Essential Oils as Antimicrobials against Acinetobacter baumannii: Experimental and Literature Data to Definite Predictive Quantitative Composition-Activity Relationship Models Using Machine Learning Algorithms.

Abstract

Essential oils (EOs) exhibit a broad spectrum of biological activities; however, their clinical application is hindered by challenges, such as variability in chemical composition and chemical/physical instability. A critical limitation is the lack of chemical consistency across EO samples, which impedes standardization. Despite this, evidence suggests that EOs with differing chemical profiles often display similar (micro)biological activities, raising the possibility of standardizing EOs based on their biological effects rather than their chemical composition. This study explored the relationship between EO chemical composition and antibacterial activity against carbapenem-resistant Acinetobacter baumannii. A dataset comprising 82 EOs with known minimal inhibitory concentration values was compiled using both experimental results and literature data sourced from the AI4EssOil database (https://www.ai4essoil.com). Machine learning classification algorithms including Support Vector Machines, Random Forest, Gradient Boosting, Decision Trees, and K-Nearest Neighbors were employed to generate quantitative composition-activity relationship models. Model performance was assessed using internal and external prediction accuracy metrics with the Matthews correlation coefficient as the primary evaluation metrics. Features importance analysis, based on the Skater methodology, identified key chemical components influencing EO activity. The single chemical components limonene, eucalyptol, alpha-pinene, linalool, beta-caryophyllene, nerol, beta-pinene, neral, and carvacrol were highlighted as critical to biological efficacy. The predictive capacity of the ML models was validated against a test set of freshly extracted and chemically characterized EOs. The models demonstrated a 91% prediction accuracy for new EO samples, and a strong correlation was observed between predicted features importance and experimental inhibitory values for six selected pure compounds (limonene, eucalyptol, alpha-pinene, linalool, carvacrol, and thymol). Additionally, the machine learning approach was extended to cytotoxicity data from 3T3-Swiss fibroblasts for 61 EOs. The analysis revealed the potential to design EOs with both high antibacterial activity and low cytotoxicity through blending or selective enrichment with identified key components. These findings pave the way for biologically standardized EOs, enabling their rational design and optimization for clinical applications.