General information
| Name: José M. |
| Surname: Padrón |
| E-mail: jmpadron@ull.es |
| Cell phone number with international prefix: +34 922 316 502 ext. 6126 |
| Country: Canary Islands, Spain |
| Affiliation: Universidad de La Laguna (ULL) |
| Gender: F □ M ■ |
| Year of the PhD title: 1996 |
| Personal web page: http://webpages.ull.es/users/jmpadron/ |
| Previous COST participation: No □ Yes ■ CM1106, CM1407, CM15106 |
List of 10 selected publications within last 5 years
| 1. Silveira-Dorta G, Sousa IJ, Fernandes MX, Martín VS, Padrón JM. Synthesis and identification of unprecedented selective inhibitors of CK1ε. Eur J Med Chem 2015;96:308-317. |
| 2. Silveira-Dorta G, Martín VS, Padrón JM. Synthesis and antiproliferative activity of glutamic acid-based dipeptides. Amino Acids 2015;47(8):1527-1532. |
| 3. Calcatierra V, López O, Fernández-Bolaños JG, Plata GB, Padrón JM. Phenolic thio- and selenosemicarbazones as multi-target drugs. Eur J Med Chem 2015;94:63-72. |
| 4. Silveira-Dorta G, Donadel OJ, Martín VS, Padrón JM. Direct stereoselective synthesis of enantiomerically pure anti-β-amino alcohols. J Org Chem 2014;79(15):6775-6782. |
| 5. Silveira-Dorta G, Martín VS, Padrón JM. Direct synthesis of polybenzylated glutamic acid monoesters: Disambiguation of N, N -dibenzylglutamic acid α- And γ-benzyl esters. Synlett 2014;25(15):2166-2170. |
| 6. Sousa IJ, Padrón JM, Fernandes MX. Generation of artificial neural networks models in anticancer study. Neural Comput Appl 2013;23(3-4):577-582. |
| 7. Karpaviciene I, Cikotiene I, Padrón JM. Synthesis and antiproliferative activity of α-branched α,β-unsaturated ketones. Eur J Med Chem 2013;70:568-578. |
| 8. Silveira-Dorta G, Sousa IJ, Ríos-Luci C, Martín VS, Fernandes MX, Padrón JM. Molecular docking studies of the interaction between propargylic enol ethers and human DNA topoisomerase IIα. Bioorg Med Chem Lett 2013;23(19):5382-5384. |
| 9. Cabrera-Benitez NE, Pérez-Roth E, Casula M, Ramos-Nuez Á, Ríos-Luci C, Rodríguez-Gallego C, et al. Anti-Inflammatory Activity of a Novel Family of Aryl Ureas Compounds in an Endotoxin-Induced Airway Epithelial Cell Injury Model. PLoS ONE 2012;7(11). |
| 10. Ríos-Luci C, Bonifazi EL, León LG, Montero JC, Burton G, Pandiella A, et al. β-Lapachone analogs with enhanced antiproliferative activity. Eur J Med Chem 2012;53:264-274. |
Main skills and expertise (up to 5)
| 1. Lead Generation to Candidate Realization |
| 2. Phenotypic Drug Discovery |
| 3. Anticancer Drugs |
| 4. Total Synthesis |
| 5. |
Main equipment/facilities available in the participants’ lab (up to 5)
| 1. Fully equipped laboratory for cell culture |
| 2. State-of-the-art facilities for organic synthesis |
| 3. Core Research Facilities of ULL (http://www.segai.ull.es/) |
| 4. |
| 5. |
Short personal activity proposal for the COST Action CA15135 (max 1000 characters)
|
The activities are related to our capacities, which are described in the next paragraphs. All of them are linked directly to the four WGs of the COST Action.
In 2004, we started at ULL a Phenotypic Drug Discovery (PDD) initiative whose goal is to use cell-based assays directed at the discovery of pharmacologically active small-molecules, trying to establish general bases for ultimately identifying their mechanism of action. At BioLab we perform the biological evaluation of synthetic and natural compounds against a panel of representative human solid tumor cell lines. At present, three divisions comprise BioLab, namely Bioevaluation, Synthesis and Computation.
Bioevaluation To date, over 3800 small-molecule compounds from both natural and synthetic origin have been analysed at BioLab. The chemical structure and the antiproliferative data are combined in the database SAR-DB, which is licensed since 2014 to the SME Mind the Byte. – We can provide our screening expertise to the COST Action
Synthesis With all the information we elaborate a list of chemical structures selected on the basis of stucture-activity relationship studies or rational design in silico. – We can provide new molecules to the COST Action
Computation We have envisioned the use of computational methods in combination with the data obtained from the phenotypic screen. We started the development of artificial neural networks modelling to correlate the chemical structure with the experimental data of the phenotypic assays in order to allow the prediction of activity, to run virtual screenings, or to anticipate pharmacokinetic and pharmacodynamics. – We can provide our expertise to the COST Action
|
Work Group preference: score from 1 (preferred) to 4 (not preferred)
| Work Group of the CA15135 COST Action | Score |
| WG1: Development of new chemical entities | 2 |
| WG2: Selection of biological targets and assessment of biological data | 4 |
| WG3: Development of chemical databases | 1 |
| WG4: Development of Computational methods for multiple ligand design and discovery | 3 |
