基因型导向模型用于预测肺移植后的塔克罗利斯初始剂量
Wenwen Du1, Xiaoxing Wang1, Dan Zhang1
1Department of Pharmacy, China-Japan Friendship Hospital, Beijing, China.
Journal of clinical pharmacology
|February 8, 2024
概括
在肺移植后优化塔克罗利斯剂量至关重要. 这项研究开发了一种基因型导向的预测模型,使用7个关键因素来个性化初始塔克罗利斯剂量,改善患者的治疗结果.
科学领域:
- 药物基因组学 药物基因组学
- 移植医学 移植医学 移植医学
- 临床药理学 临床药理学
背景情况:
- 塔克罗利斯的剂量对于肺移植接受者来说至关重要,以达到治疗水平并预防不良事件.
- 目前的试验和错误的剂量方法在确定有效的塔克罗利木斯度方面是不理想的.
- 需要个性化剂量策略来改善塔克罗利斯的疗效和患者的预后.
研究的目的:
- 开发和验证一个预测模型,以确定肺移植患者的最佳初始塔克罗利斯剂量.
- 为了确定影响塔克罗利的药理动力学和度/剂量比的遗传 (SNP) 和临床因素.
- 建立一个基因型指导的算法,用于个性化的塔克罗利斯初始剂量.
主要方法:
- 在210名肺移植受体中,在18个与塔克罗利斯药理学相关的基因中,26个单核酸多态 (SNPs) 的基因定型.
- 对SNP与塔克罗利斯度/剂量比率之间的关联进行分析.
- 构建一个预测模型,将显著的SNP与临床因素 (ALT,胆红素,血红素) 结合起来.
主要成果:
- 开发了一个最终的预测模型,包含7个预测因子 (CYP3A5 rs776746,SLCO1B3 rs4149117,SLC2A2 rs1499821,NFATc4 rs1955915,ALT,直接胆红素,血红素).
- 该模型解释了塔克罗利斯度/剂量比率变异的41.4%,AUC为0.804.
- 该模型表现出很好的适应性 (霍斯默-莱梅肖P=.790) 和预测剂量与观察到的剂量 (r=0.748,P<.001) 有很好的相关性.
结论:
- 成功建立了一个基因型导向的预测模型,用于肺移植接受者的塔克罗利斯初始剂量.
- 这种模型可以帮助个性化塔克罗利斯治疗,从而有可能改善治疗结果.
- 这些发现支持将药物基因组学纳入临床实践,以优化移植患者的免疫抑制.
更多相关视频
相关概念视频
01:01Dosage Regimens: Partial Pharmacokinetic Parameters
It is not uncommon for complete drug pharmacokinetic profiles to remain elusive in pharmacokinetics. This necessitates certain educated assumptions by pharmacokineticists to determine appropriate dosage regimens without comprehensive pharmacokinetic data from animal or human studies. One prevalent assumption is setting the bioavailability factor, denoted as F, to 1 or 100%. This assumption caters to the scenario where a drug doesn't achieve full systemic absorption, resulting in the patient...
01:25Drug Dosing in Renal Diseases: Dose Adjustments Based on Drug Clearance and Elimination Rate Constant
In patients with renal disease, dosage adjustments are necessary to maintain therapeutic plasma drug concentrations and prevent toxicity or subtherapeutic exposure. Renal impairment alters drug pharmacokinetics, especially in conditions like uremia, where changes such as prolonged elimination half-life and altered apparent volume of distribution can significantly affect drug disposition. These changes require careful modification of the dosing regimen to achieve the desired clinical...
01:27Pharmacogenetics of Phase II Enzymes: N-acetyltransferase, Thiopurine S-methyltransferase, UDP-glucuronosyltransferase
Phase II biotransformation reactions are essential for detoxifying and eliminating xenobiotics, including many pharmaceutical compounds. These reactions typically involve conjugation, the covalent attachment of polar endogenous groups such as glucuronic acid, sulfate, methyl, or acetyl moieties to functional groups introduced during Phase I metabolism. The resulting conjugates are more water-soluble, enabling efficient renal or biliary excretion.The major classes of Phase II enzymes include...
01:11Pharmacogenetics of Drug Targets: β₂-Adrenergic Receptors, Apo E, Thymidylate Synthase
Genetic polymorphisms in drug targets have emerged as critical determinants of interindividual variability in drug response and toxicity. Pharmacogenomic investigations increasingly focus on identifying these variations to personalize and optimize therapeutic interventions. A drug target may be a receptor, enzyme, or signaling protein involved in pharmacologic responses or disease-related pathways. While early pharmacogenetic studies focused primarily on drug metabolism, current research...
01:29Pharmacogenomics: Identification of New Drug Targets
Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
01:15Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions
PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure (CHF).