Abstract 6361: Identification of mitochondrial RNA polymerase inhibitors via deep convolutional neural networks: A therapeutic strategy for cancer metabolism

Abstract Dysregulated metabolism is a hallmark of cancer and a substantial subset of many cancers rely on oxidative phosphorylation (OXPHOS) for growth and viability, including AML, breast cancer, lung cancer, glioblastoma, ovarian and pancreatic cancer. The electron transport chain (ETC) in mitochondria contains both nuclear DNA- and mitochondrial DNA-encoded subunits. Mitochondrial DNA-encoded subunits are essential for ETC function and are transcribed by the mitochondrial RNA polymerase POLRMT. We have previously validated POLRMT as an anticancer target in acute myeloid leukemia models. The challenge of inhibiting POLRMT with small molecules will arise from both a) selectivity to off targets and b) transport of the drug to the mitochondria. There exist several small nucleoside / nucleotide analogues that are known to inhibit RNA polymerase. However, this target space is rife with the opportunity for off-target activities and toxicity. Therefore, we targeted a novel pocket with the aim of finding novel chemical matter. Using AtomNet, an artificial intelligence platform that utilizes deep convolutional neural networks to predict small molecule recognition, a virtual library of 8 million compounds was screened against POLRMT. The top 72 hit compounds were tested in OCI-AML2 human acute myeloid leukemia cells for a potential decrease of mitochondrial RNA levels (MT-ND1) using reverse transcriptase polymerase chain reaction (RT-PCR). OCI-AML2 cells were treated with compounds at 0.1, 1 or 10 micromolar for 72 hours, 3 days or 6 days or with the positive control POLRMT ribonucleoside analogue inhibitor, 4'-azidocytidine. Gene expression was normalized to 18S. Twelve compounds demonstrated a time-dependent decrease in mitochondrial gene expression. In summary, these data indicate that functional inhibitors of human POLRMT can be identified rapidly via deep convolutional neural networks and machine learning techniques. Financial relationships: RL received an Artificial Intelligence Molecular Screening Award from Atomwise, and funding from the Centre for Collaborative Drug Research, Toronto, Canada. JW is an employee of Atomwise. Citation Format: Noor-Ul-Ain Khalid, Jeffrey M. Warrington, Rebecca Rachelle Laposa. Identification of mitochondrial RNA polymerase inhibitors via deep convolutional neural networks: A therapeutic strategy for cancer metabolism [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6361.

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