QSAR Modelling to Identify LRRK2 Inhibitors for Parkinson's Disease

Parkinson's disease is one of the most common neurodegenerative illnesses in older persons and the leucine-rich repeat kinase 2 (LRRK2) is an auspicious target for its pharmacological treatment. In this work, quantitative structure-activity relationship (QSAR) models for identification of putative inhibitors of LRRK2 protein are developed by using an in-house chemical library and several machine learning techniques. The methodology applied in this paper has two steps: first, alternative subsets of molecular descriptors useful for characterizing LRRK2 inhibitors are chosen by a multi-objective feature selection method; secondly, QSAR models are learned by using these subsets and three different strategies for supervised learning. The qualities of all these QSAR models are compared by classical metrics and the best models are discussed in statistical and physicochemical terms.Fil: Sebastián Pérez, Víctor. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; EspañaFil: Martínez, María Jimena. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Gil, Carmen. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; EspañaFil: Campillo Martín, Nuria Eugenia. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; EspañaFil: Martínez, Ana. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; EspañaFil: Ponzoni, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentin

QSAR Classification Models for Predicting the Activity of Inhibitors of Beta-Secretase (BACE1) Associated with Alzheimer’s Disease

Alzheimer’s disease is one of the most common neurodegenerative disorders in elder population. The β-site amyloid cleavage enzyme 1 (BACE1) is the major constituent of amyloid plaques and plays a central role in this brain pathogenesis, thus it constitutes an auspicious pharmacological target for its treatment. In this paper, a QSAR model for identification of potential inhibitors of BACE1 protein is designed by using classification methods. For building this model, a database with 215 molecules collected from different sources has been assembled. This dataset contains diverse compounds with different scaffolds and physical-chemical properties, covering a wide chemical space in the drug-like range. The most distinctive aspect of the applied QSAR strategy is the combination of hybridization with backward elimination of models, which contributes to improve the quality of the final QSAR model. Another relevant step is the visual analysis of the molecular descriptors that allows guaranteeing the absence of information redundancy in the model. The QSAR model performances have been assessed by traditional metrics, and the final proposed model has low cardinality, and reaches a high percentage of chemical compounds correctly classified.Fil: Ponzoni, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Sebastián Pérez, Víctor. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; EspañaFil: Martínez, María J.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Roca, Carlos. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; EspañaFil: De la Cruz Pérez, Carlos. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; EspañaFil: Cravero, Fiorella. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaFil: Vazquez, Gustavo Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Católica del Uruguay; UruguayFil: Páez, Juan A.. Consejo Superior de Investigaciones Científicas. Instituto de Química Médica; EspañaFil: Diaz, Monica Fatima. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Química; ArgentinaFil: Campillo Martín, Nuria Eugenia. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; Españ

Effectiveness of an intervention for improving drug prescription in primary care patients with multimorbidity and polypharmacy:Study protocol of a cluster randomized clinical trial (Multi-PAP project)

This study was funded by the Fondo de Investigaciones Sanitarias ISCIII (Grant Numbers PI15/00276, PI15/00572, PI15/00996), REDISSEC (Project Numbers RD12/0001/0012, RD16/0001/0005), and the European Regional Development Fund ("A way to build Europe").Background: Multimorbidity is associated with negative effects both on people's health and on healthcare systems. A key problem linked to multimorbidity is polypharmacy, which in turn is associated with increased risk of partly preventable adverse effects, including mortality. The Ariadne principles describe a model of care based on a thorough assessment of diseases, treatments (and potential interactions), clinical status, context and preferences of patients with multimorbidity, with the aim of prioritizing and sharing realistic treatment goals that guide an individualized management. The aim of this study is to evaluate the effectiveness of a complex intervention that implements the Ariadne principles in a population of young-old patients with multimorbidity and polypharmacy. The intervention seeks to improve the appropriateness of prescribing in primary care (PC), as measured by the medication appropriateness index (MAI) score at 6 and 12months, as compared with usual care. Methods/Design: Design:pragmatic cluster randomized clinical trial. Unit of randomization: family physician (FP). Unit of analysis: patient. Scope: PC health centres in three autonomous communities: Aragon, Madrid, and Andalusia (Spain). Population: patients aged 65-74years with multimorbidity (≥3 chronic diseases) and polypharmacy (≥5 drugs prescribed in ≥3months). Sample size: n=400 (200 per study arm). Intervention: complex intervention based on the implementation of the Ariadne principles with two components: (1) FP training and (2) FP-patient interview. Outcomes: MAI score, health services use, quality of life (Euroqol 5D-5L), pharmacotherapy and adherence to treatment (Morisky-Green, Haynes-Sackett), and clinical and socio-demographic variables. Statistical analysis: primary outcome is the difference in MAI score between T0 and T1 and corresponding 95% confidence interval. Adjustment for confounding factors will be performed by multilevel analysis. All analyses will be carried out in accordance with the intention-to-treat principle. Discussion: It is essential to provide evidence concerning interventions on PC patients with polypharmacy and multimorbidity, conducted in the context of routine clinical practice, and involving young-old patients with significant potential for preventing negative health outcomes. Trial registration: Clinicaltrials.gov, NCT02866799Publisher PDFPeer reviewe

Ames Mutagenicity Dataset

El dataset contiene 5536 compuestos moleculares representados por su código SMILES y 1360 descriptores moleculares calculados con Mordred. Además, contiene las respectivas etiquetas para cada compuesto (1: mutagénico / 0: no mutagénico) para cada una de las cinco cepas (TA98, TA100, TA102, TA1535, TA1537) y una etiqueta general (Overall) que corresponde a la etiqueta de consenso utilizada para evaluar la predicción final del test de Ames. Los compuestos fueron compilados originalmente por el Istituto Superiore di Sanita’ (https://www.iss.it/isstox) y son el resultado de una etapa de preprocesamiento exhaustiva, que consta de diferentes pasos de filtrado, limpieza y canonicalización. The dataset contains 5,536 molecular compounds represented by their SMILES code and 1,360 molecular descriptors calculated with Mordred. Moreover, it contains the respective labels for each compound (1: mutagenic / 0: non-mutagenic) for each of the five strains (TA98, TA100, TA102, TA1535, TA1537) and a general label (Overall) that corresponds to the ground-truth consensus label used for evaluating the final Ames mutagenicity prediction. The compounds listed were originally compiled by the Istituto Superiore di Sanita’ (https://www.iss.it/isstox) and result from an exhaustive pre-processing stage, consisting of different filtering, sanitization, and canonicalization steps.Fil: Martínez, María Jimena. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Sabando, María Virginia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Soto, Axel Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Roca Magadán, Carlos. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas Margarita Salas; EspañaFil: Requena Triguero, Carlos. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas Margarita Salas; EspañaFil: Campillo Martín, Nuria Eugenia. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas Margarita Salas; EspañaFil: Páez, Juan A.. Consejo Superior de Investigaciones Científicas. Instituto de Química Médica; EspañaFil: Ponzoni, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentin

Multitarget drugs as potential therapeutic agents for alzheimer’s disease. A new family of 5-substituted indazole derivatives as cholinergic and BACE1 inhibitors

Multitarget drugs are a promising therapeutic approach against Alzheimer’s disease. In this work, a new family of 5-substituted indazole derivatives with a multitarget profile including cholinesterase and BACE1 inhibition is described. Thus, the synthesis and evaluation of a new class of 5-substituted indazoles has been performed. Pharmacological evaluation includes in vitro inhibitory assays on AChE/BuChE and BACE1 enzymes. Also, the corresponding competition studies on BuChE were carried out. Additionally, antioxi- dant properties have been calculated from ORAC assays. Furthermore, studies of anti-inflammatory proper- ties on Raw 264.7 cells and neuroprotective effects in human neuroblastoma SH-SY5Y cells have been performed. The results of pharmacological tests have shown that some of these 5-substituted indazole derivatives 1–4 and 6 behave as AChE/BuChE and BACE1 inhibitors, simultaneously. In addition, some indazole derivatives showed anti-inflammatory (3, 6) and neuroprotective (1–4 and 6) effects against Ab- induced cell death in human neuroblastoma SH-SY5Y cells with antioxidant properties.This work was supported from Ministerio de Economía, Industria y Competitividad, Gobierno de Espa~na under Grant RTI2018-096100B-100.Peer reviewe

Cannabinoid derivatives exert a potent anti-myeloma activity both in vitro and in vivo.

Although hematopoietic and immune system show high levels of the cannabinoid receptor CB2, the potential effect of cannabinoids on hematologic malignancies has been poorly determined. Here we have investigated their anti-tumor effect in multiple myeloma (MM). We demonstrate that cannabinoids induce a selective apoptosis in MM cell lines and in primary plasma cells of MM patients, while sparing normal cells from healthy donors, including hematopoietic stem cells. This effect was mediated by caspase activation, mainly caspase-2, and was partially prevented by a pan-caspase inhibitor. Their pro-apoptotic effect was correlated with an increased expression of Bax and Bak, a decrease of Bcl-xL and Mcl-1, a biphasic response of Akt/PKB and an increase in the levels of ceramide in MM cells. Inhibition of ceramide synthesis partially prevented apoptosis, indicating that these sphingolipids play a key role in the pro-apoptotic effect of cannabinoids in MM cells. Remarkably, blockage of the CB2 receptor also inhibited cannabinoid-induced apoptosis. Cannabinoid derivative WIN-55 enhanced the anti-myeloma activity of dexamethasone and melphalan overcoming resistance to melphalan in vitro. Finally, administration of cannabinoid WIN-55 to plasmacytoma-bearing mice significantly suppressed tumor growth in vivo. Together, our data suggest that cannabinoids may be considered as potential therapeutic agents in the treatment of MM