ChemGPS-NP Mapping of Chemical Compounds for Prediction of Anticancer Mode of Action

In vitro and in silico evaluation of Ononis isoflavonoids as molecules targeting the central nervous system

Isoflavonoids with various structural elements show a promising potential effect on central nervous system activities. Despite their favorable medicinal properties, the pharmacokinetic characteristics of this thoroughly investigated group of natural phenolics have only been described to a limited extent. Regarding the lack of information about the BBB permeability of isoflavones, isoflavanones, and pterocarpans found in Ononis species, the aim of our study was to investigate their physico-chemical properties influencing their absorption and distribution. Furthermore, we aimed to characterize the possible MAO-B inhibiting features of Ononis isoflavonoids in silico. Octanol-water partitioning and BBB-PAMPA permeability of formononetin, calycosin D, onogenin, sativanone, medicarpin and maackiain were assessed for the first time in our study. The log P values ranged from 2.21 to 3.03 and log D^7.4 values from 2.48 to 3.03, respectively, indicating optimal polarity for BBB permeation. The results of PAMPA-BBB expressed as log P_e values fell between -5.60 and -4.45, predicting their good permeation capability as well. The effective permeability values showed structure-dependent differences, indicating that the pterocarpan type skeleton was the most preferred type, followed by isoflavanones, then isoflavones. The methoxy or methylenedioxy substitution of the same skeleton did not influence the permeability significantly, contrary to an additional hydroxyl group. Membrane retention showed a similar structure dependent pattern to that of effective permeability, ranging from 16% to 70%. For the identification of volumes of chemical space related to particular biological activities the ChemGPS-NP framework was used. The MAO-B inhibitory potency and selectivity were also predicted and validated. Based on our results, MAO-B inhibitory potency could be predicted with good precision, but in the case of selectivity, only the direction could be concluded (favors MAO-B or MAO-A), not the magnitude. Our finding reflects that Ononis isoflavonoid aglycones show an excellent fit with the suggested parameters for BBB permeability and this is the first study to confirm the highly favorable position of these natural products for MAO-B inhibition.

Cheminformatics in Natural Product-based Drug Discovery

This review seeks to provide a timely survey of the scope and limitations of cheminformatics methods in natural product-based drug discovery. Following an overview of data resources of chemical, biological and structural information on natural products, we discuss, among other aspects, in silico methods for (i) data curation and natural products dereplication, (ii) analysis, visualization, navigation and comparison of the chemical space, (iii) quantification of natural product-likeness, (iv) prediction of the bioactivities (virtual screening, target prediction), ADME and safety profiles (toxicity) of natural products, (v) natural products-inspired de novo design and (vi) prediction of natural products prone to cause interference with biological assays. Among the many methods discussed are rule-based, similarity-based, shape-based, pharmacophore-based and network-based approaches, docking and machine learning methods.

Chemical space of naturally occurring compounds

The chemical space of naturally occurring compounds is vast and diverse. Other than biologics, naturally occurring small molecules include a large variety of compounds covering natural products from different sources such as plant, marine, and fungi, to name a few, and several food chemicals. The systematic exploration of the chemical space of naturally occurring compounds have significant implications in many areas of research including but not limited to drug discovery, nutrition, bio- and chemical diversity analysis. The exploration of the coverage and diversity of the chemical space of compound databases can be carried out in different ways. The approach will largely depend on the criteria to define the chemical space that is commonly selected based on the goals of the study. This chapter discusses major compound databases of natural products and cheminformatics strategies that have been used to characterize the chemical space of natural products. Recent exemplary studies of the chemical space of natural products from different sources and their relationships with other compounds are also discussed. We also present novel chemical descriptors and data mining approaches that are emerging to characterize the chemical space of naturally occurring compounds.

Prediction of anticancer molecules using hybrid model developed on molecules screened against NCI-60 cancer cell lines

Background

In past, numerous quantitative structure-activity relationship (QSAR) based models have been developed for predicting anticancer activity for a specific class of molecules against different cancer drug targets. In contrast, limited attempt have been made to predict the anticancer activity of a diverse class of chemicals against a wide variety of cancer cell lines. In this study, we described a hybrid method developed on thousands of anticancer and non-anticancer molecules tested against National Cancer Institute (NCI) 60 cancer cell lines.

Results

Our analysis of anticancer molecules revealed that majority of anticancer molecules contains 18–24 carbon atoms and are dominated by functional groups like R₂NH, R₃N, ROH, RCOR, and ROR. It was also observed that certain substructures (e.g., 1-methoxy-4-methylbenzene, 1-methoxy benzene, Nitrobenzene, Indole, Propenyl benzene) are more abundant in anticancer molecules. Next, we developed anticancer molecule prediction models using various machine-learning techniques and achieved maximum matthews correlation coefficient (MCC) of 0.81 with 90.40 % accuracy using support vector machine (SVM) based models. In another approach, a novel similarity or potency score based method has been developed using selected fragments/fingerprints and achieved maximum MCC of 0.82 with 90.65 % accuracy. Finally, we combined the strength of above methods and developed a hybrid method with maximum MCC of 0.85 with 92.47 % accuracy.

Conclusions

We developed a hybrid method utilizing the best of machine learning and potency score based method. The highly accurate hybrid method can be used for classification of anticancer and non-anticancer molecules. In order to facilitate scientific community working in the field of anticancer drug discovery, we integrate hybrid and potency method in a web server CancerIN. This server provides various facilities that includes; virtual screening of anticancer molecules, analog based drug design, and similarity with known anticancer molecules (http://crdd.osdd.net/oscadd/cancerin).

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-016-2082-y) contains supplementary material, which is available to authorized users.

Synergistic Antiproliferative Effects of a New Cucurbitacin B Derivative and Chemotherapy Drugs on Lung Cancer Cell Line A549

Nonsmall cell lung cancer (NSCLC) represents an important cause of mortality worldwide due to its aggressiveness and growing resistance to currently available therapy. Cucurbitacins have emerged as novel potential anticancer agents showing strong antiproliferative effects and can be promising candidates for combined treatments with clinically used anticancer agents. This study investigates the synergistic antiproliferative effects of a new semisynthetic derivative of cucurbitacin B (DACE) with three chemotherapy drugs: cisplatin (CIS), irinotecan (IRI), and paclitaxel (PAC) on A549 cells. The most effective combinations were selected for studies of the mechanism of action. Using an in silico tool, DACE seems to act by a different mechanism of action when compared with that of different classes of drugs already used in clinical settings. DACE also showed potent synergic effects with drugs, and the most potent combinations induced G2/M cell cycle arrest by modulating survivin and p53 expression, disruption of F-actin cytoskeleton, and cell death by apoptosis. These treatments completely inhibited the clonogenic potential and did not reduce the proliferation of nontumoral lung cells (MRC-5). DACE also showed relevant antimigratory and anti-invasive effects, and combined treatments modulated cell migration signaling pathways evolved with metastasis progression. The effects of DACE associated with drugs was potentiated by the oxidant agent l-buthionine-sulfoximine (BSO), and attenuated by N-acetilcysteine (NAC), an antioxidant agent. The antiproliferative effects induced by combined treatments were attenuated by a pan-caspase inhibitor, indicating that the effects of these treatments are dependent on caspase activity. Our data highlight the therapeutic potential of DACE used in combination with known chemotherapy drugs and offer important insights for the development of more effective and selective therapies against lung cancer.

Discovery of Tricyclic Clerodane Diterpenes as Sarco/Endoplasmic Reticulum Ca(2+)-ATPase Inhibitors and Structure-Activity Relationships

Tricyclic clerodane diterpenes (TCDs) are natural compounds that often show potent cytotoxicity for cancer cells, but their mode of action remains elusive. A computationally based similarity search (CDRUG), combined with principal component analysis (ChemGPS-NP) and docking calculations (GOLD 5.2), suggested TCDs to be inhibitors of the sarco/endoplasmic reticulum Ca(2+)-ATPase (SERCA) pump, which is also the target of the sesquiterpene lactone thapsigargin. Biochemical studies were performed with 11 TCDs on purified rabbit skeletal muscle sarcoplasmic reticulum membranes, which are highly enriched with the SERCA1a isoform. Casearborin D (2) exhibited the highest affinity, with a KD value of 2 μM and giving rise to complete inhibition of SERCA1a activity. Structure-activity relationships revealed that functionalization of two acyl side chains (R1 and R4) and the hydrophobicity imparted by the aliphatic chain at C-9, as well as a C-3,C-4 double bond, play crucial roles for inhibitory activity. Docking studies also suggested that hydrophobic interactions in the binding site, especially with Phe256 and Phe834, may be important for a strong inhibitory activity of the TCDs. In conclusion, a novel class of SERCA inhibitory compounds is presented.

Bioassays in natural product research - strategies and methods in the search for anti-inflammatory and antimicrobial activity

INTRODUCTION

Identifying bioactive molecules from complex biomasses requires careful selection and execution of relevant bioassays in the various stages of the discovery process of potential leads and targets.

OBJECTIVE

The aim of this review is to share our long-term experience in bioassay-guided isolation, and mechanistic studies, of bioactive compounds from different organisms in nature with emphasis on anti-inflammatory and antimicrobial activity.

METHODS

In the search for anti-inflammatory activity, in vivo and in vitro model combinations with enzymes and cells involved in the inflammatory process have been used, such as cyclooxygenases, human neutrophils and human cancer cell lines. Methods concerning adsorption and perforation of bacteria, fungi, human cells and model membranes, have been developed and optimised, with emphasis on antimicrobial peptides and their interaction with the membrane target, in particular their ability to distinguish host from pathogen.

RESULTS

A long-term research has provided experience of selection and combination of bioassay models, which has led to an increased understanding of ethnopharmacological and ecological observations, together with in-depth knowledge of mode of action of isolated compounds.

CONCLUSION

A more multidisciplinary approach and a higher degree of fundamental research in development of bioassays are often necessary to identify and to fully understand the mode of action of bioactive molecules with novel structure-activity relationships from natural sources.

Synthesis and biological evaluation of phenanthrenes as cytotoxic agents with pharmacophore modeling and ChemGPS-NP prediction as topo II inhibitors

In a structure-activity relationship (SAR) study, 3-methoxy-1,4-phenanthrenequinones, calanquinone A (6a), denbinobin (6b), 5-OAc-calanquinone A (7a) and 5-OAc-denbinobin (7b), have significantly promising cytotoxicity against various human cancer cell lines (IC₅₀ 0.08–1.66 µg/mL). Moreover, we also established a superior pharmacophore model for cytotoxicity (r = 0.931) containing three hydrogen bond acceptors (HBA1, HBA2 and HBA3) and one hydrophobic feature (HYD) against MCF-7 breast cancer cell line. The pharmacophore model indicates that HBA3 is an essential feature for the oxygen atom of 5-OH in 6a–b and for the carbonyl group of 5-OCOCH₃ in 7a–b, important for their cytotoxic properties. The SAR for moderately active 5a–b (5-OCH₃), and highly active 6a–b and 7a–b, are also elaborated in a spatial aspect model. Further rational design and synthesis of new cytotoxic phenanthrene analogs can be implemented via this model. Additionally, employing a ChemGPS-NP based model for cytotoxicity mode of action (MOA) provides support for a preliminary classification of compounds 6a–b as topoisomerase II inhibitors.

Orthogonal PLS (OPLS) Modeling for Improved Analysis and Interpretation in Drug Design

Partial least squares (PLS) regression is a flexible data analytical approach, which can be made even more versatile and useful by various modifications. In this article we describe the extension into orthogonal PLS modeling, in terms of two new methods, called OPLS and O2PLS, with similar prediction capacity but improved model interpretation.

Current strategies for drug discovery through natural products

IMPORTANCE TO THE FIELD

Natural products are the most consistently successful source of drug leads, both historically and currently. Despite this, the use of natural products in industrial drug discovery has fallen out of favour. Natural products are likely to continue to be sources of new commercially viable drug leads because the chemical novelty associated with natural products is higher than that of any other source: this is particularly important when searching for lead molecules against newly discovered targets for which there are no known small molecule leads. Areas to be covered: Current drug discovery strategies involving natural products are described in three sections: developments from traditionally used medicines, random testing of natural compounds on biological assays and use of virtual screening techniques with structures of natural products.

WHAT THE READER WILL GAIN

The reader will gain an insight into the potential for natural products in current drug discovery paradigms, particularly in the value of using natural products in virtual screening approaches.

TAKE HOME MESSAGE

Drug discovery would be enriched if fuller use was made of the chemistry of natural products.

Cytotoxic effects of cardiac glycosides in colon cancer cells, alone and in combination with standard chemotherapeutic drugs

Cardiac glycosides have been reported to exhibit cytotoxic activity against several different cancer types, but studies against colorectal cancer are lacking. In a screening procedure aimed at identifying natural products with activity against colon cancer, several cardiac glycosides were shown to be of interest, and five of these were further evaluated in different colorectal cancer cell lines and primary cells from patients. Convallatoxin (1), oleandrin (4), and proscillaridin A (5) were identified as the most potent compounds (submicromolar IC50 values), and digitoxin (2) and digoxin (3), which are used in cardiac disease, exhibited somewhat lower activity (IC50 values 0.27-4.1 microM). Selected cardiac glycosides were tested in combination with four clinically relevant cytotoxic drugs (5-fluorouracil, oxaliplatin, cisplatin, irinotecan). The combination of 2 and oxaliplatin exhibited synergism including the otherwise highly drug-resistant HT29 cell line. A ChemGPS-NP application comparing modes of action of anticancer drugs identified cardiac glycosides as a separate cluster. These findings demonstrate that such substances may exhibit significant activity against colorectal cancer cell lines, by mechanisms disparate from currently used anticancer drugs, but at concentrations generally considered not achievable in patient plasma.