Drug Repositioning and Response Detection Based on Randomized Control Trials for Cancer Treatment - A Review

Abstract

Drug repositioning is vital in cancer treatment, offering a swift alternative to identify existing drugs repurposable for cancer treatment, bypassing the lengthy and costly traditional drug development process. This approach not only saves resources for the pharmaceutical sector and healthcare systems but also accelerates the discovery of new drugs. Overcoming challenges like data integration and patient classification is crucial in drug repositioning, where methodological advancements utilizing randomized control trials (RCTs) become essential. RCTs provide a systematic way to assess medication efficacy in diverse cancer subpopulations, enhancing the credibility of drug repositioning outcomes. The current study integrates RCTs with advanced data analytics and machine learning to establish a Bayesian Network response detection based on randomized control (BNRD-RC). This approach allows researchers to identify promising drug candidates, predict patient responses, and optimize treatment plans by analyzing diverse datasets, including genomes, proteomics, and clinical records. Beyond personalized treatment, drug repositioning explores medication synergy and combination therapy for rare cancer types. Simulation analysis significantly aids in validating the efficacy and safety of repositioned drugs. Through simulations of clinical scenarios and treatment outcomes, researchers can assess the impact of drug repositioning on patient survival, quality of life, and healthcare costs.