In silico studies of piperazine derivatives as potent anti-proliferative agents against PC-3 prostate cancer cell lines

Abstract

Quantitative Structure Activity Relationship studies were carried out on arylpiperazine derivatives to investigate their anti-proliferate activity against prostate PC-3 cancer cell lines. The built model with statistical parameters; R 2 ¼ 0.8483, R 2 adj ¼ 0.8078, Q 2 cv ¼ 0.7122 and external validation (R 2 test) 0.6682 revealed that the antiproliferate activities were strongly dependent on the descriptors: MATS7c, MATS3e, maxwHBa and WPSA-3. The Variance Inflation Factor of the descriptors were all greater than one but less than two and all descriptors were poorly correlated (r < 0.4). A graph of the experimental activities and predicted activities showed a high correlation and a William's plot showed the presence of only one outlier compound. These results are similar to those reported for stable and robust models with high predicting power. Molecular docking studies of compounds 5 (1-phenyl-4-(4-(2-(p-tolyloxy)ethyl)benzyl)piperazine) and 17 (4-(4-((4-phenylpiperazin-1-yl)methyl)phenethoxy)benzonitrile) with the androgen receptor gave binding affinities of À7.5 and À7.1 kcal/mol respectively. Compound 5 formed a more stable complex having hydrogen, electrostatic and hydrophobic bond interactions while compound 17 had hydrogen and hydrophobic bond interactions only. This study provides a roadmap to the design of more potent anti-prostate cancer compounds.

QSAR, QSTR, and molecular docking studies of the anti-proliferative activity of phenylpiperazine derivatives against DU145 prostate cancer cell lines

“…The built model explained at least 70% (0.7) of the variation in the activity of the compounds. Q cv 2 as earlier defined measures the degree to which the built model can generalize over another independent dataset of phenylpiperazine compounds [21]. The model built had at least a 66% (0.66) probability of generalizing over any dataset of phenylpiperazine compounds.…”

“…Thus, a robust model should be able to explain at least 0.7 (70%) of the variation in the activity/toxicity of the compounds. Q 2 cv measures the degree to which the model can generalize to another independent dataset of phenylpiperazine derivatives [21]. R 2 , R 2 adj.…”

“…2. The mean effect reveals the descriptors which have the highest positive (or negative) impact on the activity/toxicity [21].…”

In silico design and molecular docking study of CDK2 inhibitors with potent cytotoxic activity against HCT116 colorectal cancer cell line

“…The Y randomization test was carried out using the DTC Lab's YRandomization software version 1.2. The zone of applicability of the model was graphed out using the leverages approach as described by Ikwu et al [22].…”

“…The zone of applicability presents those molecules whose cytotoxic activity was satisfactorily predicted by the model. Compounds within the zone of applicability can be satisfactorily employed for further in silico studies [22,26].…”