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      Minimal information for chemosensitivity assays (MICHA): a next-generation pipeline to enable the FAIRification of drug screening experiments

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          Abstract

          Chemosensitivity assays are commonly used for preclinical drug discovery and clinical trial optimization. However, data from independent assays are often discordant, largely attributed to uncharacterized variation in the experimental materials and protocols. We report here the launching of Minimal Information for Chemosensitivity Assays (MICHA), accessed via https://micha-protocol.org. Distinguished from existing efforts that are often lacking support from data integration tools, MICHA can automatically extract publicly available information to facilitate the assay annotation including: 1) compounds, 2) samples, 3) reagents and 4) data processing methods. For example, MICHA provides an integrative web server and database to obtain compound annotation including chemical structures, targets and disease indications. In addition, the annotation of cell line samples, assay protocols and literature references can be greatly eased by retrieving manually curated catalogues. Once the annotation is complete, MICHA can export a report that conforms to the FAIR principle (Findable, Accessible, Interoperable and Reusable) of drug screening studies. To consolidate the utility of MICHA, we provide FAIRified protocols from five major cancer drug screening studies as well as six recently conducted COVID-19 studies. With the MICHA web server and database, we envisage a wider adoption of a community-driven effort to improve the open access of drug sensitivity assays.

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          Most cited references33

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          A SARS-CoV-2 Protein Interaction Map Reveals Targets for Drug-Repurposing

          SUMMARY The novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 2.3 million people, killed over 160,000, and caused worldwide social and economic disruption 1,2 . There are currently no antiviral drugs with proven clinical efficacy, nor are there vaccines for its prevention, and these efforts are hampered by limited knowledge of the molecular details of SARS-CoV-2 infection. To address this, we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins physically associated with each using affinity-purification mass spectrometry (AP-MS), identifying 332 high-confidence SARS-CoV-2-human protein-protein interactions (PPIs). Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (29 FDA-approved drugs, 12 drugs in clinical trials, and 28 preclinical compounds). Screening a subset of these in multiple viral assays identified two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the Sigma1 and Sigma2 receptors. Further studies of these host factor targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19.
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            The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity

            The systematic translation of cancer genomic data into knowledge of tumor biology and therapeutic avenues remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacologic annotation is available 1 . Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number, and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacologic profiles for 24 anticancer drugs across 479 of the lines, this collection allowed identification of genetic, lineage, and gene expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Altogether, our results suggest that large, annotated cell line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of “personalized” therapeutic regimens 2 .
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              ChEMBL: towards direct deposition of bioassay data

              Abstract ChEMBL is a large, open-access bioactivity database (https://www.ebi.ac.uk/chembl), previously described in the 2012, 2014 and 2017 Nucleic Acids Research Database Issues. In the last two years, several important improvements have been made to the database and are described here. These include more robust capture and representation of assay details; a new data deposition system, allowing updating of data sets and deposition of supplementary data; and a completely redesigned web interface, with enhanced search and filtering capabilities.
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                Author and article information

                Contributors
                Journal
                Brief Bioinform
                Brief Bioinform
                bib
                Briefings in Bioinformatics
                Oxford University Press
                1467-5463
                1477-4054
                January 2022
                01 September 2021
                01 September 2021
                : 23
                : 1
                : bbab350
                Affiliations
                Research Program in Systems Oncology , Faculty of medicine, University of Helsinki , Finland
                Research Program in Systems Oncology , Faculty of medicine, University of Helsinki , Finland
                Research Program in Systems Oncology , Faculty of medicine, University of Helsinki , Finland
                Research Program in Systems Oncology , Faculty of medicine, University of Helsinki , Finland
                Research Program in Systems Oncology , Faculty of medicine, University of Helsinki , Finland
                Istituto di Ricerche Farmacologiche Mario Negri IRCCS , Italy
                Institute of Molecular and Translational Medicine , Czech
                Institute of Molecular and Translational Medicine , Czech
                European Infrastructure for Translational Medicine , UK
                Fraunhofer Institute for Molecular Biology and Applied Ecology , Germany
                Fraunhofer Institute for Molecular Biology and Applied Ecology , Germany
                National Center for Advancing Translational Sciences , USA
                National Center for Advancing Translational Sciences , USA
                National Center for Advancing Translational Sciences , USA
                Institute for Molecular Medicine Finland , University of Helsinki , Finland
                Institute for Molecular Medicine Finland , University of Helsinki , Finland
                Biotech Research & Innovation Centre (BRIC) , University of Copenhagen , Denmark
                Institute for Molecular Medicine Finland , University of Helsinki , Finland
                Research Program in Systems Oncology , Faculty of medicine, University of Helsinki , Finland
                Author notes
                Corresponding author. Jing Tang. Tel.: +358458689708; E-mail: jing.tang@ 123456helsinki.fi

                Ziaurrehman Tanoli and Jehad Aldahdooh wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors

                Article
                bbab350
                10.1093/bib/bbab350
                8769689
                34472587
                fde35221-6f3e-47ae-8329-0cc8e5166430
                © The Author(s) 2021. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 11 June 2021
                : 3 August 2021
                : 2 August 2021
                Page count
                Pages: 7
                Funding
                Funded by: EOSC-Life;
                Award ID: 824087
                Funded by: European Research Council, DOI 10.13039/501100000781;
                Award ID: 716063
                Funded by: Academy of Finland, DOI 10.13039/501100002341;
                Award ID: 317680
                Categories
                Problem Solving Protocol
                AcademicSubjects/SCI01060

                Bioinformatics & Computational biology
                drug discovery,drug sensitivity assays,data integration tools,fair research data

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