# Computational Design of Antibody-Drug-Excipient Nanoparticles

> **NIH NIH R21** · DUKE UNIVERSITY · 2024 · $193,041

## Abstract

ABSTRACT
Nanoparticles enable the delivery of therapeutics to the desired tissue and thereby improve efficacy and safety.
However, only about 30 nanoparticle therapeutics have been FDA approved, and none of these 30
nanoparticles use advanced targeting functionality. Key challenges that impede broader nanoparticle
deployment are the complexity of nanoparticle synthesis protocols, a drug loading capacity commonly below
10%, and a one-size-fits-all approach in material optimization. Novel drug-excipient co-aggregates (Reker et al,
Nat Nanotechnol 2021) address these shortcomings through facile synthesis, drug loading of up to 95%, and
by using machine learning for the rational design and optimization of new nanoparticles. However, the
functionalization of these novel materials for actively targeted drug delivery is not yet established, limiting their
deployment to only a narrow set of tissues and indications. The here presented research will address the
unmet need for novel technologies to enable the functionalization of drug-excipient co-aggregate
nanoparticles. Specifically, we will develop novel experimental (aim 1) and computational (aim 2) protocols to
functionalize drug-excipient nanoparticles with antibodies and validate their targeting capabilities in vitro and in
vivo. This project will (1) prototype machine learning for targeted nanoparticle development, (2) for the first time
functionalize drug-excipient nanoparticles to qualitatively enhance the targeting capabilities of highly loaded
nanoparticles, and (3) generate a set of novel, carefully characterized therapeutic nanoparticles with potential
for further clinical development. Through rapid synthesis and machine learning-guided design, the here
proposed platform can rapidly expand the nanomedicine toolbox and streamline nanoparticle development,
evaluation, and manufacturing. Through our modular approach to “mix-and-match” nanoparticle components,
we expect the rational selection of antibodies, drugs, and excipients to enable the design of precision
nanoparticles for personalized drug delivery.

## Key facts

- **NIH application ID:** 10818622
- **Project number:** 5R21EB034443-02
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Daniel Reker
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $193,041
- **Award type:** 5
- **Project period:** 2023-04-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10818622

## Citation

> US National Institutes of Health, RePORTER application 10818622, Computational Design of Antibody-Drug-Excipient Nanoparticles (5R21EB034443-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10818622. Licensed CC0.

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