Predicting targets of compounds against neurological diseases using cheminformatic methodology
Authorized Users Only
2015
Authors
Nikolić, KatarinaMavridis, Lazaros
Bautista-Aguilera, Oscar M.
Marco-Contelles, Jose
Stark, Holger
Carreiras, Maria do Carmo
Rossi, Ilaria
Massarelli, Paola
Agbaba, Danica
Ramsay, Rona R.
Mitchell, John B. O.
Article (Published version)
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Show full item recordAbstract
Recently developed multi-targeted ligands are novel drug candidates able to interact with monoamine oxidase A and B; acetylcholinesterase and butyrylcholinesterase; or with histamine N-methyltransferase and histamine H-3-receptor (H3R). These proteins are drug targets in the treatment of depression, Alzheimer's disease, obsessive disorders, and Parkinson's disease. A probabilistic method, the Parzen-Rosenblatt window approach, was used to build a "predictor" model using data collected from the ChEMBL database. The model can be used to predict both the primary pharmaceutical target and off-targets of a compound based on its structure. Molecular structures were represented based on the circular fingerprint methodology. The same approach was used to build a "predictor" model from the DrugBank dataset to determine the main pharmacological groups of the compound. The study of off-target interactions is now recognised as crucial to the understanding of both drug action and toxicology. Primar...y pharmaceutical targets and off-targets for the novel multi-target ligands were examined by use of the developed cheminformatic method. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. The cheminformatic targets identifications were in agreement with four 3D-QSAR (H3R/D1R/D2R/5-HT2aR) models and by in vitro assays for serotonin 5-HT1a and 5-HT2a receptor binding of the most promising ligand (71/MBA-VEG8).
Keywords:
Multi-targeted ligands / Circular fingerprints / Off-target study / ChE / MAO / Histamine H3 receptor / HMTSource:
Journal of Computer-Aided Molecular Design, 2015, 29, 2, 183-198Publisher:
- Springer, Dordrecht
Funding / projects:
- EU COST Action STSM 10295
- Scottish Universities Life Sciences Alliance (SULSA)
- Else Kroner-Fresenius-Stiftung
- Translational Research Innovation-Pharma (TRIP)
- Fraunhofer-Projektgruppe fur Translationale Medizin und Pharmakologie (TMP)
DOI: 10.1007/s10822-014-9816-1
ISSN: 0920-654X
PubMed: 25425329
WoS: 000348190700007
Scopus: 2-s2.0-84922105008
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Institution/Community
PharmacyTY - JOUR AU - Nikolić, Katarina AU - Mavridis, Lazaros AU - Bautista-Aguilera, Oscar M. AU - Marco-Contelles, Jose AU - Stark, Holger AU - Carreiras, Maria do Carmo AU - Rossi, Ilaria AU - Massarelli, Paola AU - Agbaba, Danica AU - Ramsay, Rona R. AU - Mitchell, John B. O. PY - 2015 UR - https://farfar.pharmacy.bg.ac.rs/handle/123456789/2395 AB - Recently developed multi-targeted ligands are novel drug candidates able to interact with monoamine oxidase A and B; acetylcholinesterase and butyrylcholinesterase; or with histamine N-methyltransferase and histamine H-3-receptor (H3R). These proteins are drug targets in the treatment of depression, Alzheimer's disease, obsessive disorders, and Parkinson's disease. A probabilistic method, the Parzen-Rosenblatt window approach, was used to build a "predictor" model using data collected from the ChEMBL database. The model can be used to predict both the primary pharmaceutical target and off-targets of a compound based on its structure. Molecular structures were represented based on the circular fingerprint methodology. The same approach was used to build a "predictor" model from the DrugBank dataset to determine the main pharmacological groups of the compound. The study of off-target interactions is now recognised as crucial to the understanding of both drug action and toxicology. Primary pharmaceutical targets and off-targets for the novel multi-target ligands were examined by use of the developed cheminformatic method. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. The cheminformatic targets identifications were in agreement with four 3D-QSAR (H3R/D1R/D2R/5-HT2aR) models and by in vitro assays for serotonin 5-HT1a and 5-HT2a receptor binding of the most promising ligand (71/MBA-VEG8). PB - Springer, Dordrecht T2 - Journal of Computer-Aided Molecular Design T1 - Predicting targets of compounds against neurological diseases using cheminformatic methodology VL - 29 IS - 2 SP - 183 EP - 198 DO - 10.1007/s10822-014-9816-1 ER -
@article{ author = "Nikolić, Katarina and Mavridis, Lazaros and Bautista-Aguilera, Oscar M. and Marco-Contelles, Jose and Stark, Holger and Carreiras, Maria do Carmo and Rossi, Ilaria and Massarelli, Paola and Agbaba, Danica and Ramsay, Rona R. and Mitchell, John B. O.", year = "2015", abstract = "Recently developed multi-targeted ligands are novel drug candidates able to interact with monoamine oxidase A and B; acetylcholinesterase and butyrylcholinesterase; or with histamine N-methyltransferase and histamine H-3-receptor (H3R). These proteins are drug targets in the treatment of depression, Alzheimer's disease, obsessive disorders, and Parkinson's disease. A probabilistic method, the Parzen-Rosenblatt window approach, was used to build a "predictor" model using data collected from the ChEMBL database. The model can be used to predict both the primary pharmaceutical target and off-targets of a compound based on its structure. Molecular structures were represented based on the circular fingerprint methodology. The same approach was used to build a "predictor" model from the DrugBank dataset to determine the main pharmacological groups of the compound. The study of off-target interactions is now recognised as crucial to the understanding of both drug action and toxicology. Primary pharmaceutical targets and off-targets for the novel multi-target ligands were examined by use of the developed cheminformatic method. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. The cheminformatic targets identifications were in agreement with four 3D-QSAR (H3R/D1R/D2R/5-HT2aR) models and by in vitro assays for serotonin 5-HT1a and 5-HT2a receptor binding of the most promising ligand (71/MBA-VEG8).", publisher = "Springer, Dordrecht", journal = "Journal of Computer-Aided Molecular Design", title = "Predicting targets of compounds against neurological diseases using cheminformatic methodology", volume = "29", number = "2", pages = "183-198", doi = "10.1007/s10822-014-9816-1" }
Nikolić, K., Mavridis, L., Bautista-Aguilera, O. M., Marco-Contelles, J., Stark, H., Carreiras, M. d. C., Rossi, I., Massarelli, P., Agbaba, D., Ramsay, R. R.,& Mitchell, J. B. O.. (2015). Predicting targets of compounds against neurological diseases using cheminformatic methodology. in Journal of Computer-Aided Molecular Design Springer, Dordrecht., 29(2), 183-198. https://doi.org/10.1007/s10822-014-9816-1
Nikolić K, Mavridis L, Bautista-Aguilera OM, Marco-Contelles J, Stark H, Carreiras MDC, Rossi I, Massarelli P, Agbaba D, Ramsay RR, Mitchell JBO. Predicting targets of compounds against neurological diseases using cheminformatic methodology. in Journal of Computer-Aided Molecular Design. 2015;29(2):183-198. doi:10.1007/s10822-014-9816-1 .
Nikolić, Katarina, Mavridis, Lazaros, Bautista-Aguilera, Oscar M., Marco-Contelles, Jose, Stark, Holger, Carreiras, Maria do Carmo, Rossi, Ilaria, Massarelli, Paola, Agbaba, Danica, Ramsay, Rona R., Mitchell, John B. O., "Predicting targets of compounds against neurological diseases using cheminformatic methodology" in Journal of Computer-Aided Molecular Design, 29, no. 2 (2015):183-198, https://doi.org/10.1007/s10822-014-9816-1 . .