Objective: To mitigate clinical variability of Time-Off with various therapeutic combinations

Background: There is increasing interest to use wearable-based digital health measures to continuously monitor the effect of therapeutic interventions on motor symptoms in Parkinson’s Disease (PD). Apps like Apple’s MPower2 offer the possibility to generate a more relevant clinical outcome for instance on TIme-Off for patients on a day-to-day basis in clinical trials with novel therapeutic interventions. However, this outcome is partially dependent upon the PK profiles of the comedications, including different formulations of L-DOPA.

Method: We use a combination of pharmacokinetic and pharmacodynamic modeling to generate daily profiles of clinical time-off for realistic formulations of various approved standard-of-care medications. The pharmacodynamic modeling is based on an advanced Quantitative Systems Pharmacology (QSP) computer model of human basal ganglia representing different subregions of the motor circuit with the beta/gamma ratio of local field potential in the subthalamic nucleus as a proxy silico readout for rigidity and bradykinesia. This model was previously calibrated using the UPDRS Part III scale. The beta/gamma threshold for Time-Off was determined using published clinical trial data on the impact of various L-DOPA formulations on Time-Off reported by PD patients.

Results: generate time profiles of the beta/gamma ratio in the awake situation for various combinations of Parkinson’s medications in 23 clinical trials with various interventions for which data on Time-Off are available. We report the observed difference between immediate and extended release of the carbidopa-levodopa formulation. The model is able to reproduce reductions in Time-off of other PD medications. The simulations demonstrate that the accumulated duration of Time-Off during the day heavily depends upon the formulation and nature of the comedications.

Conclusion: This approach allows for testing the impact of various comedications in combination with novel therapeutic interventions not only in clinical trials, but also in clinical practice. In addition, it can account for missing doses. It is expected that application of this QSP approach can identify and mitigate sources of variability in clinical readouts of Time-Off, in particular in trials using wearable-based digital health measures.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/impact-of-standard-of-care-medications-on-time-off-readouts-in-clinical-trials-a-quantitative-systems-pharmacology-approach/