Design of Biopharmaceutical Formulations Accelerated by Machine Learning,Molecular Pharmaceutics

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Design of Biopharmaceutical Formulations Accelerated by Machine Learning
Molecular Pharmaceutics ( IF 4.5 ) Pub Date : 2021-09-14 , DOI: 10.1021/acs.molpharmaceut.1c00469
Harini Narayanan ₁ , Fabian Dingfelder _{1,

2} , Itzel Condado Morales _{1,

2} , Bhargav Patel ₁ , Kristine Enemærke Heding ₂ , Jais Rose Bjelke ₃ , Thomas Egebjerg ₄ , Alessandro Butté ₅ , Michael Sokolov ₅ , Nikolai Lorenzen ₂ , Paolo Arosio ₁

Affiliation

In addition to activity, successful biological drugs must exhibit a series of suitable developability properties, which depend on both protein sequence and buffer composition. In the context of this high-dimensional optimization problem, advanced algorithms from the domain of machine learning are highly beneficial in complementing analytical screening and rational design. Here, we propose a Bayesian optimization algorithm to accelerate the design of biopharmaceutical formulations. We demonstrate the power of this approach by identifying the formulation that optimizes the thermal stability of three tandem single-chain Fv variants within 25 experiments, a number which is less than one-third of the experiments that would be required by a classical DoE method and several orders of magnitude smaller compared to detailed experimental analysis of full combinatorial space. We further show the advantage of this method over conventional approaches to efficiently transfer historical information as prior knowledge for the development of new biologics or when new buffer agents are available. Moreover, we highlight the benefit of our technique in engineering multiple biophysical properties by simultaneously optimizing both thermal and interface stabilities. This optimization minimizes the amount of surfactant in the formulation, which is important to decrease the risks associated with corresponding degradation processes. Overall, this method can provide high speed of converging to optimal conditions, the ability to transfer prior knowledge, and the identification of new nonlinear combinations of excipients. We envision that these features can lead to a considerable acceleration in formulation design and to parallelization of operations during drug development.

中文翻译：

机器学习加速的生物制药配方设计

除了活性之外，成功的生物药物还必须表现出一系列合适的可开发性特性，这取决于蛋白质序列和缓冲液组成。在这个高维优化问题的背景下，机器学习领域的高级算法在补充分析筛选和合理设计方面非常有益。在这里，我们提出了一种贝叶斯优化算法来加速生物制药配方的设计。我们通过在 25 个实验中确定优化三个串联单链 Fv 变体的热稳定性的配方来展示这种方法的强大功能，这个数字不到经典 DoE 方法所需实验的三分之一，与完整组合空间的详细实验分析相比要小几个数量级。我们进一步展示了这种方法相对于传统方法的优势，可以有效地将历史信息作为开发新生物制剂或新缓冲剂可用时的先验知识进行传输。此外，我们通过同时优化热稳定性和界面稳定性来强调我们的技术在设计多种生物物理特性方面的优势。这种优化最大限度地减少了配方中表面活性剂的量，这对于降低与相应降解过程相关的风险很重要。总的来说，这种方法可以提供高速收敛到最佳条件，转移先验知识的能力，以及识别赋形剂的新非线性组合。我们设想这些特性可以显着加快配方设计和药物开发过程中的操作并行化。

更新日期：2021-10-04

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