CGT Cost-of-Goods Modeling: Starting from the End to Estimate Costs and Drive Decision-Making
Whether it’s cost-of-goods or build-versus-buy analysis, eXmoor Pharma’s Harvey Branton, Evelien Stalmeijer and Ruxandra Comisel say a clear understanding of the end goal is crucial to develop useful predictive models for cell and gene therapy manufacturing.
Early-stage therapeutics development is incremental by design. Scientists slowly and methodically move forward, looking one technical step ahead at a time. In well-established areas where the path to market is clear and well-trod, there may be little impetus to step back to take a big-picture view. But in cell and gene therapy (CGT), where it has been challenging to get therapeutics to market and show profitability, developers cannot afford incremental thinking. Decision-making must begin with the end in mind.
This approach is applicable regardless of the endpoint – it’s a methodology that takes into account the relative infancy of CGT manufacturing and can be applied to model a range of questions: determining cost of goods, defining the range of commercial geographies, whether to build or buy manufacturing capacity, identifying the most suitable process design and minimal required performance or adapting for new products. It starts by defining what success looks like, then creating a model of the process embedded with assumptions based on developer data and industry experience. From there, the model can iteratively test various hypotheses or solutions, generating information that can feed back into the model to identify additional options and optimise a route to reach the goal – much more cost-effective than a solely science-driven approach.
Modelling Cost of Goods
Estimating profitability is a unique challenge in CGT, where predicting the cost of goods is complex. For example, in order to accommodate market demand at commercial scale, the makers of the first wave of approved autologous chimeric antigen receptor (CAR)-T cell therapies had to buy up the entire market for certain raw materials, affecting not only their costs but those of next-wave developers too. Furthermore, the field still lacks standards and optimised best practices, which means the cost of the scaling process can be highly variable.
However, based on standards of care and existing commercial CGT therapies, it can be straightforward to target a selling price. This helps clarify the end goal, making it possible to incorporate in-house manufacturing data to build bottom-up cost-of-goods models, with detailed analysis of various cost components. The methodology identifies the key cost drivers and enables informed decisions to take the right solutions forward. For instance, this informs process development strategy by evaluating trade-offs such as improved productivities achieved by optimised cell lines and downstream unit operations, or embarking on development of specialised equipment versus cost.
Cost modelling will become more critical in time as more CGTs make it to the market. The selling price will come under pressure as reimbursements change, driving prices down and putting manufacturing under pressure to become more efficient. Thankfully, the modelling approach can be expanded by running sensitivity analyses, changing one or multiple variables at a time whether process-related or business-related. This can inform CGT developers on how to best achieve profitability and make wise business decisions related to multiple assets in the product pipeline or reimbursement strategy. Also, running sensitivity analyses can help to evaluate manufacturing strategy, laying the foundations for deciding between building, buying, or contracting capacity.
Build vs Buy
When thinking one step at a time, early-stage CGT developers would likely choose contract manufacturing to develop their first clinical material as it will typically be the least expensive option. Yet, for years, concerns about insufficient capacity across the industry – and a desire to control long-term costs – drove CGT developers to build their own manufacturing facilities.
Whatever the prevailing wisdom was, what once seemed like a straightforward decision is a lot muddier today. Contract manufacturers have expanded capacity, and investment for a large capital expenditure is harder to come by. At the same time, it has proven challenging to predict manufacturing capacity requirements in the CGT space, especially in the autologous space, resulting in a number of companies struggling with the cost burden of unused capacity. As a result, the trend towards building seems to have reversed, with many companies spinning off or selling capacity – in some cases, before their facilities have even been completed.
This decision, too, requires starting with the end in mind. In terms of technical considerations, future capacity requirements should be forecasted based on the product’s estimated market dynamics and sales projections. For developers who haven’t yet moved beyond Phase 1, manufacturing requirements likely have been modest – timely capacity planning for later stage manufacture is paramount. Considerations such as speed to market, manufacture ramp-up profile and securing control over supply chain, amongst others, come into play when choosing between build-buy-contract and can be captured in cost models to inform this decision.
By expanding on the models built for cost-of-goods assessments, they can guide not only the build-buy-contract decision but the timing. These can be used to test different scenarios, based on how long it would take to build a new facility, capital and overhead costs, and the risk of losing momentum by switching strategies mid-stream. For companies that do stick with a contract manufacturer through proof-of-concept studies, early assessment using these models can lay the groundwork for seamlessly switching gears as validating data comes.
Maximising Company Value
Don’t underestimate the value — figuratively or literally – of having an early roadmap that accounts for complexity and rapidly identifies routes forward. This can ensure the manufacturing approach employed early in initial clinical trials will be viable for scaling up and can reduce the risk of other unplanned delays as well. Modelling by third-party partners with robust, thorough, data-driven and structured methodology can give assurance to potential investors that there is a commercial path forward.
As such, these assessments need to dovetail with the developer’s business strategy. If the plan is to spin out or seek an exit after a positive proof-of-concept, then keeping early costs low will likely be the priority, making contract manufacturing much more attractive than heavy investment in internal manufacturing capabilities.
On the other hand, teams with a vision of commercialising their own products will have a different calculus. For these CGT developers, getting the timing right is key, which will depend on understanding the product’s clinical outcome: building a facility prior to knowing if your clinical results show product efficacy may not always be the right solution. In addition, an evaluation of the company’s net present value as it moves toward the commercialisation stage can help ensure that it maintains a healthy cash flow at a critical time.
These decisions hinge on having a clear idea of what the final product should be, who it will serve and how it will be commercialised – things that are surprisingly difficult for many early-stage CGT developers to conceptualise. Few recognise the pitfalls of trying to design processes that will eventually support commercial products without setting goals. While these endpoints can and frequently do move, adopting a helicopter vision and cost-of-goods methodologies equip a company with a reliable framework that can focus the project in the midst of the inherent changes associated with commercialising CGT products.