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

Molecules ◽  
2020 ◽  
Vol 25 (10) ◽  
pp. 2452 ◽  
Author(s):  
Manuela Sabatino ◽  
Marco Fabiani ◽  
Mijat Božović ◽  
Stefania Garzoli ◽  
Lorenzo Antonini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Essential Oil ◽  
Essential Oils ◽  
Predictive Ability ◽  
Partial Least Square ◽  
Least Square ◽  
Classification Models ◽  
Machine Learning Classification ◽  
Hsv 1

In the last decade essential oils have attracted scientists with a constant increase rate of more than 7% as witnessed by almost 5000 articles. Among the prominent studies essential oils are investigated as antibacterial agents alone or in combination with known drugs. Minor studies involved essential oil inspection as potential anticancer and antiviral natural remedies. In line with the authors previous reports the investigation of an in-house library of extracted essential oils as a potential blocker of HSV-1 infection is reported herein. A subset of essential oils was experimentally tested in an in vitro model of HSV-1 infection and the determined IC50s and CC50s values were used in conjunction with the results obtained by gas-chromatography/mass spectrometry chemical analysis to derive machine learning based classification models trained with the partial least square discriminant analysis algorithm. The internally validated models were thus applied on untested essential oils to assess their effective predictive ability in selecting both active and low toxic samples. Five essential oils were selected among a list of 52 and readily assayed for IC50 and CC50 determination. Interestingly, four out of the five selected samples, compared with the potencies of the training set, returned to be highly active and endowed with low toxicity. In particular, sample CJM1 from Calaminta nepeta was the most potent tested essential oil with the highest selectivity index (IC50 = 0.063 mg/mL, SI > 47.5). In conclusion, it was herein demonstrated how multidisciplinary applications involving machine learning could represent a valuable tool in predicting the bioactivity of complex mixtures and in the near future to enable the design of blended essential oil possibly endowed with higher potency and lower toxicity.

scholarly journals Screening for Antiviral Activities of Isolated Compounds from Essential Oils

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
Akram Astani ◽  
Jürgen Reichling ◽  
Paul Schnitzler
Keyword(s):  
Essential Oil ◽  
Antiviral Activity ◽  
Essential Oils ◽  
High Selectivity ◽  
Selectivity Index ◽  
Viral Infectivity ◽  
Dependent Manner ◽  
Star Anise ◽  
Hsv 1

Essential oil of star anise as well as phenylpropanoids and sesquiterpenes, for example, trans-anethole, eugenol,β-eudesmol, farnesol,β-caryophyllene andβ-caryophyllene oxide, which are present in many essential oils, were examined for their antiviral activity against herpes simplex virus type 1 (HSV-1)in vitro. Antiviral activity was analyzed by plaque reduction assays and mode of antiviral action was determined by addition of the drugs to uninfected cells, to the virus prior to infection or to herpesvirus-infected cells. Star anise oil reduced viral infectivity by >99%, phenylpropanoids inhibited HSV infectivity by about 60–80% and sesquiterpenes suppressed herpes virus infection by 40–98%. Both, star anise essential oil and all isolated compounds exhibited anti-HSV-1 activity by direct inactivation of free virus particles in viral suspension assays. All tested drugs interacted in a dose-dependent manner with herpesvirus particles, thereby inactivating viral infectivity. Star anise oil, rich in trans-anethole, revealed a high selectivity index of 160 against HSV, whereas among the isolated compounds onlyβ-caryophyllene displayed a high selectivity index of 140. The presence ofβ-caryophyllene in many essential oils might contribute strongly to their antiviral ability. These results indicate that phenylpropanoids and sesquiterpenes present in essential oils contribute to their antiviral activity against HSV.

scholarly journals Virucidal Activity and Chemical Composition of Essential Oils from Aromatic Plants of Central West Argentina

2010 ◽  
Vol 5 (8) ◽  
pp. 1934578X1000500 ◽  
Author(s):  
Cybele C. García ◽  
Eliana G. Acosta ◽  
Ana C. Carro ◽  
María C. Fernández Belmonte ◽  
Renata Bomben ◽  
...  
Keyword(s):  
Essential Oil ◽  
Essential Oils ◽  
Virus Type ◽  
Inhibitory Activity ◽  
Gas Chromatography Mass Spectrometry ◽  
Virucidal Activity ◽  
Aromatic Plants ◽  
Simplex Virus ◽  
Hsv 1

The essential oils of seven aromatic plants from central west argentina were isolated by steam distillation and analyzed by a gas chromatography mass spectrometry technique. The oils were screened for cytotoxicity and In Vitro inhibitory activity against herpes simplex virus type 1 (HSV-1), dengue virus type 2 (DENV-2) and Junin virus (JUNV). The oils showed a variable virucidal action according to the virus. JUNV was the least susceptible virus in comparison with HSV-1 and DENV-2. The better relationship between cytotoxicity and inhibitory activity was observed for the essential oil of Lantana grisebachiii (Seckt.) var. grisebachii against DENV-2 and HSV-1 with IC50 (inhibitory concentration 50%) values of 21.1 and 26.1 ppm, respectively. This effect was specific since the selectivity indices (ratio cytotoxicity/virucidal activity) were > 23.7 and > 19.1 for DENV-2 and HSV-1, respectively. Furthermore, the oil from L. grisebachii was also an effective inhibitor of HSV-2 and acyclovir resistant variants of herpes virus. This study demonstrates the effective and selective inhibitory activity of the essential oil from Lantana grisebachii against HSV and DENV by direct virus inactivation.

scholarly journals Discrimination of the Essential Oils Obtained from Four Apiaceae Species Using Multivariate Analysis Based on the Chemical Compositions and Their Biological Activity

Plants ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 1529
Author(s):  
Dilafruz N. Jamalova ◽  
Haidy A. Gad ◽  
Davlat K. Akramov ◽  
Komiljon S. Tojibaev ◽  
Nawal M. Al Musayeib ◽  
...  
Keyword(s):  
Essential Oils ◽  
Reducing Power ◽  
Maximum Activity ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Diffusion Method ◽  
Cytotoxic Activities ◽  
Chemical Compositions

The chemical composition of the essential oils obtained from the aerial parts of four Apiaceae species, namely Elaeosticta allioides (EA), E. polycarpa (EP), Ferula clematidifolia (FC), and Hyalolaena intermedia (HI), were determined using gas chromatography. Altogether, 100 volatile metabolites representing 78.97, 81.03, 85.78, and 84.49% of the total components present in EA, EP, FC, and HI oils, respectively, were reported. allo-Ocimene (14.55%) was the major component in FC, followed by D-limonene (9.42%). However, in EA, germacrene D (16.09%) was present in a high amount, while heptanal (36.89%) was the predominant compound in HI. The gas chromatographic data were subjected to principal component analysis (PCA) to explore the correlations between these species. Fortunately, the PCA score plot could differentiate between the species and correlate Ferula to Elaeosticta species. Additionally, the antioxidant activity was evaluated in vitro using the 2,2-diphenyl-1-picryl-hydrazyl-hydrate (DPPH), 2,2-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS), and the ferric reducing power (FRAP) assays. In addition, the antimicrobial activity using the agar diffusion method was assessed, and the minimum inhibitory concentrations (MICs) were determined. Furthermore, the cell viability MTT assay was performed to evaluate the cytotoxicity of the essential oils against hepatic (HepG-2) and cervical (HeLa) cancer cell lines. In the DPPH assay, FC exhibited the maximum activity against all the antioxidant assays with IC50 values of 19.8 and 23.0 μg/mL for the DPPH and ABTS assays, respectively. Ferula showed superior antimicrobial and cytotoxic activities as well. Finally, a partial least square regression model was constructed to predict the antioxidant capacity by utilizing the metabolite profiling data. The model showed excellent predictive ability by applying the ABTS assay.

scholarly journals Machine Learning and Machine Vision Accelerate 3D Printed Orodispersible Film Development

Pharmaceutics ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2187
Author(s):  
Colm S. O’Reilly ◽  
Moe Elbadawi ◽  
Neel Desai ◽  
Simon Gaisford ◽  
Abdul W. Basit ◽  
...  
Keyword(s):  
Machine Learning ◽  
3D Printing ◽  
Machine Vision ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Linear Discriminant ◽  
On Demand

Orodispersible films (ODFs) are an attractive delivery system for a myriad of clinical applications and possess both large economical and clinical rewards. However, the manufacturing of ODFs does not adhere to contemporary paradigms of personalised, on-demand medicine, nor sustainable manufacturing. To address these shortcomings, both three-dimensional (3D) printing and machine learning (ML) were employed to provide on-demand manufacturing and quality control checks of ODFs. Direct ink writing (DIW) was able to fabricate complex ODF shapes, with thicknesses of less than 100 µm. ML algorithms were explored to classify the ODFs according to their active ingredient, by using their near-infrared (NIR) spectrums. A supervised model of linear discriminant analysis was found to provide 100% accuracy in classifying ODFs. A subsequent partial least square algorithm was applied to verify the dose, where a coefficient of determination of 0.96, 0.99 and 0.98 was obtained for ODFs of paracetamol, caffeine, and theophylline, respectively. Therefore, it was concluded that the combination of 3D printing, NIR and ML can result in a rapid production and verification of ODFs. Additionally, a machine vision tool was used to automate the in vitro testing. These collective digital technologies demonstrate the potential to automate the ODF workflow.

Hybrid Design of Isonicotinic Acid Hydrazide Derivatives: Machine Learning Studies, Synthesis and Biological Evaluation of their Antituberculosis Activity

2020 ◽  
Vol 17 (3) ◽  
pp. 365-375
Author(s):  
Vasyl Kovalishyn ◽  
Diana Hodyna ◽  
Vitaliy O. Sinenko ◽  
Volodymyr Blagodatny ◽  
Ivan Semenyuta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Isonicotinic Acid ◽  
Acid Hydrazide ◽  
Predictive Ability ◽  
Antitubercular Activity ◽  
Biological Evaluation ◽  
Starting Point ◽  
Binary Classifiers ◽  
Synthesis And Biological Evaluation

Background: Tuberculosis (TB) is an infection disease caused by Mycobacterium tuberculosis (Mtb) bacteria. One of the main causes of mortality from TB is the problem of Mtb resistance to known drugs. Objective: The goal of this work is to identify potent small molecule anti-TB agents by machine learning, synthesis and biological evaluation. Methods: The On-line Chemical Database and Modeling Environment (OCHEM) was used to build predictive machine learning models. Seven compounds were synthesized and tested in vitro for their antitubercular activity against H37Rv and resistant Mtb strains. Results: A set of predictive models was built with OCHEM based on a set of previously synthesized isoniazid (INH) derivatives containing a thiazole core and tested against Mtb. The predictive ability of the models was tested by a 5-fold cross-validation, and resulted in balanced accuracies (BA) of 61–78% for the binary classifiers. Test set validation showed that the models could be instrumental in predicting anti- TB activity with a reasonable accuracy (with BA = 67–79 %) within the applicability domain. Seven designed compounds were synthesized and demonstrated activity against both the H37Rv and multidrugresistant (MDR) Mtb strains resistant to rifampicin and isoniazid. According to the acute toxicity evaluation in Daphnia magna neonates, six compounds were classified as moderately toxic (LD50 in the range of 10−100 mg/L) and one as practically harmless (LD50 in the range of 100−1000 mg/L). Conclusion: The newly identified compounds may represent a starting point for further development of therapies against Mtb. The developed models are available online at OCHEM http://ochem.eu/article/11 1066 and can be used to virtually screen for potential compounds with anti-TB activity.

scholarly journals Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

2021 ◽  
Vol 13 (4) ◽  
pp. 641
Author(s):  
Gopal Ramdas Mahajan ◽  
Bappa Das ◽  
Dayesh Murgaokar ◽  
Ittai Herrmann ◽  
Katja Berger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Spectral Indices ◽  
Learning Models ◽  
Leaf Nutrients ◽  
Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

scholarly journals Suggestions on the Contribution of Methyl Eugenol and Eugenol to Bay Laurel (Laurus nobilis L.) Essential Oil Preservative Activity through Radical Scavenging

Molecules ◽  
2021 ◽  
Vol 26 (8) ◽  
pp. 2342
Author(s):  
Nikolaos Nenadis ◽  
Maria Papapostolou ◽  
Maria Z. Tsimidou
Keyword(s):  
Essential Oil ◽  
Radical Scavenging Activity ◽  
Pearson Correlation ◽  
Radical Scavenging ◽  
Methyl Eugenol ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Positive Contribution ◽  
Scavenging Activity

The present study examined the radical scavenging potential of the two benzene derivatives found in the bay laurel essential oil (EO), namely methyl eugenol (MEug) and eugenol (Eug), theoretically and experimentally to make suggestions on their contribution to the EO preservative activity through such a mechanism. Calculation of appropriate molecular indices widely used to characterize chain-breaking antioxidants was carried out in the gas and liquid phases (n-hexane, n-octanol, methanol, water). Experimental evidence was based on the DPPH• scavenging assay applied to pure compounds and a set of bay laurel EOs chemically characterized with GC-MS/FID. Theoretical calculations suggested that the preservative properties of both compounds could be exerted through a radical scavenging mechanism via hydrogen atom donation. Eug was predicted to be of superior efficiency in line with experimental findings. Pearson correlation and partial least square regression analyses of the EO antioxidant activity values vs. % composition of individual volatiles indicated the positive contribution of both compounds to the radical scavenging activity of bay laurel EOs. Eug, despite its low content in bay laurel EOs, was found to influence the most the radical scavenging activity of the latter.

scholarly journals Prediction of activity and selectivity profiles of human Carbonic Anhydrase inhibitors using machine learning classification models

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Annachiara Tinivella ◽  
Luca Pinzi ◽  
Giulio Rastelli
Keyword(s):  
Machine Learning ◽  
Carbonic Anhydrase ◽  
A Priori ◽  
Selective Inhibition ◽  
Great Promise ◽  
Classification Models ◽  
Machine Learning Classification ◽  
Central Interest ◽  
Human Carbonic Anhydrase ◽  
In Silico Models

AbstractThe development of selective inhibitors of the clinically relevant human Carbonic Anhydrase (hCA) isoforms IX and XII has become a major topic in drug research, due to their deregulation in several types of cancer. Indeed, the selective inhibition of these two isoforms, especially with respect to the homeostatic isoform II, holds great promise to develop anticancer drugs with limited side effects. Therefore, the development of in silico models able to predict the activity and selectivity against the desired isoform(s) is of central interest. In this work, we have developed a series of machine learning classification models, trained on high confidence data extracted from ChEMBL, able to predict the activity and selectivity profiles of ligands for human Carbonic Anhydrase isoforms II, IX and XII. The training datasets were built with a procedure that made use of flexible bioactivity thresholds to obtain well-balanced active and inactive classes. We used multiple algorithms and sampling sizes to finally select activity models able to classify active or inactive molecules with excellent performances. Remarkably, the results herein reported turned out to be better than those obtained by models built with the classic approach of selecting an a priori activity threshold. The sequential application of such validated models enables virtual screening to be performed in a fast and more reliable way to predict the activity and selectivity profiles against the investigated isoforms.

scholarly journals Global metabolomic and lipidomic analysis reveals the potential mechanisms of hemolysis effect of Ophiopogonin D and Ophiopogonin D' in vivo

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Huan-Hua Xu ◽  
Zhen-Hong Jiang ◽  
Cong-Shu Huang ◽  
Yu-Ting Sun ◽  
Long-Long Xu ◽  
...  
Keyword(s):  
Chemical Properties ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Practical Significance ◽  
Plasma Metabolites ◽  
Active Components ◽  
The Difference

Abstract Background OPD and OPD' are the two main active components of Ophiopogon japonicas in Shenmai injection (SMI). Being isomers of each other, they are supposed to have similar pharmacological activities, but the actual situation is complicated. The difference of hemolytic behavior between OPD and OPD' in vivo and in vitro was discovered and reported by our group for the first time. In vitro, only OPD' showed hemolysis reaction, while in vivo, both OPD and OPD' caused hemolysis. In vitro, the primary cause of hemolysis has been confirmed to be related to the difference between physical and chemical properties of OPD and OPD'. In vivo, although there is a possible explanation for this phenomenon, the one is that OPD is bio-transformed into OPD' or its analogues in vivo, the other one is that both OPD and OPD' were metabolized into more activated forms for hemolysis. However, the mechanism of hemolysis in vivo is still unclear, especially the existing literature are still difficult to explain why OPD shows the inconsistent hemolysis behavior in vivo and in vitro. Therefore, the study of hemolysis of OPD and OPD' in vivo is of great practical significance in response to the increase of adverse events of SMI. Methods Aiming at the hemolysis in vivo, this manuscript adopted untargeted metabolomics and lipidomics technology to preliminarily explore the changes of plasma metabolites and lipids of OPD- and OPD'-treated rats. Metabolomics and lipidomics analyses were performed on ultra-high performance liquid chromatography (UPLC) system tandem with different mass spectrometers (MS) and different columns respectively. Multivariate statistical approaches such as principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (OPLS-DA) were applied to screen the differential metabolites and lipids. Results Both OPD and OPD' groups experienced hemolysis, Changes in endogenous differential metabolites and differential lipids, enrichment of differential metabolic pathways, and correlation analysis of differential metabolites and lipids all indicated that the causes of hemolysis by OPD and OPD' were closely related to the interference of phospholipid metabolism. Conclusions This study provided a comprehensive description of metabolomics and lipidomics changes between OPD- and OPD'-treated rats, it would add to the knowledge base of the field, which also provided scientific guidance for the subsequent mechanism research. However, the underlying mechanism require further research.

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