PHARMACOEPIDEMIOLOGY OF THE ANTIEPILEPTIC DRUGS

癫痫的发病率在60岁以后迅速上升，许多老年人正在接受苯妥英钠(PHT)、卡马西平(CBZ)和丙戊酸(VPA)的治疗。这些较老的抗癫痫药物具有许多缺点，包括细胞色素P450和葡萄糖醛酸化酶系统代谢复杂，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，涉及两种清除 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联合用药而改变，而AEDs可能会影响联合用药的疗效和毒性。由于一名老年患者平均使用6种药物，这个年龄段的患者与这些老年AEDs发生药物相互作用的风险非常高。拉莫三嗪(LTG)、托吡酯(TPM)和左乙拉西坦(LEV)这三种新的AEDs似乎具有更有利的药物-药物相互作用谱；所有这些药物的蛋白质结合量都很低，代谢相互作用很少或根本没有。然而，这些新药 在老年人中还没有得到充分的研究，需要关于这些药物的药代动力学和药效学性质的更详细的信息。从这一人群中获取血液样本的困难使得纳入标准的药物动力学研究变得困难。本项目将使用结合药代动力学和统计学的非线性混合效应模型(NONMEM)来确定这三种新药的药物动力学参数。这种强大的方法允许使用常规收集的数据，并避免了在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，不仅可以确定人群的药物清除量，也可以确定个人的清除量。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，还将确定LTG、TPM和LEV的血药浓度与癫痫发作类型的关系。我们将可以访问大约450名65岁的患者接受LTG治疗，420人接受TPM治疗，337人接受LEV治疗，这些患者来自3个城市(明尼阿波利斯、迈阿密、亚特兰大)的几个活跃的癫痫实践，以及超过1500名年轻人关于这些AEDs的数据。此外，我们还将 使用我们的工具从正在进行的LTG合作研究的角度分析数据，预计纳入240名接受LTG作为初始治疗的受试者。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，为老年人提供药代动力学和药效学信息。该项目以及项目1和2将提供药代动力学数据，并确定和量化 影响LTG、LEV和TPM药代动力学的因素。这些信息可用于指导所需的剂量要求，以获得老年人的目标血清浓度，以实现癫痫控制和避免药物毒性。

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.