Topología molecular aplicada al reconocimiento de sustratos de la Proteína de Resistencia del Cáncer de Mama (BCRP)

La disminución de la eficacia de un fármaco en el tratamiento o cura de una determinada enfermedad o condición se conoce como resistencia a los fármacos. La resistencia a múltiples fármacos o MDR (por sus siglas en inglés, multidrug resistance) se puede definir, en términos generales, como la capacidad de una célula viva u organismo de ofrecer resistencia al tratamiento frente a un amplio espectro de fármacos no relacionados estructural o funcionalmente entre sí. Una forma específica de MDR es aquella mediada por una superfamilia de proteínas de membrana denominadas proteínas transportadoras de eflujo ATP dependientes o transportadores ABC (por sus siglas en inglés, ATP-binding cassette). En humanos (y eucariotas en general), los transportadores ABC utilizan la energía proveniente de la hidrólisis del ATP intracelular para exportar compuestos desde el citoplasma hacia el exterior de la célula a través de la membrana plasmática, o bien para mover moléculas en orgánulos intracelulares. En cualquier caso, el sentido de transporte en eucariotas es siempre desde el interior de la célula u orgánulo hacia el exterior de éstos. En los mamíferos, los transportadores ABC se expresan de forma ubicua en todo el organismo, pero su expresión es predominante en ciertos órganos como el hígado, el intestino y los riñones, y en tejidos tales como la barrera hematoencefálica (BHE), la barrera hematotesticular y la placenta. Están caracterizados por una amplia especificidad de sustrato y están involucrados en el eflujo tanto de moléculas endógenas como de compuestos xenobióticos, y por lo tanto están asociados a un amplio espectro de funciones fisiológicas. De los 49 transportadores humanos, sólo la proteína ABCB1 (Glicoproteína P - Pgp, MDR1), la proteína ABCG2 (Proteína de Resistencia del Cáncer de Mama - BCRP, MXR) y varios miembros de la familia ABCC (MRP) califican sin dudas como proteínas MDR-ABC y su participación en este fenómeno ha sido largamente documentada, principalmente en el cáncer, la epilepsia y las enfermedades infecciosas. La epilepsia es el trastorno cerebral crónico más frecuente, afectando a más de 50 millones de personas en todo el mundo. La farmacoterapia es el tratamiento de elección logrando éxito en el control de la epilepsia (es decir, logrando un estado libre de convulsiones sostenido en el tiempo) en alrededor del 70% de los pacientes. El 30% restante sufre de epilepsia resistente al tratamiento o refractaria, en la cual no se logra alcanzar un estado libre de convulsiones sostenido en el tiempo con al menos dos regímenes de fármacos antiepilépticos (FAE) correctamente seleccionados, bien tolerados y de uso habitual. Entre las diferentes hipótesis que proporcionan posibles explicaciones al fenómeno de la epilepsia refractaria, la hipótesis de los transportadores sostiene que la farmacorresistencia puede ser una consecuencia de una sobreexpresión (inducida por las propias convulsiones o los FAE) e hiperactividad local de los transportadores MDR-ABC en la BHE y/o los focos epilépticos, los cuales limitarían la penetración de los FAE y conducirían a niveles insuficientes de los fármacos en el tejido cerebral epileptogénico. La refractariedad ha sido atribuida principalmente a la sobreexpresión de la Pgp; sin embargo, estudios recientes evidenciaron el papel relevante de la BCRP en la restricción del acceso al cerebro de varios FAE. Cabe destacar aquí que la BCRP es el transportador con los niveles de expresión basales más altos tanto a nivel de la BHE como en todos los segmentos del intestino humano de sujetos sanos, además de ser el transportador ABC con espectro de sustratos más amplio. Por este motivo, la Administración de Alimentos y Medicamentos de Estados Unidos (FDA) y la Agencia Europea de Medicamentos (EMA) recomiendan que los fármacos en investigación sean evaluados como sustrato y/o inhibidor tanto de la Pgp como de la BCRP. Por todo esto, durante el presente plan de tesis se desarrollaron una serie de modelos computacionales químico-matemáticos basados en descriptores moleculares topológicos capaces de discriminar entre sustratos y no sustratos de la BCRP humana de tipo salvaje, utilizando distintas metodologías de modelado desde el ligando. El mejor modelo obtenido fue un ensamble de modelos de tipo no lineal desarrollado utilizando una estrategia combinada de algoritmos genéticos, algoritmo de inducción de árboles de decisión J48 y fusión de datos. El filtro ADME desarrollado es fácil y rápido de aplicar porque no requiere un análisis conformacional previo de las estructuras químicas a evaluar, lo cual es particularmente adecuado para campañas de cribado virtual en grandes bibliotecas de compuestos químicos. Las predicciones del modelo se validaron experimentalmente utilizando el modelo ex vivo de saco intestinal evertido de rata. Se evaluaron experimentalmente cinco compuestos anticonvulsivos clasificados como no sustrato por el ensamble y ninguno de los cinco compuestos demostró ser un sustrato de la BCRP en las condiciones experimentales utilizadas. Los resultados experimentales demostraron la capacidad predictiva del ensamble de modelos computacionales obtenido, el cual constituye un filtro ADME in silico que podrá aplicarse en campañas de cribado virtual orientadas al descubrimiento de nuevos fármacos para el tratamiento de patologías con alta prevalencia de fenómenos MDR asociados al transportador BCRP como la epilepsia y el cáncer, y para la predicción de potenciales reacciones medicamentosas debidas a la competencia de dos o más fármacos por dicho transportador.Facultad de Ciencias Exacta

Development of Conformation Independent Computational Models for the Early Recognition of Breast Cancer Resistance Protein Substrates

ABC efflux transporters are polyspecific members of the ABC superfamily that, acting as drug and metabolite carriers, provide a biochemical barrier against drug penetration and contribute to detoxification. Their overexpression is linked tomultidrug resistance issues in a diversity of diseases. Breast cancer resistance protein (BCRP) is the most expressed ABC efflux transporter throughout the intestine and the blood-brain barrier, limiting oral absorption and brain bioavailability of its substrates. Early recognition of BCRP substrates is thus essential to optimize oral drug absorption, design of novel therapeutics for central nervous systemconditions, and overcome BCRP-mediated cross-resistance issues. We present the development of an ensemble of ligand-based machine learning algorithms for the early recognition of BCRP substrates, from a database of 262 substrates and nonsubstrates compiled from the literature. Such dataset was rationally partitioned into training and test sets by application of a 2-step clustering procedure. The models were developed through application of linear discriminant analysis to randomsubsamples ofDragonmolecular descriptors. Simple data fusion and statistical comparison of partial areas under the curve of ROC curves were applied to obtain the best 2-model combination, which presented 82% and 74.5% of overall accuracy in the training and test set, respectively.Facultad de Ciencias Exacta

Development of Conformation Independent Computational Models for the Early Recognition of Breast Cancer Resistance Protein Substrates

ABC efflux transporters are polyspecific members of the ABC superfamily that, acting as drug and metabolite carriers, provide a biochemical barrier against drug penetration and contribute to detoxification. Their overexpression is linked tomultidrug resistance issues in a diversity of diseases. Breast cancer resistance protein (BCRP) is the most expressed ABC efflux transporter throughout the intestine and the blood-brain barrier, limiting oral absorption and brain bioavailability of its substrates. Early recognition of BCRP substrates is thus essential to optimize oral drug absorption, design of novel therapeutics for central nervous systemconditions, and overcome BCRP-mediated cross-resistance issues. We present the development of an ensemble of ligand-based machine learning algorithms for the early recognition of BCRP substrates, from a database of 262 substrates and nonsubstrates compiled from the literature. Such dataset was rationally partitioned into training and test sets by application of a 2-step clustering procedure. The models were developed through application of linear discriminant analysis to randomsubsamples ofDragonmolecular descriptors. Simple data fusion and statistical comparison of partial areas under the curve of ROC curves were applied to obtain the best 2-model combination, which presented 82% and 74.5% of overall accuracy in the training and test set, respectively.Facultad de Ciencias Exacta

Development of Conformation Independent Computational Models for the Early Recognition of Breast Cancer Resistance Protein Substrates

ABC efflux transporters are polyspecific members of the ABC superfamily that, acting as drug and metabolite carriers, provide a biochemical barrier against drug penetration and contribute to detoxification. Their overexpression is linked tomultidrug resistance issues in a diversity of diseases. Breast cancer resistance protein (BCRP) is the most expressed ABC efflux transporter throughout the intestine and the blood-brain barrier, limiting oral absorption and brain bioavailability of its substrates. Early recognition of BCRP substrates is thus essential to optimize oral drug absorption, design of novel therapeutics for central nervous systemconditions, and overcome BCRP-mediated cross-resistance issues. We present the development of an ensemble of ligand-based machine learning algorithms for the early recognition of BCRP substrates, from a database of 262 substrates and nonsubstrates compiled from the literature. Such dataset was rationally partitioned into training and test sets by application of a 2-step clustering procedure. The models were developed through application of linear discriminant analysis to randomsubsamples ofDragonmolecular descriptors. Simple data fusion and statistical comparison of partial areas under the curve of ROC curves were applied to obtain the best 2-model combination, which presented 82% and 74.5% of overall accuracy in the training and test set, respectively.Facultad de Ciencias Exacta

Development and Validation of a Computational Model Ensemble for the Early Detection of BCRP/ABCG2 Substrates during the Drug Design Stage

Breast Cancer Resistance Protein (BCRP) is an ATP-dependent efflux transporter linked to the multidrug resistance phenomenon in many diseases such as epilepsy and cancer and a potential source of drug interactions. For these reasons, the early identification of substrates and nonsubstrates of this transporter during the drug discovery stage is of great interest. We have developed a computational nonlinear model ensemble based on conformational independent molecular descriptors using a combined strategy of genetic algorithms, J48 decision tree classifiers, and data fusion. The best model ensemble consists in averaging the ranking of the 12 decision trees that showed the best performance on the training set, which also demonstrated a good performance for the test set. It was experimentally validated using the ex vivo everted rat intestinal sac model. Five anticonvulsant drugs classified as nonsubstrates for BRCP by the model ensemble were experimentally evaluated, and none of them proved to be a BCRP substrate under the experimental conditions used, thus confirming the predictive ability of the model ensemble. The model ensemble reported here is a potentially valuable tool to be used as an in silico ADME filter in computer-aided drug discovery campaigns intended to overcome BCRP-mediated multidrug resistance issues and to prevent drug−drug interactions.Facultad de Ciencias ExactasLaboratorio de Investigación y Desarrollo de Bioactivo

Strengths and Weaknesses of Docking Simulations in the SARS-CoV-2 Era: The Main Protease (Mpro) Case Study

The scientific community is working against the clock to arrive at therapeutic interventions to treat patients with COVID-19. Among the strategies for drug discovery, virtual screening approaches have the capacity to search potential hits within millions of chemical structures in days, with the appropriate computing infrastructure. In this article, we first analyzed the published research targeting the inhibition of the main protease (Mpro), one of the most studied targets of SARS-CoV-2, by docking-based methods. An alarming finding was the lack of an adequate validation of the docking protocols (i.e., pose prediction and virtual screening accuracy) before applying them in virtual screening campaigns. The performance of the docking protocols was tested at some level in 57.7% of the 168 investigations analyzed. However, we found only three examples of a complete retrospective analysis of the scoring functions to quantify the virtual screening accuracy of the methods. Moreover, only two publications reported some experimental evaluation of the proposed hits until preparing this manuscript. All of these findings led us to carry out a retrospective performance validation of three different docking protocols, through the analysis of their pose prediction and screening accuracy. Surprisingly, we found that even though all tested docking protocols have a good pose prediction, their screening accuracy is quite limited as they fail to correctly rank a test set of compounds. These results highlight the importance of conducting an adequate validation of the docking protocols before carrying out virtual screening campaigns, and to experimentally confirm the predictions made by the models before drawing bold conclusions. Finally, successful structure-based drug discovery investigations published during the redaction of this manuscript allow us to propose the inclusion of target flexibility and consensus scoring as alternatives to improve the accuracy of the methods.Fil: Llanos, Manuel. Universidad Nacional de La Plata. Facultad de Ciencas Exactas. Laboratorio de Investigación y Desarrollo de Bioactivos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Gantner, Melisa Edith. Universidad Nacional de La Plata. Facultad de Ciencas Exactas. Laboratorio de Investigación y Desarrollo de Bioactivos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Rodríguez, Santiago. Universidad Nacional de La Plata. Facultad de Ciencas Exactas. Laboratorio de Investigación y Desarrollo de Bioactivos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Bioquímicas de La Plata "Prof. Dr. Rodolfo R. Brenner". Universidad Nacional de la Plata. Facultad de Ciencias Médicas. Instituto de Investigaciones Bioquímicas de La Plata "Prof. Dr. Rodolfo R. Brenner"; ArgentinaFil: Alberca, Lucas Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular "Dr. Héctor N. Torres"; ArgentinaFil: Bellera, Carolina Leticia. Universidad Nacional de La Plata. Facultad de Ciencas Exactas. Laboratorio de Investigación y Desarrollo de Bioactivos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Talevi, Alan. Universidad Nacional de La Plata. Facultad de Ciencas Exactas. Laboratorio de Investigación y Desarrollo de Bioactivos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Gavernet, Luciana. Universidad Nacional de La Plata. Facultad de Ciencas Exactas. Laboratorio de Investigación y Desarrollo de Bioactivos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentin

Integrated Application of Enhanced Replacement Method and Ensemble Learning for the Prediction of BCRP/ABCG2 Substrates

Breast Cancer Resistance Protein (BCRP or ABCG2) is a polyspecific efflux-transporter which belongs to the ATP-binding Cassette superfamily. Up-regulation of BCRP is associated to multi-drug resistance in a number of conditions, e.g. cancer and epilepsy. Recent proteomic studies show that high-expression levels of BCRP are found in healthy human intestine and at the blood-brain barrier, limiting the absorption and brain distribution of its substrates. Here, we have jointly applied the Enhanced Replacement Method and ensemble learning approaches to obtain combinations of 2D linear classifiers capable of discriminating among substrates and non-substrates of the wild type human BCRP. The best model ensemble obtained outperforms previously reported 2D linear classifiers, showing the ability of the Enhanced Replacement Method and ensemble learning schemes to optimize the performance of individual models. This is the first report of the Enhanced Replacement Method to solve classification problems.Facultad de Ciencias Exacta

Computer-Aided Recognition of ABC Transporters Substrates and Its Application to the Development of New Drugs for Refractory Epilepsy

Despite the introduction of more than 15 third generation antiepileptic drugs to the market from 1990 to the moment, about one third of the epileptic patients still suffer from refractory to intractable epilepsy. Several hypotheses seek to explain the failure of drug treatments to control epilepsy symptoms in such patients. The most studied one proposes that drug resistance might be related with regional overactivity of efflux transporters from the ATP-Binding Cassette (ABC) superfamily at the blood-brain barrier and/or the epileptic foci in the brain. Different strategies have been conceived to address the transporter hypothesis, among them inhibiting or down-regulating the efflux transporters or bypassing them through a diversity of artifices. Here, we review scientific evidence supporting the transporter hypothesis along with its limitations, as well as computer-assisted early recognition of ABC transporter substrates as an interesting strategy to develop novel antiepileptic drugs capable of treating refractory epilepsy linked to ABC transporters overactivity.Laboratorio de Investigación y Desarrollo de Bioactivo

Applications of Nanosystems to Anticancer Drug Therapy (Part I. Nanogels, Nanospheres, Nanocapsules)

One of the greatest challenges in cancer drug therapy is to maximize the effectiveness of the active agent while reducing its systemic adverse effects. To add more, many widely-used chemoterapeutic agents present unfavorable physicochemical properties (e.g. low solubility, lack of chemical or biological stability) that hamper or limit their therapeutic applications. All these issues may be overcome by designing adequate drug delivery systems; nanocarriers are particularly suitable for this purpose. Nanosystems can be used for targeted-drug release, treatment, diagnostic imaging and therapy monitoring. They allow the formulation of drug delivery systems with user-defined characteristics regarding solubility, biodegradability, particle size, release kinetics and active targeting, among others. This review (Part I) focuses on recent patents published between 2008 and the present day, related to nanospheres, nanocapsules and nanogels applied to anticancer drug therapy. Other nanosystems will be covered in a second article (Part II), currently in preparation.Fil: Talevi, Alan. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas. Cátedra de Química Medicinal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gantner, Melisa Edith. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas. Cátedra de Química Medicinal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Ruiz, María Esperanza. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biologicas. Catedra de Control de Calidad de Medicamentos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin