IJMS, Vol. 26, Pages 7723: Towards Post-Genomic Oncology: Embracing Cancer Complexity via Artificial Intelligence, Multi-Targeted Therapeutics, Drug Repurposing, and Innovative Study Designs

International Journal of Molecular Sciences doi: 10.3390/ijms26167723

Authors:
Annabella Di Mauro
Massimiliano Berretta
Mariachiara Santorsola
Gerardo Ferrara
Carmine Picone
Giovanni Savarese
Alessandro Ottaiano

Recent advances in precision oncology have led to significant breakthroughs through the targeting of defined oncogenic drivers. However, the clinical efficacy of single-target therapies is increasingly constrained by the intrinsic complexity and adaptability of cancer. Solid tumors frequently arise from multifactorial oncogenic processes and adapt via diverse resistance mechanisms, ultimately limiting the durability of monotherapies. This review advocates for a paradigm shift toward multi-targeted, AI-enhanced strategies that harness high-throughput multi-omic data to inform the rational design of combination therapies. By leveraging artificial intelligence for drug discovery and repurposing, response prediction, and clinical trial optimization, the field of oncology is poised to transcend reductionist approaches and more fully address the biological intricacy of cancer.

Source link

Annabella Di Mauro www.mdpi.com