Optimizing treatment combination for lymphoma using an optimization heuristic

• Published in: IDEAS Working Paper Series from RePEc 2019-09, Vol.315 (315), p.108227-108227, Article 108227
• Main Authors: Houy, Nicolas, Le Grand, François
• Format: Article
• Language: English
• Subjects: Algorithms; Antineoplastic Agents - administration & dosage; Antineoplastic Protocols; Biocompatibility; Business administration; Chemotherapy; Computer simulation; Economics and Finance; Effector cells; Heuristics; High-grade non-Hodgkin lymphoma; Humanities and Social Sciences; Humans; Immunotherapy; Immunotherapy - standards; Lymphoma; Lymphoma, Non-Hodgkin - therapy; Models, Theoretical; Monte Carlo simulation; Monte-Carlo tree search; Non-Hodgkin's lymphoma; Optimization; Optimization algorithms; Pharmacodynamics; Pharmacokinetics; Pharmacology; PK/PD model; Protocol combination; Search algorithms; Survival; Toxicity
• ISSN: 0025-5564; 1879-3134
• DOI: 10.1016/j.mbs.2019.108227

•We use a heuristic-based algorithm to optimize in-silico the combination of chemotherapy and immunotherapy administration in the treatment of lymphoma.•Compared to standard protocols, optimal protocols achieve a sizable gain in two-year overall survival probabilities for our in-silico trials.•In optimal in-silico protocols, the administration schedules of chemotherapy and immunotherapy significantly differ from the standard schedules. 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.