As multi-omics technologies advance, they present transformative opportunities to refine personalized toxicology. This Research Topic calls for Forensic-Clinical Omics approaches that integrate genomics, metabolomics and proteomics and exposure biomarkers to bridge the gap between clinical toxicology and forensic investigations. This abstract proposes a comprehensive framework that addresses environmental-exposure driven interactions to create more accurate, individualized toxicological risk assessments. While multi-omics holds great promise, there remains a significant gap in standardized protocols for sampling, sample preparation, instrumental analysis, and data analysis in both forensic and clinical settings. Case studies demonstrating how AI-driven data integration can enhance predictive modelling for toxicant-induced diseases and exposure biomarkers are particularly welcomed in their application to clinical and forensic toxicology applications. Additionally, articles discussing ethical considerations around the use of omics data in clinical and forensic cases are anticipated. This work lays the foundation for a more holistic, personalized approach to public health outcomes and precision medicine in clinical and forensic toxicology. In essence submissions are welcomed regarding creating an integrated system where omics data flows seamlessly between forensic and clinical contexts, offering a more personalized and comprehensive understanding of toxicological exposures and their effects. Areas of interest include, but are not limited to, the following:

Integration of Multi-Omics in Forensic and Clinical Toxicology

- Genomics, proteomics, metabolomics, and exposure biomarkers
- Strategies for bridging clinical and forensic toxicology through omics

Personalized Toxicological Risk Assessment

- Environmental and exposure-driven interactions
- Individualized risk profiling using omics approaches

Standardization Challenges in Multi-Omics Workflows

- Sampling and sample preparation protocols
- Instrumental and data analysis techniques across forensic and clinical contexts

AI-Driven Data Integration and Predictive Modelling

- Case studies where artificial intelligence enhances toxicology
- Use of machine learning in predicting toxicant-induced disease
- Application of machine learning and AI for multi-omics data integration in exposure assessment
- Case studies using predictive modelling to identify biomarkers of toxicant exposure or disease onset
- Development of computational frameworks for individualized toxicological risk profiling
- Data fusion strategies for combining mass spectrometry, sequencing, and clinical/forensic metadata

Ethical and Legal Considerations in Omics Applications

- Data governance, consent, and privacy issues
- Implications of using omics evidence in legal or clinical settings

Translational Omics: From Bench to Courtroom or Clinic

- Applied research that demonstrates practical use of omics findings
- Seamless data flow between forensic and clinical environments

Systems Toxicology and Public Health Implications

- Multi-omics in precision medicine and public health
- Holistic approaches to understanding toxicological outcomes

We welcome a range of manuscript types—including original research articles, reviews, mini-reviews, and perspective pieces—that offer new insights into “Toward a Forensic-Clinical Omics Continuum: Advancing Personalized Exposure Assessment and Toxicology with Standardized Multi-Omics Integration”

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Toward a Forensic-Clinical Omics Continuum: Advancing Personalized Exposure Assessment and Toxicology with Standardized Multi-Omics Integration