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... Show moreThe 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. & COPY; 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. Show less
Koopmans, I.; Doll, R.J.; Wall, H. van der; Kam, M. de; Groeneveld, G.J.; Cohen, A.; Zuiker, R. 2023
IntroductionDrivers should be aware of possible impairing effects of alcohol, medicinal substance, or fatigue on driving performance. Such effects are assessed in clinical trials, including a... Show moreIntroductionDrivers should be aware of possible impairing effects of alcohol, medicinal substance, or fatigue on driving performance. Such effects are assessed in clinical trials, including a driving task or related psychomotor tasks. However, a choice between predicting tasks must be made. Here, we compare driving performance with on-the-road driving, simulator driving, and psychomotor tasks using the effect of sleep deprivation.Method This two-way cross over study included 24 healthy men with a minimum driving experience of 3000km per year. Psychomotor tasks, simulated driving, and on-the-road driving were assessed in the morning and the afternoon after a well-rested night and in the morning after a sleep-deprived night. Driving behaviour was examined by calculating the Standard Deviation of Lateral Position (SDLP).Results SDLP increased after sleep deprivation for simulated (10cm, 95%CI:6.7–13.3) and on-the-road driving (2.8cm, 95%CI:1.9–3.7). The psychomotor test battery detected effects of sleep deprivation in almost all tasks. Correlation between on-the-road tests and simulator SDLP after a well-rested night (0.63, p < .001) was not present after a night of sleep deprivation (0.31, p = .18). Regarding the effect of sleep deprivation on the psychomotor test battery, only adaptive tracking correlated with the SDLP of the driving simulator (-0.50, p = .02). Other significant correlations were related to subjective VAS scores.Discussion The lack of apparent correlations and difference in sensitivity of performance of the psychomotor tasks, simulated driving and, on-the-road driving indicates that the tasks may not be interchangeable and may assess different aspects of driving behaviour. Show less
Background: Facioscapulohumeral muscular dystrophy (FSHD) is a progressive neuromuscular disease. Its slow and variable progression makes the development of new treatments highly dependent on... Show moreBackground: Facioscapulohumeral muscular dystrophy (FSHD) is a progressive neuromuscular disease. Its slow and variable progression makes the development of new treatments highly dependent on validated biomarkers that can quantify disease progression and response to drug interventions.Objective: We aimed to build a tool that estimates FSHD clinical severity based on behavioral features captured using smartphone and remote sensor data. The adoption of remote monitoring tools, such as smartphones and wearables, would provide a novel opportunity for continuous, passive, and objective monitoring of FSHD symptom severity outside the clinic.Methods: In total, 38 genetically confirmed patients with FSHD were enrolled. The FSHD Clinical Score and the Timed Up and Go (TUG) test were used to assess FSHD symptom severity at days 0 and 42. Remote sensor data were collected using an Android smartphone, Withings Steel HR+, Body+, and BPM Connect+ for 6 continuous weeks. We created 2 single-task regression models that estimated the FSHD Clinical Score and TUG separately. Further, we built 1 multitask regression model that estimated the 2 clinical assessments simultaneously. Further, we assessed how an increasingly incremental time window affected the model performance. To do so, we trained the models on an incrementally increasing time window (from day 1 until day 14) and evaluated the predictions of the clinical severity on the remaining 4 weeks of data.Results: The single-task regression models achieved an R2 of 0.57 and 0.59 and a root-mean-square error (RMSE) of 2.09 and 1.66 when estimating FSHD Clinical Score and TUG, respectively. Time spent at a health-related location (such as a gym or hospital) and call duration were features that were predictive of both clinical assessments. The multitask model achieved an R2 of 0.66 and 0.81 and an RMSE of 1.97 and 1.61 for the FSHD Clinical Score and TUG, respectively, and therefore outperformed the single-task models in estimating clinical severity. The 3 most important features selected by the multitask model were light sleep duration, total steps per day, and mean steps per minute. Using an increasing time window (starting from day 1 to day 14) for the FSHD Clinical Score, TUG, and multitask estimation yielded an average R2 of 0.65, 0.79, and 0.76 and an average RMSE of 3.37, 2.05, and 4.37, respectively. Conclusions: We demonstrated that smartphone and remote sensor data could be used to estimate FSHD clinical severity and therefore complement the assessment of FSHD outside the clinic. In addition, our results illustrated that training the models on the first week of data allows for consistent and stable prediction of FSHD symptom severity. Longitudinal follow-up studies should be conducted to further validate the reliability and validity of the multitask model as a tool to monitor disease progression over a longer period. Show less
Thijssen, E.; Makai-Boloni, S.; Brummelen, E. van; Heijer, J. den; Yavuz, Y.; Doll, R.J.; Groeneveld, G.J. 2022
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).... Show moreBackground: 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. Show less
Aim: We assessed whether total sleep deprivation (TSD) in combination with pain tests yields a reliable method to assess altered pain thresholds, which subsequently may be used to investigate ... Show moreAim: We assessed whether total sleep deprivation (TSD) in combination with pain tests yields a reliable method to assess altered pain thresholds, which subsequently may be used to investigate (novel) analgesics in healthy subjects. Methods: This was a two-part randomized crossover study in 24 healthy men and 24 women. Subjects were randomized 1:1 to first complete a day of nonsleep-deprived nociceptive threshold testing, followed directly by a TSD night and morning of sleep-deprived testing, or first complete the TSD night and morning sleep-deprived testing, returning 7 days later for a day of nonsleep-deprived testing. A validated pain test battery (heat, pressure, electrical burst and stair, cold pressor pain test and conditioned pain modulation [CPM] paradigm) and sleep questionnaires were performed. Results: Subjects were significantly sleepier after TSD as measured using sleepiness questionnaires. Cold pressor pain tolerance (PTT, estimate of difference [ED] -10.8%, 95% CI -17.5 to -3.6%), CPM PTT (ED -0.69 mA, 95% CI -1.36 to -0.03 mA), pressure PTT (ED -11.2%, 95% CI -17.5% to -4.3%) and heat pain detection thresholds (ED -0.74 degrees C, 95% CI -1.34 to -0.14 degrees C) were significantly decreased after TSD compared to the baseline morning assessment in the combined analysis (men + women). Heat hyperalgesia was primarily driven by an effect of TSD in men, whereas cold and pressure hyperalgesia was primarily driven by the effects of TSD observed in women. Conclusions: TSD induced sex-dependent hyperalgesia on cold, heat and pressure pain, and CPM response. These results suggest that the TSD model may be suitable to evaluate (novel) analgesics in early-phase drug studies. Show less
Maleki, G.; Zhuparris, A.; Koopmans, I.; Doll, R.J.; Voet, N.; Cohen, A.; ... ; Maeyer, J. de 2022
Background: Facioscapulohumeral dystrophy (FSHD) is a progressive muscle dystrophy disorder leading to significant disability. Currently, FSHD symptom severity is assessed by clinical assessments... Show moreBackground: 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. Show less
Wall, H.E.C. van der; Hassing, G.J.; Doll, R.J.; Westen, G.J.P. van; Cohen, A.F.; Selder, J.L.; ... ; Gal, P. 2022
ObjectiveThe aim of the present study was to develop a neural network to characterize the effect of aging on the ECG in healthy volunteers. Moreover, the impact of the various ECG features on aging... Show moreObjectiveThe aim of the present study was to develop a neural network to characterize the effect of aging on the ECG in healthy volunteers. Moreover, the impact of the various ECG features on aging was evaluated.Methods & resultsA total of 6228 healthy subjects without structural heart disease were included in this study. A neural network regression model was created to predict age of the subjects based on their ECG; 577 parameters derived from a 12‑lead ECG of each subject were used to develop and validate the neural network; A tenfold cross-validation was performed, using 118 subjects for validation each fold. Using SHapley Additive exPlanations values the impact of the individual features on the prediction of age was determined. Of 6228 subjects tested, 1808 (29%) were females and mean age was 34 years, range 18-75 years. Physiologic age was estimated as a continuous variable with an average error of 6.9 ± 5.6 years (R2 = 0.72 ± 0.04). The correlation was slightly stronger for men (R2 = 0.74) than for women (R2 = 0.66). The most important features on the prediction of physiologic age were T wave morphology indices in leads V4 and V5, and P wave amplitude in leads AVR and II.ConclusionThe application of machine learning to the ECG using a neural network regression model, allows accurate estimation of physiologic cardiac age. This technique could be used to pick up subtle age-related cardiac changes, but also estimate the reversing of these age-associated effects by administered treatments. Show less
Wall, H.E.C. van der; Hassing, G.J.; Doll, R.J.; Westen, G.J.P. van; Cohen, A.F.; Selder, J.L.; ... ; Gal, P. 2022
ObjectiveThe aim of the present study was to develop a neural network to characterize the effect of aging on the ECG in healthy volunteers. Moreover, the impact of the various ECG features on aging... Show moreObjectiveThe aim of the present study was to develop a neural network to characterize the effect of aging on the ECG in healthy volunteers. Moreover, the impact of the various ECG features on aging was evaluated.Methods & resultsA total of 6228 healthy subjects without structural heart disease were included in this study. A neural network regression model was created to predict age of the subjects based on their ECG; 577 parameters derived from a 12‑lead ECG of each subject were used to develop and validate the neural network; A tenfold cross-validation was performed, using 118 subjects for validation each fold. Using SHapley Additive exPlanations values the impact of the individual features on the prediction of age was determined. Of 6228 subjects tested, 1808 (29%) were females and mean age was 34 years, range 18–75 years. Physiologic age was estimated as a continuous variable with an average error of 6.9 ± 5.6 years (R2 = 0.72 ± 0.04) . The correlation was slightly stronger for men (R2 = 0.74) than for women (R2 = 0.66). The most important features on the prediction of physiologic age were T wave morphology indices in leads V4 and V5, and P wave amplitude in leads AVR and II.ConclusionThe application of machine learning to the ECG using a neural network regression model, allows accurate estimation of physiologic cardiac age. This technique could be used to pick up subtle age-related cardiac changes, but also estimate the reversing of these age-associated effects by administered treatments. Show less
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)... Show moreAims 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 mu V; 95%CI: -644.3, -112.5 mu V; P < .01), valproic acid (ED -268.8 mu V; 95%CI: -532.9, -4.6 mu V; P = .047) and lorazepam (ED -330.7 mu V; 95%CI: -595.6, -65.8 mu V; 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. Show less
Berg, B. van den; Hijma, H.J.; Koopmans, I.; Doll, R.J.; Zuiker, R.G.J.A.; Groeneveld, G.J.; Buitenweg, J.R. 2022
Sleep deprivation has been shown to increase pain intensity and decrease pain thresholds in healthy subjects. In chronic pain patients, sleep impairment often worsens the perceived pain intensity.... Show moreSleep deprivation has been shown to increase pain intensity and decrease pain thresholds in healthy subjects. In chronic pain patients, sleep impairment often worsens the perceived pain intensity. This increased pain perception is the result of altered nociceptive processing. We recently developed a method to quantify and monitor altered nociceptive processing by simultaneous tracking of psychophysical detection thresholds and recording of evoked cortical potentials during intra-epidermal electric stimulation. In this study, we assessed the sensitivity of nociceptive detection thresholds and evoked potentials to altered nociceptive processing after sleep deprivation in an exploratory study with 24 healthy male and 24 healthy female subjects. In each subject, we tracked nociceptive detection thresholds and recorded central evoked potentials in response to 180 single- and 180 double-pulse intra-epidermal electric stimuli. Results showed that the detection thresholds for single- and double-pulse stimuli and the average central evoked potential for single-pulse stimuli were significantly decreased after sleep deprivation. When analyzed separated by sex, these effects were only significant in the male population. Multivariate analysis showed that the decrease of central evoked potential was associated with a decrease of task-related evoked activity. Measurement repetition led to a decrease of the detection threshold to double-pulse stimuli in the mixed and the female population, but did not significantly affect any other outcome measures. These results suggest that simultaneous tracking of psychophysical detection thresholds and evoked potentials is a useful method to observe altered nociceptive processing after sleep deprivation, but is also sensitive to sex differences and measurement repetition. Show less
IntroductionCoughing is a common symptom in pediatric lung disease and cough frequency has been shown to be correlated to disease activity in several conditions. Automated cough detection could... Show moreIntroductionCoughing is a common symptom in pediatric lung disease and cough frequency has been shown to be correlated to disease activity in several conditions. Automated cough detection could provide a noninvasive digital biomarker for pediatric clinical trials or care. The aim of this study was to develop a smartphone-based algorithm that objectively and automatically counts cough sounds of children.MethodsThe training set was composed of 3228 pediatric cough sounds and 480,780 noncough sounds from various publicly available sources and continuous sound recordings of 7 patients admitted due to respiratory disease. A Gradient Boost Classifier was fitted on the training data, which was subsequently validated on recordings from 14 additional patients aged 0–14 admitted to the pediatric ward due to respiratory disease. The robustness of the algorithm was investigated by repeatedly classifying a recording with the smartphone-based algorithm during various conditions.ResultsThe final algorithm obtained an accuracy of 99.7%, sensitivity of 47.6%, specificity of 99.96%, positive predictive value of 82.2% and negative predictive value 99.8% in the validation dataset. The correlation coefficient between manual- and automated cough counts in the validation dataset was 0.97 (p < .001). The intra- and interdevice reliability of the algorithm was adequate, and the algorithm performed best at an unobstructed distance of 0.5–1 m from the audio source.ConclusionThis novel smartphone-based pediatric cough detection application can be used for longitudinal follow-up in clinical care or as digital endpoint in clinical trials. Show less
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... Show moreParkinson'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. Show less
OBJECTIVESleep deprivation is known to affect driving behavior and may lead to serious car accidents similar to the effects from e.g., alcohol. In a previous study, we have demonstrated that the... Show moreOBJECTIVESleep deprivation is known to affect driving behavior and may lead to serious car accidents similar to the effects from e.g., alcohol. In a previous study, we have demonstrated that the use of machine learning techniques allows adequate characterization of abnormal driving behavior after alprazolam and/or alcohol intake. In the present study, we extend this approach to sleep deprivation and test the model for characterization of new interventions. We aimed to classify abnormal driving behavior after sleep deprivation, and, by using a machine learning model, we tested if this model could also pick up abnormal driving behavior resulting from other interventions.METHODSData were collected during a previous study, in which 24 subjects were tested after being sleep-deprived and after a well-rested night. Features were calculated from several driving parameters, such as the lateral position, speed of the car, and steering speed. In the present study, we used a gradient boosting model to classify sleep deprivation. The model was validated using a 5-fold cross validation technique. Next, probability scores were used to identify the overlap of driving behavior after sleep deprivation and driving behavior affected by other interventions. In the current study alprazolam, alcohol, and placebo are used to test/validate the approach.RESULTSThe sleep deprivation model detected abnormal driving behavior in the simulator with an accuracy of 77 ± 9%. Abnormal driving behavior after alprazolam, and to a lesser extent also after alcohol intake, showed remarkably similar characteristics to sleep deprivation. The average probability score for alprazolam and alcohol measurements was 0.79, for alcohol 0.63, and for placebo only 0.27 and 0.30, matching the expected relative drowsiness.CONCLUSIONWe developed a model detecting abnormal driving induced by sleep deprivation. The model shows the similarities in driving characteristics between sleep deprivation and other interventions, i.e., alcohol and alprazolam. Consequently, our model for sleep deprivation may serve as a next reference point for a driving test battery of newly developed drugs. Show less
Background: The cholinergic system and M-1 receptor remain an important target for symptomatic treatment of cognitive dysfunction. The selective M-1 receptor partial agonist HTL0018318 is under... Show moreBackground: The cholinergic system and M-1 receptor remain an important target for symptomatic treatment of cognitive dysfunction. The selective M-1 receptor partial agonist HTL0018318 is under development for the symptomatic treatment of Dementia's including Alzheimer's disease (AD) and dementia with Lewy bodies (DLB). We investigated the safety, tolerability, pharmacokinetics and exploratory pharmacodynamics of multiple doses of HTL0018318 in healthy younger adults and elderly subjects.Methods: This randomised, double blind, placebo-controlled study was performed, investigating oral doses of 15-35 mg/day HTL0018318 or placebo in 7 cohorts of healthy younger adult (n = 36; 3 cohorts) and elderly (n = 50; 4 cohorts) subjects. Safety, tolerability and pharmacokinetic measurements were performed. Pharmacodynamics were assessed using a battery of neurocognitive tasks and electrophysiological biomarkers of synaptic and cognitive functions.Results: HTL0018318 was generally well-tolerated in multiple doses up to 35 mg/day and were associated with mild or moderate cholinergic adverse events. There were modest increases in blood pressure and pulse rate when compared to placebo-treated subjects, with tendency for the blood pressure increase to attenuate with repeated dosing. There were no clinically significant observations or changes in blood and urine laboratory measures of safety or abnormalities in the ECGs and 24-h Holter assessments. HTL0018318 plasma exposure was dose-proportional over the range 15-35 mg. Maximum plasma concentrations were achieved after 1-2 h. The apparent terminal half-life of HTL0018318 was 16.1 h (+/- 4.61) in younger adult subjects and 14.3 h (+/- 2.78) in elderly subjects at steady state. HTL0018318 over the 10 days of treatment had significant effects on tests of short-term (working) memory (n-back) and learning (Milner maze) with moderate to large effect sizes.Conclusion: Multiple doses of HTL0018138 showed well-characterised pharmacokinetics and were safe and generally well-tolerated in the dose range studied. Pro-cognitive effects on short-term memory and learning were demonstrated across the dose range. These data provide encouraging data in support of the development of HTL0018138 for cognitive dysfunction in AD and DLB. Show less
Introduction: The duration and frequency of crying of an infant can be indicative of its health. Manual tracking and labeling of crying is laborious, subjective, and sometimes inaccurate. The aim... Show moreIntroduction: The duration and frequency of crying of an infant can be indicative of its health. Manual tracking and labeling of crying is laborious, subjective, and sometimes inaccurate. The aim of this study was to develop and technically validate a smartphone-based algorithm able to automatically detect crying.Methods: For the development of the algorithm a training dataset containing 897 5-s clips of crying infants and 1,263 clips of non-crying infants and common domestic sounds was assembled from various online sources. OpenSMILE software was used to extract 1,591 audio features per audio clip. A random forest classifying algorithm was fitted to identify crying from non-crying in each audio clip. For the validation of the algorithm, an independent dataset consisting of real-life recordings of 15 infants was used. A 29-min audio clip was analyzed repeatedly and under differing circumstances to determine the intra- and inter- device repeatability and robustness of the algorithm.Results: The algorithm obtained an accuracy of 94% in the training dataset and 99% in the validation dataset. The sensitivity in the validation dataset was 83%, with a specificity of 99% and a positive- and negative predictive value of 75 and 100%, respectively. Reliability of the algorithm appeared to be robust within- and across devices, and the performance was robust to distance from the sound source and barriers between the sound source and the microphone.Conclusion: The algorithm was accurate in detecting cry duration and was robust to various changes in ambient settings. Show less
In the current study, we aimed to develop an algorithm based on biomarkers obtained through nonor minimally invasive procedures to identify healthy elderly subjects who have an increased risk of... Show moreIn the current study, we aimed to develop an algorithm based on biomarkers obtained through nonor minimally invasive procedures to identify healthy elderly subjects who have an increased risk of abnormal cerebrospinal fluid (CSF) amyloid beta42 (Aβ) levels consistent with the presence of Alzheimer’s disease (AD) pathology. The use of the algorithm may help to identify subjects with preclinical AD who are eligible for potential participation in trials with disease modifying compounds being developed for AD. Due to this pre-selection, fewer lumbar punctures will be needed, decreasing overall burden for study subjects and costs. Show less
Novel digital endpoints gathered via wearables, small devices, or algorithms hold great promise for clinical trials. However, implementation has been slow because of a lack of guidelines regarding... Show moreNovel digital endpoints gathered via wearables, small devices, or algorithms hold great promise for clinical trials. However, implementation has been slow because of a lack of guidelines regarding the validation process of these new measurements. In this paper, we propose a pragmatic approach toward selection and fit-for-purpose validation of digital endpoints. Measurements should be value-based, meaning the measurements should directly measure or be associated with meaningful outcomes for patients. Devices should be assessed regarding technological validity. Most importantly, a rigorous clinical validation process should appraise the tolerability, difference between patients and controls, repeatability, detection of clinical events, and correlation with traditional endpoints. When technically and clinically fit-for-purpose, case building in interventional clinical trials starts to generate evidence regarding the response to new or existing health-care interventions. This process may lead to the digital endpoint replacing traditional endpoints, such as clinical rating scales or questionnaires in clinical trials. We recommend initiating more data-sharing collaborations to prevent unnecessary duplication of research and integration of value-based measurements in clinical care to enhance acceptance by health-care professionals. Finally, we invite researchers and regulators to adopt this approach to ensure a timely implementation of digital measurements and value-based thinking in clinical trial design and health care.Significance Statement-Novel digital endpoints are often cited as promising for the clinical trial of the future. However, clear validation guidelines are lacking in the literature. This paper contains pragmatic criteria for the selection, technical validation, and clinical validation of novel digital endpoints and provides recommendations for future work and collaboration. Show less
Berg, B. van den; Doll, R.J.; Mentink, A.L.H.; Siebenga, P.S.; Groeneveld, G.J.; Buitenweg, J.R. 2020
Measuring altered nociceptive processing involved in chronic pain is difficult due to a lack of objective methods. Potential methods to characterize human nociceptive processing involve measuring... Show moreMeasuring altered nociceptive processing involved in chronic pain is difficult due to a lack of objective methods. Potential methods to characterize human nociceptive processing involve measuring neurophysiological activity and psychophysical responses to well-defined stimuli. To reliably measure neurophysiological activity in response to nociceptive stimulation using EEG, synchronized activation of nerve fibers and a large number of stimuli are required. On the other hand, to reliably measure psychophysical detection thresholds, selection of stimulus amplitudes around the detection threshold and many stimulus-response pairs are required. Combining the two techniques helps in quantifying the properties of nociceptive processing related to detected and non-detected stimuli around the detection threshold. The two techniques were combined in an experiment including 20 healthy participants to study the effect of intra-epidermal electrical stimulus properties (i.e. amplitude, single- or double-pulse and trial number) on the detection thresholds and vertex potentials. Generalized mixed regression and linear mixed regression were used to quantify the psychophysical detection probability and neurophysiological EEG responses, respectively. It was shown that the detection probability is significantly modulated by the stimulus amplitude, trial number, and the interaction between stimulus type and amplitude. Furthermore, EEG responses were significantly modulated by stimulus detection and trial number. Hence, we successfully demonstrated the possibility to simultaneously obtain information on psychophysical and neurophysiological properties of nociceptive processing. These results warrant further investigation of the potential of this method to observe altered nociceptive processing. Show less
There is a lack of reliable, repeatable, and non-invasive clinical endpoints when investigating treatments for intellectual disability (ID). The aim of this study is to explore a novel approach... Show moreThere is a lack of reliable, repeatable, and non-invasive clinical endpoints when investigating treatments for intellectual disability (ID). The aim of this study is to explore a novel approach towards developing new endpoints for neurodevelopmental disorders, in this case for ARID1B-related ID. In this study, twelve subjects with ARID1B-related ID and twelve age-matched controls were included in this observational case-control study. Subjects performed a battery of non-invasive neurobehavioral and neurophysiological assessments on two study days. Test domains included cognition, executive functioning, and eye tracking. Furthermore, several electrophysiological assessments were performed. Subjects wore a smartwatch (Withings (R) Steel HR) for 6 days. Tests were systematically assessed regarding tolerability, variability, repeatability, difference with control group, and correlation with traditional endpoints. Animal fluency, adaptive tracking, body sway, and smooth pursuit eye movements were assessed as fit-for-purpose regarding all criteria, while physical activity, heart rate, and sleep parameters show promise as well. The event-related potential waveform of the passive oddball and visual evoked potential tasks showed discriminatory ability, but EEG assessments were perceived as extremely burdensome. This approach successfully identified fit-for-purpose candidate endpoints for ARID1B-related ID and possibly for other neurodevelopmental disorders. Next, results could be replicated in different ID populations or the assessments could be included as exploratory endpoint in interventional trials in ARID1B-related ID. Show less
Wall, H.E.C. van der; Doll, R.J.; Westen, G.J.P. van der; Koopmans, I.; Zuiker, R.G.; Burggraaf, J.; Cohen, A.F. 2020
