•Developed mixed QSRR models for evaluation of mixed mode interactions in RP/WCX.•QSRR models were built by gradient boosted trees algorithm.•Global and local modelling was provided for analytes’ ...retention estimation.•Presence of electrostatic, hydrophobic and hydrogen bond interactions was evaluated.
Resolving complex sample mixtures by liquid chromatography in a single run is challenging. The so-called mixed-mode liquid chromatography (MMLC) which combines several retention mechanisms within a single column, can provide resource-efficient separation of solutes of diverse nature. The Acclaim Mixed-Mode WCX-1 column, encompassing hydrophobic and weak cation exchange interactions, was employed for the analysis of small drug molecules. The stationary phase's interaction abilities were assessed by analysing molecules of different ionisation potentials. Mixed Quantitative Structure-Retention Relationship (QSRR) models were developed for revealing significant experimental parameters (EPs) and molecular features governing molecular retention. According to the plan of Face-Centred Central Composite Design, EPs (column temperature, acetonitrile content, pH and buffer concentration of aqueous mobile phase) variations were included in QSRR modelling. QSRRs were developed upon the whole data set (global model) and upon discrete parts, related to similarly ionized analytes (local models) by applying gradient boosted trees as a regression tool. Root mean squared errors of prediction for global and local QSRR models for cations, anions and neutrals were respectively 0.131; 0.105; 0.102 and 0.042 with the coefficient of determination 0.947; 0.872; 0.954 and 0.996, indicating satisfactory performances of all models, with slightly better accuracy of local ones. The research showed that influences of EPs were dependant on the molecule's ionisation potential. The molecular descriptors highlighted by models pointed out that electrostatic and hydrophobic interactions and hydrogen bonds participate in the retention process. The molecule's conformation significance was evaluated along with the topological relationship between the interaction centres, explicitly determined for each molecular species through local models. All models showed good molecular retention predictability thus showing potential for facilitating the method development.
The charged aerosol detector (CAD) is the latest representative of aerosol-based detectors that generate a response independent of the analytes’ chemical structure. This study was aimed at accurately ...predicting the CAD response of homologous fatty acids under varying experimental conditions. Fatty acids from C12 to C18 were used as model substances due to semivolatile characterics that caused non-uniform CAD behaviour. Considering both experimental conditions and molecular descriptors, a mixed quantitative structure–property relationship (QSPR) modeling was performed using Gradient Boosted Trees (GBT). The ensemble of 10 decisions trees (learning rate set at 0.55, the maximal depth set at 5, and the sample rate set at 1.0) was able to explain approximately 99% (Q
2
: 0.987, RMSE: 0.051) of the observed variance in CAD responses. Validation using an external test compound confirmed the high predictive ability of the model established (R
2
: 0.990, RMSEP: 0.050). With respect to the intrinsic attribute selection strategy, GBT used almost all independent variables during model building. Finally, it attributed the highest importance to the power function value, the flow rate of the mobile phase, evaporation temperature, the content of the organic solvent in the mobile phase and the molecular descriptors such as molecular weight (MW), Radial Distribution Function—080/weighted by mass (RDF080m) and average coefficient of the last eigenvector from distance/detour matrix (Ve2_D/Dt). The identification of the factors most relevant to the CAD responsiveness has contributed to a better understanding of the underlying mechanisms of signal generation. An increased CAD response that was obtained for acetone as organic modifier demonstrated its potential to replace the more expensive and environmentally harmful acetonitrile.
A priori estimation of analyte response is crucial for the efficient development of liquid chromatography–electrospray ionization/mass spectrometry (LC–ESI/MS) methods, but remains a demanding task ...given the lack of knowledge about the factors affecting the experimental outcome. In this research, we address the challenge of discovering the interactive relationship between signal response and structural properties, method parameters and solvent-related descriptors throughout an approach featuring quantitative structure–property relationship (QSPR) and design of experiments (DoE). To systematically investigate the experimental domain within which QSPR prediction should be undertaken, we varied LC and instrumental factors according to the Box-Behnken DoE scheme. Seven compounds, including aripiprazole and its impurities, were subjected to 57 different experimental conditions, resulting in 399 LC–ESI/MS data endpoints. To obtain a more standard distribution of the measured response, the peak areas were log-transformed before modeling. QSPR predictions were made using features selected by Genetic Algorithm (GA) and providing Gradient Boosted Trees (GBT) with training data. Proposed model showed satisfactory performance on test data with a RMSEP of 1.57 % and a of 96.48 %. This is the first QSPR study in LC–ESI/MS that provided a holistic overview of the analyte’s response behavior across the experimental and chemical space. Since intramolecular electronic effects and molecular size were given great importance, the GA–GBT model improved the understanding of signal response generation of model compounds. It also highlighted the need to fine-tune the parameters affecting desolvation and droplet charging efficiency.
•Quantitative structure−property relationship was supported by design of experiments.•Gradient boosted trees was used for the first time in LC–ESI/MS response modeling.•Intramolecular electronic effects and molecule size determine signal intensities.•Fine-tuning of capillary temperature and spray voltage is strongly recommended.
•Predictive ability of 48 mixed QSRR models was compared in terms of RMSE and Q2•Change in the set of input variables had a minor impact on the models’ performance.•Mixed models built by non-linear ...Gradient Boosted Trees showed the highest accuracy.•Contribution of steric and dipole-dipole interactions to MLC retention was stressed.•Fine-tuning of molecular geometry is recommended to increase accuracy of final model.
In micellar liquid chromatography (MLC), the addition of a surfactant to the mobile phase in excess is accompanied by an alteration of its solubilising capacity and a change in the stationary phase's properties. As an implication, the prediction of the analytes’ retention in MLC mode becomes a challenging task. Mixed Quantitative Structure – Retention Relationships (QSRR) modelling represents a powerful tool for estimating the analytes’ retention.
This study compares 48 successfully developed mixed QSRR models with respect to their ability to predict retention of aripiprazole and its five impurities from molecular structures and factors that describe the Brij - acetonitrile system. The development of the models was based on an automatic combining of six attribute (feature) selection methods with eight predictive algorithms and the optimization of hyper-parameters. The feature selection methods included Principal Component Analysis (PCA), Non-negative Matrix Factorization (NMF), ReliefF, Multiple Linear Regression (MLR), Mutual Info and F-Regression. The series of investigated predictive algorithms comprised Linear Regressions (LR), Ridge Regression, Lasso Regression, Artificial Neural Networks (ANN), Support Vector Regression (SVR), Random Forest (RF), Gradient Boosted Trees (GBT) and K-Nearest neighbourhood (k-NN).
A sufficient amount of data for building the model (78 cases in total) was provided by conducting 13 experiments for each of the 6 analytes and collecting the target responses afterwards. Different experimental settings were established by varying the values of the concentration of Brij L23, pH of the aqueous phase and acetonitrile content in the mobile phase according to the Box-Behnken design. In addition to the chromatographic parameters, the pool of independent variables was expanded by 27 molecular descriptors from all major groups (physicochemical, quantum chemical, topological and spatial structural descriptors). The best model was chosen by taking into consideration the Root Mean Square Error (RMSE) and cross-validation (CV) correlation coefficient (Q2) values.
Interestingly, the comparative analysis indicated that a change in the set of input variables had a minor impact on the performance of the final models. On the other hand, different regression algorithms showed great diversity in the ability to learn patterns conserved in the data. In this regard, testing many regression algorithms is necessary in order to find the most suitable technique for model building. In the specific case, GBT-based models have demonstrated the best ability to predict the retention factor in the MLC mode. Steric factors and dipole-dipole interactions have proven to be relevant to the observed retention behaviour. This study, although being of a smaller scale, is a most promising starting point for comprehensive MLC retention prediction.
Predicting the response signal in Atmospheric Pressure Chemical Ionization - Mass Spectrometry (APCI-MS) systems appears to be considerably challenging due to a gap in knowledge of governing factors ...and nature of their relationship with response. In this regard, signal intensity is optimized for each analyte separately through trial-and-error approach which impairs the method development and depletes numerous resources.
To tackle the given issue, here we proposed the Quantitative Structure - Property Relationship (QSPR) model that estimated the ion signal based on molecular descriptors of tested compounds. In particular, the QSPR model was developed using APCI-MS data acquired for 8 chemical compounds under 41 different experimental conditions. Antipsychotics, namely, sulpiride, risperidone, aripiprazole, bifeprunox, ziprasidone and its three impurities, were selected as model substances to undergo APCI ionization. Experimental (instrumental and solvent-related) parameters were varied according to the scheme of Box-Behnken Design. Gradient Boosted Trees (GBT) technique was used to model sophisticated inputs – output relationships of the monitored system.
The GBT algorithm with optimized hyper-parameters (16 estimators, learning rate set to 0.55 and maximal depth set to 7) built a so-called mixed model that yielded satisfactory predictive performance (Root Mean Square Error of Prediction: 5.98%; coefficient of determination: 97.1%). According to the built-in feature selection method, GBT identified experimental factors impacting nebulization and vaporization efficiency, i.e. descriptors related to hydrophobicity and molecular polarizability as the major determinants of observed APCI behavior. Therefore, the proposed model has shed light on the parameters and factors’ interactions that govern the generation of APCI ion signals for the analytes with diverse physical-chemical properties. The established QSPR patterns could be reliably used to predict APCI-MS signal in a variety of experimental environments.
•The gradient boosted tree model predicts the ion signal in APCI-MS satisfactorily.•Experimental factors and molecular features describe the process thoroughly.•Factors affecting the nebulization/vaporization efficiency determine the outcome.•Lipophilicity and electronic-specific features are vital for antipsychotics' signal.
The present study screened various fungal species for inhibitors of alpha-glucosidase, alpha-amylase, and DPP-4, enzymes that are crucial in carbohydrate metabolism. Ethanolic extracts exhibited ...superior inhibitory activity compared to water extracts, suggesting their potential as sources of anti-diabetic agents. Further fractionation revealed fomentariol from Fomes fomentarius as a potent inhibitor of alpha-glucosidase and DPP-4, with higher activity against alpha-glucosidase than acarbose. Fomentariol presents a novel avenue for diabetes management, demonstrating the simultaneous inhibition of key enzymes in glucose metabolism. However, comprehensive clinical studies are needed to evaluate its safety and efficacy in humans.
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•Ovomucoid CSP showed enantioselectivity for the separation of timolol enantiomers.•An enantioselective HPLC method was developed using AQbD methodology.•Achievement of a more ...ecologically friendly method than official one.•The validation studies confirmed adequacy of the method for its intended purpose.
Official method in Ph. Eur. for evaluation of timolol enantiomeric purity is normal-phase high performance liquid chromatography (NP-HPLC) method. Compared to other HPLC modes, NP is depicted as quite expensive with high consumption of organic solvents which leads to chronic exposure of analysts to toxic and carcinogenic effects. In order to overcome above-mentioned drawbacks, the aim of this study was to develop new method with better eco-friendly features. This was enabled by using protein type Chiral Stationary Phase (CSP) in reversed-phase mode that required up to 10 % (v/v) of organic solvent. Therefore, an enantioselective HPLC method was developed and validated for quantification of (S)-timolol and its chiral impurity, (R)-isomer. Optimized separation conditions on ovomucoid column were set using Analytical Quality by Design (AQbD) approach in method development. Optimization step was performed following the Box-Behnken experimental plan and the influence of three critical method parameters (CMPs) towards enantioseparation of the above-mentioned peak pair was examined. CMPs included variation of acetonitrile content in the mobile phase (5–10 %, v/v), pH value of the aqueous phase (6.0–7.0) and ammonium chloride concentration in the aqueous part of the mobile phase (10−30 mmol L−1). The most relevant critical method attributes (CMAs) in this case were the separation criterion between studied critical pair and retention factor of the second eluting peak, (S)-timolol. Qualitative Design Space (DS) was defined by Monte Carlo simulations providing adequate assurance of method’s qualitative robustness (π = 95 %). The selected working point situated in the middle of the DS was characterized by following combination of CMPs: acetonitrile content in the mobile phase 7 % (v/v), pH value of the aqueous phase 6.8 and concentration of ammonium chloride in aqueous phase 14 mmol L–1. In the next step, the quantitative robustness was tested by Plackett-Burman experimental design. The validation studies confirmed adequacy of the proposed method for its intended purpose. Finally, Analytical Eco-Scale metric tool was applied to confirm that developed method represents excellent green analytical method compared to the official one.
A new optimization strategy based on the mixed quantitative structure? retention relationship (QSRR) model is proposed for improving the RPHPLC separation of aripiprazole and its impurities (IMP ...A-E). Firstly, experimental parameters (EPs), namely mobile phase composition and flow rate, were varied according to Box?Behnken design and thereafter, an artificial neural network (ANN) as a QSRR model was built correlating EPs and selected molecular descriptors (ovality, torsion energy and non-1,4-van der Waals energy) with the log-transformed retention times of the analytes. Values of the root mean square error (RMSE) were used for an estimation of the quality of the ANNs (0.0227, 0.0191 and 0.0230 for the training, verification and test set, respectively). The separations of critical peak pairs on chromatogram (IMP AB and IMP D-C) were optimized using ANNs for which the EPs served as inputs and the log-transformed separation criteria s as the outputs. They were validated by application of leave-one-out cross-validation (RMSE values 0.065 and 0.056, respectively). The obtained ANNs were used for plotting response surfaces upon which the analyses chromatographic conditions resulting in optimal analytes retention behaviour and the optimal values of the separation criteria s were defined. The optimal conditions were 54 % of methanol at the beginning and 79 % of methanol at the end of gradient elution programme with a mobile phase flow rate of 460 ?L min-1.
•Chromatographic approach in complex stability constant assessment is time-consuming.•QSRR model was used to predict change in analyte retention time upon complexation.•Predicted change in retention ...time was used to calculate stability constants.•HPLC and QSRR approach were not applicable under all experimental conditions.•β-CD concentration and acetonitrile content affected stability constant calculation.
When cyclodextrins (CDs) are used in chromatography analytes’ retention time is decreased with an increase in concentration of CD in the mobile phase. Thus complex stability constants can be determined from the change in retention time of the ligand molecule upon complexation. Since the preceding approach implies extensive and time-consuming HPLC experiments, the goal of this research was to investigate the possibility of using in silico prediction tools instead. Quantitative structure–retention relationship (QSRR) model previously developed to explain the retention behavior of risperidone, olanzapine and their structurally related impurities in β-CD modified HPLC system was applied to predict retention factor under different chromatographic conditions within the examined domains. Predicted retention factors were further used for calculation of stability constants and important thermodynamic parameters, namely standard Gibbs free energy, standard molar enthalpy and entropy, contributing to inclusion phenomenon. Unexpected prolonged retention with an increase in β-CD concentration was observed, in contrast to the employed chromatographic theory used for the calculation of the stability constants. Consequently, it led to failure in stability constants and thermodynamic parameters calculation for almost all analytes when acetonitrile content was 20% (v/v) across the investigated pH range. Moreover, ionization of investigated analytes and free stationary phase silanol groups are pH dependent, leading to minimization of secondary interactions if free silanol groups are non-ionized at pH lower than 3. In order to prove accuracy of predicted retention factors, HPLC verification experiments were performed and good agreement between predicted and experimental values was obtained, confirming the applicability of proposed in-silico tool. However, the obtained results opened some novel questions and revealed that chromatographic method is not overall applicable in calculation of stability constants and thermodynamic parameters indicating the complexity of β-CD modified systems.
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