Development and validation of a HPLC method for the determination of sertraline and three non-chiral related impurities

A comparative study of the predictive performance of different descriptor calculation tools: Molecular-based elution order modeling and interpretation of retention mechanism for isomeric compounds from METLIN database

In the pharmaceutical industry, the need for analytical standards is a bottleneck for comprehensive evaluation and quality control of intermediate and end products. These are complex mixtures containing structurally related molecules. In this regard, chromatographic peak annotation, especially for critical pairs of isomers and closest structural analogs, can be supported by using a Quantitative Structure Retention Relationship (QSRR) approach. In our study, we investigated the fundamental basis of the reversed-phase (RP) retention mechanism for 1141 isomeric compounds from the METLIN SMRT dataset. Nine different descriptor calculation tools combined with different feature selection methods (genetic algorithm (GA), stepwise, Boruta) and machine learning (ML) approaches (support vector machine (SVM), multiple linear regression (MLR), random forest (RF), XGBoost) were applied to provide a reliable molecular structure-based interpretation of RP retention behaviour of the isomeric compounds. Strict internal and external validation metrics were used to select models with the best predictive capabilities (rtest > 0.73, order of elution > 60 %). For the developed models, mean absolute errors were in the range of 60 to 110 s. Stepwise and GA showed the most suitable performance as descriptor selection methods, while SVM and XGBoost modeling gave satisfactory predictive characteristics in most cases. Validation performed on the published experimental data for structurally related pharmaceutical compounds confirmed the best accuracy of MLR modeling in combination with GA feature selection of general physico-chemical properties. The resulting models will be useful for the prediction of separation and identification of structurally related compounds in pharmaceutical analysis, providing a simultaneous understanding of the interaction mechanisms leading to their retention under RP conditions.

Smoking blunts sertraline response in depression: A prospective observational cohort study

Background

Smoking is common in patients of depression and is known to affect response to antidepressants. This study was undertaken to evaluate the effect of smoking on the antidepressant effect of sertraline.

Method

Patients with depression were divided into smoker and nonsmoker cohorts and followed up for 8 weeks. Serum sertraline levels were estimated using the high-performance liquid chromatography system. Response to treatment was evaluated with the Hamilton Depression Rating Scale (HAM-D).

Results

Serum sertraline levels did not differ between smokers and nonsmokers at 4 and 8 weeks. Nonsmokers responded better to sertraline than smokers after 8 weeks. Adverse drug reaction profile did not vary between the two groups and was not impacted by serum sertraline levels. Nonsmokers showed a greater fall in the HAM-D score than smokers.

Conclusion

This study found depression among smokers to be less responsive to sertraline. This was not explained by serum sertraline levels. Treatment of depression in smokers with sertraline might require higher doses and duration, with more frequent reviews.

Comparative study of performances of UHPLC-MS/MS and HPLC/UV methods for analysis of ziprasidone and its main impurities

Ziprasidone is the second generation antipsychotic drug with unique multipotent G-protein-coupled (GPCR) receptor binding profile. Since ziprasidone is a highly lipophilic and unstable compound, development of efficient method for a concurrent assay of ziprasidone and its main impurities was a very challenging task.

The UHPLC-MS/MS method that we developed for simultaneous determination of ziprasidone and its main impurities (BITP, Chloroethyl-chloroindolinone, Zip-oxide, Zip-dimer, and Zip-BIT) was compared with some other related HPLC-UV methods of our own and other authorship. An increase of the mobile phase pH value from 2.5 to 4.7 units in the examined analytical methods influenced elution order of the investigated compounds. It was found out that the UHPLC-MS/MS method is more selective and sensitive than the earlier developed HPLC-UV method. Similar to our earlier HPLC-UV method, the UHPLC-MS/MS method is linear with a correlation coefficient (r) above 0.99 for all the analysed compounds, but with a negligibly lower precision and accuracy. Finally, with shorter analysis time, smaller column size and reduction of solvent consumption, UHPLC-MS/MS is assumed as a greener method than HPLC-UV for the ziprasidone purity assay.

After transfer of the UHPLC-MS/MS method to the UHPLC-DAD system, suitability of the UHPLC-DAD method for routine control of ziprasidone and its main impurities is examined and confirmed based on the retained good selectivity, resolution and short analysis time.

Metabolite fingerprinting by capillary electrophoresis-mass spectrometry

Changes in metabolite concentrations in response to specific diseases, treatments, diets, or other factors can be used to understand the complex mechanisms that control and regulate the human body and potentially detect the onset of disease prior to the observation of symptoms in a patient. Different analytical and chemometric platforms are necessary to detect as many metabolites as possible in different biological fluids. Capillary electrophoresis (CE) coupled to mass spectrometry (MS) is a particularly attractive, although still not common, approach for metabolomics for the detection of mainly polar and ionic metabolites. Among its main features, CE provides the capability to separate complex mixtures with high resolution and minimum sample treatment. However, the routine, automated use of CE-MS is not without challenges. In this chapter we describe a well-tested method for fingerprinting serum and urine using CE-TOF-MS. We describe below a sensitive and quite robust method for metabolomics with CE-MS including sample treatment, separation conditions, instrumental setup, and identification of 76 metabolites in the profile. Useful advice for daily practice is also included for every step of the procedure.