Keeping your pharmaceutical manufacturing processes under control is important not only to ensure a quality product, but also for regulatory compliance. Process or raw material deviations can affect the downstream quality of a product and could mean tossing out an entire batch or end product if process corrections aren’t made soon enough — or if you can’t document that a correction was made before it affected your critical quality attributes.
Continuous manufacturing is one of the key trends within the pharmaceutical industry, both for the production of ‘classical’ drugs as well as large molecules. Companies are looking for ways to shift from traditional batch processing to a continuous method of operation. The main advantages associated with these processes are more room for modularity, automation and flexibility due to a smaller footprint, as well as more consistent quality of the drug product.
For pharmaceutical companies facing challenges such as rising costs, sterner regulations and declining profit margins, innovative new technologies like artificial intelligence (AI) and digital twins have become part of an essential strategy to future-proof their businesses. A digital twin is the next evolution of machine learning combining advanced data analytics and equipment simulation with comprehensive system models that blend historical information with real-time data to predict the future of a process. According to Gartner, the digital twin concept was one of the top 10 strategic technology trends in 2019.
Biosimilars are an exciting route to increasing access to the highly effective therapy made possible by biologics, but ensuring a biosimilar meets the critical quality attributes (CQA) of the original biologic is a major challenge. Optimizing production at full scale is impractical, which makes a quality by design (QbD) approach using a reliable scale down model of the process an attractive alternative. A process development team at Zhejiang Hisun Pharmaceuticals, Taizhou, China, therefore developed a scaled down model of the cell culture process used to produce the biosimilar adalimumab. They qualified the model using multivariate data analysis (SIMCA), and explored the design space for key process attributes (KPA) and CQAs using MODDE.
One key to reducing R&D costs in the biopharmaceutical market is streamlining and speeding up process data flow for Design of Experiments (DOE). Now, a direct integration of Genedata Bioprocess® platform and Umetrics Suite MODDE® software enables seamless data flow and facilitates the design, execution and evaluation of experiments in large-molecule process development.
Pressure to cut development costs and lower regulatory barriers while assuring product quality has stimulated the pharmaceutical industry to apply Quality by Design (QbD) to manage risk and gain process and product understanding. As a result, QbD is being widely promoted by regulatory authorities such as the Food and Drug Administration, and the International Conference on Harmonization.
Making sure your data and processes from research and development through to production are compliant is essential in today's highly regulated life science, biopharma, pharmaceutical and food industries. But it's no easy task. Following all of the required steps and ensuring the integrity of your data at every stage is easier and more successful when you use a product designed to keep your data compliant.
Using a Quality by Design (QbD) approach for DOE supports ICH Q8 compliance
In pharmaceutical development, manufacturers must be able to demonstrate product robustness and deliver the intended quality of the product within allowable ranges for the claimed shelf-life period. Both international and country specific regulatory agencies, such as the FDA, pay close attention to these claims.