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.

Drug-Excipient nanoparticles have unprecedented drug loading and are rapidly synthesized and designed using machine learning but cannot yet be functionalized to enable active targeting. Here, we will invent computational and experimental protocols to design and characterize novel Antibody-Drug- Excipient nanoparticles with designer tissue targeting in vivo. The developed platform will enable the efficient creation of highly loaded nanoparticles for various indications and tissues and serve as a blueprint for future research and development into machine learning-assisted drug delivery and biomaterial design.