Disease progression describes the change of disease status over time as function of disease process and treatment effects. In practice, biomarkers are frequently used as a proxy to monitor disease status. Chronic diseases usually progress slowly over time. For example, Chronic Obstructive Pulmonary Disease can take well over 10 years to evolve from Stage I (mild) to Stage IV (very severe). It may also take 10 years for Congestive Heart Failure to progress from Stage I (mild) to Stage IV (severe). Late detection and intervention for such chronic diseases significantly increases the burden on both the patients and the healthcare system. Being able to detect the development of chronic diseases at an early stage is instrumental to preventive care and personalized medicine.
A better understanding of disease progression is beneficial for early diagnosis and appropriate individual therapy. There are many different approaches for statistical modelling of disease progression proposed in the literature, including simple path (Linear Progress) models up to complex model. In disease progression modeling (DPM), the progression in time of a disease in an individual is represented as a mathematical function. Initially, a model is produced that characterizes a given disease’s time profile in the absence of therapeutic intervention; this is a base model. Changes due to active treatment are superimposed onto the base model to simulate the effect on the disease of a drug. As the base model is not dependent on any treatment, it may be modified for use in simulating for other treatments. Disease progression models offer greater insight into data obtained from clinical trials, allowing for better study designs. DPM of the progression of a target disease with computational methods is an important technique that can help with the early detection and management of chronic diseases. By characterizing the entire disease progression trajectory, DPM also facilitates disease prognosis improvement, drug development, and clinical trial design.
Linear Progress Model: The linear model assumes constant rate of change of clinical effect or biomarker that reflects the disease status (D) at any time (T) from initial observation from the patient. The rate of change can be defined in terms of baseline disease status (D0) with a slope factor (s), reflects the change from baseline with time:
D(T) = D(0) + s*T
The linear model was further modified by the addition of an effect compartment (Ece) in order to allow for a delay between the initiation of treatment and the time to observable response:
D(T) = D(0) +(Ece(T) + s) *T
In this model, the disease status would not return to the pretreatment course when therapy was discontinued, but would be expected to result in a permanent improvement. Linear equations have been used to describe the progression of several diseases, including Alzheimer’s disease and schizophrenia. In these models, the disease progression component included a placebo response model, incorporating both the trajectory of disease as well as a transient change in disease status attributed to placebo response. DPM is an additional evaluation tool that has an improved ability to detect a drug effect and provides useful dosing information for prescribers. Modeling is also an important component in the regulatory requirements for a new drug application. Models of disease progression have been used for a wide variety of clinical indications. The use of the population approach for studies of disease progression refers to describing the natural history of disease reflected in repeated measures of disease status. Disease status is a general term that refers to any quantifiable variable describing disease at a particular point in time. Observations of disease status, like those of drug concentration or drug effect, can often be made repeatedly in the same patient.
Disease progress models incorporate both pharmacokinetics and pharmacodynamics to describe drug action. Natural history progression models incorporate the clinical and pathophysiological features of disease. By combining them, a more complete picture can be created of the roles of disease and treatments in understanding clinical pharmacology and improving patient care. The use of models to describe the disease progress is good tool that allows the modelers to assess the effect of drug treatment on the time course of disease. The mechanism of action of the drug may suggest innovative combination therapies or novel treatment approaches that would not have been considered without knowledge of the disease and the effect of drug on disease progress. In the literature there are many suggestions of DPM. Each has certain assumptions and models disease progression in a different way.