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Predicting targets of compounds against neurological diseases using cheminformatic methodology

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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).

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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

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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.

Author information

Authors and Affiliations

  1. Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Institute of Pharmaceutical Chemistry, University of Belgrade, Vojvode Stepe 450, 11000, Belgrade, Serbia

    Katarina Nikolic & Danica Agbaba

  2. Biomedical Sciences Research Complex and EaStCHEM School of Chemistry, University of St Andrews, St Andrews, KY16 9ST, Scotland, UK

    Lazaros Mavridis, Rona R. Ramsay & John B. O. Mitchell

  3. Laboratorio de Química Médica, Instituto de Química Orgánica General, Consejo Superior de Investigaciones Científicas, C/Juan de la Cierva 3, 28006, Madrid, Spain

    Oscar M. Bautista-Aguilera & José Marco-Contelles

  4. Institute of Pharmaceutical and Medicinal Chemistry, Heinrich Heine University, Universitaetsstr. 1, 40225, Düsseldorf, Germany

    Holger Stark

  5. iMed.UL - Research Institute for Medicines and Pharmaceutical Sciences, Faculty of Pharmacy, University of Lisbon, Avda. Prof. Gama Pinto, 1649-003, Lisbon, Portugal

    Maria do Carmo Carreiras

  6. Dipartimento di Scienze Mediche, Chirurgiche e Neuroscienze, University of Siena, Strada delle Scotte 6, 53100, Siena, Italy

    Ilaria Rossi & Paola Massarelli

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  1. Katarina Nikolic
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Corresponding author

Correspondence to Katarina Nikolic.

Electronic supplementary material

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10822_2014_9816_MOESM1_ESM.tif

Supplementary Figure 1: Ligand-pharmacological group associations for all examined compounds (1–134), obtained by querying the 134 compounds against the refined DrugBank dataset (TIFF 14384 kb)

10822_2014_9816_MOESM2_ESM.xls

Supplementary Table 1: Ligand-target associations for all examined compounds (1–134), obtained by querying the 134 compounds against the refined ChEMBL dataset (XLS 466 kb)

10822_2014_9816_MOESM3_ESM.xls

Supplementary Table 2: Ligand-pharmacological group associations for all examined compounds (1–134), obtained by querying the 134 compounds against the refined DrugBank dataset (XLS 546 kb)

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Nikolic, K., Mavridis, L., Bautista-Aguilera, O.M. et al. Predicting targets of compounds against neurological diseases using cheminformatic methodology. J Comput Aided Mol Des 29, 183–198 (2015). https://doi.org/10.1007/s10822-014-9816-1

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  • DOI: https://doi.org/10.1007/s10822-014-9816-1

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