Multimodal Model to Predict Tissue-to-Blood Partition Coefficients of Chemicals in Mammals and Fish.

Tissue-to-blood partition coefficients (Ptb) are key parameters for assessing toxicokinetics of xenobiotics in organisms, yet their experimental data were lacking. Experimental methods for measuring Ptb values are inefficient, underscoring the urgent need for prediction models. However, most existing models failed to fully exploit Ptb data from diverse sources, and their applicability domain (AD) was limited. The current study developed a multimodal model capable of processing and integrating textual (categorical features) and numerical information (molecular descriptors/fingerprints) to simultaneously predict Ptb values across various species, tissues, blood matrices, and measurement methods. Artificial neural network algorithms with embedding layers were used for the multimodal modeling. The corresponding unimodal models were developed for comparison. Results showed that the multimodal model outperformed unimodal models. To enhance the reliability of the model, a method considering categorical features, weighted molecular similarity density, and weighted inconsistency in molecular activities of structure-activity landscapes was used to characterize the AD. The model constrained by the AD exhibited better prediction accuracy for the validation set, with the determination coefficient, root mean-square error, and mean absolute error being 0.843, 0.276, and 0.213 log units, respectively. The multimodal model coupled with the AD characterization can serve as an efficient tool for internal exposure assessment of chemicals.

Polyparameter Linear Free Energy Relationships for Partitioning of Neutral Organic Compounds to Storage Lipids.

Storage lipids are an important compartment in the bioaccumulation of neutral organic compounds. Reliable models for predicting storage lipid-water and storage lipid-air partition coefficients (Kislip/w and Kislip/a), as well as their temperature dependence, are considered useful. Polyparameter linear free energy relationships (PP-LFERs) are accurate, general, and mechanistically clear models for predicting partitioning-related physicochemical quantities. About a decade ago, PP-LFERs were calibrated for Kislip/w at the physiological temperature of 37 °C. However, to date, a comprehensive collection and sufficiently reliable PP-LFERs for Kislip/w and Kislip/a at the most common standard temperature of 25 °C are still lacking. In this study, experimentally based Kislip/w and/or Kislip/a values at 25 °C for 278 compounds were extensively collected or converted from the literature. Subsequently, PP-LFERs were calibrated for Kislip/w and Kislip/a at 25 °C, performing well over 10 orders of magnitude with root-mean-square errors of 0.17-0.21 log units for compounds with reliable descriptors. Furthermore, standard internal energy changes of transfer from water or air to storage lipids for 158 compounds were derived and used to calibrate PP-LFERs for estimating the temperature dependence of Kislip/w or Kislip/a. Additionally, using PP-LFERs, low-density polyethylene was confirmed to be a better storage lipid analogue than silicone and polyoxymethylene in the equilibrium passive sampling of nonpolar and H-bond acceptor polar compounds.

Distribution characteristics of sulfonamide antibiotics between water and extracellular polymeric substances in municipal sludge.

The interaction between extracellular polymeric substances (EPS) in municipal sludge and antibiotics in wastewater is critical in wastewater treatment, resource recovery, and sludge management. Therefore, it is increasingly urgent to investigate the distribution coefficient (Log K) of sulfonamide antibiotics (SAs) in EPS, particularly in sludge-derived dissolved organic carbon (DOC) and aqueous phase systems. Herein, through balance experiments, the concentrations of SAs were determined using alkaline extraction EPS (AEPS) and alginate-like extracellular polymer (ALE) systems, and the Log KDOC values were determined. The results showed that the Log KDOC of AEPS was higher than that of ALE, which exhibited a negative KDOC value, indicating an inhibitory effect on dissolution. For the three SAs studied, the Log KDOC values were in the following order: sulfamethoxazole > sulfapyridine > sulfadiazine. This order can be attributed to the differing physicochemical properties, such as polarity, of the SAs. Three-dimensional excitation-emission matrix fluorescence spectra and fitting results indicated a lack of aromatic proteins dominated by tryptophan and humus-like substances in ALE. Meanwhile, the hydrophobic interaction of aromatic proteins dominated by tryptophan was the main driving force in the binding process between AEPS and SAs.

Multi-task machine learning models for simultaneous prediction of tissue-to-blood partition coefficients of chemicals in mammals.

Tissue-to-blood partition coefficients (Ptb) are crucial for assessing the distribution of chemicals in organisms. Given the lack of experimental data and laborious nature of experimental methods, there is an urgent need to develop efficient predictive models. With the help of machine learning algorithms, i,e., random forest (RF), and artificial neural network (ANN), this study developed multi-task (MT) models that can simultaneously predict Ptb values for various mammalian tissues, including liver, muscle, brain, lung, and adipose. Single-task (ST) models using partial least squares regression, RF, and ANN algorithms for each endpoint were established for comparison. Overall, the performances of MT models were superior to those of ST models. The MT model using ANN algorithms showed the highest prediction accuracy with determination coefficients ranging from 0.704 to 0.886, root mean square errors between 0.223 and 0.410, and mean absolute errors ranging from 0.178 to 0.285 log units. Results showed that lipophilicity and polarizability of molecules significantly influence their partition behavior in organisms. Applicability domains (ADs) of the models were characterized by weighted molecular similarity density, and weighted inconsistency in molecular activities of structure-activity landscapes. When constrained by ADs, the models displayed enhanced predictive accuracy, making them valuable tools for the risk assessment and management of chemicals.

Machine-Learning-Based Prediction of Plant Cuticle-Air Partition Coefficients for Organic Pollutants: Revealing Mechanisms from a Molecular Structure Perspective.

Accurately predicting plant cuticle-air partition coefficients (Kca) is essential for assessing the ecological risk of organic pollutants and elucidating their partitioning mechanisms. The current work collected 255 measured Kca values from 25 plant species and 106 compounds (dataset (I)) and averaged them to establish a dataset (dataset (II)) containing Kca values for 106 compounds. Machine-learning algorithms (multiple linear regression (MLR), multi-layer perceptron (MLP), k-nearest neighbors (KNN), and gradient-boosting decision tree (GBDT)) were applied to develop eight QSPR models for predicting Kca. The results showed that the developed models had a high goodness of fit, as well as good robustness and predictive performance. The GBDT-2 model (Radj2 = 0.925, QLOO2 = 0.756, QBOOT2 = 0.864, Rext2 = 0.837, Qext2 = 0.811, and CCC = 0.891) is recommended as the best model for predicting Kca due to its superior performance. Moreover, interpreting the GBDT-1 and GBDT-2 models based on the Shapley additive explanations (SHAP) method elucidated how molecular properties, such as molecular size, polarizability, and molecular complexity, affected the capacity of plant cuticles to adsorb organic pollutants in the air. The satisfactory performance of the developed models suggests that they have the potential for extensive applications in guiding the environmental fate of organic pollutants and promoting the progress of eco-friendly and sustainable chemical engineering.

Advances and challenges in the fractionation of edible oils and fats through supercritical fluid processing.

Petrochemical solvents are widely used for the extraction and fractionation of biomolecules from edible oils and fats at an industrial scale. However, owing to its safety concerns, toxicity, price fluctuations, and sustainability, alternative solvents and technologies have been actively explored in recent years. Technologies, such as ultrasound and microwave-assisted extraction, supercritical carbon dioxide extraction, supercritical fluid fractionation, and sub-critical water extraction, and solvents, like ionic liquids and deep eutectic solvents, are reported for extraction and fractionation of biomolecules. Among them, supercritical carbon dioxide extraction and fractionation are some of the most promising green technologies with the potential to replace petrochemical-based conventional techniques. The addition of cosolvents, such as water, ethanol, and acetone, improves the extraction of amphiphilic and polar compounds from edible oils and fats. Supercritical fluid processing has diverse applications, including concentration of solutes, selective separation of desired molecules, and separation of undesirable compounds from the feed material. Temperature, pressure, particle size, porosity, flow rate, solvent-to-feed ratio, density, viscosity, diffusivity, solubility, partition coefficient, and separation factor are the fundamental factors governing the extraction and fractionation of desired biomolecules from lipids. Supercritical fluids stand alone compared to conventional fluids, because of their tunable solvent properties. Overall, it is to be noted that supercritical fluid-based methods have lots of scope to replace conventional solvent-based methods and progress toward the creation of sustainable food-processing techniques. This review critically evaluates the parameters responsible for the extraction and fractionation of biomolecules from edible oils and fats under supercritical conditions.

Using physicochemical properties to predict the impact of natural dissolved organic carbon on transepithelial potential in the freshwater rainbow trout (Oncorhynchus mykiss) at neutral and acidic pH.

Dissolved organic carbon (DOC) is a complex mixture of molecules that varies in composition based on origin as well as spatial and temporal factors. DOC is an important water quality parameter as it regulates many biological processes in freshwater systems, including the physiological function of the gills in fish. These effects are often beneficial, especially at low pH where DOCs mitigate ion loss and protect active ion uptake. DOCs of different compositions and quality have varied ionoregulatory effects. The molecular variability of DOCs can be characterized using optical and chemical indices, but how these indices relate to the physiological effects exerted by DOCs is not well understood. We tested the effects of five naturally sourced DOCs, at both pH 7 and pH 4, on transepithelial potential (TEP) (a diffusion potential between the blood plasma and the external water) in rainbow trout. The five chosen DOCs have been well characterized and span large differences in physicochemical characteristics. Each of the DOCs significantly influenced TEP, although in a unique manner or magnitude which was likely due to their physicochemical characteristics. These TEP responses were also a function of pH. With the goal of determining which physicochemical indices are predictive of changes in TEP, we evaluated correlations between various indices and TEP at pH 7 and pH 4. The indices included: specific absorbance coefficient at 340 nm, molecular weight index, fluorescence index, octanol-water partition coefficient, molecular charge, proton binding index, % humic acid-like, % fulvic acid-like, and % protein-like components by parallel factor analysis on fluorescence data (PARAFAC). Our results demonstrate the novel finding that there are three particularly important indices that are predictors of changes in TEP across pHs in rainbow trout: specific absorbance coefficient at 340 nm, octanol-water partition coefficient; and proton binding index.

Critical Assessment of pH-Dependent Lipophilicity Profiles of Small Molecules: Which One Should We Use and In Which Cases?

Lipophilicity is a physicochemical property with wide relevance in drug design, computational biology, food, environmental and medicinal chemistry. Lipophilicity is commonly expressed as the partition coefficient for neutral molecules, whereas for molecules with ionizable groups, the distribution coefficient (D) at a given pH is used. The logDpH is usually predicted using a pH correction over the logPN using the pKa of ionizable molecules, while often ignoring the apparent ion pair partitioning ( P IP app ) ${{\rm{(}}P_{{\rm{IP}}}^{{\rm{app}}} )}$ . In this work, we studied the impact of ( P IP app ) ${{\rm{(}}P_{{\rm{IP}}}^{{\rm{app}}} )}$ on the prediction of both the experimental lipophilicity of small molecules and experimental lipophilicity-based applications and metrics such as lipophilic efficiency (LipE), distribution of spiked drugs in milk products, and pH-dependent partition of water contaminants in synthetic passive samples such as silicones. Our findings show that better predictions are obtained by considering the apparent ion pair partitioning. In this context, we developed machine learning algorithms to determine the cases that P I app ${P_{\rm{I}}^{{\rm{app}}} }$ should be considered. The results indicate that small, rigid, and unsaturated molecules with logPN close to zero, which present a significant proportion of ionic species in the aqueous phase, were better modeled using the apparent ion pair partitioning ( P IP app ) ${{\rm{(}}P_{{\rm{IP}}}^{{\rm{app}}} )}$ . Finally, our findings can serve as guidance to the scientific community working in early-stage drug design, food, and environmental chemistry.

Resin and loading condition screening for effective and robust byproducts removal by anion exchange chromatography: A case study.

In downstream processing of protein therapeutics, ion exchange (IEX) chromatography is a powerful tool for removing byproducts whose isoelectric point (pI) is appreciably different from that of the product. Although in theory for a given case cation exchange (CEX) and anion exchange (AEX) chromatography should be equally effective for separation, in reality they may show different effectiveness. In the current work, with a case study, we demonstrated that AEX is more effective than CEX chromatography at removing the associated byproducts. In addition, we screened AEX resins and loading conditions to achieve best separation. Finally, we demonstrated that effective separation was achieved with the selected resin/condition, and chromatography performance was comparable between runs conducted at low and high load densities, suggesting that the developed process was relatively robust. The procedure described in this work can be used as a general approach for selecting resin and loading condition that allow for effective and robust removal of byproduct that binds weaker than the product to the selected type of column.

Rule of five violations among the FDA-approved small molecule protein kinase inhibitors.

Because genetic alterations including mutations, overexpression, translocations, and dysregulation of protein kinases are involved in the pathogenesis of many illnesses, this enzyme family is the target of many drug discovery programs in the pharmaceutical industry. Overall, the US FDA has approved 74 small molecule protein kinase inhibitors, nearly all of which are orally effective. Of the 74 approved drugs, thirty-nine block receptor protein-tyrosine kinases, nineteen target nonreceptor protein-tyrosine kinases, twelve are directed against protein-serine/threonine protein kinases, and four target dual specificity protein kinases. The data indicate that 65 of these medicinals are approved for the management of neoplasms (51 against solid tumors such as breast, colon, and lung cancers, eight against nonsolid tumors such as leukemia, and six against both types of tumors). Nine of the FDA-approved kinase inhibitors form covalent bonds with their target enzymes and they are accordingly classified as TCIs (targeted covalent inhibitors). Medicinal chemists have examined the physicochemical properties of drugs that are orally effective. Lipinski's rule of five (Ro5) is a computational procedure that is used to estimate solubility, membrane permeability, and pharmacological effectiveness in the drug-discovery setting. It relies on four parameters including molecular weight, number of hydrogen bond donors and acceptors, and the Log of the partition coefficient. Other important descriptors include the lipophilic efficiency, the polar surface area, and the number of rotatable bonds and aromatic rings. We tabulated these and other properties of the FDA-approved kinase inhibitors. Of the 74 approved drugs, 30 fail to comply with the rule of five.

Henry's Law Constant─A General-Purpose Fragment Model to Predict Log Kaw from Molecular Structure.

Henry's law constant is important for assessing the environmental fate of organic compounds, including polar accumulation, indoor contamination, and the impact of airborne predominance on persistence. Moreover, it can be used in the context of alternative 3R bioassays to inform about the compound loss through volatilization as a confounding factor. For 2636 compounds, curated experimental log Kaw (air/water partition coefficient) data at 25° covering 23.6 orders of magnitude (from -18.6 to 5.0) have been collected from the literature. Subsequently, a new fragment model for predicting log Kaw from molecular structures has been developed. According to the root-mean-squared error (rms) and the maximum negative and positive errors (mne and mpe), this general-purpose model outperforms COSMOtherm, EPISuite HENRYWIN, OPERA, and LSER with calculated input parameters significantly (rms 0.50 vs 0.92 vs 1.25 vs 1.28 vs 1.38, mne -2.74 vs -6.78 vs -9.11 vs -6.24 vs -6.27, mpe 2.25 vs 6.22 vs 8.27 vs 11.5 vs 7.69 log units). Initial separation into a training and prediction set (80%:20%), mutual leave-50%-out validation, and target value scrambling (temporarily wrong compound-Kaw allocations) demonstrate the prediction capability, statistical robustness, and mechanistically sound basis of the fragment scheme. The new model is available to the public in fully computerized form through the ChemProp software, and can be combined with a separate existing model to extend the log Kaw prediction to temperatures different from 25 °C.

Octanol/Air Partition Coefficient─A General-Purpose Fragment Model to Predict Log Koa from Molecular Structure.

The octanol/air partition coefficient Koa is important for assessing the bioconcentration of airborne xenobiotics in foliage and in air-breathing organisms. Moreover, Koa informs about compound partitioning to aerosols and indoor dust, and complements the octanol/water partition coefficient Kow and the air/water partition coefficient Kaw for multimedia fate modeling. Experimental log Koa at 25 °C has been collected from literature for 2161 compounds with molecular weights from 16 to 959 Da. The curated data set covers 18.2 log units (from -1.0 to 17.2). A newly developed fragment model for predicting log Koa from molecular structure outperforms COSMOtherm, EPI-Suite KOAWIN, OPERA, and linear solvation energy relationships (LSERs) regarding the root-mean-squared error (rms) and the maximum negative and positive errors (mne and mpe) (rms: 0.57 vs 0.86 vs 1.09 vs 1.19 vs 1.05-1.53, mne: -2.55 vs -3.95 vs -7.51 vs -7.54 vs (-5.63) - (-7.34), mpe: 2.91 vs 5.97 vs 7.54 vs 4.24 vs 6.89-10.2 log units). The prediction capability, statistical robustness, and sound mechanistic basis are demonstrated through initial separation into a training and prediction set (80:20%), mutual leave-50%-out validation, and target value scrambling in terms of temporarily wrong compound-Koa allocations. The new general-purpose model is implemented in a fully automatized form in the ChemProp software available to the public. Regarding Koa indirectly determined through Kow and Kaw, a new approach is developed to convert from wet to dry octanol, enabling higher consistency in experimental (and thus also predicted) Koa.

Experimental Determination of Air/Water Partition Coefficients for 21 Per- and Polyfluoroalkyl Substances Reveals Variable Performance of Property Prediction Models.

Per- and polyfluoroalkyl substances (PFAS) are a group of chemicals of high environmental concern. However, reliable data for the air/water partition coefficients (Kaw), which are required for fate, exposure, and risk analysis, are available for only a few PFAS. In this study, Kaw values at 25 °C were determined for 21 neutral PFAS by using the hexadecane/air/water thermodynamic cycle. Hexadecane/water partition coefficients (KHxd/w) were measured with batch partition, shared-headspace, and/or modified variable phase ratio headspace methods and were divided by hexadecane/air partition coefficients (KHxd/air) to obtain Kaw values over 7 orders of magnitude (10-4.9 to 102.3). Comparison to predicted Kaw values by four models showed that the quantum chemically based COSMOtherm model stood out for accuracy with a root-mean-squared error (RMSE) of 0.42 log units, as compared to HenryWin, OPERA, and the linear solvation energy relationship with predicted descriptors (RMSE, 1.28-2.23). The results indicate the advantage of a theoretical model over empirical models for a data-poor class like PFAS and the importance of experimentally filling data gaps in the chemical domain of environmental interest. Kaw values for 222 neutral (or neutral species of) PFAS were predicted using COSMOtherm as current best estimates for practical and regulatory use.

Accessibility investigation of semi-volatile organic compounds in indoor dust estimated by multi-ratio equilibrium passive sampling.

Many semi-volatile organic compounds (SVOCs) accumulate in indoor dust, which serves as a repository for those compounds. The presence of SVOCs in indoor environments is of concern because many of them are suspected to have toxic effects. Total SVOC concentrations in the dust are generally used for exposure assessment to indoor contaminants, assuming that 100% of the SVOCs is accessible for human uptake. However, such an assumption may potentially lead to an overestimated risk related to dust exposure. We applied a multi-ratio equilibrium passive sampling (MR-EPS) for estimation of SVOC accessibility in indoor settled dust using silicone passive samplers and three particle size dust fractions, <0.25 mm, 0.25-0.5 mm, and 1-2 mm in dry and wet conditions. Equilibrations were performed at various sampler-dust mass ratios to achieve different degrees of SVOC depletion, allowing the construction of a desorption isotherm. The desorption isotherms provided accessible fractions (FAS), equivalent air concentrations (CAIR), dust-air partition coefficients (KDUST-AIR) and organic carbon-air partition coefficients (KOC-AIR). The highest FAS were observed in the <0.25 mm dust fraction in wet conditions which is relevant for exposure assessment via oral ingestion. The highest CAIR were estimated for several organophosphorus flame retardants (OPFRs), polycyclic aromatic hydrocarbons (PAHs) and synthetic musks. The logKOC-AIR did not differ between dust particle sizes in dry and wet conditions but within compound groups, different relationships with hydrophobicity were observed. Equivalent lipid-based concentrations (CL⇌DUST) calculated using available lipid-silicone partition coefficients (KLIP-SIL) were compared with lipid-based concentrations (CL) measured in human-related samples collected from Europeans. For hexachlorobenzene (HCB), CL⇌DUST, and CL were similar, indicating equilibrium attainment between environment and human samples. Lipid-based concentrations for persistent legacy contaminants were also similar but lower for PBDEs in human samples. Overall, accessibility estimation using MR-EPS in dust further contributes to human risk assessment.

Functionalized maghemite nanoparticles for enhanced adsorption of uranium from simulated wastewater and magnetic harvesting.

Maghemite (γ-Fe2O3) nanoparticles (MNPs) were functionalized with 3-aminopropyltriethoxysilane (APTES) to give APTES@Fe2O3 (AMNP) which was then reacted with diethylenetriamine-pentaacetic acid (DTPA) to give a nanohybrid DTPA-APTES@Fe2O3 (DAMNP). Nano-isothermal titration calorimetry shows that DTPA complexation with uranyl ions in water is exothermic and has a stoichiometry of two DTPA to three uranyl ions. Density functional theory calculations indicate the possibility of several complexes between DTPA and UO22+ with different stoichiometries. Interactions between uranyl ions and DAMNP functional groups are revealed by X-photoelectron and Fourier transform infrared spectroscopies. Spherical aberration-corrected Scanning Transmission Electron Microscopy visualizes uranium on the particle surface. Adsorbent performance metrics were evaluated by batch adsorption studies under different conditions of pH, initial uranium concentration and contact time, and the results expressed in terms of equilibrium adsorption capacities (qe) and partition coefficients (PC). By either criterion, performance increases from MNP to AMNP to DAMNP, with the maximum uptake at pH 5.5 in all cases: MNP, qe = 63 mg g-1, PC = 127 mg g-1 mM-1; AMNP, qe = 165 mg g-1, PC = 584 mg g-1 mM-1; DAMNP, qe = 249 mg g-1, PC = 2318 mg g-1 mM-1 (at 25 °C; initial U concentration 0.63 mM; 5 mg adsorbent in 10 mL of solution; contact time, 3 h). The pH maximum is related to the predominance of mono- and di-cationic uranium species. Uptake by DAMNPs follows a pseudo-first-order or pseudo-second-order kinetic model and fits a variety of adsorption models. The maximum adsorption capacity for DAMNPs is higher than for other functionalized magnetic nanohybrids. This adsorbent can be regenerated and recycled for at least 10 cycles with less than 10% loss in activity, and shows high selectivity. These findings suggest that DAMNP could be a promising adsorbent for the recovery of uranium from nuclear wastewaters.

Modeled Rat Hepatic and Plasma Concentrations of Chemicals after Virtual Administrations Using Two Sets of in Silico Liver-to-Plasma Partition Coefficients.

The hepatic elimination of chemical substances in pharmacokinetic models requires hepatic intrinsic clearance (CLh,int) parameters for unbound drug in the liver, and these are regulated by the liver-to-plasma partition coefficients (Kp,h). Both Poulin and Theil and Rodgers and Rowland have proposed in silico expressions for Kp,h for a variety of chemicals. In this study, two sets of in silico Kp,h values for 14 model substances were assessed using experimentally reported in vivo steady-state Kp,h data and time-dependent virtual internal exposures in the liver and plasma modeled by forward dosimetry in rats. The Kp,h values for 14 chemicals independently calculated using the primary Poulin and Theil method in this study were significantly correlated with those obtained using the updated Rodgers and Rowland method and with reported in vivo steady-state Kp,h data in rats. When pharmacokinetic parameters were derived based on individual in vivo time-dependent data for diazepam, phenytoin, and nicotine in rats, the modeled liver and plasma concentrations after intravenous administration of the selected substrates in rats using two sets of in silico Kp,h values were mostly similar to the reported time-dependent in vivo internal exposures. Similar results for modeled liver and plasma concentrations were observed with input parameters estimated by machine-learning systems for hexobarbital, fingolimod, and pentazocine, with no reference to experimental pharmacokinetic data. These results suggest that the output values from rat pharmacokinetic models based on in silico Kp,h values derived from the primary Poulin and Theil model would be applicable for estimating toxicokinetics or internal exposure to substances.

Nuclear magnetic resonance-based solvent system selection for counter-current chromatography separation of compounds present in the same high-performance liquid chromatography peak: Flavonoids in barley seedlings as an example.

Partition coefficient is a key parameter for counter-current chromatography separation. High-performance liquid chromatography (HPLC) is the most commonly used tool for the screening of partition coefficients. However, HPLC technology is not applicable to the compounds present in the same chromatographic peak. Nuclear magnetic resonance (NMR) technology could easily distinguish compounds according to their characteristic absorption even if they exist in the same HPLC peak. In this study, two flavonoids present in the same HPLC peak were successfully purified by counter-current chromatography with a solvent system screened by NMR to show the great potential of NMR technology in the screening of the partition coefficient of co-efflux compounds. Through NMR screening, an optimized ethyl acetate/n-buthanol/water (7:3:10, v/v/v) system was applied in this study. As a result, two flavonoids, including 4.8 mg of 3'-methoxyl-6'''-O-feruloylsaponarin and 9.8 mg of 6'''-O-feruloylsaponarin were separated from 15 mg of the mixture. There is only one methoxy group difference between the two flavonoids. This study provides a new strategy for the screening of counter-current chromatography solvent systems and broadens the application scope of counter-current chromatography.

An efficient isolation and purification of broad partition coefficient range ginsenosides from roots of Panax quinquefolium L. by linear gradient counter-current chromatography coupled with preparative high-performance liquid chromatography.

As a famous health food, roots of Panax quinquefolium L. possessed immune regulation and enhancement of the central nervous system, in which ginsenosides are the main active component with different numbers and positions of sugars, causing different chemical polarities with a challenge for the separation and isolation. In this study, a fast and effective bilinear gradient counter-current chromatography was proposed for preparative isolation ginsenosides with a broad partition coefficient range from roots of Panax quinquefolium L. In terms of the established method, the mobile phases comprising n-butanol and ethyl acetate were achieved by adjusting the proportion. Coupled with the preparative HPLC, eleven main ginsenosides were successfully separated, including ginsenoside Rg1 (1), Re (2), acetyl ginsenoside Rg1 (3), Rb1 (4), Rc (5), Rg2 (6), Rb3 (7), quinquefolium R1 (8), Rd (9), gypenoside X VII (10) and notoginsenoside Fd (11), with purities exceeding 95% according to the HPLC results. Tandem mass spectrometry and electrospray ionization mass spectrometry were adopted for recognizing the isolated compound architectures. Our study suggests that linear gradient counter-current chromatography effectively separates the broad partition coefficient range of ginsenosides compounds from the roots of Panax quinquefolium L. In addition, it can apply to active compound isolation from other complicated natural products.

Application of one-step inner-recycling counter-current chromatography for the preparative separation and purification of chemical constituents from the rhizome of Bergenia ciliate (haw.) Sternb.

Bergenia ciliata (haw.) Sternb, the renowned pharmaceutical plant in Jammu and Kashmir of Pakistan, is widely applied in treating different illnesses including diabetes, diarrhea, and vomiting. This work employed an efficient one-step inner-recycling counter-current chromatography for preparative separating and purifying compounds with similar partition coefficients from the rhizome of Bergenia ciliate (haw.). Five compounds, including quercetin rhamnodiglucoside (1), quercetin-3-O-rutinoside (2), bergenine (3), kaempferol (4), and palmatic acid (5), were successfully separated using the optimized biphasic solvent system that contained ter-butylmetylether/n-butanol/acetonitrile/water (2:2:1:5, v/v) with the purities over 98%. Mass spectrometry and nuclear magnetic resonance were conducted for structural identification. As a result, our proposed strategy might be applied in separating compounds with similar partition coefficients, which was advantageous with regard to the less solvent and time consumption, and the increased number of theoretical plates.

Traditional Chinese medicine: saponins, critical micellar concentrations and partition coefficients.

INTRODUCTION: Traditional Chinese medicine (TCM) revolves around complex mixtures bound to specific roles within the formulation, among which saponin-containing plants with alleged properties of harmonising or detoxifying other compounds present in the preparations. OBJECTIVE: This article deals with the study of these interactions with, as a model, the interaction between saponins and selected active principles. METHODS: The measurement of the partition coefficient between water and octanol (logP) was used as an indicator and determined by nuclear magnetic resonance (NMR) for these active principles in the presence of saponins. For each compound, a graph was constructed showing the evolution of logP with increasing concentrations of saponins. RESULTS: Four distinct patterns of interactions were distinguished. Pattern A showed a constant decrease of logP, pattern B showed a decrease followed by a plateau, in pattern C the logP did not vary until the critical micellar concentration (CMC) and decreased afterwards, and pattern D exhibited an increase of logP. These properties were linked to the ability of saponins to form micelles in water once the CMC is reached. The interaction of aconitine and saponins followed pattern D, thus explaining the detoxification of herbal preparations using Aconitum with licorice. The licorice facilitated the extraction of the notoriously water-insoluble artemisinin from Artemisia annua. CONCLUSION: This investigation confirms that the physical properties of micelle forming saponins are intimately linked to a modification of behaviour of the other molecules in solution, as seen with the alteration of logP and the four types of interactions presented.