TY  - JOUR
AU  - Nikolić, Katarina
AU  - Mavridis, Lazaros
AU  - Đikić, Teodora
AU  - Vučićević, Jelica
AU  - Agbaba, Danica
AU  - Yelekci, Kemal
AU  - Mitchell, John B. O.
PY  - 2016
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/2540
AB  - The diverse cerebral mechanisms implicated in Central Nervous System (CNS) diseases together with the heterogeneous and overlapping nature of phenotypes indicated that multitarget strategies may be appropriate for the improved treatment of complex brain diseases. Understanding how the neurotransmitter systems interact is also important in optimizing therapeutic strategies. Pharmacological intervention on one target will often influence another one, such as the well-established serotonin-dopamine interaction or the dopamine-glutamate interaction. It is now accepted that drug action can involve plural targets and that polypharmacological interaction with multiple targets, to address disease in more subtle and effective ways, is a key concept for development of novel drug candidates against complex CNS diseases. A multi-target therapeutic strategy for Alzheimer's disease resulted in the development of very effective Multi-Target Designed Ligands (MTDL) that act on both the cholinergic and monoaminergic systems, and also retard the progression of neurodegeneration by inhibiting amyloid aggregation. Many compounds already in databases have been investigated as ligands for multiple targets in drug discovery programs. A probabilistic method, the ParzenRosenblatt 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. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. Based on all these findings, it is concluded that multipotent ligands targeting AChE/MAO-A/MAO-B and also D-1-R/D-2-R/5-HT2A-R/H-3-R are promising novel drug candidates with improved efficacy and beneficial neuroleptic and procognitive activities in treatment of Alzheimer's and related neurodegenerative diseases. Structural information for drug targets permits docking and virtual screening and exploration of the molecular determinants of binding, hence facilitating the design of multi-targeted drugs. The crystal structures and models of enzymes of the monoaminergic and cholinergic systems have been used to investigate the structural origins of target selectivity and to identify molecular determinants, in order to design MTDLs.
PB  - Frontiers Media Sa, Lausanne
T2  - Frontiers in Neuroscience
T1  - Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies
VL  - 10
DO  - 10.3389/fnins.2016.00265
ER  - 

@article{
author = "Nikolić, Katarina and Mavridis, Lazaros and Đikić, Teodora and Vučićević, Jelica and Agbaba, Danica and Yelekci, Kemal and Mitchell, John B. O.",
year = "2016",
abstract = "The diverse cerebral mechanisms implicated in Central Nervous System (CNS) diseases together with the heterogeneous and overlapping nature of phenotypes indicated that multitarget strategies may be appropriate for the improved treatment of complex brain diseases. Understanding how the neurotransmitter systems interact is also important in optimizing therapeutic strategies. Pharmacological intervention on one target will often influence another one, such as the well-established serotonin-dopamine interaction or the dopamine-glutamate interaction. It is now accepted that drug action can involve plural targets and that polypharmacological interaction with multiple targets, to address disease in more subtle and effective ways, is a key concept for development of novel drug candidates against complex CNS diseases. A multi-target therapeutic strategy for Alzheimer's disease resulted in the development of very effective Multi-Target Designed Ligands (MTDL) that act on both the cholinergic and monoaminergic systems, and also retard the progression of neurodegeneration by inhibiting amyloid aggregation. Many compounds already in databases have been investigated as ligands for multiple targets in drug discovery programs. A probabilistic method, the ParzenRosenblatt 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. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. Based on all these findings, it is concluded that multipotent ligands targeting AChE/MAO-A/MAO-B and also D-1-R/D-2-R/5-HT2A-R/H-3-R are promising novel drug candidates with improved efficacy and beneficial neuroleptic and procognitive activities in treatment of Alzheimer's and related neurodegenerative diseases. Structural information for drug targets permits docking and virtual screening and exploration of the molecular determinants of binding, hence facilitating the design of multi-targeted drugs. The crystal structures and models of enzymes of the monoaminergic and cholinergic systems have been used to investigate the structural origins of target selectivity and to identify molecular determinants, in order to design MTDLs.",
publisher = "Frontiers Media Sa, Lausanne",
journal = "Frontiers in Neuroscience",
title = "Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies",
volume = "10",
doi = "10.3389/fnins.2016.00265"
}

Nikolić, K., Mavridis, L., Đikić, T., Vučićević, J., Agbaba, D., Yelekci, K.,& Mitchell, J. B. O.. (2016). Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies. in Frontiers in Neuroscience
Frontiers Media Sa, Lausanne., 10.
https://doi.org/10.3389/fnins.2016.00265

Nikolić K, Mavridis L, Đikić T, Vučićević J, Agbaba D, Yelekci K, Mitchell JBO. Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies. in Frontiers in Neuroscience. 2016;10.
doi:10.3389/fnins.2016.00265 .

Nikolić, Katarina, Mavridis, Lazaros, Đikić, Teodora, Vučićević, Jelica, Agbaba, Danica, Yelekci, Kemal, Mitchell, John B. O., "Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies" in Frontiers in Neuroscience, 10 (2016),
https://doi.org/10.3389/fnins.2016.00265 . .

TY  - 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 . .