Design of anticancer agents utilizing streptozocin for in silico optimization of properties and pattern recognition identification of group features.

Abstract

Streptozocin has been shown to be useful in the clinical treatment of malignant neuroendocrine tumors of the pancreas. The poor prognosis for patients having malignant tumors of pancreas suggests the investigation and development of new therapeutics. Nine analogs to streptozocin are determined by in silico physicochemical analysis and generation of structures by modeling from functional group isosteres. In these analogs is preserved the alkylating nitrosourea moiety, however, the covalently bonded substituent has significant hydrogen bonding sites and may include a ring structure. Analogs retain a broad range in lipophilicity, having a range of Log P from -2.798 (hydrophilic) to 3.001 (lipophilic). Standard deviation of molecular masses is only 12.6% of the group mean, so a small alteration in size occurs which is also reflected by only a 15.5% deviation in molecular volumes. Streptozocin and seven analogs show zero violations of the Rule of 5 which suggests favorable bioavailability. All compounds showed at least seven hydrogen bond acceptors with a strong positive correlation between hydrophilicity to the total number of hydrogen bond acceptors and donors. Analysis of similarity (ANOSIM) and discriminant analysis determined that streptozocin is highly similar to all nine analogs. However hierarchical cluster analysis and K-means cluster analysis were able to elucidate patterns of associations and differentiation among the ten compounds. This study demonstrates the efficacy of utilizing in silico optimization and pattern recognition to elucidate potential anticancer drugs.