Agentic Discovery of Cryomicroneedle Formulations

TL;DR

This paper presents an AI-driven, iterative workflow for discovering cryoprotectant formulations for cryomicroneedles, combining literature data, machine learning, and wet-lab validation to optimize cell viability and device formation.

Contribution

It introduces a novel, data-efficient, agent-assisted approach integrating literature curation, Bayesian optimization, and experimental validation for cryoprotectant discovery.

Findings

Model reduced batch RMSE from 41.21 to 6.86 percentage points.

Achieved 95.15% post-thaw cell viability with optimized formulation.

Validated the approach with a high R^2 of 0.942 between predicted and measured outcomes.

Abstract

Cryomicroneedles offer a route to minimally invasive intradermal delivery of living cells, but their cryogenic formulations must reconcile cell protection with constraints on toxicity and device fabrication. Here we report an AI-assisted, closed-loop workflow for cryomicroneedle cryoprotectant discovery that combines literature curation, Gaussian-process surrogate modelling, Bayesian optimization, and sequential wet-lab validation. A curated dataset of 198 mesenchymal stem-cell cryopreservation formulations from 42 studies was converted into 21 ingredient features and used to train an uncertainty-aware literature prior. This model captured moderate structure in the literature data but failed prospectively, motivating iterative wet-lab correction. Across ten validation iterations and 106 wet-lab observations, the model progressively adapted to cryomicroneedle-specific outcomes: batch RMSE decreased from 41.21 to 6.86 percentage points, later-stage rank correlations became consistently positive, and the cumulative wet-lab predicted-versus-measured summary reached $R^2 = 0.942$. The best validated formulation achieved 95.15\% post-thaw viability with low DMSO, ectoin, ethylene glycol, and fetal bovine serum. However, high viability alone did not ensure intact cryomicroneedle formation, highlighting the need for future multi-objective optimization. These results demonstrate that agent-assisted computational infrastructure can make data-efficient formulation discovery more accessible to labs with minimal data expertise in-house. Project code is available at https://github.com/baitmeister/ML-for-CryoMN.