Pharmacodynamic Modeling of Warfarin Dosing Algorithm for Cardiovascular Patients in Indonesia: A Tailored Method to Anticoagulation Therapy.

IF 5.1 2区医学 Q1 CHEMISTRY, MEDICINAL Drug Design, Development and Therapy Pub Date : 2025-01-29 eCollection Date: 2025-01-01 DOI:10.2147/DDDT.S497738

Norisca Aliza Putriana, Irma Rahayu Latarissa, Taofik Rusdiana, Tina Rostinawati, Mohammad Rizki Akbar

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Abstract

Purpose: Warfarin is an anticoagulant drug widely used for treating thromboembolism-related conditions. The main challenge with this drug is the high variability in patients response, which is influenced by both clinical, non-clinical, and genetic factors, such as VKORC1, CYP2C9, and CYP4F2. Therefore, this research aimed to evaluate the impact of clinical and genetic factors on warfarin dose adjustment and to develop a dosing algorithm for patients with cardiovascular disease.

Patients and methods: A total of 77 research subjects were selected using consecutive sampling based on the inclusion criteria of cardiac outpatients on warfarin for ≥3 months with PT-INR data, complete medical records, and willingness to participate. Exclusion criteria included vitamin K use and inability to follow up. Patients demographic data and clinical characteristics were collected from medical records. Blood samples were obtained for genetic testing of CYP4F2 rs2108622 (sequencing). Statistical analyses included both bivariate and multivariate analyses (logistic regression) with a significance level set at <0.05.

Results: Statistical analysis using the Kruskal-Wallis test showed that the CC, CT, and TT genotypes were significantly associated with warfarin dose (p = 0.02). Furthermore, the Mann-Whitney test results showed that gender did not have a significant relationship with warfarin dose (p = 0.16). The Spearman Rank correlation test showed that age (p = 0.02) and BMI (p = 0.03) had significant relationships with warfarin dose (p < 0.05). However, gender (p = 0.89) had no effect, while age (p = 0.01), BMI (p = 0.01), and genotype (p = 0.01) significantly influenced warfarin dose determination.

Conclusion: In conclusion, the combined contribution of age (8.76%), BMI (7.95%), and CYP4F2 genotype (8.29%) to warfarin dose adjustment was 25%. The linear regression model for predicting warfarin dose was determined to be y = 12.736-0.16*age + 0.55*BMI + 3.55*genotype, where 1 = CC, 2 = CT, and 3 = TT.

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印尼心血管患者华法林给药算法的药效学建模：一种适合抗凝治疗的方法。

目的：华法林是一种广泛用于治疗血栓相关疾病的抗凝药物。该药物的主要挑战是患者反应的高度可变性，这受到临床、非临床和遗传因素的影响，如VKORC1、CYP2C9和CYP4F2。因此，本研究旨在评估临床和遗传因素对华法林剂量调整的影响，并为心血管疾病患者制定一种给药算法。患者和方法：按照PT-INR数据、病历完整、有参与意愿且接受华法林治疗≥3个月的心脏门诊患者纳入标准，采用连续抽样的方法，共选择77例研究对象。排除标准包括维生素K的使用和无法随访。从病历中收集患者的人口学资料和临床特征。采集血样进行CYP4F2 rs2108622基因检测（测序）。统计分析包括双变量和多变量分析（逻辑回归），结果的显著性水平设置为：使用Kruskal-Wallis检验的统计分析显示，CC、CT和TT基因型与华法林剂量显著相关（p = 0.02）。此外，Mann-Whitney检验结果显示，性别与华法林剂量无显著关系（p = 0.16）。Spearman秩相关检验显示，年龄（p = 0.02）和BMI （p = 0.03）与华法林剂量有显著相关（p < 0.05）。性别（p = 0.89）对华法林剂量测定无影响，而年龄（p = 0.01）、BMI （p = 0.01）、基因型（p = 0.01）对华法林剂量测定有显著影响。结论：年龄（8.76%）、BMI（7.95%）和CYP4F2基因型（8.29%）对华法林剂量调整的综合贡献率为25%。预测华法林剂量的线性回归模型为y = 12.736 ~ 0.16*年龄+ 0.55*BMI + 3.55*基因型，其中1 = CC, 2 = CT, 3 = TT。

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来源期刊

Drug Design, Development and Therapy CHEMISTRY, MEDICINAL-PHARMACOLOGY & PHARMACY

CiteScore

9.00

自引率

0.00%

发文量

382

审稿时长

>12 weeks

期刊介绍： Drug Design, Development and Therapy is an international, peer-reviewed, open access journal that spans the spectrum of drug design, discovery and development through to clinical applications. The journal is characterized by the rapid reporting of high-quality original research, reviews, expert opinions, commentary and clinical studies in all therapeutic areas. Specific topics covered by the journal include: Drug target identification and validation Phenotypic screening and target deconvolution Biochemical analyses of drug targets and their pathways New methods or relevant applications in molecular/drug design and computer-aided drug discovery* Design, synthesis, and biological evaluation of novel biologically active compounds (including diagnostics or chemical probes) Structural or molecular biological studies elucidating molecular recognition processes Fragment-based drug discovery Pharmaceutical/red biotechnology Isolation, structural characterization, (bio)synthesis, bioengineering and pharmacological evaluation of natural products** Distribution, pharmacokinetics and metabolic transformations of drugs or biologically active compounds in drug development Drug delivery and formulation (design and characterization of dosage forms, release mechanisms and in vivo testing) Preclinical development studies Translational animal models Mechanisms of action and signalling pathways Toxicology Gene therapy, cell therapy and immunotherapy Personalized medicine and pharmacogenomics Clinical drug evaluation Patient safety and sustained use of medicines.