scholarly journals Structure Driven Prediction of Chromatographic Retention Times: Applications to Pharmaceutical Analysis

2021 ◽  
Vol 22 (8) ◽  
pp. 3848
Author(s):  
Roman Szucs ◽  
Roland Brown ◽  
Claudio Brunelli ◽  
James C. Heaton ◽  
Jasna Hradski

Pharmaceutical drug development relies heavily on the use of Reversed-Phase Liquid Chromatography methods. These methods are used to characterize active pharmaceutical ingredients and drug products by separating the main component from related substances such as process related impurities or main component degradation products. The results presented here indicate that retention models based on Quantitative Structure Retention Relationships can be used for de-risking methods used in pharmaceutical analysis and the identification of optimal separation conditions for separation of known sample constituents with postulated/hypothetical components. The prediction of retention times for hypothetical components in established methods is highly valuable as these compounds are not usually readily available for analysis. Here we discuss the development and optimization of retention models, selection of the most relevant structural molecular descriptors, regression model building and validation. We also present a practical example applied to chromatographic method development and discuss the accuracy of these models on selection of optimal separation parameters.

2011 ◽  
Vol 94 (1) ◽  
pp. 143-149 ◽  
Author(s):  
Hitesh B Patel ◽  
Arivozhi Mohan ◽  
Hitendra S Joshi

Abstract A simple RP-ultra-performance LC method was developed and validated for determination of impurities related to torsemide tablets. The rapid method provided adequate separation of all known related impurities and degradation products. Separation was achieved on a Zorbax SB-C18 column (50 × 4.6 mm id, 1.8 μm particle size) with binary gradient elution, and detection was performed at 288 nm. The drug product was subjected to oxidative, hydrolytic, photolytic, and thermal stress conditions to prove the specificity of the proposed method. The linearity and recovery were investigated for known impurities in the range of 0.025 to 1.0%, with respect to the drug concentration in the prepared sample. The linearity of the calibration curve for each of the impurities and torsemide was found to be very good (r2 > 0.999). Relative response factors for each of the known impurities were established by the slope ratio method from the linearity study.


2018 ◽  
Vol 6 (02) ◽  
pp. 53-63
Author(s):  
Narendra Singh ◽  
Yogendra Singh ◽  
R. S. Bhadauria ◽  
Jeyabalan Govindasamy

Pharmaceutical analysis is one of the most challenging fields of analytical chemistry. Pharmaceutical analysts carry out the qualitative and quantitative control of APIs and drug products and also develop and validate appropriate methods. One of my main goals was to develop modern, rapid, precise and reproducible, but also cost-effective HPLC assay methods which are generally available and applicable for most users. The aim of this work was to develop LC methods for both compounds. The assay of erythromycin by LC offers several advantages, such as high specificity, the possibility of determining and quantifying impurities and degradation products, and improved accuracy. The developed methods were validated. My whole work containing following plan of work as Selection of drug, Review Literature, FITR of both drugs and Mixture, Preparation of standard solutions, Preparation of sample of pure drug in Standard solution, Method development by HPLC (as Selection of solvents to be used as diluents and mobile phase, Selection of wavelength, Selection of mobile phase and Selection of chromatographic conditions) Preparation of Mobile phase, Preparation of standard calibration curve combination of drug, Optimization of HPLC condition using box behnken design. Validation of analytical method following parameters as per ICH guidelines. (i). System suitability (ii). Linearity and range (iii). Specificity (iv).Accuracy and precision (v). Limits of detection (LOD) and Quantitation (LOQ). (vi). Selectivity and (vii).Robustness.


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