Drug combinations screening using a Bayesian ranking approach based on dose-response models.
Bayesian model
dose-response model
drug screening
ranking
Journal
Biometrical journal. Biometrische Zeitschrift
ISSN: 1521-4036
Titre abrégé: Biom J
Pays: Germany
ID NLM: 7708048
Informations de publication
Date de publication:
20 Nov 2023
20 Nov 2023
Historique:
revised:
05
05
2023
received:
30
11
2022
accepted:
15
06
2023
medline:
21
11
2023
pubmed:
21
11
2023
entrez:
20
11
2023
Statut:
aheadofprint
Résumé
Drug combinations have been of increasing interest in recent years for the treatment of complex diseases such as cancer, as they could reduce the risk of drug resistance. Moreover, in oncology, combining drugs may allow tackling tumor heterogeneity. Identifying potent combinations can be an arduous task since exploring the full dose-response matrix of candidate combinations over a large number of drugs is costly and sometimes unfeasible, as the quantity of available biological material is limited and may vary across patients. Our objective was to develop a rank-based screening approach for drug combinations in the setting of limited biological resources. A hierarchical Bayesian 4-parameter log-logistic (4PLL) model was used to estimate dose-response curves of dose-candidate combinations based on a parsimonious experimental design. We computed various activity ranking metrics, such as the area under the dose-response curve and Bliss synergy score, and we used the posterior distributions of ranks and the surface under the cumulative ranking curve to obtain a comprehensive final ranking of combinations. Based on simulations, our proposed method achieved good operating characteristics to identifying the most promising treatments in various scenarios with limited sample sizes and interpatient variability. We illustrate the proposed approach on real data from a combination screening experiment in acute myeloid leukemia.
Identifiants
pubmed: 37984849
doi: 10.1002/bimj.202200332
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2200332Informations de copyright
© 2023 The Authors. Biometrical Journal published by Wiley-VCH GmbH.
Références
Betancourt, M., & Girolami, M. (2015). Hamiltonian Monte Carlo for hierarchical models. Current Trends in Bayesian Methodology with Applications, 79(30), 2-4.
Bliss, C. I. (1939). The toxicity of poisons applied jointly. Annals of Applied Biology, 26(3), 585-615.
Brain, P., & Cousens, R. (1989). An equation to describe dose responses where there is stimulation of growth at low doses. Weed Research, 29(2), 93-96.
Brooks, E. A., Galarza, S., Gencoglu, M. F., Cornelison, R. C., Munson, J. M., & Peyton, S. R. (2019). Applicability of drug response metrics for cancer studies using biomaterials. Philosophical Transactions of the Royal Society B, 374(1779), 20180226.
Chiocchia, V., White, I. R., & Salanti, G. (2022). The complexity underlying treatment rankings: How to use them and what to look at. BMJ Evidence-Based Medicine, 28(3), 180-182.
Cokol, M., Kuru, N., Bicak, E., Larkins-Ford, J., & Aldridge, B. B. (2017). Efficient measurement and factorization of high-order drug interactions in mycobacterium tuberculosis. Science Advances, 3(10), e1701881.
Dal Bello, R., Pasanisi, J., Joudinaud, R., Duchmann, M., Pardieu, B., Ayaka, P., Di Feo, G., Sodaro, G., Chauvel, C., Kim, R., Vasseur, L., Chat, L., Ling, F., Pacchiardi, K., Vaganay, C., Berrou, J., Benaksas, C., Boissel, N., Braun, T., … Itzykson, R. (2022). A multiparametric niche-like drug screening platform in acute myeloid leukemia. Blood Cancer Journal, 12(6), 1-12.
Foucquier, J., & Guedj, M. (2015). Analysis of drug combinations: Current methodological landscape. Pharmacology Research & Perspectives, 3(3), e00149.
Fourie Zirkelbach, J., Shah, M., Vallejo, J., Cheng, J., Ayyoub, A., Liu, J., Hudson, R., Sridhara, R., Ison, G., Amiri-Kordestani, L., Tang, S., Gwise, T., Rahman, A., Pazdur, R., & Theoret, M. R. (2022). Improving dose-optimization processes used in oncology drug development to minimize toxicity and maximize benefit to patients. Journal of Clinical Oncology, 40(30), 3489-3500.
Gorshkov, K., Chen, C. Z., Marshall, R. E., Mihatov, N., Choi, Y., Nguyen, D.-T., Southall, N., Chen, K. G., Park, J. K., & Zheng, W. (2019). Advancing precision medicine with personalized drug screening. Drug Discovery Today, 24(1), 272-278.
Hafner, M., Niepel, M., Chung, M., & Sorger, P. K. (2016). Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs. Nature Methods, 13(6), 521-527.
He, L., Kulesskiy, E., Saarela, J., Turunen, L., Wennerberg, K., Aittokallio, T., & Tang, J. (2018). Methods for high-throughput drug combination screening and synergy scoring. Cancer Systems Biology, 1711, 351-398.
Huang, S., & Pang, L. (2012). Comparing statistical methods for quantifying drug sensitivity based on in vitro dose-response assays. Assay and Drug Development Technologies, 10(1), 88-96.
Ianevski, A., Giri, A. K., Gautam, P., Kononov, A., Potdar, S., Saarela, J., Wennerberg, K., & Aittokallio, T. (2019). Prediction of drug combination effects with a minimal set of experiments. Nature Machine Intelligence, 1(12), 568-577.
Johnstone, R. H., Bardenet, R., Gavaghan, D. J., & Mirams, G. R. (2017). Hierarchical Bayesian inference for ion channel screening dose-response data. Wellcome Open Research, 1, 6, https://doi.org/10.12688/wellcomeopenres.9945.2
Khwaja, A., Bjorkholm, M., Gale, R. E., Levine, R. L., Jordan, C. T., Ehninger, G., Bloomfield, C. D., Estey, E., Burnett, A., Cornelissen, J. J., Scheinberg, D. A., Bouscary, D., & Linch, D. C. (2016). Acute myeloid leukaemia. Nature Reviews Disease Primers, 2(1), 1-22.
Kitano, H. (2002). Systems biology: A brief overview. Science, 295(5560), 1662-1664.
Kwak, E. L., Clark, J. W., & Chabner, B. (2007). Targeted agents: The rules of combination. Clinical Cancer Research, 13(18), 5232-5237.
Lee, S. M., & Cheung, Y. K. (2009). Model calibration in the continual reassessment method. Clinical Trials, 6(3), 227-238.
Loewe, S. t., & Muischnek, H. (1926). Über kombinationswirkungen. Naunyn-Schmiedebergs Archiv für experimentelle Pathologie und Pharmakologie, 114(5), 313-326.
Malani, D., Kumar, A., Brück, O., Kontro, M., Yadav, B., Hellesøy, M., Kuusanmäki, H., Dufva, O., Kankainen, M., Eldfors, S., Potdar, S., Saarela, J., Turunen, L., Parsons, A., Västrik, I., Kivinen, K., Saarela, J., Räty, R., Lehto, M., … Porkka, K. (2022). Implementing a functional precision medicine tumor board for acute myeloid leukemia (AML) functional molecular precision medicine. Cancer Discovery, 12(2), 388-401.
Malyutina, A., Majumder, M. M., Wang, W., Pessia, A., Heckman, C. A., & Tang, J. (2019). Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer. PLoS Computational Biology, 15(5), e1006752.
Meyer, C. T., Wooten, D. J., Lopez, C. F., & Quaranta, V. (2020). Charting the fragmented landscape of drug synergy. Trends in Pharmacological Sciences, 41(4), 266-280.
More, S., Benford, D., Hougaard Bennekou, S., Bampidis, V., Bragard, C., Halldorsson, T., Hernandez-Jerez, A., Koutsoumanis, K., Lambré, C., Machera, K., Mullins, E., Naegeli, H., Saxmose Nielsen, S., Schlatter, J., Silano, V., Schrenk, D., Turck, D., & Younes, M. (2021). EFSA committee. Opinion on the impact of non-monotonic dose responses on EFSAs human health risk assessments. EFSA Journal, 19(10), e06877.
Palmer, A. C., Chidley, C., & Sorger, P. K. (2019). A curative combination cancer therapy achieves high fractional cell killing through low cross-resistance and drug additivity. Elife, 8, e50036.
Ritz, C. (2010). Toward a unified approach to dose-response modeling in ecotoxicology. Environmental Toxicology and Chemistry, 29(1), 220-229.
Salanti, G., Ades, A., & Ioannidis, J. P. (2011). Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: An overview and tutorial. Journal of Clinical Epidemiology, 64(2), 163-171.
Short, N. J., Rytting, M. E., & Cortes, J. E. (2018). Acute myeloid leukaemia. The Lancet, 392(10147), 593-606.
Szymański, P., Markowicz, M., & Mikiciuk-Olasik, E. (2011). Adaptation of high-throughput screening in drug discovery-Toxicological screening tests. International Journal of Molecular Sciences, 13(1), 427-452.
Tansey, W., Li, K., Zhang, H., Linderman, S. W., Rabadan, R., Blei, D. M., & Wiggins, C. H. (2022). Dose-response modeling in high-throughput cancer drug screenings: An end-to-end approach. Biostatistics, 23(2), 643-665.
Wooten, D. J., Meyer, C. T., Lubbock, A. L., Quaranta, V., & Lopez, C. F. (2021). MuSyC is a consensus framework that unifies multi-drug synergy metrics for combinatorial drug discovery. Nature Communications, 12(1), 1-16.
Yadav, B., Pemovska, T., Szwajda, A., Kulesskiy, E., Kontro, M., Karjalainen, R., Majumder, M. M., Malani, D., Murumägi, A., Knowles, J., Porkka, K., Heckman, C., Kallioniemi, O., Wennerberg, K., & Aittokallio, T. (2014). Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies. Scientific Reports, 4(1), 1-10.
Yilmaz, E., Aslam, J. A., & Robertson, S. (2008). A new rank correlation coefficient for information retrieval. In Proceedings of the 31st annual international ACM SIGIR conference on research and development in information retrieval, Association for Computing Machinery (pp. 587-594).
Yoshimasu, T., Ohashi, T., Oura, S., Kokawa, Y., Kawago, M., Hirai, Y., Miyasaka, M., Nishiguchi, H., Kawashima, S., Yata, Y., Honda, M., Fujimoto, T., & Okamura, Y. (2015). A theoretical model for the hormetic dose-response curve for anticancer agents. Anticancer Research, 35(11), 5851-5855.