Pharmaceutical development of novel drugs is a complex and tedious process. The information shared between different multidisciplinary teams is critical to reducing the cost and ultimately accelerating the path of bringing a drug to market.
To achieve transparency and actionability of data, pharmaceutical companies are evolving their manufacturing processes by digitalizing the data from all of their equipment. Internet of Things (IoT) devices and cloud technologies allow process experts and data scientists to work together to increase process understanding and design analytical tools to maintain the processes under a defined state of control. When this is done in a continuous manner (and not per batch) it is referred to as Continuous Process Verification (CPV). This is a critical approach for pharmaceutical companies today to meet myriad demands, especially in this time of a global pandemic.
Importantly, the diseases of the 21st century have changed due to the fact that the human population has a higher expectancy of life and at the same time, new pandemic viruses are collapsing the health system. In this sense, pharmaceutical companies have developed different strategies to produce a wide variety of advanced therapy medicinal products (ATMP) and biological products, such as monoclonal antibodies, recombinant proteins, gene therapy viral vectors, CAR-T cells and oncolytic viruses, in order to deliver new medical needs in our society.
There is always a constant social pressure to deliver a drug product to the market to treat diseases that have no current cure or treatment. However, during the last months, due to COVID-19, we have observed how pharmaceutical companies have progressed very quickly through all the clinical development phases and different companies have already reached the clinical Phase III. Importantly, the regulatory authorities have marked the rules to prevent non-efficient vaccines and guarantee the safety required to deliver an effective product. In such a scenario, the process development performed by pharma teams should be reduced to deliver a viable manufacturing process to reach the GMP clinical manufacturing in the minimum period of time.
During the COVID-19 era, it is likely that process development teams generated limited data and low amounts of batches due to the time constraints imposed to deliver a product as soon as possible. Nevertheless, they have succeeded to continue the process development to the next stage. The FDA process validation guideline in combination with the ICH Q8 (R2) state advice about how to define the process design in an initial stage (stage 1) where the process knowledge in combination with the strategy of control are the main drivers.
In this webinar, panelists will present strategies to deal with limited data or low amounts of batches to understand which factors are relevant, as well as the tools to validate the choices. Importantly, we present strategies to exploit the limited process data and increase the data sets to reach better conclusions and increase the process understanding. In summary, we present how advanced analytics and artificial intelligence tools can help SMEs to discover what are the hidden relevant factors that may affect a process when pharma companies require to up-scale the process in the clinical manufacturing under time constraints.
Aizon provides solutions to speed up pharmaceutical manufacturing process development from the start in a GxP compliant platform. This accelerates the path to compliant digital transformation, and enables easy access to all process data under data integrity rules and for the application of advanced analytics and AI. With this, Aizon users can confidently achieve the status of Continued Process Verification (CPV) so that their processes remain in a state of continuous monitoring and control.
Learn more by watching this webinar on-demand: CPV + AI: The Formula To Succeed In Biopharma Manufacturing.
This article was created in collaboration with the sponsoring company and the Xtalks editorial team.