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

Ragno, Alessio; Baldisserotto, Anna; Antonini, Lorenzo; Sabatino, Manuela; Sapienza, Filippo; Baldini, Erika; Buzzi, Raissa; Vertuani, Silvia; Manfredini, Stefano

doi:10.3390/molecules26206279

Cited by 4 publications

(4 citation statements)

References 21 publications

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“…Different cut-off values related to the percentage of biofilm reduction/augmentation were used to develop ad hoc models to inspect strong, moderate, and weak biofilm inhibition and biofilm enhancement. In a departure from previous applications, a data augmentation (DA) approach was also implemented herein [ 27 ]. The EO dataset was augmented by means of composition random perturbation, while keeping the same bioactivity for each augmented related EO.…”

Section: Methodsmentioning

confidence: 99%

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Essential Oils Biofilm Modulation Activity and Machine Learning Analysis on Pseudomonas aeruginosa Isolates from Cystic Fibrosis Patients

et al. 2022

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The opportunistic pathogen Pseudomonas aeruginosa is often involved in airway infections of cystic fibrosis (CF) patients. It persists in the hostile CF lung environment, inducing chronic infections due to the production of several virulence factors. In this regard, the ability to form a biofilm plays a pivotal role in CF airway colonization by P. aeruginosa. Bacterial virulence mitigation and bacterial cell adhesion hampering and/or biofilm reduced formation could represent a major target for the development of new therapeutic treatments for infection control. Essential oils (EOs) are being considered as a potential alternative in clinical settings for the prevention, treatment, and control of infections sustained by microbial biofilms. EOs are complex mixtures of different classes of organic compounds, usually used for the treatment of upper respiratory tract infections in traditional medicine. Recently, a wide series of EOs were investigated for their ability to modulate biofilm production by different pathogens comprising S. aureus, S. epidermidis, and P. aeruginosa strains. Machine learning (ML) algorithms were applied to develop classification models in order to suggest a possible antibiofilm action for each chemical component of the studied EOs. In the present study, we assessed the biofilm growth modulation exerted by 61 commercial EOs on a selected number of P. aeruginosa strains isolated from CF patients. Furthermore, ML has been used to shed light on the EO chemical components likely responsible for the positive or negative modulation of bacterial biofilm formation.

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Section: Methodsmentioning

confidence: 99%

“…Authors are grateful to Alessio Ragno for having shared his data augmentation python code as applied in his recent report (reference [ 27 ]).…”

Section: Acknowledgmentsmentioning

confidence: 99%

Essential Oils Biofilm Modulation Activity and Machine Learning Analysis on Pseudomonas aeruginosa Isolates from Cystic Fibrosis Patients

et al. 2022

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“…Linear retention indices (LRIs) of each compound were also calculated using a mixture of aliphatic hydrocarbons. Knowing the chemical composition of the 61 EOs and their associated properties (Supplementary Material Table S2) [97] made them eligible as a dataset to develop QCARs models by means of ML algorithms [96][97][98][99][100][101][102]; therefore, they were also used herein to generate a list of ML models for antioxidant activities. For experimental purposes, the EOs were dissolved in dimethyl-sulfoxide (DMSO) at 50 mg/mL to obtain complete solubilization and further diluted in the medium for in vitro and in vivo experiments, always resulting in a DMSO concentration that does not affect experimental protocols. )…”

Section: Essential Oilsmentioning

confidence: 99%

“…Hence, a list of quantitative composition-activity relationships (QCARs) models was generated to shed light on the chemical components mainly responsible for the redox properties and to relate their contributions in terms of the positive or negative modulation of metal ion chelation/free radical neutralization. Previous applications of ML to EOs have enabled the correlation of their chemical composition to antimicrobial [96][97][98][99], antiviral [100], and anticancer properties [101,102].…”

Section: Introductionmentioning

confidence: 99%

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

Mladenović,

Astolfi,

Tomašević

et al. 2023

Antioxidants

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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.

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A Machine Learning Approach to Evaluating the Impact of Natural Oils on Alzheimer’s Disease Progression

Amawi,

Al-Hussaeni,

Keeriath

et al. 2024

Applied Sciences

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Alzheimer’s Disease is among the major chronic neurodegenerative diseases that affects more than 50 million people worldwide. This disease irreversibly destroys memory, cognition, and the overall daily activities which occur mainly among the elderly. Few drugs are approved for Alzheimer’s Disease management despite its high prevalence. To date, the available drugs in the market cannot reverse the damage of neurons caused by the disease leading to the exacerbation of symptoms and possibly death. Medicinal plants are considered a rich source of chemical constituents and have been contributing to modern drug discovery in many therapeutic areas including cancer, infectious, cardiovascular, neurodegenerative and Central Nervous System (CNS) diseases. Moreover, essential oils that are extracted from plant organs have been reported for a wide array of biological activities, and their roles as antioxidants, antiaging, cytotoxic, anti-inflammatory, antimicrobial, and enzyme inhibitory activities. This article highlights the promising potential of plants’ essential oils in the discovery of novel therapeutic options for Alzheimer’s Disease and halting its progression. In this article, 428 compounds were reported from the essential oils isolated from 21 plants. A comparative study is carried out by employing a variety of machine learning techniques, validation, and evaluation metrics, to predict essential oils’ efficacy against Alzheimer’s Disease progression. Extensive experiments on essential oil data suggest that a prediction accuracy of up to 82% can be achieved given the proper data preprocessing, feature selection, and model configuration steps. This study underscores the potential of integrating machine learning with natural product research to prioritize and expedite the identification of bioactive essential oils that could lead to effective therapeutic interventions for Alzheimer’s Disease. Further exploration and optimization of machine learning techniques could provide a robust platform for drug discovery and development, facilitating faster and more efficient screening of potential treatments.

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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

Cited by 4 publications

References 21 publications

Essential Oils Biofilm Modulation Activity and Machine Learning Analysis on Pseudomonas aeruginosa Isolates from Cystic Fibrosis Patients

Essential Oils Biofilm Modulation Activity and Machine Learning Analysis on Pseudomonas aeruginosa Isolates from Cystic Fibrosis Patients

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

A Machine Learning Approach to Evaluating the Impact of Natural Oils on Alzheimer’s Disease Progression

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