چکیده

In recent years, there have been remarkable successes in anti-cancer drugs such as monoclonal antibodies targeting PD1/PDL1 and CTLA4 inhibitors. Design of combination therapies is one of the major challenges in immuno-oncology and has attracted the attention of mathematical community that can leads to personalized therapeutic regimens. Statistical models represent good quantitative but not qualitative results. Since they are not mechanistic, it may be they give us contradictory biologic results. On the other hand, mathematical models can successfully capture qualitative behavior of system. Unfortunately, there are quantitative discrepancy between simulations and clinical trials data. Patient-specific parameter values of in-silico models is a source of this. Combination of mechanistic models, such as ODEs and PDEs, with statistical learning, such as several types of machine learning, can tackle this challenge. In this paper, we review some recent efforts for this end and argue about search for novel combination therapies.

کليدواژه ها

mathematical model, machine learning, systems biology, combination drug therapies, combination chemotherapy

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مهرداد انواری , 1400 , چگونگی رسیدن به رژیم های درمانی شخصی سازی شده در سرطان شناسی , نخستین همایش بین المللی و سومین همایش ملی ریاضیات زیستی