Abstract
Schizophrenia is a psychiatric disorder that affects 20 million people worldwide and the mortality rate is two or three times higher than the average overall population mainly due to high frequency of suicide and other associated comorbidities. The therapeutic approaches used for its treatment, besides do not combat this illness, trigger several side effects, being imperative the development of new medical countermeasures. In the search for new agents, we constructed a statistical model (model A) with a series of aripiprazole-derived using the partial least squares technique, and posteriorly we applied several validation tests to guarantee consistency of model A and its forecasting ability. In addition, molecular docking simulations were employed to extract important information on structural elements involved in the molecular recognition process from receptor-ligand complex. We designed new compounds whose biological activity values were predicted by our model. Among new designed molecules, we highlight compounds 2, 5, 9, and 11 with predicted pKi values over 8.5. We underline that the presence of ester moieties attached to the aromatic ring from region A is well tolerated in this position and contributes to increase the pKi values; otherwise it is essential that there are no bulky groups in region C. Finally, ADME/Tox properties evaluated via in silico approach for the proposed compounds shed light on their drug-likeness characteristics, indicating that they could be a reliable starting point as potent candidates to further experimental exploration (chemical synthesis, in vitro and in vivo analyses).
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Funding
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) (Finance Code 001), CNPq and FAPESP (2016/24524–7, 2017/10118–0). Our research was carried out using the computational resources of the Center for Mathematical Sciences Applied to Industry (CeMEAI) funded by FAPESP (grant 2013/07375–0).
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Aldineia P da Silva and Laise P. A. Chiari performed the analytic calculation and discussion, Aldineia P da Silva and Laise P. A. Chiari, Amanda R. Guimarães, Kathia M. Honorio and Albérico B. F. da Silva contributed to the final version of the manuscript. Alberico B. F. da Silva supervised the project.
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da Silva, A.P., Chiari, L.P.A., Guimarães, A.R. et al. New D2R partial agonist candidates: an in silico approach from statistical models, molecular docking, and ADME/Tox properties. Struct Chem 32, 2019–2033 (2021). https://doi.org/10.1007/s11224-021-01742-w
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DOI: https://doi.org/10.1007/s11224-021-01742-w