Machine learning algorithms to estimate everolimus exposure trained on simulated and patient pharmacokinetic profiles
Abstract Everolimus is an immunosuppressant with a small therapeutic index and large between‐patient variability. The area under the concentration versus time curve (AUC) is the best marker of exposure but measuring it requires collecting many blood samples. The objective of this study was to train...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-08-01
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| Series: | CPT: Pharmacometrics & Systems Pharmacology |
| Online Access: | https://doi.org/10.1002/psp4.12810 |
| _version_ | 1827583980740804608 |
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| author | Marc Labriffe Jean‐Baptiste Woillard Jean Debord Pierre Marquet |
| author_facet | Marc Labriffe Jean‐Baptiste Woillard Jean Debord Pierre Marquet |
| author_sort | Marc Labriffe |
| collection | DOAJ |
| description | Abstract Everolimus is an immunosuppressant with a small therapeutic index and large between‐patient variability. The area under the concentration versus time curve (AUC) is the best marker of exposure but measuring it requires collecting many blood samples. The objective of this study was to train machine learning (ML) algorithms using pharmacokinetic (PK) profiles from kidney transplant recipients, simulated profiles, or both types, and compare their performance for everolimus AUC0‐12h estimation using a limited number of predictors, as compared to an independent set of full PK profiles from patients, as well as to the corresponding maximum a posteriori Bayesian estimates (MAP‐BE). XGBoost was first trained on 508 patient interdose AUCs estimated using MAP‐BE, and then on 500–10,000 rich interdose PK profiles simulated using previously published population PK parameters. The predictors used were: predose, ~1 h, and ~2 h whole blood concentrations, differences between these concentrations, relative deviations from theoretical sampling times, morning dose, patient age, and time elapsed since transplantation. The best results were obtained with XGBoost trained on 5016 simulated profiles. AUC estimation achieved in an external dataset of 114 full‐PK profiles was excellent (root mean squared error [RMSE] = 10.8 μg*h/L) and slightly better than MAP‐BE (RMSE = 11.9 μg*h/L). Using more profiles (n = 10,035) did not improve the ML algorithm performance. The contribution of mixing patient and simulated profiles was significant only when they were in balanced numbers, with ~500 for each (RMSE = 12.5 μg*h/L), compared with patient data alone (RMSE = 18.0 μg*h/L). |
| first_indexed | 2024-03-08T23:20:57Z |
| format | Article |
| id | doaj.art-887aa69c8ac3439eb5e209f5d98348ca |
| institution | Directory Open Access Journal |
| issn | 2163-8306 |
| language | English |
| last_indexed | 2024-03-08T23:20:57Z |
| publishDate | 2022-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | CPT: Pharmacometrics & Systems Pharmacology |
| spelling | doaj.art-887aa69c8ac3439eb5e209f5d98348ca2023-12-15T04:11:47ZengWileyCPT: Pharmacometrics & Systems Pharmacology2163-83062022-08-011181018102810.1002/psp4.12810Machine learning algorithms to estimate everolimus exposure trained on simulated and patient pharmacokinetic profilesMarc Labriffe0Jean‐Baptiste Woillard1Jean Debord2Pierre Marquet3Pharmacology & Transplantation, INSERM U1248 Université de Limoges Limoges FrancePharmacology & Transplantation, INSERM U1248 Université de Limoges Limoges FrancePharmacology & Transplantation, INSERM U1248 Université de Limoges Limoges FrancePharmacology & Transplantation, INSERM U1248 Université de Limoges Limoges FranceAbstract Everolimus is an immunosuppressant with a small therapeutic index and large between‐patient variability. The area under the concentration versus time curve (AUC) is the best marker of exposure but measuring it requires collecting many blood samples. The objective of this study was to train machine learning (ML) algorithms using pharmacokinetic (PK) profiles from kidney transplant recipients, simulated profiles, or both types, and compare their performance for everolimus AUC0‐12h estimation using a limited number of predictors, as compared to an independent set of full PK profiles from patients, as well as to the corresponding maximum a posteriori Bayesian estimates (MAP‐BE). XGBoost was first trained on 508 patient interdose AUCs estimated using MAP‐BE, and then on 500–10,000 rich interdose PK profiles simulated using previously published population PK parameters. The predictors used were: predose, ~1 h, and ~2 h whole blood concentrations, differences between these concentrations, relative deviations from theoretical sampling times, morning dose, patient age, and time elapsed since transplantation. The best results were obtained with XGBoost trained on 5016 simulated profiles. AUC estimation achieved in an external dataset of 114 full‐PK profiles was excellent (root mean squared error [RMSE] = 10.8 μg*h/L) and slightly better than MAP‐BE (RMSE = 11.9 μg*h/L). Using more profiles (n = 10,035) did not improve the ML algorithm performance. The contribution of mixing patient and simulated profiles was significant only when they were in balanced numbers, with ~500 for each (RMSE = 12.5 μg*h/L), compared with patient data alone (RMSE = 18.0 μg*h/L).https://doi.org/10.1002/psp4.12810 |
| spellingShingle | Marc Labriffe Jean‐Baptiste Woillard Jean Debord Pierre Marquet Machine learning algorithms to estimate everolimus exposure trained on simulated and patient pharmacokinetic profiles CPT: Pharmacometrics & Systems Pharmacology |
| title | Machine learning algorithms to estimate everolimus exposure trained on simulated and patient pharmacokinetic profiles |
| title_full | Machine learning algorithms to estimate everolimus exposure trained on simulated and patient pharmacokinetic profiles |
| title_fullStr | Machine learning algorithms to estimate everolimus exposure trained on simulated and patient pharmacokinetic profiles |
| title_full_unstemmed | Machine learning algorithms to estimate everolimus exposure trained on simulated and patient pharmacokinetic profiles |
| title_short | Machine learning algorithms to estimate everolimus exposure trained on simulated and patient pharmacokinetic profiles |
| title_sort | machine learning algorithms to estimate everolimus exposure trained on simulated and patient pharmacokinetic profiles |
| url | https://doi.org/10.1002/psp4.12810 |
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