Modular manufacturing plant designs trade low capital costs for increased operational expense compared to traditional, stainless designs. These tradeoffs can be optimized given the market needs and technical requirements for new therapies.
Multimodal therapeutic pathways — those that combine small molecule, biologics, gene therapies, etc — need new manufacturing models to bring them to commercial reality.
Using data to give greater insight into therapeutic pathways and process optimization requires a strong focus on data curation and more advanced data management tools.
The 6th Accelerating Biopharmaceutical Development Conference (AccBio2019), hosted by AIChE from February 17-19, 2019 focused on the confluence of new therapy pathways, biologics process intensification, and using process development data to speed innovative therapies to market.
Companies such as Just Therapeutics and GE Healthcare have commercialized modular manufacturing facilities that offer lower capital expenditure (~$100MM) and speed to market (~18 Months). This is a great option for a new drug candidate that has been selected for FDA’s Fast-Track approval process which requires clinical manufacturing scale-up during Phase 1 and 2 reviews. Modular plants make it easy to meet these short timelines, though with higher operational expenses. Process development teams with precise insight into process specifications for these new candidates can be instrumental in engineering the most optimal process implementation and mitigate many of the operational costs.
Multimodal therapies need to leverage process development teams for scale-up
Multi-modal therapies such as gene therapies, antibody drug conjugates, or Synthetic DNA-delivered antibodies are driving a need for more flexible and cost-effective manufacturing methods. An AAV-delivered gene therapy, for example, will require very different manufacturing methods than a standard antibody biologic. It’s engineered genetic payload needs to be produced and then embedded into its AAV (virus) carrier. This process requires a different - though not unfamiliar - set of unit operations than traditional cell culture and purification process for monoclonal antibodies.
See article: Manufacturing of AAV Vectors for Gene Therapy - Genetic Engineering & Biology News, July 1, 2017
These novel processes are pressing process development teams to more rigorously determine the processes parameters needed for these therapies to reach manufacturing scale. Many teams are struggling with the speed and process insight required due to the inflexibility of internal data systems.
New therapeutic pathways and orphan drug competition are compressing the timelines for delivering high impact treatments to market. This is driving the need for much faster transfer from development to clinical manufacturing. Data from development has become critical for creating scale-up/scale-down models for next-generation manufacturing methods, particularly those selected for fast-track FDA approval.
Drug companies are working to employ predictive methods that leverage data and analytics to produce insights into new therapies. There are a few persistent milestones that must be met in order to make this a reality. First, PD groups must invest in solutions for ingesting data from their various devices into a centralized solution. Data need to be collected from devices and stored in a central data solution as soon as they are generated. Second, machine learning and AI initiatives require structured, integrated data solutions from across the process development organization. Lastly, to make sense of this data, PD groups will need to invest in flexible, scalable solutions that can contextualize, annotate, and join the various sets of data to make them suitable for data analysis and machine learning applications.
Riffyn is poised to be the platform of choice for digital tech transfer from development to manufacturing
Many of Riffyn’s customers were in attendance at the AccBio2019 conference. Riffyn’s integrated data system allows process development organizations to access their data from a centralized service and collaborate across different groups. We are helping our customers implement a digital tech transfer process. We are providing them with a seamless digital platform to transfer process designs and parameters from process development to manufacturing teams. In addition, Riffyn’s platform provides the ability to conduct multivariate data analysis across batches in both PD and manufacturing to draw insights, make decisions, and memorialize the context of the process.