No abstract

No abstract

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.

No abstract

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 cutoff score values. Method: 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.

Epilepsy is the second most common chronic brain disorder, affecting 65 million people worldwide. According to the NIH's Epilepsy Therapy Screening Program, evaluation of potential new antiepileptic drug candidates begins with assessment of their protective effects in two acute seizure models in mice, the Maximal Electroshock Seizure test and the 6 Hz test. The latter elicits partial seizures through an electrical stimulus of 44 mA, at which many clinically established anti-seizure drugs do not suppress seizures. The inclusion of this "high-hurdle" acute seizure assay at the initial stage of the drug identification phase is intended to increase the probability that agents with improved efficacy will be detected. In this work, we have used machine learning approximations to develop in silico models capable of identifying novel anticonvulsant drugs with protective effects in the 6 Hz seizure model. Linear classifiers based on Dragon conformation-independent descriptors were generated through an in-house routine in R environment and validated through standard validation procedures. They were later combined through different ensemble learning schemes. The best ensemble comprised the 29 best-performing models combined using the MIN operator. With the objective of finding new drug repurposing opportunities (i.e. identifying second or further therapeutic indications, in our case anticonvulsant activity, in existing drugs), such model ensemble was applied in a virtual screening campaign of DrugBank and Sweetlead databases. 28 approved drugs were identified as potential protective agents in the 6 Hz model. The present study constitutes an example of the use of machine learning approximations to systematically guide drug repurposing projects.

La Epilepsia es uno de los desórdenes neurológicos más comunes, afectando a alrededor de 50 millones de personas en todo el mundo sin distinguir raza, sexo, edad y/o clase social. Esta enfermedad está caracterizada por crisis recurrentes y espontáneas provocadas por una actividad neuronal excesiva. Pese a los múltiples esfuerzos por encontrar nuevos tratamientos para esta patología existe un 30% de pacientes que no logra controlar las convulsiones con la farmacoterapia disponible, lo cual se conoce como epilepsia refractaria o intratable. Esta proporción de pacientes refractarios no se ha modificado a pesar de la considerable introducción en el mercado farmacéutico de numerosos fármacos antiepilépticos (FAEs) de última generación. Con el propósito de buscar nuevos candidatos a fármacos que demuestren protección frente a distintos tipos de crisis epilépticas, el Instituto Nacional de Salud de los Estados Unidos (National Institute of Health, NIH) ha modificado en los últimos tiempos el Programa de Screening de Fármacos Anticonvulsivos, reincorporando al modelo animal de crisis epiléptica de 6 Hz para ser utilizado en el screening primario de nuevos FAEs. A través de este modelo animal se ha identificado al fármaco de uso clínico levetiracetam, el cual está asociado a un mecanismo de acción novedoso. En el presente trabajo de tesis se propone realizar una búsqueda racional mediante tamizado virtual de nuevos compuestos activos frente al modelo de 6 Hz en ratones. Se ha realizado una campaña de tamizado virtual a partir del desarrollo de modelos computacionales basados en el ligando con capacidad de identificar compuestos candidatos con actividad anticonvulsiva frente el modelo animal en cuestión. Los modelos clasificatorios individuales han sido combinados y aplicados sobre las bases de datos DrugBank y SweetLead, revelando 57 fármacos que podrían poseer la actividad deseada. A partir de ello, se han adquirido 3 compuestos para ser evaluados durante la validación experimental frente al modelo de crisis epiléptica de 6 Hz. Como resultado, uno de los candidatos demostró poseer actividad anticonvulsiva, mientras que un segundo candidato demostró una leve tendencia a proteger frente a las crisis provocadas en el modelo animal. Los resultados obtenidos demuestran que las estrategias computacionales basadas en el ligando y aplicadas durante este trabajo de tesis, como el cribado virtual y el reposicionamiento de fármacos asistido por computadoras, han sido particularmente útiles para abordar de manera sistemática la búsqueda de nuevos usos terapéuticos de fármacos ya existentes. Asimismo, se ha destacado la importancia de aplicar estas metodologías escasamente exploradas y explotadas en el campo de la epilepsia.