Autism spectrum disorders (ASD) are a complex sequelae of neurodevelopmental disorders which manifest in the form of communication and social deficits. Currently, only two agents, namely risperidone and aripiprazole have been approved for the treatment of ASD, and there is a dearth of more drugs for the disorder. The exact pathophysiology of autism is not understood clearly, but research has implicated multiple pathways at different points in the neuronal circuitry, suggesting their role in ASD. Among these, the role played by neuroinflammatory cascades like the NF-KB and Nrf2 pathways, and the excitotoxic glutamatergic system, are said to have a bearing on the development of ASD. Similarly, the GPR40 receptor, present in both the gut and the blood brain barrier, has also been said to be involved in the disorder. Consequently, molecules which can act by interacting with one or multiple of these targets might have a potential in the therapy of the disorder, and for this reason, this study was designed to assess the binding affinity of taurine, a naturally-occurring amino acid, with these target molecules. The same was scored against these targets using docking studies, with Risperidone and Aripiprazole being used as standard comparators. Encouraging docking scores were obtained for taurine across all the selected targets, indicating promising target interaction. But the affinity for targets actually varied in the order Given the potential implication of these targets in the pathogenesis of ASD, the drug might show promising results in the therapy of the disorder if subjected to further evaluations.
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http://dx.doi.org/10.1016/j.ibneur.2023.08.2191 | DOI Listing |
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State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China. Electronic address:
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Interactive methods support decision-makers in finding the most preferred solution for multiobjective optimization problems, where multiple conflicting objective functions must be optimized simultaneously. These methods let a decision-maker provide preference information iteratively during the solution process to find solutions of interest, allowing them to learn about the trade-offs in the problem and the feasibility of the preferences. Several interactive evolutionary multiobjective optimization methods have been proposed in the literature.
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