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Background <p>Achieving optimal tacrolimus dosing is vital for effectively balancing therapeutic efficacy and safety, as <i>CYP3A5</i> genetic variants and inter-patient variability emphasize the need for precision strategies.</p> Aim <p>This study aimed to optimize tacrolimus dosage prediction for renal transplant recipients by incorporating genetic polymorphisms, specifically profiling <i>CYP3A5</i> genetic variants, within the DoseOptimal framework to enhance interpretability and accuracy of dosing decisions.</p> Method <p>The dataset comprised clinical, demographic, and <i>CYP3A5</i> genetic variants information from 1045 stable tacrolimus-treated patients. The DoseOptimal framework was developed by integrating the strengths of the most effective algorithms from fifteen machine learning models. SHapley Additive exPlanations (SHAP) and decision tree insights were incorporated to enhance the framework’s interpretability. The framework’s performance was assessed using mean absolute error (MAE) and the coefficient of determination (R<sup>2</sup> score). The F-statistic and <i>p</i> value were calculated to validate the framework’s statistical significance.</p> Results <p>The DoseOptimal framework demonstrated robust performance with an R<sup>2</sup> score of 0.884 in the training set and 0.830 in the testing set. The MAE was 0.40&#xa0;mg/day (95% CI 0.38–0.43) in the training set and 0.41&#xa0;mg/day (95% CI 0.38–0.45) in the testing set. The framework predicted the ideal tacrolimus dosage in 87.6% (n = 275) of the test cohort, with 3.2% (n = 10) underestimation and 9.2% (n = 29) overestimation. The framework’s statistical significance was confirmed with an F-statistic of 266.095 and a <i>p</i> value &lt; 0.001.</p> Conclusion <p>The framework provides precision medicine-based dosing solutions tailored to individual genetic profiles, minimizing dosing errors and enhancing patient outcomes.</p>

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Optimizing tacrolimus dosage in post-renal transplantation using DoseOptimal framework: profiling CYP3A5 genetic variants for interpretability

  • 期刊论文

  • 发表日期：

    2025年3月21日

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  • Chintal Upendra Raval,
  • Ashwin Makwana,
  • Samir Patel,
  • Rashmi Hemani,
  • Sachchida Nand Pandey

摘要

Background

Achieving optimal tacrolimus dosing is vital for effectively balancing therapeutic efficacy and safety, as CYP3A5 genetic variants and inter-patient variability emphasize the need for precision strategies.

Aim

This study aimed to optimize tacrolimus dosage prediction for renal transplant recipients by incorporating genetic polymorphisms, specifically profiling CYP3A5 genetic variants, within the DoseOptimal framework to enhance interpretability and accuracy of dosing decisions.

Method

The dataset comprised clinical, demographic, and CYP3A5 genetic variants information from 1045 stable tacrolimus-treated patients. The DoseOptimal framework was developed by integrating the strengths of the most effective algorithms from fifteen machine learning models. SHapley Additive exPlanations (SHAP) and decision tree insights were incorporated to enhance the framework’s interpretability. The framework’s performance was assessed using mean absolute error (MAE) and the coefficient of determination (R2 score). The F-statistic and p value were calculated to validate the framework’s statistical significance.

Results

The DoseOptimal framework demonstrated robust performance with an R2 score of 0.884 in the training set and 0.830 in the testing set. The MAE was 0.40 mg/day (95% CI 0.38–0.43) in the training set and 0.41 mg/day (95% CI 0.38–0.45) in the testing set. The framework predicted the ideal tacrolimus dosage in 87.6% (n = 275) of the test cohort, with 3.2% (n = 10) underestimation and 9.2% (n = 29) overestimation. The framework’s statistical significance was confirmed with an F-statistic of 266.095 and a p value < 0.001.

Conclusion

The framework provides precision medicine-based dosing solutions tailored to individual genetic profiles, minimizing dosing errors and enhancing patient outcomes.