DSC Derived (Ea & ΔG) Energetics and Aggregation Predictions for mAbs

能量学 化学 生物物理学 热力学 生物 物理
作者
Ralf Joe Carrillo,Andy Semple
出处
期刊:Journal of Pharmaceutical Sciences [Elsevier BV]
卷期号:113 (8): 2140-2150 被引量:3
标识
DOI:10.1016/j.xphs.2024.05.009
摘要

The Arrhenius energy of activation of unfolding Ea unfolding and Gibbs free energy of unfolding ΔG unfolding have been calculated utilizing DSC differential scanning calorimetry for 4 mAbs (1 biosimilar) in 3 formulations. DSC derived ΔTm melting temperature changes for each mAb domain (CH2, Fab, CH3) at calorimetric scan rates at 60°C, 90°C, 150°C and 200°C / hr. were utilized to calculate the kinetic Ea unfolding. The DSC derived Ea trend with observed aggregate formation and can be used to predict %HMW formation post 9-month storage at 5°C and 40°C for all formulations analyzed. Additionally, thermodynamic ΔG unfolding energies were also derived (Tm, ΔCp and ΔH measurements) for each mAb at every scan rate to observe scan rate dependence of ΔG and for extrapolation to 0°C/hr. (to report ΔG at true equilibrium conditions). Both derived thermodynamic ΔG and kinetic Ea energies were combined to build full energetic landscapes for mAb unfolding and aggregation. Statistical multivariate analysis of kinetic (Ea CH2, Ea Fab, Ea CH3) energies, thermodynamic (ΔG5°C and ΔG40°C) energies and in-silico modeled surface properties was also performed. Analysis revealed key significant parameters contributing to aggregation. These parameters were utilized to build predictive aggregation models for 25 mg/mL mAb formulations stored 9-months at 5°C and 40°C.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
ss完成签到,获得积分10
2秒前
quup完成签到,获得积分10
3秒前
传奇3应助nansy采纳,获得10
5秒前
6秒前
共工完成签到 ,获得积分10
6秒前
hrzmlily完成签到,获得积分10
6秒前
renshiq完成签到,获得积分10
7秒前
拼搏霸发布了新的文献求助10
7秒前
研友_VZG7GZ应助quup采纳,获得10
8秒前
9秒前
CodeCraft应助LC2228采纳,获得10
11秒前
ZihaoJin发布了新的文献求助10
11秒前
Cain完成签到,获得积分10
11秒前
15秒前
milo完成签到 ,获得积分10
15秒前
JXDYYZK完成签到,获得积分0
16秒前
Cain发布了新的文献求助10
16秒前
17秒前
mmuoo完成签到,获得积分10
17秒前
woshi123发布了新的文献求助20
17秒前
lhl完成签到,获得积分0
18秒前
20秒前
森sen发布了新的文献求助10
20秒前
李爱国应助ZihaoJin采纳,获得10
21秒前
21秒前
隐形曼青应助qianlan采纳,获得10
23秒前
笨笨的元风完成签到 ,获得积分10
23秒前
muxi完成签到,获得积分20
24秒前
25秒前
26秒前
小白牛完成签到 ,获得积分10
26秒前
kathleen完成签到,获得积分10
27秒前
27秒前
28秒前
29秒前
echo完成签到,获得积分10
29秒前
PSC发布了新的文献求助10
31秒前
31秒前
coco发布了新的文献求助10
32秒前
LC2228发布了新的文献求助10
32秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Health Psychology 800
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Electric machines: theory, operating applications, and controls 500
The Analytical and Numerical Solution of Electric and Magnetic Fields 500
When Is Two-Stage Sample Robust Optimization Asymptotically Optimal? 500
Discerning Saints: Moralization of Intrinsic Motivation and Selective Prosociality at Work 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7592932
求助须知:如何正确求助?哪些是违规求助? 9170175
关于积分的说明 19627409
捐赠科研通 7170719
什么是DOI,文献DOI怎么找? 3267529
关于科研通互助平台的介绍 2432418
邀请新用户注册赠送积分活动 2260076