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• W2960156952 abstract "Background. The standard treatment for high-grade non-Hodgkin lymphoma involves the combination of chemotherapy and immunotherapy. We characterize in-silico the optimal combination protocol that maximizes the overall survival probability. We rely on a pharmacokinetics/pharmacodynamics (PK/PD) model that describes the joint evolution of tumor and effector cells, as well as the effects of both chemotherapy and immunotherapy. The toxicity is taken into account through ad-hoc constraints. We develop an optimization algorithm that belongs to the class of Monte-Carlo tree search algorithms. Our simulations rely on an in-silico population of heterogeneous patients differing with respect to their PK/PD parameters. The optimization objective consists in characterizing the combination protocol that maximizes the overall survival probability of the patient population under consideration. Results. We compare using in-silico experiments our results to standard protocols and observe a gain in overall survival probabilities that vary from 4 to 9 percentage points. The gains increase with the complexity of the potential protocol. Gains are larger in presence of a higher number of injections or of an actual combination with immunotherapy. Conclusions. In in-silico experiments, optimal protocols achieve significant gains over standard protocols when considering overall survival probabilities. Our optimization algorithm enables us to efficiently tackle this numerical problem with a large dimensionality. The in-vivo implications of our in-silico results remain to be explored." @default.
• W2960156952 created "2019-07-23" @default.
• W2960156952 creator A5053448585 @default.
• W2960156952 creator A5062105658 @default.
• W2960156952 date "2019-09-01" @default.
• W2960156952 modified "2023-09-30" @default.
• W2960156952 title "Optimizing treatment combination for lymphoma using an optimization heuristic" @default.
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• W2960156952 doi "https://doi.org/10.1016/j.mbs.2019.108227" @default.
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