In Silico Forensic Toxicology: Is It Feasible?

Abstract

In silico forensic toxicology refers to the emerging application of computational models based on Quantitative Structure-Activity Relationships (QSARs), molecular docking, and predictions regarding Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) as used to predict the toxicological behavior of various substances, particularly in medico-legal contexts. These computational models replicate metabolic pathways, providing insights into the metabolism of substances in the human body, while the results of this approach effectively reflect the necessary compounds, reducing the need for direct laboratory work. This review aims to evaluate whether forensic settings and in silico methods present a cost-effective strategy for investigating unknown substances, aiding in toxicological interpretations, and steering laboratory process analyses. Additionally, financial considerations, such as break-even analysis and Bland-Altman plots, were conducted, indicating that forensic labs conducting over 625 analyses each year can achieve cost efficiency by integrating in silico strategies, thus making them a viable alternative to conventional methods in high-throughput settings. Recent studies have emphasized how machine learning enhances predictive accuracy, thereby boosting forensic toxicology's capacity to effectively evaluate toxicity endpoints. In silico methods are essential for cases involving novel psychoactive substances (NPSs) or unclear toxicological findings. They are also useful as a supporting method in legal contexts, as they uphold expert testimonies and reinforce evidence claims. The future of forensic toxicology is likely to see the increased implementation of AI-powered techniques, streamlining toxicological investigations and enhancing overall accuracy in forensic evaluations.