Search

Quantitative structure-retention relationships (QSRRs) are used in the field of chromatography to model the relationship between an analyte structure and chromatographic retention. Such models are typically difficult to build and validate for heterogeneous compounds because of their many descriptors and relatively limited analyte-specific data. In this study, a Bayesian multilevel model is proposed to characterize the isocratic retention time data collected for 1026 heterogeneous analytes. The QSRR considers the effects of the molecular mass and 100 functional groups (substituents) on analyte-specific chromatographic parameters of the Neue model (i.e., the retention factor in water, the retention factor in acetonitrile, and the curvature coefficient). A Bayesian multilevel regression model was used to smooth noisy parameter estimates with too few data and to consider the uncertainties in the model parameters. We discuss the benefits of the Bayesian multilevel model (i) to understand chromatographic data, (ii) to quantify the effect of functional groups on chromatographic retention, and (iii) to predict analyte retention based on various types of preliminary data. The uncertainty of isocratic and gradient predictions was visualized using uncertainty chromatograms and discussed in terms of usefulness in decision making. We think that this method will provide the most benefit in providing a unified scheme for analyzing large chromatographic databases and assessing the impact of functional groups and other descriptors on analyte retention.

Retention time data for 300 analytes.

Retention time data for 300 analytes.

Retention time data for 300 analytes.

Retention time data for 300 analytes.

Chromatographic retention times are usually modeled considering only one analyte at a time. However, it has certain limitations as no information is shared between the analytes, and consequently the model predictions poorly generalize to out-of-sample analytes. In this work, a publicly available dataset was used to illustrate the benefits of pooling the individual data and analyzing them simultaneously utilizing Bayesian hierarchical approach. Statistical analysis was carried out using the Stan program coupled with R, which enables full Bayesian inference with Markov chain Monte Carlo sampling. This methodology allows (i) incorporating prior knowledge about the likely values of model parameters, (ii) considering the between-analyte variability and the correlation between the model parameters, (iii) explaining the between-analyte variability by available predictors, and (iv) sharing information across the analytes. The latter is especially valuable when only limited information is available in the data about certain model parameters. The results are obtained in the form of posterior probability distribution, which quantifies uncertainty about the model parameters and predictions. Posterior probability is also directly relevant for decision-making. In this work, we used the Neue model to describe the relationship between retention factor and acetonitrile content in the mobile phase for 1026 analytes. The model was parametrized in terms of retention factor in 100% water, retention factor in 100% acetonitrile, and curvature coefficient, and considered log P and pK

We present a review of the available literature concerning pKa values and methods for the determination of selected anticancer drugs as well as the most up-to-date knowledge on their different ionic forms depending on solution pH. Additionally, to clarify the existing state of knowledge we have presented an overview on the obtained pKa values with the use of experimental and computational methods. As a result, we have demonstrated and proposed acid-base equilibria of cyclophosphamide (CF), ifosfamide (IF), 5-fluorouracil (5-FU), methotrexate (MTX), and imatinib (IMT) in an aqueous solution, and their species distribution curves as a function of pH calculated on the basis of the acid dissociation constants, which are as follows: CF pKa1 2.3, pKa2 11.1 [CF-H2L+/CF-HL/CF-L−] IF pKa1 5-FU pKa1 7.5, pKa2 9.0 [5-FU-H2L/5-FU-HL−/5-FU-L2−] MTX pKa1 2.9, pKa2 4.6, pKa3 6.6 [MTX-H3L+/MTX-H2L/MTX-HL−/MTX-L2−] IMT pKa1 2.5, pKa2 4.0, pKa3 8.3 [IMT-H3L3+/IMT-H2L2+/IMT-HL+/MTX-L].

Imidazol(in)e derivatives, having the chemical structure similar to clonidine, exert diverse pharmacological activities connected with their interactions with alpha2-adrenergic receptors, e.g. hypotension, bradycardia, sedation as well as antinociceptive, anxiolytic, antiarrhythmic, muscle relaxant and mydriatic effects. The mechanism of pupillary dilation observed after systemic administration of imidazol(in)es to rats, mice and cats depends on the stimulation of postsynaptic alpha2-adrenoceptors within the brain. It was proved that the central nervous system (CNS)-localized I1-imidazoline receptors are not engaged in those effects. It appeared interesting to analyze the CNS-mediated pharmacodynamics of imidazole(in)e agents in terms of their chromatographic and calculation chemistry-derived parameters. In the present study a systematic determination and comparative pharmacometric analysis of mydriatic effects in rats were performed on a series of 20 imidazol(in)e agents, composed of the well-known drugs and of the substances used in experimental pharmacology. The eye pupil dilatory activities of the compounds were assessed in anesthetized Wistar rats according to the established Koss method. Among twenty imidazol(in)e derivatives studied, 18 produced diverse dose-dependent mydriatic effects. In the quantitative structure-activity relationships (QSAR) analysis, the pharmacological data (half maximum mydriatic effect - ED50 in μmol/kg) were considered along with the structural parameters of the agents from molecular modeling. The theoretically calculated lipophilicity parameters, CLOGP, of imidazol(in)es, as well as their lipophilicity parameters from HPLC, logkw, were also considered. The attempts to derive statistically significant QSAR equations for a full series of the agents under study were unsuccessful. However, for a subgroup of eight apparently structurally related imidazol(in)es a significant relationship between log(1/ED50) and logkw values was obtained. The lack of "predictive" QSAR for the whole series of the structurally diverse agents is probably due to a complex mechanism of the ligand-alpha2-adrenergic receptor interactions, which are predominantly of a highly structurally specific polar nature. Such interactions are difficult to quantify with the established chemical structural descriptors, contrary to the less specific, molecular bulkiness-related interactions.

The objective of this work was to develop a multilevel (hierarchical) model based on isocratic-reversed-phase-high-performance-chromatographic data collected in methanol and acetonitrile for 58 chemical compounds. Such a multilevel model is a regression model of the analyte-specific chromatographic measurements, in which all the regression parameters are given a probability model. It is a fundamentally different approach from the most common approach, where parameters are separately estimated for each analyte (without sharing information across analytes and different organic modifiers). The statistical analysis was done with Stan software implementing the Bayesian-statistics inference with Markov-chain Monte Carlo sampling. During the model-building process, a series of multilevel models of different complexity were obtained, such as (1) a model with no pooling (separate models were fitted for each analyte), (2) a model with partial pooling (a common distribution was used for analyte-specific parameters), and (3) a model with partial pooling as well as a regression model relating analyte-specific parameters and analyte-specific properties (QSRR equations). All the models were compared with each other using 10-fold cross-validation. The benefits of multilevel models in inference and predictions were shown. In particular the obtained models allowed us to (i) better understand the data and (ii) solve many routine analytical problems, such as obtaining well-calibrated predictions of retention factors for an analyte in acetonitrile-containing mobile phases given zero, one, or several measurements in methanol-containing mobile phases and vice versa.

Fast and reliable methods for the determination of hydrophobicity and acidity are desired in pre-clinical drug development phases to eliminate compounds with poor pharmacokinetic properties. Reversed-phase high-performance liquid chromatography (RP HPLC) coupled with time-of-flight mass spectrometry (RP HPLC-ESI-TOF-MS) is a convenient technique for that purpose. In this work we determined the chromatographic measure of hydrophobicity (logkw) and dissociation constant (pKa) simultaneously for a large and diverse group of 161 drugs. Retention times were determined by means of RP HPLC-ESI-TOF-MS for a series of pH and organic modifier gradients. We were able to measure retention times for 140 out of 161 (87%) compounds. For those analytes logkw and pKa parameters were calculated and compared with literature and ACD Labs-calculated data. The determined chromatographic measure of hydrophobicity and dissociation constant was closely related to literature and theoretically calculated values. Applied methodology achieved the medium-throughput screening rate of 100 compounds per day and proved to be a simple, fast and reliable approach of assessing important physicochemical properties of drugs. This technique has certain limitations as it is not applicable for very hydrophilic analytes (logP

Convenient drug candidates testing methods for lipophilicity and acidity are highly requested in modern pharmaceutical research and development strategy. Reversed-phase high-performance liquid chromatography (RP HPLC) might be particularly useful for the determination of both pKa and the apparent (pH-dependent) octanol–water partition coefficient, applicable in high-throughput analysis of multi-component mixtures. In this report the pH/organic modifier gradient RP HPLC is presented as a means of simultaneous determination of acidity and lipophilicity of a series of 26 imidazoline-like drugs. The previously theoretically elaborated approach has been applied consisting in retention measurements in a series of methanol gradient runs differing in pH range and duration of the gradient. The simultaneously determined lipophilicity and dissociation constants have been demonstrated to correlate to the respective parameters form calculation chemistry. The proposed approach can be applied to compound mixtures, it requires only minute amounts of substances, and pKa values can be determined in the range 3–10 units and lipophilicity log P parameter in the range 0–7 units.

The aim of this work was to develop mathematical models relating the hydrophobicity and dissociation constant of an analyte with its structure, which would be useful in predicting analyte retention times in reversed-phase liquid chromatography. For that purpose a large and diverse group of 115 drugs was used to build three QSRR models combining retention-related parameters (logkw-chromatographic measure of hydrophobicity, S-slope factor from Snyder-Soczewinski equation, and pKa) with structural descriptors calculated by means of molecular modeling for both dissociated and nondissociated forms of analytes. Lasso, Stepwise and PLS regressions were used to build statistical models. Moreover a simple QSRR equations based on lipophilicity and dissociation constant parameters calculated in the ACD/Labs software were proposed and compared with quantum chemistry-based QSRR equations. The obtained relationships were further used to predict chromatographic retention times. The predictive performances of the obtained models were assessed using 10-fold cross-validation and external validation. The QSRR equations developed were simple and were characterized by satisfactory predictive performance. Application of quantum chemistry-based and ACD-based descriptors leads to similar accuracy of retention times' prediction.

The pH of a mobile phase is one of the most important parameters affecting retention of ionic analytes in reversed phase high-performance liquid chromatography (RP-HPLC). RP-HPLC and hydrophilic interaction liquid chromatography (HILIC) can be used as a convenient tool for the analysis of ionizable analytes. The ionic analyte contains one or more acidic or basic functional groups in its molecular structure. Acidic compounds lose a proton as pH increases, whereas bases gain a proton as pH decreases The ionic analyte contains one or more acidic or basic functional groups in its molecular structure. for chromatographic retention, the presence of dissociated group(s) decreases analytes' hydrophobicity that leads to the lower retention of the dissociated form than that of the nondissociated form of an analyte. The chapter discusses effect of pH on Isocratic retention, pH effect on organic modifier gradients, pH gradient, and effect of pH in normal-phase mode. Keywords: chromatographic retention modes; hydrophilic interaction liquid chromatography; isocratic retention; isocratic retention; organic modifier gradients; pH effect; pH-gradient; reversed phase HPLC

The aim of this work was to develop a nonlinear mixed-effect chromatographic model able to describe the retention times of weak acids and bases in all possible combinations of organic modifier content and mobile-phase pH. Further, we aimed to identify the influence of basic covariates, like lipophilicity (log P), dissociation constant (pK(a)), and polar surface area (PSA), on the intercompound variability of chromatographic parameters. Lastly, we aimed to propose the optimal limited experimental design to the estimation process of parameters through a maximum a posteriori (MAP) Bayesian method to facilitate the method development process. The data set comprised retention times for two series of organic modifier content collected at different pH for a large series of acids and bases. The obtained typical parameters and their distribution were subsequently used as priors to improve the estimation process from reduced design with a variable number of preliminary experiments. The MAP Bayesian estimator was validated using two external-validation data sets. The common literature model was used to relate analyte retention time with mobile-phase pH and organic modifier content. A set of QSRR-based covariate relationships was established. It turned out that four preliminary experiments and prior information that includes analyte pK(a), log P, acid/base type, and PSA are sufficient to accurately predict analyte retention in virtually all combined changes of pH and organic modifier content. The MAP Bayesian estimator of all important chromatographic parameters controlling retention in pH/organic modifier gradient was developed. It can be used to improve parameter estimation using limited experimental design.

Lung infection by Burkholderia species, in particular Burkholderia cenocepacia, accelerates tissue damage and increases post-lung transplant mortality in cystic fibrosis patients. Host-microbe interplay largely depends on interactions between pathogen-specific molecules and innate immune receptors such as Toll-like receptor 4 (TLR4), which recognizes the lipid A moiety of the bacterial lipopolysaccharide (LPS). The human TLR4·myeloid differentiation factor 2 (MD-2) LPS receptor complex is strongly activated by hexa-acylated lipid A and poorly activated by underacylated lipid A. Here, we report that B. cenocepacia LPS strongly activates human TLR4·MD-2 despite its lipid A having only five acyl chains. Furthermore, we show that aminoarabinose residues in lipid A contribute to TLR4-lipid A interactions, and experiments in a mouse model of LPS-induced endotoxic shock confirmed the proinflammatory potential of B. cenocepacia penta-acylated lipid A. Molecular modeling combined with mutagenesis of TLR4-MD-2 interactive surfaces suggests that longer acyl chains and the aminoarabinose residues in the B. cenocepacia lipid A allow exposure of the fifth acyl chain on the surface of MD-2 enabling interactions with TLR4 and its dimerization. Our results provide a molecular model for activation of the human TLR4·MD-2 complex by penta-acylated lipid A explaining the ability of hypoacylated B. cenocepacia LPS to promote proinflammatory responses associated with the severe pathogenicity of this opportunistic bacterium.

The objective of this study was to model the retention of nucleosides and pterins in hydrophilic interaction liquid chromatography (HILIC) via QSRR-based approach. Two home-made (Amino-P-C18, Amino-P-C10) and one commercial (IAM.PC.DD2) HILIC stationary phases were considered. Logarithm of retention factor at 5% of acetonitrile (log kACN) along with descriptors obtained for 16 nucleosides and 11 pterins were used to develop QSRR models. We used and compared the predictive performance of three regression techniques: partial least square (PLS), the least absolute shrinkage and selection operator (LASSO), and the LASSO followed by stepwise multiple linear regression. The highest predictive squared correlation coefficient ( Q L O O C V 2 ) in PLS analysis was found for Amino-P-C10 ( Q L O O C V 2 = 0.687 ) and IAM.PC.DD2 ( Q L O O C V 2 = 0.506 ) and the lowest for IAM.PC.DD2 ( Q L O O C V 2 = − 0.01 ). Much higher values were obtained for the LASSO model. The Q L O O C V 2 equaled 0.9 for Amino-P-C10, 0.66 for IAM.PC.DD2 and 0.59 for Amino-P-C18. The combination of LASSO with stepwise regression provided models with comparable predictive performance as the LASSO, however with possibility of calculating the standard error of estimates. The use of LASSO itself and in combination with classical stepwise regression may offer greater stability of the developed models thanks to more smooth change of coefficients and reduced susceptibility towards chance correlation. Application of QSRR-based approach, along with the computational methods proposed in this work, may offer a useful approach in the modeling of retention of nucleoside and pterin compounds in HILIC.

Convenient methods for testing drug candidates’ lipophilicity and acidity are highly requested in modern pharmaceutical research and drug development strategies. Reversed-phase high-performance liquid chromatography (RP HPLC) might be particularly useful for the determination of both dissociation constant and the (pH-dependent) partition coefficient related parameters, applicable in high-throughput analysis of multi-component mixtures. The general theory of combined pH/organic modifier gradient has recently provided equations relating gradient retention time and pH of the mobile phase. The purpose of this work was to facilitate the identification of analytes in this technique by its transfer to RP HPLC coupled with time-of-flight mass spectrometry with electrospray ionization source (ESI-TOF-MS). The accuracy of the proposed methodology was assessed by analyzing a set of known drugs. The ammonium formate, ammonium acetate or ammonium bicarbonate buffers were used to control pH during chromatographic analysis. In result, the p K a and hydrophobicity parameters were determined and the accuracy of the estimated values was assessed by comparing them with literature data. The gradient RP HPLC coupled with ESI-TOF-MS methods allowed for the rapid determination of dissociation constant and hydrophobicity and was shown to be especially applicable for complex mixtures. The use of ESI-TOF-MS detection allowed to achieve the medium-throughput screening rate (100 compounds/day) and provided a simple approach to assess pharmacokinetically important physicochemical properties of drugs.