Delivering safe and effective therapeutic treatment to patients is one of the grand challenges in modern medicine. However, drug safety research has been progressing slowly in recent years, compared to other fields such as biotechnologies and precision medicine, due to the mechanistic complexity of adverse drug reactions (ADRs). To fill up this gap, we develop a new database, the Adverse Drug Reaction Classification System-Target Profile (ADReCS-Target, http://bioinf.xmu.edu.cn/ADReCS-Target), which provides comprehensive information about ADRs caused by drug interaction with protein, gene and genetic variation. In total, ADReCS-Target includes 66,573 pairwise relations, among which 1710 are protein–ADR associations, 2613 are genetic variation–ADR associations, and 63,298 are gene–ADR associations. In a case study of exploring the mechanism of rash, we find that HLAs, C1QA and APOA1 are the key gene players and thus can be potential targets (or biomarkers) in monitoring or countermining rashes. In summary, ADReCS-Target can be a useful resource for the biomedical scientific community by serving researchers in the fields of drug development, clinical pharmacology, precision medicine, and from web lab to high-throughput computational platform. Particularly, it helps to identify drug with better ADR profile and design safer drug therapy regimen.
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
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