Predictive modeling of drug efficacy in colorectal cancer via a computational strategy integrating structural descriptors and ranking analysis

Abstract

Colorectal cancer is challenging to treat because many anticancer drugs do not achieve optimal therapeutic effects. Moreover, these drugs can cause systemic side effects, and patients often respond differently to treatment. This study provides a computational framework that merges quantitative structure-property relationship analysis with a computational methodology combining structural descriptors and ranking analysis to systematically analyze and rank 10 United States Food and Drug Administration approved medicines for colorectal cancer. A suite of degree-based and neighborhood degree-based topological indices were generated and examined for their association with essential physicochemical properties, specially molecular weight and molecular complexity. Correlation research revealed that specific degree-based indices, and neighborhood degree-based indices, exhibited strong predictive power for these properties. By employing ratio weighting alongside with the VIekriterijumsko Kompromisno Rangiranje and Technique for Order Preference by Similarity to Ideal Solution decision techniques, the medicines were ranked based on their predicted physicochemical performance. The results from both decision methods consistently showed fluorouracil as the highest ranked therapeutic agent, followed by tipiracil hydrochloride and bevacizumab, underlining their favorable structural and pharmacological properties. This comprehensive modeling technique provides a consistent and systematic strategy to aid in early-phase drug screening and inform decision-making in colorectal cancer therapy.

Keywords

Graph theory / Drug networks / Decision-making / Optimization

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