DeepCRE: Transforming Drug R&D via AI-Driven Cross-drug Response Evaluation
This addresses the problem of high failure rates in clinical trials for drug developers and patients by providing a more accurate AI model for late-stage drug response prediction, though it builds incrementally on existing in-silico methods.
The paper tackles the challenge of predicting cross-drug response evaluation (CRE) in late-stage drug R&D, introducing DeepCRE, which improves patient-level CRE by 17.7% and indication-level CRE by 5-fold, and identifies six drug candidates more effective than approved drugs in colorectal cancer organoids.
The fields of therapeutic application and drug research and development (R&D) both face substantial challenges, i.e., the therapeutic domain calls for more treatment alternatives, while numerous promising pre-clinical drugs have failed in clinical trials. One of the reasons is the inadequacy of Cross-drug Response Evaluation (CRE) during the late stages of drug R&D. Although in-silico CRE models bring a promising solution, existing methodologies are restricted to early stages of drug R&D, such as target and cell-line levels, offering limited improvement to clinical success rates. Herein, we introduce DeepCRE, a pioneering AI model designed to predict CRE effectively in the late stages of drug R&D. DeepCRE outperforms the existing best models by achieving an average performance improvement of 17.7% in patient-level CRE, and a 5-fold increase in indication-level CRE, facilitating more accurate personalized treatment predictions and better pharmaceutical value assessment for indications, respectively. Furthermore, DeepCRE has identified a set of six drug candidates that show significantly greater effectiveness than a comparator set of two approved drugs in 5/8 colorectal cancer organoids. This demonstrates the capability of DeepCRE to systematically uncover a spectrum of drug candidates with enhanced therapeutic effects, highlighting its potential to transform drug R&D.