Use of Pattern Recognition Analysis to Identify Underlying Relationships of Doxorubicin Derivatives Optimized for Breast Cancer Treatment

Introduction. Treatment of breast cancer includes surgery, drugs (hormone therapy and chemotherapy), and radiation. A discussion of eight drug constructs for the treatment of breast cancer, derived through application of in silico optimized molecular properties and substituent substitution, are analyzed using pattern recognition techniques. Methods and Materials. Determined properties of these eight compounds (inclusive of doxorubicin) showed a Log P varying from 0.567 to 4.137, rotatable bonds from 5 to 12, polar surface area from 195.1 A2 to 206.1 A2, and water solubility from 0.00873 mg/L to 390 mg/L. Analysis of similarity (ANOSIM), hierarchical cluster analysis, and neighbor-joining cluster analysis elucidated relationships among the drugs that are useful for pharmaceutical consideration. Results and Discussion. Although the new derivatives share the same parent scaffold (doxorubicin), elucidation by analysis of similarity (ANOSIM) indicates that these assorted compounds are substantially distinct. The number of oxygen and nitrogen atoms (hydrogen bond acceptors) remained constant at 12 for compounds. Although violations of the Rule of five remained constant at three for all compounds, the variation of Log P and water solubility offers potentially beneficial medicinal activity for this group of anticancer agents that may enhance the antitumor activity of these anthracycline antibiotics. Hierarchical cluster analysis results clearly differentiated the parent doxorubicin from all higher molecular weight analogs. This outcome is confirmed with the use of neighbor-joining cluster analysis. Conclusion. By utilizing in silico optimization with pattern recognition analysis, potentially advantageous analogs can be elucidated from known effective pharmaceuticals.