A Bayesian active learning platform for scalable combination drug screens

Christopher Tosh, Mauricio Tec, Jessica B. White, Jeffrey F. Quinn, Glorymar Ibanez Sanchez, Paul Calder, Andrew L. Kung, Filemon S. Dela Cruz and Wesley Tansey ()
Additional contact information
Christopher Tosh: Memorial Sloan Kettering Cancer Center
Mauricio Tec: Harvard T.H. Chan School of Public Health
Jessica B. White: Memorial Sloan Kettering Cancer Center
Jeffrey F. Quinn: Memorial Sloan Kettering Cancer Center
Glorymar Ibanez Sanchez: Memorial Sloan Kettering Cancer Center
Paul Calder: Memorial Sloan Kettering Cancer Center
Andrew L. Kung: Memorial Sloan Kettering Cancer Center
Filemon S. Dela Cruz: Memorial Sloan Kettering Cancer Center
Wesley Tansey: Memorial Sloan Kettering Cancer Center

Nature Communications, 2025, vol. 16, issue 1, 1-18

Abstract: Abstract Large-scale combination drug screens are generally considered intractable due to the immense number of possible combinations. Existing approaches use ad hoc fixed experimental designs then train machine learning models to impute unobserved combinations. Here we propose BATCHIE, an orthogonal approach that conducts experiments dynamically in batches. BATCHIE uses information theory and probabilistic modeling to design each batch to be maximally informative based on the results of previous experiments. On retrospective experiments from previous large-scale screens, BATCHIE designs rapidly discover highly effective and synergistic combinations. In a prospective combination screen of a library of 206 drugs on a collection of pediatric cancer cell lines, the BATCHIE model accurately predicts unseen combinations and detects synergies after exploring only 4% of the 1.4M possible experiments. Further, the model identifies a panel of top combinations for Ewing sarcomas, which follow-up validation experiments confirm to be effective, including the rational and translatable top hit of PARP plus topoisomerase I inhibition. These results demonstrate that adaptive experiments can enable large-scale unbiased combination drug screens with a relatively small number of experiments. BATCHIE is open source and publicly available ( https://github.com/tansey-lab/batchie ).

Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-55287-7 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-024-55287-7

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-55287-7

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().