Toxicophoric and Metabolic In Silico Evaluation of Benzimidazole and Phenylbenzamide Derivatives with Potential Application as Anticancer Agents

Vinicius      Barreto da Silva; Andreia      Machado Leopoldino; Carlton      Anthony Taft; Carlos      Henrique Tomich de Paula da Silva

Abstract

Poor pharmacokinetics and toxicity are responsible for most drug candidate failures. In order to attempt to some degree of ADMET (Absorption, Distribution, Metabolism, Excrection and Toxicity) information, in silico predictions arise currently as an interesting alternative to evaluate prototypes during early stages of the drug design processes, especially for anticancer candidates that constitute a class of therapeutic agents that exhibit substantial toxicity. A benzimidazole and a phenylbenzamide derivatives, previously identified as novel anticancer lead compounds able to prevent DNA binding to hnRNP K protein, were evaluated in silico regarding their metabolic profile and toxicity potential in order to give insights to the design of drug candidates with an adequate pharmaceutical profile. Considering the structure of proposed metabolites for both molecules, the phenylbenzamide derivative seems to be a molecule with better pharmaceutic profile, since its possible metabolites present a milder degree of chemical structure toxic alerts than the benzimidazole derivative that can cause chromosome damage induced by the benzimidazole group. It would be desirable during optimization of the phenylbenzamide derivative to maintain these characteristics during generation of analogues with substituents that are not known as potent toxicophoric groups. For the benzimidazole derivative, if the toxic events are really severe as it seems, one possible strategy would be replace the benzimidazole ring system by bioisosteres with lower toxic potential, hoping to maintain or enhance biological activity.

Keywords: ADMET predictions, molecular modelling, hnRNP K ligands, cancer, In silico, benzimidazole, phenylbenzamide derivative, bioisosteres, signal transduction, metabolites