One defining feature of Gram-negative bacteria, mitochondria, and chloroplasts is that they are all enveloped by a double membrane consisting of an inner membrane (IM) and outer membrane (OM).These two membranes are home to a plethora of vitally important integral membrane proteins which play roles as gatekeepers for the cell, regulating nutrient import, virulence factor export, and maintaining the protective coat of the cell, cargo transport, membrane biogenesis and signaling transporters, enzymes, and receptors
Hits from high-throughput screening (HTS) of chemical libraries are often false positives due to their interference with the assay detection technology. In response to this problem, we generated the largest publicly available library of chemical liabilities and developed a free webtool to predict HTS artifacts, both of which are described herein. More specifically, we generated, curated, and integrated HTS datasets for thiol reactivity, redox activity, and luciferase (firefly and nano) activity. Using these curated datasets, we developed and validated several Quantitative Structure-Interference Relationship (QSIR) models to predict nuisance behavior. The resulting models showed balanced accuracy (BA) for external datasets ranging from 62 to 78%. These models were employed for virtual profiling of the National Center for Advancing Translational Science’s (NCATS) in-house library, and 256 compounds for each assay were selected for the experimental validation of nuisance behavior. The BA for these external predictions ranged from 58 to 78% for compounds within the applicability domains of the models. Our findings suggest that the QSIR models developed and validated herein identify nuisance compounds among experimental hits more reliably than popular PAINS filters. The models developed in this study, along with aggregation models previously developed by our group (SCAM Detective) were implemented in “Liability Predictor,” an online tool which may be used as part of chemical library design or for triaging HTS hits. Both the models and the curated datasets are publicly available at https://liability.mml.unc.edu/.
Computational models that predict PK properties, such as those related to drug absorption, metabolism, distribution, and excretion, are critical to flagging drug candidates with poor PK profiles that emerge as hits in high-throughput screening campaigns. To support the development of reliable computational models to predict key PK properties, we collected, curated, and integrated a database of compounds tested in 13 major PK endpoints containing over 10,000 unique molecules. We built classification quantitative structure-activity relationship (QSAR) models for all but one endpoint (Cmax) following best practices of model development and validation. Those with acceptable external accuracy (CCR ≥ 0.60 and SE, PPV, SP, and NPV ≥ 0.50) include hepatic stability at 15, 30, and 60 minutes, hepatic half-life at the subcellular and tissue levels, renal clearance, blood brain barrier permeability, CNS activity, Caco-2 permeability, plasma protein binding, plasma half-life, microsomal intrinsic clearance, and oral bioavailability. As a case study to illustrate model utility, we employed all developed models to predict the PK properties of all compounds in DrugBank. We also predicted PK properties of molecules hitting popular drug targets among several organs SLC6A4 (brain), ADRB2 (heart and lungs), HMGCR (liver), and CaSR (kidneys) only. These analyses revealed that nearly all experimental, investigational, and withdrawn compounds included in DrugBank are hepatically stable at 60 minutes and under, exhibit CNS activity, and permeate the Caco-2 cell line (a measure of intestinal absorption). Furthermore, our results indicate that compounds targeting different organs have distinct predicted PK profiles. This observation suggests that desired PK properties depend on compound’s indication. All models described in this paper have been integrated and made publicly available via the novel predictor of pharmacokinetic properties (PhaKinPro) web portal that can be accessed at https://phakinpro.mml.unc.edu/.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.