Abstract 4789: Predicting sensitivity to Lantern Pharma’s pipeline drug candidate LP-184 using the Response Algorithm for Drug Positioning and Rescue
Authors: Aditya Kulkarni, Umesh Kathad, Yuvanesh Vedaraju, Barry Henderson, Gregory Tobin, Panna Sharma, Arun Asaithambi
Journal: Cancer Research (2019)

Abstract LP-184 is a DNA Damage Repair inhibitor being developed by Lantern Pharma primarily as a non-hormone, non-chemotherapy option for the growing indication of taxane- and hormone-resistant metastatic prostate cancer. LP-184 is a next-generation analog of Irofulven with broad anti-tumor activity that counteracts multi-drug resistance pathways and is potentially synergistic with many classes of anticancer agents. LP-184 has a favorable therapeutic index and pharmacokinetic profile. Knowledge about its shared mechanism of action with Irofulven and potential biomarkers implicated in induction of bioactivation and synthetic lethal interactions is available. To advance LP-184 into clinical stages and achieve accelerated approval, Lantern Pharma is employing its proprietary Artificial Intelligence (AI)-driven technology. Lantern Pharma has developed a technology platform termed RADRTM that can be used to construct responder/ non-responder profiles based on gene expression signatures and predict true responders before conducting a clinical trial in order to achieve higher success rates. RADRTM is an Al-based machine learning approach for candidate biomarker identification and patient stratification. RADRTM is a combination of three automated modules working sequentially to generate drug- and tumor-specific gene signatures predictive of response. RADRTM emphasizes the integration of biological knowledge, data-driven feature selection, and robust Al algorithms to derive biomarkers in a hypothesis-free manner. In analytic demonstrations, RADRTM was able to achieve an average accuracy of 85% in validation tests using preclinical datasets associated with selected solid tumor indications and approved drugs. As part of RADRTM drug model building and development, we used a dataset showing preclinical efficacy of our pipeline oncology candidate LP-184. We obtained baseline cell line gene expression profiles covering more than 18,000 transcripts per cell line and corresponding LP-184 sensitivity records from the NCI60 cancer cell line panel. Using RADRTM, we derived a panel of 10 genes whose expression levels are predictive of overall response at an accuracy of 100%. Thus, RADRTM was able to identify the top 10 genes for prediction of either drug sensitivity or insensitivity, demonstrating the hypothesis-free identification of biomarkers with biological relevance and statistical rigor and having highest possible prediction accuracy. Genes from the final 10 predictive list were found to be functionally involved in LP-184-specific induction of bioactivation and are in agreement with its mechanism of action. These preliminary biomarker analyses will be further validated using LP-184 sensitivity and pre-treatment gene expression data derived from ex vivo models of fresh prostate tumor biopsy samples. Citation Format: Aditya Kulkarni, Umesh Kathad, Yuvanesh Vedaraju, Barry Henderson, Gregory Tobin, Panna Sharma, Arun Asaithambi. Predicting sensitivity to Lantern Pharma’s pipeline drug candidate LP-184 using the Response Algorithm for Drug Positioning and Rescue [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4789.