A machine learning and network framework to discover new indications for small molecules.

TitleA machine learning and network framework to discover new indications for small molecules.
Publication TypeJournal Article
Year of Publication2020
AuthorsGilvary C, Elkhader J, Madhukar N, Henchcliffe C, Goncalves MD, Elemento O
JournalPLoS Comput Biol
Date Published2020 08
KeywordsAlgorithms, Antiparkinson Agents, Computational Biology, Drug Repositioning, Hypoglycemic Agents, Machine Learning, Models, Statistical, Software

Drug repurposing, identifying novel indications for drugs, bypasses common drug development pitfalls to ultimately deliver therapies to patients faster. However, most repurposing discoveries have been led by anecdotal observations (e.g. Viagra) or experimental-based repurposing screens, which are costly, time-consuming, and imprecise. Recently, more systematic computational approaches have been proposed, however these rely on utilizing the information from the diseases a drug is already approved to treat. This inherently limits the algorithms, making them unusable for investigational molecules. Here, we present a computational approach to drug repurposing, CATNIP, that requires only biological and chemical information of a molecule. CATNIP is trained with 2,576 diverse small molecules and uses 16 different drug similarity features, such as structural, target, or pathway based similarity. This model obtains significant predictive power (AUC = 0.841). Using our model, we created a repurposing network to identify broad scale repurposing opportunities between drug types. By exploiting this network, we identified literature-supported repurposing candidates, such as the use of systemic hormonal preparations for the treatment of respiratory illnesses. Furthermore, we demonstrated that we can use our approach to identify novel uses for defined drug classes. We found that adrenergic uptake inhibitors, specifically amitriptyline and trimipramine, could be potential therapies for Parkinson's disease. Additionally, using CATNIP, we predicted the kinase inhibitor, vandetanib, as a possible treatment for Type 2 Diabetes. Overall, this systematic approach to drug repurposing lays the groundwork to streamline future drug development efforts.

Alternate JournalPLoS Comput Biol
PubMed ID32764756
PubMed Central IDPMC7437923
Grant ListUL1 TR002384 / TR / NCATS NIH HHS / United States
P50 CA211024 / CA / NCI NIH HHS / United States
R01 CA194547 / CA / NCI NIH HHS / United States
U24 CA210989 / CA / NCI NIH HHS / United States
F31 LM013058 / LM / NLM NIH HHS / United States