Template-type: ReDIF-Article 1.0
Title: Artificial Intelligence-Driven Antibody-Drug Conjugate Development for Chronic Neuropathic Pain: From Target Discovery to Clinical Translation
Author-Name: Liu, Yuan
Author-Name: Xie, Qin
Author-Name: Zhou, Huixian
Author-Name: Shah, Krushi
Author-Name: Song, Siyuan
Abstract: Chronic neuropathic pain affects 7–10% of the global population and imposes a substantial socioeconomic burden, yet existing pharmacological options remain inadequate in efficacy and burdened by systemic toxicity. Antibody-drug conjugates (ADCs) — engineered to deliver neuromodulatory payloads selectively to peripheral nociceptors — represent a compelling but unexplored therapeutic modality; no clinical-stage pain ADC currently exists. To the best of our knowledge based on a structured literature search (January 2000–March 2025) and targeted updates through 6 June 2026, this review provides a systematic and comprehensive conceptual framework mapping AI methodologies validated in oncology ADC development onto the unique challenges of chronic neuropathic pain. The framework spans five pipeline stages: multi-omics integration and graph neural networks for nociceptor-selective target prioritization (Nav1.7, TRPV1, P2X3, TrkA, CGRP receptor, ASIC3); structure prediction and protein language models for antibody engineering against transmembrane pain targets; generative AI for neuromodulatory payload design; deep learning for neural tissue pharmacokinetics and neurotoxicity prediction; and AI-driven patient stratification for precision clinical trial design. A unique three-layer optimization challenge — neural selectivity, analgesic potency, and systemic safety — distinguishes pain ADC AI from oncology ADC AI and defines the core design constraints of this review. Clinical precedent from FDA-approved anti-CGRP pathway antibodies (erenumab, fremanezumab, galcanezumab, eptinezumab) and Phase III anti-NGF antibody trials establishes that peripheral nociceptor-targeting biologics are both feasible and clinically active, providing the biological foundation for the pain-ADC concept. Realizing this framework requires curated pain-ADC datasets, interpretable AI, and closed-loop platforms incorporating dorsal root ganglion (DRG) organoids.
Publication-Status: published
Journal: AI Med
Volume: 2
Issue: 1
Year: 2026
Month: 6
Handle: RePEc:ayf:j00001:11
File-URL: https://www.ai-press.org/index.php/aimed/article/view/10-71423-aimed-20260606
File-Format: text/html
File-URL: https://www.ai-press.org/index.php/aimed/article/download/10-71423-aimed-20260606/10
File-Format: application/pdf
DOI: 10.71423/aimed.20260606