Stacked Meta-Model Drug Response Prediction System

Adverse drug reactions are one of the biggest problems in cancer therapy owing to the differences in tumor biology from patient to patient and the particular response of drugs to individual patients. Even though these variations further limit the effectiveness of treatment, they complicate the patient safety and quality of life dangers involved. Accurate drug response prediction would facilitate personalized identification of effective regimens for patients while minimizing the chances of resulting adverse drug reactions (ADRs). This paper presents a new stacked meta-model framework for drug response prediction based on integrating multiple structural, molecular drug, and phenotypic features. Then by joining several different base learners via meta-level integration, this model improves its performance on the prediction accuracy and robustness. Advanced feature selection makes the model interpretable. Evaluation of the constructed dataset demonstrates that the stacked meta-model outperforms traditional single-model performance. The generalized model very well predicts drug response on unseen drugs and patient profiles. In addition, the model helps give insight into the mechanisms of drugs and cancer biology, which gives better insights into therapeutic pathways and potential biomarkers and fills critical gaps in prediction accuracy and interpretability. It shows the first development towards precision oncology from this work. The model is also an excellent computational tool for personalized cancer treatment and has the potential to improve clinical outcomes while reducing ADR rates.