Drug-like Substances Research Articles

Employing artificial neural networks for the assessment of the aqueous solubility of drug –like substances

This research has advanced Quantitative Structure-Property Relationship (QSPR) models for predicting the aqueous solubility of drug-like substances. By integrating multivariate regression and neural network techniques, the study utilized the backward algorithm to strategically select 2D and 3D molecular descriptors, resulting in the development of an optimal QSPRMLR model with k = 23. The artificial neural network regression model(QSPRANN), derived from selected descriptors of the multivariable linear regression model(QSPRMLR), demonstrated enhanced predictive capabilities for logS values in both validationand prediction groups, yielding SE values of 0.786 and 0.808, respectively. The QSPRANNsignificantly improved the overall predictability of the multivariate regression model. Statistical assessments of the QSPRANN model revealed SE = 0.699, R2train = 0.918, and Q2v = 0.878. The predicted logS values from the QSPRANN model align well with experimental data,confirming the reliability and accuracy of the developed model.

Experimental study of local anesthetic and antiarrhythmic activities of fluorinated ethynylpiperidine derivatives.

The chemical structure of piperidine has a unique ability to combine with other molecular fragments. This fact makes it possible to actively use it as an effective basis for the creation of new drug-like substances. Thus, the aim of the current investigation was to study the acute toxicity, local anesthetic potency, and antiarrhythmic activity of the two new synthesized piperidine derivatives under laboratory codes LAS-286 and LAS-294 (local anesthetic substances). The Bulbring & Wajda animal model and method of determining the nociception threshold during electrical stimulation was used to investigate the action of the substance during infiltration anesthesia. An antiarrhythmic activity was observed by the aconitine-induced rat arrhythmia model. Additionally, these compounds were studied in relation to molecular docking to delineate the structure-activity relationships. The tested piperidine derivatives had a low toxicity in the subcutaneous and intravenous administration routes. The experimental results showed a higher prolonged and pronounced local anesthetic activity for LAS-286 at a 0.5% concentration, compared to the reference preparations. The low dosage of 0.1 mg/kg of LAS-294 demonstrated a pronounced preventive antiarrhythmic effect in 90% of cases on the development of mixed arrhythmia, caused by aconitine. The results of molecular docking confirmed a higher binding affinity of the tested piperidines with the Nav1.4 and Nav1.5 macromolecules. The results of the present study are very promising, because these piperidines have shown a high biological activity, which can suggest a potential therapeutic application in the future.

New sulfonamide derivatives based on 1,2,3-triazoles: synthesis, in vitro biological activities and in silico studies

Eight new hybrid constructs containing a series of sulfonamide and 1,2,3-triazole units were designed and synthesized. Anticancer, antioxidant and cholinesterase activities of these hybrid structures were investigated. In our design, the Cu(I)-catalyzed click reaction between N,4-dimethyl-N-(prop-2-yn-1-yl)benzenesulfonamide (6) and aryl azides 8a–h was used. Antioxidant activity values of 9f (IC50: 229.46 ± 0.001 μg/mL) and 9h (IC50: 254.32 ± 0.002 μg/mL) hybrid structures were higher than BHT (IC50: 286.04 ± 0.003 μg/mL) and lower than Ascorbic acid (IC50: 63.53 ± 0.001 μg/mL) and α-Tocopherol (IC50: 203.21 ± 0.002 μg/mL). We determined that the cytotoxic effects of hybrid constructs 9d (IC50: 3.81 ± 0.1084 µM) and 9g (IC50: 4.317 ± 0.0367 µM) against A549 and healthy cell line (HDF) are much better than standard cisplatin (IC50: 6.202 ± 0.0705 µM). It was determined that the AChE inhibitory activities of all synthesized compounds were much better than Galantamine used as a standard. In particular, 9c (IC50: 13.81 ± 0.0026 mM) had ten times better activity than the standard Galantamine (IC50: 136 ± 0.008 mM). The ADMET properties of the molecules have been thoroughly examined and met the criteria for drug-like substances. They also have a high oral absorption rate, as they can effectively cross the blood–brain barrier and are easily absorbed in the gastrointestinal tract. In vitro experiments were confirmed by in silico molecular docking studies. Communicated by Ramaswamy H. Sarma

SAV-Pred: A Freely Available Web Application for the Prediction of Pathogenic Amino Acid Substitutions for Monogenic Hereditary Diseases Studied in Newborn Screening.

Next Generation Sequencing (NGS) technologies are rapidly entering clinical practice. A promising area for their use lies in the field of newborn screening. The mass screening of newborns using NGS technology leads to the discovery of a large number of new missense variants that need to be assessed for association with the development of hereditary diseases. Currently, the primary analysis and identification of pathogenic variations is carried out using bioinformatic tools. Although extensive efforts have been made in the computational approach to variant interpretation, there is currently no generally accepted pathogenicity predictor. In this study, we used the sequence-structure-property relationships (SSPR) approach, based on the representation of protein fragments by molecular structural formula. The approach predicts the pathogenic effect of single amino acid substitutions in proteins related with twenty-five monogenic heritable diseases from the Uniform Screening Panel for Major Conditions recommended by the Advisory Committee on Hereditary Disorders in Newborns and Children. In order to create SSPR models of classification, we modified a piece of cheminformatics software, MultiPASS, that was originally developed for the prediction of activity spectra for drug-like substances. The created SSPR models were compared with traditional bioinformatic tools (SIFT 4G, Polyphen-2 HDIV, MutationAssessor, PROVEAN and FATHMM). The average AUC of our approach was 0.804 ± 0.040. Better quality scores were achieved for 15 from 25 proteins with a significantly higher accuracy for some proteins (IVD, HADHB, HBB). The best SSPR models of classification are freely available in the online resource SAV-Pred (Single Amino acid Variants Predictor).

Reality check: lipid-oligonucleotide conjugates for therapeutic applications

ABSTRACT Introduction Despite much progress in the field of oligonucleotide therapeutics, delivery in general remains an important aspect for innovation. Various lipids and lipophilic small molecules have long been conjugated to many oligonucleotides in hopes of creating better, drug-like substances. A few conjugates are beginning to enter clinical development as the understanding grows of how such conjugations change the pharmacology of the conjugate relative to the unmodified oligonucleotide. The delivery of different forms of oligonucleotides, such as antisense oligonucleotides and siRNA, is often a challenging, limiting aspect to this form of therapeutics. Areas Covered Herein, the origins of covalent attachment of lipophilic moieties to oligonucleotides are described as well as listing a few of those lipids commonly used for lipidation. The author also describes the mechanism by which lipidation may enhance delivery and/or exposure of oligonucleotides in vitro and in vivo. Expert opinion The covalent attachment of lipophilic moieties is one means to enhance the delivery and exposure of oligonucleotides. Such methods may also be applicable to other oligonucleotide-based modalities as long as the lipidation does not interfere with some key interaction. Lipidation may also be useful to modulate the cell type-specific delivery within tissues. As the understanding of the effects of such covalent modification grows, more lipidated oligos are soon likely to enter clinical development.

In silico High-Throughput Screening of ZINC Database of Natural Compounds to Identify Novel Histone Deacetylase Inhibitors

Histone deacetylase 8 (HDAC8) is a zinc-binding metalloprotein being involved in transcriptional regulation, cell cycle progression and cellular development. Its overexpression is associated with various pathologies, including childhood neuroblastoma, leukemia, tumor progression and lung cancer. The FDA-approved HDAC inhibitors are not very safe due to various side effects. Therefore, we implemented an in silico high-throughput screening to identify novel HDAC8 inhibitors from the natural compounds database ([Formula: see text]) using computational methods. As a result, some drug-like substances ([Formula: see text]) with nontoxic profiles were identified as hit compounds after using molecular docking, including quantum polarized ligand and induced-fit docking approaches. One hit compound (ZINC02106564) was determined to be highly chemically reactive by using the DFT calculation. Finally, the top binder (ZINC12601598) to HDAC8 together with the reference inhibitor, was studied by 100[Formula: see text]ns molecular dynamics simulations, confirming previously obtained data. Overall, the proposed computational protocol might be prospective at the early stage of rational design for novel and less toxic HDAC8 inhibitors for the treatment of diverse diseases.

Computational Prediction of Inhibitors and Inducers of the Major Isoforms of Cytochrome P450.

Human cytochrome P450 enzymes (CYPs) are heme-containing monooxygenases. This superfamily of drug-metabolizing enzymes is responsible for the metabolism of most drugs and other xenobiotics. The inhibition of CYPs may lead to drug–drug interactions and impair the biotransformation of drugs. CYP inducers may decrease the bioavailability and increase the clearance of drugs. Based on the freely available databases ChEMBL and PubChem, we have collected over 70,000 records containing the structures of inhibitors and inducers together with the IC50 values for the inhibitors of the five major human CYPs: 1A2, 3A4, 2D6, 2C9, and 2C19. Based on the collected data, we developed (Q)SAR models for predicting inhibitors and inducers of these CYPs using GUSAR and PASS software. The developed (Q)SAR models could be applied for assessment of the interaction of novel drug-like substances with the major human CYPs. The created (Q)SAR models demonstrated reasonable accuracy of prediction. They have been implemented in the web application P450-Analyzer that is freely available via the Internet.

Implications for herbal polypharmacy: coumarin-induced hepatotoxicity increased through common herbal phytochemicals astragaloside IV and atractylenolide I

Hepatotoxicity is a well-known adverse effect of many substances, with toxicity often resulting from interactions of drugs with other drug-like substances. With the increased availability of complementary and alternative medicines, including herbal medicines, the likelihood of adverse interactions between drugs and drug-like substances in herbs increases. However, the impact of potential herb-herb interactions is little understood. To assess the potential of two cytochrome P450 enzyme modulating phytochemicals common to many herbal medicines, atractylenolide I (ATR-I) and astragaloside IV (AST-IV), to interact with coumarin, another phytochemical common in many foods, a hepatocyte function model with a liver carcinoma cell line, HepG2, was exposed to these agents. To determine the effects of cytochrome P450 modulation by these phytochemicals certain cells were induced with rifampicin to induce cytochrome P450. Increasing concentrations of ATR-I combined with a fixed, nontoxic concentration of coumarin (200 µM), demonstrated significant additive interactions. 300 µM ATR-I produced a 31% reduction in cell viability (p < 0.01) with coumarin in rifampicin uninduced cells. In rifampicin-induced cells, ATR-I (100–300 µM) produced a significant reduction in cell viability (p < 0.01) with coumarin (200 µM). AST-IV with fixed coumarin (200 µM) showed 27% toxicity at 300 µM AST-IV in rifampicin uninduced cells (p < 0.05) and 30% toxicity in rifampicin induced cells (p < 0.05). However, when fixed coumarin and AST-IV were combined with increasing concentrations of ATR-I no further significant increase in toxicity was observed (p > 0.05). These results demonstrate the potential toxic interactive capabilities of common traditional Chinese herbal medicine phytochemicals and underline the potential importance of coumarin-mediated toxicity.

Predicting the membrane permeability of organic fluorescent probes by the deep neural network based lipophilicity descriptor DeepFl-LogP

Light microscopy has become an indispensable tool for the life sciences, as it enables the rapid acquisition of three-dimensional images from the interior of living cells/tissues. Over the last decades, super-resolution light microscopy techniques have been developed, which allow a resolution up to an order of magnitude higher than that of conventional light microscopy. Those techniques require labelling of cellular structures with fluorescent probes exhibiting specific properties, which are supplied from outside and therefore have to surpass cell membranes. Currently, major efforts are undertaken to develop probes which can surpass cell membranes and exhibit the photophysical properties required for super-resolution imaging. However, the process of probe development is still based on a tedious and time consuming manual screening. An accurate computer based model that enables the prediction of the cell permeability based on their chemical structure would therefore be an invaluable asset for the development of fluorescent probes. Unfortunately, current models, which are based on multiple molecular descriptors, are not well suited for this task as they require high effort in the usage and exhibit moderate accuracy in their prediction. Here, we present a novel fragment based lipophilicity descriptor DeepFL-LogP, which was developed on the basis of a deep neural network. DeepFL-LogP exhibits excellent correlation with the experimental partition coefficient reference data (R2 = 0.892 and MSE = 0.359) of drug-like substances. Further a simple threshold permeability model on the basis of this descriptor allows to categorize the permeability of fluorescent probes with 96% accuracy. This novel descriptor is expected to largely simplify and speed up the development process for novel cell permeable fluorophores.

Computer-aided drug design: from discovery of novel pharmaceutical agents to systems pharmacology

New drug discovery is based on the analysis of public information about the mechanisms of the disease, molecular targets, and ligands, which interaction with the target could lead to the normalization of the pathological process. The available data on diseases, drugs, pharmacological effects, molecular targets, and drug-like substances, taking into account the combinatorics of the associative relations between them, correspond to the Big Data. To analyze such data, the application of computer-aided drug design methods is necessary. An overview of the studies in this area performed by the Laboratory for Structure-Function Based Drug Design of IBMC is presented. We have developed the approaches to identifying promising pharmacological targets, predicting several thousand types of biological activity based on the structural formula of the compound, analyzing protein-ligand interactions based on assessing local similarity of amino acid sequences, identifying likely molecular mechanisms of side effects of drugs, calculating the integral toxicity of drugs taking into account their metabolism, have been developed in the human body, predicting sustainable and sensitive options strains and evaluating the effectiveness of combinations of antiretroviral drugs in patients, taking into account the molecular genetic characteristics of the clinical isolates of HIV-1. Our computer programs are implemented as the web-services freely available on the Internet, which are used by thousands of researchers from many countries of the world to select the most promising substances for the synthesis and determine the priority areas for experimental testing of their biological activity.

A robotic platform for flow synthesis of organic compounds informed by AI planning.

The synthesis of complex organic molecules requires several stages, from ideation to execution, that require time and effort investment from expert chemists. Here, we report a step toward a paradigm of chemical synthesis that relieves chemists from routine tasks, combining artificial intelligence-driven synthesis planning and a robotically controlled experimental platform. Synthetic routes are proposed through generalization of millions of published chemical reactions and validated in silico to maximize their likelihood of success. Additional implementation details are determined by expert chemists and recorded in reusable recipe files, which are executed by a modular continuous-flow platform that is automatically reconfigured by a robotic arm to set up the required unit operations and carry out the reaction. This strategy for computer-augmented chemical synthesis is demonstrated for 15 drug or drug-like substances.

Toward Rational Design of Novel Anti-Cancer Drugs Based on Targeting, Solubility, and Bioavailability Exemplified by 1,3,4-Thiadiazole Derivatives Synthesized Under Solvent-Free Conditions.

The 1,3,4-thiadiazole derivatives (9a–i) were synthesized under solvent free conditions and their chemical composition was confirmed using different spectral tools (IR, Mass, and NMR spectrometry). All the synthesized compounds were screened for their anti-cancer potentiality over human breast carcinoma (MCF-7) and human lung carcinoma (A-549). Most of the tested compounds showed remarkable anti-breast cancer activity. However, compound 4 showed the most anti-lung cancer activity. Then, compounds with cytotoxic activity ≥ 80% over breast and lung cells were subjected to investigate their specificity on human normal skin cell line (BJ-1). Compounds 9b and 9g were chosen owing to their high breast anti-cancer efficacy and their safety, in order to study the possible anti-cancer mode of action. Otherwise, drug delivery provides a means to overcome the low solubility, un-targeted release, and limited bioavailability of the prepared 1,3,4-thiadiazole drug-like substances. Compounds 9b and 9g were chosen to be encapsulated in Na-alginate microspheres. The release profile and mechanism of both compounds were investigated, and the results revealed that the release profiles of both microspheres showed a sustained release, and the release mechanism was controlled by Fickian diffusion. Accordingly, these compounds are promising for their use in chemotherapy for cancer treatment, and their hydrophilicity was improved by polymer encapsulation to become more effective in their pharmaceutical application.

Consensus QSAR modeling of toxicity of pharmaceuticals to different aquatic organisms: Ranking and prioritization of the DrugBank database compounds

In the present work, quantitative structure-activity relationship (QSAR) models have been developed for ecotoxicity of pharmaceuticals on four different aquatic species namely Pseudokirchneriella subcapitata, Daphnia magna, Oncorhynchus mykiss and Pimephales promelas using genetic algorithm (GA) for feature selection followed by Partial Least Squares regression technique according to the Organization for Economic Co-operation and Development (OECD) guidelines. Double cross-validation methodology was employed for selecting suitable models. Only 2D descriptors were used for capturing chemical information and model building, whereas validation of the models was performed by considering various stringent internal and external validation metrics. Interestingly, models could be developed even without using any LogP terms in contrary to the usual dependence of toxicity on lipophilicity. However, the current manuscript proposes highly robust and more predictive models employing computed logP descriptors. The applicability domain study was performed in order to set a predefined chemical zone of applicability for the obtained QSAR models, and the test compounds falling outside the domain were not taken for further analysis while making a prioritized list. An additional comparison was made with ECOSAR, an online expert system for toxicity prediction of organic pollutants, in order to prove predictability of the obtained models. The obtained robust consensus models were utilized to predict the toxicity of a large dataset of approximately 9300 drug-like molecules in order to prioritize the existing drug-like substances in accordance to their acute predicted aquatic toxicities following a scaling technique. Finally, prioritized lists of 500 most toxic chemicals obtained by respective consensus models and those predicted from ECOSAR tool have been reported.

Discriminating Drug-Like Compounds by Partition Trees with Quantum Similarity Indices and Graph Invariants.

The search for new drug candidates in databases is of paramount importance in pharmaceutical chemistry. The selection of molecular subsets is greatly optimized and much more promising when potential drug-like molecules are detected a priori. In this work, about one hundred thousand molecules are ranked following a new methodology: a drug/non-drug classifier constructed by a consensual set of classification trees. The classification trees arise from the stochastic generation of training sets, which in turn are used to estimate probability factors of test molecules to be drug-like compounds. Molecules were represented by Topological Quantum Similarity Indices and their Graph Theoretical counterparts. The contribution of the present paper consists of presenting an effective ranking method able to improve the probability of finding drug-like substances by using these types of molecular descriptors.

Aryl Sulfonamide Inhibitors of Insulin-Regulated Aminopeptidase Enhance Spine Density in Primary Hippocampal Neuron Cultures.

The zinc metallopeptidase insulin regulated aminopeptidase (IRAP), which is highly expressed in the hippocampus and other brain regions associated with cognitive function, has been identified as a high-affinity binding site of the hexapeptide angiotensin IV (Ang IV). This hexapeptide is thought to facilitate learning and memory by binding to the catalytic site of IRAP to inhibit its enzymatic activity. In support of this hypothesis, low molecular weight, nonpeptide specific inhibitors of IRAP have been shown to enhance memory in rodent models. Recently, it was demonstrated that linear and macrocyclic Ang IV-derived peptides can alter the shape and increase the number of dendritic spines in hippocampal cultures, properties associated with enhanced cognitive performance. After screening a library of 10 500 drug-like substances for their ability to inhibit IRAP, we identified a series of low molecular weight aryl sulfonamides, which exhibit no structural similarity to Ang IV, as moderately potent IRAP inhibitors. A structural and biological characterization of three of these aryl sulfonamides was performed. Their binding modes to human IRAP were explored by docking calculations combined with molecular dynamics simulations and binding affinity estimations using the linear interaction energy method. Two alternative binding modes emerged from this analysis, both of which correctly rank the ligands according to their experimental binding affinities for this series of compounds. Finally, we show that two of these drug-like IRAP inhibitors can alter dendritic spine morphology and increase spine density in primary cultures of hippocampal neurons.

Interactions of Scalenohedral Calcite Crystals with Acidic Amino Acid Derivatives of Salicylic Acid

The kinetics of scalenohedral calcite crystal growth in the presence of N-salicyloil-l-aspartic (N-Sal-Asp) and N-salicyloil-l-glutamic acid (N-Sal-Glu), as selected models of anti-inflammatory drug-like substances, were analyzed in order to study the molecular interactions of the selected salicylates with scalenohedral {2 1 4} surfaces. The results revealed that the growth is controlled by surface reactions, proceeding at the spiral step. The presence of the additives caused the inhibition of the growth, wherein the N-Sal-Asp interacted more strongly than N-Sal-Glu. The obtained data were supported with the electrokinetic, spectroscopic, and adsorption measurements. The interactions between the selected additives and the scalenohedral calcite crystals were compared with the rhombohedral modification. The stronger interactions of salicylates with scalenohedral surfaces are explained in terms of their higher reactivity and, most likely, better structural matching of their surface calcium ions with the sali...

Effect of the structure, solid state and lipophilicity on the solubility of novel bicyclic derivatives

Novel bicyclic derivatives have been synthesized. The solubility of drug-like substances in phosphate buffer рН 7.4 has been measured within the range of (9.02·10−5 to 1.05·10−4)mol/l. The relationship between the chemical nature and the structure of the aryl substituents and the solubility parameter was investigated. The fusion temperatures, enthalpies and entropies have been determined experimentally. The influence of thermophysical characteristics and lipophilicity on the solubility was studied using regression analysis. The calculations by the solubility/lipophilicity equation showed an overall improvement of the predictions equal to 0.5 log units. It was concluded that the solvation has a considerable influence on the solubility of the compounds under consideration. It was also determined that the alkyl- and halogen-derivatives solubility values correlate with HYBOT descriptors characterizing the (donor+acceptor) properties of the substances. The thermodynamic parameters of the solubility process were calculated using the temperature dependences. The study also revealed that the solubility of the bicyclic compounds is characterized by high endothermicity of the processes and negative entropies.

Identification of Drug-Induced Myocardial Infarction-Related Protein Targets through the Prediction of Drug–Target Interactions and Analysis of Biological Processes

Drug-induced myocardial infarction (DIMI) is one of the most serious adverse drug effects that often lead to death. Therefore, the identification of DIMI at the early stages of drug development is essential. For this purpose, the in vitro testing and in silico prediction of interactions between drug-like substances and various off-target proteins associated with serious adverse drug reactions are performed. However, only a few DIMI-related protein targets are currently known. We developed a novel in silico approach for the identification of DIMI-related protein targets. This approach is based on the computational prediction of drug-target interaction profiles based on information from approximately 1738 human targets and 828 drugs, including 254 drugs that cause myocardial infarction. Through a statistical analysis, we revealed the 155 most significant associations between protein targets and DIMI. Because not all of the identified associations may lead to DIMI, an analysis of the biological functions of these proteins was performed. The Random Walk with Restart algorithm based on a functional linkage gene network was used to prioritize the revealed DIMI-related protein targets according to the functional similarity between their genes and known genes associated with myocardial infarction. The biological processes associated with the 155 selected protein targets were determined by gene ontology and pathway enrichment analysis. This analysis indicated that most of the processes leading to DIMI are associated with atherosclerosis. The revealed proteins were manually annotated with biological processes using functional and disease-related data extracted from the literature. Finally, the 155 protein targets were classified into three categories of confidence: (1) high (the protein targets are known to be involved in DIMI via atherosclerotic progression; 50 targets), (2) medium (the proteins are known to participate in biological processes related with DIMI; 65 targets), and (3) low (the proteins are indirectly involved in DIMI pathogenesis; 40 proteins).

Lipid nanoparticles: Drug localization is substance-specific and achievable load depends on the size and physical state of the particles

Lipid nanoemulsions and -suspensions are being intensively investigated as carriers for poorly water soluble drugs. The question on where model compounds or probes are localized within the dispersions has been the subject of several studies. However, only little data exists for pharmaceutically relevant molecules in dispersions composed of pharmaceutically relevant excipients. In this work, the localization of drugs and drug-like substances was studied in lipid nanoemulsions and -suspensions. Conclusions about the drug localization were drawn from the relations between lipid mass, specific particle surface area and drug load in the dispersions. Additionally, the achievable drug loads of the liquid and the solid lipid particles were compared. Nanoemulsions and -suspensions comprised trimyristin as lipid matrix and poloxamer 188 as emulsifier and were prepared with different well-defined particle sizes. These pre-formed dispersions were passively loaded with either amphotericin B, curcumin, dibucaine, fenofibrate, mefenamic acid, propofol, or a porphyrin derivative. The physico-chemical properties of the particles were characterized; drug load and lipid content were quantified by UV spectroscopy and high performance liquid chromatography, respectively. For all drugs the passive loading procedure was successful in both emulsions and suspensions. Solid particles accommodate drug molecules preferably at the particle surface. Liquid particles can accommodate drugs at the particle surface as well as in the core; the distribution between the two sites is drug specific. It is also drug specific whether solid or liquid particles yield higher drug loads. As a general rule, smaller particles led to higher drug loads than larger ones. Propofol and the porphyrin derivative displayed eutectic interaction with the lipid and crystal growth after loading, respectively.

An insight into the cytotoxic activity of phytol at in vitro conditions

The cytotoxicity of the diterpene alcohol, phytol, was evaluated by using the MTT assay in vitro against seven tumour cells and one normal cell of human origin. The compound tested induced concentration-dependent cytotoxic response in all cell lines, demonstrating to be most and least effective against the breast adenocarcinoma MCF-7 and the prostate adenocarcinoma PC-3 cells, respectively (IC50 8.79 ± 0.41 μM and 77.85 ± 1.93 μM). The IC50 values towards the other five tumours (HeLa, HT-29, A-549, Hs294T and MDA-MB-231) ranged from 15.51 to 69.67 μM. However, mild toxicity was detected against the foetal lung fibroblast MRC-5 cells at the concentrations used (IC50 124.84 ± 1.59 μM). According to the experimental data obtained, this cost-effective natural product widely present in the biosphere may inspire the development of new drug-like substances with improved cytotoxic activity on breast cancer.