An automated high inoculation density fed-batch bioreactor, enabled through N-1 perfusion, accommodates clonal diversity and doubles titers.

An important consideration for biopharmaceutical processes is the cost of goods (CoGs) of biotherapeutics manufacturing. CoGs can be reduced by dramatically increasing the productivity of the bioreactor process. In this study, we demonstrate that an intensified process which couples a perfused N-1 seed reactor and a fully automated high inoculation density (HID) N stage reactor substantially increases the bioreactor productivity as compared to a low inoculation density (LID) control fed-batch process. A panel of six CHOK1SV GS-KO® CHO cell lines expressing three different monoclonal antibodies was evaluated in this intensified process, achieving an average 85% titer increase and 132% space-time yield (STY) increase was demonstrated when comparing the 12-day HID process to a 15-day LID control process. These productivity increases were enabled by automated nutrient feeding in both the N-1 and N stage bioreactors using in-line process analytical technologies (PAT) and feedback control. The N-1 bioreactor utilized in-line capacitance to automatically feed the bioreactor based on a capacitance-specific perfusion rate (CapSPR). The N-stage bioreactor utilized in-line Raman spectroscopy to estimate real-time concentrations of glucose, phenylalanine, and methionine, which are held to target set points using automatic feed additions. These automated feeding methodologies were shown to be generalizable across six cell lines with diverse feed requirements. We show this new process can accommodate clonal diversity and reproducibly achieve substantial titer uplifts compared to traditional cell culture processes, thereby establishing a baseline technology platform upon which further increases bioreactor productivity and CoGs reduction can be achieved.

Healthcare experience of adults with COPD during the COVID-19 pandemic: a rapid review of international literature.

BACKGROUND: People living with chronic obstructive pulmonary disease (COPD) are a group who may be particularly vulnerable to COVID-19. This vulnerability has been associated with increased anxiety or fear about exposure to the virus, which may also impact upon experience in healthcare settings. AIM/OBJECTIVES: The aim of this narrative mixed-methods review was to systematically scope, identify and synthesise findings from peer-reviewed qualitative, quantitative and mixed-methods studies published in academic journals describing the healthcare experiences of adults living with COPD independently in the community, following the emergence of COVID-19 in December 2019-June 2022. METHODS: Databases including Ovid MEDLINE, PsychINFO, Ovid Emcare and CINAHL Plus were searched. Studies were uploaded to Covidence to support selection and appraisal of studies. Studies were appraised for quality using the Mixed Methods Appraisal Tool. A narrative synthesis of these themes was provided, and qualitative and quantitative findings are interpreted together in the discussion. FINDINGS: The quality and experience of care for patients with COPD was impacted through the COVID-19 pandemic. Innovations and adoption of technologies such as telehealth and telerehabilitation were well received and mitigated the potential implications of severe disruption to care access to some extent. Patients feared feeling forgotten and experienced isolation and anxiety; however, telerehabilitation and exercise through modalities such as Zoom classes help support social connection and physical activity. IMPLICATIONS: These innovations are likely to be useful to be offered to patients on an ongoing basis, and education and standardised protocols around their use will benefit healthcare providers and patients alike. PROSPERO REGISTRATION NUMBER: CRD42022341168.

Using Self-Management and Visual Cues to Improve Responses to Nonverbal Social Cues in Adults With Autism Spectrum Disorder.

Many individuals with Autism Spectrum Disorder (ASD) experience challenges with social communication, including recognizing and responding to non-verbal cues. The purpose of this study was to assess the efficacy of self-management combined with textual cues to teach adults with ASD to recognize and respond to nonverbal expressions of boredom and confusion during social conversation. A multiple baseline across participants design was used to assess the efficacy of this intervention for three participants. Results showed substantial gains across all participants in their recognition and responsiveness to the targeted nonverbal cues. Moreover, this skill maintained after the completion of intervention and generalized to novel conversation partners and settings with large effect sizes. The findings add to the literature base on interventions for adults with ASD, and further support the use of self-management and textual cues as effective intervention strategies for improving nonverbal communication.

Forecasting and control of lactate bifurcation in Chinese hamster ovary cell culture processes.

Biomanufacturing exhibits inherent variability that can lead to variation in performance attributes and batch failure. To help ensure process consistency and product quality the development of predictive models and integrated control strategies is a promising approach. In this study, a feedback controller was developed to limit excessive lactate production, a widespread metabolic phenomenon that is negatively associated with culture performance and product quality. The controller was developed by applying machine learning strategies to historical process development data, resulting in a forecast model that could identify whether a run would result in lactate consumption or accumulation. In addition, this exercise identified a correlation between increased amino acid consumption and low observed lactate production leading to the mechanistic hypothesis that there is a deficiency in the link between glycolysis and the tricarboxylic acid cycle. Using the correlative process parameters to build mechanistic insight and applying this to predictive models of lactate concentration, a dynamic model predictive controller (MPC) for lactate was designed. This MPC was implemented experimentally on a process known to exhibit high lactate accumulation and successfully drove the cell cultures towards a lactate consuming state. In addition, an increase in specific titer productivity was observed when compared with non-MPC controlled reactors.

A system identification approach for developing model predictive controllers of antibody quality attributes in cell culture processes.

As the biopharmaceutical industry evolves to include more diverse protein formats and processes, more robust control of Critical Quality Attributes (CQAs) is needed to maintain processing flexibility without compromising quality. Active control of CQAs has been demonstrated using model predictive control techniques, which allow development of processes which are robust against disturbances associated with raw material variability and other potentially flexible operating conditions. Wide adoption of model predictive control in biopharmaceutical cell culture processes has been hampered, however, in part due to the large amount of data and expertise required to make a predictive model of controlled CQAs, a requirement for model predictive control. Here we developed a highly automated, perfusion apparatus to systematically and efficiently generate predictive models using application of system identification approaches. We successfully created a predictive model of %galactosylation using data obtained by manipulating galactose concentration in the perfusion apparatus in serialized step change experiments. We then demonstrated the use of the model in a model predictive controller in a simulated control scenario to successfully achieve a %galactosylation set point in a simulated fed-batch culture. The automated model identification approach demonstrated here can potentially be generalized to many CQAs, and could be a more efficient, faster, and highly automated alternative to batch experiments for developing predictive models in cell culture processes, and allow the wider adoption of model predictive control in biopharmaceutical processes. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1647-1661, 2017.