Incorporating AI, in silico, and CRISPR technologies to uncover the potential of repurposed drugs in cancer therapy

Abstract

Patients with cancer have faced exhausting physical and mental obstacles as a result of traditional treatment methods including chemotherapy and radiation therapy. In cancer, drug repurposing—the use of already-approved medications for novel therapeutic indications—has become a game-changing tactic. This method greatly lowers development costs and durations by utilizing the wealth of safety and pharmacokinetic data available for licensed medications. Large-scale databases and advanced computer techniques enable it to logically find either combinations of traditional medications or selective “non-selective” target medications. Furthermore, repurposing cancer drugs can undergo a significant and profound change thanks to genome-editing technologies like CRISPR-dCas9. It is recognized that there is yet unrealized potential of these advanced methods in further applications. Understanding the pros and cons of these technologies can provide valuable insights for clinical practice and fundamental research projects. This research will explore various innovative methods, including artificial intelligence (AI) algorithms, supervised machine learning (ML), data resources for in silico, microbial clustered regularly interspaced short palindromic repeats-dCas9 (CRISPR-dCas9) based artificial transcription factors, and combination therapy. This comprehensive guide outlines various methods for repurposing drugs, addressing effects, trials, barriers, and potential solutions to aid clinicians and researchers in maximizing efficacy and efficiency.