QSAR modeling, molecular docking and ADMET/pharmacokinetic studies: a chemometrics approach to search for novel inhibitors of norepinephrine transporter as potent antipsychotic drugs
Sabitu Babatunde Olasupo, Adamu Uzairu, Gideon Adamu Shallangwa, Sani Uba · Journal of the Iranian Chemical Society · 2020
Abstract Chemometrics study that relates biological activity to physicochemical descriptors of a molecule and the prediction of absorption, distribution, metabolism, excretion and toxicity (ADMET) properties in advance are important steps in drugs discovery. In this study, a chemometrics approach was employed on some molecules (inhibitors) of norepinephrine transporter to assess their inhibitory potencies, interactions with the receptor and predict their ADMET/pharmacokinetic properties for identification of novel antipsychotic drugs. The molecules were optimized by using density functional theory at the basis set of B 3 LYP/6-31G*. The genetic function algorithm technique was used to generate a statistically significant model with a good correlation coefficient R 2 Train = 0.952 Cross-validated coefficient Q 2 cv = 0.870, and adjusted squared correlation coefficient R 2 adj = 0.898. The molecular docking simulation using a neurotransmitter transporter receptor (PDB Code 2A65) revealed that three inhibitors (molecule No 38, 44 and 12) exhibited the highest binding affinity of − 10.3, − 9.9 and − 9.3 kcal/mol, respectively, were observed to inhibit the target by forming strong hydrogen bonds with hydrophobic interactions. The physicochemical and ADMET/pharmacokinetic properties result showed that these three molecules are orally bioavailable, high gastrointestinal absorption, good permeability and non-inhibitors of CYP3A4 and CYP2D6 except for molecule No 38. Also, Molecules No 38 and 44 proved to be non-substrate of P-glycoprotein and nontoxicity to a human ether-a-go-go-related gene with predicted hERG toxicity endpoints (pIC 50 < 6) and low ADMET_Risk (< 7.0). The results of this study would provide physicochemical and pharmacokinetics properties needed to identify potent antipsychotic drugs and other relevant information in drug discovery.