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352 Computational methods for predicting drug combinations for targeting KRAS mutations relevant to non-small cell lung cancer

OBJECTIVES/GOALS: Our goal is to develop a cost-effective approach for precision medicine treatment by providing computational predictions for new uses of currently available FDA approved, and experimental drugs for NSCLC. METHODS/STUDY POPULATION: Cell Lines: A549 (ATCC- CCL-185) Human epithelial L...

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Autores principales: Bruggemann, Liana, Falls, Zackary, Mangione, William, Mahajan, Supriya, Samudrala, Ram
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9209286/
http://dx.doi.org/10.1017/cts.2022.200
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author Bruggemann, Liana
Falls, Zackary
Mangione, William
Mahajan, Supriya
Samudrala, Ram
author_facet Bruggemann, Liana
Falls, Zackary
Mangione, William
Mahajan, Supriya
Samudrala, Ram
author_sort Bruggemann, Liana
collection PubMed
description OBJECTIVES/GOALS: Our goal is to develop a cost-effective approach for precision medicine treatment by providing computational predictions for new uses of currently available FDA approved, and experimental drugs for NSCLC. METHODS/STUDY POPULATION: Cell Lines: A549 (ATCC- CCL-185) Human epithelial Lung Carcinoma cells, H1792 (ATCC-CRL-5895) Human Lung Carcinoma cells. In Vitro Cytotoxicity Assay: A Vybrant® MTT Cell Proliferation Assay was used. Colony Formation Assay: NCI-H1792, A549 cells were seeded at a density of 500 cells/ dish, then treated with ARS-1620, Osimertinib. The Computational Analysis of Novel Drug Opportunities (CANDO):  Herein, we employed the bioanalytic docking (BANDOCK) protocol within CANDO to calculate the compound-protein interaction scores for a library of 13,218 compounds from DrugBank against a library of 5,317 protein structures from the Protein Data Bank, resulting in a proteomic interaction signature for each compound, and identified Osimertinib as the most likely EGFR/ErbB inhibitor to synergize with ARS-1620. RESULTS/ANTICIPATED RESULTS: ARS-1620 and Osimertinib in combination displays potent anti-tumor activity as evident by a decrease in cell viability with cytotoxicity assays, as well as reduced number of colonies in the colony formation assay for both A549 and H1792 cells. By using CANDO, and cross-referencing the obtained rankings with known experimental information, we have obtained drug predictions within the context of precision medicine. Our preliminary data indicates that EGFR inhibitor Osimertinib may be most structurally similar to KRAS G12C inhibitors overall, compared to other ErbB/ EGFR inhibitors. Validations with human cancer cell lines A549 and H1792 have confirmed that Osimertinib in combination with KRAS G12C inhibitor ARS-1620 may exhibit a synergistic effect in decreasing cellular proliferation and colony formation. DISCUSSION/SIGNIFICANCE: This suggests that this innovative drug combination therapy may help improve treatment outcomes for KRAS G12C(H1792) and KRASG12S(A549) mutant cancers. Cell migration and cell invasion studies in response to treatment with Osimertinib and ARS-1620 are currently ongoing.
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spelling pubmed-92092862022-07-01 352 Computational methods for predicting drug combinations for targeting KRAS mutations relevant to non-small cell lung cancer Bruggemann, Liana Falls, Zackary Mangione, William Mahajan, Supriya Samudrala, Ram J Clin Transl Sci Valued Approaches OBJECTIVES/GOALS: Our goal is to develop a cost-effective approach for precision medicine treatment by providing computational predictions for new uses of currently available FDA approved, and experimental drugs for NSCLC. METHODS/STUDY POPULATION: Cell Lines: A549 (ATCC- CCL-185) Human epithelial Lung Carcinoma cells, H1792 (ATCC-CRL-5895) Human Lung Carcinoma cells. In Vitro Cytotoxicity Assay: A Vybrant® MTT Cell Proliferation Assay was used. Colony Formation Assay: NCI-H1792, A549 cells were seeded at a density of 500 cells/ dish, then treated with ARS-1620, Osimertinib. The Computational Analysis of Novel Drug Opportunities (CANDO):  Herein, we employed the bioanalytic docking (BANDOCK) protocol within CANDO to calculate the compound-protein interaction scores for a library of 13,218 compounds from DrugBank against a library of 5,317 protein structures from the Protein Data Bank, resulting in a proteomic interaction signature for each compound, and identified Osimertinib as the most likely EGFR/ErbB inhibitor to synergize with ARS-1620. RESULTS/ANTICIPATED RESULTS: ARS-1620 and Osimertinib in combination displays potent anti-tumor activity as evident by a decrease in cell viability with cytotoxicity assays, as well as reduced number of colonies in the colony formation assay for both A549 and H1792 cells. By using CANDO, and cross-referencing the obtained rankings with known experimental information, we have obtained drug predictions within the context of precision medicine. Our preliminary data indicates that EGFR inhibitor Osimertinib may be most structurally similar to KRAS G12C inhibitors overall, compared to other ErbB/ EGFR inhibitors. Validations with human cancer cell lines A549 and H1792 have confirmed that Osimertinib in combination with KRAS G12C inhibitor ARS-1620 may exhibit a synergistic effect in decreasing cellular proliferation and colony formation. DISCUSSION/SIGNIFICANCE: This suggests that this innovative drug combination therapy may help improve treatment outcomes for KRAS G12C(H1792) and KRASG12S(A549) mutant cancers. Cell migration and cell invasion studies in response to treatment with Osimertinib and ARS-1620 are currently ongoing. Cambridge University Press 2022-04-19 /pmc/articles/PMC9209286/ http://dx.doi.org/10.1017/cts.2022.200 Text en © The Association for Clinical and Translational Science 2022 https://creativecommons.org/licenses/by-nc-nd/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
spellingShingle Valued Approaches
Bruggemann, Liana
Falls, Zackary
Mangione, William
Mahajan, Supriya
Samudrala, Ram
352 Computational methods for predicting drug combinations for targeting KRAS mutations relevant to non-small cell lung cancer
title 352 Computational methods for predicting drug combinations for targeting KRAS mutations relevant to non-small cell lung cancer
title_full 352 Computational methods for predicting drug combinations for targeting KRAS mutations relevant to non-small cell lung cancer
title_fullStr 352 Computational methods for predicting drug combinations for targeting KRAS mutations relevant to non-small cell lung cancer
title_full_unstemmed 352 Computational methods for predicting drug combinations for targeting KRAS mutations relevant to non-small cell lung cancer
title_short 352 Computational methods for predicting drug combinations for targeting KRAS mutations relevant to non-small cell lung cancer
title_sort 352 computational methods for predicting drug combinations for targeting kras mutations relevant to non-small cell lung cancer
topic Valued Approaches
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9209286/
http://dx.doi.org/10.1017/cts.2022.200
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