(541b) Next-Generation Vaccines and Therapeutics: Towards Resilient Pharmaceutical Supply Chains
AIChE Annual Meeting
Wednesday, November 10, 2021 - 3:53pm to 4:16pm
Given the high demand and limited manufacturing capacity, viral vector manufacturers are asked to make decisions on how to best utilise and expand the available manufacturing resources in the presence of numerous demand uncertainties. Some of the challenges are related to production and capacity planning under uncertain clinical trial outcomes and dosage requirements. Furthermore, manufacturers of viral vector-based COVID-19 vaccines tackle the hurdles of rapid and in-risk manufacturing, transitioning to large-scale deployment of novel vaccine products in record times. In this case, SARS-Cov-2 has clearly demonstrated the need for sophisticated tools that can assist responsive decision-making and support planning under uncertainty.
This work presents a novel mixed-integer linear programming (MILP) model for the design and optimisation of viral vector supply chains (Figure 1). An array of applications for viral vectors and their respective demand scales are considered, thereby capturing the emerging paradigm shift from one-size-fits-all manufacturing and distribution to targeted healthcare . The framework relies on techno-economic analyses of well-established viral vector manufacturing protocols, which are modelled and simulated in SuperPro Designer. The formulated optimisation model identifies good candidate supply chain structures, capacities and production plans for multi-product multi-suite facilities, distribution plans and transportation flows. For this, we consider (i) a set of products and demand scenarios, (ii) capital and operating manufacturing expenditures for a range of production scales, as well as storage and transport costs for sensitive formulated products, (iii) candidate locations for network nodes, namely primary, secondary manufacturing and distribution centres. The resilience of the strategic decisions is then assessed under demand uncertainty through comparisons of network structures with respect to (i) cost, (ii), scalability and (iii) flow time.
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