Issue 19, 2024

Polymer design via SHAP and Bayesian machine learning optimizes pDNA and CRISPR ribonucleoprotein delivery

Abstract

We present the facile synthesis of a clickable polymer library with systematic variations in length, binary composition, pKa, and hydrophobicity (clog P) to optimize intracellular pDNA and CRISPR-Cas9 ribonucleoprotein (RNP) performance. We couple physicochemical characterization and machine learning to interpret quantitative structure–property relationships within the combinatorial design space. For the first time, we reveal unexpected disparate design parameters for nucleic acid carriers; via explainable machine learning on 432 formulations, we discover that lower polymer pKa and higher percentages of benzimidazole ethanethiol enhance pDNA delivery, yet polymer length and captamine cation identity improve RNP delivery. Closed-loop Bayesian optimization of 552 formulation ratios further enhances in vitro performance. The top three polymers yield a higher signal and stable transgene expression over 20 days in vivo, and a 1.7-fold enhancement over controls. Our facile coupling of synthesis, characterization, and machine analysis provides powerful tools to quantitate performance parameters accelerating next-generation vehicles for nucleic acid medicines.

Graphical abstract: Polymer design via SHAP and Bayesian machine learning optimizes pDNA and CRISPR ribonucleoprotein delivery

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Article information

Article type
Edge Article
Submitted
23 Dec 2023
Accepted
25 Mar 2024
First published
22 Apr 2024
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY-NC license

Chem. Sci., 2024,15, 7219-7228

Polymer design via SHAP and Bayesian machine learning optimizes pDNA and CRISPR ribonucleoprotein delivery

R. J. Dalal, F. Oviedo, M. C. Leyden and T. M. Reineke, Chem. Sci., 2024, 15, 7219 DOI: 10.1039/D3SC06920F

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