Comprehensive safety evaluation of <i>Withania somnifera</i> (Ashwagandha): an AI-driven meta-analysis and quantitative structure-activity relationship based toxicity assessment
Ronen Y, Ebert C, Tamim-Yecheskel BC, Zev S, Kantor O, Arbel HB
Frontiers in nutrition · 2 citations
Review labels
Neutral facts our review recorded about how this study was done. They describe method, never whether we like the result.
How it was studied
- Design
- Meta-analysis (classified by our AI screen)
- Main outcome
- No health outcome
Who paid for it
- Funding
- Independent funding
- Authors
- At least one author declares a financial tie to industry
Based on full-text disclosure statement.
Publication
- Published
- 2025-11-24 · Front Nutr · vol. 12 · p. 1658265
- Publisher
- Frontiers Media
- Cited
- 3 citations · more than 93% of similar papers · 3.4× the field average
- Impact
- Top 10% most cited in its field
- References
- 58 works
- Access
- Open access (journal) · CC-BY
- Research areas
- Phytochemicals and Medicinal Plants · Drug-Induced Hepatotoxicity and Protection · Computational Drug Discovery Methods
- Keywords
- Withania somnifera, Risk assessment, Preference, Complement (music), Liver toxicity
6 authors
From IL
- Yotam RonenAlzheimer's Association of Israel
- Coralie Ebert · correspondingAlzheimer's Association of Israel
- Bat-Chen Tamim-YecheskelAlzheimer's Association of Israel
- Shani ZevAlzheimer's Association of Israel
- Ophir KantorAlzheimer's Association of Israel
- Hilla Ben-Hamo Arbel · correspondingAlzheimer's Association of Israel
Abstract
Objective
This study evaluates the safety profiles of Withania somnifera (Ashwagandha), an adaptogenic herb prevalent in Ayurvedic medicine, focusing on liver and reproductive toxicity. Utilizing advanced AI methodologies, we conducted a comprehensive meta-data analysis to assess the safety of the plant's root and non-root parts, comparing Ashwagandha's safety to other herbal supplements.
Methods
We employed natural language processing (NLP) to systematically review existing literature and utilized quantitative structure-activity relationship (QSAR) models to predict liver and reproductive toxicity at the molecular level. Special attention was given to withanolides, the bioactive compounds in Ashwagandha, due to conflicting safety information. Additionally, we reviewed case studies reporting liver toxicity, noting that many involved supplements containing both leaves and roots, complicating the identification of the toxicity source.
Results
Our analysis indicated that Ashwagandha root exhibits a superior safety profile compared to non-root parts, particularly concerning liver and reproductive toxicity. When compared to a broad set of other herbal supplements, Ashwagandha root was found to have a better safety profile than most, making it a first-choice ingredient for safe and effective use in supplements. While non-root parts of Ashwagandha showed higher toxicity potential than the root, their safety profile was still comparable to other edible plants and herbal supplements.
Conclusion
This study suggests that the root of Withania somnifera (Ashwagandha) demonstrates a favorable safety profile, particularly concerning liver and reproductive toxicity, when compared to other herbal supplements. Our findings support the traditional preference for root-based formulations and highlight the importance of distinguishing between plant parts in safety assessments. While these results strengthen the evidence supporting the safe use of Ashwagandha root, further experimental and clinical validation would be valuable to confirm these AI-driven predictions and literature-based findings. The study also illustrates how artificial intelligence approaches can complement traditional toxicological evaluations and enhance safety assessment frameworks in the herbal supplement industry.
Abstract via Europe PMC. Copyright remains with the authors or publisher (CC BY).
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