DrugCentral

DrugCentral aggregates and integrates drug chemical structures, bioactivity, regulatory status, pharmacologic actions, indications, pharmacokinetic properties, adverse event data, and drug–target interaction data for FDA and international active pharmaceutical ingredients (APIs) to support drug discovery, pharmacovigilance, and repositioning analyses.

Key Features:

• Up-to-Date Drug Approvals: Continuous monitoring and inclusion of FDA and other international regulatory approvals for active pharmaceutical ingredients (APIs).
• Comprehensive Data Integration: Integration and indexing of active ingredients with pharmaceutical formulations and label annotations.
• Pharmacokinetic Properties: Inclusion of pharmacokinetic annotations for approximately 1,000 drugs.
• Sex-Stratified Adverse Events (FAERS): Separation of side effects by sex derived from the FDA Adverse Event Reporting System (FAERS).
• Drug Repositioning Prioritization: A prioritization scheme for FDA-approved drugs based on market availability and intellectual property rights.
• REDIAL-2020 Machine Learning: Integration of REDIAL-2020 machine learning estimates for anti-SARS-CoV-2 activities.
• Molecular-Level Drug–Target Integration: Linking of drug–target interactions with pharmacologic actions and therapeutic indications.
• Text Mining of FDA Drug Labels: Extraction of adverse events and clinical trial information from FDA drug labels via text mining.
• Cross-Referencing to External Databases: Cross-references to external resources and databases to complement integrated data.

Scientific Applications:

• Drug Discovery: Support for identification and evaluation of candidate molecules and drug–target relationships using integrated chemical and bioactivity data.
• Pharmacovigilance and Safety Monitoring: Analysis of adverse events, including sex-based differences from FAERS, and assessment of pharmacokinetic profiles.
• Drug Repositioning and Prioritization: Prioritization of repurposing opportunities using market/IP criteria and REDIAL-2020 predictions for SARS-CoV-2 activity.
• Personalized Medicine Investigations: Investigation of sex-based adverse event differences and pharmacokinetic variability to inform individualized risk assessment.

Methodology:

Computational methods explicitly include continuous integration of regulatory approval and chemical/bioactivity data, indexing of API and formulation annotations, incorporation of pharmacokinetic properties, sex-based separation of FAERS adverse events, a repositioning prioritization scheme based on market availability and intellectual property, integration of REDIAL-2020 machine learning estimates for anti-SARS-CoV-2 activity, cross-referencing to external databases, and text mining of FDA drug labels to extract adverse events and clinical trial information.