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Journal of Computer-Aided Molecular Design

, Volume 29, Issue 2, pp 183–198 | Cite as

Predicting targets of compounds against neurological diseases using cheminformatic methodology

  • Katarina Nikolic
  • Lazaros Mavridis
  • Oscar M. Bautista-Aguilera
  • José Marco-Contelles
  • Holger Stark
  • Maria do Carmo Carreiras
  • Ilaria Rossi
  • Paola Massarelli
  • Danica Agbaba
  • Rona R. Ramsay
  • John B. O. Mitchell
Article

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 H3-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).

Keywords

Multi-targeted ligands Circular fingerprints Off-target study ChE MAO Histamine H3 receptor HMT 

Abbreviations

AD

Alzheimer’s disease

AChE

Acetylcholinesterase

BuChE

Butyrylcholinesterase

CFP

Circular fingerprint

3D-QSAR

3D-Quantitive structure–activity relationship

EDTA

Ethylenediaminetetraacetic acid

FP

False positive

GSK-3

Glycogen synthase kinase 3

HMT

Histamine N-methyltransferase

H3R

Histamine H3-receptor

5-HT1a

5-Hydroxytryptamine-1a (serotonin)

5-HT2a

5-Hydroxytryptamine-2a (serotonin)

5-HT2c

5-Hydroxytryptamine-2c (serotonin)

MAO-A

Monoamine oxidase A

MAO-B

Monoamine oxidase B

MCC

Matthews correlation coefficient

MTDL

Multi-target-directed ligand

NMDA receptors

N-Methyl-d-aspartate receptor

nAChRs

Nicotinic acetylcholine receptors

8-OH-DPAT

(±)-8-Hydroxy-2-dipropylaminotetralin

PDE-4

Phosphodiesterase 4

PD

Parkinson’s disease

RMSEE

Root main square error of estimation

RMSEP

Root main square error of prediction

SERT

Serotonin transporter

TP

True positive

Tris

Tris(hydroxymethyl)aminomethane

WADA

World Anti-Doping Agency

Notes

Acknowledgments

This project has been carried out with the support of WADA. We also acknowledge financial support from the Scottish Universities Life Sciences Alliance (SULSA). OMBA and JMC thenk MINECO (Spain) for a fellowship, and support (SAF2012-33304), respectively. KN and DA acknowledge project supported by the Ministry of Education and Science of the Republic of Serbia, Contract No. 172033. Further supports by Else Kröner-Fresenius-Stiftung, Translational Research Innovation—Pharma (TRIP), Fraunhofer-Projektgruppe für Translationale Medizin und Pharmakologie (TMP) (to HS) and the European COST Actions BM1007, CM1103 (including STSM 10295 to KN), and CM1207 are also gratefully acknowledged.

Conflict of interest

The authors (JBOM, LM) have received funding from WADA. Other then this sponsorship, the authors declare no conflict of interest.

Supplementary material

10822_2014_9816_MOESM1_ESM.tif (14 mb)
Supplementary Figure 1: Ligand-pharmacological group associations for all examined compounds (1134), obtained by querying the 134 compounds against the refined DrugBank dataset (TIFF 14384 kb)
10822_2014_9816_MOESM2_ESM.xls (466 kb)
Supplementary Table 1: Ligand-target associations for all examined compounds (1134), obtained by querying the 134 compounds against the refined ChEMBL dataset (XLS 466 kb)
10822_2014_9816_MOESM3_ESM.xls (546 kb)
Supplementary Table 2: Ligand-pharmacological group associations for all examined compounds (1134), obtained by querying the 134 compounds against the refined DrugBank dataset (XLS 546 kb)

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Katarina Nikolic
    • 1
  • Lazaros Mavridis
    • 2
  • Oscar M. Bautista-Aguilera
    • 3
  • José Marco-Contelles
    • 3
  • Holger Stark
    • 4
  • Maria do Carmo Carreiras
    • 5
  • Ilaria Rossi
    • 6
  • Paola Massarelli
    • 6
  • Danica Agbaba
    • 1
  • Rona R. Ramsay
    • 2
  • John B. O. Mitchell
    • 2
  1. 1.Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Institute of Pharmaceutical ChemistryUniversity of BelgradeBelgradeSerbia
  2. 2.Biomedical Sciences Research Complex and EaStCHEM School of ChemistryUniversity of St AndrewsSt AndrewsScotland, UK
  3. 3.Laboratorio de Química Médica, Instituto de Química Orgánica GeneralConsejo Superior de Investigaciones CientíficasMadridSpain
  4. 4.Institute of Pharmaceutical and Medicinal ChemistryHeinrich Heine UniversityDüsseldorfGermany
  5. 5.iMed.UL - Research Institute for Medicines and Pharmaceutical Sciences, Faculty of PharmacyUniversity of LisbonLisbonPortugal
  6. 6.Dipartimento di Scienze Mediche, Chirurgiche e NeuroscienzeUniversity of SienaSienaItaly

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