Parameter Identifiability of Artemisinin Synthesis using Design of Experiments
Artemisinin-based combination therapies are recommended by the World Health Organization to treat malaria, one of the most abundant infectious diseases in the world. Recently, a novel production route, which combines the extraction and the catalyzed chemical synthesis, has been shown to be a promising sustainable processing alternative [Triemer, 2018]. To exploit its mechanism, operational settings and limits, mathematical modeling might be beneficial when thorough system insight is required. In a first step, we consider the catalyzed synthesis step from dihydroartemisinic acid to artemisinin, and we show that only a subset of the parameters of the considered model is identifiable with the available sparse data using a singular value decomposition approach. In a second step, within the framework of design of experiments (DoE), we demonstrate the effect of additional experimental data to overcome the non-identifiability problem of the model parameters.
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