Prediction of rat, dog, monkey, and human volume of distribution (VDss) by Rodgers-Lukacova model was evaluated using a data set of more than 100 compounds.The prediction accuracy was best for humans followed by monkeys and dogs with 59, 52, and 41% of compounds within 2-fold, respectively.The accuracy of predictions in preclinical species was indicative of the human situation. This was particularly true for monkeys, where 87% of the compounds that were predicted within 2-fold in monkeys were also predicted within 2-fold in humans.The model's tendency to underestimate VDss was higher in rats and dogs compared to humans and monkeys for all ion classes but zwitterions. Hence, correction of human predictions using prediction errors in rats and dogs resulted in overestimation of VDss.The model had a similar degree of underestimation in humans and monkeys. Correction using monkeys improved the accuracy of the human estimate, especially for basic and zwitterion compounds.A strategy is proposed based on the accuracy of prediction in monkey and monkey scalars for prediction and prospective assessment of the accuracy of human VDss. Prediction of rat, dog, monkey, and human volume of distribution (VDss) by Rodgers-Lukacova model was evaluated using a data set of more than 100 compounds. The prediction accuracy was best for humans followed by monkeys and dogs with 59, 52, and 41% of compounds within 2-fold, respectively. The accuracy of predictions in preclinical species was indicative of the human situation. This was particularly true for monkeys, where 87% of the compounds that were predicted within 2-fold in monkeys were also predicted within 2-fold in humans. The model's tendency to underestimate VDss was higher in rats and dogs compared to humans and monkeys for all ion classes but zwitterions. Hence, correction of human predictions using prediction errors in rats and dogs resulted in overestimation of VDss. The model had a similar degree of underestimation in humans and monkeys. Correction using monkeys improved the accuracy of the human estimate, especially for basic and zwitterion compounds. A strategy is proposed based on the accuracy of prediction in monkey and monkey scalars for prediction and prospective assessment of the accuracy of human VDss.

The search for new molecular constructs that resemble the critical two-metal binding pharmacophore required for HIV integrase strand transfer inhibition represents a vibrant area of research within drug discovery. Here we present the discovery of a new class of HIV integrase strand transfer inhibitors based on the 2-pyridinone core of MK-0536. These efforts led to the identification of two lead compounds with excellent antiviral activity and preclinical pharmacokinetic profiles to support a once-daily human dose prediction. Dose escalating PK studies in dog revealed significant issues with limited oral absorption and required an innovative prodrug strategy to enhance the high-dose plasma exposures of the parent molecules.

Reliable prediction of two fundamental human pharmacokinetic (PK) parameters, systemic clearance (CL) and apparent volume of distribution (Vd), determine the size and frequency of drug dosing and are at the heart of drug discovery and development. Traditionally, estimated CL and Vd are derived from preclinical in vitro and in vivo absorption, distribution, metabolism, and excretion (ADME) measurements. In this paper, we report quantitative structure–activity relationship (QSAR) models for prediction of systemic CL and steady-state Vd (Vdss) from intravenous (iv) dosing in humans. These QSAR models avoid uncertainty associated with preclinical-to-clinical extrapolation and require two-dimensional structure drawing as the sole input. The clean, uniform training sets for these models were derived from the compilation published by Obach et al. (Drug Metab. Disp. 2008, 36, 1385–1405). Models for CL and Vdss were developed using both a support vector regression (SVR) method and a multiple linear regression (MLR) method. The SVR models employ a minimum of 2048-bit fingerprints developed in-house as structure quantifiers. The MLR models, on the other hand, are based on information-rich electro-topological states of two-atom fragments as descriptors and afford reverse QSAR (RQSAR) analysis to help model-guided, in silico modulation of structures for desired CL and Vdss. The capability of the models to predict iv CL and Vdss with acceptable accuracy was established by randomly splitting data into training and test sets. On average, for both CL and Vdss, 75% of test compounds were predicted within 2.5-fold of the value observed and 90% of test compounds were within 5.0-fold of the value observed. The performance of the final models developed from 525 compounds for CL and 569 compounds for Vdss was evaluated on an external set of 56 compounds. The predictions were either better or comparable to those predicted by other in silico models reported in the literature. To demonstrate the practical application of the RQSAR approach, the structure of vildagliptin, a high-CL and a high-Vdss compound, is modified based on the atomic contributions to its predicted CL and Vdss to propose compounds with lower CL and lower Vdss.

A series of acidic diaryl ether heterocyclic sulfonamides that are potent and subtype selective NaV1.7 inhibitors is described. Optimization of early lead matter focused on removal of structural alerts, improving metabolic stability and reducing cytochrome P450 inhibition driven drug–drug interaction concerns to deliver the desired balance of preclinical in vitro properties. Concerns over nonmetabolic routes of clearance, variable clearance in preclinical species, and subsequent low confidence human pharmacokinetic predictions led to the decision to conduct a human microdose study to determine clinical pharmacokinetics. The design strategies and results from preclinical PK and clinical human microdose PK data are described leading to the discovery of the first subtype selective NaV1.7 inhibitor clinical candidate PF-05089771 (34) which binds to a site in the voltage sensing domain.

Protein-Protein, Genetic, and Chemical Interactions for Obermeier M (2010):In vitro characterization and pharmacokinetics of dapagliflozin (BMS-512148), a potent sodium-glucose cotransporter type II inhibitor, in animals and humans. curated by BioGRID (https://thebiogrid.org); ABSTRACT: (2S,3R,4R,5S,6R)-2-(3-(4-Ethoxybenzyl)-4-chlorophenyl)-6-hydroxymethyl-tetrahydro-2H-pyran-3,4,5-triol (dapagliflozin; BMS-512148) is a potent sodium-glucose cotransporter type II inhibitor in animals and humans and is currently under development for the treatment of type 2 diabetes. The preclinical characterization of dapagliflozin, to allow compound selection and prediction of pharmacological and dispositional behavior in the clinic, involved Caco-2 cell permeability studies, cytochrome P450 (P450) inhibition and induction studies, P450 reaction phenotyping, metabolite identification in hepatocytes, and pharmacokinetics in rats, dogs, and monkeys. Dapagliflozin was found to have good permeability across Caco-2 cell membranes. It was found to be a substrate for P-glycoprotein (P-gp) but not a significant P-gp inhibitor. Dapagliflozin was not found to be an inhibitor or an inducer of human P450 enzymes. The in vitro metabolic profiles of dapagliflozin after incubation with hepatocytes from mice, rats, dogs, monkeys, and humans were qualitatively similar. Rat hepatocyte incubations showed the highest turnover, and dapagliflozin was most stable in human hepatocytes. Prominent in vitro metabolic pathways observed were glucuronidation, hydroxylation, and O-deethylation. Pharmacokinetic parameters for dapagliflozin in preclinical species revealed a compound with adequate oral exposure, clearance, and elimination half-life, consistent with the potential for single daily dosing in humans. The pharmacokinetics in humans after a single dose of 50 mg of [(14)C]dapagliflozin showed good exposure, low clearance, adequate half-life, and no metabolites with significant pharmacological activity or toxicological concern.

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.

Drug-induced liver injury (DILI) has been a significant challenge in drug discovery, often leading to clinical trial failures and necessitating drug withdrawals. Over the last decade, the existing suite of in vitro proxy-DILI assays has generally improved at identifying compounds with hepatotoxicity. However, there is considerable interest in enhancing the in silico prediction of DILI because it allows for evaluating large sets of compounds more quickly and cost-effectively, particularly in the early stages of projects. In this study, we aim to study ML models for DILI prediction that first predict nine proxy-DILI labels and then use them as features in addition to chemical structural features to predict DILI. The features include in vitro (e.g., mitochondrial toxicity, bile salt export pump inhibition) data, in vivo (e.g., preclinical rat hepatotoxicity studies) data, pharmacokinetic parameters of maximum concentration, structural fingerprints, and physicochemical parameters. We trained DILI-prediction models on 888 compounds from the DILI data set (composed of DILIst and DILIrank) and tested them on a held-out external test set of 223 compounds from the DILI data set. The best model, DILIPredictor, attained an AUC-PR of 0.79. This model enabled the detection of the top 25 toxic compounds (2.68 LR+, positive likelihood ratio) compared to models using only structural features (1.65 LR+ score). Using feature interpretation from DILIPredictor, we identified the chemical substructures causing DILI and differentiated cases of DILI caused by compounds in animals but not in humans. For example, DILIPredictor correctly recognized 2-butoxyethanol as nontoxic in humans despite its hepatotoxicity in mice models. Overall, the DILIPredictor model improves the detection of compounds causing DILI with an improved differentiation between animal and human sensitivity and the potential for mechanism evaluation. DILIPredictor required only chemical structures as input for prediction and is publicly available at https://broad.io/DILIPredictor for use via web interface and with all code available for download.

Degraders are an increasingly important sub-modality of small molecules as illustrated by an ever-expanding number of publications and clinical candidate molecules in human trials. Nevertheless, their preclinical optimization of ADME and PK/PD properties has remained challenging. Significant research efforts are being directed to elucidate the underlying principles and to derive rational optimization strategies. In this review, the authors summarize currently best practices in terms of in vitro assays and in vivo experiments. Furthermore, the authors collate and comment on the current understanding of optimal physicochemical characteristics and their impact on absorption, distribution, metabolism, and excretion properties, including the current knowledge of drug–rug interactions. Finally, the authors describe the pharmacokinetic prediction and Pharmacokinetic/Pharmacodynamic -concepts unique to degraders and how to best implement these in research projects. Despite many recent advances in the field, continued research will further our understanding of rational design regarding degrader optimization. Machine-learning and computational approaches will become increasingly important once larger, more robust datasets become available. Furthermore, tissue-targeting approaches (particularly regarding the central nervous system will be increasingly studied to elucidate efficacious drug regimens that capitalize on the catalytic mode of action. Finally, additional specialized approaches (e.g. covalent degraders, LOVdegs) can further enrich the field and offer interesting alternative approaches.