Summary: Gareth Jenkins discusses FlowInova, a platform for continuous drug substance manufacturing developed in collaboration with the University of Nottingham. The FlowInova platform offers a more sustainable and less wasteful manufacturing approach that can reduce the time and cost associated with new drug development.
With a growing trend towards more targeted drugs, small-molecule active pharmaceutical ingredient (API) candidates are becoming more potent.
Consequently, the amount of drug substance required through development, from clinic to commercial, has reduced significantly, while the molecular complexity and demands on chemical manufacture have increased. This necessitates a radical rethink in terms of the technology required for manufacturing these APIs.
Continuous manufacturing facilities that can meet development and small-scale manufacturing requirements will be a critical solution to this problem.
The FlowInova platform started at Arcinova, A Quotient Sciences Company in 2018 as a research project when we set out to develop a data-driven methodology for process development and continuous manufacturing that enables efficient and intensive scale-up for early-phase development of API candidates.
The novel approach combines high-throughput experimentation with process modeling and uses this to rapidly complete a number of plan-do-review cycles of process development. This leads to highly efficient and scalable processes that can produce kilograms of material within a laboratory environment, reducing the timescales for scale-up and the quantities of material required during development.
In collaboration with the Department of Chemistry at the University of Nottingham, the project was the first of its kind to be implemented by a UK-based contract drug development and manufacturing organization (CDMO). It enabled the development of innovative continuous processes and significantly reduces the time and cost associated with drug development, scale-up, and manufacture for customers across the globe.
The FlowInova project was the first to demonstrate that using automated reaction equipment and building models early in the process enables better decision-making for the next round of experiments. As more data is acquired and knowledge of the process increases, the process models become more predictive and allow for ‘virtual design of experiments’ to be carried out. This permits a greater focus on confirming and optimizing the predicted process parameters, leading to more robust and reliable scale-up.
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