Optimization of Mixed Micelles Based on Oppositely Charged Block Copolymers by Machine Learning for Application in Gene Delivery - Research Article

Aug 5, 2026

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The study addresses the need for improved non-viral gene carriers for genetic material delivery by targeting the toxicity–efficiency dilemma that limits polymer-based systems. Two sets of diblock terpolymers are designed with a common hydrophobic poly(n-butylacrylate) core, a hydrophilic NAM segment, and either cationic GPAm or anionic CEAm. Mixed micelles are co-assembled across varied GPAm/CEAm ratios, and machine learning is used to rapidly identify an optimal ratio balancing high transfection efficiency with cell viability while minimizing resource expenses. Results demonstrate machine learning integration as an efficient strategy for exploring vast polymer-chemistry combinations.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Introduction

Non-viral gene carriers and the toxicity–efficiency dilemma

Polymer delivery systems and block-copolymer micelles

Stealth functionality and PEG-related trade-offs

Anionic shielding strategies in nanocarriers

Mixed micelle co-assembly and ML optimization

RESEARCH ARTICLE [www.small-journal.com](www.small-journal.com) Optimization of Mixed Micelles Based on Oppositely Charged Block Copolymers by Machine Learning for Application in Gene Delivery Katharina Leer, Liên S. Reichel, Julian Kimmig, Friederike Richter, Stephanie Hoeppener, Johannes C. Brendel, Stefan Zechel, Ulrich S. Schubert,* and Anja Traeger* The COVID-19 mRNA vaccines represent a milestone in developing non-viral gene carriers, and their success highlights the crucial need for continued research in this ﬁeld to address further challenges. Polymer-based delivery systems are particularly promising due to their versatile chemical structure and convenient adaptability, but struggle with the toxicity-eﬃciency dilemma. Introducing anionic, hydrophilic, or “stealth” functionalities represents a promising approach to overcome this dilemma in gene delivery. Here, two sets of diblock terpolymers are created comprising hydrophobic poly(n-butylacrylate) (PnBA), a copolymer segment made of hydrophilic 4-acryloylmorpholine (NAM), and either the cationic 3-guanidinopropyl acrylamide (GPAm) or the 2-carboxyethyl acrylamide (CEAm), which is negatively charged at neutral conditions. These oppositely charged sets of diblocks are co-assembled in diﬀerent ratios to form mixed micelles. Since this experimental design enables countless mixing possibilities, a machine learning approach is applied to identify an optimal GPAm/CEAm ratio for achieving high transfection eﬃciency and cell viability with little resource expenses. After two runs, an optimal ratio to overcome the toxicity-eﬃciency dilemma is identiﬁed. The results highlight the remarkable potential of integrating machine learning into polymer chemistry to eﬀectively tackle the enormous number of conceivable combinations for identifying novel and powerful gene transporters. 1. Introduction In recent decades, research on non-viral gene carriers has advanced tremendously creating safe and eﬀective vaccines based on lipid nanoparticles. [1,2] However, the limited success of this technology for more complex applications underscores the urgent need for stable delivery systems to effectively address future gene delivery challenges. Eﬃcient delivery systems for genetic material have to meet a range of requirements: i) Stable packaging, ii) protection, iii) safe transport of the genetic payload, iv) high eﬃciency, and v) a safe proﬁle.[3] Besides lipid-based delivery systems, cationic polymers represent a promising material class, oﬀering the advantage of chemical variety and synthetic versatility with deﬁned structure and composition.[4–7] In particular, amphiphilic block copolymers have gained attention as delivery systems in biomedicine due to the increased stability and eﬃciency.[8–11] Amphiphilic block copolymers containing a hydrophobic and a hydrophilic segment can self-assemble into core-shell micelles. [12,13] K. Leer, L. S. Reichel, J. Kimmig, F. Richter[+], S. Hoeppener, J. C. Brendel, S. Zechel, U. S. Schubert, A. Traeger Laboratory of Organic and Macromolecular Chemistry Friedrich Schiller University Jena Humboldtstrasse 10, 07743 Jena, Germany E-mail: [Ulrich.Schubert@uni-jena.de](Ulrich.Schubert@uni-jena.de); [anja.traeger@uni-jena.de](anja.traeger@uni-jena.de) The ORCID identiﬁcation number(s) for the author(s) of this article can be found under [https://doi.org/10.1002/smll.202306116](https://doi.org/10.1002/smll.202306116) [+] Present address: NGP Polymers GmbH, Botzstrasse 5, 07743 Jena, Germany © 2023 The Authors. Small published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. DOI: 10.1002/smll.202306116 J. Kimmig, S. Hoeppener, J. C. Brendel, S. Zechel, U. S. Schubert, A. Traeger Jena Center for Soft Matter (JC

FAQs

Why are mixed micelles for gene delivery considered in this research?

Mixed micelles are used to combine oppositely charged block copolymer segments, enabling tuning of GPAm/CEAm ratios to achieve high transfection efficiency while preserving cell viability.

What terpolymer components are used to build the mixed micelles?

The design includes hydrophobic poly(n-butylacrylate) (PnBA), a hydrophilic NAM segment, and either the cationic GPAm or the negatively charged-at-neutral-conditions CEAm.

How does machine learning contribute to the optimization process?

Machine learning is applied to select an optimal GPAm/CEAm mixing ratio after limited experimental runs, targeting the balance between toxicity and efficiency with reduced resource usage.

Aug 5, 2026

Upload by Levi