Information Technology

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…

Working with Big Data in Healthcare and Bioprocessing Settings: A Brief Introduction to Key Components and Considerations

Artificial intelligence (AI) in healthcare and biopharmaceutical industries still is in early stages of development and use, yet proper data management is critical to successful implementation at any stage of the enterprise continuum. AI is applied to help researchers understand and analyze biology, diagnose diseases, design new drugs, and predict clinical potential and treatment outcomes. AI has been applied in the discovery and development of novel drugs and for repurposing existing drugs, manufacturing different types of drug products, and optimizing…

Pathogen Safety Digital Platform for Biopharmaceuticals: The Journey from Ground to Cloud

Digital transformation is at the heart of many biopharmaceutical companies’ strategies for ongoing success. However, the definition of digital transformation and what it consists of differ within the bioprocess industry (and might even vary within a single company), specifically as it applies to the overall value chain from R&D to clinical trials, manufacturing, supply chain, and eventually commercial operations. To provide a perspective on what digital transformation could look like in bioprocessing, we present a case study about an exploratory…

Realization of Quality By Design and Beyond: The Intelligent Cell Processing System

Cells are used as raw materials, intermediates, and final products in biopharmaceutical and cell therapy manufacturing. Because living cells are always in a dynamic state, their characteristics must be kept within specified ranges throughout bioprocessing to preserve their utility. Cell population expansion without changing the original cell properties is key for obtaining the required number of cells at the next step. However, limited process data are collected during the expansion phase — information that could be used to understand and…