Predicting Drug Blood-Brain Barrier Penetration with Adverse Event Report Embeddings

Overview

Journal AMIA Annu Symp Proc

Specialty Medical Informatics

Date 2023 May 2

PMID 37128462

Authors

Yifan Wu

Justin Mower

Xiruo Ding

Oliver Li

Devika Subramanian

Trevor Cohen

Affiliations

Soon will be listed here.

Abstract

Adverse event reports (AER) are widely used for post-market drug safety surveillance and drug repurposing, with the assumption that drugs with similar side-effects may have similar therapeutic effects also. In this study, we used distributed representations of drugs derived from the Food and Drug Administration (FDA) AER system using aer2vec, a method of representing AER, with drug embeddings emerging from a neural network trained to predict the probability of adverse drug effects given observed drugs. We combined these representations with molecular features to predict permeability of the blood-brain barrier to drugs, a prerequisite to their application to treat conditions of the central nervous system. Across multiple machine learning classifiers, the addition of distributed representations improved performance over prior methods using drug-drug similarity estimates derived from discrete representations of AER system data. Embedding-based approaches outperformed those using discrete statistics, with improvements in absolute AUC of 5% and 9%, corresponding to improvements of 9% and 13% over performance with molecular features only. Performance was retained when reducing embedding dimensions from 500 to 6, indicating that they are neither attributable to overfitting, nor to a difference in the number of trainable parameters. These results indicate that aer2vec distributed representations carry information that is valuable for drug repurposing.

References

Doniger G, Foxe J, Murray M, Higgins B, Javitt D . Impaired visual object recognition and dorsal/ventral stream interaction in schizophrenia. Arch Gen Psychiatry. 2002; 59(11):1011-20. DOI: 10.1001/archpsyc.59.11.1011. View

Campillos M, Kuhn M, Gavin A, Jensen L, Bork P . Drug target identification using side-effect similarity. Science. 2008; 321(5886):263-6. DOI: 10.1126/science.1158140. View

Hennessy S, Strom B . Improving postapproval drug safety surveillance: getting better information sooner. Annu Rev Pharmacol Toxicol. 2014; 55:75-87. PMC: 4677571. DOI: 10.1146/annurev-pharmtox-011613-135955. View

Evans S, Waller P, Davis S . Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports. Pharmacoepidemiol Drug Saf. 2002; 10(6):483-6. DOI: 10.1002/pds.677. View

Tatonetti N, Fernald G, Altman R . A novel signal detection algorithm for identifying hidden drug-drug interactions in adverse event reports. J Am Med Inform Assoc. 2011; 19(1):79-85. PMC: 3240755. DOI: 10.1136/amiajnl-2011-000214. View

Banda J, Evans L, Vanguri R, Tatonetti N, Ryan P, Shah N . A curated and standardized adverse drug event resource to accelerate drug safety research. Sci Data. 2016; 3:160026. PMC: 4872271. DOI: 10.1038/sdata.2016.26. View

Miao R, Xia L, Chen H, Huang H, Liang Y . Improved Classification of Blood-Brain-Barrier Drugs Using Deep Learning. Sci Rep. 2019; 9(1):8802. PMC: 6584536. DOI: 10.1038/s41598-019-44773-4. View

Wang K, Wan M, Wang R, Weng Z . Opportunities for Web-based Drug Repositioning: Searching for Potential Antihypertensive Agents with Hypotension Adverse Events. J Med Internet Res. 2016; 18(4):e76. PMC: 4833875. DOI: 10.2196/jmir.4541. View

Edwards I, Aronson J . Adverse drug reactions: definitions, diagnosis, and management. Lancet. 2000; 356(9237):1255-9. DOI: 10.1016/S0140-6736(00)02799-9. View

10.

Martins I, Teixeira A, Pinheiro L, Falcao A . A Bayesian approach to in silico blood-brain barrier penetration modeling. J Chem Inf Model. 2012; 52(6):1686-97. DOI: 10.1021/ci300124c. View

11.

Ashburn T, Thor K . Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov. 2004; 3(8):673-83. DOI: 10.1038/nrd1468. View

12.

Gottlieb A, Stein G, Ruppin E, Sharan R . PREDICT: a method for inferring novel drug indications with application to personalized medicine. Mol Syst Biol. 2011; 7:496. PMC: 3159979. DOI: 10.1038/msb.2011.26. View

13.

Harpaz R, DuMouchel W, LePendu P, Bauer-Mehren A, Ryan P, Shah N . Performance of pharmacovigilance signal-detection algorithms for the FDA adverse event reporting system. Clin Pharmacol Ther. 2013; 93(6):539-46. PMC: 3857139. DOI: 10.1038/clpt.2013.24. View

14.

De Silva T, Macdonald D, Paterson G, Sikdar K, Cochrane B . Systematized nomenclature of medicine clinical terms (SNOMED CT) to represent computed tomography procedures. Comput Methods Programs Biomed. 2011; 101(3):324-9. DOI: 10.1016/j.cmpb.2011.01.002. View

15.

Kortagere S, Chekmarev D, Welsh W, Ekins S . New predictive models for blood-brain barrier permeability of drug-like molecules. Pharm Res. 2008; 25(8):1836-45. PMC: 2803117. DOI: 10.1007/s11095-008-9584-5. View

16.

Tatonetti N, Ye P, Daneshjou R, Altman R . Data-driven prediction of drug effects and interactions. Sci Transl Med. 2012; 4(125):125ra31. PMC: 3382018. DOI: 10.1126/scitranslmed.3003377. View

17.

Gao Z, Chen Y, Cai X, Xu R . Predict drug permeability to blood-brain-barrier from clinical phenotypes: drug side effects and drug indications. Bioinformatics. 2016; 33(6):901-908. PMC: 5860495. DOI: 10.1093/bioinformatics/btw713. View

18.

Pushpakom S, Iorio F, Eyers P, Jane Escott K, Hopper S, Wells A . Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discov. 2018; 18(1):41-58. DOI: 10.1038/nrd.2018.168. View

19.

Wang Z, Yang H, Wu Z, Wang T, Li W, Tang Y . In Silico Prediction of Blood-Brain Barrier Permeability of Compounds by Machine Learning and Resampling Methods. ChemMedChem. 2018; 13(20):2189-2201. DOI: 10.1002/cmdc.201800533. View

20.

McCoy Jr T, Perlis R . A tool to utilize adverse effect profiles to identify brain-active medications for repurposing. Int J Neuropsychopharmacol. 2015; 18(3). PMC: 4360243. DOI: 10.1093/ijnp/pyu078. View