate of clinical trials. Since clearance (CL) which indicates the capacity of the entire body to process a drug is one of the most important parameters, many methods have been developed. However, there are still rooms to be improved for practical use in drug discovery research; "improving CL prediction accuracy" and "understanding the chemical structure of compounds in terms of pharmacokinetics". To improve those, this research proposes a multimodal learning method based on deep learning that takes not only the chemical structure of a drug but also rat CL as inputs. Good results were obtained compared with the conventional animal scale-up method; the geometric mean fold error was 2.68 and the proportion of compounds with prediction errors of 2-fold or less was 48.5%. Furthermore, it was found to be possible to infer the partial structure useful for CL prediction by a structure contributing factor inference method. The validity of these results of structural interpretation of metabolic stability was confirmed by chemists.

Interspecies Scaling of Antibody-Drug Conjugates (ADC) for the Prediction of Human Clearance.

Allometric scaling is a useful tool for the extrapolation of pharmacokinetic parameters from animals to humans. The objective of this study was to predict human clearance of antibody–drug conjugates (ADC) allometrically from one to three animal species and compare the predicted human clearance with the observed human clearance. For three animal species allometric scaling, the “Rule of Exponents” (ROE) was used. The results of the study indicated that three-species allometric scaling in association with the ROE provides acceptable prediction (within 0.5–2-fold prediction error) of human clearance. The two-species allometric scaling resulted in substantial prediction error. One-species scaling using a fixed exponent of 1.0 provided acceptable prediction error (within 0.5–2-fold) by monkey, rat, and mouse, in which monkey and rat were comparable. Overall, the predicted human clearance values of ADCs from animal(s) was good. The allometric method proposed in this article can be used to predict human clearance from the animal data and subsequently to select the first-in-human dose of ADCs.

Predicting human pharmacokinetics from preclinical data: clearance.

We have streamlined known in vitro methods used to predict the clearance (CL) of small molecules in humans in this tutorial. There have been many publications on in vitro methods that are used at different steps of human CL prediction. The steps from initial intrinsic CL measurement in vitro to the final application of the well-stirred model to obtain predicted hepatic CL (CLH) are somewhat complicated. Except for the experts on drug metabolism and PBPK, many drug development scientists found it hard to figure out the entire picture of human CL prediction. To help readers overcome this barrier, we introduce each method briefly and demonstrate its usage in the chain of related equations destined to the CLH. Despite efforts in the laboratory steps, huge in vitro (predicted CLH)-in vivo (observed CLH) discrepancy is not rare. A simple remedy to this discrepancy is to correct human predicted CLH using the ratio of in vitro-in vivo CLH obtained from animal species.

Albumin-Mediated Uptake Improves Human Clearance Prediction for Hepatic Uptake Transporter Substrates Aiding a Mechanistic In Vitro-In Vivo Extrapolation (IVIVE) Strategy in Discovery Research.

This study focused on exploring various in vitro to in vivo extrapolation (IVIVE) approaches with the primary goal of improving human hepatic clearance (CL) prediction for OATP substrates. To that effect, the impact of albumin-mediated uptake in human hepatocytes was investigated. In vitro hepatic uptake assay using suspended human hepatocytes was performed with 16 selected OATP substrates to determine the uptake CL in the absence and presence of 4% BSA and unbound hepatocyte to media partition coefficient (Kpuu). Substantial enhancement of the unbound uptake CL (PSu,inf) was observed in the presence of 4% BSA, demonstrating "albumin-mediated" uptake. Prediction of human hepatic CL was performed using two non-traditional IVIVE approaches: initial uptake CL (PSu,inf) and intrinsic metabolic CL (CLint,met) corrected by Kpuu based on extended clearance concept. Compared to traditional IVIVE using CLint,met only, the two tested IVIVE approaches significantly improved the prediction of human hepatic CL. Particularly, direct extrapolation from PSu,inf (+BSA) showed the most robust correlation with in vivo human hepatic CL for all 16 compounds with bias of 1.9-2.0 for two lots of human hepatocytes, respectively. In addition, PSu,inf (+BSA) and Kpuu were also determined in suspended cynomolgus monkey hepatocytes. Prediction of monkey hepatic CL was improved by both approaches, although with more bias compared to human. These results suggested supplementing 4% BSA in human hepatocyte uptake assay provides a useful tool to characterize hepatic uptake CL for OATP substrates, enabling more accurate human CL prediction without any empirical scaling factor (ESF).

Predicting the Human Hepatic Clearance of Acidic and Zwitterionic Drugs

Prospective predictions of human hepatic clearance for anionic/zwitterionic compounds, which are oftentimes subjected to transporter-mediated uptake, are challenging in drug discovery. We evaluated the utility of preclinical species, rats and cynomolgus monkeys [nonhuman primates (NHPs)], to predict the human hepatic clearance using a diverse set of acidic/zwitterionic drugs. Preclinical clearance data were generated following intravenous dosing in rats/NHPs and compared to the human clearance data (n = 18/27). Single-species scaling of NHP clearance with an allometric exponent of 0.50 allowed for good prediction of human clearance (fold error ∼2.1, bias ∼1.0), with ∼86% predictions within 3-fold. In comparison, rats underpredicted the clearance of lipophilic acids, while overprediction was noted for hydrophilic acids. Finally, an in vitro clearance assay based on human hepatocytes, which is routinely used in discovery setting, markedly underpredicted human clearance (bias ∼0.12). Collectively, this study provides insights into the usefulness of the preclinical models in enabling pharmacokinetic optimization for acid/zwitterionic drug candidates.

Application of the Extended Clearance Classification System (ECCS) in Drug Discovery and Development: Selection of Appropriate In Vitro Tools and Clearance Prediction.

In vitro to in vivo extrapolation (IVIVE) to predict human hepatic clearance, including metabolism and transport, requires extensive experimental resources. In addition, there may be technical challenges to measure low clearance values. Therefore, prospective identification of rate-determining step(s) in hepatic clearance through application of the Extended Clearance Classification System (ECCS) could be beneficial for optimal compound characterization. IVIVE for hepatic intrinsic clearance (CLint,h) prediction is conducted for a set of 36 marketed drugs with low-to-high in vivo clearance, which are substrates of metabolic enzymes and active uptake transporters in the liver. The compounds were assigned to the ECCS classes, and CLint,h, estimated with HepatoPac (a micropatterned hepatocyte coculture system), was compared with values calculated based on suspended hepatocyte incubates. An apparent permeability threshold (apical to basal) of 50 nm/s in LLC-PK1 cells proved optimal for ECCS classification. A reasonable performance of the IVIVE for compounds across multiple classes using HepatoPac was achieved (with 2-3-fold error), except for substrates of uptake transporters (class 3b), for which scaling of uptake clearance using plated hepatocytes is more appropriate. Irrespective of the ECCS assignment, metabolic clearance can be estimated well using HepatoPac. The validation and approach elaborated in the present study can result in proposed decision trees for the selection of the optimal in vitro assays guided by ECCS class assignment, to support compound optimization and candidate selection. SIGNIFICANCE STATEMENT: Characterization of the rate-determining step(s) in hepatic elimination could be on the critical path of compound optimization during drug discovery. This study demonstrated that HepatoPac and plated hepatocytes are suitable tools for the estimation of metabolic and active uptake clearance, respectively, for a larger set of marketed drugs, supporting a comprehensive strategy to select optimal in vitro tools and to achieve Extended Clearance Classification System-dependent in vitro to in vivo extrapolation for human clearance prediction.

Absorption, distribution, metabolism, excretion (ADME), drug-drug interaction potential and prediction of human pharmacokinetics of SUVN-G3031, a novel histamine 3 receptor (H3R) inverse agonist in clinical development for the treatment of narcolepsy

SUVN-G3031 is a potent and selective inverse agonist of Histmine-3 (H3) receptor that is being investigated for the treatment of narcolepsy. SUVN-G3031 has high passive permeability, not a substrate for P-glycoprotein, has high plasma unbound fractions and was equally distributed between blood and plasma. Major routes of metabolism in vitro were cyclization (Metabolite A) in microsomes and dealkylation (Metabolite D) in hepatocytes. Intrinsic clearance in liver microsomes and hepatocytes was low as monitored by metabolite formation approach. CYP3A4 and MAO-A were the major enzymes involved in the formation of metabolite A and metabolite D respectively. The human hepatic clearance estimated by well-stirred model from hepatocytes was low (2.7 L.h −1) illustrating the importance of metabolite formation kinetics for prediction of human clearance for SUVN-G3031. Renal clearance in humans (9.7 L.h −1) was predicted from dog renal clearance and accounts for ~78% of the total clearance. SUVN-G3031 was neither an inhibitor nor inducer of the P450 enzymes at clinically relevant concentrations. SUVN-G3031 did not inhibit the major uptake transporters and was not a substrate for the uptake transporters. The potential of SUVN-G3031 as a victim and perpetrator of drug-drug interactions is remote. The predicted human pharmacokinetic parameters were consistent with those observed in the first-in-human study.