Browse
Submit Data
Databases
API
Help

Dataset Information

24 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

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

ABSTRACT: 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 for the identification of optimal conditions for separation of known sample constituents from 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.

SUBMITTER: Szucs R

PROVIDER: S-EPMC8068189 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Retention projection enables accurate calculation of liquid chromatographic retention times across labs and methods.

Project description:Identification of small molecules by liquid chromatography-mass spectrometry (LC-MS) can be greatly improved if the chromatographic retention information is used along with mass spectral information to narrow down the lists of candidates. Linear retention indexing remains the standard for sharing retention data across labs, but it is unreliable because it cannot properly account for differences in the experimental conditions used by various labs, even when the differences are relatively small and unintentional. On the other hand, an approach called "retention projection" properly accounts for many intentional differences in experimental conditions, and when combined with a "back-calculation" methodology described recently, it also accounts for unintentional differences. In this study, the accuracy of this methodology is compared with linear retention indexing across eight different labs. When each lab ran a test mixture under a range of multi-segment gradients and flow rates they selected independently, retention projections averaged 22-fold more accurate for uncharged compounds because they properly accounted for these intentional differences, which were more pronounced in steep gradients. When each lab ran the test mixture under nominally the same conditions, which is the ideal situation to reproduce linear retention indices, retention projections still averaged 2-fold more accurate because they properly accounted for many unintentional differences between the LC systems. To the best of our knowledge, this is the most successful study to date aiming to calculate (or even just to reproduce) LC gradient retention across labs, and it is the only study in which retention was reliably calculated under various multi-segment gradients and flow rates chosen independently by labs.

| S-EPMC4556278 | biostudies-literature

Quantitative Structure-Retention Relationships with Non-Linear Programming for Prediction of Chromatographic Elution Order.

Project description:In this work, we employed a non-linear programming (NLP) approach via quantitative structure-retention relationships (QSRRs) modelling for prediction of elution order in reversed phase-liquid chromatography. With our rapid and efficient approach, error in prediction of retention time is sacrificed in favor of decreasing the error in elution order. Two case studies were evaluated: (i) analysis of 62 organic molecules on the Supelcosil LC-18 column; and (ii) analysis of 98 synthetic peptides on seven reversed phase-liquid chromatography (RP-LC) columns with varied gradients and column temperatures. On average across all the columns, all the chromatographic conditions and all the case studies, percentage root mean square error (%RMSE) of retention time exhibited a relative increase of 29.13%, while the %RMSE of elution order a relative decrease of 37.29%. Therefore, sacrificing %RMSE(tR) led to a considerable increase in the elution order predictive ability of the QSRR models across all the case studies. Results of our preliminary study show that the real value of the developed NLP-based method lies in its ability to easily obtain better-performing QSRR models that can accurately predict both retention time and elution order, even for complex mixtures, such as proteomics and metabolomics mixtures.

| S-EPMC6678770 | biostudies-literature

Predicting reversed-phase liquid chromatographic retention times of pesticides by deep neural networks.

Project description:To be able to predict reversed phase liquid chromatographic (RPLC) retention times of contaminants is an asset in order to solve food contamination issues. The development of quantitative structure-retention relationship models (QSRR) requires selection of the best molecular descriptors and machine-learning algorithms. In the present work, two main approaches have been tested and compared, one based on an extensive literature review to select the best set of molecular descriptors (16), and a second with diverse strategies in order to select among 1545 molecular descriptors (MD), 16 MD. In both cases, a deep neural network (DNN) were optimized through a gridsearch.

| S-EPMC8671870 | biostudies-literature

Prediction of Chromatographic Elution Order of Analytical Mixtures Based on Quantitative Structure-Retention Relationships and Multi-Objective Optimization.

Project description:Prediction of the retention time from the molecular structure using quantitative structure-retention relationships is a powerful tool for the development of methods in reversed-phase HPLC. However, its fundamental limitation lies in the fact that low error in the prediction of the retention time does not necessarily guarantee a prediction of the elution order. Here, we propose a new method for the prediction of the elution order from quantitative structure-retention relationships using multi-objective optimization. Two case studies were evaluated: (i) separation of organic molecules in a Supelcosil LC-18 column, and (ii) separation of peptides in seven columns under varying conditions. Results have shown that, when compared to predictions based on the conventional model, the relative root mean square error of the elution order decreases by 48.84%, while the relative root mean square error of the retention time increases by 4.22% on average across both case studies. The predictive ability in terms of both retention time and elution order and the corresponding applicability domains were defined. The models were deemed stable and robust with few to no structural outliers.

| S-EPMC7411958 | biostudies-literature

Retention time prediction for chromatographic enantioseparation by quantile geometry-enhanced graph neural network.

Project description:The enantioseparation of chiral molecules is a crucial and challenging task in the field of experimental chemistry, often requiring extensive trial and error with different experimental settings. To overcome this challenge, here we show a research framework that employs machine learning techniques to predict retention times of enantiomers and facilitate chromatographic enantioseparation. A documentary dataset of chiral molecular retention times in high-performance liquid chromatography (CMRT dataset) is established to handle the challenge of data acquisition. A quantile geometry-enhanced graph neural network is proposed to learn the molecular structure-retention time relationship, which shows a satisfactory predictive ability for enantiomers. The domain knowledge of chromatography is incorporated into the machine learning model to achieve multi-column prediction, which paves the way for chromatographic enantioseparation prediction by calculating the separation probability. The proposed research framework works well in retention time prediction and chromatographic enantioseparation facilitation, which sheds light on the application of machine learning techniques to the experimental scene and improves the efficiency of experimenters to speed up scientific discovery.

| S-EPMC10227049 | biostudies-literature

LogD7.4 prediction enhanced by transferring knowledge from chromatographic retention time, microscopic pKa and logP.

Project description:Lipophilicity is a fundamental physical property that significantly affects various aspects of drug behavior, including solubility, permeability, metabolism, distribution, protein binding, and toxicity. Accurate prediction of lipophilicity, measured by the logD7.4 value (the distribution coefficient between n-octanol and buffer at physiological pH 7.4), is crucial for successful drug discovery and design. However, the limited availability of data for logD modeling poses a significant challenge to achieving satisfactory generalization capability. To address this challenge, we have developed a novel logD7.4 prediction model called RTlogD, which leverages knowledge from multiple sources. RTlogD combines pre-training on a chromatographic retention time (RT) dataset since the RT is influenced by lipophilicity. Additionally, microscopic pKa values are incorporated as atomic features, providing valuable insights into ionizable sites and ionization capacity. Furthermore, logP is integrated as an auxiliary task within a multitask learning framework. We conducted ablation studies and presented a detailed analysis, showcasing the effectiveness and interpretability of RT, pKa, and logP in the RTlogD model. Notably, our RTlogD model demonstrated superior performance compared to commonly used algorithms and prediction tools. These results underscore the potential of the RTlogD model to improve the accuracy and generalization of logD prediction in drug discovery and design. In summary, the RTlogD model addresses the challenge of limited data availability in logD modeling by leveraging knowledge from RT, microscopic pKa, and logP. Incorporating these factors enhances the predictive capabilities of our model, and it holds promise for real-world applications in drug discovery and design scenarios.

| S-EPMC10478446 | biostudies-literature

Using LC Retention Times in Organic Structure Determination: Drug Metabolite Identification.

Project description:BackgroundThere is a continued need for improvements in the efficiency of metabolite structure elucidation.ObjectiveWe propose to take LC Retention Time (RT) into consideration during the process of structure determination.MethodsHerein, we develop a simple methodology that employs a Chromatographic Hydrophobicity Index (CHI) framework for standardizing LC conditions and introduce and utilize the concept of a predictable CHI change upon Phase 1 biotransformation (CHIbt). Through the analysis of literature examples, we offer a Quantitative Structure-Retention Relationship (QSRR) for several types of biotransformation (especially hydroxylation) using physicochemical properties (clogP, hydrogen bonding).ResultsThe CHI system for retention indexing is shown to be practical and simple to implement. A database of CHIbt values has been created from re-incubation of 3 compounds and from analysis of an additional 17 datasets from the literature. Application of this database is illustrated.ConclusionIn our experience, this simple methodology allows complementing the discovery efforts that saves resources for in-depth characterization using NMR.

| S-EPMC6350196 | biostudies-literature

Mechanistic Chromatographic Column Characterization for the Analysis of Flavonoids Using Quantitative Structure-Retention Relationships Based on Density Functional Theory.

Project description:This work aimed to unravel the retention mechanisms of 30 structurally different flavonoids separated on three chromatographic columns: conventional Kinetex C18 (K-C18), Kinetex F5 (K-F5), and IAM.PC.DD2. Interactions between analytes and chromatographic phases governing the retention were analyzed and mechanistically interpreted via quantum chemical descriptors as compared to the typical 'black box' approach. Statistically significant consensus genetic algorithm-partial least squares (GA-PLS) quantitative structure retention relationship (QSRR) models were built and comprehensively validated. Results showed that for the K-C18 column, hydrophobicity and solvent effects were dominating, whereas electrostatic interactions were less pronounced. Similarly, for the K-F5 column, hydrophobicity, dispersion effects, and electrostatic interactions were found to be governing the retention of flavonoids. Conversely, besides hydrophobic forces and dispersion effects, electrostatic interactions were found to be dominating the IAM.PC.DD2 retention mechanism. As such, the developed approach has a great potential for gaining insights into biological activity upon analysis of interactions between analytes and stationary phases imitating molecular targets, giving rise to an exceptional alternative to existing methods lacking exhaustive interpretations.

| S-EPMC7139519 | biostudies-literature

Easy and accurate calculation of programmed temperature gas chromatographic retention times by back-calculation of temperature and hold-up time profiles.

Project description:Linear retention indices are commonly used to identify compounds in programmed-temperature gas chromatography (GC), but they are unreliable unless the original experimental conditions used to measure them are stringently reproduced. However, differences in many experimental conditions may be properly taken into account by calculating programmed-temperature retention times of compounds from their measured isothermal retention vs. temperature relationships. We call this approach "retention projection". Until now, retention projection has been impractical because it required very precise, meticulous measurement of the temperature vs. time and hold-up time vs. temperature profiles actually produced by a specific GC instrument to be accurate. Here we present a new, easy-to-use methodology to precisely measure those profiles: we spike a sample with 25 n-alkanes and use their measured, programmed-temperature retention times to precisely back-calculate what the instrument profiles must have been. Then, when we use those back-calculated profiles to project retention times of 63 chemically diverse compounds, we found that the projections are extremely accurate (e.g. to ±0.9 s in a 40 min ramp). They remained accurate with different temperature programs, GC instruments, inlet pressures, flow rates, and with columns taken from different batches of stationary phase while the accuracy of retention indices became worse the more the experimental conditions were changed from the original ones used to measure them. We also developed new, open-source software (http://www.retentionprediction.org/gc) to demonstrate the system.

| S-EPMC3478941 | biostudies-literature

Structure Characteristics, Biochemical Properties, and Pharmaceutical Applications of Alginate Lyases.

Project description:Alginate, the most abundant polysaccharides of brown algae, consists of various proportions of uronic acid epimers α-L-guluronic acid (G) and β-D-mannuronic acid (M). Alginate oligosaccharides (AOs), the degradation products of alginates, exhibit excellent bioactivities and a great potential for broad applications in pharmaceutical fields. Alginate lyases can degrade alginate to functional AOs with unsaturated bonds or monosaccharides, which can facilitate the biorefinery of brown algae. On account of the increasing applications of AOs and biorefinery of brown algae, there is a scientific need to explore the important aspects of alginate lyase, such as catalytic mechanism, structure, and property. This review covers fundamental aspects and recent developments in basic information, structural characteristics, the structure-substrate specificity or catalytic efficiency relationship, property, molecular modification, and applications. To meet the needs of biorefinery systems of a broad array of biochemical products, alginate lyases with special properties, such as salt-activated, wide pH adaptation range, and cold adaptation are outlined. Withal, various challenges in alginate lyase research are traced out, and future directions, specifically on the molecular biology part of alginate lyases, are delineated to further widen the horizon of these exceptional alginate lyases.

| S-EPMC8618178 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data