Broad‐Spectrum Profiling of Drug Safety via Learning Complex Network

Abstract

Drug safety is a severe clinical pharmacology and toxicology problem that has caused immense medical and social burdens every year. Regretfully, a reproducible method to assess drug safety systematically and quantitatively is still missing. In this study, we developed an advanced machine learning model for de novo drug safety assessment by solving the multilayer drug-gene-adverse drug reaction (ADR) interaction network. For the first time, the drug safety was assessed in a broad landscape of 1,156 distinct ADRs. We also designed a parameter ToxicityScore to quantify the overall drug safety. Moreover, we determined association strength for every 3,807,631 gene-ADR interactions, which clues mechanistic exploration of ADRs. For convenience, we deployed the model as a web service ADRAlertgene at http://www.bio-add.org/ADRAl ert/. In summary, this study offers insights into prioritizing safe drug therapy. It helps reduce the attrition rate of new drug discovery by providing a reliable ADR profile in the early preclinical stage.Drug safety is a severe clinical problem in drug therapy. It has caused immense medical and social burdens around the world every year. Drug safety research answers why a particular drug causes side effects or adverse drug reactions (ADRs) in a particular patient. The World Health Organization (WHO) defines ADR as "responses to a medicine which is noxious and unintended, and which occurs at doses normally used in man." Severe ADRs (SADRs) often lead to hospitalization, prolonged hospital staying, increased cost of care, disability, and even death. [1][2][3] It was reported that fatal ADRs answered for more than 100,000 deaths in US hospitals in 1994. 4 The ADR-related mortality increased significantly over time at a rate of 0.58% per year since 1999. 5 Besides, ADRs accounted for about 24% of all failures in clinical trials of new drug discovery, second to unsatisfied efficacy. 6 Therefore, it is extremely important to monitor and assess drug safety throughout the life cycle of drug development, from early drug discovery to postmarket surveillance. 7 Conventionally, both in vitro and in vivo tests are undertaken before clinical trials to help rapidly remove those highly toxic drugs. Be that as it may, > 20% of drug candidates still failed in clinical trials due to their poor toxicity profiles. 8 Regretfully, the toxicity information collected from cell experiments, like MTT assays and animal studies, cannot be fully transferred to humans if they are not interpreted prudently. More reliable ADR profiles are usually created in clinical trials among small but carefully recruited patient populations and from

DrugMAP: molecular atlas and pharma-information of all drugs

“…With the wide application of artificial intelligence (AI) techniques in current drug discovery, it is crucial to construct a comprehensive and precise ‘network’ (based on the findings of previous publications) to describe a large number of drugs and their interacting molecules of pharmacological importance ( 30 , 37 , 38 ). In other words, it is key to have a database that provides such valuable network data for facilitating the discovery of efficacious combination therapy ( 39 ), the understanding of off-target mechanisms and undesirable side effects ( 40–42 ), etc.…”