Retention prediction using quantitative structure-retention relationships combined with the hydrophobic subtraction model in reversed-phase liquid chromatography.

The hydrophobic subtraction model (HSM) combined with quantitative structure-retention relationships (QSRR) methodology was utilized to predict retention times in reversed-phase liquid chromatography (RPLC). A selection of new analytes and new RPLC columns that had never been used in the QSRR modeling process were used to verify the proposed approach. This work is designed to facilitate early prediction of co-elution of analytes in pharmaceutical drug discovery applications where it is advantageous to predict whether impurities might be co-eluted with the active drug component. The QSRR models were constructed through partial least squares regression combined with a genetic algorithm (GA-PLS) which was employed as a feature selection method to choose the most informative molecular descriptors calculated using VolSurf+ software. The analyte hydrophobicity coefficient of the HSM was predicted for subsequent calculation of retention. Clustering approaches based on the local compound type and the local second dominant interaction were investigated to select the most appropriate training set of analytes from a larger database. Predicted retention times of five new compounds on five new RPLC C18 columns were compared with their measured retention times with percentage root-mean-square errors of 15.4 and 24.7 for the local compound type and local second dominant interaction clustering methods, respectively.

Retention Index Prediction Using Quantitative Structure-Retention Relationships for Improving Structure Identification in Nontargeted Metabolomics.

Structure identification in nontargeted metabolomics based on liquid-chromatography coupled to mass spectrometry (LC-MS) remains a significant challenge. Quantitative structure-retention relationship (QSRR) modeling is a technique capable of accelerating the structure identification of metabolites by predicting their retention, allowing false positives to be eliminated during the interpretation of metabolomics data. In this work, 191 compounds were grouped according to molecular weight and a QSRR study was carried out on the 34 resulting groups to eliminate false positives. Partial least squares (PLS) regression combined with a Genetic algorithm (GA) was applied to construct the linear QSRR models based on a variety of VolSurf+ molecular descriptors. A novel dual-filtering approach, which combines Tanimoto similarity (TS) searching as the primary filter and retention index (RI) similarity clustering as the secondary filter, was utilized to select compounds in training sets to derive the QSRR models yielding R2 of 0.8512 and an average root mean square error in prediction (RMSEP) of 8.45%. With a retention index filter expressed as ±2 standard deviations (SD) of the error, representative compounds were predicted with >91% accuracy, and for 53% of the groups (18/34), at least one false positive compound could be eliminated. The proposed strategy can thus narrow down the number of false positives to be assessed in nontargeted metabolomics.

Retention prediction in reversed phase high performance liquid chromatography using quantitative structure-retention relationships applied to the Hydrophobic Subtraction Model.

Quantitative Structure-Retention Relationships (QSRR) methodology combined with the Hydrophobic Subtraction Model (HSM) have been utilized to accurately predict retention times for a selection of analytes on several different reversed phase liquid chromatography (RPLC) columns. This approach is designed to facilitate early prediction of co-elution of analytes, for example in pharmaceutical drug discovery applications where it is advantageous to predict whether impurities might be co-eluted with the active drug component. The QSRR model utilized VolSurf+ descriptors and a Partial Least Squares regression combined with a Genetic Algorithm (GA-PLS) to predict the solute coefficients in the HSM. It was found that only the hydrophobicity (η'H) term in the HSM was required to give the accuracy necessary to predict potential co-elution of analytes. Global QSRR models derived from all 148 compounds in the dataset were compared to QSRR models derived using a range of local modelling techniques based on clustering of compounds in the dataset by the structural similarity of compounds (as represented by the Tanimoto similarity index), physico-chemical similarity of compounds (represented by log D), the neutral, acidic, or basic nature of the compound, and the second dominant interaction between analyte and stationary phase after hydrophobicity. The global model showed reasonable prediction accuracy for retention time with errors of 30 s and less for up to 50% of modeled compounds. The local models for Tanimoto, nature of the compound and second dominant interaction approaches all exhibited prediction errors less than 30 s in retention time for nearly 70% of compounds for which models could be derived. Predicted retention times of five representative compounds on nine reversed-phase columns were compared with known experimental retention data for these columns and this comparison showed that the accuracy of the proposed modelling approach is sufficient to reliably predict the retention times of analytes based only on their chemical structures.

Towards a chromatographic similarity index to establish localized quantitative structure-retention models for retention prediction: Use of retention factor ratio.

Quantitative Structure-Retention Relationships (QSRR) have the potential to speed up the screening phase of chromatographic method development as the initial exploratory experiments are replaced by prediction of analyte retention based solely on the structure of the molecule. The present study offers further proof-of-concept of localized QSRR modelling, in which the retention of any given compound is predicted using only the most chromatographically similar compounds in the available dataset. To this end, each compound in the dataset was sequentially removed from the database and individually utilized as a test analyte. In this study, we propose the retention factor k as the most relevant chromatographic similarity measure and compare it with the Tanimoto index, the most popular similarity measure based on chemical structure. Prediction error was reduced by up to 8 fold when QSRR was based only on chromatographically similar compounds rather than using the entire dataset. The study therefore shows that the design of a practically useful structural similarity index should select the same compounds in the dataset as does the k-similarity filter in order to establish accurate predictive localized QSRR models. While low average prediction errors (Mean Absolute Error (MAE)<0.5min) and slopes of the regression lines through the origin close to 1.00 were obtained using k-similarity searching, the use of the structural Tanimoto similarity index, considered as the gold standard in Quantitative Structure-Activity Relationships (QSAR) studies, generally resulted in much higher prediction errors (MAE>1min) and significant deviations from the reference slope of 1.0. The Tanomoto similarity index therefore appears to have limited general utility in QSRR studies. Future studies therefore aim at designing a more appropriate chromatographic similarity index that can then be applied for unknown compounds (that is, compounds which have not been tested previously on the chromatographic system used, but for which the chemical structures are known).

Online solid phase extraction and liquid chromatography-mass spectrometric determination of nucleoside drugs in plasma.

The bioanalysis and especially the sample preparation of nucleoside drugs in complex media, such as human plasma, has been challenging due to the high polarity and high solubility of these drugs in water. Online solid phase extraction (SPE) offers significant advantages, such as automation and timesaving. Thus, several types of SPE columns have been developed for compounds with different polarities. In this study, SPE was applied to overcome the issue of sample pretreatment of nucleoside drugs in human plasma, with the final aim of establishing a robust analytical platform for drugs with similar structures. A simple, easy-to-use, and efficient method is described for the simultaneous determination of lamivudine, zidovudine, didanosine and emtricitabine in human plasma via online SPE and high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Following a simple centrifugation step, a 10µL plasma sample was injected directly onto the HPLC system. The Oasis MCX cartridge was washed, and the analytes were removed by back-flushing directly onto the analytical column. The analytes were quantified using a triple-quadrupole tandem mass spectrometer in multiple-reaction monitoring mode. Similarly, with the development and application of a Bond Elut phenylboronic acid (PBA) SPE cartridge, a fully automated online SPE-HPLC-MS/MS method was established for the simultaneous determination of ribavirin and taribavirin in human plasma. Linear calibration curves were obtained over the range of 0.5-2000ngmL-1, and the limit of quantification ranged from 0.5ngmL-1 to 10ngmL-1, which is sensitive enough for clinical drug monitoring. The intra- and inter-day precisions were in the range of 0.2-8.9%, and the trueness ranged between 88.9% and 113.1%. Excellent recoveries from plasma were achieved with a range between 86.7% and 105.1%. This procedure is easier to perform and requires less sample handling compared to methods previously described in the literature. This high-throughput method involving the direct injection of plasma samples may provide a practical solution for the analysis of multiple nucleoside drugs in clinical research. The method was tested in plasma samples from some patients and showed good performance.

Optimization of on-line solid phase extraction and HPLC conditions using response surface methodology for determination of WM-5 in mouse plasma and its application to pharmacokinetic study.

Response surface methodology (RSM) was utilized for rapid and systematic optimization of on-line solid-phase extraction (SPE) parameters to maximize the response and separation of WM-5. The optimization was performed with Box-Behnken designs. Four major parameters were investigated for their contributions to the response and separation of WM-5, with a total of 29 experiments being performed for each instrument, respectively. Quantitative determination of WM-5 in mouse plasma was performed to evaluate the statistical significance of the parameters on chromatographic response. A fully automated on-line SPE and high-performance liquid chromatography (HPLC) with diode array detection (DAD) method was developed for the determination of WM-5 in mouse plasma. Calibration curve with good linearity (r=0.9989) was obtained in the range of 20-4000 ng/mL in mouse plasma. The limit of detection (LOD) and lower limit of quantification (LLOQ) of the assay were 6 ng/mL and 20 ng/mL, respectively. The overall intra-day and the inter-day variations were less than 1.90%. The recovery of the method was in the range of 93.74-96.33% with RSD less than 3.06%. The optimized method demonstrated good performance in terms of specificity, LLOQ, linearity, recovery, precision and accuracy, and was successfully applied to quantify WM-5 in mouse plasma to support the pharmacokinetic study.

Association of weight-adjusted body fat and fat distribution with bone mineral density in middle-aged chinese adults: a cross-sectional study.

BACKGROUND: Although it is well established that a higher body weight is protective against osteoporosis, the effects of body fat and fat distribution on bone mineral density (BMD) after adjustment for body weight remains uncertain. OBJECTIVE: To examine the relationship between body fat and fat distribution and BMD beyond its weight-bearing effect in middle-aged Chinese adults. METHOD: The study had a community-based cross-sectional design and involved 1,767 women and 698 men aged 50-75 years. The BMD of the lumbar spine, total hip, and whole body, and the fat mass (FM) and percentage fat mass (%FM) of the total body and segments of the body were measured by dual-energy X-ray absorptiometry. General information on the participants was collected using structured questionnaire interviews. RESULT: After adjusting for potential confounders, an analysis of covariance showed the weight-adjusted (WA-) total FM (or %FM) to be negatively associated with BMD in all of the studied sites (P<0.05) in both women and men. The unfavorable effects of WA-total FM were generally more substantial in men than in women, and the whole body was the most sensitive site related to FM, followed by the total hip and the lumbar spine, in both genders. The mean BMD of the lumbar spine, total hip, and whole body was 3.93%, 3.01%, and 3.65% (in women) and 5.02%, 5.57%, 6.03% (in men) lower in the highest quartile (vs. lowest quartile) according to the WA-total FM (all p<0.05). Similar results were noted among the groups for WA-total FM%. In women, abdominal fat had the most unfavorable association with BMD, whereas in men it was limb fat. CONCLUSION: FM (or %FM) is inversely associated with BMD beyond its weight-bearing effect. Abdominal fat in women and limb fat in men seems to have the greatest effect on BMD.

Development of a fully automated on-line solid phase extraction and high-performance liquid chromatography with diode array detection method for the pharmacokinetic evaluation of bavachinin: a study on absolute bioavailability and dose proportionality.

A fully automated on-line solid-phase extraction (SPE) and high-performance liquid chromatography (HPLC) with diode array detection (DAD) method was developed for determination of bavachinin in mouse plasma. Analytical process was performed on two reversed-phase columns (SPE cartridge and analytical column) connected via a Valco 6-port switching valve. Plasma samples (10 µL) were injected directly onto a C18 SPE cartridge (MF Ph-1 C18, 10 mm × 4 mm, 5 µm) and the biological matrix was washed out for 2 min with the loading solvent (5 mM NaH(2)PO(4) buffer, pH 3.5) at a flow rate of 1 mL/min. By rotation of the switching valve, bavachinin was eluted from the SPE cartridge in the back-flush mode and transferred to the analytical column (Venusil MP C18, 4.6 mm × 150 mm, 5 µm) by the chromatographic mobile phase consisted of acetonitrile-5mM NaH(2)PO(4) buffer 65/35 (v/v, pH 3.5) at a flow rate of 1 mL/min. The complete cycle of the on-line SPE purification and chromatographic separation of the analyte was 13 min with UV detection performed at 236 nm. Calibration curve with good linearity (r=0.9997) was obtained in the range of 20-4000 ng/mL in mouse plasma. The intra-day and inter-day precisions (RSD) of bavachinin were in the range of 0.20-2.32% and the accuracies were between 98.47% and 102.95%. The lower limit of quantification (LLOQ) of the assay was 20 ng/mL. In conclusion, the established automated on-line SPE-HPLC-DAD method demonstrated good performance in terms of linearity, specificity, detection and quantification limits, precision and accuracy, and was successfully utilized to quantify bavachinin in mouse plasma to support the pharmacokinetic (PK) studies. The PK properties of bavachinin were characterized as rapid oral absorption, high clearance, and poor absolute bioavailability.