Aims: The purpose of this study was to investigate pharmacodynamic effects of drugs targeting cortical excitability using transcranial magnetic stimulation (TMS) combined with electromyography (EMG) and electroencephalography (EEG) in healthy subjects, to further develop TMS outcomes as biomarkers for proof‐of‐mechanism in early‐phase clinical drug development. Antiepileptic drugs presumably modulate cortical excitability. Therefore, we studied effects of levetiracetam, valproic acid and lorazepam on cortical excitability in a double‐blind, placebo‐controlled, 4‐way cross‐over study.Methods: In 16 healthy male subjects, single‐ and paired‐pulse TMS‐EMG–EEG measurements were performed predose and 1.5, 7 and 24 hours postdose. Treatment effects on motor‐evoked potential, short and long intracortical inhibition and TMS‐evoked potential amplitudes, were analysed using a mixed model ANCOVA and cluster‐based permutation analysis.Results: We show that motor‐evoked potential amplitudes decreased after administration of levetiracetam (estimated difference [ED] −378.4 μV; 95%CI: −644.3, −112.5 μV; P < .01), valproic acid (ED −268.8 μV; 95%CI: −532.9, −4.6 μV; P = .047) and lorazepam (ED −330.7 μV; 95%CI: −595.6, −65.8 μV; P = .02) when compared with placebo. Long intracortical inhibition was enhanced by levetiracetam (ED −60.3%; 95%CI: −87.1%, −33.5%; P < .001) and lorazepam (ED −68.2%; 95%CI: −94.7%, −41.7%; P < .001) at a 50‐ms interstimulus interval. Levetiracetam increased TMS‐evoked potential component N45 (P = .004) in a central cluster and decreased N100 (P < .001) in a contralateral cluster.Conclusion: This study shows that levetiracetam, valproic acid and lorazepam decrease cortical excitability, which can be detected using TMS‐EMG–EEG in healthy subjects. These findings provide support for the use of TMS excitability measures as biomarkers to demonstrate pharmacodynamic effects of drugs that influence cortical excitability.

The validation of objective and easy‐to‐implement biomarkers that can monitor the effects of fast‐acting drugs among Parkinson's disease (PD) patients would benefit antiparkinsonian drug development. We developed composite biomarkers to detect levodopa/carbidopa effects and to estimate PD symptom severity. For this development, we trained machine learning algorithms to select the optimal combination of finger tapping task features to predict treatment effects and disease severity. Data were collected during a placebo‐controlled, crossover study with 20 PD patients. The alternate index and middle finger tapping (IMFT), alternative index finger tapping (IFT), and thumb–index finger tapping (TIFT) tasks and the Movement Disorder Society‐Unified Parkinson's Disease Rating Scale (MDS‐UPDRS) III were performed during treatment. We trained classification algorithms to select features consisting of the MDS‐UPDRS III item scores; the individual IMFT, IFT, and TIFT; and all three tapping tasks collectively to classify treatment effects. Furthermore, we trained regression algorithms to estimate the MDS‐UPDRS III total score using the tapping task features individually and collectively. The IFT composite biomarker had the best classification performance (83.50% accuracy, 93.95% precision) and outperformed the MDS‐UPDRS III composite biomarker (75.75% accuracy, 73.93% precision). It also achieved the best performance when the MDS‐UPDRS III total score was estimated (mean absolute error: 7.87, Pearson's correlation: 0.69). We demonstrated that the IFT composite biomarker outperformed the combined tapping tasks and the MDS‐UPDRS III composite biomarkers in detecting treatment effects. This provides evidence for adopting the IFT composite biomarker for detecting antiparkinsonian treatment effect in clinical trials. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Background: Movement Disorder Society–Unified Parkinson's Rating Scale Part III (MDS‐UPDRS III) is the gold standard for assessing medication effects in patients with Parkinson's disease (PD). However, short and rater‐independent measurements would be ideal for future trials.Objectives: To assess the ability of 3 different finger tapping tasks to detect levodopa/carbidopa‐induced changes over time and to determine their correlation and compare their discriminatory power with MDS‐UPDRS III.Methods: This was a randomized, double‐blind, crossover study in 20 patients with PD receiving levodopa/carbidopa and placebo capsules after overnight medication withdrawal. Pre‐ and up to 3.5 hours postdose, MDS‐UPDRS III and tapping tasks were performed. Tasks included 2 touchscreen‐based alternate finger tapping tasks (index finger versus index–middle finger tapping) and a thumb–index finger task using a goniometer.Results: In the alternate index finger tapping task, levodopa/carbidopa compared with placebo resulted in significantly faster (total taps: 12.5 [95% confidence interval, CI, 6.7–18.2]) and less accurate tapping (total spatial error: 240 mm [95% CI, 123–357 mm]) with improved rhythm (intertap interval standard deviation [SD], −16.3% [95% CI, −29.9% to 0.0%]). In the thumb–index finger task, tapping was significantly faster (mean opening velocity, 151 degree/s [64–237 degree/s]), with a higher mean amplitude (8.4 degrees [3.7–13.0 degrees]) and improved rhythm (intertap interval SD, −46.4% [95% CI, −63.7% to −20.9%]). The speed‐related endpoints showed a moderate‐to‐strong correlation with the MDS‐UPDRS III (r = 0.45–0.70). The effect sizes of total taps and spatial error in the alternate index finger tapping task and opening velocity in the thumb–index finger task were comparable with the MDS‐UPDRS III. In contrast, the MDS‐UPDRS III performed better than the alternate index–middle finger task.Conclusion: The alternate index finger and the thumb–index finger tapping tasks provide short, rater‐independent measurements that are sensitive to levodopa/carbidopa effects with a similar effect size as the MDS‐UPDRS III.

Background: Central nervous system (CNS) disorders benefit from ongoing monitoring to assess disease progression and treatment efficacy. Mobile health (mHealth) technologies offer a means for the remote and continuous symptom monitoring of patients. Machine Learning (ML) techniques can process and engineer mHealth data into a precise and multidimensional biomarker of disease activity. Objective: This narrative literature review aims to provide an overview of the current landscape of biomarker development using mHealth technologies and ML. Additionally, it proposes recommendations to ensure the accuracy, reliability, and interpretability of these biomarkers. Methods: This review extracted relevant publications from databases such as PubMed, IEEE, and CTTI. The ML methods employed across the selected publications were then extracted, aggregated, and reviewed. Results: This review synthesized and presented the diverse approaches of 66 publications that address creating mHealth-based biomarkers using ML. The reviewed publications provide a foundation for effective biomarker development and offer recommendations for creating representative, reproducible, and interpretable biomarkers for future clinical trials. Conclusion: mHealth-based and ML-derived biomarkers have great potential for the remote monitoring of CNS disorders. However, further research and standardization of study designs are needed to advance this field. With continued innovation, mHealth-based biomarkers hold promise for improving the monitoring of CNS disorders.

Background: Facioscapulohumeral dystrophy (FSHD) is a progressive muscle dystrophy disorder leading to significant disability. Currently, FSHD symptom severity is assessed by clinical assessments such as the FSHD clinical score and the Timed Up-and-Go test. These assessments are limited in their ability to capture changes continuously and the full impact of the disease on patients’ quality of life. Real-world data related to physical activity, sleep, and social behavior could potentially provide additional insight into the impact of the disease and might be useful in assessing treatment effects on aspects that are important contributors to the functioning and well-being of patients with FSHD. Objective: This study investigated the feasibility of using smartphones and wearables to capture symptoms related to FSHD based on a continuous collection of multiple features, such as the number of steps, sleep, and app use. We also identified features that can be used to differentiate between patients with FSHD and non-FSHD controls. Methods: In this exploratory noninterventional study, 58 participants (n=38, 66%, patients with FSHD and n=20, 34%, non-FSHD controls) were monitored using a smartphone monitoring app for 6 weeks. On the first and last day of the study period, clinicians assessed the participants’ FSHD clinical score and Timed Up-and-Go test time. Participants installed the app on their Android smartphones, were given a smartwatch, and were instructed to measure their weight and blood pressure on a weekly basis using a scale and blood pressure monitor. The user experience and perceived burden of the app on participants’ smartphones were assessed at 6 weeks using a questionnaire. With the data collected, we sought to identify the behavioral features that were most salient in distinguishing the 2 groups (patients with FSHD and non-FSHD controls) and the optimal time window to perform the classification. Results: Overall, the participants stated that the app was well tolerated, but 67% (39/58) noticed a difference in battery life using all 6 weeks of data, we classified patients with FSHD and non-FSHD controls with 93% accuracy, 100% sensitivity, and 80% specificity. We found that the optimal time window for the classification is the first day of data collection and the first week of data collection, which yielded an accuracy, sensitivity, and specificity of 95.8%, 100%, and 94.4%, respectively. Features relating to smartphone acceleration, app use, location, physical activity, sleep, and call behavior were the most salient features for the classification. Conclusions: Remotely monitored data collection allowed for the collection of daily activity data in patients with FSHD and non-FSHD controls for 6 weeks. We demonstrated the initial ability to detect differences in features in patients with FSHD and non-FSHD controls using smartphones and wearables, mainly based on data related to physical and social activity. Trial Registration: ClinicalTrials.gov NCT04999735; https://www.clinicaltrials.gov/ct2/show/NCT04999735

Parkinson’s disease (PD) is a progressive neurodegenerative disease that affects almost 2% of the population above the age of 65. To better quantify the effects of new medications, fast and objective methods are needed. Touchscreen-based tapping tasks are simple yet effective tools for quantifying drug effects on PD-related motor symptoms, especially bradykinesia. However, there is no consensus on the optimal task set-up. The present study compares four tapping tasks in 14 healthy participants. In alternate finger tapping (AFT), tapping occurred with the index and middle finger with 2.5 cm between targets, whereas in alternate side tapping (AST) the index finger with 20 cm between targets was used. Both configurations were tested with or without the presence of a visual cue. Moreover, for each tapping task, within- and between-day repeatability and (potential) sensitivity of the calculated parameters were assessed. Visual cueing reduced tapping speed and rhythm, and improved accuracy. This effect was most pronounced for AST. On average, AST had a lower tapping speed with impaired accuracy and improved rhythm compared to AFT. Of all parameters, the total number of taps and mean spatial error had the highest repeatability and sensitivity. The findings suggest against the use of visual cueing because it is crucial that parameters can vary freely to accurately capture medication effects. The choice for AFT or AST depends on the research question, as these tasks assess different aspects of movement. These results encourage further validation of non-cued AFT and AST in PD patients.