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      CurveCurator: Decrypting drugs by identifying relevant concentration-response curves in large-scale screens

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            Abstract

            Understanding how (targeted) drugs act on cellular systems is crucial for drug discovery and repurposing as wellas personalized drug recommendations in precision medicine. To address this, a variety of high-throughput drugassays are regularly employed, most notably phenotypic cell viability screens, activity-, affinity- or thermal stability-based drug-target binding assays, and proteome-wide drug-response profiling of post-translational modifications(PTMs). In such screens, it is often unclear whether or not a response significantly differs from a curve withoutregulation, making large-scale interpretation cumbersome. For example, treating potency and effect size estimatesfrom random and true curves with the same level of confidence leads to incorrect hypotheses and issues in trainingmachine learning models.

            Here, we present CurveCurator, an open-source software with interactive dashboard that provides reliableconcentration-response characteristics. It does this by computing p-values and false discovery rates based on arecalibrated F-statistic and a novel thresholding procedure. To demonstrate its broad utility, CurveCurator wasapplied to three concentration-response data sets (>450k curves) from different assays. Drug-phenotype, drug-target binding, and drug-PTM response were linked by taking advantage of CurveCurator’s functionalities, such as,regulation classification, robust potency estimation, and interactive dashboards. This unified approach enableselucidation of the full cellular MoAs of drugs by leveraging the potency dimension of regulated curves. As anexample, for Afatinib in the cell line A431, the drug effect was traced from its inhibition of the carcinoma’s maindriver (drug-target binding) to the shut-down of key downstream survival signals (drug-PTM response) and theeventual reduction in cell growth (drug-phenotype).

            CurveCurator is the first tool that provides reliable p-values for assessing the statistical likelihood of regulation inconcentration–response experiments. The objective categorization of concentration–response curves combinedwith an interactive dashboard accelerates data analysis and fills the need for a helpful tool in times of ever-increasing experimental throughput.

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            Author and article information

            Conference
            RExPO24 Conference
            REPO4EU
            3 May 2024
            Affiliations
            [1 ] Proteomics and Bioanalytics, School of Life Sciences, Technical University of Munich, 85354 Freising, Germany;
            [2 ] German Cancer Consortium (DKTK), Partner Site Munich, 80336 Munich, Germany;
            Author notes
            Author information
            https://orcid.org/0000-0002-5401-5553
            Article
            10.58647/REXPO.24000051.v1
            b2c5f17c-04c8-4120-93ce-4c01a017dfa6

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

            RExPO24
            3
            Munich, Germany
            3-5 July 2024
            History
            : 3 May 2024
            Product

            REPO4EU

            Categories

            The datasets generated during and/or analysed during the current study are available in the repository: https://doi.org/10.5281/zenodo.8399823
            Bioinformatics & Computational biology
            concentration-response curves,drug response profiling,phenotypic screening,statistics,F-statistic,drug-target binding,post-translation modifications,interactive dashboard,machine learning,high-throughput screening

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