Biomarkers for epileptogenesis after brain injury

描述（由申请人提供）：脑损伤与继发癫痫之间的潜伏期为数月至数年，为抗癫痫治疗提供了独特的窗口期。在克服以下三个障碍之前，最近有希望的实验性疾病改善疗法的报道无法进入临床试验：首先，量化癫痫是评估干预措施有效性的必要条件，但脑损伤后的癫痫很难量化：第一次癫痫发作的潜伏期很长且多变，早期癫痫发作通常很微妙，不频繁，并且在很长的簇间间隔之间聚集。其次，由于损伤和癫痫之间的潜伏期很长，临床试验需要相当长的时间，因此成本高昂。第三，由于约20%的中度脑损伤患者发生癫痫，尽管暴露于风险和副作用，但大多数接受治疗的患者无法从长期抗癫痫治疗中获益。这三个障碍可以通过足够精确的生物标志物来克服。我们最近证明，早期癫痫样电活动是凯尼克酸所致脑损伤后癫痫的一个有希望的预测指标。在这里，我们建议解决关于癫痫发生的电图生物标志物的关键知识空白。电图生物标志物在更多临床相关损伤（如创伤和缺氧缺血性损伤）后的预测能力尚未得到评估。电图生物标志物的预测能力尚未被系统地与传统的损伤物理描述符（如病变大小和位置）的预测能力进行比较。此外,

DESCRIPTION (provided by applicant): The latent period between brain injury and subsequent epilepsy is months to years in duration, providing a unique window for antiepileptogenic therapy. Recent reports of promising experimental disease-modifying therapies can't advance to clinical trials until three hurdles are overcome: First, quantification f epilepsy is necessary to assess efficacy of interventions, but epilepsy after brain injury is very difficult to quantify: the latency to first seizure is long and variable, and early seizures are ofen subtle, infrequent, and clustered between long inter-cluster intervals. Second, because of the long latency between injury and epilepsy, clinical trials need to be quite prolonged and therefore prohibitively expensive. Third, because ~ 20% of moderately brain-injured patients develop epilepsy, most of the treated patients could not benefit from long-term antiepileptogenic therapy, despite exposure to the risks and side effects. These three hurdles could be overcome with sufficiently accurate biomarkers. We recently demonstrated that early electrographic epileptiform activity is a promising predictor of epilepsy after brain injury induced by kainic aci. Here we propose to address critical knowledge gaps regarding electrographic biomarkers of epileptogenesis. The predictive power of electrographic biomarkers has not been assessed after more clinically relevant injuries such as trauma and hypoxic-ischemic injury. The predictive power of electrographic biomarkers has not been systematically compared to the predictive power of traditional physical descriptors of injury, such as lesion size and location. Furthermore, it has not been determined whether combining electrographic and physical-injury parameters would improve their predictive power. We will employ well-established models of clinical injuries, the lateral fluid percussion (LFP) trauma model and the Rice- Vannucci model of focal hypoxia-ischemia in P30 rats. The incidence of epilepsy in these models is close to the human experience, and thus provides a more rigorous test of the predictive power of these biomarkers than the kainate model. Further, the latency to seizures is sufficiently long in these models to enable testing as to whether the appearance of early electrographic biomarkers is more closely related to the time elapsed since the injury, or to the time remaining prior to the first seizure; he nature of these relationships will significantly impact the design of clinical studies of these biomarkers. In Aim 1, we will use a novel miniature telemetry device for continuous recording of video-EEG together with validated, unbiased computer detection algorithms to quantify early epileptiform activity and seizures in these brain injury models. We will optimize EEG sampling and develop the best predictive model based on epileptiform electrographic activity and injury descriptors, and then prospectively test this model in a second group of animals. In Aim 2, we will use the same approach to test whether early electrographic epileptiform activity and injury descriptors predict the severity of epilepsy, including latency to first seizure and seizure frequency, once epilepsy is fully developed.