Individual dose-response models for levodopa infusion dose optimization

Machine learning with optimization to create medicine intake schedules for Parkinson's disease patients

This paper presents a solution for creating individualized medicine intake schedules for Parkinson's disease patients. Dosing medicine in Parkinson's disease is a difficult and a time-consuming task and wrongly assigned therapy affects patient's quality of life making the disease more uncomfortable. The method presented in this paper may decrease errors in therapy and time required to establish a suitable medicine intake schedule by using objective measures to predict patient's response to medication. Firstly, it demonstrates the use of machine learning models to predict the patient's medicine response based on their state evaluation acquired during examination with biomedical sensors. Two architectures, a multilayer perceptron and a deep neural network with LSTM cells are proposed to evaluate the patient's future state based on their past condition and medication history, with the best patient-specific models achieving R2 value exceeding 0.96. These models serve as a foundation for conventional optimization, specifically genetic algorithm and differential evolution. These methods are applied to find optimal medicine intake schedules for patient's daily routine, resulting in a 7% reduction in the objective function value compared to existing approaches. To achieve this goal and be able to adapt the schedule during the day, reinforcement learning is also utilized. An agent is trained to suggest medicine doses that maintain the patient in an optimal state. The conducted experiments demonstrate that machine learning models can effectively model a patient's response to medication and both optimization approaches prove capable of finding optimal medicine schedules for patients. With further training on larger datasets from real patients the method has the potential to significantly improve the treatment of Parkinson's disease.

Scoring the Sit-to-Stand Performance of Parkinson's Patients with a Single Wearable Sensor

Monitoring disease progression in Parkinson's disease is challenging. Postural transfers by sit-to-stand motions are adapted to trace the motor performance of subjects. Wearable sensors such as inertial measurement units allow for monitoring motion performance. We propose quantifying the sit-to-stand performance based on two scores compiling kinematics, dynamics, and energy-related variables. Three groups participated in this research: asymptomatic young participants (n = 33), senior asymptomatic participants (n = 17), and Parkinson's patients (n = 20). An unsupervised classification was performed of the two scores to differentiate the three populations. We found a sensitivity of 0.4 and a specificity of 0.96 to distinguish Parkinson's patients from asymptomatic subjects. In addition, seven Parkinson's patients performed the sit-to-stand task "ON" and "OFF" medication, and we noted the scores improved with the patients' medication states (MDS-UPDRS III scores). Our investigation revealed that Parkinson's patients demonstrate a wide spectrum of mobility variations, and while one inertial measurement unit can quantify the sit-to-stand performance, differentiating between PD patients and healthy adults and distinguishing between "ON" and "OFF" periods in PD patients is still challenging.

Investigating Stochastic Differential Equations Modelling for Levodopa Infusion in Patients with Parkinson's Disease

BACKGROUND AND OBJECTIVES

Levodopa concentration in patients with Parkinson's disease is frequently modelled with ordinary differential equations (ODEs). Here, we investigate a pharmacokinetic model of plasma levodopa concentration in patients with Parkinson's disease by introducing stochasticity to separate the intra-individual variability into measurement and system noise, and to account for auto-correlated errors. We also investigate whether the induced stochasticity provides a better fit than the ODE approach.

METHODS

In this study, a system noise variable is added to the pharmacokinetic model for duodenal levodopa/carbidopa gel (LCIG) infusion described by three ODEs through a standard Wiener process, leading to a stochastic differential equations (SDE) model. The R package population stochastic modelling (PSM) was used for model fitting with data from previous studies for modelling plasma levodopa concentration and parameter estimation. First, the diffusion scale parameter (σ_w), measurement noise variance, and bioavailability are estimated with the SDE model. Second, σ_w is fixed to certain values from 0 to 1 and bioavailability is estimated. Cross-validation was performed to compare the average root mean square errors (RMSE) of predicted plasma levodopa concentration.

RESULTS

Both the ODE and the SDE models estimated bioavailability to be approximately 75%. The SDE model converged at different values of σ_w that were significantly different from zero. The average RMSE for the ODE model was 0.313, and the lowest average RMSE for the SDE model was 0.297 when σ_w was fixed to 0.9, and these two values are significantly different.

CONCLUSIONS

The SDE model provided a better fit for LCIG plasma levodopa concentration by approximately 5.5% in terms of mean percentage change of RMSE.

Evaluation of a sensor algorithm for motor state rating in Parkinson's disease

INTRODUCTION

A treatment response objective index (TRIS) was previously developed based on sensor data from pronation-supination tests. This study aimed to examine the performance of TRIS for medication effects in a new population sample with Parkinson's disease (PD) and its usefulness for constructing individual dose-response models.

METHODS

Twenty-five patients with PD performed a series of tasks throughout a levodopa challenge while wearing sensors. TRIS was used to determine motor changes in pronation-supination tests following a single levodopa dose, and was compared to clinical ratings including the Treatment Response Scale (TRS) and six sub-items of the UPDRS part III.

RESULTS

As expected, correlations between TRIS and clinical ratings were lower in the new population than in the initial study. TRIS was still significantly correlated to TRS (r_s = 0.23, P < 0.001) with a root mean square error (RMSE) of 1.33. For the patients (n = 17) with a good levodopa response and clear motor fluctuations, a stronger correlation was found (r_s = 0.38, RMSE = 1.29, P < 0.001). The mean TRIS increased significantly when patients went from the practically defined off to their best on state (P = 0.024). Individual dose-response models could be fitted for more participants when TRIS was used for modelling than when TRS ratings were used.

CONCLUSION

The objective sensor index shows promise for constructing individual dose-response models, but further evaluations and retraining of the TRIS algorithm are desirable to improve its performance and to ensure its clinical effectiveness.

Sensor-based algorithmic dosing suggestions for oral administration of levodopa/carbidopa microtablets for Parkinson's disease: a first experience

Objective

Dosing schedules for oral levodopa in advanced stages of Parkinson’s disease (PD) require careful tailoring to fit the needs of each patient. This study proposes a dosing algorithm for oral administration of levodopa and evaluates its integration into a sensor-based dosing system (SBDS).

Materials and methods

In collaboration with two movement disorder experts a knowledge-driven, simulation based algorithm was designed and integrated into a SBDS. The SBDS uses data from wearable sensors to fit individual patient models, which are then used as input to the dosing algorithm. To access the feasibility of using the SBDS in clinical practice its performance was evaluated during a clinical experiment where dosing optimization of oral levodopa was explored. The supervising neurologist made dosing adjustments based on data from the Parkinson’s KinetiGraph™ (PKG) that the patients wore for a week in a free living setting. The dosing suggestions of the SBDS were compared with the PKG-guided adjustments.

Results

The SBDS maintenance and morning dosing suggestions had a Pearson’s correlation of 0.80 and 0.95 (with mean relative errors of 21% and 12.5%), to the PKG-guided dosing adjustments. Paired t test indicated no statistical differences between the algorithmic suggestions and the clinician’s adjustments.

Conclusion

This study shows that it is possible to use algorithmic sensor-based dosing adjustments to optimize treatment with oral medication for PD patients.

The effect of continuous levodopa treatment during the afternoon hours

OBJECTIVE

The aim of this retrospective study was to investigate whether patients with Parkinson's disease, who are treated with levodopa-carbidopa intestinal gel (LCIG), clinically worsen during the afternoon hours and if so, to evaluate whether this occurs in all LCIG-treated patients or in a subgroup of patients.

METHODS

Three published studies were identified and included in the analysis. All studies provided individual response data assessed on the treatment response scale (TRS), and patients were treated with continuous LCIG. Ninety-eight patients from the three studies fulfilled the criteria. t tests were performed to find differences on the TRS values between the morning and the afternoon hours, linear mixed effect models were fitted on the afternoon hours' evaluations to find trends of wearing-off, and patients were classified into three TRS categories (meaningful increase in TRS, meaningful decrease in TRS, non-meaningful increase or decrease).

RESULTS

In all three studies, significant statistical differences were found between the morning TRS values and the afternoon TRS values (P-value <=0.001 in all studies). The linear mixed effect models had significant negative coefficients for time in two studies, and 48 out of 98 patients (49%) showed a meaningful decrease in TRS during the afternoon hours.

CONCLUSION

The results from all studies were consistent, both in the proportion of patients in the three groups and in the value of TRS decrease in the afternoon hours. Based on these findings, there seems to be a group of patients with predictable "off" behavior in the later parts of the day.

Vinpocetine regulates levels of circulating TLRs in Parkinson's disease patients

BACKGROUND

The pathogenesis of Parkinson's disease (PD) is complex; it includes mitochondrial dysfunction, oxidative stress, and neuroinflammation. Notably, Toll-like receptors (TLRs) may activate inflammatory or anti-inflammatory responses in Parkinson's disease. Vinpocetine has been tested as an anti-inflammatory in both animal and in vitro research. Thus, it is important to test whether the anti-inflammatory properties of vinpocetine may have a protective effect in PD patients.

METHODS

Eighty-nine Parkinson's disease patients and 42 healthy controls were recruited for this study. All patients were randomly assigned to either the traditional therapy group (T PD group, n = 46) or the vinpocetine group (V PD group, n = 43), in a blinded manner. Both treatments were administered for 14 days.

RESULTS

Administration of vinpocetine reduced mRNA levels of TLR2/4, as well as protein levels of the downstream signalling molecules, MyD88 and NF-κB; moreover, it lowered the expression levels of serum inflammatory cytokines, TNF-α and MCP-1. Notably, vinpocetine increased TLR3 mRNA levels, as well as protein levels of the downstream signalling molecules TRIF-β and IRF-3, and serum levels of the anti-inflammatory cytokines IL-10 and IL-8. Furthermore, vinpocetine produced a robust increase in the Mini Mental State Examination score, compared to that achieved by using levodopa therapy.

CONCLUSION

Vinpocetine treatment may exhibit anti-inflammatory activity and alleviate cognitive impairment.