115: Precision PainRx: AI-Driven Personalized Cancer Pain Therapeutics Management Platform

Topic: Basic Science
Subtopic: Pain category (ICD-11), Acute cancer related pain, , , , , , , , , , Developing methodology/technology, Omics (Proteomics/Metabolomics/Transcriptomics/Genomics), Artificial Intelligence/Machine Learning

Background and Aims: Cancer pain is common yet often undertreated due to reliance on generalized, stepwise protocols such as the WHO analgesic ladder, which require trial-and-error titration and can delay optimal relief. This project aims to modernize cancer pain management using the PainRx AI platform, a two-phase precision medicine strategy that personalizes both analgesic dosing and drug selection. Phase 1 applies real-time, patient-specific dosing optimization; Phase 2 performs functional precision profiling using patient-derived sensory neurons to identify optimal drug combinations in advance. Together, these approaches aim to deliver faster, safer, and more effective pain control.

Methods: Phase 1 is a clinical pilot implementing PainRx AI for real-time analgesic dose optimization in patients with moderate to severe cancer pain. After a brief calibration phase to capture initial dose–response data, PainRx AI provides iterative dose recommendations reviewed by clinicians. Pain intensity, side effects, and quality-of-life metrics are tracked.

Phase 2 develops ex vivo patient-specific neuronal cultures and uses PainRx AI to evaluate targeted analgesic drug combinations through small-data functional profiling. The resulting response surfaces identify individualized optimal regimens. Insights from both phases inform each other to refine dosing guidance and combination selection.

Results: Phase 1 is expected to show improved pain control, faster achievement of target analgesia, fewer severe pain episodes, and reduced cumulative opioid exposure. High clinician acceptance of AI-recommended doses will support feasibility.

Phase 2 is anticipated to demonstrate that ex vivo functional assays can predict patient-specific optimal drug combinations that differ from empiric standards. Early correlations between neuronal responses and clinical pain outcomes will support predictive validity. Collectively, results will establish PainRx AI as an integrated platform that delivers personalized dosing and drug selection for precision-guided cancer pain management.

Conclusions: The PainRx AI platform represents a fundamentally new paradigm in cancer pain management by unifying real-time clinical dosing optimization with functional precision drug profiling. This dual strategy moves the field beyond empirical titration toward truly individualized therapy informed by each patient’s biology and response dynamics. Clinically, PainRx AI has the potential to deliver faster, safer, and more durable pain relief while optimizing opioid exposure and enhancing quality of life. For research, it establishes a scalable framework for discovering novel analgesic combinations, validating mechanistic biomarkers, and generating high-resolution datasets that can reshape future pain science and therapeutic development.

References: CDC, 2024. Understanding the Opioid Overdose Epidemic. (~108,000 overdose deaths in 2022; ~82,000 opioid-related, a tenfold rise since 1999).

Jain PN et al., 2015. Indian J Palliative Care. (52–77% of cancer patients report pain; one-third have moderate–severe pain despite WHO guidelines).

Schuft K., 2018. Wolters Kluwer Health Insights. (5–10% CYP2D6 poor metabolizers; 1–2% ultra-rapid; one-third receive inadequate opioid relief).

Nadeau SE et al., 2021. Front Pain Res. (~66% misuse opioids due to uncontrolled pain).

KYAN Technologies, 2025. (Optim.AI shows ~85% concordance across 20+ cancers).

Presenting Author: Muhammad Kumail
Submitting Author: Yacine HADJIAT

Senior Author: Yacine HADJIAT

IASP 2026 Global Year theme of Neuropathic Pain: No