DrugCentral 2021 supports drug discovery and repositioning.

DrugCentral is a public resource (http://drugcentral.org) that serves the scientific community by providing up-to-date drug information, as described in previous papers. The current release includes 109 newly approved (October 2018 through March 2020) active pharmaceutical ingredients in the US, Europe, Japan and other countries; and two molecular entities (e.g. mefuparib) of interest for COVID19. New additions include a set of pharmacokinetic properties for ∼1000 drugs, and a sex-based separation of side effects, processed from FAERS (FDA Adverse Event Reporting System); as well as a drug repositioning prioritization scheme based on the market availability and intellectual property rights forFDA approved drugs. In the context of the COVID19 pandemic, we also incorporated REDIAL-2020, a machine learning platform that estimates anti-SARS-CoV-2 activities, as well as the 'drugs in news' feature offers a brief enumeration of the most interesting drugs at the present moment. The full database dump and data files are available for download from the DrugCentral web portal.

TCRD and Pharos 2021: mining the human proteome for disease biology.

In 2014, the National Institutes of Health (NIH) initiated the Illuminating the Druggable Genome (IDG) program to identify and improve our understanding of poorly characterized proteins that can potentially be modulated using small molecules or biologics. Two resources produced from these efforts are: The Target Central Resource Database (TCRD) (http://juniper.health.unm.edu/tcrd/) and Pharos (https://pharos.nih.gov/), a web interface to browse the TCRD. The ultimate goal of these resources is to highlight and facilitate research into currently understudied proteins, by aggregating a multitude of data sources, and ranking targets based on the amount of data available, and presenting data in machine learning ready format. Since the 2017 release, both TCRD and Pharos have produced two major releases, which have incorporated or expanded an additional 25 data sources. Recently incorporated data types include human and viral-human protein-protein interactions, protein-disease and protein-phenotype associations, and drug-induced gene signatures, among others. These aggregated data have enabled us to generate new visualizations and content sections in Pharos, in order to empower users to find new areas of study in the druggable genome.

Combining machine learning and quantum mechanics yields more chemically aware molecular descriptors for medicinal chemistry applications.

Molecular interaction fields (MIFs), describing molecules in terms of their ability to interact with any chemical entity, are one of the most established and versatile concepts in drug discovery. Improvement of this molecular description is highly desirable for in silico drug discovery and medicinal chemistry applications. In this work, we revised a well-established molecular mechanics' force field and applied a hybrid quantum mechanics and machine learning approach to parametrize the hydrogen-bonding (HB) potentials of small molecules, improving this aspect of the molecular description. Approximately 66,000 molecules were chosen from available drug databases and subjected to density functional theory calculations (DFT). For each atom, the molecular electrostatic potential (EP) was extracted and used to derive new HB energy contributions; this was subsequently combined with a fingerprint-based description of the structural environment via partial least squares modeling, enabling the new potentials to be used for molecules outside of the training set. We demonstrate that parameter prediction for molecules outside of the training set correlates with their DFT-derived EP, and that there is correlation of the new potentials with hydrogen-bond acidity and basicity scales. We show the newly derived MIFs vary in strength for various ring substitution in accordance with chemical intuition. Finally, we report that this derived parameter, when extended to non-HB atoms, can also be used to estimate sites of reaction.

State of the Art and Uses for the Biopharmaceutics Drug Disposition Classification System (BDDCS): New Additions, Revisions, and Citation References.

The Biopharmaceutics Drug Disposition Classification system (BDDCS) is a four-class approach based on water solubility and extent of metabolism/permeability rate. Based on the BDDCS class to which a drug is assigned, it is possible to predict the role of metabolic enzymes and transporters on the drug disposition of a new molecular entity (NME) prior to its administration to animals or humans. Here, we report a total of 1475 drugs and active metabolites to which the BDDCS is applied. Of these, 379 are new entries, and 1096 are revisions of former classification studies with the addition of references for the approved maximum dose strength, extent of the systemically available drug excreted unchanged in the urine, and lowest solubility over the pH range 1.0-6.8 when such information is available in the literature. We detail revised class assignments of previously misclassified drugs and the literature analyses to classify new drugs. We review the process of solubility assessment for NMEs prior to drug dosing in humans and approved dose classification, as well as the comparison of Biopharmaceutics Classification System (BCS) versus BDDCS assignment. We detail the uses of BDDCS in predicting, prior to dosing animals or humans, disposition characteristics, potential brain penetration, food effect, and drug-induced liver injury (DILI) potential. This work provides an update on the current status of the BDDCS and its uses in the drug development process.

GLUT3 inhibitor discovery through in silico ligand screening and in vivo validation in eukaryotic expression systems.

The passive transport of glucose and related hexoses in human cells is facilitated by members of the glucose transporter family (GLUT, SLC2 gene family). GLUT3 is a high-affinity glucose transporter primarily responsible for glucose entry in neurons. Changes in its expression have been implicated in neurodegenerative diseases and cancer. GLUT3 inhibitors can provide new ways to probe the pathophysiological role of GLUT3 and tackle GLUT3-dependent cancers. Through in silico screening of an ~ 8 million compounds library against the inward- and outward-facing models of GLUT3, we selected ~ 200 ligand candidates. These were tested for in vivo inhibition of GLUT3 expressed in hexose transporter-deficient yeast cells, resulting in six new GLUT3 inhibitors. Examining their specificity for GLUT1-5 revealed that the most potent GLUT3 inhibitor (G3iA, IC50 ~ 7 µM) was most selective for GLUT3, inhibiting less strongly only GLUT2 (IC50 ~ 29 µM). None of the GLUT3 inhibitors affected GLUT5, three inhibited GLUT1 with equal or twofold lower potency, and four showed comparable or two- to fivefold better inhibition of GLUT4. G3iD was a pan-Class 1 GLUT inhibitor with the highest preference for GLUT4 (IC50 ~ 3.9 µM). Given the prevalence of GLUT1 and GLUT3 overexpression in many cancers and multiple myeloma's reliance on GLUT4, these GLUT3 inhibitors may discriminately hinder glucose entry into various cancer cells, promising novel therapeutic avenues in oncology.

Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer.

Inhibiting protein kinases (PKs) that cause cancers has been an important topic in cancer therapy for years. So far, almost 8% of >530 PKs have been targeted by FDA-approved medications, and around 150 protein kinase inhibitors (PKIs) have been tested in clinical trials. We present an approach based on natural language processing and machine learning to investigate the relations between PKs and cancers, predicting PKs whose inhibition would be efficacious to treat a certain cancer. Our approach represents PKs and cancers as semantically meaningful 100-dimensional vectors based on word and concept neighborhoods in PubMed abstracts. We use information about phase I-IV trials in ClinicalTrials.gov to construct a training set for random forest classification. Our results with historical data show that associations between PKs and specific cancers can be predicted years in advance with good accuracy. Our tool can be used to predict the relevance of inhibiting PKs for specific cancers and to support the design of well-focused clinical trials to discover novel PKIs for cancer therapy.

REDIAL-2020: A suite of machine learning models to estimate Anti-SARS-CoV-2 activities.

Strategies for drug discovery and repositioning are an urgent need with respect to COVID-19. We developed "REDIAL-2020", a suite of machine learning models for estimating small molecule activity from molecular structure, for a range of SARS-CoV-2 related assays. Each classifier is based on three distinct types of descriptors (fingerprint, physicochemical, and pharmacophore) for parallel model development. These models were trained using high throughput screening data from the NCATS COVID19 portal (https://opendata.ncats.nih.gov/covid19/index.html), with multiple categorical machine learning algorithms. The "best models" are combined in an ensemble consensus predictor that outperforms single models where external validation is available. This suite of machine learning models is available through the DrugCentral web portal (http://drugcentral.org/Redial). Acceptable input formats are: drug name, PubChem CID, or SMILES; the output is an estimate of anti-SARS-CoV-2 activities. The web application reports estimated activity across three areas (viral entry, viral replication, and live virus infectivity) spanning six independent models, followed by a similarity search that displays the most similar molecules to the query among experimentally determined data. The ML models have 60% to 74% external predictivity, based on three separate datasets. Complementing the NCATS COVID19 portal, REDIAL-2020 can serve as a rapid online tool for identifying active molecules for COVID-19 treatment. The source code and specific models are available through Github (https://github.com/sirimullalab/redial-2020), or via Docker Hub (https://hub.docker.com/r/sirimullalab/redial-2020) for users preferring a containerized version.

Virtual and In Vitro Antiviral Screening Revive Therapeutic Drugs for COVID-19.

The urgent need for a cure for early phase COVID-19 infected patients critically underlines drug repositioning strategies able to efficiently identify new and reliable treatments by merging computational, experimental, and pharmacokinetic expertise. Here we report new potential therapeutics for COVID-19 identified with a combined virtual and experimental screening strategy and selected among already approved drugs. We used hydroxychloroquine (HCQ), one of the most studied drugs in current clinical trials, as a reference template to screen for structural similarity against a library of almost 4000 approved drugs. The top-ranked drugs, based on structural similarity to HCQ, were selected for in vitro antiviral assessment. Among the selected drugs, both zuclopenthixol and nebivolol efficiently block SARS-CoV-2 infection with EC50 values in the low micromolar range, as confirmed by independent experiments. The anti-SARS-CoV-2 potential of ambroxol, amodiaquine, and its active metabolite (N-monodesethyl amodiaquine) is also discussed. In trying to understand the "hydroxychloroquine" mechanism of action, both pK a and the HCQ aromatic core may play a role. Further, we show that the amodiaquine metabolite and, to a lesser extent, zuclopenthixol and nebivolol are active in a SARS-CoV-2 titer reduction assay. Given the need for improved efficacy and safety, we propose zuclopenthixol, nebivolol, and amodiaquine as potential candidates for clinical trials against the early phase of the SARS-CoV-2 infection and discuss their potential use as adjuvant to the current (i.e., remdesivir and favipiravir) COVID-19 therapeutics.

How to Illuminate the Druggable Genome Using Pharos.

Pharos is an integrated web-based informatics platform for the analysis of data aggregated by the Illuminating the Druggable Genome (IDG) Knowledge Management Center, an NIH Common Fund initiative. The current version of Pharos (as of October 2019) spans 20,244 proteins in the human proteome, 19,880 disease and phenotype associations, and 226,829 ChEMBL compounds. This resource not only collates and analyzes data from over 60 high-quality resources to generate these types, but also uses text indexing to find less apparent connections between targets, and has recently begun to collaborate with institutions that generate data and resources. Proteins are ranked according to a knowledge-based classification system, which can help researchers to identify less studied "dark" targets that could be potentially further illuminated. This is an important process for both drug discovery and target validation, as more knowledge can accelerate target identification, and previously understudied proteins can serve as novel targets in drug discovery. Two basic protocols illustrate the levels of detail available for targets and several methods of finding targets of interest. An Alternate Protocol illustrates the difference in available knowledge between less and more studied targets. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Search for a target and view details Alternate Protocol: Search for dark target and view details Basic Protocol 2: Filter a target list to get refined results.

Can BDDCS illuminate targets in drug design?

The fact that pharmacokinetic (PK) properties of drugs influence their interaction with protein targets is a principle known for decades. The same cannot be said for the opposite, namely that targets influence the PK properties of drugs. Evidence confirming this possibility is introduced here for the first time, as we show that certain protein families have a clear preference for drugs with specific PK properties. We investigate this by cross-referencing 'druggable target' annotations for >1000 US Food and Drug Administration (FDA)-approved drugs with their PK profile, as defined by the Biopharmaceutics Drug Disposition Classification System (BDDCS) criteria, and then examine the BDDCS preference for several major target protein families and therapeutic categories. Our findings suggest a novel way to conduct drug discovery by focusing PK profiles at the very early stage of target selection.

New insights in the in vitro characterisation and molecular modelling of the P-glycoprotein inhibitory promiscuity.

The presence of several binding sites for both substrates and inhibitors is yet a poorly explored thematic concerning the assessment of the drug-drug interactions risk due to interactions of multiple drugs with the human transport protein P-glycoprotein (P-gp or MDR1, gene ABCB1). In this study we measured the inhibitory behaviour of a set of known drugs towards P-gp by using three different probe substrates (digoxin, Hoechst 33,342 and rhodamine 123). A structure-based model was built to unravel the different substrates binding sites and to rationalize the cases where drugs were not inhibiting all the substrates. A separate set of experiments was used to validate the model and confirmed its suitability to either detect the substrate-dependent P-gp inhibition and to anticipate proper substrates for in vitro experiments case by case. The modelling strategy described can be applied for either design safer drugs (P-gp as antitarget) or to target specific sub-site inhibitors towards other drugs (P-gp as target).

ADME-Space: a new tool for medicinal chemists to explore ADME properties.

We introduce a new chemical space for drugs and drug-like molecules, exclusively based on their in silico ADME behaviour. This ADME-Space is based on self-organizing map (SOM) applied to 26,000 molecules. Twenty accurate QSPR models, describing important ADME properties, were developed and, successively, used as new molecular descriptors not related to molecular structure. Applications include permeability, active transport, metabolism and bioavailability studies, but the method can be even used to discuss drug-drug interactions (DDIs) or it can be extended to additional ADME properties. Thus, the ADME-Space opens a new framework for the multi-parametric data analysis in drug discovery where all ADME behaviours of molecules are condensed in one map: it allows medicinal chemists to simultaneously monitor several ADME properties, to rapidly select optimal ADME profiles, retrieve warning on potential ADME problems and DDIs or select proper in vitro experiments.

Side-Chain Modified Ergosterol and Stigmasterol Derivatives as Liver X Receptor Agonists.

A series of stigmasterol and ergosterol derivatives, characterized by the presence of oxygenated functions at C-22 and/or C-23 positions, were designed as potential liver X receptor (LXR) agonists. The absolute configuration of the newly created chiral centers was definitively assigned for all the corresponding compounds. Among the 16 synthesized compounds, 21, 27, and 28 were found to be selective LXRα agonists, whereas 20, 22, and 25 showed good selectivity for the LXRß isoform. In particular, 25 showed the same degree of potency as 22R-HC (3) at LXRß, while it was virtually inactive at LXRα (EC50 = 14.51 µM). Interestingly, 13, 19, 20, and 25 showed to be LXR target gene-selective modulators, by strongly inducing the expression of ABCA1, while poorly or not activating the lipogenic genes SREBP1 and SCD1 or FASN, respectively.