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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

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

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.