Project Objectives
- Optimise performance of a deep‑neural‑network drug synergy prediction model.
- Integrate drug-drug interaction and protein‑network information into predictions.
- Develop a globally accessible, user‑friendly web platform.
- Validate predicted drug synergies ex vivo using cancer cell lines and patient‑derived organoids.
3Rs Impact
- Reduces animals used in drug‑combination screening by replacing experimental testing with in silico prediction.
- Replaces toxicity studies in animals with computational filtering and organoid‑based testing.
- Provides clinically relevant, reproducible organoid alternatives for pre-clinical validation.
- Supports broad adoption of computational 3Rs‑aligned tools via an openly available web platform.
Background
In precision oncology, single‑drug treatments often lead to resistance and tumor relapse. Although new drugs are in development, this process involves substantial animal use, high costs, and high failure rates. Drug repurposing is an attractive alternative because it relies on compounds that are available. We also know that combining multiple drugs can increase efficacy and reduce the development of resistance. However, testing all possible combinations is impossible due to the vast number of compounds and the dependence of drug responses on each tumour’s profile. In silico prediction can help address this challenge.
Advances in high‑throughput screening and expanding datasets now allow machine‑learning and deep‑learning models to improve drug synergy predictions, but these methods still lack biological explainability. User‑friendly platforms for interpreting predictions and ex vivo validation are also missing, which further limits clinical translation.
In response, the research team will develop an explainable, user‑friendly deep‑learning approach to predict and understand novel drug synergies. Drug-drug interaction analysis will reduce toxic combinations, and validation in patient‑derived tumor organoids will support robust clinical implementation, facilitate drug repurposing, reduce toxicity, and ultimately reduce and replace animal use.
