Back to publications
DeepSynBa: A deep learning method to improve drug combination predictions using full dose-response matrices and contextual features of cell lines and cancer indications Free
Paper Details
Published: 2025/04/21
Journal: Cancer Research
Volume: Volume 85
Number: Issue 8
DOI: 10.1158/1538-7445.AM2025-3646
Container Title: Supplement 1
Paper Links
HTMLMany cancer monotherapies have limited activity in clinic, making combinations a relevant treatment strategy. The number of possible combinations is vast, and the responses can be context-specific, making it challenging to predict combination effects. Existing computational models typically predict a single aggregated synergy score, i.e. Bliss or Loewe, for a given drug combination. However, these approaches exhibit high prediction uncertainty and limited actionability because they fail to differentiate between potency and efficacy by oversimplifying the drug-response surface using a single synergy score.
To address these limitations, we introduce DeepSynBa, which models the full dose-response matrix of drug pairs rather than an aggregated synergy score. DeepSynBa formulates this task as a regression problem and uses cell line-specific features and drug embeddings to predict the entire drug-response matrix within a deep learning framework. Following SynBa’s approach of modelling the dose-response surface [1], DeepSynBa includes an intermediate layer that estimates pharmacological parameters, which are then used to predict dose-response values across dosages. This design also enables post-hoc calculation of traditional synergy scores like Loewe and Bliss, maintaining compatibility with existing synergy predictors.
Authors
Marta Milo
Haoting Zhang
Halil ibrahim Kuru
A. Ercument Cicek
Oznur Tastan