Computational Design of Antibody-Drug-Excipient Nanoparticles

抗体药物赋形剂纳米颗粒的计算设计

• 批准号: 10647403
• 负责人: Daniel Reker
• 金额: $ 19.33万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10647403
• 关键词: Address; Antibodies; Behavior; Biocompatible Materials; Cells; Complex; Computer Models; Development; Drug Combinations; Drug Delivery Systems; Drug Design; Drug Targeting; Evaluation; Excipients; FDA approved; Fc domain; Generations; Goals; High Performance Computing; Hydrophobicity; In Vitro; Laboratories; Libraries; Machine Learning; Modeling; Molecular; Molecular Computations; Outcome Study; Pharmaceutical Preparations; Positioning Attribute; Protocols documentation; Research; Research Proposals; Safety; Surface; Testing; Therapeutic; Time; Tissues; Translations; Treatment Efficacy; Vaccines; antibody libraries; clinical development; clinically relevant; cluster computing; combinatorial; computational pipelines; design; experience; improved; in vivo; innovation; insight; invention; manufacture; nanoformulation; nanomedicine; nanoparticle; new technology; novel; novel therapeutics; precision drugs; preservation; prototype; rational design; research and development; simulation; therapeutic nanoparticles; uptake

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.

摘要 纳米颗粒能够将治疗剂递送至所需组织，从而提高功效和安全性。 然而，只有大约30种纳米颗粒治疗剂被FDA批准，并且这30种治疗剂中没有一种被FDA批准。 纳米颗粒使用先进的靶向功能。阻碍更广泛纳米颗粒的关键挑战 部署是纳米颗粒合成方案的复杂性，载药能力通常低于 10%，并在材料优化中采用一刀切的方法。新型药物-赋形剂共聚集体（Reker等人， Nat Nanotechnol 2021）通过简单的合成、高达95%的载药量和 通过使用机器学习来合理设计和优化新的纳米颗粒。但 用于主动靶向药物递送的这些新材料的功能化尚未建立，限制了它们的应用。 仅部署到一组狭窄的组织和适应症。这里介绍的研究将解决 对能够使药物-赋形剂共聚集体官能化的新技术的未满足的需求 纳米粒子具体来说，我们将开发新的实验（目标1）和计算（目标2）协议， 用抗体功能化药物-赋形剂纳米颗粒，并在体外和体内验证其靶向能力。 vivo.该项目将（1）为靶向纳米颗粒开发提供机器学习原型，（2）首次 功能化药物赋形剂纳米颗粒，以定性增强高载药的靶向能力 纳米颗粒，和（3）产生一组新的，仔细表征的治疗性纳米颗粒， 用于进一步的临床开发。通过快速合成和机器学习引导的设计， 所提出的平台可以快速扩展纳米医学工具箱并简化纳米颗粒的开发， 评估和制造。通过我们的模块化方法来“混合和匹配”纳米颗粒组件， 我们期望合理选择抗体、药物和赋形剂，以实现精密度设计 用于个性化药物递送的纳米颗粒。