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Advancing Therapeutic Protein Discovery and Development through Comprehensive Computational and Biophysical Characterization.
Molecular Pharmaceutics ( IF 4.5 ) Pub Date : 2020-01-07 , DOI: 10.1021/acs.molpharmaceut.9b00852
Lorenzo Gentiluomo 1, 2 , Hristo L Svilenov 2 , Dillen Augustijn 3 , Inas El Bialy 2 , Maria Laura Greco 4 , Alina Kulakova 5 , Sowmya Indrakumar 5 , Sujata Mahapatra 6 , Marcello Martinez Morales 4 , Christin Pohl 6 , Aisling Roche 7 , Andreas Tosstorff 2 , Robin Curtis 7 , Jeremy P Derrick 8 , Allan Nørgaard 6 , Tarik A Khan 9 , Günther H J Peters 5 , Alain Pluen 7 , Åsmund Rinnan 3 , Werner W Streicher 6 , Christopher F van der Walle 4 , Shahid Uddin 4 , Gerhard Winter 2 , Dierk Roessner 1 , Pernille Harris 5 , Wolfgang Frieß 2
Affiliation  

Therapeutic protein candidates should exhibit favorable properties that render them suitable to become drugs. Nevertheless, there are no well-established guidelines for the efficient selection of proteinaceous molecules with desired features during early stage development. Such guidelines can emerge only from a large body of published research that employs orthogonal techniques to characterize therapeutic proteins in different formulations. In this work, we share a study on a diverse group of proteins, including their primary sequences, purity data, and computational and biophysical characterization at different pH and ionic strength. We report weak linear correlations between many of the biophysical parameters. We suggest that a stability comparison of diverse therapeutic protein candidates should be based on a computational and biophysical characterization in multiple formulation conditions, as the latter can largely determine whether a protein is above or below a certain stability threshold. We use the presented data set to calculate several stability risk scores obtained with an increasing level of analytical effort and show how they correlate with protein aggregation during storage. Our work highlights the importance of developing combined risk scores that can be used for early stage developability assessment. We suggest that such scores can have high prediction accuracy only when they are based on protein stability characterization in different solution conditions.

中文翻译:

通过全面的计算和生物物理表征推进治疗性蛋白质的发现和开发。

候选治疗性蛋白质应表现出有利的特性,使其适合用作药物。然而,对于在早期开发过程中有效选择具有所需特征的蛋白质分子,尚无完善的指南。此类指南只能从大量已发表的研究中得出,这些研究采用正交技术来表征不同制剂中的治疗性蛋白质。在这项工作中,我们分享了对多种蛋白质的研究,包括其主要序列,纯度数据以及在不同pH和离子强度下的计算和生物物理表征。我们报告了许多生物物理参数之间的弱线性相关性。我们建议,各种治疗性蛋白质候选物的稳定性比较应基于多种制剂条件下的计算和生物物理表征,因为后者可以在很大程度上确定蛋白质是高于还是低于某个稳定性阈值。我们使用提出的数据集来计算随着分析水平的提高而获得的几个稳定性风险评分,并显示它们在存储过程中与蛋白质聚集之间的关系。我们的工作突出了开发可用于早期可开发性评估的组合风险评分的重要性。我们建议仅当这些分数基于不同溶液条件下的蛋白质稳定性表征时,它们才能具有较高的预测准确性。因为后者可以在很大程度上确定蛋白质是高于还是低于某个稳定性阈值。我们使用提出的数据集来计算随着分析水平的提高而获得的几个稳定性风险评分,并显示它们在存储过程中如何与蛋白质聚集相关。我们的工作突显了开发可用于早期可开发性评估的组合风险评分的重要性。我们建议仅当这些分数基于不同溶液条件下的蛋白质稳定性表征时,它们才能具有较高的预测准确性。因为后者可以在很大程度上确定蛋白质是高于还是低于某个稳定性阈值。我们使用提出的数据集来计算随着分析水平的提高而获得的几个稳定性风险评分,并显示它们在存储过程中与蛋白质聚集之间的关系。我们的工作突显了开发可用于早期可开发性评估的组合风险评分的重要性。我们建议仅当这些分数基于不同溶液条件下的蛋白质稳定性表征时,它们才能具有较高的预测准确性。我们的工作突显了开发可用于早期可开发性评估的组合风险评分的重要性。我们建议仅当这些分数基于不同溶液条件下的蛋白质稳定性表征时,它们才能具有较高的预测准确性。我们的工作突显了开发可用于早期可开发性评估的组合风险评分的重要性。我们建议仅当这些分数基于不同溶液条件下的蛋白质稳定性表征时,它们才能具有较高的预测准确性。
更新日期:2020-01-07
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