Olivia Seifert
Biotech and pharma companies developing therapies for rare genetic diseases always need to take into consideration varying patient profiles as, even in patients with the “same” disease, different genetic mutations can lead to very different symptoms, risks, and responses to treatment. With the advancement of precision medicine, genetic stratification – dividing patients into subgroups based on their specific mutations – is a key starting point in the drug development lifecycle for rare genetic diseases.
Incorporating a clear stratification plan is a key part of developing an effective Target Product Profile (TPP), which serves as a blueprint for product development by defining what “success” looks like early on, and aligning clinical, regulatory, and commercial strategies to meet patient needs, and regulatory and market requirements. Federal agencies such as the NIH and FDA provide TPP templates, underscoring the central role of the target patient population in early planning for new therapies.
Importantly, a TPP is not a single static document. Building multiple TPP scenarios to reflect different clinical and commercial realities is needed – for example, by setting a Minimum, Base Case, and Best Case profile. These scenarios can differ significantly in terms of the patient subgroups the therapy will serve and the magnitude of benefit expected. For example, a Base Case might target patients with a particular high-risk mutation, while a Best Case could include broader groups if efficacy across variants is demonstrated. This scenario planning enables more flexible and informed decision-making throughout the development process.
Leveraging genetic stratification for stronger TPPs
Creating an integrated TPP is complex, especially for drugs with multiple patient profiles – whether for small molecule, protein or gene therapy approaches – and a one-size-fits-all development strategy rarely works in genetically stratified rare diseases. Every element of the drug development process – from candidate selection to pivotal trial endpoint choices – should align with the biology of the target population. This will ensure the most promising therapies are selected, and that efficacy can be demonstrated optimally.
Genetic stratification provides critical data to help inform robust TPPs, offering insights that help answer key questions early on, such as:
What is the range and size of the potential target populations? Researching and evaluating patient sub-groups can be used to better understand patient subpopulation size and disease risk across ancestry, which is key to confidently determining the final regulatory label – as well as building investor confidence in the global market opportunity.
What is the optimal patient population to target? Genetic stratification provides insights regarding which patients to recruit for trials, and which are most likely to benefit based on severity, prevalence and the potential to demonstrate measurable outcomes. There are many examples where HTA bodies have only chosen to fund the most severe sub-groups of patients, where there is the strongest evidence of the drug’s efficacy, while excluding other sub-groups in the same disease from reimbursement – for example, in the case of paroxysmal nocturnal haemoglobinuria, or spinal muscular atrophy.
What is the clinical and economic value proposition of the therapy? Assessing the value and return on investment of a prospective therapy relies on understanding the disease severity and mutation types, how many patients could benefit from a therapy, and how and what level of clinical improvement the therapy might offer. By integrating genetic stratification data with insights from epidemiological modelling that forecast the future impact of new therapies, alongside pricing assumptions and anticipated market share across regions, drug developers can robustly evaluate therapy effectiveness and value. This informs indication prioritisation and payer engagement plans – and allows drug developers to adjust their TPP and pricing strategy early on, avoiding costly rework later in the development process. Having a clear, data-driven value proposition from the outset is particularly important in rare disease drug development, given the small patient populations.
Genetic database analysis: A powerful methodology for genetic stratification
Thanks to large-scale genetic databases and biobanks such as gnomAD, UK Biobank, Genomics England’s 100K Genomes, and All of Us, it is now possible to estimate how many people carry genetic variants associated with rare disease, which ancestries are most at risk, and how different mutations might affect patients.
The process of analysing large genetic datasets for subpopulation mapping involves several steps:
This methodology provides robust rare disease prevalence estimates that, combined with burden of disease modelling studies can then accurately project a prospective therapy’s impact on a population of interest.
Genetic database analysis case study: Fabry disease
HealthLumen’s genetic database analysis of Fabry disease showed the condition may be over three times as common than previous estimates, which were based on clinically ascertained cases. By screening for pathogenic variants in the GLA gene within the gnomAD database and stratifying by sex and ethnicity, our study estimated that in the US in 2024, there were:
The highest pathogenic allele carrier frequencies were found in the European (non-Finnish) ancestry group, followed by the South Asian, East Asian and African / African American ancestry groups.
Such insights can help redefine the value of prospective Fabry therapies, strengthen the case for internal investment, allow for more effective clinical trial design, and de-risk the drug development lifecycle with accurate patient subpopulation numbers.
Conclusion: Building better TPPs through robust genetic stratification using genetic database analysis
In rare disease drug development, a TPP is only as strong as its alignment with the patient population’s underlying biology. Understanding genetic stratification adds essential clarity throughout the drug development process – from selecting the best drug candidate to recruiting the right patients and measuring the appropriate clinical outcomes using an optimal trial design.
Genetic database analysis offers a powerful methodology for genetic stratification, enabling drug developers to identify and quantify subgroups based on variant type, predicted severity, and population ancestry distribution. For biotech and pharma teams working on rare genetic diseases, early investment in genetic insights to inform robust TPPs can help reduce development risk, accelerate time to market, and ultimately improve outcomes for patients.
For information on how HealthLumen can help you with genetic stratification to inform robust TPPs, please get in touch with us today.