Abstract
An effective approach for assessing a drug’s potential to induce autoimmune diseases (ADs) is needed in drug development. Here, we aim to develop a workflow to examine the association between structural alerts and drugs-induced ADs to improve toxicological prescreening tools. Considering reactive metabolite (RM) formation as a well-documented mechanism for drug-induced ADs, we investigated whether the presence of certain RM-related structural alerts was predictive for the risk of drug-induced AD. We constructed a database containing 171 RM-related structural alerts, generated a dataset of 407 AD- and non-AD-associated drugs, and performed statistical analysis. The nitrogen-containing benzene substituent alerts were found to be significantly associated with the risk of drug-induced ADs (odds ratio = 2.95, p = 0.0036). Furthermore, we developed a machine-learning-based predictive model by using daily dose and nitrogen-containing benzene substituent alerts as the top inputs and achieved the predictive performance of area under curve (AUC) of 70%. Additionally, we confirmed the reactivity of the nitrogen-containing benzene substituent aniline and related metabolites using quantum chemistry analysis and explored the underlying mechanisms. These identified structural alerts could be helpful in identifying drug candidates that carry a potential risk of drug-induced ADs to improve their safety profiles.
Highlights
Autoimmune disease (AD) is a clinical condition that occurs when the immune system mistakenly attacks one’s own normal cells
We first built a database by collecting published structural alerts for reactive metabolite formation from the literature and generated a dataset containing both drugs with determined potential to induce autoimmune diseases (ADs) (AD-positive) and those not associated with ADs (AD-negative) (Figure 1)
We performed statistical analyses to identify the association between the structural alerts and the risk of drug-induced ADs, and daily dose was to the strengthen the relationship
Summary
Autoimmune disease (AD) is a clinical condition that occurs when the immune system mistakenly attacks one’s own normal cells. More than 100 types of autoimmune diseases have been identified, affecting about 7–9% of the population [1], who are mostly female. The annual medical cost of treating ADs in the United States (U.S.) healthcare system was estimated to be greater than USD 100 billion [2]. Drugs are known to account for a significant subset of common clinical ADs. For example, 10% of lupus erythematosus cases and 12–17% of autoimmune hepatitis cases were estimated to be caused by drugs [3,4]. Recent reports suggested that ADs are common conditions following COVID-19, which could be attributable to the acute respiratory distress syndrome or medical treatment [5,6].
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