Academic Journal
Drug response profile-based machine learning enables strategic cell line and compound selection for drug development.
| Τίτλος: | Drug response profile-based machine learning enables strategic cell line and compound selection for drug development. |
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| Συγγραφείς: | Abdel-Rehim A; Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, CB3 0AS, United Kingdom., Tate E; Arctoris Ltd, Abingdon, OX14 4SA, United Kingdom., Soldatova LN; Department of Mathematics, University College London, London, WC1H 0AY, United Kingdom., King RD; Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, CB3 0AS, United Kingdom.; Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, 412 96, Sweden.; Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, 412 96, Sweden.; The Alan Turing Institute, London, NW1 2DB, United Kingdom. |
| Πηγή: | Bioinformatics (Oxford, England) [Bioinformatics] 2026 Jun 01; Vol. 42 (6). |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Oxford University Press Country of Publication: England NLM ID: 9808944 Publication Model: Print Cited Medium: Internet ISSN: 1367-4811 (Electronic) Linking ISSN: 13674803 NLM ISO Abbreviation: Bioinformatics Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Oxford : Oxford University Press, c1998- |
| Ιατρικοί όροι (MeSH): | Antineoplastic Agents*/pharmacology , Drug Development*/methods , Drug Discovery*/methods , Boosting Machine Learning Algorithms* , Machine Learning*, Humans ; Cell Line, Tumor |
| Περίληψη: | Motivation: Early-stage drug discovery relies on testing compounds across a limited set of cell lines, making it challenging to capture biological diversity while maintaining experimental efficiency. Current predictive approaches for identifying responsive cell lines often depend on high-dimensional omics data, which can be costly and difficult to interpret. We therefore evaluated whether drug-response panel (DRP) descriptors, which capture sensitivity profiles to a reference set of compounds, can provide an efficient and informative alternative for modelling drug response in cell lines. Results: Using gradient boosting models across GDSC and CCLE datasets, DRP descriptors consistently outperformed mRNA expression features in predicting drug sensitivity (-log10(IC50)), although performance varied across compounds. DRP-guided cell line selection enabled downstream omics-based modelling that recovered known MAPK-associated sensitivity signatures and identified potential biomarkers for MEK1/2 and BTK/MNK inhibitors. Extending this framework, we demonstrated its utility in compound prioritisation by distinguishing between tumourigenic MCF7 and non-tumourigenic MCF10A cells, successfully identifying compounds with selective activity. Together, these results show that DRP-based representations, derived from compact screening panels, support efficient cell line selection, biomarker discovery, and compound prioritisation in early-stage drug development. Availability: Code and data uploaded to https://github.com/abbiAR/-Strategic-Cell-Line-and-Compound-Selection-Using-Drug-Response-Profiles. (© The Author(s) 2026. Published by Oxford University Press.) |
| Grant Information: | EP/R022925/2 UK Engineering and Physical Sciences Research Council (EPSRC); EP/W004801/1 UK Engineering and Physical Sciences Research Council (EPSRC); EP/X032418/1 UK Engineering and Physical Sciences Research Council (EPSRC) |
| Substance Nomenclature: | 0 (Antineoplastic Agents) |
| Entry Date(s): | Date Created: 20260508 Date Completed: 20260612 Latest Revision: 20260726 |
| Update Code: | 20260726 |
| PubMed Central ID: | PMC13242297 |
| DOI: | 10.1093/bioinformatics/btag293 |
| PMID: | 42103986 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1367-4811 |
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| DOI: | 10.1093/bioinformatics/btag293 |