preprints.org > medicine and pharmacology > oncology and oncogenics > doi: 10.20944/preprints202108.0327.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 12 August 2021 / Approved: 16 August 2021 / Online: 16 August 2021 (11:34:01 CEST)
Version 2 : Received: 17 August 2021 / Approved: 17 August 2021 / Online: 17 August 2021 (11:00:27 CEST)
Version 3 : Received: 12 June 2023 / Approved: 12 June 2023 / Online: 12 June 2023 (10:22:29 CEST)

Skin melanoma presents increasing prevalence and poor outcomes. Progression to aggressive stages is characterized by overexpression of the transcription factor E2F1 and activation of downstream pro-metastatic gene regulatory networks (GRNs). Appropriate therapeutic manipulation of the E2F1-governed GRNs holds potential to prevent metastasis, however these networks entail complex feedback and feedforward regulatory motifs among various regulatory layers, which challenge the characterization of drug targetablemake it difficult to identify druggable components. To this end, computational approaches such as mathematical modeling and virtual screening are important tools to unveil the dynamics of these signaling networks and comprehensively identify critical components that could be further explored as therapeutic targets. Herein, we integrated a well-established E2F1-mediated epithelial-mesenchymal transition (EMT) map with transcriptomics data from E2F1-expressing melanoma cells to reconstruct a core regulatory network underlying aggressive melanoma. Using logic-based in silico perturbation experiments of a core regulatory network, we identifiedy that simultaneous perturbation of AKT1 and MDM2 drastically reduces EMT in metastatic melanoma. Using the structures of the two protein signatures along with virtual screening of lead-like compound library available in ZINC12 database, we identified a number of lead compounds that efficiently inhibit AKT1 and MDM2 without eliciting toxicities. We propose that these compounds could be taken into account in the design of novel therapeutic strategies for the management of aggressive melanoma. were identified using virtual screening of lead-like compound library available in ZINC12 database. Subsequent high-throughput virtual screening of drug library using the structures of the two protein signatures predicted a number of lead compounds that efficiently inhibit AKT1 and MDM2 without eliciting toxicities. These can be experimentally evaluated and further considered as new anti-melanoma metastatic agents, in monotherapies or combination regimens.

Melanoma; Core regulatory network; in silico perturbationSystems pharmacology; Boolean model; Small molecule inhibitors; Virtual screening, ADME, E2F1

Medicine and Pharmacology, Oncology and Oncogenics

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