通过使用代数学和人工中性网络建模方法的离子交换色谱来分离Fab治疗电荷变体
Anupa Anupa1, Pratik Punj2, Lalita Kanwar Shekhawat2
1School of Interdisciplinary Research, Indian Institute of Technology Delhi, New Delhi, India.
Journal of chromatography. A
|May 11, 2025
概括
开发一个强大的染色体净化工艺对于Fab抗体疗法至关重要. 这项研究引入了一种新的方法,使用数学和人工智能建模将线性梯度转换为阶梯度,简化生物制造.
科学领域:
- 生物制药制造业 生物制药制造业
- 染色体学 染色体学 是一种染色学.
- 蛋白质净化 蛋白质净化
背景情况:
- 线性渐变化是通过离子交换染色学净化单克隆和Fab抗体的标准.
- 在生物制造中,精确的梯度控制带来了重大挑战.
- 目前的方法需要进行广泛的优化,以实现强大的净化.
研究的目的:
- 为Fab抗体疗法开发一种简化和强大的染色学净化工艺.
- 为了将具有挑战性的线性梯度化方法转换为更容易管理的梯度化.
- 利用数学和人工神经网络建模来预测色谱中的蛋白质行为.
主要方法:
- 采用代数学和人工神经网络 (ANN) 建模方法.
- 利用经典的山本和莫勒鲁普的热力学方法来预测分布系数.
- 建模蛋白质物种 (Fab,A1,A2变体) 的行为,基于静态位移和结合电荷.
主要成果:
- 成功预测了Fab及其酸性变体在不同pH值和盐度的分布系数.
- 在固定的pH值下实现了三步盐梯度化,绕过了进一步的优化.
- 通过使用开发的阶梯梯度方法,获得了92%纯度和76%产量的纯化Fab.
结论:
- 使用数学和ANN建模将线性变化为步骤梯度化是可行的和有效的.
- 这种方法为商业规模的复杂生物分子的染色学净化提供了一个强大的和简化的方法.
- 开发的战略提高了生物制药净化过程的可重复性和效率.
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