Experimental Data Based Machine Learning Classification Models with Predictive Ability to Select in Vitro Active Antiviral and Non-Toxic Essential Oils

Screening of the activity of sixty essential oils against plasmodium early mosquito stages in vitro and machine learning analysis reveals new putative inhibitors of malaria parasites

Malaria, an infectious disease with a tremendous impact on human health is caused by Plasmodium parasites, and transmitted by Anopheles mosquitoes. New approaches to control the disease involve transmission blocking strategies aiming to target the parasite in the mosquito. Here, we investigated the putative inhibitory activity of essential oils and their components on the early mosquito stages of the parasite. We employed an in vitro assay of gametocyte-to-ookinete development of the rodent model parasite Plasmodium berghei combined with high content screening. 60 essential oils with known composition were tested. The results revealed that fifteen EOs had inhibitory activity. Furthermore, a machine learning approach was used to identify the putative inhibitory components. Five of the most important chemical components indicated by the machine learning-based models were actually confirmed by the experimental approach. This combined approach was used for the first time to identify the potential transmission blocking activity of essential oils and single components at the zygote and ookinete stages.

In Vitro Antioxidant and In Vivo Antigenotoxic Features of a Series of 61 Essential Oils and Quantitative Composition-Activity Relationships Modeled through Machine Learning Algorithms

The antioxidant activity of essential oils (EOs) is an important and frequently studied property, yet it is not sufficiently understood in terms of the contribution of EOs mixtures' constituents and biological properties. In this study, a series of 61 commercial EOs were first evaluated as antioxidants in vitro, following as closely as possible the cellular pathways of reactive oxygen species (ROS) generation. Hence, EOs were assessed for the ability either to chelate metal ions, thus interfering with ROS generation within the respiratory chain, or to neutralize 2,2-diphenyl-1-picrylhydrazyl (DPPH•) and lipid peroxide radicals (LOO•), thereby halting lipid peroxidation, as well as to neutralize 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid cation radicals (ABTS•+) and hydroxyl radicals (OH•), thereby preventing the ROS species from damaging DNA nucleotides. Showing noteworthy potencies to neutralize all of the radicals at the ng/mL level, the active EOs were also characterized as protectors of DNA double strands from damage induced by peroxyl radicals (ROO•), emerging from 2,2'-azobis-2-methyl-propanimidamide (AAPH) as a source, and OH•, indicating some genome protectivity and antigenotoxicity effectiveness in vitro. The chemical compositions of the EOs associated with the obtained activities were then analyzed by means of machine learning (ML) classification algorithms to generate quantitative composition-activity relationships (QCARs) models (models published in the AI4EssOil database available online). The QCARs models enabled us to highlight the key features (EOSs' chemical compounds) for exerting the redox potencies and to define the partial dependencies of the features, viz. percentages in the mixture required to exert a given potency. The ML-based models explained either the positive or negative contribution of the most important chemical components: limonene, linalool, carvacrol, eucalyptol, α-pinene, thymol, caryophyllene, p-cymene, eugenol, and chrysanthone. Finally, the most potent EOs in vitro, Ylang-ylang (Cananga odorata (Lam.)) and Ceylon cinnamon peel (Cinnamomum verum J. Presl), were promptly administered in vivo to evaluate the rescue ability against redox damage caused by CCl4, thereby verifying their antioxidant and antigenotoxic properties either in the liver or in the kidney.

Effect of Different Soil Treatments on Production and Chemical Composition of Essential Oils Extracted from Foeniculum vulgare Mill., Origanum vulgare L. and Thymus vulgaris L

The aim of the study was to investigate how essential oil production and associated chemical composition and related biological activity could be influenced by different cultivation treatments and distillation methods. Foeniculum vulgare Mill. (fennel), Origanum vulgare L. (oregano) and Thymus vulgaris L. (thyme) were cultivated in absence of any fertilizer (control) and in presence of three different fertilizers: a chemical one with augmented mineral phosphorus and potassium, a second added with hydrolyzed organic substance and mineral phosphorus and potassium (organic-mineral) and a third one treated with a high content of organic nitrogen of protein origin (organic). The plants were subjected to steam distillation using two modalities, recycled and continuous, to obtain 32 essential oil samples. Chemical composition analysis was performed using gas chromatography-mass spectrometry; in vitro antimicrobial activity was evaluated using a broth microdilution method. In general, the recycled distillation method appeared to have a slightly higher yield than the continuous method. The "mineral" and "organic-mineral" treatments resulted in a higher yield compared to the "organic" or "control" treatments, and this was particularly evident in the recycled method. The "control" plants had a lower yield of essential oils. Anethole (13.9-59.5%) and estragole (13.4-52.2%) were the main constituents of the fennel oils; p-cymene and its derivatives carvacrol and thymol were the main constituents of the oregano and thyme samples. The antimicrobial activity of the thyme oils on Staphylococcus aureus ranged from 0.31 to 0.16% (v/v); a lower effect of the oregano samples and no activity of the fennel samples were observed. The essential oils failed to inhibit the growth of Pseudomonas aeruginosa strains.

Essential Oils from Mediterranean Plants Inhibit In Vitro Monocyte Adhesion to Endothelial Cells from Umbilical Cords of Females with Gestational Diabetes Mellitus

Essential oils (EOs) are mixtures of volatile compounds belonging to several chemical classes derived from aromatic plants using different distillation techniques. Recent studies suggest that the consumption of Mediterranean plants, such as anise and laurel, contributes to improving the lipid and glycemic profile of patients with diabetes mellitus (DM). Hence, the aim of the present study was to investigate the potential anti-inflammatory effect of anise and laurel EOs (AEO and LEO) on endothelial cells isolated from the umbilical cord vein of females with gestational diabetes mellitus (GDM-HUVEC), which is a suitable in vitro model to reproduce the pro-inflammatory phenotype of a diabetic endothelium. For this purpose, the Gas Chromatographic/Mass Spectrometric (GC-MS) chemical profiles of AEO and LEO were first analyzed. Thus, GDM-HUVEC and related controls (C-HUVEC) were pre-treated for 24 h with AEO and LEO at 0.025% v/v, a concentration chosen among others (cell viability by MTT assay), and then stimulated with TNF-α (1 ng/mL). From the GC-MS analysis, trans-anethole (88.5%) and 1,8-cineole (53.9%) resulted as the major components of AEO and LEO, respectively. The results in C- and GDM-HUVEC showed that the treatment with both EOs significantly reduced: (i) the adhesion of the U937 monocyte to HUVEC; (ii) vascular adhesion molecule-1 (VCAM-1) protein and gene expression; (iii) Nuclear Factor-kappa B (NF-κB) p65 nuclear translocation. Taken together, these data suggest the anti-inflammatory efficacy of AEO and LEO in our in vitro model and lay the groundwork for further preclinical and clinical studies to study their potential use as supplements to mitigate vascular endothelial dysfunction associated with DM.

In Vivo Antiphytoviral and Aphid Repellency Activity of Essential Oils and Hydrosols from Mentha suaveolens and Foeniculum vulgare to Control Zucchini Yellow Mosaic Virus and Its Vector Aphis gossypii

In recent years, natural compounds have gained attention in many fields due to their wide-range biological activity. In particular, essential oils and their associated hydrosols are being screened to control plant pests, exerting antiviral, antimycotic and antiparasitic actions. They are more quickly and cheaply produced and are generally considered safer for the environment and non-target organisms than conventional pesticides. In this study, we report the evaluation of the biological activity of two essential oils and their corresponding hydrosols obtained from Mentha suaveolens and Foeniculum vulgare in the control of zucchini yellow mosaic virus and its vector, Aphis gossypii, in Cucurbita pepo plants. The control of the virus was ascertained with treatments applied either concurrently with or after virus infection; choice tests were performed to verify repellency activity against the aphid vector. The results indicated that treatments could decrease virus titer as measured using real-time RT-PCR, while the experiments on the vector showed that the compounds effectively repelled aphids. The extracts were also chemically characterized using gas chromatography-mass spectrometry. Mentha suaveolens and Foeniculum vulgare hydrosol extracts mainly comprised fenchone and decanenitrile, respectively, while essential oils analysis returned a more complex composition, as expected.

Pinus mugo Essential Oil Impairs STAT3 Activation through Oxidative Stress and Induces Apoptosis in Prostate Cancer Cells

Essential oils (EOs) and their components have been reported to possess anticancer properties and to increase the sensitivity of cancer cells to chemotherapy. The aim of this work was to select EOs able to downregulate STAT3 signaling using Western blot and RT-PCR analyses. The molecular mechanism of anti-STAT3 activity was evaluated through spectrophotometric and fluorometric analyses, and the biological effect of STAT3 inhibition was analyzed by flow cytometry and wound healing assay. Herein, Pinus mugo EO (PMEO) is identified as an inhibitor of constitutive STAT3 phosphorylation in human prostate cancer cells, DU145. The down-modulation of the STAT3 signaling cascade decreased the expression of anti-proliferative as well as anti-apoptotic genes and proteins, leading to the inhibition of cell migration and apoptotic cell death. PMEO treatment induced a rapid drop in glutathione (GSH) levels and an increase in reactive oxygen species (ROS) concentration, resulting in mild oxidative stress. Pretreatment of cells with N-acetyl-cysteine (NAC), a cell-permeable ROS scavenger, reverted the inhibitory action of PMEO on STAT3 phosphorylation. Moreover, combination therapy revealed that PMEO treatment displayed synergism with cisplatin in inducing the cytotoxic effect. Overall, our data highlight the importance of STAT3 signaling in PMEO cytotoxic activity, as well as the possibility of developing adjuvant therapy or sensitizing cancer cells to conventional chemotherapy.

In vivo Antiphytoviral Activity of Essential Oils and Hydrosols From Origanum vulgare, Thymus vulgaris, and Rosmarinus officinalis to Control Zucchini Yellow Mosaic Virus and Tomato Leaf Curl New Delhi Virus in Cucurbita pepo L

In the last decades, the interest in biological activity of natural compounds has been growing. In plant protection, essential oils have been reported to exhibit antiviral, antimycotic, and antiparasitic activities, and are regarded as promising for the formulation of safe antimicrobial agents. Attention has also been focused on hydrosols, the by-products of hydro-distillation of essential oils. Their production is easy, fast, and cheap, and they seem to arise less concern for human health than essential oils. Plant viruses represent a major concern for agricultural crops since no treatment compound is available for virus control. This work was aimed at evaluating the antiphytoviral effectiveness of treatments with three essential oils and corresponding hydrosols extracted from Origanum vulgare, Thymus vulgaris, and Rosmarinus officinalis on Cucurbita pepo plants infected by zucchini yellow mosaic virus or tomato leaf curl New Delhi virus. Treatments were applied either concurrently or after virus inoculation to ascertain an inhibition or curative activity, respectively. Symptoms were observed and samplings were performed weekly. Virus titer and expression levels of phenylalanine ammonia lyase gene (PAL) were measured on treated and untreated infected plants by real-time PCR. PAL gene plays an important role in plant defense response as it is involved in tolerance/resistance to phytopathogens. Results indicated that treatments were effective against tomato leaf curl New Delhi virus whether applied simultaneously with the inoculation or after. A major inhibition was observed with O. vulgare essential oil and hydrosol, resulting in 10^–4-fold decrease of virus titer 3 weeks after treatment. Curative activity gave maximum results with all three essential oils and T. vulgaris and R. officinalis hydrosols, recording from 10^–2-fold decrease to virus not detected 4 weeks after treatment. An induction of PAL gene expression was recorded at 12 d.p.i. and then was restored to the levels of untreated control. This allows to hypothesize an early plant defense response to virus infection, possibly boosted by treatments. Plant extracts’ composition was characterized by gas chromatography-mass spectrometry. Phenols were largely main components of O. vulgare and T. vulgaris extracts (carvacrol and thymol, respectively), while extracts from R. officinalis were based on monoterpene hydrocarbons (essential oil) and oxygenated monoterpenes (hydrosol).

Machine Learning Analysis of Essential Oils from Cuban Plants: Potential Activity against Protozoa Parasites

Essential oils (EOs) are a mixture of chemical compounds with a long history of use in food, cosmetics, perfumes, agricultural and pharmaceuticals industries. The main object of this study was to find chemical patterns between 45 EOs and antiprotozoal activity (antiplasmodial, antileishmanial and antitrypanosomal), using different machine learning algorithms. In the analyses, 45 samples of EOs were included, using unsupervised Self-Organizing Maps (SOM) and supervised Random Forest (RF) methodologies. In the generated map, the hit rate was higher than 70% and the results demonstrate that it is possible find chemical patterns using a supervised and unsupervised machine learning approach. A total of 20 compounds were identified (19 are terpenes and one sulfur-containing compound), which was compared with literature reports. These models can be used to investigate and screen for bioactivity of EOs that have antiprotozoal activity more effectively and with less time and financial cost.

Effective Search of Triterpenes with Anti-HSV-1 Activity Using a Classification Model by Logistic Regression

Natural products are an excellent source of skeletons for medicinal seeds. Triterpenes and saponins are representative natural products that exhibit anti-herpes simplex virus type 1 (HSV-1) activity. However, there has been a lack of comprehensive information on the anti-HSV-1 activity of triterpenes. Therefore, expanding information on the anti-HSV-1 activity of triterpenes and improving the efficiency of their exploration are urgently required. To improve the efficiency of the development of anti-HSV-1 active compounds, we constructed a predictive model for the anti-HSV-1 activity of triterpenes by using the information obtained from previous studies using machine learning methods. In this study, we constructed a binary classification model (i.e., active or inactive) using a logistic regression algorithm. As a result of the evaluation of predictive model, the accuracy for the test data is 0.79, and the area under the curve (AUC) is 0.86. Additionally, to enrich the information on the anti-HSV-1 activity of triterpenes, a plaque reduction assay was performed on 20 triterpenes. As a result, chikusetsusaponin IVa (11: IC₅₀ = 13.06 μM) was found to have potent anti-HSV-1 with three potentially anti-HSV-1 active triterpenes. The assay result was further used for external validation of predictive model. The prediction of the test compounds in the activity test showed a high accuracy (0.83) and AUC (0.81). We also found that this predictive model was found to be able to successfully narrow down the active compounds. This study provides more information on the anti-HSV-1 activity of triterpenes. Moreover, the predictive model can improve the efficiency of the development of active triterpenes by integrating many previous studies to clarify potential relationships.

Machine Learning Data Augmentation as a Tool to Enhance Quantitative Composition-Activity Relationships of Complex Mixtures. A New Application to Dissect the Role of Main Chemical Components in Bioactive Essential Oils

Scientific investigation on essential oils composition and the related biological profile are continuously growing. Nevertheless, only a few studies have been performed on the relationships between chemical composition and biological data. Herein, the investigation of 61 assayed essential oils is reported focusing on their inhibition activity against Microsporum spp. including development of machine learning models with the aim of highlining the possible chemical components mainly related to the inhibitory potency. The application of machine learning and deep learning techniques for predictive and descriptive purposes have been applied successfully to many fields. Quantitative composition-activity relationships machine learning-based models were developed for the 61 essential oils tested as Microsporum spp. growth modulators. The models were built with in-house python scripts implementing data augmentation with the purpose of having a smoother flow between essential oils' chemical compositions and biological data. High statistical coefficient values (Accuracy, Matthews correlation coefficient and F1 score) were obtained and model inspection permitted to detect possible specific roles related to some components of essential oils' constituents. Robust machine learning models are far more useful tools to reveal data augmentation in comparison with raw data derived models. To the best of the authors knowledge this is the first report using data augmentation to highlight the role of complex mixture components, in particular a first application of these data will be for the development of ingredients in the dermo-cosmetic field investigating microbial species considering the urge for the use of natural preserving and acting antimicrobial agents.

Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade

Introduction: Drug design and discovery of new antivirals will always be extremely important in medicinal chemistry, taking into account known and new viral diseases that are yet to come. Although machine learning (ML) have shown to improve predictions on the biological potential of chemicals and accelerate the discovery of drugs over the past decade, new methods and their combinations have improved their performance and established promising perspectives regarding ML in the search for new antivirals.Areas covered: The authors consider some interesting areas that deal with different ML techniques applied to antivirals. Recent innovative studies on ML and antivirals were selected and analyzed in detail. Also, the authors provide a brief look at the past to the present to detect advances and bottlenecks in the area.Expert opinion: From classical ML techniques, it was possible to boost the searches for antivirals. However, from the emergence of new algorithms and the improvement in old approaches, promising results will be achieved every day, as we have observed in the case of SARS-CoV-2. Recent experience has shown that it is possible to use ML to discover new antiviral candidates from virtual screening and drug repurposing.

Progress of Plant Medicine Derived Extracts and Alkaloids on Modulating Viral Infections and Inflammation

Viral infectious diseases are serious threats to human health in both developing and developed countries. Although there is the continued development of new drugs from synthetic sources as antiviral agents, medicinal plants continue to provide the basic raw materials for some of the most important antiviral drugs. Alkaloids are a class of pharmacologically active plant compounds that are usually alkaline in nature. In this review, we tried to summarize recent progress in herb-based antiviral research, the advantages of using active plant compounds as antiviral agents, and the inflammatory responses initiated by alkaloids, based on the literature from 2009 to 2019, for the treatment of conditions, including influenza, human immunodeficiency virus, herpes simplex virus, hepatitis, and coxsackievirus infections. Articles are retrieved from PubMed, Google Scholar, and Web of Science using relevant keywords. In particular, the alkaloids from medicinal plants responsible for the molecular mechanisms of anti-inflammatory actions are identified and discussed. This review can provide a theoretical basis and approaches for using various alkaloids as antiviral treatments. More research is needed to develop alkaloidal compounds as antiviral therapeutic agents and potential regulators of the anti-inflammatory response.

Data handling and data analysis in metabolomic studies of essential oils using GC-MS

Gas chromatography electron impact ionization mass spectrometry (GC-EI-MS) has been, and remains, the most widely applied analytical technique for metabolomic studies of essential oils. GC-EI-MS analysis of complex samples, such as essential oils, creates a large volume of data. Creating predictive models for such samples and observing patterns within complex data sets presents a significant challenge and requires application of robust data handling and data analysis methods. Accordingly, a wide variety of software and algorithms has been investigated and developed for this purpose over the years. This review provides an overview and summary of that research effort, and attempts to classify and compare different data handling and data analysis procedures that have been reported to-date in the metabolomic study of essential oils using GC-EI-MS.

Essential Oils Biofilm Modulation Activity, Chemical and Machine Learning Analysis. Application on Staphylococcus aureus Isolates from Cystic Fibrosis Patients

Bacterial biofilm plays a pivotal role in chronic Staphylococcus aureus (S. aureus) infection and its inhibition may represent an important strategy to develop novel therapeutic agents. The scientific community is continuously searching for natural and “green alternatives” to chemotherapeutic drugs, including essential oils (EOs), assuming the latter not able to select resistant strains, likely due to their multicomponent nature and, hence, multitarget action. Here it is reported the biofilm production modulation exerted by 61 EOs, also investigated for their antibacterial activity on S. aureus strains, including reference and cystic fibrosis patients’ isolated strains. The EOs biofilm modulation was assessed by Christensen method on five S. aureus strains. Chemical composition, investigated by GC/MS analysis, of the tested EOs allowed a correlation between biofilm modulation potency and putative active components by means of machine learning algorithms application. Some EOs inhibited biofilm growth at 1.00% concentration, although lower concentrations revealed different biological profile. Experimental data led to select antibiofilm EOs based on their ability to inhibit S. aureus biofilm growth, which were characterized for their ability to alter the biofilm organization by means of SEM studies.

Essential Oils and Their Main Chemical Components: The Past 20 Years of Preclinical Studies in Melanoma

Simple Summary

In the last years, targeted therapy and immunotherapy modified the landscape for metastatic melanoma treatment. These therapeutic approaches led to an impressive improvement in patients overall survival. Unfortunately, the emergence of drug resistance and side effects occurring during therapy strongly limit the long-term efficacy of such treatments. Several preclinical studies demonstrate the efficacy of essential oils as antitumoral agents, and clinical trials support their use to reduce side effects emerging during therapy. In this review we have summarized studies describing the molecular mechanism through which essential oils induce in vitro and in vivo cell death in melanoma models. We also pointed to clinical trials investigating the use of essential oils in reducing the side effects experienced by cancer patients or those undergoing anticancer therapy. From this review emerged that further studies are necessary to validate the effectiveness of essential oils for the management of melanoma.

Abstract

The last two decades have seen the development of effective therapies, which have saved the lives of a large number of melanoma patients. However, therapeutic options are still limited for patients without BRAF mutations or in relapse from current treatments, and severe side effects often occur during therapy. Thus, additional insights to improve treatment efficacy with the aim to decrease the likelihood of chemoresistance, as well as reducing side effects of current therapies, are required. Natural products offer great opportunities for the discovery of antineoplastic drugs, and still represent a useful source of novel molecules. Among them, essential oils, representing the volatile fraction of aromatic plants, are always being actively investigated by several research groups and show promising biological activities for their use as complementary or alternative medicine for several diseases, including cancer. In this review, we focused on studies reporting the mechanism through which essential oils exert antitumor action in preclinical wild type or mutant BRAF melanoma models. We also discussed the latest use of essential oils in improving cancer patients’ quality of life. As evidenced by the many studies listed in this review, through their effect on apoptosis and tumor progression-associated properties, essential oils can therefore be considered as potential natural pharmaceutical resources for cancer management.