Abstract

Developing safe and effective therapies for neurological disorders such as Alzheimer’s, Parkinson’s, and Epilepsy, is essential given that these can affect millions of people across the globe that result in significant challenge to healthcare system. Unfortunately, the development of neurological does drugs face a lack of abandonment owing to severe toxicity risks which include neurotoxicity, hepatotoxicity, and cardiotoxicity, drugs fail in the later stages of clinical trials due to unpredictable side effects, metabolic instability, poor bioavailability. Many drugs which are neurotoxic have a high risk of toxicity and predictive harmful effects. Prior to the commencement of clinical trials, predictive toxicology plays a crucial role in identifying potential adverse effects before clinical trials by the combination of computational technologies and experimental designs. The in vitro and in vivo systems from traditional approaches are now being supplemented with quantum mechanics (QM) and rational drug design (RDD), which have markedly advanced toxicity prediction. Identifying reactive metabolites and toxicity pathways is aided by QM’s atomic-level analysis of molecular interactions. To maximize effectiveness and reduce side effects, RDD uses a drug design that combines structures and functions. This article reviews the QM and RD approach to predictive toxicology with a focus on the need for stronger integration of both technologies to increase the safety of neurological drugs. Improvements in drug prediction in toxicology may be provided by the further development of AI modeling and quantum mechanical simulations in the future.

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