HTL0018318 is a selective M 1 receptor partial agonist currently under development for the symptomatic treatment of cognitive and behavioural symptoms in Alzheimer's disease and other dementias. We investigated safety, tolerability, pharmacokinetics and exploratory pharmacodynamics (PD) of HTL0018318 following single ascending doses.Methods: This randomized, double-blind, placebo-controlled study in 40 healthy younger adult and 57 healthy elderly subjects, investigated oral doses of 1-35 mg HTL0018318. Pharmacodynamic assessments were performed using a battery of neurocognitive tasks and electrophysiological measurements. Cerebrospinal fluid concentrations of HTL0018318 and food effects on pharmacokinetics of HTL0018318 were investigated in an open label and partial cross-over design in 14 healthy subjects.Results: Pharmacokinetics of HTL0018318 were well-characterized showing dose proportional increases in exposure from 1-35 mg. Single doses of HTL0018318 were associated with mild dose-related adverse events of low incidence in both younger adult and elderly subjects. The most frequently reported cholinergic AEs included hyperhidrosis and increases in blood pressure up to 10.3 mmHg in younger adults (95% CI [4.2-16.3], 35-mg dose) and up to 11.9 mmHg in elderly subjects (95% CI [4.9-18.9], 15-mg dose). There were no statistically significant effects on cognitive function but the study was not powered to detect small to moderate effect sizes of clinical relevance.Conclusion: HTL0018318 showed well-characterized pharmacokinetics and following single doses were generally well tolerated in the dose range studied. TheseThe authors confirm that the PI for this paper is Geert Jan Groeneveld and that he had direct clinical responsibility for patients.

Mecamylamine administration induced slowing of the electroencephalogram and produced decrease in performance of tests evaluating motor coordination, sustained attention and short- and long-term memory. These effects could be partially reversed by the coadministration of nicotine, and to a lesser extent by galantamine.

Background This study investigated plasma biomarkers for neuroinflammation associated with Alzheimer’s disease (AD) in subjects with preclinical AD compared to healthy elderly. How these biomarkers behave in patients with AD, compared to healthy elderly is well known, but determining these in subjects with preclinical AD is not and will add information related to the onset of AD. When found to be different in preclinical AD, these inflammatory biomarkers may be used to select preclinical AD subjects who are most likely to develop AD, to participate in clinical trials with new disease-modifying drugs. Methods Healthy elderly (n= 50; age 71.9; MMSE >24) and subjects with preclinical AD (n=50; age 73.4; MMSE >24) defined by CSF Aβ1-42 levels < 1000 pg/mL were included. Four neuroinflammatory biomarkers were determined in plasma, GFAP, YKL-40, MCP-1, and eotaxin-1. Differences in biomarker outcomes were compared using ANCOVA. Subject characteristics age, gender, and APOE ε4 status were reported per group and were covariates in the ANCOVA. Least square means were calculated for all 4 inflammatory biomarkers using both the Aβ+/Aβ− cutoff and Ptau/Aβ1-42 ratio. Results The mean (standard deviation, SD) age of the subjects (n=100) was 72.6 (4.6) years old with 62 male and 38 female subjects. Mean (SD) overall MMSE score was 28.7 (0.49) and 32 subjects were APOE ε4 carriers. The number of subjects in the different APOE ε4 status categories differed significantly between the Aβ+ and Aβ− groups. Plasma GFAP concentration was significantly higher in the Aβ+ group compared to the Aβ− group with significant covariates age and sex, variables that also correlated significantly with GFAP. Conclusion GFAP was significantly higher in subjects with preclinical AD compared to healthy elderly which agrees with previous studies. When defining preclinical AD based on the Ptau181/Aβ1-42 ratio, YKL-40 was also significantly different between groups. This could indicate that GFAP and YKL-40 are more sensitive markers of the inflammatory process in response to the Aβ misfolding and aggregation that is ongoing as indicated by the lowered Aβ1-42 levels in the CSF. Characterizing subjects with preclinical AD using neuroinflammatory biomarkers is important for subject selection in new disease-modifying clinical trials. Trial registration ISRCTN.org identifier: ISRCTN79036545 (retrospectively registered).

This novel measure for quantifying nicotinic cholinergic effects on the EEG could serve as a useful tool in drug development of pro-cognitive compounds.

To diagnose and treat early-stage (preclinical) Alzheimer’s disease (AD) patients, we need body-fluid-based biomarkers that reflect the processes that occur in this stage, but current knowledge on associated processes is lacking. As human studies on (possible) onset and early-stage AD would be extremely expensive and time-consuming, we investigate the potential value of animal AD models to help to fill this knowledge gap. We provide a comprehensive overview of processes associated with AD pathogenesis and biomarkers, current knowledge on AD-related biomarkers derived from on human and animal brains and body fluids, comparisons of biomarkers obtained in human AD and frequently used animal AD models, and emerging body-fluid-based biomarkers. In human studies, amyloid beta (Aβ), hyperphosphorylated tau (P-tau), total tau (T-tau), neurogranin, SNAP-25, glial fibrillary acidic protein (GFAP), YKL-40, and especially neurofilament light (NfL) are frequently measured. In animal studies, the emphasis has been mostly on Aβ. Although a direct comparison between human (familial and sporadic) AD and (mostly genetic) animal AD models cannot be made, still, in brain, cerebrospinal fluid (CSF), and blood, a majority of similar trends are observed for human AD stage and animal AD model life stage. This indicates the potential value of animal AD models in understanding of the onset and early stage of AD. Moreover, animal studies can be smartly designed to provide mechanistic information on the interrelationships between the different AD processes in a longitudinal fashion and may also include the combinations of different conditions that may reflect comorbidities in human AD, according to the Mastermind Research approach.

Background In the current study, we aimed to develop an algorithm based on biomarkers obtained through non- or 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. Methods Healthy elderly subjects (n = 200; age 65–70 (N = 100) and age > 70 (N = 100)) with an MMSE > 24 were recruited. An automated central nervous system test battery was used for cognitive profiling. CSF Aβ1-42 concentrations, plasma Aβ1-40, Aβ1-42, neurofilament light, and total Tau concentrations were measured. Aβ1-42/1-40 ratio was calculated for plasma. The neuroinflammation biomarker YKL-40 and APOE ε4 status were determined in plasma. Different mathematical models were evaluated on their sensitivity, specificity, and positive predictive value. A logistic regression algorithm described the data best. Data were analyzed using a 5-fold cross validation logistic regression classifier. Results Two hundred healthy elderly subjects were enrolled in this study. Data of 154 subjects were used for the per protocol analysis. The average age of the 154 subjects was 72.1 (65–86) years. Twenty-four (27.3%) were Aβ positive for AD (age 65–83). The results of the logistic regression classifier showed that predictive features for Aβ positivity/negativity in CSF consist of sex, 7 CNS tests, and 1 plasma-based assay. The model achieved a sensitivity of 70.82% (± 4.35) and a specificity of 89.25% (± 4.35) with respect to identifying abnormal CSF in healthy elderly subjects. The receiver operating characteristic curve showed an AUC of 65% (± 0.10). Conclusion This algorithm would allow for a 70% reduction of lumbar punctures needed to identify subjects with abnormal CSF Aβ levels consistent with AD. The use of this algorithm can be expected to lower overall subject burden and costs of identifying subjects with preclinical AD and therefore of total study costs. Trial registration ISRCTN.org identifier: ISRCTN79036545 (retrospectively registered).

Background Although electrocardiography is the gold standard for heart rate (HR) recording in clinical trials, the increasing availability of smartwatch-based HR monitors opens up possibilities for drug development studies. Smartwatches allow for inexpensive, unobtrusive, and continuous HR estimation for potential detection of treatment effects outside the clinic, during daily life. Objective The aim of this study is to evaluate the repeatability and sensitivity of smartwatch-based HR estimates collected during a randomized clinical trial. Methods The data were collected as part of a multiple-dose, investigator-blinded, randomized, placebo-controlled, parallel-group study of 12 patients with Parkinson disease. After a 6-day baseline period, 4 and 8 patients were treated for 7 days with an ascending dose of placebo and clenbuterol, respectively. Throughout the study, the smartwatch provided HR and sleep state estimates. The HR estimates were quantified as the 2.5th, 50th, and 97.5th percentiles within awake and asleep segments. Linear mixed models were used to calculate the following: (1) the intraclass correlation coefficient (ICC) of estimated sleep durations, (2) the ICC and minimum detectable effect (MDE) of the HR estimates, and (3) the effect sizes of the HR estimates. Results Sleep duration was moderately repeatable (ICC=0.64) and was not significantly affected by study day (P=.83), clenbuterol (P=.43), and study day by clenbuterol (P=.73). Clenbuterol-induced changes were detected in the asleep HR as of the first night (+3.79 beats per minute [bpm], P=.04) and in the awake HR as of the third day (+8.79 bpm, P=.001). The median HR while asleep had the highest repeatability (ICC=0.70). The MDE (N=12) was found to be smaller when patients were asleep (6.8 bpm to 11.7 bpm) than while awake (10.7 bpm to 22.1 bpm). Overall, the effect sizes for clenbuterol-induced changes were higher while asleep (0.49 to 2.75) than while awake (0.08 to 1.94). Conclusions We demonstrated the feasibility of using smartwatch-based HR estimates to detect clenbuterol-induced changes during clinical trials. The asleep HR estimates were most repeatable and sensitive to treatment effects. We conclude that smartwatch-based HR estimates obtained during daily living in a clinical trial can be used to detect and track treatment effects. Trial Registration Netherlands Trials Register NL8002; https://www.trialregister.nl/trial/8002

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