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Abstract Controlling chromatography systems for downstream processing of biotherapeutics is challenging because of the highly nonlinear behavior of feed components and complex interactions with binding phases. This challenge is exacerbated by the highly variable binding properties of the chromatography columns. Furthermore, the inability to collect information inside chromatography columns makes real-time control even more problematic. Typical static control policies either perform sub optimally on average owing to column variability or need to be adapted for each column requiring expensive experimentation. Exploiting the recent advances in simulation-based data generation and deep reinforcement learning, we present an adaptable control policy that is learned in a data-driven manner. Our controller learns a control policy by directly manipulating the inlet and outlet flow rates to optimize a reward function that specifies the desired outcome. Training our controller on columns with high variability enables us to create a single policy that adapts to multiple variable columns. Moreover, we show that our learned policy achieves higher productivity, albeit with a somewhat lower purity, than a human-designed benchmark policy. Our study shows that deep reinforcement learning offers a promising route to develop adaptable control policies for more efficient liquid chromatography processing.

This manuscript addresses the problem of determining input excitation for data driven model identification appropriate for cell culture bio-processes in general, and for an industrial bioreactor used for the production of monoclonal antibodies, in particular. The design space is set up to give us the operating parameters for the key objective of demonstrating the feasibility of using far more perturbations than typically done in bio process identification, although significantly less than other applications, to yield data rich enough for the purpose of data driven modelling (and subsequently, control). A proprietary mechanistic model developed by Sartorius for their Cellca cell line is first introduced to serve as a test bed, based on AMBR 250® (Sartorius registered trademark for integrated high throughput bioreactor systems). Subsequently, this test bed is used to address the question of determining the frequency of input perturbation sufficient to identify a data driven dynamic model. To this end, the test bed is used to generate data at various frequencies and a linear time invariant model identified. The predictive capability of the identified model is used to ascertain the frequency of changes in data generation such that the changes are acceptable from a biological standpoint, and yet generate sufficiently rich data. In particular, a frequency of perturbations at once every three days is found to balance these tradeoffs for the monoclonal antibody process under consideration. The results from the manuscript are meaningful both from a specific results standpoint (as illustrated by subsequent adoption by Sartorius), but also by providing a mechanism to ascertain such information for other bio-processes.

There is a growing interest in continuous processing of the biopharmaceutical industry. However, the technology transfer from traditional batch-based processes is considered a challenge as protocol and tools still remain to be established for their usage at the manufacturing scale. Here, we present a model-based approach to design optimized perfusion cultures of Chinese Hamster Ovary cells using only the knowledge captured during small-scale fed-batch experiments. The novelty of the proposed model lies in the simplicity of its structure. Thanks to the introduction of a new catch-all variable representing a bulk of by-products secreted by the cells during their cultivation, the model was able to successfully predict cellular behavior under different operating modes without changes in its formalism. To our knowledge, this is the first experimentally validated model capable, with a single set of parameters, to capture culture dynamic under different operating modes and at different scales.

Patients requiring mechanical ventilation (MV) beyond 21 days, usually referred to as prolonged MV, represent a unique group with significant medical needs and a generally poor prognosis. Research suggests that approximately 10% of all MV patients will need prolonged ventilatory care, and that number will continue to rise. Although we have extensive knowledge of MV in the acute care setting, less is known about care in the post-ICU setting. More than 50% of patients who were deemed unweanable in the ICU will be liberated from MV in the post-acute setting. Prolonged MV also presents a challenge in care for medically complex, elderly, socioeconomically disadvantaged and marginalized individuals, usually at the end of their life. Patients and their families often rely on ventilator weaning facilities and skilled nursing homes for the continuation of care, but home ventilation is becoming more common. The focus of this review is to discuss recent advances in the weaning strategies in prolonged MV, present their outcomes and provide insight into the complexity of care. [ABSTRACT FROM AUTHOR]