为了在结核性脑膜炎中获得最佳的莫西弗洛克萨剂量:一种基于转化生理学的药理动力学建模方法
Ming Sun1, Katie Lynch2, Theis Mariager3
1Systems Pharmacology and Pharmacy, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, the Netherlands; ESCMID Study Group for Infectious Diseases of the Brain (ESGIB), Basel, Switzerland.
概括
结核性脑膜炎 (TBM) 可能需要更高的莫西素剂量,即使是与里芬素. 超过800毫克的剂量需要在临床试验中进行安全评估,以确保有效治疗这种严重的中枢神经系统感染.
科学领域:
- 药理动力学和药理动力学
- 传染性疾病 传染性疾病
- 神经科学是一个神经科学.
背景情况:
- 结核性脑膜炎 (TBM) 是一种严重的中枢神经系统感染,死亡率高.
- 莫西弗洛克萨具有抗菌细菌活性和中枢神经系统透,但在TBM中最佳的剂量尚不清楚,特别是与里芬素.
- 利芬素显著降低了莫西弗洛克萨辛的暴露.
研究的目的:
- 为了确定TBM.moxifloxacin的最佳剂量.
- 为了考虑区域中枢神经系统的药理动力学 (PK) 和利芬素的同时使用.
- 使用跨物种的转化生理学基础药理动力学 (PBPK) 模型.
主要方法:
- 使用猪血和中枢神经系统微透析数据开发了一个PBPK模型.
- 将模型转化为人类,使用全米缩放和文献数据.
- 用人类PK数据验证了该模型,并模拟了与/或没有利法素的各种莫西素剂量 (400-1000毫克).
主要成果:
- 该PBPK模型准确地描述了猪和人类中枢神经系统中的莫西素度.
- 观察到区域性PK差异,其中度最高的是在下大脑节空间,最低的是在脑细胞外液中.
- 利芬素将中枢神经系统的暴露量降低了26%;需要更高的莫西素剂量 (高达1000毫克) 来达到目标水平,特别是利芬素.
结论:
- 对于TBM来说,可能需要更高的莫西弗洛克萨辛剂量来实现PK目标.
- 剂量调整可能是必要的,不管是与里芬素同时服用.
- 超过800毫克的莫西弗洛克萨辛疗程的安全性需要严格的临床调查.
相关概念视频
01:28Pulmonary Tuberculosis V
Medical management of tuberculosis (TB) patients involves a comprehensive approach that includes diagnosis, treatment, and monitoring. The specific strategies can vary depending on the type of tuberculosis (latent or active), the patient's overall health status, and other considerations.
Latent tuberculosis infection occurs when TB bacteria are present in a person's body, but are not causing illness or symptoms. It is not contagious, and preventive treatment is crucial to avoid the progression...
Latent tuberculosis infection occurs when TB bacteria are present in a person's body, but are not causing illness or symptoms. It is not contagious, and preventive treatment is crucial to avoid the progression...
01:26Pharmacokinetic Models: Comparison and Selection Criterion
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
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...
01:27Pharmacodynamic Models: Overview
Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
01:15Pharmacodynamic Models: Linear Concentration–Effect Model
The linear concentration–effect model, underpinned by the principle that pharmacological effect (E) is directly proportional to plasma drug concentration (C), emerges as a pivotal simplification of the Emax model for conditions where C is significantly less than EC50. This model portrays a linear trajectory of the concentration–effect relationship when drug levels are markedly below the EC50 threshold.Despite its inherent assumption of continuous effect augmentation with increasing drug...
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).