Deep learning, deeper relief: pipeline toward tailored analgesia for experimental animal models

Effective pain management in animal models is crucial for maintaining ethical and scientific integrity. However, commonly used analgesics may affect immune responses and disturb signaling pathways, thereby potentially confounding the experimental outcomes. In mouse colitis models, opioids and non-steroidal anti-inflammatory drugs have been shown to interfere with the immune response and the activation of the central regulator of inflammation, the transcription factor nuclear factor kappa B (NF-κB). Here, we propose a tailored pipeline for the identification and the validation of analgesics with minimal off-target effects. This approach combines protein-centered relation extraction using deep language models and distant supervision via the Protein-Centered Association Extraction with Deep Language (PEDL⁺) together with an <i>in vivo</i> experimental validation with a NF-κB reporter mouse model that enables unambiguous visualization of direct NF-κB activity across different tissues. Our findings indicate that commonly used analgesics, such as tramadol and acetaminophen, not only interfere with immune cell recruitment and NF-κB activation but also skew the differentiation of epithelial stem cells into goblet cells, affecting epithelial functions even after short exposures. Conversely, the analgesics selected by our PEDL⁺-based workflow, such as piritramide, demonstrated no significant interference with NF-κB signaling. To validate our findings <i>in vivo</i>, we treated our NF-κB reporter mice with the analgesics selected by our computational pipeline. Amantadine demonstrated the least impact on the inflammatory responses and NF-κB activation. We then predicted and identified the signaling pathways that are impacted by amantadine treatment. In summary, our proposed pipeline facilitates a shift from one-size-fits-all analgesics to a precision medicine approach that considers the unique molecular interactions associated with each model.