Correction to Designing QSARs for Parameters of High Throughput Toxicokinetic Models Using Open-Source Descriptors

Area of Science:

• Environmental Chemistry
• Toxicology
• Computational Chemistry

Context:

• Limited experimental data exists for intrinsic metabolic clearance (Clint) and unbound fraction (fup), crucial for toxicokinetic (TK) modeling.
• High-throughput screening requires reliable in silico prediction methods for chemical safety assessments.
• US-regulated chemicals, including pharmaceuticals, pesticides, and industrial agents, necessitate robust risk evaluation tools.

Purpose:

• To develop open-source quantitative structure-activity relationship (QSAR) models for predicting Clint and fup.
• To provide reliable in silico toxicokinetic parameters for diverse chemical datasets.
• To demonstrate the utility of these QSAR models in a risk-based prioritization framework.

Summary:

• Open-source QSAR models were created for intrinsic metabolic clearance (Clint) and unbound fraction (fup).
• These models offer reliable in silico predictions for pharmaceuticals, pesticides, and industrial chemicals.
• Model predictions were integrated into a Bioactivity: Exposure Ratio (BER) risk assessment, showing similar results to in vitro data.

Impact:

• The developed QSAR models can effectively prioritize chemical risks, especially when in vitro toxicokinetic data is unavailable.
• A case study using the Tox21 screening library demonstrated high concordance (91.30%) between in silico and in vitro parameters for risk classification.
• These tools support regulatory decision-making by enabling efficient screening of large chemical libraries.