DOI: 10.25934/pr00009328
title: Developing a quantitative base for more effective and individualized ropinirole dosing regimen

description: Ropinirole is used for the management of symptoms of Parkinson’s disease. The drug administration is started from a small dose with a gradual increase to the therapeutically active dose which is usually established based on clinical observations, rather than on previous knowledge. Long titration regimens create difficulties for patients and can decrease patient compliance. Moreover, when the patients do not experience the improvement of the symptoms, they might increase the dose on their own without medical supervision which can result in more serious adverse events such as dyskinesia. Therefore, knowledge about the behavior of ropinirole in the human body is required to provide a reliable base for drug dosing. This will also allow to predict the effective dose for individual patient rather than rely on trial and error approach.
The aim of our research is to develop a quantitative base for personalized ropinirole therapy which will inform the drug dosing. Our research consists of 4 steps, which are summarized below.
Step 1. Predicting ropinirole concentration in plasma and brain (PBPK modeling and simulation). Due to the availability of numerous GSK Trial Reports with pharmacokinetic data for healthy volunteers and Parkinson’s disease patients we were able to develop and verify a PBPK model for ropinirole. The ropinirole PBPK model includes virtual patient physiological and demographic characteristics, drug physico-chemical and ADME parameters and is able to predict ropinirole concentration in plasma and different organs.
Step 2. Understanding ropinirole effect (identification of the most important items of UPDRS score for further PK-PD analysis). One of the standard tools in the estimation of the effect of antiparkinsonian drugs is Unified Parkinson's Disease Rating Scale (UPDRS). The analysis of the relationship between ropinirole concentration and effect expressed as total UPDRS score in GSK Study SK&F-101468\168indicated that total UPDRS score does not change with the dose change. Because of this reason, the drug dosing is based rather on observations than on a mechanistic understanding of drug action and makes the optimization of therapy complicated. However, the UPDRS score is a composite score and consists of 44 items estimating different patient activities such as daily living and motor activities. It can be hypothesized that some of the items are more relevant to the drug antiparkinsonian effect, and some create noise. For the analysis of such scores, the Item Response Theory (IRT) can be applied. IRT, instead of analyzing the total score, takes into account the varying importance of individual items of the score which increases the statistical power of the analysis. Based on the analysis using Item Response Theory we plan to identify those items that contribute to the total UPDRS score the most.
Step 3. Estimating ropinirole concentration-effect relationship. By applying PBPK-PD analysis based on the results obtained in the previous step we will build a PD model which will be able to predict the ropinirole effect based on patients characteristics.
Step 4. Ropinirole and neurocomputational model. Finally, for a better understanding of ropinirole’s mechanism of action, we plan to use the neurocomputational model of the patient brain region and one of the UPDRS score items – frequency of tapping. Using the above model, we will simulate the drug’s impact on the relevant neural circuit. Knowing the ropinirole concentration in the striatum (which will be predicted using the PBPK model) and by modeling ligand-receptor binding, drug concentration will be translated into the activity of neurons in the basal ganglia. This activity, then, will be mapped into effects that are measured during UPDRS scoring of patients, for example, finger tapping frequencies. Hence, it will be possible to link ropinirole administration which follows a certain protocol with the predicted outcome.