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FormulationBCS: A Machine Learning Platform Based on Diverse Molecular Representations for Biopharmaceutical Classification System (BCS) Class Prediction.FormulationBCS:一种基于多种分子表征的机器学习平台,用于生物药剂分类系统(BCS)类别预测。
Mol Pharm. 2025 Jan 6;22(1):330-342. doi: 10.1021/acs.molpharmaceut.4c00946. Epub 2024 Dec 8.
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Machine Learning Models for Predicting Monoclonal Antibody Biophysical Properties from Molecular Dynamics Simulations and Deep Learning-Based Surface Descriptors.用于从分子动力学模拟和基于深度学习的表面描述符预测单克隆抗体生物物理特性的机器学习模型
Mol Pharm. 2025 Jan 6;22(1):142-153. doi: 10.1021/acs.molpharmaceut.4c00804. Epub 2024 Nov 28.
4
A New Analytical Method for Quantifying Acid-End-Cap PLGA in Sub-Milligram Quantities.一种用于定量亚毫克量酸封端聚乳酸-羟基乙酸共聚物的新分析方法。
Mol Pharm. 2025 Jan 6;22(1):446-458. doi: 10.1021/acs.molpharmaceut.4c01057. Epub 2024 Nov 20.
5
In vitro-in vivo correlation (IVIVC) development for long-acting injectable drug products based on poly(lactide-co-glycolide).基于聚(丙交酯-乙交酯)的长效注射用药物产品的体外-体内相关性(IVIVC)研究
J Control Release. 2025 Jan 10;377:186-196. doi: 10.1016/j.jconrel.2024.11.021. Epub 2024 Nov 19.
6
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Mechanistic Model for Drug Release from PLGA-Based Biodegradable Implants for Release Testing: Development and Validation.基于 PLGA 的可生物降解植入物的药物释放机制模型用于释放测试:开发和验证。
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Patient-Centric Long-Acting Injectable and Implantable Platforms─An Industrial Perspective.患者为中心的长效注射和植入式平台—工业视角。
Mol Pharm. 2024 Sep 2;21(9):4238-4258. doi: 10.1021/acs.molpharmaceut.4c00665. Epub 2024 Aug 19.
9
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基于聚乳酸-羟基乙酸共聚物的长效注射剂(LAI)制剂。

PLGA-based long-acting injectable (LAI) formulations.

作者信息

Park Kinam

机构信息

Purdue University, Weldon School of Biomedical Engineering and Department of Industrial and Molecular Pharmaceutics, West Lafayette, IN 47907, USA; Akina, Inc., 3495 Kent Avenue, West Lafayette, IN 47906, USA.

出版信息

J Control Release. 2025 Jun 10;382:113758. doi: 10.1016/j.jconrel.2025.113758. Epub 2025 Apr 21.

DOI:10.1016/j.jconrel.2025.113758
PMID:40268201
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12065662/
Abstract

Long-acting injectable (LAI) formulations, which deliver drugs over weeks or months, have been in use for more than three decades. Most clinically approved LAI products are formulated using poly(lactide-co-glycolide) (PLGA) polymers. Historically, the development of PLGA-based LAI formulations has relied predominantly on trial-and-error methods, primarily due to a limited understanding of the complex factors involved in LAI formulations and insufficient analytical techniques available for characterizing individual PLGA polymers of the prepared formulations. This article offers a personal perspective on recent advancements in characterization methods for PLGA polymers within final formulations, i.e., products, as well as enhanced insights into the drug release mechanisms associated with LAI products. With a deeper understanding of PLGA polymer properties and drug release mechanisms, the formulation development process can transition from traditional trial-and-error practices to a more systematic Quality by Design (QbD) approach. Additionally, this article explores the emerging role of artificial intelligence (AI) in formulation science and its potential, when applied carefully, to enhance the future development of PLGA-based LAI formulations.

摘要

长效注射(LAI)制剂可在数周或数月内持续释放药物,已使用超过三十年。大多数临床批准的LAI产品是使用聚(丙交酯-共-乙交酯)(PLGA)聚合物配制的。从历史上看,基于PLGA的LAI制剂的开发主要依赖于反复试验方法,这主要是由于对LAI制剂中涉及的复杂因素了解有限,以及用于表征所制备制剂中各个PLGA聚合物的分析技术不足。本文就最终制剂(即产品)中PLGA聚合物表征方法的最新进展提供个人观点,并深入探讨与LAI产品相关的药物释放机制。随着对PLGA聚合物性质和药物释放机制的更深入理解,制剂开发过程可以从传统的反复试验做法转变为更系统的设计质量(QbD)方法。此外,本文探讨了人工智能(AI)在制剂科学中的新兴作用,以及在谨慎应用时其对未来基于PLGA的LAI制剂开发的潜在促进作用。