Gaussian Processes for Modeling Drug Response in Cancer Treatment

Abstract

The main objective is to get a better knowledge of how general practitioners can accurately forecast the effectiveness of cancer drugs and the results for their patients. This research intends to tackle the difficulties of personalized cancer treatment by making use of the adaptability and uncertainty quantification that Primary Care Physicians (PCPs) provide via extensive analysis. The goal is to provide the groundwork for effective, individualized treatment plans that maximize therapeutic treatments with few side effects. The objective is to create prediction models that can efficiently anticipate how a patient will react to a therapy by combining data that is unique to everyone, such as their genetic makeup and clinical characteristics. This study aims to show that GP-based methods may optimize cancer treatments and might be useful in the clinic by conducting extensive experiments and validation. Implications for better patient outcomes and treatment decision-making stem from this investigation's contribution to cancer precision medicine paradigm advancements. Data from six patients' samples for Drugs A, B, and C were analyzed in the Genomics of Drug Sensitivity in Cancer (GDSC) database. Dosage ranges include 50–85 mg, response time is 5.8–8.1 days, side effect severity is 1.8–4.8 days, and treatment lasts 8–15 weeks.