Andrew Hooker, Lena Friberg, Siv Jönsson, Mats Karlsson, Elodie Plan


Multiple Sclerosis is both a complex and chronic neurological disease of the CNS. The natural course of MS is slow and difficult to monitor clinically. The overall aim of this project is to establish 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. Model building involves sequential development of (i) separate models for all com­ponents of interest (disease progression, relapse rate dynamics, MRI dynamics and lymphocytes including CD4+, CD8+) and (ii) the covariate model, which explore the underlying patient factors influencing within- and between-subject variability in treatment response. The final anticipated model shall enable simultaneous characterization of the interplay between relapse rate dynamics, total CD4+ and CD8+ lymphocytes and their ratio, and MRI readout dynamics in the evolution of disease and, most importantly, link the time-course of MRI and clinical outcome (relapse rate and disability) and lymphocyte data.

In rheumatoid arthritis a range of variables of the disease are summarized into a clinical endpoint for evaluation of drug response - the dichotomous ACR20 score. Integrated longitudinal transition models with dropout are useful for understanding the outcome of different dosing schedules and by expanding such models to also include the more stringent ACR70 criteria more information can be preserved. To increase the information on the concentration-effect relationship in the available data, a longitudinal transition model describing the pro­bability of ACR20, ACR50 and ACR70 responses have been developed. 


Biological medicinal products are an important contributor in the treatment of many diseases, e.g. multiple sclerosis, rheumatoid arthritis, cancer and psoriasis. Characterization of biologics benefit from pharma­cometric modelling, since they exhibit complex disposition characteristics, quite different to the processes and pathways utilized for small molecules, e.g. monoclonal antibodies exhibit target mediated drug disposition (TMDD) and disease-related changes in protein turnover

The study design of biological products needs to consider the special disposition features. We explore study design options for studies in different stages of drug development, optimal design methodology is applied to TMDD models. 

A complicating factor for biologics is the occurrence of antidrug antibodies (ADA), which may affect the pharmacokinetic and efficacy features. The identification of ADAs is usually confounded by the presence of the drug itself and therefore the result from an analysis is that ADA is present, absent or unknown (missing information). Thus, there is a need to develop adequate methods to incorporate the ADA information in pharmacometric models. Currently, we are using mixed hidden Markov models (MHMM) to model the underlying unobservable ADA states.