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

Abstract

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.

Economic Analysis of Batch and Continuous Biopharmaceutical Antibody Production: a Review

“…In order to model biopharmaceutical processes, GAMS, MATLAB and other modeling software can be used to model continuous manufacturing, especially to enable process optimization. Raftery et al [120] use GAMS to optimize the process operation of multi-feed bioreactor by increasing the productivity and decreasing operating costs for beta-carotene production process. Using these software platforms, users have more flexibility on building process models, but modeling is more complicated and difficult to adapt to different processes.…”

Integration of reinforcement learning and model predictive control to optimize semi‐batch bioreactor

“…18,19 To date, most of the studies apply modelbased optimal control with two-stage architecture where the openloop optimization is performed to obtain the state and input reference trajectories and a local controller tracks those references. The openloop optimization is numerically solved with the direct method, 18,20,21 indirect method, or dynamic programming based methods. 19,22 On the other hand, model predictive control (MPC) is predominantly utilized as the local controller.…”

Model predictive control and moving horizon estimation for adaptive optimal bolus feeding in high-throughput cultivation of \textit{E. coli}

“…Model predictive control (MPC) and moving horizon estimation (MHE) are popular methods in engineering (Rawlings et al, 2017) and there has been a large number of applications for fed-batch bioreactor cultivations. Given the state trajectory from the pre-defined operating strategy, MPC has been applied for the optimal tracking control of the bioprocesses described with basic Monod equations (Ramaswamy et al, 2005;Tebbani et al, 2008), and further extended to economic objectives such as to maximize the product (Ashoori et al, 2009;Raftery et al, 2017). In the presence of measure-ment and model uncertainties, optimal state or parameter estimators such as Kalman filter (Markana et al, 2018) or MHE (Abdollahi and Dubljevic, 2012;del Rio-Chanona et al, 2016) are combined with MPC in order to adapt to the data.…”