Quantification of Acetaminophen, Caffeine and Ibuprofen in Solid Dosage Forms by UV Spectroscopy coupled with Multivariate analysis

Quantification of acetaminophen, caffeine and ibuprofen in ternary mixtures was studied by using UV spectroscopy coupled with multivariate analysis [tri-linear regression-calibration (TLRC), classical least squares (CLS) and multi linear regression calibration (MLRC)]. To validate UV spectroscopic methods, the wavelength range of 215 – 290nm and a matrix (composed of 21 standard solutions containing acetaminophen 20-32.5mg/L, caffeine 1-3.5mg/L and ibuprofen 12-32mg/L) were selected. With the exception of TLRC, CLS and MLRS algorithms proved to be relevant for obtaining UV spectroscopic assay results and dissolution profile of acetaminophen, caffeine and ibuprofen in their combined capsules and tablets, with precision (RSD < 3%) and accuracy (98.3 – 101.9% recovery). It is also suggested that these UV spectroscopic methods could replace UHPLC analysis in the routine quality control of these compounds in their solid pharmaceutical dosage forms (p > 0.05).

On the Use of Regression Calibration in a Complex Sampling Design With Application to the Hispanic Community Health Study/Study of Latinos

Regression calibration is the most widely used method to adjust regression parameter estimates for covariate measurement error. Yet, its application in the context of a complex sampling design, for which the common bootstrap variance estimator can be less straightforward, has been less studied. We propose two variance estimators for a multi-stage probability-based sampling design, a parametric and a resampling-based multiple imputation approach, where a latent mean exposure needed for regression calibration is the target of imputation. This work was motivated by the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), data from 2006 to 2011, for which relationships between several outcomes and diet, an error-prone self-reported exposure, are of interest. We assessed the relative performance of these variance estimation strategies in an extensive simulation study built on the HCHS/SOL data. We further illustrate the proposed estimators with an analysis of the cross-sectional association of dietary sodium intake with hypertension-related outcomes in a subsample of the HCHS/SOL cohort. We provide investigators guidelines for the application of regression models with regression-calibrated exposures. Practical considerations for implementation of these two variance estimators in the setting of a large multi-center study are also discussed. Code to replicate the presented results is available online.

Using enhanced regression calibration to combine dietary intake estimates from 24 h recall and FFQ reduces bias in diet–disease associations

Objective:To illustrate the impact of combining 24 h recall (24hR) and FFQ estimates using regression calibration (RC) and enhanced regression calibration (ERC) on diet–disease associations.Setting:Wageningen area, the Netherlands, 2011–2013.Design:Five approaches for obtaining self-reported dietary intake estimates of protein and K were compared: (i) uncorrected FFQ intakes (FFQ); (ii) uncorrected average of two 24hR ( $\overline {\rm R}$ ); (iii) average of FFQ and $\overline {\rm R}$ ( ${\overline {\rm F}}\,\overline {\rm R}}$ ); (iv) RC from regression of 24hR v. FFQ; and (v) ERC by adding individual random effects to the RC approach. Empirical attenuation factors (AF) were derived by regression of urinary biomarker measurements v. the resulting intake estimates.Participants:Data of 236 individuals collected within the National Dietary Assessment Reference Database.Results:Both FFQ and 24hR dietary intake estimates were measured with substantial error. Using statistical techniques to correct for measurement error (i.e. RC and ERC) reduced bias in diet–disease associations as indicated by their AF approaching 1 (RC 1·14, ERC 0·95 for protein; RC 1·28, ERC 1·34 for K). The larger sd and narrower 95% CI of AF obtained with ERC compared with RC indicated that using ERC has more power than using RC. However, the difference in AF between RC and ERC was not statistically significant, indicating no significantly better de-attenuation by using ERC compared with RC. AF larger than 1, observed for the ERC for K, indicated possible overcorrection.Conclusions:Our study highlights the potential of combining FFQ and 24hR data. Using RC and ERC resulted in less biased associations for protein and K.