Forward or Backward: Lessons Learned from Small Molecule Drugs Approved by FDA from 2012 to 2022.

At every juncture in history, the design and identification of new drugs pose significant challenges. To gain valuable insights for future drug development, we conducted a detailed analysis of New Molecular Entitiy (NME) approved by the Food and Drug Administration (FDA) from 2012 to 2022 and focused on the analysis of first-in-class (FIC) small-molecules from a perspective of a medicinal chemist. We compared the change of numbers between all the FDA-approved NMEs and FIC, which could be more visual to analyze the changing trend of FIC. To get a more visual change of molecular physical properties, we computed the annual average trends in molecular weight for FIC across various therapeutic fields. Furthermore, we consolidated essential information into three comprehensive databases, which covered the indications, canonical SMILES, structural formula, research and development (R&D) institutions, molecular weight, calculated LogP (CLogP), and route of administration on all the small-molecule pharmaceutical. Through the analysis of the database of 11 years of approvals, we forecast the development trend of NME approval in the future.

Does healthy lifestyle attenuate the detrimental effects of urinary polycyclic aromatic hydrocarbons on phenotypic aging? An analysis from NHANES 2001-2010.

Existing evidence has showed that exposure to polycyclic aromatic hydrocarbons (PAHs) increases the risk of many chronic diseases. Given the close connection between aging (a major risk factor) and chronic diseases, however, very few studies have evaluated the association between PAHs and aging. Furthermore, whether modifiable healthy lifestyle could attenuate the detrimental effect of PAHs on aging remains unknown. Therefore, we conducted this study, aiming to: (1) examine the associations of urinary monohydroxy polycyclic aromatic hydrocarbons (OH-PAHs) and lifestyle with Phenotypic Age Acceleration (PhenoAge.Accel), a novel aging measure that captures morbidity and mortality risk; and (2) evaluate the potential interaction effects of OH-PAHs and lifestyle on PhenoAge.Accel. Cross-sectional data of 2,579 participants (aged 20-84 years, n = 1,292 females) from the National Health and Nutrition Examination Survey for years 2001-2010 were analyzed. A lifestyle index was constructed based on five components (drinking, smoking, body mass index, physical activity, and diet), ranging from 0 to 5. We calculated PhenoAge.Accel using algorithms developed previously. General linear regression models were used to examine the associations. We observed strong associations of OH-PAHs and lifestyle with PhenoAge.Accel. For instance, one unit increase in ∑NAP (sum of 1- and 2-hydroxynaphthalene) was associated with 0.37 year (95% confidence interval [CI]: 0.26, 0.48) increase in PhenoAge.Accel. We did not observe statistically significant interaction effects between OH-PAHs and lifestyle on PhenoAge.Accel. After stratified by sex, we observed strong associations as well as statistically significant interactions of OH-PAHs and lifestyle with PhenoAge.Accel among females. In conclusion, both OH-PAHs and lifestyle were independently associated with phenotypic aging and there were statistically significant interactions between OH-PAHs and lifestyle on phenotypic aging among females. The findings highlight the importance of adherence to a healthy lifestyle to attenuate the detrimental effects of exposures to PAHs on phenotypic aging among females.

The effect of weighted blankets on sleep and related disorders: a brief review.

Background: Sleep disorders such as insomnia can lead to a range of health problems. The high risk of side effects and drug abuse of traditional pharmacotherapy calls for a safer non-pharmacotherapy. Aims: To examine the use and efficacy of weighted blankets in improving sleep and related disorders in different populations and explore the possible mechanisms. Methods: A literature search was conducted using PubMed, Embase, Web of Science, MEDLINE, Cochrane Library and CNKI databases. Eligible studies included an intervention with weighted blankets and outcomes covering sleep and/or related disorders (behavioral disturbance, negative emotions and daytime symptoms). Studies using other deep pressure, compression, or exercise-related interventions were excluded. Conclusions: Most of the included studies showed that weighted blankets could effectively improve sleep quality and alleviate negative emotions and daytime symptoms in patients with sleep disorders, attention deficit hyperactivity disorder, autism spectrum disorder, and other related disorders, with a possible mechanism of deep pressure touch. Recommendations: Weighted blankets might be a promising tool for sleep interventions among individuals with sleep disorders in clinical settings. More high-quality and large-scale randomized controlled trials are needed to further validate the safety and efficacy of weighted blankets and explore precise mechanisms.

Understanding common human driving semantics for autonomous vehicles.

Autonomous vehicles will share roads with human-driven vehicles until the transition to fully autonomous transport systems is complete. The critical challenge of improving mutual understanding between both vehicle types cannot be addressed only by feeding extensive driving data into data-driven models but by enabling autonomous vehicles to understand and apply common driving behaviors analogous to human drivers. Therefore, we designed and conducted two electroencephalography experiments for comparing the cerebral activities of human linguistics and driving understanding. The results showed that driving activates hierarchical neural functions in the auditory cortex, which is analogous to abstraction in linguistic understanding. Subsequently, we proposed a neural-informed, semantics-driven framework to understand common human driving behavior in a brain-inspired manner. This study highlights the pathway of fusing neuroscience into complex human behavior understanding tasks and provides a computational neural model to understand human driving behaviors, which will enable autonomous vehicles to perceive and think like human drivers.