Abstract 901: BATCHIE: An active learning platform for scalable combination drug screens
Authors: Tosh Christopher , Tec Mauricio , White Jessica , Quinn Jeffrey F. , Sanchez Glorymar Ibanez , Calder Paul , Kung Andrew L. , Dela Cruz Filemon S. , Tansey Wesley 
Published: March 2024

Abstract
Large-scale combination drug screens are largely 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 novel combinations. We introduce BATCHIE, an orthogonal approach that adaptively conducts experiments in batches. BATCHIE uses information theory and probabilistic modeling to design each batch to be maximally informative based on the results of previous experiments. BATCHIE is fully modular, allowing any Bayesian probabilistic model to be used and any study constraints to be incorporated while maintaining optimality guarantees.
Results: In retrospective simulations on public combination screens, BATCHIE saved 10s of thousands to 100s of thousands of experiments relative to non-adaptive baselines. We conducted a prospective study focusing on pediatric sarcomas with BATCHIE. Our study covered 16 cell lines spanning Ewing sarcoma (EWS), osteosarcoma, rhabdomyosarcoma, as well as non-sarcoma cancers and non-cancer lines. We used a drug library of 206 drugs at two doses. After 15 rounds of BATCHIE-driven data collection, we observed 54K unique combinations, covering 4% of the experimental landscape. On unobserved validation data, the BATCHIE model predictions were highly accurate (Pearson’s rho=0.91, p&lt;10-30) and detected the rare (0.004%) combinations with significant synergy (AUC of ROC=0.85, p&lt;10-5). We further investigated 10 combinations that BATCHIE predicted to have high therapeutic index (TI) for a broad selection of EWS lines, meaning a large differential effect between the predicted viabilities of the control lines and the target lines. We found that the TI scores for the top hits were significantly larger than the rest of the screen (p&lt;10−50), with the median top hit TI score lying in the 98th percentile of observed TI scores. We further validated 6 of the top hits in an ex vivo study on 2 patient-derived EWS samples, again finding significantly large TI values (p&lt;10-13), with the median ex vivo TI score lying in the 96th percentile of observed TI scores. The top hits also exhibited biologically plausible rationales including combining PARP inhibitors with topoisomerase 1 inhibitors and alkylating agents, despite our model utilizing no prior knowledge on the molecular targets of our drug library. Combining PARP inhibitors with topoisomerase 1 inhibitors comprise 3 of the 6 currently open phase II clinical combination trials for EWS.
Citation Format: Christopher Tosh, Mauricio Tec, Jessica White, Jeffrey F. Quinn, Glorymar Ibanez Sanchez, Paul Calder, Andrew L. Kung, Filemon S. Dela Cruz, Wesley Tansey. BATCHIE: An active learning platform for scalable combination drug screens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 901.