The Molecular Pharmacology of G Protein Signaling Then and Now: A Tribute to

ABSTRACT The recent, unfortunate death of Alfred G. ("Al") Gilman, M.D., Ph.D., represents a sad signpost for an era spanning over 40 years in molecular pharmacology. Gilman's discoveries, influence, and persona were dominant forces in research and training in pharmacology. Here, we review the progression of ideas and knowledge that spawned early work by Gilman and collaborators (among them, one of the authors) and later efforts (including those of the other author) that have recently yielded a comprehensive and precise structural understanding of fundamental topics in pharmacology: the binding of ligands to G proteincoupled receptors (GPCRs) and the interaction of GPCRs with heterotrimeric G proteins and effector molecules. Those data provide new and important insights into the molecular basis that underlies affinity and efficacy, two of the most important features of drug action, which represent the latest chapter in the saga that Al Gilman's work helped launch

Prepregnancy obesity is associated with lower psychomotor development scores in boys at age 3 in a low-income, minority birth cohort

Whether maternal obesity and gestational weight gain (GWG) are associated with early-childhood development in low-income, urban, minority populations, and whether effects differ by child sex remain unknown. This study examined the impact of prepregnancy BMI and GWG on early childhood neurodevelopment in the Columbia Center for Children's Environmental Health Mothers and Newborns study. Maternal prepregnancy weight was obtained by self-report, and GWG was assessed from participant medical charts. At child age 3 years, the Psychomotor Development Index (PDI) and Mental Development Index (MDI) of the Bayley Scales of Infant Intelligence were completed. Sex-stratified linear regression models assessed associations between prepregnancy BMI and pregnancy weight gain z-scores with child PDI and MDI scores, adjusting for covariates. Of 382 women, 48.2% were normal weight before pregnancy, 24.1% overweight, 23.0% obese, and 4.7% underweight. At 3 years, mean scores on the PDI and MDI were higher among girls compared to boys (PDI: 102.3 vs. 97.2, P = 0.0002; MDI: 92.8 vs. 88.3, P = 0.0001). In covariate-adjusted models, maternal obesity was markedly associated with lower PDI scores in boys [b = -7.81, 95% CI: (-13.08, -2.55), P = 0.004], but not girls. Maternal BMI was not associated with MDI in girls or boys, and GWG was not associated with PDI or MDI among either sex (all-P > 0.05). We found that prepregnancy obesity was associated with lower PDI scores at 3 years in boys, but not girls. The mechanisms underlying this sex-specific association remain unclear, but due to elevated obesity exposure in urban populations, further investigation is warranted

Innovative solutions to novel drug development in mental health

There are many new advances in neuroscience and mental health which should lead to a greater understanding of the neurobiological dysfunction in neuropsychiatric disorders and new developments for early, effective treatments. To do this, a biomarker approach combining genetic, neuroimaging, cognitive and other biological measures is needed. The aim of this article is to highlight novel approaches for pharmacological and non-pharmacological treatment development. This article suggests approaches that can be taken in the future including novel mechanisms with preliminary clinical validation to provide a toolbox for mechanistic studies and also examples of translation and back-translation. The review also emphasizes the need for clinician-scientists to be trained in a novel way in order to equip them with the conceptual and experimental techniques required, and emphasizes the need for private-public partnership and pre-competitive knowledge exchange. This should lead the way for important new holistic treatment developments to improve cognition, functional outcome and well-being of people with neuropsychiatric disorders

The alimentary impact of the hemp seed

Hemp seed and hemp seed oil can supply us with many important substances. Their essential fatty acid compositions are favourable, but they may contain non-psychotropic cannabinoids. Emerging data show that these components can influence the health status of the population beneficially. Some data also showed trace amounts of tetrahydrocannabinol in seed oils, the main psychotropic cannabinoid that is contraindicated.Our aim was to examine cannabinoids and fatty acid composition as well as metal and non-metal element compositions in products, like hemp seed oil and chopped hemp seed capsule.The cannabinoids were separated by thin layer chromatography. Fatty acid composition was determined with gas chromatography, and elements (Al, B, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, P, Pb, S, Si, Sn, Sr, V, and Zn) were measured by inductively coupled plasma optical emission spectrometric method. Selenium was determined with polarographic analyser.Cannabinoids were not detectable by thin layer chromatography, so hemp seed oil, as well as the capsule, have no psychotropic adverse effect. Our data showed that hemp seed contains essential fatty acids close to the recommended ratio. The B and Se concentrations of the oils and the P concentration of the capsule are also relevant

The BJP expects authors to share data

Editoria

Group interventions to improve health outcomes : a framework for their design and delivery

Peer reviewedPublisher PD

Strategies to reduce sample sizes in Alzheimer’s disease primary and secondary prevention trials using longitudinal amyloid PET imaging

BACKGROUND: Detecting subtle-to-moderate biomarker changes such as those in amyloid PET imaging becomes increasingly relevant in the context of primary and secondary prevention of Alzheimer's disease (AD). This work aimed to determine if and when distribution volume ratio (DVR; derived from dynamic imaging) and regional quantitative values could improve statistical power in AD prevention trials. METHODS: Baseline and annualized % change in [11C]PIB SUVR and DVR were computed for a global (cortical) and regional (early) composite from scans of 237 cognitively unimpaired subjects from the OASIS-3 database ( www.oasis-brains.org ). Bland-Altman and correlation analyses were used to assess the relationship between SUVR and DVR. General linear models and linear mixed effects models were used to determine effects of age, sex, and APOE-ε4 carriership on baseline and longitudinal amyloid burden. Finally, differences in statistical power of SUVR and DVR (cortical or early composite) were assessed considering three anti-amyloid trial scenarios: secondary prevention trials including subjects with (1) intermediate-to-high (Centiloid > 20.1), or (2) intermediate (20.1 < Centiloid ≤ 49.4) amyloid burden, and (3) a primary prevention trial focusing on subjects with low amyloid burden (Centiloid ≤ 20.1). Trial scenarios were set to detect 20% reduction in accumulation rates across the whole population and in APOE-ε4 carriers only. RESULTS: Although highly correlated to DVR (ρ = .96), cortical SUVR overestimated DVR cross-sectionally and in annual % change. In secondary prevention trials, DVR required 143 subjects per arm, compared with 176 for SUVR. Both restricting inclusion to individuals with intermediate amyloid burden levels or to APOE-ε4 carriers alone further reduced sample sizes. For primary prevention, SUVR required less subjects per arm (n = 855) compared with DVR (n = 1508) and the early composite also provided considerable sample size reductions (n = 855 to n = 509 for SUVR, n = 1508 to n = 734 for DVR). CONCLUSION: Sample sizes in AD secondary prevention trials can be reduced by the acquisition of dynamic PET scans and/or by restricting inclusion to subjects with intermediate amyloid burden or to APOE-ε4 carriers only. Using a targeted early composite only leads to reductions of sample size requirements in primary prevention trials. These findings support strategies to enable smaller Proof-of-Concept Phase II clinical trials to better streamline drug development

Strategies to reduce sample sizes in Alzheimer’s disease primary and secondary prevention trials using longitudinal amyloid PET imaging

BACKGROUND: Detecting subtle-to-moderate biomarker changes such as those in amyloid PET imaging becomes increasingly relevant in the context of primary and secondary prevention of Alzheimer's disease (AD). This work aimed to determine if and when distribution volume ratio (DVR; derived from dynamic imaging) and regional quantitative values could improve statistical power in AD prevention trials. METHODS: Baseline and annualized % change in [11C]PIB SUVR and DVR were computed for a global (cortical) and regional (early) composite from scans of 237 cognitively unimpaired subjects from the OASIS-3 database ( www.oasis-brains.org ). Bland-Altman and correlation analyses were used to assess the relationship between SUVR and DVR. General linear models and linear mixed effects models were used to determine effects of age, sex, and APOE-ε4 carriership on baseline and longitudinal amyloid burden. Finally, differences in statistical power of SUVR and DVR (cortical or early composite) were assessed considering three anti-amyloid trial scenarios: secondary prevention trials including subjects with (1) intermediate-to-high (Centiloid > 20.1), or (2) intermediate (20.1 < Centiloid ≤ 49.4) amyloid burden, and (3) a primary prevention trial focusing on subjects with low amyloid burden (Centiloid ≤ 20.1). Trial scenarios were set to detect 20% reduction in accumulation rates across the whole population and in APOE-ε4 carriers only. RESULTS: Although highly correlated to DVR (ρ = .96), cortical SUVR overestimated DVR cross-sectionally and in annual % change. In secondary prevention trials, DVR required 143 subjects per arm, compared with 176 for SUVR. Both restricting inclusion to individuals with intermediate amyloid burden levels or to APOE-ε4 carriers alone further reduced sample sizes. For primary prevention, SUVR required less subjects per arm (n = 855) compared with DVR (n = 1508) and the early composite also provided considerable sample size reductions (n = 855 to n = 509 for SUVR, n = 1508 to n = 734 for DVR). CONCLUSION: Sample sizes in AD secondary prevention trials can be reduced by the acquisition of dynamic PET scans and/or by restricting inclusion to subjects with intermediate amyloid burden or to APOE-ε4 carriers only. Using a targeted early composite only leads to reductions of sample size requirements in primary prevention trials. These findings support strategies to enable smaller Proof-of-Concept Phase II clinical trials to better streamline drug development

Pathways to new drug discovery in neuropsychiatry

There is currently a crisis in drug discovery for neuropsychiatric disorders, with a profound, yet unexpected drought in new drug development across the spectrum. In this commentary, the sources of this dilemma and potential avenues to redress the issue are explored. These include a critical review of diagnostic issues and of selection of participants for clinical trials, and the mechanisms for identifying new drugs and new drug targets. Historically, the vast majority of agents have been discovered serendipitously or have been modifications of existing agents. Serendipitous discoveries, based on astute clinical observation or data mining, remain a valid option, as is illustrated by the suggestion in the paper by Wahlqvist and colleagues that treatment with sulfonylurea and metformin reduces the risk of affective disorder. However, the identification of agents targeting disorder-related biomarkers is currently proving particularly fruitful. There is considerable hope for genetics as a purist, pathophysiologically valid pathway to drug discovery; however, it is unclear whether the science is ready to meet this promise. Fruitful paradigms will require a break from the orthodoxy, and creativity and risk may well be the fingerprints of success

Prescription Drug Labeling Medication Errors: A Big Deal for Pharmacists

Today, in the health care profession, all types of medication errors including missed dose, wrong dosage forms, wrong time interval, wrong route, etc., are a big deal for better patient care. Today, problems related to medications are common in the healthcare profession, and are responsible for significant morbidity, mortality, and cost. Several recent studies have demonstrated that patients frequently have difficulty in reading and understanding medication labels. According to the Institute of Medicine report, “Preventing Medication Errors”, cited poor labeling as a central cause for medication errors in the USA. Evidence suggests that specific content and format of prescription drug labels facilitate communication with and comprehension by patients. Efforts to improve the labels should be guided by such evidence, although an additional study assessing the influence of label design on medication-taking behavior and health outcomes is needed. Several policy options exist to require minimal standards to optimize medical therapy, particularly in light of the new Medicare prescription drug benefit