Experimental optimization of a real time fed-batch fermentation process using Markov decision process

Economic improvement of continuous pharmaceutical production via the optimal control of a multifeed bioreactor

Projections on the profitability of the pharmaceutical industry predict a large amount of growth in the coming years. Stagnation over the last 20 years in product development has led to the search for new processing methods to improve profitability by reducing operating costs or improving process productivity. This work proposes a novel multifeed bioreactor system composed of independently controlled feeds for substrate(s) and media used that allows for the free manipulation of the bioreactor supply rate and substrate concentrations to maximize bioreactor productivity and substrate utilization while reducing operating costs. The optimal operation of the multiple feeds is determined a priori as the solution of a dynamic optimization problem using the kinetic models describing the time-variant bioreactor concentrations as constraints. This new bioreactor paradigm is exemplified through the intracellular production of beta-carotene using a three feed bioreactor consisting of separate glucose, ethanol and media feeds. The performance of a traditional bioreator with a single substrate feed is compared to that of a bioreactor with multiple feeds using glucose and/or ethanol as substrate options. Results show up to a 30% reduction in the productivity with the addition of multiple feeds, though all three systems show an improvement in productivity when compared to batch production. Additionally, the breakeven selling price of beta-carotene is shown to decrease by at least 30% for the multifeed bioreactor when compared to the single feed counterpart, demonstrating the ability of the multifeed reactor to reduce operating costs in bioreactor systems. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:902-912, 2017.

Comparative effectiveness research on patients with acute ischemic stroke using Markov decision processes

Background

Several methodological issues with non-randomized comparative clinical studies have been raised, one of which is whether the methods used can adequately identify uncertainties that evolve dynamically with time in real-world systems. The objective of this study is to compare the effectiveness of different combinations of Traditional Chinese Medicine (TCM) treatments and combinations of TCM and Western medicine interventions in patients with acute ischemic stroke (AIS) by using Markov decision process (MDP) theory. MDP theory appears to be a promising new method for use in comparative effectiveness research.

Methods

The electronic health records (EHR) of patients with AIS hospitalized at the 2^nd Affiliated Hospital of Guangzhou University of Chinese Medicine between May 2005 and July 2008 were collected. Each record was portioned into two "state-action-reward" stages divided by three time points: the first, third, and last day of hospital stay. We used the well-developed optimality technique in MDP theory with the finite horizon criterion to make the dynamic comparison of different treatment combinations.

Results

A total of 1504 records with a primary diagnosis of AIS were identified. Only states with more than 10 (including 10) patients' information were included, which gave 960 records to be enrolled in the MDP model. Optimal combinations were obtained for 30 types of patient condition.

Conclusion

MDP theory makes it possible to dynamically compare the effectiveness of different combinations of treatments. However, the optimal interventions obtained by the MDP theory here require further validation in clinical practice. Further exploratory studies with MDP theory in other areas in which complex interventions are common would be worthwhile.

A new bioreactor adapts to materials state and builds a growth model for vascular tissue engineering

Bioreactors are a promising enabling technology for vascular tissue engineering. Beyond their value for the scale-up and manufacturing of tissue-engineered blood vessels, bioreactors represent a potential path toward the understanding of the regeneration process of tissues in vitro, toward the development of mathematical models for growth and remodeling in tissue engineering, and toward the study of pathological conditions. To achieve these promises, bioreactors must overcome the paradigm of a black box for the growth of tissues and become a tool for the study of growth in tissue engineering. An advanced control strategy was developed to study and maximize growth in bioreactors. The aim of this paper is to validate experimentally the ability of this controller to build knowledge during the culture of a tissue-engineered blood vessel. During the experiments, the controller proposed linear regression models, therefore making hypotheses on the parameters that influence growth; then, it chose experiments to refine these models, therefore verifying these hypotheses. These results show that tissue maturation in bioreactors can become more efficient by acquiring information about the process, and by dynamically adapting culture conditions according to this information input.