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Toxicol Lett. 2016 Mar 14;245:1-6. doi: 10.1016/j.toxlet.2016.01.001. Epub 2016 Jan 12.

Synthetic cannabinoids: In silico prediction of the cannabinoid receptor 1 affinity by a quantitative structure-activity relationship model.

Author information

1
Institute of Legal Medicine, University of Frankfurt/Main, Kennedyallee 104, D-60596 Frankfurt/Main, Germany. Electronic address: paulke@em.uni-frankfurt.de.
2
Institute of Pharmaceutical Chemistry, University of Frankfurt/Main, Max-von-Laue Straße 9, D-60438 Frankfurt/Main, Germany.
3
Institute of Legal Medicine, University of Frankfurt/Main, Kennedyallee 104, D-60596 Frankfurt/Main, Germany.

Abstract

The number of new synthetic psychoactive compounds increase steadily. Among the group of these psychoactive compounds, the synthetic cannabinoids (SCBs) are most popular and serve as a substitute of herbal cannabis. More than 600 of these substances already exist. For some SCBs the in vitro cannabinoid receptor 1 (CB1) affinity is known, but for the majority it is unknown. A quantitative structure-activity relationship (QSAR) model was developed, which allows the determination of the SCBs affinity to CB1 (expressed as binding constant (Ki)) without reference substances. The chemically advance template search descriptor was used for vector representation of the compound structures. The similarity between two molecules was calculated using the Feature-Pair Distribution Similarity. The Ki values were calculated using the Inverse Distance Weighting method. The prediction model was validated using a cross validation procedure. The predicted Ki values of some new SCBs were in a range between 20 (considerably higher affinity to CB1 than THC) to 468 (considerably lower affinity to CB1 than THC). The present QSAR model can serve as a simple, fast and cheap tool to get a first hint of the biological activity of new synthetic cannabinoids or of other new psychoactive compounds.

KEYWORDS:

In-silico prediction model; QSAR; Synthetic cannabinoids

PMID:
26795018
DOI:
10.1016/j.toxlet.2016.01.001
[Indexed for MEDLINE]

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