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 preferr...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | mAbs |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19420862.2024.2352887 |
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| author | Ronghua Bei Justin Thomas Shiven Kapur Mahlet Woldeyes Adam Rauk Jason Robarge Jiangyan Feng Kaoutar Abbou Oucherif |
| author_facet | Ronghua Bei Justin Thomas Shiven Kapur Mahlet Woldeyes Adam Rauk Jason Robarge Jiangyan Feng Kaoutar Abbou Oucherif |
| author_sort | Ronghua Bei |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-59a1999e102a4d11ae76b4e1586bf416 |
| institution | Kabale University |
| issn | 1942-0862 1942-0870 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | mAbs |
| spelling | doaj-art-59a1999e102a4d11ae76b4e1586bf4162025-01-31T04:19:38ZengTaylor & Francis GroupmAbs1942-08621942-08702024-12-0116110.1080/19420862.2024.2352887Predicting the clinical subcutaneous absorption rate constant of monoclonal antibodies using only the primary sequence: a machine learning approachRonghua Bei0Justin Thomas1Shiven Kapur2Mahlet Woldeyes3Adam Rauk4Jason Robarge5Jiangyan Feng6Kaoutar Abbou Oucherif7Lilly Research Laboratories, Lilly Corporate Center, Eli Lilly and Company, Indianapolis, IN, USALilly Research Laboratories, Lilly Corporate Center, Eli Lilly and Company, Indianapolis, IN, USALilly Research Laboratories, Lilly Corporate Center, Eli Lilly and Company, Indianapolis, IN, USALilly Research Laboratories, Lilly Corporate Center, Eli Lilly and Company, Indianapolis, IN, USALilly Research Laboratories, Lilly Corporate Center, Eli Lilly and Company, Indianapolis, IN, USALilly Research Laboratories, Lilly Corporate Center, Eli Lilly and Company, Indianapolis, IN, USALilly Research Laboratories, Lilly Corporate Center, Eli Lilly and Company, Indianapolis, IN, USALilly Research Laboratories, Lilly Corporate Center, Eli Lilly and Company, Indianapolis, IN, USASubcutaneous 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.https://www.tandfonline.com/doi/10.1080/19420862.2024.2352887Machine learningmonoclonal antibodyrate constantsubcutaneous absorptionXgboost |
| spellingShingle | Ronghua Bei Justin Thomas Shiven Kapur Mahlet Woldeyes Adam Rauk Jason Robarge Jiangyan Feng Kaoutar Abbou Oucherif Predicting the clinical subcutaneous absorption rate constant of monoclonal antibodies using only the primary sequence: a machine learning approach mAbs Machine learning monoclonal antibody rate constant subcutaneous absorption Xgboost |
| title | Predicting the clinical subcutaneous absorption rate constant of monoclonal antibodies using only the primary sequence: a machine learning approach |
| title_full | Predicting the clinical subcutaneous absorption rate constant of monoclonal antibodies using only the primary sequence: a machine learning approach |
| title_fullStr | Predicting the clinical subcutaneous absorption rate constant of monoclonal antibodies using only the primary sequence: a machine learning approach |
| title_full_unstemmed | Predicting the clinical subcutaneous absorption rate constant of monoclonal antibodies using only the primary sequence: a machine learning approach |
| title_short | Predicting the clinical subcutaneous absorption rate constant of monoclonal antibodies using only the primary sequence: a machine learning approach |
| title_sort | predicting the clinical subcutaneous absorption rate constant of monoclonal antibodies using only the primary sequence a machine learning approach |
| topic | Machine learning monoclonal antibody rate constant subcutaneous absorption Xgboost |
| url | https://www.tandfonline.com/doi/10.1080/19420862.2024.2352887 |
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