2-(Fluoromethoxy)-4'-(S-methanesulfonimidoyl)-1,1'-biphenyl (UCM-1306), an Orally Bioavailable Positive Allosteric Modulator of the Human Dopamine D1 Receptor for Parkinson's Disease.

Tolerance development caused by dopamine replacement with l-DOPA and therapeutic drawbacks upon activation of dopaminergic receptors with orthosteric agonists reveal a significant unmet need for safe and effective treatment of Parkinson's disease. In search for selective modulators of the D1 receptor, the screening of a chemical library and subsequent medicinal chemistry program around an identified hit resulted in new synthetic compound 26 [UCM-1306, 2-(fluoromethoxy)-4'-(S-methanesulfonimidoyl)-1,1'-biphenyl] that increases the dopamine maximal effect in a dose-dependent manner in human and mouse D1 receptors, is inactive in the absence of dopamine, modulates dopamine affinity for the receptor, exhibits subtype selectivity, and displays low binding competition with orthosteric ligands. The new allosteric modulator potentiates cocaine-induced locomotion and enhances l-DOPA recovery of decreased locomotor activity in reserpinized mice after oral administration. The behavior of compound 26 supports the interest of a positive allosteric modulator of the D1 receptor as a promising therapeutic approach for Parkinson's disease.

Novel Antagonist of the Type 2 Lysophosphatidic Acid Receptor (LPA2), UCM-14216, Ameliorates Spinal Cord Injury in Mice.

Spinal cord injuries (SCIs) irreversibly disrupt spinal connectivity, leading to permanent neurological disabilities. Current medical treatments for reducing the secondary damage that follows the initial injury are limited to surgical decompression and anti-inflammatory drugs, so there is a pressing need for new therapeutic strategies. Inhibition of the type 2 lysophosphatidic acid receptor (LPA2) has recently emerged as a new potential pharmacological approach to decrease SCI-associated damage. Toward validating this receptor as a target in SCI, we have developed a new series of LPA2 antagonists, among which compound 54 (UCM-14216) stands out as a potent and selective LPA2 receptor antagonist (Emax = 90%, IC50 = 1.9 µM, KD = 1.3 nM; inactive at LPA1,3-6 receptors). This compound shows efficacy in an in vivo mouse model of SCI in an LPA2-dependent manner, confirming the potential of LPA2 inhibition for providing a new alternative for treating SCI.

An Open Drug Discovery Competition: Experimental Validation of Predictive Models in a Series of Novel Antimalarials.

The Open Source Malaria (OSM) consortium is developing compounds that kill the human malaria parasite, Plasmodium falciparum, by targeting PfATP4, an essential ion pump on the parasite surface. The structure of PfATP4 has not been determined. Here, we describe a public competition created to develop a predictive model for the identification of PfATP4 inhibitors, thereby reducing project costs associated with the synthesis of inactive compounds. Competition participants could see all entries as they were submitted. In the final round, featuring private sector entrants specializing in machine learning methods, the best-performing models were used to predict novel inhibitors, of which several were synthesized and evaluated against the parasite. Half possessed biological activity, with one featuring a motif that the human chemists familiar with this series would have dismissed as "ill-advised". Since all data and participant interactions remain in the public domain, this research project "lives" and may be improved by others.

Individual and collective human intelligence in drug design: evaluating the search strategy.

In recent years, individual and collective human intelligence, defined as the knowledge, skills, reasoning and intuition of individuals and groups, have been used in combination with computer algorithms to solve complex scientific problems. Such approach was successfully used in different research fields such as: structural biology, comparative genomics, macromolecular crystallography and RNA design. Herein we describe an attempt to use a similar approach in small-molecule drug discovery, specifically to drive search strategies of de novo drug design. This is assessed with a case study that consists of a series of public experiments in which participants had to explore the huge chemical space in silico to find predefined compounds by designing molecules and analyzing the score associate with them. Such a process may be seen as an instantaneous surrogate of the classical design-make-test cycles carried out by medicinal chemists during the drug discovery hit to lead phase but not hindered by long synthesis and testing times. We present first findings on (1) assessing human intelligence in chemical space exploration, (2) comparing individual and collective human intelligence performance in this task and (3) contrasting some human and artificial intelligence achievements in de novo drug design.

A Novel Agonist of the Type 1 Lysophosphatidic Acid Receptor (LPA1), UCM-05194, Shows Efficacy in Neuropathic Pain Amelioration.

Neuropathic pain (NP) is a complex chronic pain state with a prevalence of almost 10% in the general population. Pharmacological options for NP are limited and weakly effective, so there is a need to develop more efficacious NP attenuating drugs. Activation of the type 1 lysophosphatidic acid (LPA1) receptor is a crucial factor in the initiation of NP. Hence, it is conceivable that a functional antagonism strategy could lead to NP mitigation. Here we describe a new series of LPA1 agonists among which derivative (S)-17 (UCM-05194) stands out as the most potent and selective LPA1 receptor agonist described so far (Emax = 118%, EC50 = 0.24 µM, KD = 19.6 nM; inactive at autotaxin and LPA2-6 receptors). This compound induces characteristic LPA1-mediated cellular effects and prompts the internalization of the receptor leading to its functional inactivation in primary sensory neurons and to an efficacious attenuation of the pain perception in an in vivo model of NP.

Structuring Chemical Space: Similarity-Based Characterization of the PubChem Database.

The ensemble of conceivable molecules is referred to as the Chemical Space. In this article we describe a hierarchical version of the Affinity Propagation (AP) clustering algorithm and apply it to analyze the LINGO-based similarity matrix of a 500 000-molecule subset of the PubChem database, which contains more than 19 million compounds. The combination of two highly efficient methods, namely the AP clustering algorithm and LINGO-based molecular similarity calculations, allows the unbiased analysis of large databases. Hierarchical clustering generates a numerical diagonalization of the similarity matrix. The target-independent, intrinsic structure of the database , derived without any previous information on the physical or biological properties of the compounds, maps together molecules experimentally shown to bind the same biological target or to have similar physical properties.

An improved scoring function for suboptimal polar ligand complexes.

Learning strategies can be used to improve the efficiency of virtual screening of very large databases. In these strategies new compounds to be screened are selected on the basis of the results obtained in previous stages, even if truly good ligands have not yet been identified. This approach requires that the scoring function used correctly predicts the energy and geometry of suboptimal complexes, i.e. weak complexes that are not the final solution of the screening but help direct the search toward the most productive regions of chemical space. We show that a small modification in the treatment of the solvation of polar atoms corrects the tendency of the original Autodock 3.0 scoring function to bury ligand polar atoms away from solvent, even if no complementary groups are present in the target and improves the performance of Autodock 3.0 and 4.0 in reproducing the experimental docking energies of weak complexes, resembling the suboptimal complexes encountered in the intermediate stages of virtual screening.