Nut consumption and age-related disease

Current knowledge on the effects of nut consumption on human health has rapidly increased in recent years and it now appears that nuts may play a role in the prevention of chronic age-related diseases. Frequent nut consumption has been associated with better metabolic status, decreased body weight as well as lower body weight gain over time and thus reduce the risk of obesity. The effect of nuts on glucose metabolism, blood lipids, and blood pressure is still controversial. However, significant decreased cardiovascular risk has been reported in a number of observational and clinical intervention studies. Thus, findings from cohort studies show that increased nut consumption is associated with a reduced risk of cardiovascular disease and mortality (especially that due to cardiovascular-related causes). Similarly, nut consumption has been also associated with reduced risk of certain cancers, such as colorectal, endometrial, and pancreatic neoplasms. Evidence regarding nut consumption and neurological or psychiatric disorders is scarce, but a number of studies suggest significant protective effects against depression, mild cognitive disorders and Alzheimer’s disease. The underlying mechanisms appear to include antioxidant and anti-inflammatory actions, particularly related to their mono- and polyunsaturated fatty acids (MUFA and PUFA, as well as vitamin and polyphenol content). MUFA have been demonstrated to improve pancreatic beta-cell function and regulation of postprandial glycemia and insulin sensitivity. PUFA may act on the central nervous system protecting neuronal and cell-signaling function and maintenance. The fiber and mineral content of nuts may also confer health benefits. Nuts therefore show promise as useful adjuvants to prevent, delay or ameliorate a number of chronic conditions in older people. Their association with decreased mortality suggests a potential in reducing disease burden, including cardiovascular disease, cancer, and cognitive impairments.     

State-of-the-art Tools for Computational Site of Metabolism Predictions: Comparative Analysis,Mechanistical Insights,and Future Applications

In drug design, it is crucial to have reliable information on how a chemical entity behaves in the presence of metabolizing enzymes. This requires substantial experimental efforts. Consequently, being able to predict the likely site/s of metabolism in any compound, synthesized or virtual, would be highly beneficial and time efficient.

In this work, six different methodologies for predictions of the site of metabolism (SOM) have been compared and validated using structurally diverse data sets of drug-like molecules with well-established metabolic pattern in CYP3A4, CYP2C9, or both. Three of the methods predict the SOM based on the ligand's chemical structure, two additional methods use structural information of the enzymes, and the sixth method combines structure and ligand similarity and reactivity. The SOM is correctly predicted in 50 to 90% of the cases, depending on method and enzyme, which is an encouraging rate. We also discuss the underlying mechanisms of cytochrome P450 metabolism in the light of the results from this comparison.     

Impaired function of fibroblast growth factor 23 / Klotho protein axis in prediabetes and diabetes mellitus: Promising predictor of cardiovascular risk

The discovery of clear molecular mechanisms of early cardiac and vascular complications in patients with prediabetes and known diabetes mellitus are core element of stratification at risk with predictive model creation further. Previous clinical studies have shown a pivotal role of impaired signaling axis of fibroblast growth factor 23 (FGF23), FGF23 receptor isoforms and its co-factor Klotho protein in cardiovascular (CV) complications in prediabetes and diabetes. Although there were data received in clinical studies, which confirmed a causative role of altered function of FGF-23/Klotho protein axis in manifestation of CV disease in prediabetes and type 2 diabetes mellitus (T2DM), the target therapy of these diseases directing on improvement of metabolic profiles, systemic and adipokine-relating inflammation by beneficial restoring of dysregulation in FGF-23/Klotho protein axis remain to be not fully clear. The aim of the review was to summarize findings regarding the role of impaired FGF-23/Klotho protein axis in developing CV complications in patients with prediabetes and type 2 diabetes mellitus. It has been elucidated that elevated levels of FGF-23 and deficiency of Klotho protein in peripheral blood are predictors of CV disease and CV outcomes in patients with (pre) diabetes, while predictive values of dynamic changes of the concentrations of these biomarkers require to be elucidated in detail in the future.     

Extrapolation for a pharmacokinetic model for acetaminophen from adults to neonates: A Latin Hypercube Sampling analysis

Physiological and drug-specific parameters need to be adjusted when extrapolating a pharmacokinetic (PK) model from adults to neonates, so as to reproduce the time profiles of the studied drug(s) consistent with clinical, in vivo data or in vitro cell line measurements. In this paper we present a parameter analysis method, i.e. the Latin Hypercube Sampling (LHS) method for an acetaminophen (APAP) PK model. The original model consists of two compartments (the blood and the urine) with Michaelis-Menten kinetic parameters determined for APAP and its metabolites. The physiological parameters are scaled through allometric laws from adults to neonates, and APAP-specific parameters are adjusted for enzymatic maturational changes. The LHS method is used to statistically investigate the interplay between these parameters. The results for the extrapolated APAP model are consistent with published APAP PK data in neonates. We found the sulphation clearance parameter played a crucial role in the neonatal PK model, but its influence was weakened if the volume of distribution parameters were included. We suggest that this kind of in silico experiment could be valuable as the first step in PK model extrapolation between different ages.