A highly automated, continuous method for developing active controllers of product quality attributes in early phase clinical development

The biotherapeutics industry is aggressively targeting increases in product quality. It has been recently suggested that a 10x increase in robustness of product quality will be required in the next 5-10 years to meet the changing market forces of our industry1. This step-increase in quality will likely only be achieved by actively controlling product quality attributes in bioproduction processes, using techniques like model predictive control (MPC)2. Adoption of MPC of product quality attributes in bioproduction processes has been somewhat sluggish, despite the recent introduction of enabling technologies, such as aseptic auto samplers. One barrier for adoption of MPC is the current difficulty involved in developing MPC controllers. This difficulty stems from the fact that critical to quality process technologies like MPC must be adopted early in the drug development process to achieve consistent clinical material throughout the drug development process. There remains a need for a method to quickly and cheaply develop MPC strategies during early phase development for biomanufacturing processes Please click Additional Files below to see the full abstract

Data integration methodology that couples novel bioreactor monitoring tools, automated sampling, and applied mathematics to redefine bioproduction processes

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Use of an automated, integrated laboratory environment to enable predictive modeling approaches for identifying critical process parameters and controlling key quality attributes

An essential part of ensuring a high quality medicine is being able to reliably control Critical Quality Attributes (CQA’s). In the cell culture process, bioreactor conditions, feeds, cell state are some of the many variables that affect CQA’s. Out of this very large set of possible variables, the small subset of these (i.e., critical process parameters, or CPP’s) that have a large effect on the CQA’s must be identified and understood such that those CPP’s can be controlled to ensure quality product. Here, we demonstrate the use of predictive modeling techniques to supplement experimental bioreactor studies when defining critical process parameters (CPP’s) and generating process control strategies. Using predictive models to relate culture process conditions to CQA’s has the benefit of enabling both: 1) using model predictions to supplement experimental data when determining critical process parameters (CPP’s) and the resulting control strategy, and 2) active control of CQA’s based on model forecasts to achieve finer control of CQA’s. In order to support this predictive forecasting approach for bioreactor process definition and control, Bend Research has developed a new bioreactor laboratory environment that allows us to run the right experiments, take the right data, and determine which measurements are actually important in determining CQA’s, and to generate model predictions based on those data sets. Here we demonstrate the application of this new laboratory paradigm in practice, using galactosylation, an important product quality attribute, as the “CQA” of interest. We show how through using automated, perfusion-type systems identification experiments, combined with automated data-generation and reduction tools, we can generate a prediction of the effect of galactose feeding on product qualit

The effects of three varying seat positions and three cadences on six physiological measurements associated with cycling

Twelve male, categorized United States Cycling Federation (USCF) cyclists (23.1 yrs, 70 in, 163 lbs, 60.37 Q02 ml . kg-1 . min-1, 4.37 V OL~ m in-1) performed 3 submaximal tests while riding a standard, USCF legal road bicycle mounted on a Giant C-Force indoor trainer. Seat position was altered for each submaximal test (Forward = 88 degrees, Middle = 85 degrees, Back = 82 degrees). The Ss cycled at a constant workload of 19 mph throughout all tests. This workload was attained using 3 varying cadences: Fast = 130 rpm, Medium = 90 rpm, Slow = 50 rpm. HR. RER, RPE VEO2, VE, and VO2 ml. kg-1.m in-1 & ~ VO2 L min -1 were compared with seat position coupled with cadence and cadence independent of seat position. The data were collapsed for each stage of each test. For each S there were 9 submax values for each variable analyzed by a 2-way ANOVA with repeated measures. Significant values were further analyzed using a Tukey's post hoc test. No significant differences in physiological variables were found between 3 seat positions at 3 different cadences (p > .05). Significant differences were found between varying cadences (p > .05) independent of seat position. These differences demonstrated significant increases in physiologicai variables at high cadences