What is Disease Progression Modeling?

What is Disease Progression Modeling?

Disease progression modeling involves the use of mathematical and statistical functions to quantitatively describe how a disease develops and progresses over time. The disease status over time may be described by longitudinal data that describe disease severity, such as biomarker data (e.g. troponin) or clinical outcome (e.g. survival status). These models can describe the natural progression of the disease by predicting the likelihood of different outcomes at different stages of the disease and, may also be linked to pharmacokinetic-pharmacodynamic models so that the effect of a drug intervention on the disease progression can be evaluated.

What are the Components of a Disease Progression Model?

Disease progression models are comprised of the following components:

1. Baseline disease state

2. Natural disease progression

3. Active drug response

4. Placebo response.

Disease progression models begin at the baseline disease state (i.e. status prior to disease progress). Any change from the baseline disease state, in the absence of any drug intervention, describes the natural disease progression. Drug response refers to the situation when a pharmacological intervention modifies disease status. In the setting of a placebo-controlled clinical trial, it is worth considering the potential placebo response following administration of the inactive treatment. Using the four listed components of a disease progression model, disease progression can be described using the following equation:

\[ S(t) = S0 + NDP + D+ P \]

Here, S(t) is disease progression, S0 is baseline disease state, D is drug response and P is placebo response. As shown, disease progression is a combination of baseline disease state, natural disease progression and, as applicable, drug and placebo response. However, in practice, it is often difficult to separate placebo response from the natural disease progression. Thus, in such situations, the equation simplifies to,

\[S(t) = S0 + NDP + D\]

where, the natural disease progression and placebo response are lumped together.

Overall, disease progression models are a powerful tool in drug development as they can help researchers and clinicians understand how a disease progresses over time, identify risk factors for disease progression and assess the potential impact different pharmacological treatments may have on disease outcomes. Predictions made from a disease progression model can be used to help inform clinical trial design, guide treatment decisions, and support the development of new therapies.