Advances in Clinical and Experimental Medicine

Title abbreviation: Adv Clin Exp Med
JCR Impact Factor (IF) – 1.736
5-Year Impact Factor – 2.135
Index Copernicus  – 168.52
MEiN – 70 pts

ISSN 1899–5276 (print)
ISSN 2451-2680 (online)
Periodicity – monthly

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Advances in Clinical and Experimental Medicine

Ahead of print

doi: 10.17219/acem/161155

Publication type: research letter

Language: English

License: Creative Commons Attribution 3.0 Unported (CC BY 3.0)

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Sridharan K, Al Banna RA, Husain A. Time-to-event modeling for achieving a stable warfarin dose using genetic and non-genetic covariates [published online as ahead of print on March 10, 2023]. Adv Clin Exp Med. 2023. doi:10.17219/acem/161155

Time-to-event modeling for achieving a stable warfarin dose using genetic and non-genetic covariates

Kannan Sridharan1,A,B,C,D,E,F, Rashed Abdulla Al Banna2,B,E,F, Aisha Husain2,B,E,F

1 Department of Pharmacology and Therapeutics, College of Medicine and Medical Sciences, Arabian Gulf University, Manama, Bahrain

2 Department of Cardiology, Salmaniya Medical Complex, Manama, Bahrain


Background. Time-to-event modeling is gaining importance in drug dosage determination, particularly using pharmacometrics approaches.
Objectives. To evaluate the various time-to-event models for estimating the time to achieve a stable warfarin dose in the Bahraini population.
Material and Methods. A cross-sectional study evaluating the non-genetic and genetic covariates (single nucleotide polymorphisms (SNPs) in CYP2C9, VKORC1 and CYP4F2 genotypes) was conducted in patients receiving warfarin for at least 6 months. Time to achieving a stable dose of warfarin was defined as the duration (in days) from the day of initiating warfarin until 2 consecutive prothrombin time-international normalized ratio (PT-INR) values were observed in the therapeutic range with a gap of at least 7 days. Exponential, Gompertz, log-logistics, and Weibull models were tested, and the model with the lowest objective function value (OFV) was chosen. The covariate selection was carried out using the Wald test and OFV. A hazard ratio (HR) with a 95% confidence interval (95% CI) was estimated.
Results. A total of 218 participants were included in the study. The Weibull model was observed to have the lowest OFV (1989.82). The expected time to reach a stable dose in the population was 21.35 days. The CYP2C9 genotypes were identified as the only significant covariate. The HR (95% CI) for achieving a stable warfarin dose within 6 months of initiation for individuals with CYP2C9 *1/*2 was 0.2 (0.09, 0.3), 0.2 (0.1, 0.5) for CYP2C9 *1/*3, 0.14 (0.04, 0.6) for CYP2C9 *2/*2, 0.2 (0.03, 0.9) for CYP2C9 *2/*3, and 0.8 (0.45, 0.9) for those with the C/T genotype for CYP4F2.
Conclusion. We estimated the population time-to-event parameters for achieving a stable warfarin dose in our population and observed CYP2C9 genotypes to be the main predictor covariate followed by CYP4F2. The influence of these SNPs needs to be validated in a prospective study and an algorithm to predict a stable warfarin dose and the time to achieve it needs to be developed.

Key words

warfarin, Weibull, Gompertz, time-to-event, exponential

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References (8)

  1. Patel S, Singh R, Preuss C, Patel N. Warfarin. In: StatPearls. Treasure Island, USA: StatPearls Publishing; 2022. Accessed June 1, 2022.
  2. Dean L. Warfarin therapy and VKORC1 and CYP genotype. In: Dean L, Pratt V, Scott S, et al., eds. Medical Genetic Summaries. Bethesda, USA: National Center for Biotechnology Information (US); 2018.
  3. Holford N. A time to event tutorial for pharmacometricians. CPT Pharmacometrics Syst Pharmacol. 2013;2(5):e43. doi:10.1038/psp.2013.18
  4. Gong X, Hu M, Zhao L. Big data toolsets to pharmacometrics: Application of machine learning for time-to-event analysis. Clin Transl Sci. 2018;11(3):305–311. doi:10.1111/cts.12541
  5. Sridharan K, Al Banna R, Malalla Z, et al. Influence of CYP2C9, VKORC1, and CYP4F2 polymorphisms on the pharmacodynamic parameters of warfarin: A cross-sectional study. Pharmacol Rep. 2021;73(5):1405–1417. doi:10.1007/s43440-021-00256-w
  6. Sridharan K, Banny RA, Husain A. Evaluation of stable doses of warfarin in a patient cohort. Drug Res (Stuttg). 2020;70(12):570–575. doi:10.1055/a-1228-5033
  7. Teuscher N. What is the -2LL or the log-likelihood ratio? 2013. Accessed November 1, 2022.
  8. Sedgwick P. Hazards and hazard ratios. BMJ. 2012;345:e5980. doi:10.1136/bmj.e5980