In vitro and in silico studies of the membrane permeability of natural flavonoids from Silybum marianum (L.) Gaertn. and their derivatives

Medicinal Plant-derived Phytochemicals in Detoxification

The average worldwide human life expectancy is 70 years, with a significantly higher value in Western societies. Many modern diseases are not associated with premature mortality but with a decreased quality of life in aged patients and an excessive accumulation of various toxic compounds in the human body during life. Today, scientists are especially interested in finding compounds that can help increase a healthy lifespan by detoxifying the body. Phytotherapy with specific approaches is used in alternative medicine to remove toxins from the body. Worldwide, research is conducted to identify medicinal plant-derived molecules that, with few or no side effects, may protect the liver and other organs. This review provides updated information about the detoxification process, the traditional and modern use of the most effective medicinal plants, their active metabolites as detoxifying agents, and the mechanisms and pathways involved in the detoxification process. Among medicinal plants with substantial detoxifying properties, a major part belongs to the Asteraceae family (Silybum marianum, Cynara scolymus, Arctium lappa, Helichrysum species, Inula helenium, and Taraxacum officinale). The most widely used hepatoprotective phytocomponent is silymarin, a standardized extract from the Silybum marianum seeds containing a mixture of flavonolignans. Many polysaccharides, polyphenols, and terpenoids have a detoxifying effect. Overall, scientific data on medicinal plants used in phytotherapeutic practice worldwide provides an understanding and awareness of their efficacy in detoxification.

A Shared Perspective on in Vitro and in Vivo Models to Assay Intestinal Transepithelial Transport of Food Compounds

Assessing nutrient bioavailability is complex, as the process involves multiple digestion steps, several cellular environments, and regulatory-metabolic mechanisms. Several in vitro models of different physiological relevance are used to study nutrient absorption, providing significant challenges in data evaluation. However, such in vitro models are needed for mechanistic studies as well as to screen for biological functionality of the food structures designed. This collaborative work aims to put into perspective the wide-range of models to assay the permeability of food compounds considering the particular nature of the different molecules, and, where possible, in vivo data are provided for comparison.

Extending the limitations in the prediction of PAMPA permeability with machine learning algorithms

Gastrointestinal absorption is a key factor amongst the ADME-related (absorption, distribution, metabolism and excretion) pharmacokinetic properties; therefore, it has a major role in drug discovery and drug safety determinations. The Parallel Artificial Membrane Permeability Assay (PAMPA) can be considered as the most popular and well-known screening assay for the measurement of gastrointestinal absorption. Our study provides quantitative structure-property relationship (QSPR) models based on experimental PAMPA permeability data for almost four hundred diverse molecules, which is a great extension of the applicability of the models in the chemical space. Two- and three-dimensional molecular descriptors were applied for the model building in every case. We have compared the performance of a classical partial least squares regression (PLS) model with two major machine learning algorithms: artificial neural networks (ANN) and support vector machine (SVM). Due to the applied gradient pH in the experiments, we have calculated the descriptors for the model building at pH values of 7.4 and 6.5, and compared the effect of pH on the performance of the models. After a complex validation protocol, the best model had an R2=0.91 for the training set, and R2= 0.84 for the external test set. The developed models are capable for the robust and fast prediction of new compounds with an excellent accuracy compared to the previous QSPR models.

Development of artificial neural network models to predict the PAMPA effective permeability of new, orally administered drugs active against the coronavirus SARS-CoV-2

Responding to the pandemic caused by SARS-CoV-2, the scientific community intensified efforts to provide drugs effective against the virus. To strengthen these efforts, the "COVID Moonshot" project has been accepting public suggestions for computationally triaged, synthesized, and tested molecules. The project aimed to identify molecules of low molecular weight with activity against the virus, for oral treatment. The ability of a drug to cross the intestinal cell membranes and enter circulation decisively influences its bioavailability, and hence the need to optimize permeability in the early stages of drug discovery. In our present work, as a contribution to the ongoing scientific efforts, we employed artificial neural network algorithms to develop QSAR tools for modelling the PAMPA effective permeability (passive diffusion) of orally administered drugs. We identified a set of 61 features most relevant in explaining drug cell permeability and used them to develop a stacked regression ensemble model, subsequently used to predict the permeability of molecules included in datasets made available through the COVID Moonshot project. Our model was shown to be robust and may provide a promising framework for predicting the potential permeability of molecules not yet synthesized, thus guiding the process of drug design.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13721-023-00410-9.

Advanced Analytical Tools for the Estimation of Gut Permeability of Compounds of Pharmaceutical Interest

The present study aims at developing a quantitative structure–activity relationship (QSAR) model for the determination of gut permeability of 228 pharmacological drugs at different pH conditions (3, 5, 7.4, 9, intrinsic). As a consequence, five different datasets (according to the diverse permeability shown by the compounds at the different pH values) were handled, with the aim of discriminating compounds as low-permeable or high-permeable. In order to achieve this goal, molecular descriptors for all the investigated compounds were computed and then classification models calculated by means of partial least squares discriminant analysis (PLS-DA). A high predictive capability was achieved for all models, providing correct classification rates in external validation between 80% and 96%. In order to test whether a reduction in the molecular descriptors would improve predictions and provide information about the most relevant variables, a feature selection approach, covariance selection, was used to select the most relevant subsets of predictors. This led to a slight improvement in the predictive accuracies, and it has indicated that the most relevant descriptors for the discrimination of the investigated compounds into low- and high-permeable were associated with the 2D and 3D structures.

Anticancer Activity of Natural and Synthetic Chalcones

Cancer is a condition caused by many mechanisms (genetic, immune, oxidation, and inflammatory). Anticancer therapy aims to destroy or stop the growth of cancer cells. Resistance to treatment is theleading cause of the inefficiency of current standard therapies. Targeted therapies are the most effective due to the low number of side effects and low resistance. Among the small molecule natural compounds, flavonoids are of particular interest for theidentification of new anticancer agents. Chalcones are precursors to all flavonoids and have many biological activities. The anticancer activity of chalcones is due to the ability of these compounds to act on many targets. Natural chalcones, such as licochalcones, xanthohumol (XN), panduretin (PA), and loncocarpine, have been extensively studied and modulated. Modification of the basic structure of chalcones in order to obtain compounds with superior cytotoxic properties has been performed by modulating the aromatic residues, replacing aromatic residues with heterocycles, and obtaining hybrid molecules. A huge number of chalcone derivatives with residues such as diaryl ether, sulfonamide, and amine have been obtained, their presence being favorable for anticancer activity. Modification of the amino group in the structure of aminochalconesis always favorable for antitumor activity. This is why hybrid molecules of chalcones with different nitrogen hetercycles in the molecule have been obtained. From these, azoles (imidazole, oxazoles, tetrazoles, thiazoles, 1,2,3-triazoles, and 1,2,4-triazoles) are of particular importance for the identification of new anticancer agents.

Natural Chain-Breaking Antioxidants and Their Synthetic Analogs as Modulators of Oxidative Stress

Oxidative stress is associated with the increased production of reactive oxygen species or with a significant decrease in the effectiveness of antioxidant enzymes and nonenzymatic defense. The penetration of oxygen and free radicals in the hydrophobic interior of biological membranes initiates radical disintegration of the hydrocarbon “tails” of the lipids. This process is known as “lipid peroxidation”, and the accumulation of the oxidation products as peroxides and the aldehydes and acids derived from them are often used as a measure of oxidative stress levels. In total, 40 phenolic antioxidants were selected for a comparative study and analysis of their chain-breaking antioxidant activity, and thus as modulators of oxidative stress. This included natural and natural-like ortho-methoxy and ortho-hydroxy phenols, nine of them newly synthesized. Applied experimental and theoretical methods (bulk lipid autoxidation, chemiluminescence, in silico methods such as density functional theory (DFT) and quantitative structure–activity relationship ((Q)SAR) modeling) were used to clarify their structure–activity relationship. Kinetics of non-inhibited and inhibited lipid oxidation in close connection with inhibitor transformation under oxidative stress is considered. Special attention has been paid to chemical reactions resulting in the initiation of free radicals, a key stage of oxidative stress. Effects of substituents in the side chains and in the phenolic ring of hydroxylated phenols and biphenols, and the concentration were discussed.

Simulated Gastrointestinal Biotransformation of Chlorogenic Acid, Flavonoids, Flavonolignans and Triterpenoid Saponins in Cecropia obtusifolia Leaf Extract

It is well known that biotransformation processes in the human body are crucial to form potentially bioactive metabolites from particular classes of natural products. However, little research has been conducted concerning the bioavailability of polyphenols, especially in the colon. The gastrointestinal stability and colonic biotransformation of the crude extract of the leaves of Cecropia obtusifolia, rich in flavone C-glycosides, was investigated under in vitro conditions, and the processing and interpretation of results were facilitated by using an automated machine learning model. This investigation revealed that flavone C-glycosides and flavonolignans from C. obtusifolia were stable throughout their passage in the simulated gastrointestinal tract including the colon phase. On the other hand, the colon bacteria extensively metabolized chlorogenic acid, flavonol, and triterpenoid O-glycosides. This investigation revealed that the colonic microbiota has an important role in the biotransformation of some chemical constituents of this extract.

Predicting PAMPA permeability using the 3D-RISM-KH theory: are we there yet?

The parallel artificial membrane permeability assay (PAMPA), a non-cellular lab-based assay, is extensively used to measure the permeability of pharmaceutical compounds. PAMPA experiments provide a working mimic of a molecule passing through cells and PAMPA values are widely used to estimate drug absorption parameters. There is an increased interest in developing computational methods to predict PAMPA permeability values. We developed an in silico model to predict the permeability of compounds based on the PAMPA assay. We used the three-dimensional reference interaction site model (3D-RISM) theory with the Kovalenko-Hirata (KH) closure to calculate the excess chemical potentials of a large set of compounds and predicted their apparent permeability with good accuracy (mean absolute error or MAE = 0.69 units) when compared to a published experimental data set. Furthermore, our in silico PAMPA protocol performed very well in the binary prediction of 288 compounds as being permeable or impermeable (precision = 94%, accuracy = 93%). This suggests that our in silico protocol can mimic the PAMPA assay and could aid in the rapid discovery or screening of potentially therapeutic drug leads that can be delivered to a desired tissue.

Identification of Human Sulfotransferases Active towards Silymarin Flavonolignans and Taxifolin

Natural phenolic compounds are known to be metabolized by phase II metabolic reactions. In this study, we examined the in vitro sulfation of the main constituents of silymarin, an herbal remedy produced from the fruits of the milk thistle. The study focused on major flavonolignan constituents, including silybin A, silybin B, isosilybin A, isosilybin B, silychristin, and silydianin, as well as the flavonoid taxifolin. Using ultra-high-performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS), individual flavonolignans and taxifolin were found to be sulfated by human liver and human intestinal cytosols. Moreover, experiments with recombinant enzymes revealed that human sulfotransferases (SULTs) 1A1*1, 1A1*2, 1A2, 1A3, 1B1, 1C4, and 1E1 catalyzed the sulfation of all of the tested compounds, with the exception of silydianin, which was not sulfated by SULT1B1 and SULT1C4. The sulfation products detected were monosulfates, of which some of the major ones were identified as silybin A 20-O-sulfate, silybin B 20-O-sulfate, and isosilybin A 20-O-sulfate. Further, we also observed the sulfation of the tested compounds when they were tested in the silymarin mixture. Sulfates of flavonolignans and of taxifolin were produced by incubating silymarin with all of the above SULT enzymes, with human liver and intestinal cytosols, and also with human hepatocytes, even though the spectrum and amount of the sulfates varied among the metabolic models. Considering our results and the expression patterns of human sulfotransferases in metabolic tissues, we conclude that flavonolignans and taxifolin can potentially undergo both intestinal and hepatic sulfation, and that SULTs 1A1, 1A3, 1B1, and 1E1 could be involved in the biotransformation of the constituents of silymarin.

In Vitro Permeability of Saponins and Sapogenins from Seed Extracts by the Parallel Artificial Membrane Permeability Assay: Effect of in Vitro Gastrointestinal Digestion

The permeability of saponins and sapogenins from fenugreek and quinoa extracts, as well as dioscin and diosgenin, was evaluated by the parallel artificial membrane permeability assay (PAMPA). The effect of the digestion process on permeability was determined, with previous development of a gastrointestinal process coupled to PAMPA. Saponins from both seeds displayed a moderate-to-poor permeability (>1 × 10^-6 cm/s), although the digestion enhanced their permeability values in the order of 10^-5 cm/s (p < 0.001). Sapogenins exhibited a similar permeability to that of saponins, although the digestion enhanced the permeability of sapogenins from quinoa (1.14 ± 0.47 × 10^-5 cm/s) but not from fenugreek (2.33 ± 0.99 × 10^-6 cm/s). An overall positive impact of coexisting lipids on the permeability was evidenced. PAMPA is shown as a useful, rapid, and easy tool for assessing the permeability of bioactive compounds from complex matrices, with the previous gastrointestinal process being a relevant step.

Efficacy evaluation of Qingyan formulation in a smoking environment and screening of anti-inflammatory compounds

Qingyan formulation (QF) is a common preparation that is often used to control inflammation in the haze environment. However, the efficacy and effective constituents of QF are still uncertain and difficult to identify. This paper aims to evaluate the efficacy by simulating a haze environment and determine its anti-inflammatory compounds by UPLC/Q-TOF-MS/MS combing with bioactivity screening. The therapeutic effect of QF in the simulated haze environment was confirmed from the aspects of lung histomorphology and inflammatory factor expression levels. QF showed strong anti-inflammatory activity with the minimum effective concentration reaching 1.5 g/kg. Potential anti-inflammatory components were screened by the NF-κB activity assay system and simultaneously identified based on mass spectral data. Then, the potential active compounds were verified by molecular biological methods, the minimum effective concentration can reach 0.1 mg/L. Six structural types of NF-κB inhibitors (phenolic acid, scopolamine, hydroxycinnamic acid, flavonoid, dihydroflavone and steroid) were identified. Further cytokine assays confirmed their potential anti-inflammatory effects of NF-κB inhibitors. This strategy clearly demonstrates that QF has a significant therapeutic effect on respiratory diseases caused by haze, so it is necessary to promote its commercialization and wider application.

In Silico Prediction of PAMPA Effective Permeability Using a Two-QSAR Approach

Oral administration is the preferred and predominant route of choice for medication. As such, drug absorption is one of critical drug metabolism and pharmacokinetics (DM/PK) parameters that should be taken into consideration in the process of drug discovery and development. The cell-free in vitro parallel artificial membrane permeability assay (PAMPA) has been adopted as the primary screening to assess the passive diffusion of compounds in the practical applications. A classical quantitative structure–activity relationship (QSAR) model and a machine learning (ML)-based QSAR model were derived using the partial least square (PLS) scheme and hierarchical support vector regression (HSVR) scheme to elucidate the underlying passive diffusion mechanism and to predict the PAMPA effective permeability, respectively, in this study. It was observed that HSVR executed better than PLS as manifested by the predictions of the samples in the training set, test set, and outlier set as well as various statistical assessments. When applied to the mock test, which was designated to mimic real challenges, HSVR also showed better predictive performance. PLS, conversely, cannot cover some mechanistically interpretable relationships between descriptors and permeability. Accordingly, the synergy of predictive HSVR and interpretable PLS models can be greatly useful in facilitating drug discovery and development by predicting passive diffusion.