A human metabolic map of pharmacological perturbations reveals drug modes of action – Nature Biotechnology

Gregori-Puigjané, E. et al. Identifying mechanism-of-action targets for drugs and probes. Proc. Natl Acad. Sci. USA 109, 11178–11183 (2012).
Google Scholar
Emmerich, C. H. et al. Improving target assessment in biomedical research: the GOT-IT recommendations. Nat. Rev. Drug Discov. 20, 64–81 (2021).
Google Scholar
Vincent, F. et al. Phenotypic drug discovery: recent successes, lessons learned and new directions. Nat. Rev. Drug Discov. 21, 899–914 (2022).
Google Scholar
Moffat, J. G., Vincent, F., Lee, J. A., Eder, J. & Prunotto, M. Opportunities and challenges in phenotypic drug discovery: an industry perspective. Nat. Rev. Drug Discov. 16, 531–543 (2017).
Google Scholar
Corsello, S. M. et al. Discovering the anticancer potential of non-oncology drugs by systematic viability profiling. Nat. Cancer 1, 235–248 (2020).
Google Scholar
Pemovska, T. et al. Metabolic drug survey highlights cancer cell dependencies and vulnerabilities. Nat. Commun. 12, 7190 (2021).
Google Scholar
Subramanian, A. et al. A next generation Connectivity Map: L1000 platform and the first 1,000,000 profiles. Cell 171, 1437–1452 (2017).
Google Scholar
Way, G. P. et al. Morphology and gene expression profiling provide complementary information for mapping cell state. Cell Syst. 13, 911–923 (2022).
Google Scholar
Mitchell, D. C. et al. A proteome-wide atlas of drug mechanism of action. Nat. Biotechnol. 41, 845–857 (2023).
Google Scholar
Messner, C. B. et al. Ultra-fast proteomics with Scanning SWATH. Nat. Biotechnol. 39, 846–854 (2021).
Google Scholar
Anglada-Girotto, M. et al. Combining CRISPRi and metabolomics for functional annotation of compound libraries. Nat. Chem. Biol. 18, 482–491 (2022).
Zecha, J. et al. Decrypting drug actions and protein modifications by dose- and time-resolved proteomics. Science 380, 93–101 (2023).
Google Scholar
Filzen, T. M., Kutchukian, P. S., Hermes, J. D., Li, J. & Tudor, M. Representing high throughput expression profiles via perturbation barcodes reveals compound targets. PLoS Comput. Biol. 13, e1005335 (2017).
Google Scholar
Feng, Y., Mitchison, T. J., Bender, A., Young, D. W. & Tallarico, J. A. Multi-parameter phenotypic profiling: using cellular effects to characterize small-molecule compounds. Nat. Rev. Drug Discov. 8, 567–578 (2009).
Google Scholar
Kang, J. et al. Improving drug discovery with high-content phenotypic screens by systematic selection of reporter cell lines. Nat. Biotechnol. 34, 70–77 (2016).
Google Scholar
Badwan, B. A. et al. Machine learning approaches to predict drug efficacy and toxicity in oncology. Cell Rep. Methods 3, 100413 (2023).
Google Scholar
Zimmermann, M., Zimmermann-Kogadeeva, M., Wegmann, R. & Goodman, A. L. Separating host and microbiome contributions to drug pharmacokinetics and toxicity. Science 363, eaat9931 (2019).
Google Scholar
Gonçalves, E. et al. Pan-cancer proteomic map of 949 human cell lines. Cancer Cell 40, 835–849 (2022).
Google Scholar
Frejno, M. et al. Proteome activity landscapes of tumor cell lines determine drug responses. Nat. Commun. 11, 3639 (2020).
Google Scholar
Cohen, A. A. et al. Dynamic proteomics of individual cancer cells in response to a drug. Science 322, 1511–1516 (2008).
Google Scholar
van’t Veer, L. J. et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536 (2002).
Google Scholar
Behan, F. M. et al. Prioritization of cancer therapeutic targets using CRISPR–Cas9 screens. Nature 568, 511–516 (2019).
Google Scholar
Douglass, E. F. et al. A community challenge for a pancancer drug mechanism of action inference from perturbational profile data. Cell Rep. Med. 3, 100492 (2022).
Google Scholar
Nair, N. U. et al. A landscape of response to drug combinations in non-small cell lung cancer. Nat. Commun. 14, 3830 (2023).
Google Scholar
Bansal, M. et al. A community computational challenge to predict the activity of pairs of compounds. Nat. Biotechnol. 32, 1213–1222 (2014).
Google Scholar
Duran-Frigola, M. et al. Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker. Nat. Biotechnol. 38, 1087–1096 (2020)
Ortmayr, K., Dubuis, S. & Zampieri, M. Metabolic profiling of cancer cells reveals genome-wide crosstalk between transcriptional regulators and metabolism. Nat. Commun. 10, 1841 (2019).
Google Scholar
Dubuis, S., Ortmayr, K. & Zampieri, M. A framework for large-scale metabolome drug profiling links coenzyme A metabolism to the toxicity of anti-cancer drug dichloroacetate. Commun. Biol. 1, 101 (2018).
Google Scholar
Brunk, E. et al. Recon3D enables a three-dimensional view of gene variation in human metabolism. Nat. Biotechnol. 36, 272–281 (2018).
Google Scholar
Wishart, D. S. et al. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 46, D608–D617 (2018).
Google Scholar
Campos, A. I. & Zampieri, M. Metabolomics-driven exploration of the chemical drug space to predict combination antimicrobial therapies. Mol. Cell 74, 1291–1303 (2019).
Google Scholar
Blasi, F., Sommariva, D., Cosentini, R., Cavaiani, B. & Fasoli, A. Bezafibrate inhibits HMG-CoA reductase activity in incubated blood mononuclear cells from normal subjects and patients with heterozygous familial hypercholesterolaemia. Pharmacol. Res. 21, 247–254 (1989).
Google Scholar
Elis, J. & Rašková, H. New indications for 6-azauridine treatment in man. A review. Eur. J. Clin. Pharmacol. 4, 77–81 (1972).
Google Scholar
González-Aragón, D., Ariza, J. & Villalba, J. M. Dicoumarol impairs mitochondrial electron transport and pyrimidine biosynthesis in human myeloid leukemia HL-60 cells. Biochem. Pharmacol. 73, 427–439 (2007).
Google Scholar
Cao, S. et al. Tiratricol, a thyroid hormone metabolite, has potent inhibitory activity against human dihydroorotate dehydrogenase. Chem. Biol. Drug Des. 102, 1–13 (2023).
Google Scholar
Lyu, J. et al. Ultra-large library docking for discovering new chemotypes. Nature 566, 224–229 (2019).
Google Scholar
Diao, Y. et al. Discovery of diverse human dihydroorotate dehydrogenase inhibitors as immunosuppressive agents by structure-based virtual screening. J. Med. Chem. 55, 8341–8349 (2012).
Google Scholar
Chilingaryan, G. et al. Combination of consensus and ensemble docking strategies for the discovery of human dihydroorotate dehydrogenase inhibitors. Sci. Rep. 11, 11417 (2021).
Google Scholar
Wierbowski, S. D., Wingert, B. M., Zheng, J. & Camacho, C. J. Cross-docking benchmark for automated pose and ranking prediction of ligand binding. Protein Sci. 29, 298–305 (2020).
Google Scholar
Trott, O. & Olson, A. J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31, 455–461 (2010).
Google Scholar
Corso, G. et al. Deep confident steps to new pockets: strategies for docking generalization. Preprint at arXiv https://doi.org/10.48550/arXiv.2402.18396 (2024).
Mysinger, M. M., Carchia, M., Irwin, J. J. & Shoichet, B. K. Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J. Med. Chem. 55, 6582–6594 (2012).
Google Scholar
Bender, B. J. et al. A practical guide to large-scale docking. Nat. Protoc. 16, 4799–4832 (2021).
Google Scholar
Hwangbo, H., Patterson, S. C., Dai, A., Plana, D. & Palmer, A. C. Additivity predicts the efficacy of most approved combination therapies for advanced cancer. Nat. Cancer 4, 1693–1704 (2023).
Google Scholar
Hardy, R. S., Raza, K. & Cooper, M. S. Therapeutic glucocorticoids: mechanisms of actions in rheumatic diseases. Nat. Rev. Rheumatol. 16, 133–144 (2020).
Google Scholar
Caratti, B. et al. The glucocorticoid receptor associates with RAS complexes to inhibit cell proliferation and tumor growth. Sci. Signal. 15, eabm4452 (2022).
Google Scholar
Snijder, B. et al. Image-based ex-vivo drug screening for patients with aggressive haematological malignancies: interim results from a single-arm, open-label, pilot study. Lancet Haematol. 4, e595–e606 (2017).
Google Scholar
Gonçalves, E. et al. Drug mechanism-of-action discovery through the integration of pharmacological and CRISPR screens. Mol. Syst. Biol. 16, e9405 (2020).
Google Scholar
Breinig, M., Klein, F. A., Huber, W. & Boutros, M. A chemical-genetic interaction map of small molecules using high-throughput imaging in cancer cells. Mol. Syst. Biol. 11, 846 (2015).
Google Scholar
di Bernardo, D. et al. Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks. Nat. Biotechnol. 23, 377–383 (2005).
Google Scholar
Carraro, C. et al. Decoding mechanism of action and sensitivity to drug candidates from integrated transcriptome and chromatin state. eLife 11, e78012 (2022).
Google Scholar
Franken, H. et al. Thermal proteome profiling for unbiased identification of direct and indirect drug targets using multiplexed quantitative mass spectrometry. Nat. Protoc. 10, 1567–1593 (2015).
Google Scholar
Piazza, I. et al. A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes. Nat. Commun. 11, 4200 (2020).
Google Scholar
Ye, C. et al. DRUG-seq for miniaturized high-throughput transcriptome profiling in drug discovery. Nat. Commun. 9, 4307 (2018).
Google Scholar
Sinha, S., Sinha, N. & Ruppin, E. Deep characterization of cancer drugs mechanism of action by integrating large-scale genetic and drug screens. Preprint at bioRxiv https://doi.org/10.1101/2022.10.17.512424 (2022).
Stokes, J. M. et al. A deep learning approach to antibiotic discovery. Cell 180, 688–702 (2020).
Google Scholar
Cappelletti, V. et al. Dynamic 3D proteomes reveal protein functional alterations at high resolution in situ. Cell 184, 545–559 (2021).
Google Scholar
Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016).
Google Scholar
Okimoto, R. A. et al. Inactivation of Capicua drives cancer metastasis. Nat. Genet. 49, 87–96 (2017).
Google Scholar
Ortmayr, K. & Zampieri, M. Sorting-free metabolic profiling uncovers the vulnerability of fatty acid β-oxidation in in vitro quiescence models. Mol. Syst. Biol. 18, e10716 (2022).
Google Scholar
Zimmermann, M., Sauer, U. & Zamboni, N. Quantification and mass isotopomer profiling of α-keto acids in central carbon metabolism. Anal. Chem. 86, 3232–3237 (2014).
Google Scholar
Storey, J. D. A direct approach to false discovery rates. J. R. Stat. Soc. Ser. B Stat. Methodol. 64, 479–498 (2002).
Google Scholar
Sunseri, J. & Koes, D. R. Virtual screening with Gnina 1.0. Molecules 26, 7369 (2021).
Google Scholar