Valgenesis Portugal, Lda, R. Castilho 50 4th Floor, 1250-071, Lisbon, Portugal.
Pieris Pharmaceuticals GmbH, Carl-Zeiss-Ring 15a, 85737, Ismaning, Germany.
J Comput Aided Mol Des. 2024 Aug 20;38(1):30. doi: 10.1007/s10822-024-00569-x.
The development of novel therapeutic proteins is a lengthy and costly process, with an average attrition rate of 91% (Thomas et al. Clinical Development Success Rates and Contributing Factors 2011-2020, 2021). To increase the probability of success and ensure robust drug supply beyond approval, it is essential to assess the developability profile of new potential drug candidates as early and broadly as possible in development (Jain et al. MAbs, 2023. https://doi.org/10.1016/j.copbio.2011.06.002 ). Predicting these properties in silico is expected to be the next leap in innovation as it would enable significantly reduced development timelines combined with broader screens at lower costs. However, developing predictive algorithms typically requires substantial datasets generated under very defined conditions, a limiting factor especially for new classes of therapeutic proteins that hold immense clinical promise. Here we describe a strategy for assessing the developability of a novel class of small therapeutic Anticalin® proteins using machine learning in conjunction with a knowledge-driven approach. The knowledge-driven approach considers developability attributes such as aggregation propensity, charge variants, immunogenicity, specificity, thermal stability, hydrophobicity, and potential post-translational modifications, to calculate a holistic developability score. Based on sequence-derived descriptors as input parameters we established novel statistical models designed to predict the developability scores for Anticalin proteins. The best models yielded low root mean square errors across the entire dataset and were further validated by removing input data from individual screening campaigns and predicting developability scores for those drug candidates. The adoption of the described workflow will enable significantly streamlined preclinical development of Anticalin drug candidates and could potentially be applied to other therapeutic protein scaffolds.
新型治疗蛋白的开发是一个漫长而昂贵的过程，平均淘汰率为 91%（Thomas 等人，Clinical Development Success Rates and Contributing Factors 2011-2020，2021）。为了提高成功的可能性并确保在获得批准后有稳健的药物供应，尽早广泛地评估新的潜在药物候选物的可开发性特征至关重要（Jain 等人，MAbs，2023. https://doi.org/10.1016/j.copbio.2011.06.002）。预计通过计算预测这些特性将是创新的下一个飞跃，因为它将使开发时间大大缩短，并以更低的成本进行更广泛的筛选。然而，开发预测算法通常需要在非常明确的条件下生成大量数据集，这对于具有巨大临床前景的新型治疗蛋白类别来说是一个限制因素。在这里，我们描述了一种使用机器学习结合知识驱动方法评估新型小分子治疗性 Anticalin®蛋白可开发性的策略。知识驱动方法考虑了可开发性属性，如聚集倾向、电荷变体、免疫原性、特异性、热稳定性、疏水性和潜在的翻译后修饰，以计算整体可开发性评分。基于序列衍生的描述符作为输入参数，我们建立了新的统计模型，旨在预测 Anticalin 蛋白的可开发性评分。最佳模型在整个数据集上的均方根误差都较低，并通过从各个筛选活动中删除输入数据并预测这些候选药物的可开发性评分来进一步验证。采用描述的工作流程将显著简化 Anticalin 候选药物的临床前开发，并可能应用于其他治疗性蛋白支架。
