Machine learning in forensic toxicology: Concepts, applications and challenges in bioanalysis, ADME, and...

Area of Science:

• Forensic Toxicology
• Analytical Chemistry
• Data Science

Background:

• Forensic toxicology analyzes drugs and chemicals in biological samples using chromatography and mass spectrometry.
• The field faces challenges from emerging new psychoactive substances (NPS) and complex data generation.

Purpose of the Study:

• To review the applications of machine learning (ML) in forensic toxicology.
• To highlight the role of ML in bioanalysis, metabolomics, and toxicodynamics.
• To discuss the challenges and requirements for effective ML implementation.

Main Methods:

• Review of existing literature on machine learning applications in forensic toxicology.
• Discussion of analytical techniques like chromatography and mass spectrometry.
• Exploration of data challenges including dataset size and quality.

Main Results:

• Machine learning algorithms are increasingly used to address analytical challenges in forensic toxicology.
• ML aids in analyzing vast datasets generated by complex analytical methods and novel markers.
• Key limitations include the need for large, high-quality datasets and interdisciplinary collaboration.

Conclusions:

• Machine learning offers significant potential to advance forensic toxicology.
• Overcoming data limitations and fostering interdisciplinary collaboration are crucial for successful ML implementation.
• ML integration requires expertise in analytical chemistry, biochemistry, pharmacology, and data science.