# Accelerated discovery of synthetic polymers for ribonucleoprotein delivery through the integration of active learning, machine learning, and polymer science

> **NIH NIH R21** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2022 · $228,903

## Abstract

PROJECT SUMMARY/ABSTRACT
Gene editing systems such as CRISPR/Cas9 have rapidly grown in popularity as research tools and hold the
potential to cure a diverse set of genetic disorders. However, effective, safe, and effective delivery remains a
significant challenge for therapeutic translation and for application to cell types that are difficult to culture ex vivo.
Ideally, intact Cas9 protein would be delivered with its guide RNA (sgRNA) as a purified ribonucleoprotein (RNP),
as opposed to Cas9-encoding mRNA or plasmids, to minimize off-target effects. Viral vectors (e.g., AAVs) cannot
deliver such large cargo due to their limited capsid size, which exhibit additional challenges with respect to
immunogenicity, cost, and manufacturability. Fortunately, synthetic polymers--widely studied in the context of
nucleic acid delivery and as biomaterials--have recently shown promise as vehicles for in vivo delivery of sgRNA-
Cas9 RNPs. However, there are no consistent design principles by which novel synthetic polymers with improved
delivery efficiency, tissue specificity, and safety can be developed. There are far too many polymer structures to
test exhaustively or through ad hoc experimentation, so a systematic approach to polymer design, synthesis,
and evaluation is required to identify promising candidates. This proposal presents a framework for the discovery
of functional polymers through Bayesian experimental design. Machine learning models trained on experimental
outcomes will serve as surrogates for experimentation in order to virtually screen a massive library of potential
polymer candidates. Polymer candidates will be selected algorithmically through Bayesian Optimization to
balance exploration of unknown chemical space and exploitation of structures known to effectively deliver RNPs.
Aim 1 will involve (a) the synthesis of a diverse library of biodegradable poly(ester urea amines) (PEUAs), (b)
the evaluation of their functional performance using a model fluorescent reporter knock-in/knock-out assay, a
cell viability assay, and a metabolic activity assay, and (c) the development and validation of a machine learning
model to learn a quantitative relationship between polymer structure/composition and these multiple performance
metrics. Aim 2 will involve (a) the enumeration of the chemical space of synthetically accessible PEUAs, and (b)
the development and application of a Bayesian Optimization framework leveraging the machine learning model
from Aim 1 to guide the selection of candidate polymers from the enumerated space through iterative rounds of
experimentation. The outcome of the proposed work will be an integrated tool combining machine learning and
polymer science for the unbiased exploration of a broad biomaterial design space, validated through the
development of effective and safe RNP delivery vehicles for gene editing that outperform existing commercial
polymeric vehicle solutions.

## Key facts

- **NIH application ID:** 10492434
- **Project number:** 5R21GM141616-02
- **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Connor Wilson Coley
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $228,903
- **Award type:** 5
- **Project period:** 2021-09-22 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10492434

## Citation

> US National Institutes of Health, RePORTER application 10492434, Accelerated discovery of synthetic polymers for ribonucleoprotein delivery through the integration of active learning, machine learning, and polymer science (5R21GM141616-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10492434. Licensed CC0.

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