Some antiseizure drugs (ASDs) are associated with cognitive liability in patients with epilepsy, thus ASDs without this risk would be preferred. Little comparative pharmacology exists with ASDs in preclinical models of cognition. Few pharmacologic studies exist on the acute effects in rodents with chronic seizures. Predicting risk for cognitive impact with preclinical models may supply valuable ASD differentiation data.
ASDs (phenytoin [PHT]; carbamazepine [CBZ]; valproic acid [VPA]; lamotrigine [LTG]; phenobarbital [PB]; tiagabine [TGB]; retigabine [RTG]; topiramate [TPM]; and levetiracetam [LEV]) were administered equivalent to maximal electroshock median effective dose ([ED50]; mice, rats), or median dose necessary to elicit minimal motor impairment (median toxic dose [TD50]; rats). Cognition models with naive adult rodents were novel object/place recognition (NOPR) task with CF-1 mice, and Morris water maze (MWM) with Sprague-Dawley rats. Selected ASDs were also administered to rats prior to testing in an open field. The effect of chronic seizures and ASD administration on cognitive performance in NOPR was also determined with corneal-kindled mice. Mice that did not achieve kindling criterion (partially kindled) were included to examine the effect of electrical stimulation on cognitive performance. Sham-kindled and age-matched mice were also tested.
No ASD (ED50) affected latency to locate the MWM platform; TD50 of PB, RTG, TPM, and VPA reduced this latency. In naive mice, CBZ and VPA (ED50) reduced time with the novel object. Of interest, no ASD (ED50) affected performance of fully kindled mice in NOPR, whereas CBZ and LEV improved cognitive performance of partially kindled mice.
Standardized approaches to the preclinical evaluation of an ASD’s potential cognitive impact are needed to inform drug development. This study demonstrated acute, dose- and model-dependent effects of therapeutically relevant doses of ASDs on cognitive performance of naive mice and rats, and corneal-kindled mice. This study highlights the challenge of predicting clinical adverse effects with preclinical models.