PHARMACOEPIDEMIOLOGY OF THE ANTIEPILEPTIC DRUGS

60岁以后癫痫的发病率迅速上升，许多老年人正在接受苯妥英（PHT）、卡马西平（CBZ）和丙戊酸（VPA）治疗。这些较老的抗癫痫药物（AED）有许多缺点，包括细胞色素P450和葡萄糖醛酸化酶系统的复杂代谢，并已被证明是这些系统的诱导剂和抑制剂。这使得它们容易发生许多药物相互作用， 和蛋白质结合。这是一个多方面的问题; AED的疗效和毒性可能会因合并用药而改变，并且AED会影响合并用药的疗效和毒性。由于老年患者平均使用6种药物，因此该年龄组与这些老年AED发生药物相互作用的风险非常高。三种较新的AED，拉莫三嗪（LTG），托吡酯（TPM）和左乙拉西坦（LEV）似乎具有更有利的药物相互作用概况;所有药物的蛋白结合率低，代谢相互作用较少或没有。然而，这些新药 尚未在老年人中进行充分研究，需要关于这些药物的药代动力学和药效学特性的更详细信息。从该人群中获得血液样本的困难使得难以纳入标准药代动力学研究。本项目将采用非线性混合效应模型（NONMEM），该模型采用药代动力学和统计学，将用于确定这三种新药的药代动力学参数。这种强大的方法允许使用常规收集的数据，并避免了密集的药代动力学研究中遇到的风险和费用。使用这种方法，不仅可以确定群体的药物清除率，还可以确定个体的药物清除率。因素（年龄、种族、性别、吸烟等）也可以确定影响药物清除的因素。此外，将确定LTG、TPM和LEV的血清药物浓度与癫痫发作类型之间的关系。我们将访问来自3个城市（明尼阿波利斯、迈阿密、亚特兰大）的几个活跃癫痫实践的约450名>65岁接受LTG的患者、420名接受TPM的患者和337名接受LEV的患者，以及来自1500多名使用这些AED的年轻成人的数据。此外，我们将 使用我们的工具来分析数据，从正在进行的角度VA合作研究LTG预计招募240名受试者接受LTG作为初始治疗。VA数据将确定LTG的药物浓度与不良事件和癫痫发作频率之间的关系，提供老年人的药代动力学和药效学信息。本项目沿着项目1和2将提供药代动力学数据，并确定和定量 影响LTG、LEV和TPM药代动力学的因素。该信息可用于指导获得老年人目标血清浓度所需的给药要求，以实现癫痫控制并避免药物毒性。

The incidence of epilepsy rises rapidly after age 60, and many elderly are being treated with phenytoin (PHT), carbamazepine (CBZ) and valproic acid (VPA). These older antiepileptic drugs (AEDs) have many shortcomings including complex metabolism by the cytochrome P450 and glucuronidation enzyme systems and have been shown to be inducers and inhibitors in these systems. This makes them prone to many drug interactions involving both clearance and protein binding. This is a multifaceted issue; AED efficacy and toxicity may be altered by co-medications, and AEDs can affect the efficacy and toxicity of co-medications. Because an elderly patient uses an average of 6 medications, the risk of medication interactions in this age group with these older AEDs is very high. Three newer AEDs, lamotrigine (LTG), topiramate (TPM) and levetiracetam (LEV) appear to have more favorable drug-drug interaction profiles; all have low protein binding and fewer or no metabolic interactions. However, these newer drugs have not been studied sufficiently in the eldedy and more detailed information regarding the pharmacokinetio and pharmacodynamic properties of these is needed. The difficulty in obtaining blood samples from this population makes inclusion in standard pharrnacokinetic studies difficult. This project will use nonlinear mixed effects model (NONMEM) that employs both pharmacokinetics and statistics will be used to determine pharrnacokinetic parameters of these three new drugs. This powerful method allows the use of routinely collected data to be used and avoids the risks and expense encountered in intensive pharmacokinetic studies. With this method, not only can the drug clearance be determined for a population, but it can also be determined for an individual. Factors (age, race, gender, smoking, etc.) that affect drug clearance can also be determined. In addition, the relationship between serum drug concentration and seizure type will be determined for LTG, TPM, and LEV. We will have access to approximately 450 persons >65 years of age receiving LTG, 420 receiving TPM and 337 receiving LEV from several active epilepsy practices in 3 cities (Minneapolis, Miami, Atlanta) and data from more than 1500 younger adults on each of these AEDs. In addition we will use our tools to analyze data from the ongoing perspective VA cooperative study of LTG projected to enroll 240 subjects receiving LTG as initial treatment. The VA data will determine the relationship between drug concentrations and adverse events and seizure frequency for LTG providing both pharmacokinetic and pharmacodynamic information in the elderly. This project along with Projects 1 and 2 will provide pharmacokinetic data and identify and quantitate the factors that influence the pharmacokinetics of LTG, LEV, and TPM. This information can be used to guide dosing requirements needed to obtain target serum concentrations in the elderly to achieve seizure control and avoid drug toxicity.