Computer modelling tool can optimise personalised medicine supply chains

A new computer modelling tool can help optimise the distribution of patient-specific cancer treatments.

Researchers from the Department of Chemical Engineering at Imperial College London have designed a new computer modelling tool to improve the distribution of a type of patient-specific immunotherapy treatment for treating aggressive blood cancers, known as Chimeric Antigen Receptor (CAR) T cell therapy.

The computer model uses real world information on CAR T cell therapy supply chains and proposes candidate solutions to reduce costs and minimise the turnaround time of the therapy.

The research, published in Computers & Chemical Engineering addresses the main challenges associated with CAR T therapies: the highly personalised nature of the therapy, the significant therapy costs, and the tight time constraints due to the sensitive nature of the therapies and the criticality of the patient condition.

The research team interviewed industrialists, stakeholders and patients, to understand their needs and current supply chains which they translated into a mathematical model to help ensure treatments are delivered as quickly as possible.

What is CAR T cell therapy?

Chimeric Antigen Receptor (CAR) T cell therapy is a type of cancer treatment where the patient’s own T cells are genetically engineered to create a synthetic receptor that binds to the tumour antigen.

The patients’ cells are used as the raw material and therefore manufacturing, and distribution are exclusively dedicated to a singe patient therapy. This means that CAR T cell therapy is expensive and at present volumetric scale-up is not possible.

Currently, it takes between 15 and 24 days for the treatments to become commercially available and in addition to this, the materials involved are sensitive with a short shelf life. This translates into tight time constraints which further challenges the supply chain network.

Mixed Integer Linear Programming model

A Mixed Integer Linear Programming (MILP) model is a mathematical modelling approach that helps decision-makers to solve large, complex optimisation problems. The model was used to assess the supply chain network performance under different time constraint scenarios on the time it took to produce and distribute the therapy.

"This [work] will be a great step towards the digitalisation of supply chains in Advanced Medicinal Products and next generation vaccines”. Dr Maria Papathanasiou Department of Chemical Engineering

The main objectives of the model are to design networks that can be agile and responsive to varying demand and tight time constraints, while minimising distribution and storage costs. From their study, the researchers found that adding storage where applicable, allows for improved scheduling and coordination of the manufacturing facilities.

According to lead author Dr Maria Papathanasiou: “In our study we were able to demonstrate how to use a mathematical tool for the assessment of candidate supply chain network structures in autologous cell therapies.

We will proceed to compare the current supply chain (hospital-manufacturing-hospital) to a forward-looking scenario, where storage is introduced as an option upstream.”

Next steps

According to co-author, Dr Andrea Bernardi: “This network yields interesting findings, as it shows that storage, where applicable, allows for improved scheduling and coordination of the manufacturing facilities.” The next steps for the team will be looking into integrating detailed scheduling of the manufacturing process in the model.

Dr Papathanasiou added: “In addition, one of the key raw materials for these therapies are the viral vectors, the supply of which is considered guaranteed at the moment. We are currently working on incorporating these aspects in the model.

This will be a great step towards the digitalisation of supply chains in Advanced Medicinal Products and next generation vaccines”.

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‘Assessment of intermediate storage and distribution nodes in personalised medicine’ by Bernardi et al., published on 22 January 2022 in Computers & Chemical Engineering.

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