A model based on machine learning for the prediction of cyclosporin A trough concentration in Chinese allo-HSCT patients

ABSTRACT

Background

Cyclosporin A is a calcineurin inhibitor which has a narrow therapeutic window and high interindividual variability. Various population pharmacokinetic models have been reported; however, professional software and technical personnel were needed and the variables of the models were limited. Therefore, the aim of this study was to establish a model based on machine learning to predict CsA trough concentrations in Chinese allo-HSCT patients.

Methods

A total of 7874 cases of CsA therapeutic drug monitoring data from 2069 allo-HSCT patients were retrospectively included. Sequential forward selection was used to select variable subsets, and eight different algorithms were applied to establish the prediction model.

Results

XGBoost exhibited the highest prediction ability. Except for the variables that were identified by previous studies, some rarely reported variables were found, such as norethindrone, WBC, PAB, and hCRP. The prediction accuracy within ±30% of the actual trough concentration was above 0.80, and the predictive ability of the models was demonstrated to be effective in external validation.

Conclusion

In this study, models based on machine learning technology were established to predict CsA levels 3–4 days in advance during the early inpatient phase after HSCT. A new perspective for CsA clinical application is provided.

KEYWORDS:

• Blood concentration prediction
• cyclosporine
• hematopoietic stem cell transplantation
• machine learning
• XGBoost

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Author contributions

L Song, C-R Huang, L Xue, J-G Zhu and L-Y Miao designed the research. L Song, S-Z Pan, Z-Q Cheng, X Yu and J-Y Zhang collected the data and were responsible for statistical analysis. L Xue and F Xia contributed to data interpretation. L Song drafted and wrote the manuscript and all authors revised and reviewed the manuscript.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/17512433.2023.2142561

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This manuscript was funded by the Suzhou talent research project (GSWS2019036); Jiangsu Research Hospital Association for Precision Medication (JY202004); National Clinical Research Center for Hematologic Diseases, the First Affiliated Hospital of Soochow University (2020WSC07) and Key R&D Program of Jiangsu Province (BE2021644).