Interpretable Artificial Neural Network Models for Predicting Anti-Adalimumab Immune Complex and Serum Drug Level in Crohn's Disease: A Proof-of-Concept Study.

ABSTRACT: Background: The development of anti-drug antibodies (ADAs) and resulting immune complexes are key mechanisms behind the secondary loss of response to adalimumab in Crohn's disease (CD). Despite their clinical importance, routine immunogenicity assays are limited, underscoring the need for alternative predictive approaches. Objective: This study aimed to develop interpretable artificial neural network (ANN) models to predict immune complex formation and estimate serum adalimumab levels using routinely available clinical and laboratory data from CD patients. Methods: A prospective analysis was performed on 58 CD patients on maintenance adalimumab. Immune complexes and serum adalimumab were measured via ELISA and lateral flow assays. ANN and ensemble regression models wer

SUBMITTER: Genaro LM 

PROVIDER: S-EPMC12735940 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature