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Computer-assisted multifactorial method development for the streamlined separation and analysis of multicomponent mixtures in (Bio)pharmaceutical settings.计算机辅助多因素方法开发,用于在(生物)制药环境中对多组分混合物进行简化分离和分析。
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2
The quest for more effective vaccine markets - Opportunities, challenges, and what has changed with the SARS-CoV-2 pandemic.追求更有效的疫苗市场 - 机遇、挑战以及 SARS-CoV-2 大流行带来的变化。
Vaccine. 2024 Apr 8;42 Suppl 1(Suppl 1):S64-S72. doi: 10.1016/j.vaccine.2022.07.032. Epub 2022 Oct 21.
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Small molecule modulators of immune pattern recognition receptors.免疫模式识别受体的小分子调节剂
RSC Chem Biol. 2023 Oct 23;4(12):1014-1036. doi: 10.1039/d3cb00096f. eCollection 2023 Nov 29.
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Anal Chem. 2023 Dec 12;95(49):18130-18138. doi: 10.1021/acs.analchem.3c03528. Epub 2023 Nov 28.
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Optimization of HPLCCAD method for simultaneous analysis of different lipids in lipid nanoparticles with analytical QbD.采用分析 QbD 对脂质纳米粒中不同脂质进行高效液相色谱-串联质谱同时分析方法的优化。
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数字化通用分析框架加速疫苗佐剂制剂液相色谱方法开发进程

Digitally Enabled Generic Analytical Framework Accelerating the Pace of Liquid Chromatography Method Development for Vaccine Adjuvant Formulations.

作者信息

Hemida Mohamed, Barrientos Rodell C, Kinsey Caleb, Kuster Nathan, Bhavsar Mayank, Beck Armen G, Wang Heather, Singh Andrew, Aggarwal Pankaj, Arcinas Arthur, Mukherjee Malini, Appiah-Amponsah Emmanuel, Regalado Erik L

机构信息

Analytical Research and Development, MRL, Merck & Co., Inc., 126 E. Lincoln Avenue, Rahway, New Jersey 07065, United States.

出版信息

ACS Pharmacol Transl Sci. 2024 Sep 11;7(10):3108-3118. doi: 10.1021/acsptsci.4c00306. eCollection 2024 Oct 11.

DOI:10.1021/acsptsci.4c00306
PMID:39421659
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11480893/
Abstract

The growing use of adjuvants in the fast-paced formulation of new vaccines has created an unprecedented need for meaningful analytical assays that deliver reliable quantitative data from complex adjuvant and adjuvant-antigen mixtures. Due to their complex chemical and physical properties, method development for the separation of vaccine adjuvants is considered a highly challenging and laborious task. Reversed-phase liquid chromatography (RPLC) is among the most important tests in the (bio)pharmaceutical industry for release and stability indicating measurements including adjuvant content, identity, and purity profile. However, the time constraints of developing "on-demand" robust quantitative methods prior to each change in formulation can easily lead to sample analysis becoming a bottleneck in vaccine development. Herein, a simple and efficient generic analytical framework capable of chromatographically resolving the most commonly used non-aluminum-based adjuvants across academic and industrial sectors is introduced. This was designed to seek a more proactive approach for fast-paced assay development endeavors that evolved from extensive stationary phase screening in conjunction with multifactorial simulations of adjuvant retention time (RT) as a function of gradient time, temperature, organic modifier blending, and buffer concentration. The multifactorial retention models yield 3D resolution maps with excellent baseline separation of all adjuvants in a single run, which was found to be very accurate, with differences between experimental and simulated retention times of less than 1%. The analytical framework described here also includes the introduction of a more versatile approach to method development by introducing a dynamic RT database for adjuvants covering the entire library of adjuvants with broad mechanisms of action across numerous vaccine formulations with excellent linearity, accuracy, precision, and specificity. The power of this framework was also demonstrated with numerous analytical assays that can be generated rapidly from simulations guiding vaccine processes in the development of new adjuvant formulations. Analytical assay in this work covers content, purity profile by LC with diode array detector (DAD) and charged aerosol detector (CAD), and component identification by LC with mass spectrometry (MS) across complex vaccine formulations, including the use of surfactants (, polysorbates) as well as their separation from adjuvant targets.

摘要

在新疫苗的快速配方中,佐剂的使用日益增加,这就产生了前所未有的需求,即需要有意义的分析方法,以便从复杂的佐剂和佐剂 - 抗原混合物中提供可靠的定量数据。由于其复杂的化学和物理性质,疫苗佐剂分离的方法开发被认为是一项极具挑战性和费力的任务。反相液相色谱(RPLC)是(生物)制药行业中用于放行和稳定性检测的最重要测试之一,包括佐剂含量、鉴别和纯度分析。然而,在每次配方变更之前开发“按需”稳健定量方法的时间限制很容易导致样品分析成为疫苗开发的瓶颈。本文介绍了一种简单高效的通用分析框架,能够通过色谱法分离学术和工业领域中最常用的非铝基佐剂。该框架旨在寻求一种更积极主动的方法来进行快速的分析方法开发,这是通过广泛的固定相筛选以及结合佐剂保留时间(RT)作为梯度时间、温度、有机改性剂混合和缓冲液浓度函数的多因素模拟而发展而来的。多因素保留模型可生成3D分辨率图,一次运行就能实现所有佐剂的出色基线分离,结果发现该模型非常准确,实验保留时间与模拟保留时间之间的差异小于1%。这里描述的分析框架还包括引入一种更通用的方法开发途径,即通过引入一个动态RT数据库,该数据库涵盖了具有广泛作用机制的整个佐剂库,适用于众多具有出色线性、准确性、精密度和特异性的疫苗配方。该框架的强大功能还通过大量分析方法得到了证明,这些方法可以从指导新佐剂配方开发中疫苗工艺的模拟中快速生成。这项工作中的分析方法涵盖了复杂疫苗配方中的含量、使用二极管阵列检测器(DAD)和带电气溶胶检测器(CAD)的LC纯度分析以及使用质谱(MS)的LC成分鉴定,包括表面活性剂(如聚山梨酯)的使用及其与佐剂目标物的分离。