Abstract 1121: Patient-centric Discovery by Active Learning (PeDAL) platform to predict drug response in a heterogeneous patient population

Abstract

Abstract Even after thorough preclinical testing, only 5% of oncology drug candidates entering clinical trials ultimately get approved for use in patients. To find one drug that will be effective in the clinic, drug developers have to test 20 agents, 19 of which will ultimately prove ineffective. Such high attrition rates drive up the cost of treatment and delay getting effective drugs to the market. Here we describe the Patient-centric Discovery by Active Learning (PeDAL) platform and present results of a study that shows it can predict the effectiveness of a drug or a set of drugs in a patient population with 92% accuracy in only 11 weeks while only performing in vitro assays on a fraction of the drug-patient combinations. The power of PeDAL is derived from a combination of AI technology, an extensive drug response and biomarker database, and a biobank of 150,000 tumor samples from 137 tumor types. The study consists of two phases, the first demonstrates that PeDAL’s predictive model efficiently reveals drug-response patterns that provide insight into the treatment of ovarian cancers. The second phase focuses on the in vitro validation of the accuracy of the predictions. The experimental space was 175 FDA-approved drugs and 130 primary ovarian tumor samples. PeDAL’s active machine learning algorithm iteratively optimizes predictive models by learning from in vitro experiments which take into account tumor-stromal interactions and cellular heterogeneity. Importantly, the ability to model patient-drug combinations across a diverse population of primary samples adds a patient heterogeneity dimension to the generated drug response predictions. After performing 720 high throughput experiments, PeDAL was able to make drug-response predictions for an additional 4,600 drug/tumor predictions. Additional in vitro validation experiments confirm the reproducibility and accuracy of the high-confidence predictions generated by PeDAL with area under the curve (AUC) values >0.95. These results have strong implications not only for the new drug discovery process, but also for identifying opportunities for drug repurposing. We demonstrate that the PeDAL platform is a highly efficient tool to make confident predictions about drug-tumor pairings allowing the discovery of many more drug-tumor responses than are practical to test in vitro. Citation Format: Robert J. Montgomery, Aaron W. Gilkey, Joshua D. Kangas, Robert F. Murphy, Arlette H. Uihlein. Patient-centric Discovery by Active Learning (PeDAL) platform to predict drug response in a heterogeneous patient population [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1121.