Variable inhibitory effect of herbal supplements of different brands on human P450 CYP1A2

Herbal supplements are not governed by the same regulations as prescription drugs, we hy-pothesize that the content of their active ingredients may vary largely among different manu-facturers. This may produce variable therapeutic outcomes. This study aims to examine this hypothesis on commonly used herbal supplements among cancer patients. CYP1A2 has been implicated in the activation of many carcinogens and alteration in its activity may be a mech-anism associated with the protective effect of herbal products. Activity of human CYP1A2 was used to determine the effect of four herbal supplements of different brands, namely, black cohosh (BC), ginseng, grape seed extract (GSE) and green tea extract (GTE). The herbal con-tent was extracted with methanol, and extract aliquots were used to determine their effect on CYP1A2. Human liver microsomes, the CYP1A2 probe (7-ethoxyresorufin) and NADPH in buffer were incubated with and without herbal extract. Metabolite (resorufin) formation was monitored by HPLC. Seven BC products caused a mild inhibition of CYP1A2, ranging from 2.4 % by GNC Plus to 21.9 % by Nature’s Resource. Among nine ginseng products tested, the inhibitory effect varied from 4.2 % by Imperial to 44.6 % by Solarays. The effect of nine GSE brands also varied, ranging from 1.7 % (Country Life) to 26.5 % (Veg Life). Of twelve GTE products, the inhibitory effect varied from 2.9 % by Henry’s to 46.6 % by GNC Plus. It ap-pears that the inhibition of selected herbal supplements on CYP1A2 activity varies considera-bly among different brands of the products. This may be due to variations in the herbal prod-ucts’ active ingredients content

Cytochrome P450 enzyme mediated herbal drug interactions (Part 1)

It is well recognized that herbal supplements or herbal medicines are now commonly used. As many patients taking prescription medications are concomitantly using herbal supplements, there is considerable risk for adverse herbal drug interactions. Such interactions can enhance the risk for an individual patient, especially with regard to drugs with a narrow therapeutic index such as warfarin, cyclosporine A and digoxin. Herbal drug interactions can alter pharmacokinetic or/and pharmacodynamic properties of administered drugs. The most common pharmacokinetic interactions usually involve either the inhibition or induction of the metabolism of drugs catalyzed by the important enzymes, cytochrome P450 (CYP). The aim of the present article is to provide an updated review of clinically relevant metabolic CYP-mediated drug interactions between selected herbal supplements and prescription drugs. The commonly used herbal supplements selected include Echinacea, Ginkgo biloba, garlic, St. John's wort, goldenseal, and milk thistle. To date, several significant herbal drug interactions have their origins in the alteration of CYP enzyme activity by various phytochemicals. Numerous herbal drug interactions have been reported. Although the significance of many interactions is uncertain but several interactions, especially those with St. John’s wort, may have critical clinical consequences. St. John’s wort is a source of hyperforin, an active ingredient that has a strong affinity for the pregnane xenobiotic receptor (PXR). As a PXR ligand, hyperforin promotes expression of CYP3A4 enzymes in the small intestine and liver. This in turn causes induction of CYP3A4 and can reduce the oral bioavailability of many drugs making them less effective. The available evidence indicates that, at commonly recommended doses, other selected herbs including Echinacea, Ginkgo biloba, garlic, goldenseal and milk thistle do not act as potent or moderate inhibitors or inducers of CYP enzymes. A good knowledge of the mechanisms of herbal drug interactions is necessary for assessing and minimizing clinical risks. These processes help prediction of interactions between herbal supplements and prescription drugs. Healthcare professionals should remain vigilant for potential interactions between herbal supplements/medicines and prescription drugs, especially for drugs with a narrow therapeutic index are used

High therapeutic potential of Spilanthes acmella

Spilanthes acmella, a well known antitoothache plant with high medicinal usages, has been recognized as an important medicinal plant and has an increasingly high demand worldwide. From its traditional uses in health care and food, extensive phytochemical studies have been reported. This review provides an overview and general description of the plant species, bioactive metabolites and important pharmacological activities including the preparation, purification and in vitro large-scale production. Structure-activity relationships of the bioactive compounds have been discussed. Considering data from the literature, it could be demonstrated that S. acmella possesses diverse bioactive properties and immense utilization in medicine, health care, cosmetics and as health supplements. As a health food, it is enriched with high therapeutic value with high potential for further development

Effect of cranberry dietary supplements with different brands on human CYP3A4 enzyme

The use of dietary supplements has increased dramatically, making drug interactions with those supplements a major concern. Because dietary supplements are not subject to the same regulations as prescription drugs, we hypothesize that the content of their active ingredients may vary among manufacturers, potentially causing a large variation in therapeutic outcome. The current study aimed to test this hypothesis on commonly used cranberry dietary supple-ments. Activity of human CYP3A4 enzyme was used as a parameter to determine the effect of cranberry supplement from nine manufacturers. The content of a cranberry product, equivalent to one capsule, was extracted with methanol. Aliquots of the extract were tested for their ability to inhibit the metabolism of the human CYP3A4 substrate quinine, using an in vitro liver microsomal technique. Human liver microsomes and quinine were incubated with or without (i.e. as control) cranberry extract. Formation of quinine’s metabolite 3-hydroxyquinine, generated by the CYP3A4-mediated reaction was measured by a HPLC method. Of nine cranberry products tested, eight products had little or no effect but only one brand (Nature’s Herbs 600 mg) caused very strong inhibition (67.2 %) of CYP3A4. The reason for this inhibition is unknown. The effect of cranberry was varied and ranged from 4.4 % activation by Ride Aid 800 mg to 67.2% inhibition by Nature’s Herbs 600 mg. Lack of effect on human CYP3A4 activity suggests that use of cranberry dietary supplement is unlikely to cause signif-icant interactions with drugs metabolized by CYP3A4

Novel activities of 1-adamantylthiopyridines as antibacterials, antimalarials and anticancers

To discover new bioactive lead compounds for medicinal purposes, herein, 2(1-adamantylthio)pyridine and derivatives (1-10) were prepared and tested for antibacterial (agar dilution method against 27 strains of microorganisms), antimalarial (against Plasmodium falciparum) and anticancer (MOLT-3, HepG2, HuCCA-1 and A549) activities. Results showed that all the tested derivatives selectively exerted antigrowth activity against Streptococci at 15-30 µg/mL. 3-Substituted (R) thiopyridines; 3 (R = NAc2), 5 (R = OH) and 6 (R = Br) exhibited antibacterials, antimalarials and anticancers. Significantly, 6-(1-adamantylthio) nicotinonitrile (10) is a promising antibacterial which selectively displays antigrowth activity against Vibrio cholerae, Vibrio parahaemolyticus, Edwardsiella tarda and beta-hemolytic Streptococcus group A with minimum inhibitory concentration of 30 µg/mL. The findings reveal that these 1-adamantylthiopyridines represent a novel class of antibacterial, antimalarial and anticancer agents with potential medicinal values

Classification of P-glycoprotein-interacting compounds using machine learning methods

P-glycoprotein (Pgp) is a drug transporter that plays important roles in multidrug resistance and drug pharmacokinetics. The inhibition of Pgp has become a notable strategy for combating multidrug-resistant cancers and improving therapeutic outcomes. However, the polyspecific nature of Pgp, together with inconsistent results in experimental assays, renders the determination of endpoints for Pgp-interacting compounds a great challenge. In this study, the classification of a large set of 2,477 Pgp-interacting compounds (i.e., 1341 inhibitors, 913 noninhibitors, 197 substrates and 26 non-substrates) was performed using several machine learning methods (i.e., decision tree induction, artificial neural network modelling and support vector machine) as a function of their physicochemical properties. The models provided good predictive performance, producing MCC values in the range of 0.739-1 for internal cross-validation and 0.665-1 for external validation. The study provided simple and interpretable models for important properties that influence the activity of Pgp-interacting compounds, which are potentially beneficial for screening and rational design of Pgp inhibitors that are of clinical importance

Prediction of aromatase inhibitory activity using the efficient linear method (ELM)

Aromatase inhibition is an effective treatment strategy for breast cancer. Currently, several in silico methods have been developed for the prediction of aromatase inhibitors (AIs) using artificial neural network (ANN) or support vector machine (SVM). In spite of this, there are ample opportunities for further improvements by developing a simple and interpretable quantitative structure-activity relationship (QSAR) method. Herein, an efficient linear method (ELM) is proposed for constructing a highly predictive QSAR model containing a spontaneous feature importance estimator. Briefly, ELM is a linear-based model with optimal parameters derived from genetic algorithm. Results showed that the simple ELM method displayed robust performance with 10-fold cross-validation MCC values of 0.64 and 0.56 for steroidal and non-steroidal AIs, respectively. Comparative analyses with other machine learning methods (i.e. ANN, SVM and decision tree) were also performed. A thorough analysis of informative molecular descriptors for both steroidal and non-steroidal AIs provided insights into the mechanism of action of compounds. Our findings suggest that the shape and polarizability of compounds may govern the inhibitory activity of both steroidal and non-steroidal types whereas the terminal primary C(sp3) functional group and electronegativity may be required for non-steroidal AIs. The R code of the ELM method is available at http://dx.doi.org/10.6084/m9.figshare.1274030

QSAR-driven rational design of novel DNA methyltransferase 1 inhibitors

DNA methylation, an epigenetic modification, is mediated by DNA methyltransferases (DNMTs), a family of enzymes. Inhibitions of these enzymes are considered a promising strategy for the treatment of several diseases. In this study, a quantitative structure-activity relationship (QSAR) modeling was employed to understand the structure-activity relationship (SAR) of currently available non-nucleoside DNMT1 inhibitors (i.e., indole and oxazoline/1,2-oxazole scaffolds). Two QSAR models were successfully constructed using multiple linear regression (MLR) and provided good predictive performance (R2Tr = 0.850-0.988 and R2CV = 0.672-0.869). Bond information content index (BIC1) and electronegativity (R6e+) are the most influential descriptors governing the activity of compounds. The constructed QSAR models were further applied for guiding a rational design of novel inhibitors. A novel set of 153 structurally modified compounds were designed in silico according to the important descriptors deduced from the QSAR finding, and their DNMT1 inhibitory activities were predicted. This result demonstrated that 86 newly designed inhibitors were predicted to elicit enhanced DNMT1 inhibitory activity when compared to their parent compounds. Finally, a set of promising compounds as potent DNMT1 inhibitors were highlighted to be further developed. The key SAR findings may also be beneficial for structural optimization to improve properties of the known inhibitors

Rational design of novel coumarins

Coumarins are well-known for their antioxidant effect and aromatic property, thus, they are one of ingredients commonly added in cosmetics and personal care products. Quantitative structure-activity relationships (QSAR) modeling is an in silico method widely used to facilitate rational design and structural optimization of novel drugs. Herein, QSAR modeling was used to elucidate key properties governing antioxidant activity of a series of the reported coumarin-based antioxidant agents (1-28). Several types of descriptors (calculated from 4 softwares i.e., Gaussian 09, Dragon, PaDEL and Mold2 softwares) were used to generate three multiple linear regression (MLR) models with preferable predictive performance (Q2LOO-CV = 0.813-0.908; RMSELOO-CV = 0.150-0.210; Q2Ext = 0.875-0.952; RMSEExt = 0.104-0.166). QSAR analysis indicated that number of secondary amines (nArNHR), polarizability (G2p), electronegativity (D467, D580, SpMin2_Bhe, and MATS8e), van der Waals volume (D491 and D461), and H-bond potential (SHBint4) are important properties governing antioxidant activity. The constructed models were also applied to guide in silico rational design of an additional set of 69 structurally modified coumarins with improved antioxidant activity. Finally, a set of 9 promising newly design compounds were highlighted for further development. Structure-activity analysis also revealed key features required for potent activity which would be useful for guiding the future rational design. In overview, our findings demonstrated that QSAR modeling could possibly be a facilitating tool to enhance successful development of bioactive compounds for health and cosmetic applications