Retrospective assessment of rat liver microsomal stability at NCATS: data and QSAR models.

Hepatic metabolic stability is a key pharmacokinetic parameter in drug discovery. Metabolic stability is usually assessed in microsomal fractions and only the best compounds progress in the drug discovery process. A high-throughput single time point substrate depletion assay in rat liver microsomes (RLM) is employed at the National Center for Advancing Translational Sciences. Between 2012 and 2020, RLM stability data was generated for ~ 24,000 compounds from more than 250 projects that cover a wide range of pharmacological targets and cellular pathways. Although a crucial endpoint, little or no data exists in the public domain. In this study, computational models were developed for predicting RLM stability using different machine learning methods. In addition, a retrospective time-split validation was performed, and local models were built for projects that performed poorly with global models. Further analysis revealed inherent medicinal chemistry knowledge potentially useful to chemists in the pursuit of synthesizing metabolically stable compounds. In addition, we deposited experimental data for ~ 2500 compounds in the PubChem bioassay database (AID: 1508591). The global prediction models are made publicly accessible ( https://opendata.ncats.nih.gov/adme ). This is to the best of our knowledge, the first publicly available RLM prediction model built using high-quality data generated at a single laboratory.

Predictive models of aqueous solubility of organic compounds built on A large dataset of high integrity.

Aqueous solubility is one of the most important properties in drug discovery, as it has profound impact on various drug properties, including biological activity, pharmacokinetics (PK), toxicity, and in vivo efficacy. Both kinetic and thermodynamic solubilities are determined during different stages of drug discovery and development. Since kinetic solubility is more relevant in preclinical drug discovery research, especially during the structure optimization process, we have developed predictive models for kinetic solubility with in-house data generated from 11,780 compounds collected from over 200 NCATS intramural research projects. This represents one of the largest kinetic solubility datasets of high quality and integrity. Based on the customized atom type descriptors, the support vector classification (SVC) models were trained on 80% of the whole dataset, and exhibited high predictive performance for estimating the solubility of the remaining 20% compounds within the test set. The values of the area under the receiver operating characteristic curve (AUC-ROC) for the compounds in the test sets reached 0.93 and 0.91, when the threshold for insoluble compounds was set to 10 and 50 µg/mL respectively. The predictive models of aqueous solubility can be used to identify insoluble compounds in drug discovery pipeline, provide design ideas for improving solubility by analyzing the atom types associated with poor solubility and prioritize compound libraries to be purchased or synthesized.

Discovery of 3-(4-sulfamoylnaphthyl)pyrazolo[1,5-a]pyrimidines as potent and selective ALK2 inhibitors.

The pyrazolo[1,5-a]pyrimidine LDN-193189 is a potent inhibitor of activin receptor-like kinase 2 (ALK2) but is nonselective for highly homologous ALK3 and shows only modest kinome selectivity. Herein, we describe the discovery of a novel series of potent and selective ALK2 inhibitors by replacing the quinolinyl with a 4-(sulfamoyl)naphthyl, yielding ALK2 inhibitors that exhibit not only excellent discrimination versus ALK3 but also high kinome selectivity. In addition, the optimized compound 23 demonstrates good ADME and in vivo pharmacokinetic properties.

Highly predictive and interpretable models for PAMPA permeability.

Cell membrane permeability is an important determinant for oral absorption and bioavailability of a drug molecule. An in silico model predicting drug permeability is described, which is built based on a large permeability dataset of 7488 compound entries or 5435 structurally unique molecules measured by the same lab using parallel artificial membrane permeability assay (PAMPA). On the basis of customized molecular descriptors, the support vector regression (SVR) model trained with 4071 compounds with quantitative data is able to predict the remaining 1364 compounds with the qualitative data with an area under the curve of receiver operating characteristic (AUC-ROC) of 0.90. The support vector classification (SVC) model trained with half of the whole dataset comprised of both the quantitative and the qualitative data produced accurate predictions to the remaining data with the AUC-ROC of 0.88. The results suggest that the developed SVR model is highly predictive and provides medicinal chemists a useful in silico tool to facilitate design and synthesis of novel compounds with optimal drug-like properties, and thus accelerate the lead optimization in drug discovery.

Discovery of NCT-501, a Potent and Selective Theophylline-Based Inhibitor of Aldehyde Dehydrogenase 1A1 (ALDH1A1).

Aldehyde dehydrogenases (ALDHs) metabolize reactive aldehydes and possess important physiological and toxicological functions in areas such as CNS, metabolic disorders, and cancers. Increased ALDH (e.g., ALDH1A1) gene expression and catalytic activity are vital biomarkers in a number of malignancies and cancer stem cells, highlighting the need for the identification and development of small molecule ALDH inhibitors. A new series of theophylline-based analogs as potent ALDH1A1 inhibitors is described. The optimization of hits identified from a quantitative high throughput screening (qHTS) campaign led to analogs with improved potency and early ADME properties. This chemotype exhibits highly selective inhibition against ALDH1A1 over ALDH3A1, ALDH1B1, and ALDH2 isozymes as well as other dehydrogenases such as HPGD and HSD17ß4. Moreover, the pharmacokinetic evaluation of selected analog 64 (NCT-501) is also highlighted.

Regenerating matrix-based therapy for chronic wound healing: a prospective within-subject pilot study.

The aim of this study was to determine whether a skin-specific bioengineered regenerating agent (RGTA) heparan sulphate mimetic (CACIPLIQ20) improves chronic wound healing. The design of this article is a prospective within-subject study. The setting was an urban hospital. Patients were 16 African-American individuals (mean age 42 years) with 22 wounds (mean duration 2.5 years) because of either pressure, diabetic, vascular or burn wounds. Two participants each were lost to follow-up or removed because of poor compliance, resulting in 18 wounds analysed. Sterile gauze was soaked with CACIPLIQ20 saline solution, placed on the wound for 5 min, then removed twice weekly for 4 weeks. Wounds were otherwise treated according to the standard of care. Twenty-two percent of wounds fully healed during the treatment period. Wounds showed a 15.2-18.1% decrease in wound size as measured by the vision engineering research group (VERG) digital wound measurement system and total PUSH scores, respectively, at 4 weeks (P = 0.014 and P = 0.003). At 8 weeks there was an 18-26% reduction in wound size (P = 0.04) in the remaining patients. Wound-related pain measured by the visual analogue pain scale and the wound pain scale declined 60% (P = 0.024) and 70% (P = 0.001), respectively. Patient and clinician satisfaction remained positive throughout the treatment period. It is concluded that treatment with CACIPLIQ20 significantly improved wound-related pain and may facilitate wound healing. Patient and clinician satisfaction remained high throughout the trial.