In Silico Forensic Toxicology: Is It Feasible?

Area of Science:

• Computational toxicology
• Forensic science
• Medicolegal investigations

Background:

• In silico forensic toxicology utilizes computational models like QSARs, molecular docking, and ADMET predictions.
• These methods simulate metabolic pathways, offering insights into substance metabolism and reducing the need for extensive laboratory testing.

Purpose of the Study:

• To evaluate the cost-effectiveness of in silico methods in forensic toxicology.
• To assess their utility in investigating unknown substances and guiding laboratory analyses.
• To determine the viability of these computational strategies in high-throughput forensic settings.

Main Methods:

• Quantitative Structure-Activity Relationships (QSARs)
• Molecular docking
• Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) predictions
• Break-even analysis and Bland-Altman plots for financial assessment
• Review of recent machine learning applications in toxicology

Main Results:

• In silico strategies are cost-efficient for forensic labs performing over 625 analyses annually.
• Machine learning significantly improves predictive accuracy for toxicity endpoints.
• These methods are crucial for novel psychoactive substances (NPSs) and unclear toxicological findings.

Conclusions:

• In silico methods present a viable, cost-effective alternative to conventional techniques in high-throughput forensic toxicology.
• AI-powered techniques are expected to streamline investigations and improve accuracy in future forensic evaluations.
• These computational tools support expert testimonies and strengthen evidence claims in legal contexts.