Levodopa/carbidopa microtablets in Parkinson's disease: a study of pharmacokinetics and blinded motor assessment

Revolutionizing Precision Medicine: Exploring Wearable Sensors for Therapeutic Drug Monitoring and Personalized Therapy

Precision medicine, particularly therapeutic drug monitoring (TDM), is essential for optimizing drug dosage and minimizing toxicity. However, current TDM methods have limitations, including the need for skilled operators, patient discomfort, and the inability to monitor dynamic drug level changes. In recent years, wearable sensors have emerged as a promising solution for drug monitoring. These sensors offer real-time and continuous measurement of drug concentrations in biofluids, enabling personalized medicine and reducing the risk of toxicity. This review provides an overview of drugs detectable by wearable sensors and explores biosensing technologies that can enable drug monitoring in the future. It presents a comparative analysis of multiple biosensing technologies and evaluates their strengths and limitations for integration into wearable detection systems. The promising capabilities of wearable sensors for real-time and continuous drug monitoring offer revolutionary advancements in diagnostic tools, supporting personalized medicine and optimal therapeutic effects. Wearable sensors are poised to become essential components of healthcare systems, catering to the diverse needs of patients and reducing healthcare costs.

Negative pressure pulmonary edema due to upper airway obstruction after general anesthesia in a patient with Parkinson's disease: A case report

In a patient with Parkinson's disease (PD) who underwent spine surgery 13 h after the last anti-Parkinson medications, negative pressure pulmonary edema from upper airway obstruction developed immediately after extubation. Although oxygenation improved with high-flow nasal cannula therapy, such complications might develop due to abrupt discontinuation of medication for PD.

Levodopa Dose Equivalency in Parkinson's Disease: Updated Systematic Review and Proposals

BACKGROUND

To compare drug regimens across clinical trials in Parkinson's disease (PD) conversion formulae between antiparkinsonian drugs have been developed. These are reported in relation to levodopa as the benchmark drug in PD pharmacotherapy as 'levodopa equivalent dose' (LED). Currently, the LED conversion formulae proposed in 2010 by Tomlinson et al. based on a systematic review are predominantly used. However, new drugs with established and novel mechanisms of action and novel formulations of longstanding drugs have been developed since 2010. Therefore, consensus proposals for updated LED conversion formulae are needed.

OBJECTIVES

To update LED conversion formulae based on a systematic review.

METHODS

The MEDLINE, CENTRAL, and Embase databases were searched from January 2010 to July 2021. Additionally, in a standardized process according to the GRADE grid method, consensus proposals were issued for drugs with scarce data on levodopa dose equivalency.

RESULTS

The systematic database search yielded 3076 articles of which 682 were eligible for inclusion in the systematic review. Based on these data and the standardized consensus process, we present proposals for LED conversion formulae for a wide range of drugs that are currently available for the pharmacotherapy of PD or are expected to be introduced soon.

CONCLUSIONS

The LED conversion formulae issued in this Position Paper will serve as a research tool to compare the equivalence of antiparkinsonian medication across PD study cohorts and facilitate research on the clinical efficacy of pharmacological and surgical treatments as well as other non-pharmacological interventions in PD. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Personalized Medicine Approach in Treating Parkinson's Disease, Using Oral Administration of Levodopa/Carbidopa Microtablets in Clinical Practice

Background: The most effective symptomatic treatment in Parkinson’s disease (PD) is levodopa in standard doses. However, as the disease progresses, there may be a need for a more personalized approach and fine tuning, in accordance with the patients’ needs. This study aims to evaluate the individual experience of levodopa/carbidopa 5/1.25 mg microtablets (LC-5) in clinical practice with respect to efficacy, tolerability, and usability. The method used was as follows: patients answered a questionnaire concerning the effect and usability of LC-5, and their medical records were reviewed. Regarding results, thirty-five survey responses were obtained, and 29 patients’ medical records were reviewed. The LC-5 dose dispenser usability was generally rated positively and facilitated medication adherence. The majority (85%) of patients reported symptom improvement while using LC-5, compared with previous standard treatments. These results suggest that LC-5 therapy is generally well-tolerated, with favorable patient-reported efficacy and user friendliness, as well as the possibility for an individualized, fine-tuned PD treatment. Further studies with a prospective design and larger study population are needed to confirm the results.

Discrimination of idiopathic Parkinson's disease and vascular parkinsonism based on gait time series and the levodopa effect

Idiopathic Parkinson's disease (IPD) and vascular parkinsonism (VaP) present highly overlapping phenotypes, making it challenging to distinguish between these two parkinsonian syndromes. Recent evidence suggests that gait assessment and response to levodopa medication may assist in the objective evaluation of clinical differences. In this paper, we propose a new approach for gait pattern differentiation that uses convolutional neural networks (CNNs) based on gait time series with and without the influence of levodopa medication. Wearable sensors positioned on both feet were used to acquire gait data from 14 VaP patients, 15 IPD patients, and 34 healthy subjects. An individual's gait features are affected by physical characteristics, including age, height, weight, sex, and walking speed or stride length. Therefore, to reduce bias due to intersubject variations, a multiple regression normalization approach was used to obtain gait data. Recursive feature elimination using the linear support vector machine, lasso, and random forest were applied to infer the optimal feature subset that led to the best results. CNNs were implemented by means of various hyperparameters and feature subsets. The best CNN classifiers achieved accuracies of 79.33%±6.46, 82.33%±10.62, and 86.00%±7.12 without (off state), with (on state), and with the simultaneous consideration of the effect of levodopa medication (off/on state), respectively. The response to levodopa medication improved classification performance. Based on gait time series and response to medication, the proposed approach differentiates between IPD and VaP gait patterns and reveals a high accuracy rate, which might prove useful when distinguishing other diseases related to movement disorders.

Initial Experience of the Levodopa-Entacapone-Carbidopa Intestinal Gel in Clinical Practice

Patients in fluctuating stages of Parkinson’s disease (PD) require device-aided treatments. Continuous infusion of levodopa–carbidopa intestinal gel (LCIG) is a well-proven option in clinical practice. We now report the first clinical experience of levodopa–entacapone–carbidopa intestinal gel (LECIG) therapy. An observational study of the first patients to start LECIG in our clinic was performed. Twenty-four patients (11 females, 13 males) were included. The median age was 71.5 years, and the median duration since PD diagnosis was 15.5 years. The median treatment duration was 305 days. Median doses were: 6.0 mL as morning dose, 2.5 mL/h as infusion rate, and 1.0 mL as extra dose. Half of the patients were switched directly from LCIG. These patients express improvements in the size and weight of the pump. Furthermore, most of them considered the new pump to be improved regarding user-friendliness. Six patients discontinued LECIG, three due to diarrhea, one due to hallucinations and two deceased (one cardiac arrest and one COVID-19). LECIG has shown to be possible to use in patients with PD, efficacy and safety as expected. Patients are generally happy with the size and usability of the pump, but some technical improvements of the software are warranted, as well as larger, prospective studies.

Automatic Resting Tremor Assessment in Parkinson's Disease Using Smartwatches and Multitask Convolutional Neural Networks

Resting tremor in Parkinson's disease (PD) is one of the most distinctive motor symptoms. Appropriate symptom monitoring can help to improve management and medical treatments and improve the patients' quality of life. Currently, tremor is evaluated by physical examinations during clinical appointments; however, this method could be subjective and does not represent the full spectrum of the symptom in the patients' daily lives. In recent years, sensor-based systems have been used to obtain objective information about the disease. However, most of these systems require the use of multiple devices, which makes it difficult to use them in an ambulatory setting. This paper presents a novel approach to evaluate the amplitude and constancy of resting tremor using triaxial accelerometers from consumer smartwatches and multitask classification models. These approaches are used to develop a system for an automated and accurate symptom assessment without interfering with the patients' daily lives. Results show a high agreement between the amplitude and constancy measurements obtained from the smartwatch in comparison with those obtained in a clinical assessment. This indicates that consumer smartwatches in combination with multitask convolutional neural networks are suitable for providing accurate and relevant information about tremor in patients in the early stages of the disease, which can contribute to the improvement of PD clinical evaluation, early detection of the disease, and continuous monitoring.

Chitosan-Coated Hydroxypropylmethyl Cellulose Microparticles of Levodopa (and Carbidopa): In Vitro and Rat Model Kinetic Characteristics

Background

Parkinson's disease is a neurodegenerative disorder, and a major cause of disability. Levodopa, a prodrug of dopamine, remains the gold standard in the pharmacological management of Parkinson's disease. Despite several attempts to improve the clinical efficacy of levodopa, new oral levodopa formulations are needed to overcome irregular absorption and variable plasma concentrations.

Objective

The aim of this study was to evaluate the in vitro and in vivo kinetic properties of chitosan-coated hydroxypropylmethyl cellulose microparticles of levodopa (and carbidopa).

Methods

Microparticles were formulated by encapsulating levodopa powder in chitosan-coated hydroxypropylmethyl cellulose using the spray-drying method. Levodopa microparticles were evaluated for size, zeta potential, drug loading capacity, encapsulation efficiency and in vitro release. In evaluating in vivo pharmacokinetics, Sprague Dawley rats were administered either levodopa/carbidopa powder, levodopa/carbidopa microparticles, or Sinemet CR (a controlled release formulation of levodopa/carbidopa). The dose of respective formulations administered was 20/5 mg/kg; 20 mg levodopa combined with 5 mg carbidopa per kilogram body weight of animals. Treatments were administered via the oral route every 12 hours. Blood samples were collected after predetermined times following the third dose. Plasma was obtained from blood collected, and levodopa levels determined by HPLC. Pharmacokinetic parameters, including C_max, T_max, AUC, and t_½ of the various formulations, were estimated.

Results

The mean (SD) size of levodopa microparticles was 0.5 (0.05) µm with polydispersity index of 0.41 and a zeta potential of 10.8 mV. Of the expected 20% drug loading, the actual drug loading capacity of levodopa microparticles was found to be 19.1%, giving an encapsulation efficiency of 95.7%. The in vitro release kinetics of levodopa microparticles showed a controlled and sustained release, with about 80% release occurring after 12 hours. In vivo pharmacokinetic studies showed that rats administered levodopa/carbidopa microparticles had greater AUC (612.7 [17.42] ng.h/mL) and higher C_max (262.4 [38.86] ng/mL) compared with Sinemet CR: AUC 354.7 (98.09) ng.h/mL and C_max 95.5 (20.87) ng/mL. However, Sinemet CR had a much longer half-life (6.1 [2.58] hours) compared with levodopa/carbidopa microparticles (2.0 [0.31] hours).

Conclusions

Findings from this study suggest that chitosan-coated hydroxypropylmethyl cellulose microparticles of levodopa/carbidopa may give relatively high levels of levodopa in circulation. (Curr Ther Res Clin Exp. 2020; 81:XXX–XXX)

© 2020 Elsevier HS Journals, Inc.

Characterization, passive and active treatment in strabismic amblyopia: a narrative review

Strabismic amblyopia is characterized by a distorted spatial perception. In this condition, the neurofunctional disorder occurring during first years of life provoke several monocular and binocular anomalies such as crowding, deficits in the accommodative response, contrast sensitivity, and ocular motility abilities. The inhibition of the binocular function of the brain by the misaligned amblyopic eye induces a binocular imbalance leading to interocular suppression and the reduction or lack of stereoacuity. Passive treatments such as occlusion, optical and/or pharmacological penalization, and Bangerter foils has been demonstrated to be potentially useful treatments for strabismic amblyopia. Recent researches have proved new pharmacological options to improve and maintain visual acuity after occlusion treatment in strabismic amblyopia. Likewise, the active vision therapy, in the last years, is becoming a very relevant therapeutic option in combination with passive treatments, especially during and after monocular therapy, in the attempt of recovering the imbalanced binocular vision.

A multiple motion sensors index for motor state quantification in Parkinson's disease

AIM

To construct a Treatment Response Index from Multiple Sensors (TRIMS) for quantification of motor state in patients with Parkinson's disease (PD) during a single levodopa dose. Another aim was to compare TRIMS to sensor indexes derived from individual motor tasks.

METHOD

Nineteen PD patients performed three motor tests including leg agility, pronation-supination movement of hands, and walking in a clinic while wearing inertial measurement unit sensors on their wrists and ankles. They performed the tests repeatedly before and after taking 150% of their individual oral levodopa-carbidopa equivalent morning dose.Three neurologists blinded to treatment status, viewed patients' videos and rated their motor symptoms, dyskinesia, overall motor state based on selected items of Unified PD Rating Scale (UPDRS) part III, Dyskinesia scale, and Treatment Response Scale (TRS). To build TRIMS, out of initially 178 extracted features from upper- and lower-limbs data, 39 features were selected by stepwise regression method and were used as input to support vector machines to be mapped to mean reference TRS scores using 10-fold cross-validation method. Test-retest reliability, responsiveness to medication, and correlation to TRS as well as other UPDRS items were evaluated for TRIMS.

RESULTS

The correlation of TRIMS with TRS was 0.93. TRIMS had good test-retest reliability (ICC = 0.83). Responsiveness of the TRIMS to medication was good compared to TRS indicating its power in capturing the treatment effects. TRIMS was highly correlated to dyskinesia (R = 0.85), bradykinesia (R = 0.84) and gait (R = 0.79) UPDRS items. Correlation of sensor index from the upper-limb to TRS was 0.89.

CONCLUSION

Using the fusion of upper- and lower-limbs sensor data to construct TRIMS provided accurate PD motor states estimation and responsive to treatment. In addition, quantification of upper-limb sensor data during walking test provided strong results.

[New Therapeutic Options for the Individualised Titration of Levodopa]

Levodopa is the most effective medication in the treatment of Parkinson's disease. In the course of the disease the storage facility of dopaminergic neurones deteriorates, so that the duration of the half-life period likewise converges. This results in fluctuations in performance, and also in dyskinesias as a further consequence of the narrowing therapeutic window. Therapeutically, this in turn leads to further fractioning of the levodopa dosage and a reduction of single-dose levels. There is, however, only limited scope for doing this with the conventional levodopa formulations. For this reason, the introduction of water-soluble microtablets à 5 / 1.25 mg levodopa / carbidopa can be regarded as a beneficial extension permitting for fine titration of the dopaminergic stimulation. Here we present this new therapeutic principle, the available data and concepts for clinical use.

A comparison of feature selection methods when using motion sensors data: a case study in Parkinson's disease

The objective of this study is to investigate the effects of feature selection methods on the performance of machine learning methods for quantifying motor symptoms of Parkinson's disease (PD) patients. Different feature selection methods including step-wise regression, Lasso regression and Principal Component Analysis (PCA) were applied on 88 spatiotemporal features that were extracted from motion sensors during hand rotation tests. The selected features were then used in support vector machines (SVM), decision trees (DT), linear regression, and random forests models to calculate a so-called treatment-response index (TRIS). The validity, testretest reliability and sensitivity to treatment were assessed for each combination (feature selection method plus machine learning method). There were improvements in correlation coefficients and root mean squared error (RMSE) for all the machine learning methods, except DTs, when using the selected features from step-wise regression inputs. Using step-wise regression and SVM was found to have better sensitivity to treatment and higher correlation to clinical ratings on the Unified PD Rating Scale as compared to the combination of PCA and SVM. When assessing the ability of the machine learning methods to discriminate between tests performed by PD patients and healthy controls the results were mixed. These results suggest that the choice of feature selection methods is crucial when working with data-driven modelling. Based on our findings the step-wise regression can be considered as the method with the best performance.

Wearable Sweat Band for Noninvasive Levodopa Monitoring

Levodopa is the standard medication clinically prescribed to patients afflicted with Parkinson's disease. In particular, the monitoring and optimization of levodopa dosage are critical to mitigate the onset of undesired fluctuations in the patients' physical and emotional conditions such as speech function, motor behavior, and mood stability. The traditional approach to optimize levodopa dosage involves evaluating the subjects' motor function, which has many shortcomings due to its subjective and limited quantifiable nature. Here, we present a wearable sweat band on a nanodendritic platform that quantitatively monitors levodopa dynamics in the body. Both stationary iontophoretic induction and physical exercise are utilized as our methods of sweat extraction. The sweat band measures real-time pharmacokinetic profiles of levodopa to track the dynamic response of the drug metabolism. We demonstrated the sweat band's functionalities on multiple subjects with implications toward the systematic administering of levodopa and routine management of Parkinson's disease.

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.

Motion Sensor-Based Assessment of Parkinson's Disease Motor Symptoms During Leg Agility Tests: Results From Levodopa Challenge

Parkinson's disease (PD) is a degenerative, progressive disorder of the central nervous system that mainly affects motor control. The aim of this study was to develop data-driven methods and test their clinimetric properties to detect and quantify PD motor states using motion sensor data from leg agility tests. Nineteen PD patients were recruited in a levodopa single dose challenge study. PD patients performed leg agility tasks while wearing motion sensors on their lower extremities. Clinical evaluation of video recordings was performed by three movement disorder specialists who used four items from the motor section of the unified PD rating scale (UPDRS), the treatment response scale (TRS) and a dyskinesia score. Using the sensor data, spatiotemporal features were calculated and relevant features were selected by feature selection. Machine learning methods like support vector machines (SVM), decision trees, and linear regression, using ten-fold cross validation were trained to predict motor states of the patients. SVM showed the best convergence validity with correlation coefficients of 0.81 to TRS, 0.83 to UPDRS #31 (body bradykinesia and hypokinesia), 0.78 to SUMUPDRS (the sum of the UPDRS items: #26-leg agility, #27-arising from chair, and #29-gait), and 0.67 to dyskinesia. Additionally, the SVM-based scores had similar test-retest reliability in relation to clinical ratings. The SVM-based scores were less responsive to treatment effects than the clinical scores, particularly with regards to dyskinesia. In conclusion, the results from this study indicate that using motion sensors during leg agility tests may lead to valid and reliable objective measures of PD motor symptoms.

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.

Using measurements from wearable sensors for automatic scoring of Parkinson's disease motor states: Results from 7 patients

The objective of this study was to investigate the validity of an objective gait measure for assessment of different motor states of advanced Parkinson's disease (PD) patients. Seven PD patients performed a gait task up to 15 times while wearing sensors on their upper and lower limbs. Each task was performed at specific points during a test day, following a single dose of levodopa-carbidopa. At the time of the tasks the patients were video recorded and three movement disorder experts rated their motor function on three clinical scales: a treatment response scale (TRS) that ranged from -3 (very bradykinetic) to 0 (ON) to +3 (very dyskinetic), a dyskinesia score that ranged from 0 (no dyskinesia) to 4 (extreme dyskinesia), and a bradykinesia score that ranged from 0 (no bradykinesia) to 4 (extreme bradykinesia). Raw accelerometer and gyroscope data of the sensors were processed and analyzed with time series analysis methods to extract features. The utilized features quantified separate limb movements as well as movement symmetries between the limbs. The features were processed with principal component analysis and the components were used as predictors for separate support vector machine (SVM) models for each of the three scales. The performance of each model was evaluated in a leave-one-patient out setting where the observations of a single patient were used as the testing set and the observations of the other 6 patients as the training set. Root mean square error (RMSE) and correlation coefficients for the predictions showed a good ability of the models to map the sensor data into the rating scales. There were strong correlations between the SVM models and the mean ratings of TRS (0.79; RMSE=0.70), bradykinesia score (0.79; RMSE=0.47), and bradykinesia score (0.78; RMSE=0.46). The results presented in this paper indicate that the use of wearable sensors when performing gait tasks can generate measurements that have a good correlation to subjective expert assessments.

Population pharmacokinetics of levodopa/carbidopa microtablets in healthy subjects and Parkinson's disease patients

OBJECTIVES

Low dose, dispersible, levodopa/carbidopa microtablets with an automatic dose dispenser have been developed to facilitate individualized levodopa treatment. The aim of this study was to characterize the pharmacokinetics (PK) of levodopa and carbidopa after microtablet administration, and evaluate the impact of potential covariates.

METHODS

The population PK analysis involved data from 18 healthy subjects and 18 Parkinson's disease patients included in two single-dose, open-label levodopa/carbidopa microtablet studies. The analysis was carried out using non-linear mixed effects modeling. Bodyweight was included on all disposition parameters according to allometric scaling. Potential influence of additional covariates was investigated using graphical evaluation and adjusted adaptive least absolute shrinkage and selection operator.

RESULTS

Dispositions of levodopa and carbidopa were best described by a two- and one-compartment model respectively. Double-peak profiles were described using two parallel absorption compartments. Levodopa apparent clearance was found to decrease with increasing carbidopa dose (15% lower with 75 compared to 50 mg of carbidopa) and disease stage (by 18% for Hoehn and Yahr 1 to 4). Carbidopa apparent clearance was found to decrease with age (28% between the age of 60 and 80 years). An external evaluation showed the model to be able to reasonably well predict levodopa concentrations following multiple-dose microtablet administration in healthy subjects.

CONCLUSIONS

The presented models adequately described the PK of levodopa and carbidopa, following microtablet administration. The developed model may in the future be combined with a pharmacokinetic-pharmacodynamic target and used for individualized dose selection, utilizing the flexibility offered by the microtablets.

Individual dose-response models for levodopa infusion dose optimization

BACKGROUND AND OBJECTIVE

To achieve optimal effect with continuous infusion treatment in Parkinson's disease (PD), the individual doses (morning dose and continuous infusion rate) are titrated by trained medical personnel. This study describes an algorithmic method to derive optimized dosing suggestions for infusion treatment of PD, by fitting individual dose-response models. The feasibility of the proposed method was investigated using patient chart data.

METHODS

Patient records were collected at Uppsala University hospital which provided dosing information and dose-response evaluations. Mathematical optimization was used to fit individual patient models using the records' information, by minimizing an objective function. The individual models were passed to a dose optimization algorithm, which derived an optimized dosing suggestion for each patient model.

RESULTS

Using data from a single day's admission the algorithm showed great ability to fit appropriate individual patient models and derive optimized doses. The infusion rate dosing suggestions had 0.88 correlation and 10% absolute mean relative error compared to the optimal doses as determined by the hospital's treating team. The morning dose suggestions were consistency lower that the optimal morning doses, which could be attributed to different dosing strategies and/or lack of on-off evaluations in the morning.

CONCLUSION

The proposed method showed promise and could be applied in clinical practice, to provide the hospital personnel with additional information when making dose adjustment decisions.

Individualization of levodopa treatment using a microtablet dispenser and ambulatory accelerometry

AIM

This 4-week open-label observational study describes the effect of introducing a microtablet dose dispenser and adjusting doses based on objective free-living motor symptom monitoring in individuals with Parkinson's disease (PD).

METHODS

Twenty-eight outpatients with PD on stable levodopa treatment with dose intervals of ≤4 hour had their daytime doses of levodopa replaced with levodopa/carbidopa microtablets, 5/1.25 mg (LC-5) delivered from a dose dispenser device with programmable reminders. After 2 weeks, doses were adjusted based on ambulatory accelerometry and clinical monitoring.

RESULTS

Twenty-four participants completed the study per protocol. The daily levodopa dose was increased by 15% (112 mg, P < 0.001) from period 1 to 2, and the dose interval was reduced by 12% (22 minutes, P = 0.003). The treatment adherence to LC-5 was high in both periods. The MDS-UPDRS parts II and III, disease-specific quality of life (PDQ-8), wearing-off symptoms (WOQ-19), and nonmotor symptoms (NMS Quest) improved after dose titration, but the generic quality-of-life measure EQ-5D-5L did not. Blinded expert evaluation of accelerometry results demonstrated improvement in 60% of subjects and worsening in 25%.

CONCLUSIONS

The introduction of a levodopa microtablet dispenser and accelerometry aided dose adjustments improve PD symptoms and quality of life in the short term.

First clinical experience with levodopa/carbidopa microtablets in Parkinson's disease

BACKGROUND

Levodopa is the most effective symptomatic treatment throughout the course of Parkinson's disease, but as the disease progresses, there may be a need for individualized, fine-tuned treatments.

AIM

To evaluate individualized levodopa/carbidopa dosing using microtablets dispensed with a dose dispenser, with respect to efficacy and usability as perceived by patients.

METHODS

Patient records and dose dispenser reports from patients previously or currently treated with microtablets and a dose dispenser were reviewed, and a patient questionnaire concerning effect and usability was sent to patients.

RESULTS

Eleven patient records, four dose dispenser reports and nine survey responses were obtained. The treatment effect was considered to be improved by six of nine patients. One-third found their bradykinesia to be improved, and the non-troublesome dyskinesia was unchanged according to a majority of patients; however, some experienced the duration and magnitude of troublesome dyskinesia to be worse. The usability was generally rated as good. The four dose dispenser reports obtained showed 97(±5)% total adherence.

CONCLUSIONS

The experienced effect of treatment can, for some patients, be improved by the use of microtablets, and the dose dispenser was considered user-friendly. Further studies with a larger study population and prospective design are needed to confirm the results.

Verification of a Method for Measuring Parkinson's Disease Related Temporal Irregularity in Spiral Drawings

Parkinson’s disease (PD) is a progressive movement disorder caused by the death of dopamine-producing cells in the midbrain. There is a need for frequent symptom assessment, since the treatment needs to be individualized as the disease progresses. The aim of this paper was to verify and further investigate the clinimetric properties of an entropy-based method for measuring PD-related upper limb temporal irregularities during spiral drawing tasks. More specifically, properties of a temporal irregularity score (TIS) for patients at different stages of PD, and medication time points were investigated. Nineteen PD patients and 22 healthy controls performed repeated spiral drawing tasks on a smartphone. Patients performed the tests before a single levodopa dose and at specific time intervals after the dose was given. Three movement disorder specialists rated videos of the patients based on the unified PD rating scale (UPDRS) and the Dyskinesia scale. Differences in mean TIS between the groups of patients and healthy subjects were assessed. Test-retest reliability of the TIS was measured. The ability of TIS to detect changes from baseline (before medication) to later time points was investigated. Correlations between TIS and clinical rating scores were assessed. The mean TIS was significantly different between healthy subjects and patients in advanced groups (p-value = 0.02). Test-retest reliability of TIS was good with Intra-class Correlation Coefficient of 0.81. When assessing changes in relation to treatment, TIS contained some information to capture changes from Off to On and wearing off effects. However, the correlations between TIS and clinical scores (UPDRS and Dyskinesia) were weak. TIS was able to differentiate spiral drawings drawn by patients in an advanced stage from those drawn by healthy subjects, and TIS had good test-retest reliability. TIS was somewhat responsive to single-dose levodopa treatment. Since TIS is an upper limb high-frequency-based measure, it cannot be detected during clinical assessment.

Development of new levodopa treatment strategies in Parkinson's disease-from bedside to bench to bedside

This review will illustrate the process of moving from an idea through preclinical research and Galenic developments into clinical investigations and finally to approval by regulatory agencies within the European Union. The two new treatment strategies described, levodopa/carbidopa intestinal gel and levodopa/carbidopa microtablets, for advanced Parkinson's disease, have been developed in collaborative research within departments at Uppsala University. With this historical approach, reference priority is given to reports considered to be of special importance for this more than two decades long process 'from bedside to bench to bedside'.