Pharos 2023: an integrated resource for the understudied human proteome.

The Illuminating the Druggable Genome (IDG) project aims to improve our understanding of understudied proteins and our ability to study them in the context of disease biology by perturbing them with small molecules, biologics, or other therapeutic modalities. Two main products from the IDG effort are the Target Central Resource Database (TCRD) (http://juniper.health.

Rare disease-based scientific annotation knowledge graph.

Rare diseases (RDs) are naturally associated with a low prevalence rate, which raises a big challenge due to there being less data available for supporting preclinical and clinical studies. There has been a vast improvement in our understanding of RD, largely owing to advanced big data analytic approaches in genetics/genomics. Consequently, a large volume of RD-related publications has been accumulated in recent years, which offers opportunities to utilize these publications for accessing the full spectrum of the scientific research and supporting further investigation in RD.

Using in vitro ADME data for lead compound selection: An emphasis on PAMPA pH 5 permeability and oral bioavailability.

Membrane permeability plays an important role in oral drug absorption. Caco-2 and Madin-Darby Canine Kidney (MDCK) cell culture systems have been widely used for assessing intestinal permeability. Since most drugs are absorbed passively, Parallel Artificial Membrane Permeability Assay (PAMPA) has gained popularity as a low-cost and high-throughput method in early drug discovery when compared to high-cost, labor intensive cell-based assays.

Scientific evidence based rare disease research discovery with research funding data in knowledge graph.

Background: Limited knowledge and unclear underlying biology of many rare diseases pose significant challenges to patients, clinicians, and scientists. To address these challenges, there is an urgent need to inspire and encourage scientists to propose and pursue innovative research studies that aim to uncover the genetic and molecular causes of more rare diseases and ultimately to identify effective therapeutic solutions. A clear understanding of current research efforts, knowledge/research gaps, and funding patterns as scientific evidence is crucial to systematically accelerate the pace of research discovery in rare diseases, which is an overarching goal of this study.

NCATS Inxight Drugs: a comprehensive and curated portal for translational research.

The United States has a complex regulatory scheme for marketing drugs. Understanding drug regulatory status is a daunting task that requires integrating data from many sources from the United States Food and Drug Administration (FDA), US government publications, and other processes related to drug development. At NCATS, we created Inxight Drugs (https://drugs.

Triplex Direct Quantitative Polymerase Chain Reaction for the Identification of Streptococcus pneumoniae Serotypes.

The quantitative polymerase chain reaction (qPCR) method presented in this study allows the identification of pneumococcal capsular serotypes in cerebrospinal fluid without first performing DNA extraction. This testing approach, which saves time and resources, demonstrated similar sensitivity and a high level of agreement between cycle threshold values when it was compared side-by-side with the standard qPCR method with extracted DNA.

Development of Robust Quantitative Structure-Activity Relationship Models for CYP2C9, CYP2D6, and CYP3A4 Catalysis and Inhibition.

Cytochrome P450 enzymes are responsible for the metabolism of >75% of marketed drugs, making it essential to identify the contributions of individual cytochromes P450 to the total clearance of a new candidate drug. Overreliance on one cytochrome P450 for clearance levies a high risk of drug-drug interactions; and considering that several human cytochrome P450 enzymes are polymorphic, it can also lead to highly variable pharmacokinetics in the clinic. Thus, it would be advantageous to understand the likelihood of new chemical entities to interact with the major cytochrome P450 enzymes at an early stage in the drug discovery process.

Validating ADME QSAR Models Using Marketed Drugs.

Problems with drug ADME are responsible for many clinical failures. By understanding the ADME properties of marketed drugs and modeling how chemical structure contributes to these inherent properties, we can help new projects reduce their risk profiles. Kinetic aqueous solubility, the parallel artificial membrane permeability assay (PAMPA), and rat liver microsomal stability constitute the Tier I ADME assays at the National Center for Advancing Translational Sciences (NCATS).

CATMoS: Collaborative Acute Toxicity Modeling Suite.

Background: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests.

Predicting liver cytosol stability of small molecules.

Over the last few decades, chemists have become skilled at designing compounds that avoid cytochrome P (CYP) 450 mediated metabolism. Typical screening assays are performed in liver microsomal fractions and it is possible to overlook the contribution of cytosolic enzymes until much later in the drug discovery process. Few data exist on cytosolic enzyme-mediated metabolism and no reliable tools are available to chemists to help design away from such liabilities.

Critical Assessment of Artificial Intelligence Methods for Prediction of hERG Channel Inhibition in the "Big Data" Era.

The rise of novel artificial intelligence (AI) methods necessitates their benchmarking against classical machine learning for a typical drug-discovery project. Inhibition of the potassium ion channel, whose alpha subunit is encoded by the human -related gene (hERG), leads to a prolonged QT interval of the cardiac action potential and is a significant safety pharmacology target for the development of new medicines. Several computational approaches have been employed to develop prediction models for the assessment of hERG liabilities of small molecules including recent work using deep learning methods.

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.

An integrative knowledge graph for rare diseases, derived from the Genetic and Rare Diseases Information Center (GARD).

Background: The Genetic and Rare Diseases (GARD) Information Center was established by the National Institutes of Health (NIH) to provide freely accessible consumer health information on over 6500 genetic and rare diseases. As the cumulative scientific understanding and underlying evidence for these diseases have expanded over time, existing practices to generate knowledge from these publications and resources have not been able to keep pace. Through determining the applicability of computational approaches to enhance or replace manual curation tasks, we aim to both improve the sustainability and relevance of consumer health information, but also to develop a foundational database, from which translational science researchers may start to unravel disease characteristics that are vital to the research process.

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.

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.

Leveraging the UMLS As a Data Standard for Rare Disease Data Normalization and Harmonization.

Objective: In this study, we aimed to evaluate the capability of the Unified Medical Language System (UMLS) as one data standard to support data normalization and harmonization of datasets that have been developed for rare diseases. Through analysis of data mappings between multiple rare disease resources and the UMLS, we propose suggested extensions of the UMLS that will enable its adoption as a global standard in rare disease.

Methods: We analyzed data mappings between the UMLS and existing datasets on over 7,000 rare diseases that were retrieved from four publicly accessible resources: Genetic And Rare Diseases Information Center (GARD), Orphanet, Online Mendelian Inheritance in Men (OMIM), and the Monarch Disease Ontology (MONDO).

Global Substance Registration System: consistent scientific descriptions for substances related to health.

The US Food and Drug Administration (FDA) and the National Center for Advancing Translational Sciences (NCATS) have collaborated to publish rigorous scientific descriptions of substances relevant to regulated products. The FDA has adopted the global ISO 11238 data standard for the identification of substances in medicinal products and has populated a database to organize the agency's regulatory submissions and marketed products data. NCATS has worked with FDA to develop the Global Substance Registration System (GSRS) and produce a non-proprietary version of the database for public benefit.

Phenotypically Similar Rare Disease Identification from an Integrative Knowledge Graph for Data Harmonization: Preliminary Study.

Background: Although many efforts have been made to develop comprehensive disease resources that capture rare disease information for the purpose of clinical decision making and education, there is no standardized protocol for defining and harmonizing rare diseases across multiple resources. This introduces data redundancy and inconsistency that may ultimately increase confusion and difficulty for the wide use of these resources. To overcome such encumbrances, we report our preliminary study to identify phenotypical similarity among genetic and rare diseases (GARD) that are presenting similar clinical manifestations, and support further data harmonization.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity.

Background: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) approaches and computational modeling.

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.

Novel Consensus Architecture To Improve Performance of Large-Scale Multitask Deep Learning QSAR Models.

Advances in the development of high-throughput screening and automated chemistry have rapidly accelerated the production of chemical and biological data, much of them freely accessible through literature aggregator services such as ChEMBL and PubChem. Here, we explore how to use this comprehensive mapping of chemical biology space to support the development of large-scale quantitative structure-activity relationship (QSAR) models. We propose a new deep learning consensus architecture (DLCA) that combines consensus and multitask deep learning approaches together to generate large-scale QSAR models.