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The large heterogeneity of cancers and the large number of available drugs renders exhaustive\n                    <jats:italic>in vivo<\/jats:italic>\n                    or even\n                    <jats:italic>in vitro<\/jats:italic>\n                    investigation of possible treatments impractical. In recent years, sophisticated mechanistic, ordinary differential equation-based pathways models that can predict treatment responses at a\n                    <jats:italic>molecular<\/jats:italic>\n                    level have been developed. However, surprisingly little effort has been put into leveraging these models to find novel therapies. In this paper we use for the first time, to our knowledge, a large-scale state-of-the-art pan-cancer signaling pathway model to identify candidates for novel combination therapies to treat individual cancer cell lines from various tissues (e.g., minimizing proliferation while keeping dosage low to avoid adverse side effects) and populations of heterogeneous cancer cell lines (e.g., minimizing the maximum or average proliferation across the cell lines while keeping dosage low). We also show how our method can be used to optimize the drug combinations used in\n                    <jats:italic>sequential<\/jats:italic>\n                    treatment plans\u2014that is, optimized sequences of potentially different drug combinations\u2014providing additional benefits. In order to solve the treatment optimization problems, we combine the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm with a significantly more scalable sampling scheme for truncated Gaussian distributions, based on a Hamiltonian Monte-Carlo method. These optimization techniques are independent of the signaling pathway model, and can thus be adapted to find treatment candidates for other complex diseases than cancers as well, as long as a suitable predictive model is available.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1009689","type":"journal-article","created":{"date-parts":[[2021,12,28]],"date-time":"2021-12-28T14:45:25Z","timestamp":1640702725000},"page":"e1009689","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":14,"title":["Combination treatment optimization using a pan-cancer pathway 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