Information Technology

eBook: Bioprocess and Analytical Laboratories — Proving the Power of Data in Drug Development

Analytics pervade the entire biopharmaceutical development process — from protein characterization through biomanufacturing process optimization to final-product formulation and clinical testing. Every technical article in BPI requires data to back up the statements made, whether the topic is upstream/production, downstream processing, product development, or otherwise focused. And never mind publishing: Even more detailed documentation is required for regulatory submissions. If a company can’t back up the choices made and results obtained in development, manufacturing, and testing of its biopharmaceutical product,…

Data Science, Modeling, and Advanced PAT Tools Enable Continuous Culture

Bioprocesses traditionally use (fed-)batch cell culture processes for production of recombinant proteins and therapeutics. In batch bioprocessing, material flow is discrete, with a hold step between two unit operations, and product is harvested only once for each unit operation. Batch processes have been studied extensively and optimized through numerous advancements in experimental design (1, 2), monitoring (3–5), measurement techniques (6–9), and control strategies (10–12). However, such processes require large facility footprints for equipment (13) as well as sterilization, load, and…

Addressing the Challenge of Complex Buffer Management: An In-Line Conditioning Collaboration

Preparation and storage of buffers is a challenge for biopharmaceutical companies developing protein-based pharmaceuticals. The need for volumes of buffer to purify increasing upstream titers have become a major bottleneck in biopharmaceutical downstream processing. Italian biopharmaceutical company Kedrion Biopharma collects and fractionates blood plasma to produce plasma-derived therapeutic products for treating and preventing serious diseases, disorders, and conditions such as hemophilia and immune-system deficiencies. To expand its offerings and include the immunoglobulin G fractionate of blood plasma (IgG, an antibody…

Accelerating Process Development Through Flexible Automated Workflows

Synthace began as a bioprocess optimization company in 2011, spun out of University College, London. The company worked on multifactorial approaches with 15–30 factors simultaneously instead of seven or eight. The work investigated genetic strain engineering factors alongside process parameters, defining deep interactions between the way strains were designed and the way they were treated in bioprocesses. Those complex experiments gave unique insight into the complexities of biological processes, but they were exceptionally taxing to plan and carryout manually. Automation…

Model Predictive Control for Bioprocess Forecasting and Optimization

Automation hierarchy in bioprocess manufacturing consists of a regulatory layer, process analytics technology (PAT), and (potentially) a top-level model-predictive or supervisory layer. The regulatory layer is responsible for keeping typical process measurements such as temperature, pressure, flows, and pH on target. In some cases, spectral instrumentation in combination with multivariate analysis (MVA) can be configured to measure parameters such as glucose concentration. A cascade control structure can be set up when the nutrient flow setpoint is adjusted to maintain the…

Data Analysis and Visualization to Improve Biopharmaceutical Operations Part 1: What Are You Trying To Measure?

This begins a five-article series of “how-to” guides for tackling the most common obstacles in assessing, measuring, analyzing, and improving the performance of global biopharmaceutical manufacturing operations. Each installment covers a component of proper collection, analysis, and use of data for the best possible performance outcomes. When taken as a whole, the series should provide imperative best practices for handling business-performance data. First, consider what you want to know about your bioprocesses. How can you more appropriately measure those data…

The Era of Digital Biomanufacturing

The digital revolution in manufacturing began with an explosion in monitoring, analytics, and new computing capabilities. Combined with such advances as artificial intelligence (AI), automation, and robotics, they are changing our concepts of manufacturing in general — from product development and factory operations to materials supply. This evolution also connects product and process designers and leaders in manufacturing engineering. Digital manufacturing (DM) isn’t a dream or a concept on some advanced developer’s design table; it’s occurring now and will change…

Harnessing the Power of Big Data to Improve Drug R&D

Like many other industries, biotechnology is being transformed by the emergence of big data — extremely large data sets that can be analyzed to reveal patterns, trends, and associations — and advanced analytics. Information from multiple sources such as electronic health records, payer claims, and mobile health platforms is growing exponentially. When used and harnessed properly, these data can boost the efficiency of drug research and development (R&D) in three critical areas: early R&D investment, drug development, and personalized medicine.…

Flexible Automation for Continuous Unit Operations

Continuous processing has the potential to provide significant cost and time savings for biopharmaceutical manufacturing, but that potential can be realized only if appropriate automation solutions are available for continuous flow between disparate upstream and downstream operations. Pall Life Sciences’ Allegro MVP system, a fully automated bioprocessing system designed for use in upstream and downstream single-use processing, enables flexible automation and thus facilitates continuous biopharmaceutical manufacturing. This article presents the results obtained using the Allegro MVP system in combination with…

The Year of Data Integrity: 2015 Brought a Worldwide Focus on Training, System Design and Control, and Data Management

Each year, regulatory agencies from around the world focus on critical aspects of the pharmaceutical quality management system, bringing awareness to the industry and continuing to effect positive change. In the past five years, risk assessments, electronic records, and outsourced activities have been in the spotlight. As 2015 closed out, it was clearly the year of data integrity. In March 2015, the UK’s Medicines and Healthcare Products Regulatory Agency (MHRA) published its GMP Data Integrity Definitions and Guidance for Industry,…