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Updated: Aug 1, 2025

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Predicting binding between 55 cannabinoids and 4,799 biological targets by in silico methods.

Michael F Santillo¹, Robert L Sprando¹

¹Division of Toxicology, Office of Applied Research and Safety Assessment, Center for Food Safety and Applied Nutrition, US Food and Drug Administration, Laurel, Maryland, USA.

Journal of Applied Toxicology : JAT

|April 26, 2023

Summary

This study used computational methods to predict interactions between 55 cannabis compounds and thousands of biological targets. The findings help identify potential health risks from cannabis products.

Keywords:

QSAR cannabinoids cannabis computational toxicology in silico receptors safety pharmacology secondary pharmacology structure-activity relationship target screening

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Published on: June 20, 2025

Area of Science:

Pharmacology and Toxicology
Computational Chemistry
Cannabinoid Science

Background:

Increasing market availability of cannabis-derived products necessitates understanding their physiological effects.
Cannabis contains numerous cannabinoids with largely unknown biological activities.
Limited availability of cannabinoids for in vitro testing poses a challenge for safety assessment.

Purpose of the Study:

To predict potential cannabinoid-target interactions using an in silico approach.
To identify potential human health hazards associated with these interactions.
To provide a rapid screening method for prioritizing further safety testing.

Main Methods:

Utilized Chemotargets Clarity software for in silico prediction of binding between 55 cannabinoids and 4,799 biological targets.
Employed quantitative structure-activity relationships (QSAR) and structural similarity analyses.
Validated in silico predictions against available in vitro binding data and identified clinical adverse effects from an online database.

Main Results:

Predicted 827 cannabinoid-target binding pairs involving 143 unique biological targets.
Observed similar binding profiles for cannabinoids with shared core structures and those with carboxylic acid groups.
Demonstrated good agreement between in silico predictions and in vitro data, with a median fourfold difference in binding concentrations.
Identified 22 predicted targets linked to clinical adverse effects.

Conclusions:

In silico prediction is a rapid and effective method for identifying potential hazards of cannabinoid-target interactions.
The generated data can guide prioritization of in vitro and in vivo studies for safety evaluation.
This approach offers valuable insights into the potential risks of consumer products containing cannabis-derived compounds.