Information Technology

Hardware, Software, and Wetware: 20 Years of Advancements in Biopharmaceutical Production, Part 2

The past couple of decades have witnessed significant advances in upstream bioprocess technologies and approaches. Since its establishment, BPI has been a facilitator of discussion both in print and at professional conferences, as well as in webcasts and news online. To mark the 20th anniversary of the publication, we surveyed articles published over the past two decades and found hundreds that highlight significant advances in both emerging and established themes in biopharmaceutical production: • “hardware” technology (e.g., analytical instrumentation, bioreactors,…

Leveraging Prior Knowledge to Demonstrate Analytical Competency

Analytical groups are developing more methods than ever to address mounting demand for biopharmaceuticals. Still, such teams need to work within tight timelines to help candidate therapies advance quickly through clinical trials. During a May 2022 presentation, Rajgopal Rudrarapu (senior scientist at the Almac Group) pointed out that regulatory agencies allow biomanufacturers to apply prior knowledge to facilitate analytical development and validation. He described how his company leveraged prior knowledge to develop and validate a capillary isoelectric focusing (cIEF) method…

Biomanufacturing from 2002 to 2022: How Far the Biopharmaceutical Industry Has Come

It has been two decades since the theatrical release of the first Spider Man film with Tobey Maguire, the euro became the official currency for the European Union, and Anne Montgomery and Cheryl Scott began piecing together the inaugural issue of BioProcess International. In celebration of the 20th anniversary of this staple publication in the bioprocessing arena, we compared products and manufacturing capacity in 2002 with those in 2022 and delved into what has driven the changes that have occurred.…

Elevating Your Pharmaceutical Facility to the Next Digital Plant Maturity Level

Pharma 4.0 technologies, offshoots from the Industry 4.0 model, focus on introducing new technologies for increased levels of digitalization within the pharmaceutical manufacturing industry. Many companies hesitate to embrace the Pharma 4.0 concept fully even though digitalization efforts are leading the way toward new levels of efficiency and productivity. The biopharmaceutical industry currently lags behind other industries in implementing digital technologies because of its rigorous and strict regulatory requirements. Adopting Pharma 4.0 tools and concepts benefits manufacturers by harmonizing the…

Robots in Biomanufacturing: A Road Map for Automation of Biopharmaceutical Operations

As BioPhorum authors stated in 2019, “It should come as no surprise to anyone familiar with biomanufacturing that current designs of bioprocess facilities as well as associated manufacturing spaces and support operations require excessive amounts of manual labor and manual interventions that lead to high labor costs and, consequently, total cost to supply” (1). Back then, a realization was starting to take hold in the biopharmaceutical industry that modern robotics showed great potential. However, a cohesive vision of their future…

Growing Value of Artificial Intelligence in Biopharmaceutical Operations

Some people have found significant disillusionment regarding artificial intelligence’s (AI’s) limitations in application. For example, mass-media productions (e.g., Ex Machina) encourage the goal of achieving general AI or super AI, which supplies comprehensive, self-instituted results. In truth, narrow AI — which addresses only one task and provides specific results — is growing rapidly, both in power and number of applications (1). Although many different modeling methods remain dominant, AI is providing significant and increasing value in drug discovery, process development,…

Process Intelligence: Gene Therapy Case Study Shows That the Journey to Improved Capabilities Starts with One Step

The product development team at a gene therapy contract development and manufacturing organization (CDMO) was working on a high-priority drug-substance project for a key client. The material was crucial to that client’s early stage clinical trial, with an immediate value over US$500,000 to both the client and the CDMO. Unfortunately, the bioreactor used in the upstream process — a transfection unit operation for an adenoassociated virus (AAV) vector — had developed an intermittent problem that could force it to shut…

Smart, Real-Time Quality Insights Boost Life Sciences Manufacturing

The COVID-19 pandemic has shone a light on restrictive business processes, information silos, and poor supply-chain visibility in many sectors. In biopharmaceutical manufacturing, for example, difficulties associated with product-quality management have been exposed and starkly felt. However, public healthcare measures over the past 18 months have put physical distance between team members, thereby hampering the usual form-filling, manual sign-offs and spreadsheet-based recordkeeping associated with monitoring traditional manufacturing processes. In some cases, a lack of formal face-to-face discussions in the workplace…

Establishing a Digital Platform for Data Science Applications in Biopharmaceutical Manufacturing

Biopharmaceutical manufacturing consists of multiple processes with complex unit operations. Those include mammalian cell culture in upstream operations and downstream chromatography steps for removing impurities from production streams and purifying the therapeutic biological molecule (1). Biomanufacturers need enhanced understanding to ensure the process control and manufacture of safe and efficacious drug products. Process understanding also enables opportunities for improving manufacturing efficiency. Both process understanding and optimization can be facilitated by leveraging large volumes of biotechnology data — typically generated during…

Protecting Artificial Intelligence Inventions in Drug Development

Artificial intelligence (AI) is a lot more sophisticated today than it was just a few years ago. Its impacts are felt outside of technology applications such as computing, malware, and natural language processing. For example, Google’s AI (called DeepMind) recently solved one of biology’s greatest problems: It determined a protein’s three-dimensional shape from its amino acid sequence. Now, AI is making a foray into drug development processes. Drug development is a risky, expensive, time-consuming, yet often lucrative venture. The costs…