现实世界中细胞色素 P450 诱导剂和抑制剂的处方模式及其潜在的药物相互作用:一项横断面研究。

IF 1.9 Q3 PHARMACOLOGY & PHARMACY Drugs - Real World Outcomes Pub Date : 2024-09-07 DOI:10.1007/s40801-024-00450-1
Luis Fernando Valladales-Restrepo, Juan Alberto Ospina-Cano, Brayan Stiven Aristizábal-Carmona, Jorge Enrique Machado-Alba
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引用次数: 0

摘要

导言:细胞色素 P450 的诱导和抑制都与多种药理作用有关,这些作用可能导致药效丧失或增加药物不良反应的风险:旨在确定哥伦比亚一组患者的细胞色素 P450 诱导药和抑制药处方模式及其禁忌和主要药理相互作用:这项横断面观察研究包括接受诱导或抑制新陈代谢药物治疗的患者,并研究了这些药物的禁忌和主要药理作用。这些患者是从一个基于人口的配药数据库中确定的。纳入患者的时间为 2021 年 12 月 1 日至 12 月 31 日。细胞色素 P450 的抑制剂和诱导剂根据 FDA(美国食品和药物管理局)指南进行分类。使用 Micromedex® 数据库确定药物相互作用。进行了描述性分析、双变量分析和多变量分析:共分析了 63,433 名患者。抗癫痫药(35.9%)和抗真菌药(27.6%)是使用最多的诱导剂和抑制剂。共有 30.1%的患者有潜在的禁忌或更大的相互作用。13)、患有风湿病(OR 1.32;95% CI 1.23-1.41)、服用他汀类药物(OR 1.14;95% CI 1.08-1.19)、服用镇痛剂(OR 1.33;95% CI 1.27-1.38)、服用抗寄生虫药物(OR 2.88;95% CI 2.66-3.11)以及药物数量增加(OR 1.24;95% CI 1.23-1.25):在细胞色素 P450 抑制剂和诱导剂的使用者中,潜在的禁忌症和更大的相互作用非常普遍,尤其是在 65 岁以下患有合并症和多种药物的男性中。
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Prescription Patterns of Inducers and Inhibitors of Cytochrome P450 and Their Potential Drug Interactions in the Real World: A Cross-Sectional Study.

Introduction: Both the induction and inhibition of cytochrome P450 are associated with multiple pharmacological interactions, which can lead to loss of efficacy or increase the risk of adverse drug reactions.

Objective: The aim was to determine the prescription patterns of cytochrome P450-inducing and -inhibiting drugs and their contraindicated and major pharmacological interactions in a group of patients from Colombia.

Methods: This cross-sectional observational study included patients who received drugs that induce or inhibit metabolism and examined their contraindicated and major pharmacological interactions. The patients were identified from a population-based database of drug dispensing. Patients were included between December 1 and December 31, 2021. Inhibitors and inducers of cytochrome P450 were classified based on FDA (Food and Drug Administration) guidelines. Drug interactions were identified using the Micromedex® database. Descriptive, bivariate and multivariable analysis was performed.

Results: A total of 63,433 patients were analyzed. Antiseizure medications (35.9%) and antifungals (27.6%) were the most used inducers and inhibitors. A total of 30.1% of patients had potential contraindicated or greater interactions. The following factors were associated with a higher probability of presenting a potential pharmacological interaction: being male (OR 1.14; 95% CI 1.10-1.19), aged 18-39 years (OR 1.77; 95% CI 1.67-1.89) or 40-64 years (OR 1.64; 95% CI 1.56-1.72), having neurological diseases (OR 1.28; 95% CI 1.21-1.35), having psychiatric diseases (OR 3.84; 95% CI 3.58-4.13), having rheumatologic diseases (OR 1.32; 95% CI 1.23-1.41), receiving comedications with statins (OR 1.14; 95% CI 1.08-1.19), receiving comedications with analgesics (OR 1.33; 95% CI 1.27-1.38), receiving comedications with antiparasitics (OR 2.88; 95% CI 2.66-3.11) and an increase in the number of medications (OR 1.24; 95% CI 1.23-1.25).

Conclusion: Among the users of cytochrome P450 inhibitors and inducers, potential contraindications and greater interactions are very common, especially in men under 65 years of age with comorbidities and polypharmacy.

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来源期刊
Drugs - Real World Outcomes
Drugs - Real World Outcomes PHARMACOLOGY & PHARMACY-
CiteScore
3.60
自引率
5.00%
发文量
49
审稿时长
8 weeks
期刊介绍: Drugs - Real World Outcomes targets original research and definitive reviews regarding the use of real-world data to evaluate health outcomes and inform healthcare decision-making on drugs, devices and other interventions in clinical practice. The journal includes, but is not limited to, the following research areas: Using registries/databases/health records and other non-selected observational datasets to investigate: drug use and treatment outcomes prescription patterns drug safety signals adherence to treatment guidelines benefit : risk profiles comparative effectiveness economic analyses including cost-of-illness Data-driven research methodologies, including the capture, curation, search, sharing, analysis and interpretation of ‘big data’ Techniques and approaches to optimise real-world modelling.
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