AI in Preclinical Research
AI and ML are increasingly applied to nonclinical datasets, including images, behavioral videos, multi-omics, and PK/PD time series. Regulators now discuss the use of AI across the medicinal product lifecycle, including nonclinical domains, and call for risk-based, human-centered, and transparent approaches. (U.S. Food and Drug Administration, European Medicines Agency (EMA))
Below are preclinical applications where evidence and methods are most mature today. We will dig into specifics in the role-guides.
Toxicologic pathology and digital histology
Deep learning methods perform cell and tissue segmentation, lesion detection, and severity grading on whole-slide images. Reviews in toxicologic pathology summarize emerging performance and workflow integration, while also noting challenges such as domain shift across scanners, stains, and laboratories. Recent field overviews track regulatory posture and validation practices for digital pathology and AI in nonclinical settings. (PubMed Central, SAGE Journals)
Takeaway: prioritize datasets with consistent scanning and staining protocols, maintain pathologist-in-the-loop review, and lock model versions with full audit trails for GLP environments. (PubMed Central)
Automated behavioral analysis in vivo
Markerless pose estimation and behavior classification enable high-throughput, objective quantification in rodent studies. Tools such as DeepLabCut and related pipelines have reached human-level accuracy in specific tasks and are widely adopted across behavior labs, improving sensitivity and throughput over manual scoring. Comparative studies benchmark open-source and commercial solutions. (PubMed Central, PubMed, Frontiers)
Takeaway: standardize camera geometry and lighting, predefine ethograms, and validate automated labels against blinded human scoring on held-out data before using outputs in decision making. (PubMed Central)
High-content and phenotypic screening
AI assists cell segmentation, morphology profiling, and hit triage in high-content imaging, improving signal detection and reducing analyst time. Recent methods integrate deep learning pipelines for phenotype-driven screening and quality control in organoid and 3D models. (Wiley Online Library, PubMed Central)
Takeaway: use reference plates and batch controls to monitor drift, and document model performance per assay and hardware configuration.
Digital toxicology and home-cage monitoring
Sensors and continuous monitoring combined with AI can detect subtle toxicity phenotypes and improve preclinical-to-clinical translation by capturing richer longitudinal data under less stressful conditions. Reviews argue that this digitization may strengthen external validity when paired with rigorous study design. (PubMed Central)
PK/PD and pharmacometrics augmentation
ML is explored for forecasting concentration-time profiles, learning covariate effects, and accelerating simulation in quantitative systems pharmacology workflows. Reviews caution that ML should complement, not replace, mechanistic modeling, and should include interpretability and uncertainty quantification. (accp1.org, PubMed Central)
Takeaway: couple ML surrogates with established population PK/PD models and report model limits, input ranges, and error bars that map to dosing decisions.
Regulatory context for AI in nonclinical work
FDA’s discussion paper and CDER resources recognize AI across discovery, nonclinical, clinical, and chemistry manufacturing. They stress data quality, traceability, model risk management, and appropriate validation for intended use. EMA’s final reflection paper similarly adopts a risk-based, human-centered stance and signals forthcoming guidance. These documents are not approvals of specific tools but set expectations for documentation and oversight. (U.S. Food and Drug Administration, European Medicines Agency (EMA))
Limitations and good practice
Data provenance and bias. Curate datasets with clear lineage, minimize leakage, and monitor class balance and site effects. Required for credibility under GLP. (U.S. Food and Drug Administration)
Domain shift. Validate models across scanners, labs, and species where applicable, or constrain use to validated domains. (PubMed Central)
Human oversight. Keep expert review in the loop for safety-critical calls and document decision pathways. (European Medicines Agency (EMA))
Reproducibility. Predefine analysis plans, version datasets and models, and report performance with confidence intervals to support regulatory review. (Grants.gov)
