Machine learning-guided dose-time optimization and experimental validation enhance coumarin therapeutics in oncology
4 affiliations
- 1Department of Computer Science, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad
- 2Department of Biology, Faculty of Science, Ferdowsi University of Mashhad
- 3Department of Applied Mathematics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad
- 4Biotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences
Abstract
Coumarins are a group of naturally occurring compounds that have garnered significant interest for their potential anticancer properties, characterized by targeted effects and low toxicity. Despite this promise, the therapeutic application of certain coumarins is limited by insufficient data and inconsistencies in experimental results. To tackle these challenges, this study aims to explore the use of regression-based machine learning (ML) models to infer dose–exposure time windows corresponding to approximately 50% cell viability for natural coumarins esculetin, umbelliprenin, auraptene and galbanic acid across multiple human cancer cell lines. Extensive in vitro data from 64 peer-reviewed studies were extracted based on inclusion criteria, focusing on dose, time, and cell viability for four coumarins. Eight highly utilized ML regression algorithms, including Gradient Boosting Regressor (GBR), Histogram-based Gradient Boosting Regressor (HGBR), Random Forest Regressor (RFR), Extreme Gradient Boosting Regressor (XGBR), Support Vector Regressor (SVR), and regularized linear models Lasso, Ridge, and Elastic Net were employed to analyze the dataset and elucidate dose-response relationships. To validate ML predictions, in vitro experiments on a human colon carcinoma cell line, specifically not included in the original dataset, were conducted. Auraptene and galbanic acid were extracted from Ferula szowitsiana using thin layer chromatography, and LoVo cells were treated for 24, 48, and 72 h and evaluated for viability and apoptosis. The data analysis demonstrated that esculetin, umbelliprenin, and auraptene consistently exhibited robust dose-response dynamics, with notable time-dependent enhancements in their anticancer activity. Galbanic acid, however, showed cytotoxicity primarily at higher concentrations and with prolonged exposure. Interestingly, the predictions made by the gradient boosting ensemble models regarding the effects of auraptene and galbanic acid were validated through in vitro experiments on LoVo cells. Predictions from the remaining non-ensemble models, although in some cases consistent with the experimental outcomes, were not considered reliable for validation due to their comparatively low R2 scores. This research establishes grounds and proof of principle for understanding the efficacy of coumarins utilizing ML, highlighting their potential in contemporary oncology. Experimental analysis showing that the ensemble model predictions align with in vitro results provides initial support for the potential of ML to help reduce experimental complexity, though further validation across additional cell lines and coumarins is warranted.
Related
- Optimal capacitor value calculation for self excited induction generators using hybrid grey wolf differential evolution optimization with experimental validation
- Integrative Bioinformatics, Experimental Validation, and Interpretable Machine Learning Reveal Oxyresveratrol‐Mediated Protection Against Cadmium‐Induced Lung Adenocarcinoma‐Related Transcriptional Dysregulation
- Machine-learning-assisted target prioritization and experimental validation of the neoeriocitrin–mTOR axis in impaired osteogenic differentiation
- Exploring the potential mechanism of artemisinin against gastric cancer based on network pharmacology, machine learning and experimental validation
- HMOX2-driven crosstalk between vascular aging and heart failure: A multimodal bioinformatics and explainable machine learning approach with experimental validation
BibTeX
@article{nasiri2026machine,
title = {{Machine learning-guided dose-time optimization and experimental validation enhance coumarin therapeutics in oncology}},
author = {Jalal A. Nasiri and Mohammad Sadra Moazzen and Sara Seyedshazileh and Arash Pourhasani and Mehrdad Iranshahi and Fatemeh B. Rassouli},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-62953-x},
url = {https://doi.org/10.1038/s41598-026-62953-x},
note = {CC BY},
}
RIS
TY - JOUR
TI - Machine learning-guided dose-time optimization and experimental validation enhance coumarin therapeutics in oncology
AU - Jalal A. Nasiri
AU - Mohammad Sadra Moazzen
AU - Sara Seyedshazileh
AU - Arash Pourhasani
AU - Mehrdad Iranshahi
AU - Fatemeh B. Rassouli
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-62953-x
UR - https://doi.org/10.1038/s41598-026-62953-x
AB - Coumarins are a group of naturally occurring compounds that have garnered significant interest for their potential anticancer properties, characterized by targeted effects and low toxicity. Despite this promise, the therapeutic application of certain coumarins is limited by insufficient data and inconsistencies in experimental results. To tackle these challenges, this study aims to explore the use of regression-based machine learning (ML) models to infer dose–exposure time windows corresponding to approximately 50% cell viability for natural coumarins esculetin, umbelliprenin, auraptene and galbanic acid across multiple human cancer cell lines. Extensive in vitro data from 64 peer-reviewed studies were extracted based on inclusion criteria, focusing on dose, time, and cell viability for four coumarins. Eight highly utilized ML regression algorithms, including Gradient Boosting Regressor (GBR), Histogram-based Gradient Boosting Regressor (HGBR), Random Forest Regressor (RFR), Extreme Gradient Boosting Regressor (XGBR), Support Vector Regressor (SVR), and regularized linear models Lasso, Ridge, and Elastic Net were employed to analyze the dataset and elucidate dose-response relationships. To validate ML predictions, in vitro experiments on a human colon carcinoma cell line, specifically not included in the original dataset, were conducted. Auraptene and galbanic acid were extracted from Ferula szowitsiana using thin layer chromatography, and LoVo cells were treated for 24, 48, and 72 h and evaluated for viability and apoptosis. The data analysis demonstrated that esculetin, umbelliprenin, and auraptene consistently exhibited robust dose-response dynamics, with notable time-dependent enhancements in their anticancer activity. Galbanic acid, however, showed cytotoxicity primarily at higher concentrations and with prolonged exposure. Interestingly, the predictions made by the gradient boosting ensemble models regarding the effects of auraptene and galbanic acid were validated through in vitro experiments on LoVo cells. Predictions from the remaining non-ensemble models, although in some cases consistent with the experimental outcomes, were not considered reliable for validation due to their comparatively low R2 scores. This research establishes grounds and proof of principle for understanding the efficacy of coumarins utilizing ML, highlighting their potential in contemporary oncology. Experimental analysis showing that the ensemble model predictions align with in vitro results provides initial support for the potential of ML to help reduce experimental complexity, though further validation across additional cell lines and coumarins is warranted.
SN - 2045-2322
ER -