| Virtual Screening to find multitargeting candidates for CB1 and CB2 receptors

Shayma El Atawneh and Amiram Goldblum Ph.D

Multi-targeted single molecules have many advantages over multiple agents such as “drug cocktails”, linked drugs and others. In order to design in silico multi-targeted agents there is a need to provide models based on ligands, or based on protein targets’ structures, or both 1. There are many possible combinations of requirements for targets and anti targets concerning agonists and antagonists of CB1 and CB2 in the CNS and in the periphery.

Peripheral treatment with a CB1 antagonist reduced food intake and body weight 2. Selective CB2 activation may serve therapeutic manipulation of untoward immune responses including those associated with a variety of neuropathies that exhibit a hyper-inflammatory component 3 (devoid of psychoactive effects of CB1 activation).

Combination of targeting specific receptors as well as anti-targets, could be achieved using our in-house Iterative Stochastic Elimination (ISE) Algorithm4 for constructing activity models of the cannabinoid receptors. ISE models are based on the idea of classification but provide an ensemble of filters of molecular properties that enable the quantification of each molecule’s chances due to the differences in classification ability between filters. These models will form the basis for subsequent virtual screenings of huge libraries of commercially available molecules to filter candidates that will be purchased and tested5. Thus, ISE models serve to discover new multitargeting candidates.

 * Molecular Modeling and Drug Discovery Lab, Institute for Drug Research, The Hebrew University of Jerusalem, Israel

  1. Boran, A. D. W. & Iyengar, R. Systems pharmacology. Mt. Sinai J. Med. 77, 333–44
  2. Nogueiras, R. et al. Peripheral, but not central, CB1 antagonism provides food intake-independent metabolic benefits in diet-induced obese rats. Diabetes 57, 2977–91 (2008).
  3. Cabral, G. A. & Griffin-Thomas, L. Emerging Role of the CB2 Cannabinoid Receptor in Immune Regulation and Therapeutic Prospects. Expert Rev. Mol. Med. 11, e3
  4. Stern N and Goldblum A. Iterative Stochastic Elimination for Solving Complex Combinatorial Problems in Drug Discovery Isr. J. Chem. 54, 1338-67 (2014)
  5. Zatsepin M et al. Computational Discovery and Ecperimnetal Confirmation of TLR9 Receptor Antagonist Leads, http://pubs.acs.org/doi/abs/10.1021/acs.jcim.6b00070