Discovery of Thioether-Cyclized Macrocyclic Covalent Inhibitors by mRNA Display.

Macrocyclic peptides are promising scaffolds for the covalent ligand discovery. However, platforms enabling the direct identification of covalent macrocyclic ligands in a high-throughput manner are limited. In this study, we present an mRNA display platform allowing selection of covalent macrocyclic inhibitors using 1,3-dibromoacetone-vinyl sulfone (DBA-VS). Testcase selections on TEV protease resulted in potent covalent inhibitors with diverse cyclic structures, among which cTEV6-2, a macrocyclic peptide with a unique C-terminal cyclization, emerged as the most potent covalent inhibitor of TEV protease described to-date. This study outlines the workflow for integrating chemical functionalization─installation of a covalent warhead─with mRNA display and showcases its application in targeted covalent ligand discovery.

Roflumilast foam 0.3% for adolescent and adult patients with seborrheic dermatitis: A randomized, double-blinded, vehicle-controlled, phase 3 trial.

BACKGROUND: The topical phosphodiesterase 4 inhibitor roflumilast has been studied in several dermatologic conditions. OBJECTIVE: Roflumilast foam 0.3% is being investigated as a topical treatment for seborrheic dermatitis (SD). METHODS: In this phase 3, double-blinded trial, patients with SD were randomly assigned (2:1 ratio) to once-daily roflumilast foam 0.3% or vehicle foam for 8 weeks. The primary efficacy outcome was Investigator Global Assessment (IGA) Success at week 8, defined as IGA of 0 (Clear) or 1 (Almost Clear) plus ≥2-point improvement from baseline. Safety was also assessed. RESULTS: 79.5% of roflumilast-treated and 58.0% of vehicle-treated patients met the primary endpoint (P < .001); statistically significant differences in IGA Success also favored roflumilast at week 2 (roflumilast: 43.0%; vehicle: 25.7%; P < .001) and week 4 (roflumilast: 73.1%; vehicle: 47.1%; P < .001). Roflumilast was well-tolerated with a low rate of treatment-emergent adverse events. LIMITATIONS: Study limitations include the 8-week treatment period for this chronic condition. CONCLUSIONS: Once-daily roflumilast foam was superior to vehicle in leading to IGA of Clear or Almost Clear plus ≥2-point improvement from baseline at 8 weeks in patients with SD. Longer trials are needed to determine durability and safety of roflumilast foam in SD.

Long-term safety and effectiveness of roflumilast cream 0.3% in adults with chronic plaque psoriasis: A 52-week, phase 2, open-label trial.

BACKGROUND: Efficacy and/or safety profiles limit topical psoriasis treatments. OBJECTIVE: Evaluate long-term effects of once-daily roflumilast cream 0.3% in patients with psoriasis. METHODS: In this open-label phase 2 trial, adult patients (N = 332) with psoriasis who completed the phase 2b parent trial or were newly enrolled applied roflumilast once-daily for 52 weeks. Safety and effectiveness were assessed. RESULTS: Overall, 244 patients (73.5%) completed the trial; 13 patients (3.9%) discontinued due to adverse events (AEs) and 3 (0.9%) due to lack of efficacy. Twelve patients (3.6%) reported treatment-related AEs; none were serious. ≥97% of patients had no irritation. No tachyphylaxis was observed with 44.8% of the patients achieving Investigator Global Assessment (IGA) Clear or Almost Clear at Week 52. LIMITATIONS: Intertriginous-IGA and Psoriasis Area and Severity Index (PASI) were not evaluated in all patients. CONCLUSIONS: In this long-term trial, once-daily roflumilast cream was well-tolerated and efficacious up to 64 weeks in patients in the earlier trial, suggesting it is suitable for chronic treatment, including the face and intertriginous areas.

Estimating the synaptic density deficit in Alzheimer's disease using multi-contrast CEST imaging.

In vivo noninvasive imaging of neurometabolites is crucial to improve our understanding of the underlying pathophysiological mechanism in neurodegenerative diseases. Abnormal changes in synaptic organization leading to synaptic degradation and neuronal loss is considered as one of the primary factors driving Alzheimer's disease pathology. Magnetic resonance based molecular imaging techniques such as chemical exchange saturation transfer (CEST) and magnetic resonance spectroscopy (MRS) can provide neurometabolite specific information which may relate to underlying pathological and compensatory mechanisms. In this study, CEST and short echo time single voxel MRS was performed to evaluate the sensitivity of cerebral metabolites to beta-amyloid (Aß) induced synaptic deficit in the hippocampus of a mouse model of Alzheimer's disease. The CEST based spectra (Z-spectra) were acquired on a 9.4 Tesla small animal MR imaging system with two radiofrequency (RF) saturation amplitudes (1.47 µT and 5.9 µT) to obtain creatine-weighted and glutamate-weighted CEST contrasts, respectively. Multi-pool Lorentzian fitting and quantitative T1 longitudinal relaxation maps were used to obtain metabolic specific apparent exchange-dependent relaxation (AREX) maps. Short echo time (TE = 12 ms) single voxel MRS was acquired to quantify multiple neurometabolites from the right hippocampus region. AREX contrasts and MRS based metabolite concentration levels were examined in the ARTE10 animal model for Alzheimer's disease and their wild type (WT) littermate counterparts (age = 10 months). Using MRS voxel as a region of interest, group-wise analysis showed significant reduction in Glu-AREX and Cr-AREX in ARTE10, compared to WT animals. The MRS based results in the ARTE10 mice showed significant decrease in glutamate (Glu) and glutamate-total creatine (Glu/tCr) ratio, compared to WT animals. The MRS results also showed significant increase in total creatine (tCr), phosphocreatine (PCr) and glutathione (GSH) concentration levels in ARTE10, compared to WT animals. In the same ROI, Glu-AREX and Cr-AREX demonstrated positive associations with Glu/tCr ratio. These results indicate the involvement of neurotransmitter metabolites and energy metabolism in Aß-mediated synaptic degradation in the hippocampus region. The study also highlights the feasibility of CEST and MRS to identify and track multiple competing and compensatory mechanisms involved in heterogeneous pathophysiology of Alzheimer's disease in vivo.

Moderate-to-Vigorous Physical Activity and Response Inhibition Predict Balance in Adults with Attention Deficit/Hyperactivity Disorder.

Background: Some evidence indicates that adults with attention deficit hyperactivity disorder (ADHD) may have balance impairments. This study examined the associations between moderate-to-vigorous physical activity (MVPA), response inhibition (RI), and static balance in this population while off and on psychostimulant medication (PS). Methods: Participants (n = 40; 30 females; M age = 29.0; SD = 6.3 years) wore an ActiGraph GT9X-link around their waist to estimate MVPA levels (minutes/day). To assess RI, participants completed the Delis-Kaplan Executive Function System (D-KEFS) subtests Trail-Making Test (TMT) and Color-Word Interference Test (CWIT). To evaluate static balance, participants completed postural sway area (cm2) assessments in four conditions: feet-apart eyes-open (FAEO), feet-apart eyes-closed (FAEC), feet-together eyes-open (FTEO), and feet-together eyes-closed (FTEC). Participants also completed the single-leg standing tests (seconds) with eyes open (SLEO) and with eyes closed (SLEC). Results: When off medication, MVPA significantly predicted SLEC (ß = 0.30; p = 0.017). MVPA and TMT significantly predicted FTEO, explaining ~19% of the variance in FTEO; both MVPA and TMT were significant predictors (ß = -0.33, p = 0.027 and ß = -0.31, p = 0.039, respectively). When on medication, TMT significantly predicted FAEC (ß = 0.17; p = 0.047). Conclusions: MVPA and RI may be effective parameters in predicting static balance in adults with ADHD when off medication only.

Development and validation of a HPLC method for the determination of chemical and radiochemical purity of O-(2-[18F]fluoroethyl-l-tyrosine ([18F]FET)), a PET radiotracer for the imaging of brain tumor.

A novel HPLC method was developed and validated to determine radiochemical identity, radiochemical purity and chemical purity for the analysis of O-(2-[18F]fluoroethyl-l-tyrosine ([18F]FET). In this method, an analytical Phenomenex Gemini C18 column was used with an isocratic eluent of 7 % ethanol and 93 % 50 mM potassium phosphate buffer (pH = 6.9). The flow rate was 1.0 mL/min and the injection volume was 10 µL. A photo-diode array detector set at 220 nm was used for UV mass detection and a single channel, high sensitivity radiation detector was used. The method validation assays including specificity, linearity, precision, accuracy, and robustness were evaluated. Results show that the method was suitable for qualitative and quantitative determination of radiochemical and chemical purity of [18F]FET. This system has been routinely used for the analysis of more than 120 batches of [18F]FET with radiochemical yield 23.7 ± 6 % (no decay corrected) and molar activity 593 ± 284 GBq/µmole in our facility to support human use.

Predicting the Hallucinogenic Potential of Molecules Using Artificial Intelligence.

The development of new drugs addressing serious mental health and other disorders should avoid the psychedelic experience. Analogs of psychedelic drugs can have clinical utility and are termed "psychoplastogens". These represent promising candidates for treating opioid use disorder to reduce drug dependence, with rarely reported serious adverse effects. This drug abuse cessation is linked to the induction of neuritogenesis and increased neuroplasticity, a hallmark of psychedelic molecules, such as lysergic acid diethylamine. Some, but not all psychoplastogens may act through the G-protein coupled receptor (GPCR) 5HT2A whereas others may display very different polypharmacology making prediction of hallucinogenic potential challenging. In the process of developing tools to help design new psychoplastogens, we have used artificial intelligence in the form of machine learning classification models for predicting psychedelic effects using a published in vitro data set from PsychLight (support vector classification (SVC), area under the curve (AUC) 0.74) and in vivo human data derived from books from Shulgin and Shulgin (SVC, AUC, 0.72) with nested five-fold cross validation. We have also explored conformal predictors with ECFP6 and electrostatic descriptors in an effort to optimize them. These models have been used to predict known 5HT2A agonists to assess their potential to act as psychedelics and induce hallucinations for PsychLight (SVC, AUC 0.97) and Shulgin and Shulgin (random forest, AUC 0.71). We have tested these models with head twitch data from the mouse. This predictive capability is desirable to reliably design new psychoplastogens that lack in vivo hallucinogenic potential and help assess existing and future molecules for this potential. These efforts also provide useful insights into understanding the psychedelic structure activity relationship.

The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applications.

Recent advances in machine learning (ML) have led to newer model architectures including transformers (large language models, LLMs) showing state of the art results in text generation and image analysis as well as few-shot learning (FSLC) models which offer predictive power with extremely small datasets. These new architectures may offer promise, yet the 'no-free lunch' theorem suggests that no single model algorithm can outperform at all possible tasks. Here, we explore the capabilities of classical (SVR), FSLC, and transformer models (MolBART) over a range of dataset tasks and show a 'goldilocks zone' for each model type, in which dataset size and feature distribution (i.e. dataset "diversity") determines the optimal algorithm strategy. When datasets are small ( < 50 molecules), FSLC tend to outperform both classical ML and transformers. When datasets are small-to-medium sized (50-240 molecules) and diverse, transformers outperform both classical models and few-shot learning. Finally, when datasets are of larger and of sufficient size, classical models then perform the best, suggesting that the optimal model to choose likely depends on the dataset available, its size and diversity. These findings may help to answer the perennial question of which ML algorithm is to be used when faced with a new dataset.

[11C]-methionine positron emission tomography in the evaluation of pediatric low-grade gliomas.

Background: [11C]-Methionine positron emission tomography (PET; [11C]-MET-PET) is principally used for the evaluation of brain tumors in adults. Although amino acid PET tracers are more commonly used in the evaluation of pediatric brain tumors, data on [11C]-MET-PET imaging of pediatric low-grade gliomas (pLGG) is scarce. This study aimed to investigate the roles of [11C]-MET-PET in the evaluation of pLGGs. Methods: Eighteen patients with newly diagnosed pLGG and 26 previously treated pLGG patients underwent [11C]-MET-PET met the inclusion and exclusion criteria. Tumor-to-brain uptake ratio (TBR) and metabolic tumor volumes were assessed for diagnostic performances (newly diagnosed, 15; previously treated 26), change with therapy (newly diagnosed, 9; previously treated 7), and variability among different histology (n = 12) and molecular markers (n = 7) of pLGGs. Results: The sensitivity of [11C]-MET-PET for diagnosing pLGG, newly diagnosed, and previously treated combined was 93% for both TBRmax and TBRpeak, 76% for TBRmean, and 95% for qualitative evaluation. TBRmax showed a statistically significant reduction after treatment, while other PET parameters showed a tendency to decrease. Median TBRmax, TBRpeak, and TBRmean values were slightly higher in the BRAFV600E mutated tumors compared to the BRAF fused tumors. Median TBRmax, and TBRpeak in diffuse astrocytomas were higher compared to pilocytic astrocytomas, but median TBRmean, was slightly higher in pilocytic astrocytomas. However, formal statistical analysis was not done due to the small sample size. Conclusions: Our study shows that [11C]-MET-PET reliably characterizes new and previously treated pLGGs. Our study also shows that quantitative parameters tend to decrease with treatment, and differences may exist between various pLGG types.

Older Drivers' Attitudes and Preferences about Instrument Cluster Designs in Vehicles Revealed by the Dashboard Questionnaire.

Background: Older drivers are a rapidly growing demographic group worldwide; many have visual processing impairments. Little is known about their preferences about vehicle instrument cluster design. Methods: We evaluated the psychometric properties of a questionnaire on "dashboard" design for a population-based sample of 1000 older drivers. Topics included gauges, knobs/switches, and interior lighting; items were statements about their visual design. Response options used a Likert-scale ("Definitely True" to "Definitely False"). Factor and Rasch analyses identified underlying subscales. Results: Driver responses revealed four thematic subscales fitting the Rasch model: cognitive processing, lighting, pattern recognition, and obstructions. Internal consistency of subscales was acceptable (0.70-0.87); all possessed a sufficiently unidimensional structure. Opportunities for improvement were identified (item scope, category ordering, discrimination of respondents' perception levels). Conclusions: Assessment of motor vehicle dashboard preferences indicated cognitive processing, lighting, pattern recognition, and obstructions are areas relevant to older drivers. Future work will examine the relationship between older drivers' visual function (e.g., contrast sensitivity, visual processing speed) and their design preferences as revealed by the Dashboard Questionnaire, with the aim to optimize instrument cluster displays for older drivers.

Associations of visual functions with attitudes about motor vehicle dashboards among older drivers.

Purpose: Proper understanding and interaction with the dashboard is an essential aspect of safely operating a motor vehicle. A portion of this task is dependent on vision, yet no published information exists regarding dashboard ergonomics and visual function. This study sought to associate visual functions and person abilities of dashboard ergonomic dimensions relevant to older driver design preferences and attitudes. Methods: In this population-based study of drivers, participants completed functional testing for habitual distance visual acuity, contrast sensitivity, visual field sensitivity, visual processing speed, and spatial ability. A questionnaire assessed attitudes and understanding of dashboard design, with questionnaire items generated from the content of focus groups of older drivers. Dashboard design domains identified in Rasch analysis of questionnaire responses were quantified using person ability measures for the cognitive processing, lighting, obstructions, and pattern recognition domains. Visual functions and person abilities were correlated using Spearman partial correlations, adjusting for age and sex. Results: A total of 997 participants completed functional testing and the dashboard questionnaire. The mean age was 77.4 ± 4.6 years, and the majority were male (55%) and white (81%). The sample had a range of person abilities and visual functions. Contrast and visual field sensitivities were significantly associated with the cognitive processing, lighting, and pattern recognition dashboard design dimensions (p ≤ 0.0052). For all significant associations, increased visual function was indicative of better person ability. Visual processing speed, as measured by Trails B and UFOV2, was significantly associated with the lighting domain (p = 0.0008 and p = 0.0007, respectively). The UFOV2 measure was correlated with pattern recognition (p = 0.0165). Spatial ability was the only visual function associated with the visual obstruction dimension (p = 0.0347). Conclusions: Person ability for dashboard design domains are related to visual function in older drivers. Results show person ability for domains increased with improved visual function. Future automotive engineering and design initiatives should consider these associations in improving dashboard designs to increase vehicle utility and accessibility.