Ronghua Bei, Justin Thomas, Shiven Kapur, Mahlet Woldeyes, Adam Rauk, Jason Robarge, Jiangyan Feng, Kaoutar Abbou Oucherif
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Once trained, our model (enabled by the XGBoost algorithm in machine learning) can predict the k<sub>a</sub> of a mAb following a subcutaneous injection using <i>in silico</i> molecular properties alone (generated from the primary sequence). Our model does not need clinically observed plasma concentration-time data; this is a novel capability not previously achieved in predictive pharmacokinetic models. The model also showed improved performance when benchmarked against a recently reported mechanistic model that relied on clinical data to predict subcutaneous absorption of mAbs. We further interpreted the model to understand which molecular properties affect the absorption rate and showed that our findings are consistent with previous studies evaluating subcutaneous absorption through direct experimentation. 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引用次数: 0
摘要
皮下注射是一种越来越普遍的生物疗法给药途径,包括单克隆抗体(mAbs)。与静脉给药相比,皮下注射可降低给药成本、缩短给药时间,而且从患者体验的角度来看,皮下注射更受青睐。了解 mAb 从注射部位到全身循环的吸收过程对于皮下注射 mAb 制剂的开发至关重要。在这项研究中,我们建立了一个模型来预测吸收率常数 (ka),它表示 mAb 从给药部位吸收的速度。训练完成后,我们的模型(通过机器学习中的 XGBoost 算法实现)就能仅利用硅分子特性(由主序列生成)预测 mAb 皮下注射后的 ka。我们的模型不需要临床观察到的血浆浓度-时间数据;这是预测性药代动力学模型以前从未实现过的新功能。与最近报道的依赖临床数据预测 mAbs 皮下吸收的机理模型相比,该模型的性能也有所提高。我们进一步解释了该模型,以了解哪些分子特性会影响吸收率,结果表明我们的发现与之前通过直接实验评估皮下吸收的研究结果一致。总之,本研究报告了一个模型的开发、验证、基准测试和解释,该模型可以使用 mAb 的主序列作为唯一输入来预测其临床 ka。
Predicting the clinical subcutaneous absorption rate constant of monoclonal antibodies using only the primary sequence: a machine learning approach.
Subcutaneous injections are an increasingly prevalent route of administration for delivering biological therapies including monoclonal antibodies (mAbs). Compared with intravenous delivery, subcutaneous injections reduce administration costs, shorten the administration time, and are strongly preferred from a patient experience point of view. An understanding of the absorption process of a mAb from the injection site to the systemic circulation is critical to the process of subcutaneous mAb formulation development. In this study, we built a model to predict the absorption rate constant (ka), which denotes how fast a mAb is absorbed from the site of administration. Once trained, our model (enabled by the XGBoost algorithm in machine learning) can predict the ka of a mAb following a subcutaneous injection using in silico molecular properties alone (generated from the primary sequence). Our model does not need clinically observed plasma concentration-time data; this is a novel capability not previously achieved in predictive pharmacokinetic models. The model also showed improved performance when benchmarked against a recently reported mechanistic model that relied on clinical data to predict subcutaneous absorption of mAbs. We further interpreted the model to understand which molecular properties affect the absorption rate and showed that our findings are consistent with previous studies evaluating subcutaneous absorption through direct experimentation. Taken altogether, this study reports the development, validation, benchmarking, and interpretation of a model that can predict the clinical ka of a mAb using its primary sequence as the only input.
期刊介绍:
ACS Applied Materials & Interfaces is a leading interdisciplinary journal that brings together chemists, engineers, physicists, and biologists to explore the development and utilization of newly-discovered materials and interfacial processes for specific applications. Our journal has experienced remarkable growth since its establishment in 2009, both in terms of the number of articles published and the impact of the research showcased. We are proud to foster a truly global community, with the majority of published articles originating from outside the United States, reflecting the rapid growth of applied research worldwide.