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. 2025 Mar 25:14:102008.
doi: 10.1016/j.toxrep.2025.102008. eCollection 2025 Jun.

Risk-based in silico mutagenic assessment of benzodiazepine impurities using three QSAR tools

Srinivas Birudukota  1   2 , Bhaskar Mangalapu  1   3 , Ramesha Andagar Ramakrishna  3 , Swagata Halder  1
Affiliations

Risk-based in silico mutagenic assessment of benzodiazepine impurities using three QSAR tools

Srinivas Birudukota et al. Toxicol Rep. .

Abstract

Benzodiazepines, widely prescribed psychoactive drugs, may contain DNA-reactive (mutagenic) impurities formed during synthesis, posing significant health risks. Owing to animal testing requirements, traditional in vitro and in vivo methods for assessing mutagenicity are time-consuming, costly, and ethically challenging. Computational approaches, particularly in silico (Q)SAR models, provide an efficient alternative for predicting toxicity based on chemical structure. This study evaluated the mutagenic potential of 88 benzodiazepine-related impurities using three freely accessible (Q)SAR tools: TOXTREE (Ames Test Alert by ISS), Toxicity Estimation Software Tool (TEST) with nearest neighbour and consensus models, and VEGA, a QSAR tool that integrates multiple mutagenicity prediction models, including the CAESAR Ames Mutagenicity Model. The tools were validated using a dataset of 99 chemicals with known Ames test results. TOXTREE exhibited the highest sensitivity (80.7 %) and accuracy (72.2 %) for predicting mutagenicity, whereas VEGA and TEST provided balanced accuracy (66.2 % and 66.7 %, respectively) and high specificity (74.5 % and 76.6 %, respectively). The risk assessment categorised 21 impurities as high risk, 11 as moderate-high risk, 28 as moderate-low risk, 22 as low risk, and 6 as equivocal, with expert review finalising classifications. The findings emphasise the integration of multiple (Q)SAR tools for early mutagenicity detection, regulatory compliance, and reduced reliance on animal testing. Further refinement of predictive models and additional computational approaches are recommended to enhance the accuracy of the risk assessment.

Keywords: (Q)SAR tools; Benzodiazepines; In silico evaluation; Mutagenic impurities; Regulatory implications.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

None
Graphical abstract
Fig. 1
Fig. 1
Flowchart of In-Silico Evaluation and Assessment of Mutagenic Impurities in Benzodiazepine Molecules.
Fig. 2
Fig. 2
Chemical Structures of Common Benzodiazepine Active Pharmaceutical Ingredients (API's).
Fig. 3
Fig. 3
Distribution of High, Moderate, and Low-Risk Levels in Various Benzodiazepine APIs.
Fig. 4
Fig. 4
High-Risk Predicted Values of TEST QSAR Tool for Mutagenicity Endpoint: Consensus Comparison with Nearest Neighbor Method.
See this image and copyright information in PMC

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