Development of QSAR and Read-across prediction tools for toxicokinetic, repeated dose toxicity, PBPK and AOP endpoints of industrial chemicals

P.I.: Prof. Kunal Roy,

Drug Theoretics and Cheminformatics Laboratory,

Jadavpur University, Kolkata-700032

Funded by (2023-2026)

GPC Regulatory India Pvt Ltd, Nagpur, India

Objectives of the Project

1.  To develop and validate read-across and/or QSAR prediction tools for the toxicokinetic and repeated dose toxicity endpoints of industrial chemicals

2.  To develop and validate read-across and/or QSAR prediction tools for various AOP and PBPK endpoints of industrial chemicals

3.  To virtually screen databases of industrial chemicals for their ranking and prioritization based on the developed models for toxicokinetic/repeated toxicity/AOP/PBPK endpoints

Work Element 1

OECD TG 305 (Bio-Concentration in Fish)

Pore, S., Pelloux, A., Chatterjee, M., Banerjee, A. and Roy, K., 2024. Machine learning-based q-RASAR predictions of the bioconcentration factor of organic molecules estimated following the organisation for economic co-operation and development guideline 305. Journal of Hazardous Materials, 479, p.135725. https://doi.org/10.1016/j.jhazmat.2024.135725

Developed Tool: BCF Predictor v1.0 (This tool computes the repeated dose toxicity from the required input descriptors of the query compounds)

 (https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home/bcf-predictor).

Work element 2

OECD TG 210 (Fish, Early-life Stage Toxicity Test)

Pore S, Pelloux A, Bergqvist A, Chatterjee M, Roy K, Intelligent Consensus-Based Predictions of Early Life Stage Toxicity in Fish Tested in Compliance with OECD Test Guideline 210. Aquatic Toxicology, 279, 2025, 107216, https://doi.org/10.1016/j.aquatox.2024.107216

Work element 3

OECD TGs 421, 422 (Reproduction/Developmental Toxicity Screening Test)

Chatterjee, M., Pore, S., Szepesi, Z. and Roy, K., 2025. Read-across-driven binary classification for the developmental and reproductive toxicity of organic compounds tested according to the OECD test guidelines 421/422. SAR and QSAR in Environmental Research, 36(3), pp.247-270. https://doi.org/10.1080/1062936X.2025.2483765 

Work element 4

OECD TGs 407, 408, and 422 (Repeated Dose Toxicity in Rodents)

Pore, S., Szepesi, Z. and Roy, K., 2025 (Communicated). Machine Learning-Based Quantitative Structure Activity Relationship Modeling of Repeated Dose Toxicity: A Data-Driven Approach Following OECD Test Guidelines 407, 408, and 422 Supported by Experimental Validation.

Developed Tool: RDTox (This tool computes the repeated dose toxicity from the required input descriptors of the query compounds)

(https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home/rdtox).   

Acknowledgement: Funding by Global Product Compliance Group 

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Last updated on January 08, 2026