ABSTRACT
The glutamate hypothesis of schizophrenia suggests that hypofunction of N-methyl-D-aspartate (NMDA) receptors may be critical for schizophrenic symptoms; therefore, pharmacological approaches that enhance NMDA function may be beneficial for the treatment of schizophrenia. Several lines of evidence indicate that NMDA and metabotropic glutamate (mGlu) 5 receptors are closely associated signaling partners and that a selective mGlu5 receptor agonist might provide a viable approach for increasing NMDA receptor function in the treatment of schizophrenia. The orthosteric binding sites across members of the mGlu receptor subtype for a particular endogenous ligand are often highly conserved, making it difficult to achieve high selectivity for the specific mGlu5 receptor. The advanced currents of drug discovery have developed multiple highly selective allosteric modulators of mGlu5 receptors. In the present review, we provide an update of the recent patents and development of positive allosteric modulators of the mGlu5 receptor and explore their therapeutic potential for schizophrenia.
Subject(s)
Antipsychotic Agents/therapeutic use , Excitatory Amino Acid Agonists/therapeutic use , Patents as Topic , Receptors, Metabotropic Glutamate/chemistry , Receptors, Metabotropic Glutamate/metabolism , Schizophrenia/drug therapy , Allosteric Regulation/drug effects , Animals , Antipsychotic Agents/pharmacology , Disease Models, Animal , Excitatory Amino Acid Agonists/pharmacology , Glutamic Acid/metabolism , Humans , Models, Biological , Receptor, Metabotropic Glutamate 5 , Receptors, Metabotropic Glutamate/agonists , Schizophrenia/metabolismABSTRACT
The concept of drug-likeness, an important characteristic for any compound in a screening library, is nevertheless difficult to pin down. Based on our belief that this concept is implicit within the collective experience of working chemists, we devised a data set to capture an intuitive human understanding of both this characteristic and ease of synthesis, a second key characteristic. Five chemists assigned a pair of scores to each of 3980 diverse compounds, with the component scores of each pair corresponding to drug-likeness and ease of synthesis, respectively. Using this data set, we devised binary classifiers with an artificial neural network and a support vector machine. These models were found to efficiently eliminate compounds that are not drug-like and/or hard-to-synthesize derivatives, demonstrating the suitability of these models for use as compound acquisition filters.
