Current State and Future Perspectives in QSAR Models to Predict Blood Brain Barrier penetration in Central Nervous System Drug R&D

The blood brain barrier (BBB) is a physical and biochemical barrier that restricts the entry of certain drugs to the Central Nervous System (CNS), while allowing the passage of others. The ability to predict the permeability of a given molecule through the BBB is a key aspect in CNS drug discovery and development, since neurotherapeutic agents with molecular targets in the CNS should be able to cross the BBB, whereas peripherally acting agents should not, to minimize the risk of CNS adverse effects. In this review we examine and discuss QSAR approaches and current availability of experimental data for the construction of BBB permeability predictive models, focusing on the modeling of the biorelevant parameter unbound partitioning coefficient (Kp,uu) . Emphasis is made on two possible strategies to overcome the current limitations of in silico models: considering the prediction of brain penetration as a multifactorial problem, and increasing experimental datasets through accurate and standardized experimental techniques.Facultad de Ciencias Exacta

Review of QSAR Models and Software Tools for predicting Biokinetic Properties

In the assessment of industrial chemicals, cosmetic ingredients, and active substances in pesticides and biocides, metabolites and degradates are rarely tested for their toxicologcal effects in mammals. In the interests of animal welfare and cost-effectiveness, alternatives to animal testing are needed in the evaluation of these types of chemicals. In this report we review the current status of various types of in silico estimation methods for Absorption, Distribution, Metabolism and Excretion (ADME) properties, which are often important in discriminating between the toxicological profiles of parent compounds and their metabolites/degradation products. The review was performed in a broad sense, with emphasis on QSARs and rule-based approaches and their applicability to estimation of oral bioavailability, human intestinal absorption, blood-brain barrier penetration, plasma protein binding, metabolism and. This revealed a vast and rapidly growing literature and a range of software tools. While it is difficult to give firm conclusions on the applicability of such tools, it is clear that many have been developed with pharmaceutical applications in mind, and as such may not be applicable to other types of chemicals (this would require further research investigation). On the other hand, a range of predictive methodologies have been explored and found promising, so there is merit in pursuing their applicability in the assessment of other types of chemicals and products. Many of the software tools are not transparent in terms of their predictive algorithms or underlying datasets. However, the literature identifies a set of commonly used descriptors that have been found useful in ADME prediction, so further research and model development activities could be based on such studies.JRC.DG.I.6-Systems toxicolog

Desarrollo de filtros <i>in silico</i> orientados a optimizar el desarrollo de nuevos fármacos destinados al sistema nervioso central

La incidencia y costo socioeconómico de los desórdenes del sistema nervioso central (SNC) se está incrementando de manera alarmante en las últimas décadas. Por otro lado, el descubrimiento y desarrollo de nuevos fármacos destinados al SNC es una de las áreas más riesgosas, con los mayores índices de fracaso. La barrera hematoencefálica (BHE) constituye uno de los principales obstáculos farmacocinéticos para el éxito de la terapia dirigida al SNC, ya que impone serias dificultades para la biodistribución de los fármacos a sus blancos moleculares en el cerebro. Por lo tanto, durante el desarrollo de nuevos fármacos para el tratamiento de trastornos del SNC resulta de suma importancia la evaluación de la capacidad de los mismos para atravesar eficazmente la BHE y así lograr niveles terapéuticos en el cerebro. En el presente trabajo de tesis nos hemos propuesto el desarrollo de modelos computacionales para predecir el parámetro farmacocinético Kp,uu, el cual está asociado al pasaje de fármacos a través de la BHE. El proceso para la generación de los modelos se estructuró en dos etapas bien diferenciadas. En una primera instancia, se compiló un conjunto de datos de compuestos con valores de Kp,uu obtenidos en estado estacionario por cualquiera de las tres metodologías experimentales disponibles para dicho fin: Microdialisis, Slice y Homogenato (conjunto de datos MSH). Dicha base de datos MSH se utilizó para generar modelos clasificatorios mediante el uso de distintos algoritmos, algunos de los cuales lograron discernir adecuadamente entre compuestos de baja y alta biodisponibilidad de fármaco libre en el SNC. Todos los modelos fueron validados computacionalmente y demostraron un buen poder predictivo. Se validó también experimental y prospectivamente el modelo individual seleccionado como el mejor. Para ello se determinó experimentalmente el parámetro Kp,uu mediante la técnica de homogenato de cinco compuestos que no habían formado parte del conjunto de datos original. La tasa de buenas clasificaciones fue de 80,0% (4/5) para la validación experimental prospectiva. Para la segunda etapa de desarrollo de modelos se consideraron únicamente aquellos valores de Kp,uu obtenidos por las técnicas de Microdialisis y/o Slice. Se conformó así un nuevo conjunto de datos (conjunto MS), con el objetivo de disminuir la variabilidad/ruido de la base de datos a utilizar en la obtención de los modelos. La misma metodología de modelado generó modelos con mejor poder predictivo que los obtenidos con el conjunto MSH. En esta oportunidad la validación prospectiva del mejor modelo individual se llevó a cabo desde un enfoque diferente al descripto para el conjunto de datos MSH. Se utilizaron compuestos que no formaron parte del conjunto de datos MS, y cuyos datos observados del parámetro modelado fueron obtenidos de diferentes maneras: datos publicados con posterioridad al armado del conjunto de datos MS y datos proporcionados por la Escuela de Medicina de la Universidad de Indiana (EE. UU.). De esta forma, el conjunto de datos para esta nueva validación prospectiva quedó conformado por 10 compuestos. De éstos, 9 fueron bien clasificados por el mejor modelo, lo que representó una de tasa de buenas clasificaciones del 90,0%. El uso de estos modelos computacionales como filtros in silico durante el desarrollo de nuevos medicamentos para el tratamiento de las enfermedades del SNC, podría optimizar la utilización de recursos y, por ende, disminuir el impacto de la baja en la inversión sufrida en los últimos tiempos en dicha área terapéutica.Facultad de Ciencias Exacta

Pharmacophore analysis, design and in vitro testing of multi-target ligands as potentially effective therapeutics of complex neurological and mental disorders

Disfunkcija serotoninske i dopaminske neurotransmisije u mozgu je u osnovi patofiziologijebrojnih neuroloških i mentalnih oboljenja. Definisanje protokola koji integriše in silico i in vitrometode, u cilju prouĉavanja farmakofore multi-potentnih jedinjenja koja deluju na nivou centralnognervnog sistema (CNS), predstavlja vaţan korak u racionalizaciji procesa otkrivanja novih lekova.Primenom simulacija molekulske dinamike i molekulskog dokinga, kao i analize kvantitativnogodnosa strukture i aktivnosti (eng. 3D-Quantitative Structure Activity Relationship, 3D-QSAR)definisane su kljuĉne strukturne karakteristike dualnih antagonista 5-HT2A i D2 receptora, sasmanjenim afinitetom za H1 receptor. Na osnovu dobijenih rezultata izvršeno je pretraţivanje bazafragmenata primenom metode virtuelnog skrininga (eng. Virtual Screening, VS) u cilju dizajniranjapotencijalno bezbednijih i efikasnijih liganada sa višestrukim delovanjem (eng. multi-target),pruţajući smernice za razvoj novih lekova u terapiji sloţenih CNS oboljenja. 3D-QSAR analizombicikliĉnih α-iminofosfonata definisana je struktura farmakofore selektivnih liganadaimidazolinskih I2 receptora, kao potencijalno novih lekova za leĉenje kognitivnih poremećaja. Invitro paralelni test permeabilnosti na veštaĉkim membranama (eng. Parallel Artificial MembranePermeability Assay, PAMPA) je korišćen za odreĊivanje efektivne permeabilnosti (logPe) krozkrvno-moţdanu barijeru (KMB) jedinjenja koja utiĉu na modulaciju aktivnosti serotoninskog idopaminskog sistema u mozgu. Dobijeni rezultati su korišćeni u analizi kvantitativnog odnosastrukture i osobina (eng. Quantitative Structure-Property Relationship, QSPR) u cilju razumevanjastrukturnih karakteristika koje najviše utiĉu na prolazak jedinjenja kroz KMB. Model formiranprimenom metode podrţavajućih vektora (eng. Support-Vector Machine, SVM) i validiranopseţnom statistiĉkom analizom, je korišćen za predviĊanje logPe vrednosti dizajniranih dualnihantagonista i liganada I2 receptora, svrstavajući ih u grupu visoko permeabilnih jedinjenja. Sa ciljemda se dodatno analizira i vizuelizuje proces permeabilnosti centralnodelujućih jedinjenja kroz KMBna molekulskom nivou, korišćene su simulacije usmerene molekulske dinamike (eng. SteeredMolecular Dynamics, SMD).Disturbances in serotoninergic and dopaminergic neurotransmissions in the central nervoussystem (CNS) play a key role in the pathophysiology of various neurological and mental disorders.Developing an integrative approach through application of in silico and in vitro methods, in order toanalyse pharmacophore of multi-target neuroactive compounds, presents a promising strategy inrationalization of drug design process. Molecular dynamics simulations and molecular dockingmethods in combination with 3D-quantitative structure activity relationship analysis (3D-QSAR)were used to evaluate crucial structural features of potent dual antagonists of 5-HT2A i D2 receptors,with lower antagonistic activity on H1 receptors. The virtual screening of the available fragmentlibraries was performed with the aim to design novel multi-target compounds with a more effectiveand safer profile, laying a good foundation for the therapy of complex brain diseases. Moreover,3D-QSAR analysis of bicyclic α-iminophosphonates was used to reveal the pharmacophorestructure of selective imidazoline I2 receptor (I2-IR) ligands, as potentially new drugs for thetreatment of cognitive disorders. In vitro parallel artificial membrane permeability assay (PAMPA)was further employed to examine the effective permeability (logPe) through blood brain barrier(BBB) of compounds that affect serotonin and dopamine levels in the CNS. Based on the obtainedresults, quantitative structure-property relationship (QSPR) analysis was performed with the aim todefine structural features that mostly affect the permeability of compounds through BBB. Support-vector machine (SVM) method was used to create predictable and reliable QSPR model that wasfurther employed to predict logPe values of new designed dual antagonists of 5-HT2A/D2 receptorsand I2-IR ligands, classifying them into a group of highly permeable compounds. Steered moleculardynamics (SMD) simulations have been carried out to additionally explain and visualize the entireBBB permeation pathway at the molecular level

Pharmacophore analysis, design and in vitro testing of multi-target ligands as potentially effective therapeutics of complex neurological and mental disorders

Disfunkcija serotoninske i dopaminske neurotransmisije u mozgu je u osnovi patofiziologije brojnih neuroloških i mentalnih oboljenja. Definisanje protokola koji integriše in silico i in vitro metode, u cilju prouĉavanja farmakofore multi-potentnih jedinjenja koja deluju na nivou centralnog nervnog sistema (CNS), predstavlja vaţan korak u racionalizaciji procesa otkrivanja novih lekova. Primenom simulacija molekulske dinamike i molekulskog dokinga, kao i analize kvantitativnog odnosa strukture i aktivnosti (eng. 3D-Quantitative Structure Activity Relationship, 3D-QSAR) definisane su kljuĉne strukturne karakteristike dualnih antagonista 5-HT2A i D2 receptora, sa smanjenim afinitetom za H1 receptor. Na osnovu dobijenih rezultata izvršeno je pretraţivanje baza fragmenata primenom metode virtuelnog skrininga (eng. Virtual Screening, VS) u cilju dizajniranja potencijalno bezbednijih i efikasnijih liganada sa višestrukim delovanjem (eng. multi-target), pruţajući smernice za razvoj novih lekova u terapiji sloţenih CNS oboljenja. 3D-QSAR analizom bicikliĉnih α-iminofosfonata definisana je struktura farmakofore selektivnih liganada imidazolinskih I2 receptora, kao potencijalno novih lekova za leĉenje kognitivnih poremećaja. In vitro paralelni test permeabilnosti na veštaĉkim membranama (eng. Parallel Artificial Membrane Permeability Assay, PAMPA) je korišćen za odreĊivanje efektivne permeabilnosti (logPe) kroz krvno-moţdanu barijeru (KMB) jedinjenja koja utiĉu na modulaciju aktivnosti serotoninskog i dopaminskog sistema u mozgu. Dobijeni rezultati su korišćeni u analizi kvantitativnog odnosa strukture i osobina (eng. Quantitative Structure-Property Relationship, QSPR) u cilju razumevanja strukturnih karakteristika koje najviše utiĉu na prolazak jedinjenja kroz KMB. Model formiran primenom metode podrţavajućih vektora (eng. Support-Vector Machine, SVM) i validiran opseţnom statistiĉkom analizom, je korišćen za predviĊanje logPe vrednosti dizajniranih dualnih antagonista i liganada I2 receptora, svrstavajući ih u grupu visoko permeabilnih jedinjenja. Sa ciljem da se dodatno analizira i vizuelizuje proces permeabilnosti centralnodelujućih jedinjenja kroz KMB na molekulskom nivou, korišćene su simulacije usmerene molekulske dinamike (eng. Steered Molecular Dynamics, SMD).Disturbances in serotoninergic and dopaminergic neurotransmissions in the central nervous system (CNS) play a key role in the pathophysiology of various neurological and mental disorders. Developing an integrative approach through application of in silico and in vitro methods, in order to analyse pharmacophore of multi-target neuroactive compounds, presents a promising strategy in rationalization of drug design process. Molecular dynamics simulations and molecular docking methods in combination with 3D-quantitative structure activity relationship analysis (3D-QSAR) were used to evaluate crucial structural features of potent dual antagonists of 5-HT2A i D2 receptors, with lower antagonistic activity on H1 receptors. The virtual screening of the available fragment libraries was performed with the aim to design novel multi-target compounds with a more effective and safer profile, laying a good foundation for the therapy of complex brain diseases. Moreover, 3D-QSAR analysis of bicyclic α-iminophosphonates was used to reveal the pharmacophore structure of selective imidazoline I2 receptor (I2-IR) ligands, as potentially new drugs for the treatment of cognitive disorders. In vitro parallel artificial membrane permeability assay (PAMPA) was further employed to examine the effective permeability (logPe) through blood brain barrier (BBB) of compounds that affect serotonin and dopamine levels in the CNS. Based on the obtained results, quantitative structure-property relationship (QSPR) analysis was performed with the aim to define structural features that mostly affect the permeability of compounds through BBB. Support- vector machine (SVM) method was used to create predictable and reliable QSPR model that was further employed to predict logPe values of new designed dual antagonists of 5-HT2A/D2 receptors and I2-IR ligands, classifying them into a group of highly permeable compounds. Steered molecular dynamics (SMD) simulations have been carried out to additionally explain and visualize the entire BBB permeation pathway at the molecular level