What Is Disease Progression Modeling, And When Is The Right Time To Use It?

By Lindsay Kehoe, Clinical Trials Transformation Initiative

At a time when AI is the forefront topic, the importance of foundational models that AI can optimize is being overshadowed. One such model, disease progression modeling, is the subject of recent recommendations released by the Clinical Trials Transformation Initiative (CTTI). These recommendations illuminate three basic considerations: the importance of defining the question of interest, the need for cross-functional communication to decide on the optimal tool for answering the question of interest, and the potential for disease progression modeling to be the tool of choice.

What Is A Disease Progression Model?

A disease progression model is a mathematical model that quantitatively describes the time course or trajectory of a disease. Disease progression modeling (DPM) can link disease progress, treatment effects, or patient behavior, either individually or in combination, with clinical trial outcomes to inform decision-making throughout the medical product development process. ^1,2

The field of modeling and simulation is vast. DPM is one of numerous models that support model-informed drug development (MIDD), including pharmacokinetic (PK), pharmacokinetic/pharmacodynamic (PK/PD), physiologically based pharmacokinetic (PBPK), quantitative structure-activity relationship (QSAR) models, and others. Even within DPM, various methodology forms exist, including statistical empirical models and mechanistic models such as systems biology and quantitative systems pharmacology (QSP).

Modeling and simulation approaches to enable drug development have been used for decades,³ with PK and PK/PD approaches having matured into regular use. Now, through advanced analytic and technological capabilities, a push to maximally leverage various data sources, collaborative frameworks to improve credibility and standardize approaches, and current regulatory support globally,^4, 5,6 the potential of disease progression modeling is achieving greater recognition.  

The Opportunity To Advance Disease Progression Modeling

In response to growing interest in advancing disease progression modeling, CTTI, a public-private partnership cofounded by Duke University and the FDA with more than 70 members across the clinical trials enterprise, set out in late 2021 to promote the recognition, value, and consistent use of DPM to support decision-making in trials.

While model developers recognize the value of DPM, cross-functional leaders in medical product development, such as chief medical officers, therapeutic leads, or heads of innovation, may not realize its potential to answer development decisions. Through a multi-partner project team, CTTI developed a set of recommendations and supporting resources to encourage cross-functional communication between decision makers and modeling subject matter experts to advance the uptake of DPM in medical product development.

The DPM recommendations highlight the unique benefits of using this type of model, when it should be considered over other tools or approaches, and considerations for what is needed and how to incorporate it.

CTTI’s DPM Recommendations And Resources

The unique value of disease progression modeling in medical product development is laid out at the start of the CTTI recommendations document, noting that DPM has the unique ability to integrate multidisciplinary knowledge and data from different sources, including translational, clinical trial, and real-world data to:

• improve trial efficiency, especially for trials with diseases that progress over a long duration of time
• answer questions of uncertainty, including providing clarity around heterogeneity across populations
• tailor trials toward precision medicine, addressing individual factors or covariates affecting progression
• address unmet needs and knowledge gaps, including supplementing for a small population size with digital twins, stratifying a population for patient enrichment, or supporting a limited understanding of the disease by modeling disease course for endpoint selection
• inform regulatory decision-making by contributing to the totality of understanding of the disease and a medical product’s benefits and risks to slow or prevent a disease’s progression.

After acknowledging its value, CTTI provides nine specific recommendations related to the use and implementation of DPM.

The first recommendation addresses when to consider it, noting that if answering a question of interest requires longitudinal data about the disease course, then DPM should be considered. Potential question of interest topics ideal for DPM are provided, such as determining the optimal patient population, selecting an endpoint or biomarker, selecting the dosage, enhancing trial design (e.g., informing study power, duration, predicting and quantifying dropout rates), understanding covariate effects, predicting probability of success, creating virtual control arms/digital twins, and supporting cross-population extrapolation, among others.

CTTI’s second recommendation further assists decision makers in evaluating whether a DPM is the right tool compared to other approaches. This recommendation provides questions to ask of modeling experts and other subject matter experts within an organization through the DPM Considerations Framework, which walks one through assessing the benefit and risk, the data, and the existing resources.

The next three recommendations go deeper into data and the resources needed for disease progression modeling. Recommendations 3 and 4 discuss the availability of current disease models, exploring what data exists and whether that data is relevant and reliable. These recommendations provide questions to evaluate data reliability and relevance and provide examples of model and data resources. Recommendation 5 focuses on whether technological resources, as well as the right skillsets, exist or can be obtained to support the DPM development and implementation.

After assessing the data and resources, Recommendation 6 moves into implementation considerations, noting that once the decision is made to use a DPM, it should be introduced early in the medical product development life cycle to build confidence in the model for later stages of development.

Recommendation 7 emphasizes the importance of continually evaluating the model’s performance to ensure it is fit for purpose for the intended application. Questions are provided to think through the disease landscape for ongoing performance of a DPM.

The final two recommendations discuss ways to optimize the use of DPM by encouraging the use of technological capabilities such as artificial intelligence for greater efficiency and knowledge enhancement. These final recommendations also encourage information sharing to promote standard practices, enhance alignment in terminology, and foster confidence.

Throughout the document, discussion prompts are given to provide cross-functional decision makers a starting point for what to ask subject matter experts. Literature examples are also provided in the appendix to support the various recommendations.

The Future Of DPM

While the majority of DPM use has traditionally focused on drug development, the recommendations acknowledge that DPM can support device development and drug-device combinations. Disease progression models can also aid in predicting clinical response trajectories in healthcare settings, with opportunities beyond therapeutic development and into clinical practice to provide more accurate predictions of disease progression and treatment outcomes.

Ultimately, the potential for DPM to answer strategic development questions is there. It can maximally leverage and integrate the various data sources available to clinical researchers today and be used with other models to improve the quality and efficiency of trials. The new CTTI DPM recommendations and supporting resources provide an avenue to recognize its potential and help advance its use.

References:

1. Barrett JS, Nicholas T, Azer K, Corrigan BW. Role of disease progression models in drug development. Pharm Res. Aug 2022;39(8): 1803-1815. doi:10.1007/s11095-022-03257-3
2. Starling, S. The Potential of Disease Progression Modeling to Advance Clinical Development and Decision Making Clin Pharmacol Ther. Oct 15 2024; https://doi.org/10.1002/cpt.3467
3. Kimko, H. & Pinheiro, J. Model-based clinical drug development in the past, present and future: a commentary. Br. J. Clin. Pharmacol. 79, 108-116 (2015)
4. United States Food and Drug Administration. PDUFA VI commitment letter (2016).
5. United States Food and Drug Administration. PDUFA reauthorization performance goals and procedures fiscal years 2023 through 2027
6. European Medicines Agency. EMA regulatory science to 2025: Strategic reflection (2020).

About The Author:

Lindsay Kehoe manages the development and implementation of CTTI projects. She has convened teams around disease progression modeling, embedding trials into healthcare settings, and the use of digital health technologies to advance novel endpoint acceptance. Along with her CTTI colleagues, Ms. Kehoe is accelerating progress toward the Transforming Trials 2030 Vision. As the lead for emerging programs, Ms. Kehoe manages CTTI strategy and activities regarding innovations in the clinical trials enterprise. This includes investigating and sharing clinical trials trends with the internal CTTI team, convening external thought leaders to ideate around emerging topics, recommending how the emerging topics/themes can be incorporated into CTTI’s work, and developing and maintaining partnerships with internal partners and external organizations regarding CTTI activities. Ms. Kehoe has a bachelor’s degree from the University of Virginia and a master's in genetic counseling from Boston University School of Medicine.