Compound Library
The PASA Compound Library is a compiled list of existing compounds that have been assessed for their potential to be repurposed as treatments for ASUD. The library currently comprises evidence from two non-clinical PASA research projects: AS170014-A9 (Dr. Webb) Multi-omic Data and AS210006-A1 (Dr. Wang) EMR Data.
AS170014-A9 (Dr. Webb) Multi-omic Data: Identifies compounds that target genes associated with AUD and OUD and represent potential candidates for therapeutic strategies. Associated genes were determined by leveraging multiple lines of biological evidence and resources. Compounds targeting associated genes were identified by querying multiple drug repurposing database. For additional details on how the compounds have been chosen and prioritized, please click here.
AS210006-A1 (Dr. Wang) EMR Data: Identifies compounds associated with higher or lower rates of adverse events in PTSD as well as PTSD with AUD populations. Associations were established by measuring compounds' effects in deep learning models using electronic medical records to predict those events. Compound relative contributions (RCs) were calculated independently for each of three models predicting: ASUD among those with PTSD; suicide-related events among those with PTSD; and any of OUD, suicide-related events, depression, or death among those with PTSD and AUD. RCs above one represent a harmful association (red highlighting), while those below one have a protective association (green highlighting); the p-values indicate the statistical significance for a given compound and outcome.
For more information about ASUD, Suicide Risk, and Adverse Event Risk please click the following links:
ASUD reference
Suicide Risk reference
Adverse Event Risk reference
Compound Library Information
Identifying Compound to Treat Opiate Use Disorder by Leveraging Multi-Omic Data Integration and Multiple Drug Repurposing Databases
Abstract:
Genes influencing opioid use disorder (OUD) biology have been identified via genome-wide association studies (GWAS), gene expression, and network analyses. These discoveries provide opportunities to identifying existing compounds targeting these genes for drug repurposing studies. However, systematically integrating discovery results and identifying relevant available pharmacotherapies for OUD repurposing studies is challenging. To address this, we’ve constructed a framework that leverages existing results and drug databases to identify candidate pharmacotherapies.
For this study, two independent OUD related meta-analyses were used including a GWAS and a differential gene expression (DGE) study of post-mortem human brain. Protein-Protein Interaction (PPI) sub-networks enriched for GWAS risk loci were identified via network analyses. Drug databases Pharos, Open Targets, Therapeutic Target Database (TTD), and DrugBank were queried for clinical status and target selectivity. Cross-omic and drug query results were then integrated to identify candidate compounds.
GWAS and DGE analyses revealed 3 and 335 target genes (FDR q < 0.05), respectively, while network analysis detected 70 genes in 22 enriched PPI networks. Four selection strategies were implemented, which yielded between 72 and 676 genes with statistically significant support and 110 to 683 drugs targeting these genes, respectively. The compound list shared here. After filtering out less specific compounds or those targeting well-established psychiatric-related receptors (OPRM1 and DRD2), between 2 and 329 approved drugs remained across the four strategies.
By leveraging multiple lines of biological evidence and resources, we identified many FDA approved drugs that target genes associated with OUD. This approach a) allows high-throughput querying of OUD-related genes, b) detects OUD-related genes and compounds not identified using a single domain or resource, and c) produces a succinct summary of FDA approved compounds eligible for efficient expert review. Identifying larger pools of candidate pharmacotherapies and summarizing the supporting evidence bridges the gap between discovery and drug repurposing studies.
Identifying Candidate Pharmacotherapies for Alcohol Use Disorder by Leveraging Multi-Omic Data Integration and Multiple Drug Repurposing Databases
Purpose:
More genes related to Alcohol Use Disorder (AUD) are being identified via genome-wide association studies (GWAS), gene expression, methylation, and network analyses. These discoveries offer a significant opportunity for discovering novel drug targets for AUD treatment. However, systematically integrating evidence and querying drug databases to identify candidate pharmacotherapies is challenging and time consuming.
Methods:
To address this challenge, we have constructed an integrated framework within a R Shiny app to identify compounds targeting genes with single or cross-domain omic evidence from knowledge across four drug databases including Pharos, Open Targets, Therapeutic Target Database (TTD), and DrugBank. Features of the framework include a) the custom selection of drug databases, b) flexible ingest and merging of gene-based evidence, c) on demand meta-analysis and gene ranking, d) compound identification, and e) output of an integrated summary of compounds.
For this single omic domain pilot, four GWAS studies for alcohol related outcomes including drinks per week, predicted AUDIT-P, DSM-IV alcohol dependence, and a common addiction factor were integrated to discover robust SNP and genes with evidence across studies. Therapeutic compounds targeting genes with converging evidence were identified.
Results:
Cross GWAS analysis revealed 333 semi-independent genes with significant (Bonferroni p < 0.05) SNP or MAGMA gene-level evidence, with 70 genes being significant in at least two studies. Drug databases queries identified 345 approved compounds targeting any of the 70 genes. Thirty compounds targeted at least 2 of the 70 high priority genes including acamprosate, topiramate, aripiprazole, and esketamine, which are either approved, used off-label, or have evidence supporting use in AUD treatment.
Conclusion:
By leveraging an integrated framework and multiple sources of evidence, known and candidate compounds that target AUD related genes were identified. This approach a) allows high-throughput querying of AUD-related genes, b) detects compounds missed using a single data source, and c) produces a summary of compounds eligible for repurposing to facilitate efficient expert review. The app is available for beta testing to Pharmacotherapies for Alcohol and Substance Use Disorder Alliance (PASA) consortium investigators. By providing an integrated framework and app, we hope to facilitate easier identification of candidate AUD pharmacotherapies.