New anticonvulsant candidates prevent P-glycoprotein (P-gp) overexpression in a pharmacoresistant seizure model in mice.

Despite the approval of a considerable number of last generation antiepileptic drugs (AEDs) (only in the last decade, six drugs have gained Food and Drug Administration approval), the global figures of seizure control have seemingly not improved, and available AED can still be regarded as symptomatic treatments. Fresh thinking in AEDs drug discovery, including the development of drugs with novel mechanisms of action, is required to achieve truly innovative antiepileptic medications. The transporter hypothesis proposes that inadequate penetration of AEDs across the blood-brain barrier, caused by increased expression of efflux transporters such as P-glycoprotein (P-gp), contributes to drug-resistant epilepsy. Neuroinflammation due to high levels of glutamate has been identified as one of the causes of P-gp upregulation, and several studies in animal models of epilepsy suggest that antiinflammatory drugs might prevent P-gp overexpression and, thus, avoid the development of refractory epilepsy. We have applied ligand-based in silico screening to select compounds that exert dual anticonvulsant and antiinflammatory effects. Five of the hits were tested in animal models of seizure, with protective effects. Later, two of them (sebacic acid (SA) and gamma-decanolactone) were submitted to the recently described MP23 model of drug-resistant seizures. All in all, SA displayed the best profile, showing activity in the maximal electroshock seizure (MES) and pentylenetetrazol (PTZ) seizure models, and reversing resistance to phenytoin (PHT) and decreasing the P-gp upregulation in the MP23 model. Furthermore, pretreatment with SA in the pilocarpine status epilepticus (SE) model resulted in decreased histamine release in comparison with nontreated animals. This is the first report of the use of the MP23 model to screen for novel anticonvulsant compounds that may avoid the development of P-gp-related drug resistance.

Positive Predictive Value Surfaces as a Complementary Tool to Assess the Performance of Virtual Screening Methods.

BACKGROUND: Since their introduction in the virtual screening field, Receiver Operating Characteristic (ROC) curve-derived metrics have been widely used for benchmarking of computational methods and algorithms intended for virtual screening applications. Whereas in classification problems, the ratio between sensitivity and specificity for a given score value is very informative, a practical concern in virtual screening campaigns is to predict the actual probability that a predicted hit will prove truly active when submitted to experimental testing (in other words, the Positive Predictive Value - PPV). Estimation of such probability is however, obstructed due to its dependency on the yield of actives of the screened library, which cannot be known a priori. OBJECTIVE: To explore the use of PPV surfaces derived from simulated ranking experiments (retrospective virtual screening) as a complementary tool to ROC curves, for both benchmarking and optimization of score cutoff values. METHODS: The utility of the proposed approach is assessed in retrospective virtual screening experiments with four datasets used to infer QSAR classifiers: inhibitors of Trypanosoma cruzi trypanothione synthetase; inhibitors of Trypanosoma brucei N-myristoyltransferase; inhibitors of GABA transaminase and anticonvulsant activity in the 6 Hz seizure model. RESULTS: Besides illustrating the utility of PPV surfaces to compare the performance of machine learning models for virtual screening applications and to select an adequate score threshold, our results also suggest that ensemble learning provides models with better predictivity and more robust behavior. CONCLUSION: PPV surfaces are valuable tools to assess virtual screening tools and choose score thresholds to be applied in prospective in silico screens. Ensemble learning approaches seem to consistently lead to improved predictivity and robustness.

Searching for New Leads To Treat Epilepsy: Target-Based Virtual Screening for the Discovery of Anticonvulsant Agents.

The purpose of this investigation is to contribute to the development of new anticonvulsant drugs to treat patients with refractory epilepsy. We applied a virtual screening protocol that involved the search into molecular databases of new compounds and known drugs to find small molecules that interact with the open conformation of the Nav1.2 pore. As the 3D structure of human Nav1.2 is not available, we first assembled 3D models of the target, in closed and open conformations. After the virtual screening, the resulting candidates were submitted to a second virtual filter, to find compounds with better chances of being effective for the treatment of P-glycoprotein (P-gp) mediated resistant epilepsy. Again, we built a model of the 3D structure of human P-gp, and we validated the docking methodology selected to propose the best candidates, which were experimentally tested on Nav1.2 channels by patch clamp techniques and in vivo by the maximal electroshock seizure (MES) test. Patch clamp studies allowed us to corroborate that our candidates, drugs used for the treatment of other pathologies like Ciprofloxacin, Losartan, and Valsartan, exhibit inhibitory effects on Nav1.2 channel activity. Additionally, a compound synthesized in our lab, N, N'-diphenethylsulfamide, interacts with the target and also triggers significant Na1.2 channel inhibitory action. Finally, in vivo studies confirmed the anticonvulsant action of Valsartan, Ciprofloxacin, and N, N'-diphenethylsulfamide.

New model of pharmacoresistant seizures induced by 3-mercaptopropionic acid in mice.

About 30% of the patients with epilepsy do not respond to clinically established anticonvulsants, despite having effective concentrations of the antiepileptic drug in plasma. Therefore, new preclinical models of epilepsy are needed to identify more efficacious treatments. We describe here a new drug-resistant seizure model in mice to be used at the early stages of pre-clinical trials. This model consists in inducing daily generalized seizures for 23 consecutive days by administration of 3-mercaptopropionic acid (MP). As a result, 100% of animals become resistant to phenytoin and 80% to phenobarbital. Such resistance is strongly associated with the overexpression of P-glycoprotein (Pgp), observed in cerebral cortex, hippocampus and striatum while resistance to Pgp nonsubstrate drugs such as carbamazepine, diazepam and levetiracetam is not observed. This model could be useful for screening novel anticonvulsant drugs with a potential effect on pharmacoresistant seizures treatment.

Novel sulfamides and sulfamates derived from amino esters: Synthetic studies and anticonvulsant activity.

We report herein the design and optimization of a novel series of sulfamides and sulfamates derived from amino esters with anticonvulsant properties. The structures were designed based on the pharmacophoric pattern previously proposed, with the aim of improving the anticonvulsant action. The compounds were obtained by a new synthetic procedure with microwave assisted heating and the use of adsorbents in the isolation process. All the derivatives showed protection against the maximal electroshock seizure test (MES test) in mice at the lowest dose tested (30 mg/kg) but they did not show significant protection against the chemical induced convulsion by pentylenetetrazole. These results verify the ability of the computational model for designing new anticonvulsants structures with anti-MES activity. Additionally, we evaluated the capacity of the synthesized structures to bind to the benzodiazepine binding site (BDZ-bs) of the γ-aminobutiric acid receptor (GABAA receptor). Some of them showed medium to low affinity for the BDZ-bs.