Exploring Movement Impairments in Patients With Parkinson's Disease Using the Microsoft Kinect Sensor: A Feasibility Study

Background: Current assessments of motor symptoms in Parkinson's disease are often limited to clinical rating scales. Objectives: To develop a computer application using the Microsoft Kinect sensor to assess performance-related bradykinesia. Methods: The developed application (Motorgame) was tested in patients with Parkinson's disease and healthy controls. Participants were assessed with the Movement Disorder Society Unified Parkinson's disease Rating Scale (MDS-UPDRS) and standardized clinical side effect rating scales, i.e., UKU Side Effect Rating Scale and Simpson-Angus Scale. Additionally, tests of information processing (Symbol Coding Task) and motor speed (Token Motor Task), together with a questionnaire, were applied. Results: Thirty patients with Parkinson's disease and 33 healthy controls were assessed. In the patient group, there was a statistically significant (p < 0.05) association between prolonged time of motor performance in the Motorgame and upper body rigidity and bradykinesia (MDS-UPDRS) with the strongest effects in the right hand (p < 0.001). In the entire group, prolonged time of motor performance was significantly associated with higher Simson-Angus scale rigidity score and higher UKU hypokinesia scores (p < 0.05). A shortened time of motor performance was significantly associated with higher scores on information processing (p < 0.05). Time of motor performance was not significantly associated with Token Motor Task, duration of illness, or hours of daily physical activity. The Motorgame was well-accepted. Conclusions: In the present feasibility study the Motorgame was able to detect common motor symptoms in Parkinson's disease in a statistically significant and clinically meaningful way, making it applicable for further testing in larger samples.

In silico modelling of permeation enhancement potency in Caco-2 monolayers based on molecular descriptors and random forest

Structural traits of permeation enhancers are important determinants of their capacity to promote enhanced drug absorption. Therefore, in order to obtain a better understanding of structure-activity relationships for permeation enhancers, a Quantitative Structural Activity Relationship (QSAR) model has been developed. The random forest-QSAR model was based upon Caco-2 data for 41 surfactant-like permeation enhancers from Whitehead et al. (2008) and molecular descriptors calculated from their structure. The QSAR model was validated by two test-sets: (i) an eleven compound experimental set with Caco-2 data and (ii) nine compounds with Caco-2 data from literature. Feature contributions, a recent developed diagnostic tool, was applied to elucidate the contribution of individual molecular descriptors to the predicted potency. Feature contributions provided easy interpretable suggestions of important structural properties for potent permeation enhancers such as segregation of hydrophilic and lipophilic domains. Focusing on surfactant-like properties, it is possible to model the potency of the complex pharmaceutical excipients, permeation enhancers. For the first time, a QSAR model has been developed for permeation enhancement. The model is a valuable in silico approach for both screening of new permeation enhancers and physicochemical optimisation of surfactant enhancer systems.