Predicting the clinical subcutaneous absorption rate constant of monoclonal antibodies using only the primary sequence: a machine learning approach

ABSTRACT

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 (k_a), 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 k_a 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 k_a of a mAb using its primary sequence as the only input.

KEYWORDS:

• Machine learning
• monoclonal antibody
• rate constant
• subcutaneous absorption
• Xgboost

Acknowledgments

The authors are grateful to Patricia L. Brown-Augsburger for compiling some of the absorption rate constants from the literature; Yuhao Lin, Peter Thomas, and Qing Chai for their help with generating the molecular properties; and Nagarajan R. Thyagarajapuram for his continuous support.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The datasets for the absorption rate constants and the molecular properties are available in the Supplementary Information.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/19420862.2024.2352887.

Additional information

Funding

This study was entirely funded by Eli Lilly & Company.