Clinical studies on chronic disease progression, especially on the trajectory of biochemical or imaging markers that reflect the pathophysiology of a disease (so-called “biomarkers”), provide important insights into how the disease develops and progresses, and how it should be treated or managed (Jewell, 2016). One classic approach for studying disease progression is symptom-based staging, in which patients are divided into several stages based on their clinical symptoms, such as mild, moderate, and severe (Ridha et al., 2006). However, understanding the continuous progression of chronic disease as a concatenation of a small number of discrete stages defined by a reliance on limited measures is not a rational approach that takes full advantage of current knowledge and technology.

Disease progression modeling with temporal realignment (DPM-TR) is an emerging computational approach that has been proposed to study and predict the long-term progression of chronic diseases over decades using biomarker changes within a limited duration of clinical studies. DPM-TR has the potential to revolutionize healthcare in various therapeutic areas by enabling a holistic understanding of the natural history of chronic diseases using available clinical data, which has previously been achieved through time-consuming epidemiological studies. Indeed, DPM-TR has been applied to many chronic diseases, mainly neurodegenerative diseases, to provide deeper insights into their underlying mechanisms of action. More recently, the application of DPM-TR in precision medicine and clinical drug development has been extensively investigated. Here, we review such cutting-edge developments in DPM-TR for chronic diseases, including a brief comparison of the analytical techniques used. Note that different TD-DPM methods have been proposed (Table 1), which have been previously reviewed elsewhere as “data-driven DPM” (Oxtoby, 2023; Oxtoby & Alexander, 2017; Young et al., 2024). The present review aims to provide more specific perspectives on the opportunities for DPM-TR in clinical practice and drug development, along with some important recent applications.

We begin our discussion with an additional introduction to the development of mathematical modeling and the use of DPM in pharmacology (Section 2). A detailed description of the DPM-TR, including its concepts, methodologies, and applications, is provided in 3 DPM-TR: An emerging approach to modeling chronic disease progression, 4 Applications of DPM-TR. Finally, the limitations of DPM-TR and the steps necessary to bring such an advanced approach to the forefront are discussed (Section 5). This review focuses on top-down approaches, where models are obtained through the analysis of clinical data, and does not address bottom-up approaches such as quantitative systems pharmacology (QSP), which theoretically describes disease progression based on biochemical mechanisms. These two approaches are complementary and should be appropriately selected or used together depending on the nature of the problem (Cook & Bies, 2016).