An agile discovery method of drugs for infectious diseases and tentative exploration of its application in drug discovery for COVID-19

ABSTRACT

OBJECTIVE To establish an agile discovery method of drugs or natural products for epidemics (aCODE) for the development of anti-infectious disease drugs. METHODS Five infectious diseases (HIV infection, human influenza, Paramyxoviridae infections, bacterial infections and whooping cough) involving more than 40 drugs approved by the United States Food and Drug Administration (FDA) were selected. An experimental group and two negative control groups (A and B) for each disease were set up. The experimental group randomly selected (500 times) M FDA-approved indications as seed drugs for the disease, while negative control group A used all FDA-approved infectious drugs for non-current diseases instead of seed drugs, and negative control group B used all non-infectious disease drugs for non-infectious diseases instead of seed drugs. M ranged from 2 to 20, the target gene infor mation of the seed drug was input, and the feature vector of the seed drug set was calculated. Candi date compounds were predicted through similarity search of drug feature vectors. The size of the inter section between the predicted drug and the positive set of drugs approved by the FDA for the disease, and the significance of the intersection were calculated. After the establishment of the aCODE method, four drugs (lopinavir, ribavirin, ritonavir and chloroquine) were selected as seed drugs for COVID-19 to predict the composition of natural products. Using natural products with known anti-coronavirus activi ties as the verification set, the significance of the prediction results was calculated. RESULTS In the case of the five infectious diseases, the proportion of positive drugs in the results of prediction in the experimental group increased with the number of seed drugs, while the positive rate of the two negative control groups remained basically unchanged or somewhat trended down. The aCODE method, when applied to COVID-19 drug screening, could effectively predict drugs with potential anti-SARS-Cov-2 activity (P=0.0046). CONCLUSION With the aCODE method, the more the seed drugs, the more accu rate the characteristics of the disease-related gene modules calculated from this group of seed drugs, and the higher the proportion of positive drugs in the prediction result. This method may contribute to the discovery of drugs for COVID-19. © 2020 Chinese Journal of Pharmacology and Toxicology. All rights reserved.