Model-Based Tools for Pharmaceutical Manufacturing Processes

Schenkendorf, René; Gerogiorgis, Dimitrios I.; Mansouri, Seyed Soheil; Gernaey, Krist V.

doi:10.3390/pr8010049

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…To obtain an optimal overall process of artemisinin production, the partial synthesis step needs to be designed with respect to all upstream and downstream units. For this task, model-based tools have been proven essential in academia and the pharmaceutical industry [15,16] to facilitate system understanding and control [17]. The description of the process chain constitutes a highly complex mathematical problem with multiple process variables, degrees of freedom and at the same time various constraints, e.g.…”

Section: Introductionmentioning

confidence: 99%

Kinetic analysis of the partial synthesis of artemisinin: Photooxygenation to the intermediate hydroperoxide

et al. 2021

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The price of the currently best available antimalarial treatment is driven in large part by the limited availability of its base drug compound artemisinin. One approach to reduce the artemisinin cost is to efficiently integrate the partial synthesis of artemisinin starting from its biological precursor dihydroartemisinic acid (DHAA) into the production process. The optimal design of such an integrated process is a complex task that is easier to solve through simulations studies and process modelling. In this article, we present a quantitative kinetic model for the photooxygenation of DHAA to an hydroperoxide, the essential initial step of the partial synthesis to artemisinin. The photooxygenation reactions were studied in a two-phase photo-flow reactor utilizing Taylor flow for enhanced mixing and fast gas-liquid mass transfer. A good agreement of the model and the experimental data was achieved for all combinations of photosensitizer concentration, photon flux, fluid velocity and both liquid and gas phase compositions. Deviations between simulated predictions and measurements for the amount of hydroperoxide formed are 7.1 % on average. Consequently, the identified and parameterized kinetic model is exploited to investigate different behaviors of the reactor under study. In a final step, the kinetic model is utilized to suggest attractive operating windows for future applications of the photooxygenation of DHAA exploiting reaction rates that are not affected by mass transfer limitations.

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Section: Introductionmentioning

confidence: 99%

Kinetic analysis of the partial synthesis of artemisinin: Photooxygenation to the intermediate hydroperoxide

et al. 2021

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“…Mathematical models are playing an important role in simulation and prediction of bioprocesses. Model-based methods are applied in the context of model-assisted Design of Experiments, the design, layout and optimization of production processes [ 19 , 20 , 28 , 31 , 32 , 36 , 38 , 44 ]. Furthermore, they are used as predictive models, enabling prediction of the process future, featuring the development of decision making, optimization and control strategies [ 9 , 11 , 29 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

Dynamic parameter estimation and prediction over consecutive scales, based on moving horizon estimation: applied to an industrial cell culture seed train

Rodríguez

Posch

Pörtner

et al. 2020

Bioprocess Biosyst Eng

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Bioprocess modeling has become a useful tool for prediction of the process future with the aim to deduce operating decisions (e.g. transfer or feeds). Due to variabilities, which often occur between and within batches, updating (re-estimation) of model parameters is required at certain time intervals (dynamic parameter estimation) to obtain reliable predictions. This can be challenging in the presence of low sampling frequencies (e.g. every 24 h), different consecutive scales and large measurement errors, as in the case of cell culture seed trains. This contribution presents an iterative learning workflow which generates and incorporates knowledge concerning cell growth during the process by using a moving horizon estimation (MHE) approach for updating of model parameters. This estimation technique is compared to a classical weighted least squares estimation (WLSE) approach in the context of model updating over three consecutive cultivation scales (40–2160 L) of an industrial cell culture seed train. Both techniques were investigated regarding robustness concerning the aforementioned challenges and the required amount of experimental data (estimation horizon). It is shown how the proposed MHE can deal with the aforementioned difficulties by the integration of prior knowledge, even if only data at two sampling points are available, outperforming the classical WLSE approach. This workflow allows to adequately integrate current process behavior into the model and can therefore be a suitable component of a digital twin.

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Digital Seed Train Twins and Statistical Methods

Rodríguez

Frahm

2020

Advances in Biochemical Engineering/Biotechnology

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Model-Based Tools for Pharmaceutical Manufacturing Processes

Abstract: Active pharmaceutical ingredients (APIs) are highly valuable, highly sensitive products resulting from production processes with strict quality control specifications and regulations that are required for the safety of patients [...]

Cited by 6 publications

References 9 publications

Kinetic analysis of the partial synthesis of artemisinin: Photooxygenation to the intermediate hydroperoxide

Kinetic analysis of the partial synthesis of artemisinin: Photooxygenation to the intermediate hydroperoxide

Dynamic parameter estimation and prediction over consecutive scales, based on moving horizon estimation: applied to an industrial cell culture seed train

Digital Seed Train Twins and Statistical Methods

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