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Forecasting cell and gene therapies, one model at a time

Article-Forecasting cell and gene therapies, one model at a time

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Forecasters need to carefully plan for high revenue volatility and alternative payment structures and develop models that reflect the realities of the market.

With the ever-changing cell and gene therapy (CGT) market dynamics, it is increasingly complex to forecast and enable decision-making in the field but the markets in which CGT operates have a solid influence.  

In a broader market upon which to spread the development costs, such as oncology, where there is usually a larger patient population, therapies can hit a lower price point. For rare or orphan diseases, it may be harder to spread out costs, not to mention geographic considerations that can make the forecasting model quite complex. Reimbursement and cost are crucial parts of the forecasting process as each vial costs thousands or perhaps millions of dollars. Forecasters need to carefully plan for high revenue volatility and alternative payment structures and develop models reflecting the realities of the market.  

Forecasting CGT is not for the faint-hearted and even the most experienced forecasters can get goosebumps. Forecasters need to consider the macro environment factors that are likely to contribute to the growth of the cell and gene therapy market: merger and acquisitions, expansion of technological advancement, expanding application for cell and gene therapies, growing demand for CAR T-Cell therapies as well as new products approvals and increasing pipeline products. A forecaster might have to use not only technical skills but also a deep understanding of this disease and sector to really carve out the insight and be able to predict the future. It is also important to understand that navigating the CGT market can be a challenging task for forecasters. Unlike other therapeutic areas, forecasting CGT treatments and drugs require a unique approach due to the complexity of the disease. 

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Since CGT treatments are designed to target a particular patient population depending on the disease, forecasting models should factor in every critical element such as patient identification, duration, and time of therapy for different patients. However, it is not always easy to develop detailed and accurate forecasts in this space, especially when the CGT environment is a rapidly evolving one. Hence, there is a significant need to adopt CGT forecasting best practices for better accuracy, increased reliability, and model robustness. 

So, to predict any aspect of the future of this industry, a forecaster can closely monitor the following areas that can help the forecaster win. These include: 

External indicators and competitive intelligence 

Tracking industry pipelines (both preclinical and clinical), US FDA approvals, R&D spending, and M&A trends to provide modality and technology trend insights that directly shape the disease portfolio. These measures are mostly retrospective, but with cautious extrapolation, they can provide a picture of future trends. This includes the size of the clinical development pipeline, the shape of the pipeline, and transition times in the therapy area the forecaster is developing the model. In the industry, real advances and value often occur at the intersections of new therapeutics targeting new mechanisms of disease, enhancements to known mechanisms, and synergistic combinations of these. 

Identify the right target patient pool 

Forecasting in CGT is different from other therapeutic areas because of the significant need to follow patients through different stages, lines, and treatments as they progress through the disease. As important as this is to do, inaccurate identification of the target patient pool has been a common pitfall in CGT forecasting. Forecasters should split the population into smaller and more specific segments, and accurately model them based on incidence, recurrence, diagnosis, treatment, and other important factors to maximise the accuracy of forecast outputs. 

Understanding dynamic patient flows 

Forecasters must be able to model patients through the different stages of the disease as CGT therapy models have become more complex. They need to assess the advancement of each patient segment, understand how patients move between the lines of therapy, analyse dosing regimens, rates of progression, remission, and discontinuation, patient dependency on old and new drugs or therapies, and more. A holistic understanding of the disease space is the need of the hour. 

Longevity and duration of treatment 

There are no longer relevant to the forecasting process, at least not in the same way. Every time a patient is successfully treated, the prevalent population shrinks and, ultimately, demand is limited to incident patients. As one-time therapies, the duration of treatment is not relevant. But at the same time, patient drop-off throughout the treatment journey is essential to capture in modeling. 

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Supply chain, digital and market access 

Referrals to treatment centres and proximity to patients have become highly relevant. CGTs can only be administered at authorised treatment centres, requiring referrals for most patients. Referral rates themselves, the proximity of patients and referring sites to treatment centres all affect treatment volume. Supply chain and manufacturing complexity must be reflected in forecasts that previously assumed “infinite” manufacturing capacity.  

Digital enhancements and foundation elements (for example, telehealth, semi-automated remote monitoring, and eSource-centric clinical trial solutions) assure more precise and efficient care, access, and research; their usage to garner important patient-level data. It is important that RWE is adequately incorporated into the modelling process. 

Maintain peer-to-peer relationships 

It is important to maintain an open dialogue with peers in the industry to constantly learn about their pain points and be active leaders in scientific societies and conferences across drug discovery and development. The interplay with these industry leaders will enable the forecaster to validate their perspectives and assumptions which make the models closer to reality.  


Once the above is identified the forecaster can now integrate their understanding of the market and deep dive into the following to build robust platforms to enable decision making.  

Over the past few years, we have seen the ability of the industry to remain agile and adapt to new challenges like the COVID-19 pandemic. Patient needs are one of the key indicators that have been, currently is, and will remain on top to ensure the future success of the industry.  

While the CGT industry has seen life-changing innovations, there is still a substantial unmet need to address indications and disease areas that have not been successful to date. The future of the industry must remain focused on the ability to meet patient needs with innovative medicines, which will continue to be relative to what the market needs. A forecaster’s role thus becomes extremely critical and non-negotiable to ensure that the organisation wins. After all, an organisation wins if a patient is cured.

This article appears in Omnia Health magazine. Read the full issue online today.

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