Immune Checkpoint Inhibitors in Therapeutics and their Adverse Effect Profile: A Review

Immune Checkpoint Inhibitors (ICIs) have transformed the field of oncology by improving the capacity of the immune system to combat malignancies. This review investigates the mechanisms of ICIs, their adverse effects, resistance mechanisms, and the role of Artificial Intelligence (AI) And Machine Learning (ML) in predicting treatment outcomes.

A literature search was conducted using PubMed, Google Scholar, and Web of Science to identify pertinent studies, clinical trials, and review articles. The study concentrated on seven ICIs that have been approved and are designed to target the PD-1, PD-L1, and CTLA-4 pathways. The data were derived from clinical guidelines and expert opinions.

ICIs have illustrated efficacy in a variety of malignancies, such as renal cell carcinoma, non-small cell lung cancer, and melanoma. Their utilization, whether as monotherapy or in conjunction with chemotherapy, radiotherapy, or targeted therapies, has substantially enhanced survival. Nevertheless, the management of Immune-Related Adverse Events (irAEs) that affect multiple organ systems is imperative. In certain patients, the efficacy of ICI is also restricted by resistance mechanisms. AI/ML-driven models demonstrate potential for anticipating patient responses, optimizing treatment strategies, and reducing toxicity risks.

ICIs have revolutionized cancer therapy; however, there are still obstacles in predicting responses and managing adverse effects. This review emphasizes the innovative use of AI/ML to improve the precision and safety of ICI. Nevertheless, additional research is required due to the absence of reliable predictive biomarkers and the variability of patient responses. In order to enhance treatment outcomes and reduce toxicity, future research should enhance AI-driven models and incorporate multi-omics approaches.