Deep‐NCA: A deep learning methodology for performing noncompartmental analysis of pharmacokinetic data
Abstract Noncompartmental analysis (NCA) is a model‐independent approach for assessing pharmacokinetics (PKs). Although the existing NCA algorithms are very well‐established and widely utilized, they suffer from low accuracies in the setting of sparse PK samples. In response, we developed Deep‐NCA,...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-05-01
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| Series: | CPT: Pharmacometrics & Systems Pharmacology |
| Online Access: | https://doi.org/10.1002/psp4.13124 |
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| author | Gengbo Liu Logan Brooks John Canty Dan Lu Jin Y. Jin James Lu |
| author_facet | Gengbo Liu Logan Brooks John Canty Dan Lu Jin Y. Jin James Lu |
| author_sort | Gengbo Liu |
| collection | DOAJ |
| description | Abstract Noncompartmental analysis (NCA) is a model‐independent approach for assessing pharmacokinetics (PKs). Although the existing NCA algorithms are very well‐established and widely utilized, they suffer from low accuracies in the setting of sparse PK samples. In response, we developed Deep‐NCA, a deep learning (DL) model to improve the prediction of key noncompartmental PK parameters. Our methodology utilizes synthetic PK data for model training and uses an innovative patient‐specific normalization method for data preprocessing. Deep‐NCA demonstrated adequate performance across six previously unseen simulated drugs under multiple dosing, showcasing effective generalization. Compared to traditional NCA, Deep‐NCA exhibited superior performance for sparse PK data. This study advances the application of DL to PK studies and introduces an effective method for handling sparse PK data. With further validation and refinement, Deep‐NCA could significantly enhance the efficiency of drug development by providing more accurate NCA estimates while requiring fewer PK samples. |
| format | Article |
| id | doaj-art-0f1da517afe54d93b2c330aa4e23a06c |
| institution | Kabale University |
| issn | 2163-8306 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | CPT: Pharmacometrics & Systems Pharmacology |
| spelling | doaj-art-0f1da517afe54d93b2c330aa4e23a06c2025-08-20T03:31:27ZengWileyCPT: Pharmacometrics & Systems Pharmacology2163-83062024-05-0113587087910.1002/psp4.13124Deep‐NCA: A deep learning methodology for performing noncompartmental analysis of pharmacokinetic dataGengbo Liu0Logan Brooks1John Canty2Dan Lu3Jin Y. Jin4James Lu5Modeling and Simulation/Clinical Pharmacology Genentech Inc. South San Francisco California USAModeling and Simulation/Clinical Pharmacology Genentech Inc. South San Francisco California USACancer Immunology Genentech Inc. South San Francisco California USAModeling and Simulation/Clinical Pharmacology Genentech Inc. South San Francisco California USAModeling and Simulation/Clinical Pharmacology Genentech Inc. South San Francisco California USAModeling and Simulation/Clinical Pharmacology Genentech Inc. South San Francisco California USAAbstract Noncompartmental analysis (NCA) is a model‐independent approach for assessing pharmacokinetics (PKs). Although the existing NCA algorithms are very well‐established and widely utilized, they suffer from low accuracies in the setting of sparse PK samples. In response, we developed Deep‐NCA, a deep learning (DL) model to improve the prediction of key noncompartmental PK parameters. Our methodology utilizes synthetic PK data for model training and uses an innovative patient‐specific normalization method for data preprocessing. Deep‐NCA demonstrated adequate performance across six previously unseen simulated drugs under multiple dosing, showcasing effective generalization. Compared to traditional NCA, Deep‐NCA exhibited superior performance for sparse PK data. This study advances the application of DL to PK studies and introduces an effective method for handling sparse PK data. With further validation and refinement, Deep‐NCA could significantly enhance the efficiency of drug development by providing more accurate NCA estimates while requiring fewer PK samples.https://doi.org/10.1002/psp4.13124 |
| spellingShingle | Gengbo Liu Logan Brooks John Canty Dan Lu Jin Y. Jin James Lu Deep‐NCA: A deep learning methodology for performing noncompartmental analysis of pharmacokinetic data CPT: Pharmacometrics & Systems Pharmacology |
| title | Deep‐NCA: A deep learning methodology for performing noncompartmental analysis of pharmacokinetic data |
| title_full | Deep‐NCA: A deep learning methodology for performing noncompartmental analysis of pharmacokinetic data |
| title_fullStr | Deep‐NCA: A deep learning methodology for performing noncompartmental analysis of pharmacokinetic data |
| title_full_unstemmed | Deep‐NCA: A deep learning methodology for performing noncompartmental analysis of pharmacokinetic data |
| title_short | Deep‐NCA: A deep learning methodology for performing noncompartmental analysis of pharmacokinetic data |
| title_sort | deep nca a deep learning methodology for performing noncompartmental analysis of pharmacokinetic data |
| url | https://doi.org/10.1002/psp4.13124 |
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