(541b) Next-Generation Vaccines and Therapeutics: Towards Resilient Pharmaceutical Supply Chains

The promising clinical outcomes of genetic engineering have paved the way to curing life-threatening diseases, including cancers. Several innovative gene therapy drugs have already been launched over the last few years and this rapid growth is forecasted to continue with the U.S. Food and Drug Administration (FDA) anticipating the approval of 10-20 cell and gene therapy products per year by 2025 [1]. Approximately 70% of the cell and gene therapies in the clinical trials utilise viral vectors for gene transfer [2]. The versatility of viral vectors has also emerged in preventive healthcare, with their recent approval as vaccine platforms (e.g. Vaxzevria (AstraZeneca) and the Janssen COVID-19 vaccine) [3][4].

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 [5]. 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.

References

[1] U.S. Food and Drug Administration, “Statement from FDA Commissioner Scott Gottlieb, M.D. and Marks, M.D., Ph.D., Director of the Centre for Biologics Evaluation and Research on new policies to advance development of safe and effective cell and gene therapies”, https://www.fda.gov/news-events/press-announcements/statement-fda-commissioner-scott-gottlieb-md-and-peter-marks-md-phd-director-center-biologics, (2019).

[2] Ginn, S., Amaya, A. K., Alexander, I. E., Edelstein, M., Abedi, M.R., “Gene therapy clinical trials worldwide to 2017: An update”, The Journal of Gene Medicine, 20(5), e3015, (2018).

[3] European Medicines Agency, “Vaxzevria (previously COVID-19 Vaccine AstraZeneca)”, https://www.ema.europa.eu/en/medicines/human/EPAR/vaxzevria-previously-covid-19-vaccine-astrazeneca#overview-section, (2021).

[4] U.S. Food and Drug Administration, "Janssen COVID-19 Vaccine”, https://www.fda.gov/emergency-preparedness-and-response/coronavirus-disease-2019-covid-19/janssen-covid-19-vaccine, (2021).

[5] Sarkis, M., Bernardi, A., Shah, N., Papathanasiou, M., “Emerging Challenges and Opportunities in Pharmaceutical Manufacturing and Distribution”, Processes, 9(3), 457, (2021).