Application of machine learning approaches to identify new anticonvulsant compounds active in the 6 Hz seizure model

GOICOECHEA SOFIA; SBARAGLINI ML; CHUGURANSKY SR; MORALES JF; RUIZ ME; TALEVI A; BELLERA CL

Communications in Computer and Information Science

II Latin American Workshop on Computational Neuroscience (LAWCN 2019)

Año: 2019

1865-0929

Epilepsy is the second most common chronic brain disorder, affecting 65 million people worldwide. According to the NIH?s Epilepsy Therapy Screening Program, evaluation of potential new antiepileptic drug candidates begins with assessment of their protective effects in two acute seizure models in mice, the Maximal Electroshock Seizure test and the 6 Hz test. The latter elicits partial seizures through an electrical stimulus of 44 mA, at which many clinically established anti-seizure drugs do not suppress seizures. The inclusion of this ?high-hurdle? acute seizure assay at the initial stage of the drug identification phase is intended to increase the probability that agents with improved efficacy relative to existing agents will be detected. In this work, we have used machine learning approximations to develop in silico models capable of identifying novel active anticonvulsant drugs against the 6 Hz seizure model. Linear classifiers based on Dragon conformation-independent descriptors were generated through an in-house routine in R environment and validated through standard validation procedures. They were later combined through different ensemble learning schemes. The best ensemble comprised the 29 best-performing models combined using the MIN operator. Such model ensemble was applied in a virtual screening campaign of DrugBank and Sweetlead databases, where 28 approved drugs were identified as potential protective agents in the 6 Hz model. The present study constitutes an example of the use of machine learning approximations to detect drug repurposing opportunities