Andrew Hooker, Lena Friberg, Mats Karlsson, Maria Kjellsson, Elodie Plan, Sebastian Ueckert
The term degenerative or progressive disorders commonly summarizes diseases in which the function or structure of the affected tissues or organs worsens over time. In most cases, progressive disorders are caused by degenerative processes that gradually reduce the physiologic operation of organs and tissues. These disorders share several features that complicate the evaluation of treatment effects in a clinical trial, such as increased drop out, complex clinical endpoints, challenging patient populations, and relatively small effect sizes. In this research area, we focus describing these data in models and with modelling techniques addressing these challenges in many disease areas.
Multiple Sclerosis, for example, is both a complex and chronic neurological disease of the CNS. The natural course of MS is slow and difficult to monitor clinically. For this disease, we are developing the first population, data-driven, MS disease progression model in order to construct a mathematical modelling platform where the interplay between the majority of relevant aspects of the disease, such as time course of disability progression, relapse rate dynamics, time course of the imaging data, time course of lymphocytes and population characteristics are incorporated.
Another example is rheumatoid arthritis, a progressive, autoimmune disease that causes inflammation, swelling, and pain in the joints. Here we developed integrated longitudinal transition models for the dichotomous ACR20 score to understand the outcome of different dosing schedules.
We also introduced the item response theory approach to the field, that is uniquely well suited to describe score based clinical outcomes often used in this class of diseases. The application of IRT does not only results in a more exact description of the assessment score and increased statistical power but also provides insight into the assessment properties. We have applied such models to Alzheimer’s (with ADAS-cog), Schizophrenia (with PANSS), multiple sclerosis (with EDSS) and Parkinson’s (with UPDRS). Currently, we aim to continue to extend this technique to more diseases.