Pitfalls of computational drug response prediction

Abstract

Drug response prediction is pivotal in personalized medicine as it allows healthcare providers to tailor treatments to individual patients for improved therapeutic outcomes. Machine learning has emerged as a promising tool for predicting drug responses; however, this burgeoning field faces three substantial challenges that remain inadequately addressed. State-of-the-art methods, including deep learning approaches, do not systematically compare themselves against simple baseline models. We found that published methods lack reproducible implementations, suffer from overfitting, and do not pass fundamental consistency and robustness tests. This leads to over-optimism about prediction results, which can often be matched by a simple mean predictor. In addition, frequently used measures to quantify and summarize drug response, such as IC50, EC50, and AUC values, do not capture the dynamic of drug response adequately, leading to poor generalization of findings across data sets. Finally, drug response prediction methods typically rely exclusively on transcriptomics measurements, neglecting proteomics and phospho-proteomics data, which offer unique insights into the response of the drug targets themselves. To address these research gaps, we propose the development of a new pipeline for systematically benchmarking drug response prediction methods against baseline and state-of-the-art predictors on curated multi-omics data in ProteomicsDB. Through these concerted efforts, we pave the way for more accurate and clinically relevant drug response predictions, ultimately enhancing patient care and treatment outcomes.