Efficient design of cell based product manufacturing using low data demand modelling approaches

Background & Aim Advanced Therapy Medicinal Products (ATMPs) pose a continuing challenge to manufacturing process development. Despite the adoption of a structured approach to development through systematic frameworks such as Quality by Design, a costly rate of process failure or underperformance is encountered at key transitions such as transfer to contract manufacture organizations or changes of scale or equipment. The complexity of the products, particularly the cell culture step, contributes to such issues; this complexity challenges many process development tools, in particular the number of potential process variables and consequences results in a lower quality of evidence base informing early risk assessments, creates difficulties in experimental prioritization and efficiency, and can result in poor experimental coherence over the course of product development. Our aim is to develop efficient modelling approaches to reduce cost of manufacturing process optimisation and improve characterisation of risk. Methods, Results & Conclusion We have developed a modelling framework that takes advantage of the commonality across ATMP manufacturing processes and consumables. Features such as cell growth, cell modification of milieu, and consequences such as lineage selection rate and cell death are representable by a limited set of mathematical building blocks. These behaviors interact with process operation to determine critical manufacturing outcomes such as product cost and identity. From an operational perspective there are a limited number of common process operations such as dilutions, purification or factor additions. This enables a modelling framework that can be constrained whilst still representing a wide range of process dynamic hypotheses and associated manufacturing scenarios. Case studies will be presented across a variety of platforms providing a basis to specify manufacture based on cost targets, operational constraints (e.g. feed frequency, reactor size) and risk tolerance. Application of these approaches can be used to rapidly gain insight into optimization of key process elements with a high impact on manufacturing outcomes Advanced Therapy Medicinal Products (ATMPs) pose a continuing challenge to manufacturing process development. Despite the adoption of a structured approach to development through systematic frameworks such as Quality by Design, a costly rate of process failure or underperformance is encountered at key transitions such as transfer to contract manufacture organizations or changes of scale or equipment. The complexity of the products, particularly the cell culture step, contributes to such issues; this complexity challenges many process development tools, in particular the number of potential process variables and consequences results in a lower quality of evidence base informing early risk assessments, creates difficulties in experimental prioritization and efficiency, and can result in poor experimental coherence over the course of product development. Our aim is to develop efficient modelling approaches to reduce cost of manufacturing process optimisation and improve characterisation of risk. We have developed a modelling framework that takes advantage of the commonality across ATMP manufacturing processes and consumables. Features such as cell growth, cell modification of milieu, and consequences such as lineage selection rate and cell death are representable by a limited set of mathematical building blocks. These behaviors interact with process operation to determine critical manufacturing outcomes such as product cost and identity. From an operational perspective there are a limited number of common process operations such as dilutions, purification or factor additions. This enables a modelling framework that can be constrained whilst still representing a wide range of process dynamic hypotheses and associated manufacturing scenarios.