Development of prediction models for risk of anti-cancer drug

Abstract

In modern medicine, personalized cancer treatment aims to balance drug efficacy with patient safety. Accurately predicting drug responses in cancer cells while minimizing adverse side effects is critical for optimizing anticancer therapies. This dissertation addresses this challenge through two key studies. The first study focuses on developing a predictive model to estimate drug responses in cancer cells. This model leverages advanced machine-learning techniques to analyze large-scale genomic data, enabling accurate predictions of which drugs may be most effective for specific cancer types. The study emphasizes the importance of identifying existing drugs that can be repurposed for alternative cancer treatments, thereby facilitating the drug discovery process. This approach has the potential to reduce the time and cost associated with developing new drugs while improving the success rate of treatment outcomes for cancer patients. However, selecting drugs based exclusively on their effectiveness against cancer cells may overlook the potential for adverse side effects. To address this, the second study introduces a transformer-based model, Crossfeat, which predicts both the occurrence and frequency of drug side effects. Unlike existing models that rely on historical data, Crossfeat independently predicts side effect frequencies, making it more applicable to new drugs. By estimating the likelihood of side effects, this model supports the identification of drugs that are both effective against cancer and carry reduced risks of harm to patients. Together, these models form a comprehensive framework for selecting anticancer therapies that are not only effective but also minimize side effects. The results of these studies have the potential to significantly enhance personalized medicine by offering more precise and safer cancer treatments. This research represents a step forward in advancing AI-driven methods in pharmacogenomics, with broad applications in both clinical practice and pharmaceutical development.