Shimadzu’s Smart Forensic Database Enables Fast, Accurate Analysis of TOXICOLOGICAL SUBSTANCES in Whole Blood

Significance of the Topic

Forensic analysis of toxicological substances in biological matrices is critical to legal investigations and clinical toxicology. Laboratories require methods that offer high sensitivity, selectivity, and throughput to handle diverse sample loads and ensure reliable results.

Study Objectives and Overview

This work introduces Shimadzu’s Smart Forensic Database designed to automate the creation of multiple reaction monitoring (MRM) methods for GC-MS/MS toxicological analysis. The database aims to streamline method development, improve accuracy, and expand compound coverage.

Methodology and Instrumentation

The Smart Forensic Database includes 486 compounds with over 1,200 MRM transitions, collision energies, confirmation ion ratios, and retention indices. Automated Adjustment of Retention Time (AART) and Smart MRM software set optimal dwell, event, and loop times.
Sample preparation employs liquid-liquid extraction via Extrelut® columns, with separate acidic and basic fractions, followed by elution in chloroform:isopropanol and reconstitution.
Analysis uses a GCMS-TQ8040 system equipped with an Rxi-5Sil MS column (30 m×0.25 mm, 0.25 μm). Key parameters:

• Injection: splitless, 1 μL at 260–280 °C
• Oven program: 60 °C→320 °C
• Detection: simultaneous scan (m/z 43–600) and MRM

Main Results and Discussion

Application of the Smart Forensic Database enabled clear detection of chlorpromazine and triazolam at 50 ng/mL in whole blood, demonstrating high sensitivity and selectivity. The combined scan/MRM approach facilitated identification of pentedrone among 1,400 screened compounds. Automated method creation reduced development time and minimized background interferences.

Benefits and Practical Applications

• Rapid, hands-free MRM method generation
• High-throughput screening of 486 targeted and 1,400 exploratory compounds
• Enhanced confidence in qualitative and quantitative results
• Reduced data analysis times due to dual-stage mass separation

Future Trends and Potential Applications

Advancements may include expansion of compound libraries, integration of machine learning for retention time prediction, automation of sample prep workflows, and adaptation to high-resolution mass spectrometry. These developments will further improve forensic throughput and data quality.

Conclusion

Shimadzu’s Smart Forensic Database and Smart MRM software offer a streamlined solution for GC-MS/MS toxicology, delivering fast, accurate, and reproducible analyses. The combined use with broader toxicological databases supports comprehensive screening and reliable quantitation in forensic laboratories.