Chronotherapy for Hypertension.

PURPOSE OF REVIEW: Given the emerging knowledge that circadian rhythmicity exists in every cell and all organ systems, there is increasing interest in the possible benefits of chronotherapy for many diseases. There is a well-documented 24-h pattern of blood pressure with a morning surge that may contribute to the observed morning increase in adverse cardiovascular events. Historically, antihypertensive therapy involves morning doses, usually aimed at reducing daytime blood pressure surges, but an absence of nocturnal dipping blood pressure is also associated with increased cardiovascular risk. RECENT FINDINGS: To more effectively reduce nocturnal blood pressure and still counteract the morning surge in blood pressure, a number of studies have examined moving one or more antihypertensives from morning to bedtime dosing. More recently, such studies of chronotherapy have studied comorbid populations including obstructive sleep apnea, chronic kidney disease, or diabetes. Here, we summarize major findings from recent research in this area (2013-2017). In general, nighttime administration of antihypertensives improved overall 24-h blood pressure profiles regardless of disease comorbidity. However, inconsistencies between studies suggest a need for more prospective randomized controlled trials with sufficient statistical power. In addition, experimental studies to ascertain mechanisms by which chronotherapy is beneficial could aid drug design and guidelines for timed administration.

Correction to: Chronotherapy for Hypertension.

The meta-analysis referenced in the "Obstructive Sleep Apnea" section should instead refer to a meta-analysis for chronic kidney disease. Additionally, there are two mis-numbered reference citations in the "chronic kidney disease" section (ref. 107 should ref. 104 [Wang C et al. 2014] and ref. 105.

Determination of copolymer ratios of poly(lactide-co-glycolide) using near-infrared spectroscopy.

The near infrared (NIR) spectroscopic technique was used to determine copolymer ratios of polylactide-co-glycolide samples. Appropriate quantities of DL-polylactic acid and lactic-co-glycolic acid polymers with 86:14, 75:25, 64:36 and 52:48 lactide to glycolide ratios were dissolved in methylene chloride to obtain 5% (w/w) solutions. NIR spectra of the samples were obtained from the solutions using a Polyol Analyzer operated in the transmittance mode. Linear regression calibration models were generated at 2130 and 2288 nm from the second derivative spectral data obtained from the NIR technique. The lowest and highest standard errors of calibration (SEC) at 2130 nm were 1.29 and 1.63%, whereas those obtained from the calibration models generated at 2288 nm were 2.00 and 2.03%, respectively. Partial least squares (PLS) calibration models were also generated from the second derivative spectral data from 1100 to 2500 nm. The lowest and the highest SEC for the models were 1.46 and 1.53%, respectively. The calibration models were then used to predict the lactide contents of the unknown (test) samples. The highest percent error of prediction was 2.56% for samples with 86% lactide content when the linear regression calibration at 2130 nm was used, whereas the highest percent error of prediction was 1.56% for samples with 64% lactide content when the linear regression calibration at 2288 nm was used. The highest percent error of prediction was 1.73% for samples with 75% lactide content when the two-factor PLS calibration model was used.

Prediction of drug content and hardness of intact tablets using artificial neural network and near-infrared spectroscopy.

The purpose of this study was to predict drug content and hardness of intact tablets using artificial neural networks (ANN) and near-infrared spectroscopy (NIRS). Tablets for the drug content study were compressed from mixtures of Avicel PH-101, 0.5% magnesium stearate, and varying concentrations (0%, 1%, 2%, 5%, 10%, 20%, and 40% w/w) of theophylline. Tablets for the hardness study were compressed from mixtures of Avicel PH-101 and 0.5% magnesium stearate at varying compression forces ranging from 0.4 to 1 ton. An Intact Analyzer was used to obtain near infrared spectra from the tablets with varying drug contents, whereas a Rapid Content Analyzer (RCA) was used to obtain spectral data from the tablets with varying hardness. Two sets of tablets from each batch (i.e., tablets with varying drug content and hardness) were randomly selected. One set of tablets was used to generate appropriate calibration models, while the other set was used as the unknown (test) set. A total of 10 ANN calibration models (5 each with 10 and 160 inputs at appropriate wavelengths) and five separate 4-factor partial least squares (PLS) calibration models were generated to predict drug contents of the test tablets from the spectral data. For the prediction of tablet hardness, two ANN calibration models (one each with 10 and 160 inputs) and two 4-factor PLS calibration models were generated and used to predict the hardness of test tablets. The PLS calibration models were generated using Vision software. Prediction of drug contents of test tablets using the ANN calibration models generated with 10 inputs was significantly better than the prediction obtained with the ANN calibration models with 160 inputs. For tablets with low drug concentrations (less than or equal to 2% w/w) prediction of drug content was better with either of the two ANN calibration models than with the PLS calibration models. However, prediction of drug contents of tablets with greater than or equal to 5% w/w drug was better with the PLS calibration models than with the ANN calibration models. Prediction of tablet hardness was better with the ANN calibration models generated with either 10 or 160 inputs than with the PLS calibration models. This work demonstrated that a well-trained ANN model is a powerful alternative technique for analysis of NIRS data. Moreover, the technique could be used in instances when the conventional modeling of data does not work adequately.

Application of near-infrared spectroscopy for nondestructive analysis of Avicel powders and tablets.

The purpose of this study was to use near-infrared spectroscopy (NIRS) as a nondestructive technique to (a) differentiate three Avicel products (microcrystalline cellulose [MCC] PH-101, PH-102, and PH-200) in powdered form and in compressed tablets with and without 0.5% w/w magnesium stearate as a lubricant; (b) determine the magnesium stearate concentrations in the tablets; and (c) measure hardness of tablets compressed at several compression forces. Diffuse reflectance NIR spectra from Avicel powders and tablets (compression forces ranging from 0.2 to 1.2 tons) were collected and distance scores calculated from the second-derivative spectra were used to distinguish the different Avicel products. A multiple linear regression model was generated to determine magnesium stearate concentrations (from 0.25 to 2% w/w), and partial least squares (PLS) models were generated to predict hardness of tablets. The NIRS technique could distinguish between the three different Avicel products, irrespective of lubricant concentration, in both the powdered form and in the compressed tablets because of the differences in the particle size of the Avicel products. The percent error for predicting the lubricant concentration of tablets ranged from 0.2 to 10% w/w. The maximum percent error of prediction of hardness of tablets compressed at the various compression forces was 8.8% for MCC PH-101, 5.3% for MCC PH-102, and 4.6% for MCC PH-200. The NIRS nondestructive technique can be used to predict the Avicel type in both powdered and tablet forms as well as to predict the lubricant concentration and hardness.

A comparison of reflectance and transmittance near-infrared spectroscopic techniques in determining drug content in intact tablets.

Drug contents of intact tablets were determined using non-destructive near infrared (NIR) reflectance and transmittance spectroscopic techniques. Tablets were compressed from blends of Avicel PH-101 and 0.5% w/w magnesium stearate with varying concentrations of anhydrous theophylline (0, 1, 2, 5, 10, 20 and 40% w/w). Ten tablets from each drug content batch were randomly selected for spectral analysis. Both reflectance and transmittance NIR spectra were obtained from these intact tablets. Actual drug contents of the tablets were then ascertained using a UV-spectrophotometer at 268 nm. Multiple linear regression (MLR) models at 1116 nm and partial least squares (PLS) calibration models were generated from the second derivative spectral data of the tablets in order to predict drug contents of intact tablets. Both the reflectance and the transmittance techniques were able to predict the drug contents in intact tablets over a wide range. However, a comparison of the results of the study indicated that the lowest percent errors of prediction were provided by the PLS calibration models generated from spectral data obtained using the transmittance technique.