Skip to main content
SpringerLink
Log in

A Hybrid Model Associating Population Pharmacokinetics with Machine Learning: A Case Study with Iohexol Clearance Estimation

  • Original Research Article
  • Published:
Clinical Pharmacokinetics Aims and scope Submit manuscript

Abstract

Background

Maximum a posteriori Bayesian estimation (MAP-BE) based on a limited sampling strategy and a population pharmacokinetic model is frequently used to estimate pharmacokinetic parameters in individuals, however with some uncertainty (bias). Recent works have shown that the performance in individual estimation or pharmacokinetic parameters can be improved by combining population pharmacokinetic and machine learning algorithms. Objective: The objective of this work was to investigate the use of a hybrid machine learning/population pharmacokinetic approach to improve individual iohexol clearance estimation.

Methods

The reference iohexol clearance values were derived from 500 simulated profiles (samples collected between 0.1 and 24.7 h) using a population pharmacokinetic model we recently developed in Monolix and obtained using all the concentration timepoints available. Xgboost and glmnet algorithms able to predict the error of MAP-BE clearance estimates based on a limited sampling strategy (0.1 h, 1 h, and 9 h) versus reference values were developed in a training subset (75%) and were evaluated in a testing subset (25%) and in 36 real patients.

Results

The MAP-BE limited sampling strategy estimated clearance was corrected by the machine learning predicted error leading to a decrease in root mean squared error by 29% and 24%, and in the percentage of profiles with the mean prediction error out of the ± 20% bias by 60% and 40% in the external validation dataset for the glmnet and Xgboost machine learning algorithms, respectively. These results were attributable to a decrease in the eta-shrinkage (shrinkage for a MAP-BE limited sampling strategy = 32.4%, glmnet = 18.2%, and Xgboost = 19.4% in the external dataset).

Conclusions

 In conclusion, this hybrid algorithm represents a significant improvement in comparison to MAP-BE estimation alone.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Leger F, Debord J, Le Meur Y, Rousseau A, Buchler M, Lachatre G, et al. Maximum a posteriori Bayesian estimation of oral cyclosporin pharmacokinetics in patients with stable renal transplants. Clin Pharmacokinet. 2002;41:71–80.

    Article  CAS  Google Scholar 

  2. Marquet P, Destère A, Monchaud C, Rérolle J-P, Buchler M, Mazouz H, et al. Clinical pharmacokinetics and Bayesian estimators for the individual dose adjustment of a generic formulation of tacrolimus in adult kidney transplant recipients. Clin Pharmacokinet. 2021;60:611–22.

    Article  CAS  Google Scholar 

  3. Guo W, Yu Z, Gao Y, Lan X, Zang Y, Yu P, et al. A machine learning model to predict risperidone active moiety concentration based on initial therapeutic drug monitoring. Front Psychiatry. 2021;12: 711868.

    Article  Google Scholar 

  4. Woillard J, Labriffe M, Debord J, Marquet P. Tacrolimus exposure prediction using machine learning. Clin Pharmacol Ther. 2021;110:361–9.

    Article  CAS  Google Scholar 

  5. Sibieude E, Khandelwal A, Girard P, Hesthaven JS, Terranova N. Population pharmacokinetic model selection assisted by machine learning. J Pharmacokinet Pharmacodyn. 2022;49:257–70.

    Article  Google Scholar 

  6. Koch G, Pfister M, Daunhawer I, Wilbaux M, Wellmann S, Vogt JE. Pharmacometrics and machine learning partner to advance clinical data analysis. Clin Pharmacol Ther. 2020;107:926–33.

    Article  Google Scholar 

  7. Tang B-H, Guan Z, Allegaert K, Wu Y-E, Manolis E, Leroux S, et al. Drug clearance in neonates: a combination of population pharmacokinetic modelling and machine learning approaches to improve individual prediction. Clin Pharmacokinet. 2021;60:1435–48.

    Article  CAS  Google Scholar 

  8. Hughes JH, Keizer RJ. A hybrid machine learning/pharmacokinetic approach outperforms maximum a posteriori Bayesian estimation by selectively flattening model priors. CPT Pharmacometrics Syst Pharmacol. 2021;10:1150–60.

    Article  CAS  Google Scholar 

  9. Baklouti S, Gandia P, Concordet D. “De-shrinking” EBEs: the solution for Bayesian therapeutic drug monitoring. Clin Pharmacokinet. 2022;61:749–57.

    Article  CAS  Google Scholar 

  10. Åsberg A, Bjerre A, Almaas R, Luis-Lima S, Robertsen I, Salvador CL, et al. Measured GFR by utilizing population pharmacokinetic methods to determine iohexol clearance. Kidney Int Rep. 2020;5:189–98.

    Article  Google Scholar 

  11. Benz-de Bretagne I, Le Guellec C, Halimi JM, Gatault P, Barbet C, Alnajjar A, et al. New sampling strategy using a Bayesian approach to assess iohexol clearance in kidney transplant recipients. Ther Drug Monit. 2012;34:289–97.

    Article  CAS  Google Scholar 

  12. Taubert M, Ebert N, Martus P, van der Giet M, Fuhr U, Schaeffner E. Using a three-compartment model improves the estimation of iohexol clearance to assess glomerular filtration rate. Sci Rep. 2018;8:17723.

    Article  CAS  Google Scholar 

  13. Taubert M, Schaeffner E, Martus P, van der Giet M, Fuhr U, Lösment A, et al. Advancement of pharmacokinetic models of iohexol in patients aged 70 years or older with impaired kidney function. Sci Rep. 2021;11:22656.

    Article  CAS  Google Scholar 

  14. Salmon Gandonnière C, Helms J, Le Tilly O, Benz-de Bretagne I, Bretagnol A, Bodet-Contentin L, et al. Glomerular hyper- and hypofiltration during acute circulatory failure: iohexol-based gold-standard descriptive study. Crit Care Med. 2019;47:e623–9.

    Article  Google Scholar 

  15. Woillard J-B, Salmon Gandonnière C, Destere A, Ehrmann S, Merdji H, Mathonnet A, et al. A machine learning approach to estimate the glomerular filtration rate in intensive care unit patients based on plasma iohexol concentrations and covariates. Clin Pharmacokinet. 2021;60:223–33.

    Article  CAS  Google Scholar 

  16. Destere A, Gandonnière CS, Åsberg A, Loustaud-Ratti V, Carrier P, Ehrmann S, et al. A single Bayesian estimator for iohexol clearance estimation in ICU, liver failure and renal transplant patients. Br J Clin Pharmacol. 2021;88:15197.

    Google Scholar 

  17. SimulX version 2020R1. Antony: Lixoft SAS; 2019. http://lixoft.com/products/monolix/. Accessed 18 May 2022.

  18. Kuhn M, Wickham H. Tidymodels: a collection of packages for modeling and machine learning using tidyverse principles. 2020. https://www.tidymodels.org. Accessed 18 May 2022.

  19. Chen T, Guestrin C. XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco (CA): ACM; 2016: 785–94. https://doi.org/10.1145/2939672.2939785. Accessed 24 Dec 2021.

  20. Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33:1–22.

    Article  Google Scholar 

  21. Monolix version 2018R1. Antony: Lixoft SAS; 2018. http://lixoft.com/products/monolix/. Accessed 18 May 2022.

  22. Woillard J, Labriffe M, Debord J, Marquet P. Mycophenolic acid exposure prediction using machine learning. Clin Pharmacol Ther. 2021;110:370–9.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors acknowledge Karen Poole for her manuscript editing.

Author information

Authors and Affiliations

  1. Pharmacology and Transplantation, INSERM U1248, Université de Limoges, 2 rue du Pr Descottes, 87000, Limoges, France

    Alexandre Destere, Pierre Marquet, Véronique Loustaud-Ratti, Chantal Barin-Le Guellec, Aurélie Premaud & Jean-Baptiste Woillard

  2. Department of Pharmacology, Toxicology and Pharmacovigilance, CHU de Limoges, Limoges, France

    Alexandre Destere, Pierre Marquet & Jean-Baptiste Woillard

  3. Department of Pharmacology and Toxicology, University Hospital of Nice, Nice, France

    Alexandre Destere

  4. Médecine Intensive Réanimation, INSERM CIC 1415, CRICS-TriggerSep Research Network, CHRU de Tours, Tours, France

    Charlotte Salmon Gandonnière & Stephan Ehrmann

  5. Department of Transplantation Medicine, Oslo University Hospital Rikshospitalet, Oslo, Norway

    Anders Åsberg

  6. Department of Pharmacy, University of Oslo, Oslo, Norway

    Anders Åsberg

  7. Department of Hepato-Gastro-Enterology, University Hospital of Limoges, Limoges, France

    Véronique Loustaud-Ratti & Paul Carrier

  8. Centre d’Etude des Pathologies Respiratoires INSERM U1100, Faculté de Médecine, Université de Tours, Tours, France

    Stephan Ehrmann

Authors
  1. Alexandre Destere
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pierre Marquet
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Charlotte Salmon Gandonnière
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anders Åsberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Véronique Loustaud-Ratti
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Paul Carrier
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Stephan Ehrmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Chantal Barin-Le Guellec
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Aurélie Premaud
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jean-Baptiste Woillard
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jean-Baptiste Woillard.

Ethics declarations

Funding

No funding was received for the conduct of this study or the preparation of this article.

Conflicts of interest/competing interests

None of the authors has any conflicts of interest that are directly relevant to the content of this study.

Ethics approval

Ethics approval was obtained.

Consent to participate

The patients included in the validation set had participated in clinical trials for which they had given their consent and they signed informed consent regarding publishing their data.

Availability of data and material

The data that support the findings of this study can be obtained from Alexandre Destere on reasonable request (alexandre.destere@gmail.com).

Code availability

The code is available as Electronic Supplementary Material.

Author contributions

AD, JBW, PM, and AP contributed to the study conception and design. Material preparation and data collections were performed by CSG, AA, VLR, PC, SE, and CBLG and the analysis was conducted by AD and JBW The first draft of the manuscript was written by AD, JBW, and PM and all authors read and commented on the previous versions of the manuscript.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 17 kb)

Supplementary file2 (DOCX 14 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Destere, A., Marquet, P., Gandonnière, C.S. et al. A Hybrid Model Associating Population Pharmacokinetics with Machine Learning: A Case Study with Iohexol Clearance Estimation. Clin Pharmacokinet 61, 1157–1165 (2022). https://doi.org/10.1007/s40262-022-01138-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40262-022-01138-x

Search

Navigation