Modulating inhibitory response control through potentiation of GluN2D subunit-containing NMDA receptors.

NMDA receptors containing GluN2D subunits are expressed in the subthalamic nucleus and external globus pallidus, key nuclei of the indirect and hyperdirect pathways of the basal ganglia. This circuitry integrates cortical input with dopaminergic signaling to select advantageous behaviors among available choices. In the experiments described here, we characterized the effects of PTC-174, a novel positive allosteric modulator (PAM) of GluN2D subunit-containing NMDA receptors, on response control regulated by this circuitry. The indirect pathway suppresses less advantageous behavioral choices, a manifestation of which is suppression of locomotor activity in rats. Systemic administration of PTC-174 produced a dose-dependent reduction in activity in rats placed in a novel open field or administered the stimulants MK-801 or amphetamine. The hyperdirect pathway controls release of decisions from the basal ganglia to the cortex to optimize choice processing. Such response control was modeled in rats as premature responding in the 5-choice serial reaction time (5-CSRT) task. PTC-174 produced a dose-dependent reduction in premature responding in this task. These data suggest that potentiation of GluN2D receptor activity by PTC-174 facilitates the complex basal ganglia information processing that underlies response control. The behavioral effects occurred at estimated free PTC-174 brain concentrations predicted to induce 10-50% increases in GluN2D activity. The present findings suggest the potential of GluN2D PAMs to modulate basal ganglia function and to treat neurological disorders related to dysfunctional response control.

PTC-174, a positive allosteric modulator of NMDA receptors containing GluN2C or GluN2D subunits.

NMDA receptors are ionotropic glutamate receptors that mediate excitatory neurotransmission. The diverse functions of these receptors are tuned by deploying different combinations of GluN1 and GluN2 subunits (GluN2A-D) to form either diheteromeric NMDA receptors, which contain two GluN1 and two identical GluN2 subunits, or triheteromeric NMDA receptors, which contain two GluN1 and two distinct GluN2 subunits. Here, we characterize PTC-174, a novel positive allosteric modulator (PAM) of receptors containing GluN2C or GluN2D subunits. PTC-174 potentiates maximal current amplitudes by 1.8-fold for diheteromeric GluN1/2B receptors and by > 10-fold for GluN1/2C and GluN1/2D receptors. PTC-174 also potentiates responses from triheteromeric GluN1/2B/2D and GluN1/2A/2C receptors by 4.5-fold and 1.7-fold, respectively. By contrast, PTC-174 produces partial inhibition of responses from diheteromeric GluN1/2A and triheteromeric GluN1/2A/2B receptors. PTC-174 increases potencies of co-agonists glutamate and glycine by 2- to 5-fold at GluN1/2C and GluN1/2D receptors, and NMDA receptor activation facilitates allosteric modulation by PTC-174. At native NMDA receptors in GluN2D-expressing subthalamic nucleus neurons, PTC-174 increases the amplitude of responses to NMDA application and slows the decay of excitatory postsynaptic currents (EPSCs) evoked by internal capsule stimulation. Furthermore, PTC-174 increases the amplitude and slows the decay of EPSCs in hippocampal interneurons, but has not effect on the amplitudes of NMDA receptor-mediated EPSCs in hippocampal CA1 pyramidal neurons. Thus, PTC-174 provides a useful new pharmacological tool to investigate the molecular pharmacology and physiology of GluN2C- and GluN2D-containing NMDA receptors.

Structural Basis for Negative Allosteric Modulation of GluN2A-Containing NMDA Receptors.

NMDA receptors mediate excitatory synaptic transmission and regulate synaptic plasticity in the central nervous system, but their dysregulation is also implicated in numerous brain disorders. Here, we describe GluN2A-selective negative allosteric modulators (NAMs) that inhibit NMDA receptors by stabilizing the apo state of the GluN1 ligand-binding domain (LBD), which is incapable of triggering channel gating. We describe structural determinants of NAM binding in crystal structures of the GluN1/2A LBD heterodimer, and analyses of NAM-bound LBD structures corresponding to active and inhibited receptor states reveal a molecular switch in the modulatory binding site that mediate the allosteric inhibition. NAM binding causes displacement of a valine in GluN2A and the resulting steric effects can be mitigated by the transition from glycine bound to apo state of the GluN1 LBD. This work provides mechanistic insight to allosteric NMDA receptor inhibition, thereby facilitating the development of novel classes NMDA receptor modulators as therapeutic agents.

MPX-004 and MPX-007: New Pharmacological Tools to Study the Physiology of NMDA Receptors Containing the GluN2A Subunit.

GluN2A is the most abundant of the GluN2 NMDA receptor subunits in the mammalian CNS. Physiological and genetic evidence implicate GluN2A-containing receptors in susceptibility to autism, schizophrenia, childhood epilepsy and neurodevelopmental disorders such as Rett Syndrome. However, GluN2A-selective pharmacological probes to explore the therapeutic potential of targeting these receptors have been lacking. Here we disclose a novel series of pyrazine-containing GluN2A antagonists exemplified by MPX-004 (5-(((3-chloro-4-fluorophenyl)sulfonamido)methyl)-N-((2-methylthiazol-5-yl)methyl)pyrazine-2-carboxamide) and MPX-007 (5-(((3-fluoro-4-fluorophenyl)sulfonamido)methyl)-N-((2-methylthiazol-5-yl)methyl)methylpyrazine-2-carboxamide). MPX-004 and MPX-007 inhibit GluN2A-containing NMDA receptors expressed in HEK cells with IC50s of 79 nM and 27 nM, respectively. In contrast, at concentrations that completely inhibited GluN2A activity these compounds have no inhibitory effect on GluN2B or GluN2D receptor-mediated responses in similar HEK cell-based assays. Potency and selectivity were confirmed in electrophysiology assays in Xenopus oocytes expressing GluN2A-D receptor subtypes. Maximal concentrations of MPX-004 and MPX-007 inhibited ~30% of the whole-cell current in rat pyramidal neurons in primary culture and MPX-004 inhibited ~60% of the total NMDA receptor-mediated EPSP in rat hippocampal slices. GluN2A-selectivity at native receptors was confirmed by the finding that MPX-004 had no inhibitory effect on NMDA receptor mediated synaptic currents in cortical slices from GRIN2A knock out mice. Thus, MPX-004 and MPX-007 offer highly selective pharmacological tools to probe GluN2A physiology and involvement in neuropsychiatric and developmental disorders.

Allosteric modulators for the treatment of schizophrenia: targeting glutamatergic networks.

Schizophrenia is a highly debilitating mental disorder which afflicts approximately 1% of the global population. Cognitive and negative deficits account for the lifelong disability associated with schizophrenia, whose symptoms are not effectively addressed by current treatments. New medicines are needed to treat these aspects of the disease. Neurodevelopmental, neuropathological, genetic, and behavioral pharmacological data indicate that schizophrenia stems from a dysfunction of glutamate synaptic transmission, particularly in frontal cortical networks. A number of novel pre- and postsynaptic mechanisms affecting glutamatergic synaptic transmission have emerged as viable targets for schizophrenia. While developing orthosteric glutamatergic agents for these targets has proven extremely difficult, targeting allosteric sites of these targets has emerged as a promising alternative. From a medicinal chemistry perspective, allosteric sites provide an opportunity of finding agents with better drug-like properties and greater target specificity. Furthermore, allosteric modulators are better suited to maintaining the highly precise temporal and spatial aspects of glutamatergic synaptic transmission. Herein, we review neuropathological and genomic/genetic evidence underscoring the importance of glutamate synaptic dysfunction in the etiology of schizophrenia and make a case for allosteric targets for therapeutic intervention. We review progress in identifying allosteric modulators of AMPA receptors, NMDA receptors, and metabotropic glutamate receptors, all with the aim of restoring physiological glutamatergic synaptic transmission. Challenges remain given the complexity of schizophrenia and the difficulty in studying cognition in animals and humans. Nonetheless, important compounds have emerged from these efforts and promising preclinical and variable clinical validation has been achieved.

Analysis of information flows in interaction networks: implication for drug discovery and pharmacological research.

Frequent failures of experimental medicines in clinical trials question current concepts for predicting drug-effects in the human body. Improving the probability for success in drug discovery requires a better understanding of cause-effect relationships at the organism, organ, tissue, cellular, and molecular levels, each having a different degree of complexity. Despite the longstanding realization that clinical and preclinical drug-effect information needs to be integrated for generating more accurate forecasts of drug-effects, a road map for linking these disparate sources of information currently does not exist. This review focuses on a possible approach for obtaining these relationships by analyzing causes and effects on the basis of the topology of network interaction systems that process information at the cellular and organ system levels.

Cause-effect relationships in medicine: a protein network perspective.

Current target-based drug discovery platforms are not able to predict drug efficacy and the full spectrum of drug effects in organisms. Hence, many experimental drugs do not survive the lengthy and costly process of drug development. Understanding how drugs affect cellular network structures and how the resulting signals are translated into drug effects is extremely important for the discovery of new medicines. This requires a greater understanding of cause-effect relationships at the organism, organ, tissue, cellular, and molecular level. There is a growing recognition that this information must be integrated into discovery paradigms, but a 'road map' for obtaining and integrating information about heterogeneous networks into drug-discovery platforms currently does not exist. This review explores recent network-centered approaches developed to investigate the genesis of medicine and disease effects, specifically highlighting protein-protein interaction network models and their use in cause-effect analyses in medicine.

Drug effects viewed from a signal transduction network perspective.

Understanding how drugs affect cellular network structures and how resulting signals are translated into drug effects holds the key to the discovery of medicines. Herein we examine this cause-effect relationship by determining protein network structures associated with the generation of specific in vivo drug-effect patterns. Medicines having similar in vivo pharmacology have been identified by a comparison of drug-effect profiles of 1320 medicines. Protein network positions reached by these medicines were ascertained by examining the coinvestigation frequency of these medicines and 1179 protein network constituents in millions of scientific investigations. Interestingly, medicine associations obtained by comparing by drug-effect profiles mirror those obtained by comparing drug-protein coinvestigation frequency profiles, demonstrating that these drug-protein reachability profiles are relevant to in vivo pharmacology. By using protein associations obtained in these investigations and independent, curated protein interaction information, drug-mediated protein network topology models can be constructed. These protein network topology models reveal that drugs having similar pharmacology profiles reach similar discrete positions in cellular protein network systems and provide a network view of medicine cause-effect relationships.

Analysis of system structure-function relationships.

Preclinical pharmacology studies conducted with experimental medicines currently focus on assessments of drug effects attributed to a drug's putative mechanism of action. The high failure rate of medicines in clinical trials, however, underscores that the information gathered from these studies is insufficient for forecasting drug effect profiles actually observed in patients. Improving drug effect predictions and increasing success rates of new medicines in clinical trials are some of the key challenges currently faced by the pharmaceutical industry. Addressing these challenges requires development of new methods for capturing and comparing "system-wide" structure-effect information for medicines at the cellular and organism levels. The current investigation describes a strategy for moving in this direction by using six different descriptor sets for examining the relationship between molecular structure and broad effect information of 1064 medicines at the cellular and the organism level. To compare broad drug effect information between different medicines, information spectra for each of the 1064 medicines were created, and the similarity between information spectra was determined through hierarchical clustering. The structure-effect relationships ascertained through these comparisons indicate that information spectra similarity obtained through preclinical ligand binding experiments using a model proteome provide useful estimates for the broad drug effect profiles of these 1064 medicines in organisms. This premise is illustrated using the ligand binding profiles of selected medicines in the dataset as biomarkers for forecasting system-wide effect observations of medicines that were not included in the incipient 1064-medicine analysis.

Analysis of drug-induced effect patterns to link structure and side effects of medicines.

The high failure rate of experimental medicines in clinical trials accentuates inefficiencies of current drug discovery processes caused by a lack of tools for translating the information exchange between protein and organ system networks. Recently, we reported that biological activity spectra (biospectra), derived from in vitro protein binding assays, provide a mechanism for assessing a molecule's capacity to modulate the function of protein-network components. Herein we describe the translation of adverse effect data derived from 1,045 prescription drug labels into effect spectra and show their utility for diagnosing drug-induced effects of medicines. In addition, notwithstanding the limitation imposed by the quality of drug label information, we show that biospectrum analysis, in concert with effect spectrum analysis, provides an alignment between preclinical and clinical drug-induced effects. The identification of this alignment provides a mechanism for forecasting clinical effect profiles of medicines.

Biospectra analysis: model proteome characterizations for linking molecular structure and biological response.

Establishing quantitative relationships between molecular structure and broad biological effects has been a long-standing goal in drug discovery. Evaluation of the capacity of molecules to modulate protein functions is a prerequisite for understanding the relationship between molecular structure and in vivo biological response. A particular challenge in these investigations is to derive quantitative measurements of a molecule's functional activity pattern across different proteins. Herein we describe an operationally simple probabilistic structure-activity relationship (SAR) approach, termed biospectra analysis, for identifying agonist and antagonist effect profiles of medicinal agents by using pattern similarity between biological activity spectra (biospectra) of molecules as the determinant. Accordingly, in vitro binding data (percent inhibition values of molecules determined at single high drug concentration in a battery of assays representing a cross section of the proteome) are useful for identifying functional effect profile similarity between medicinal agents. To illustrate this finding, the relationship between biospectra similarity of 24 molecules, identified by hierarchical clustering of a 1567 molecule dataset as being most closely aligned with the neurotransmitter dopamine, and their agonist or antagonist properties was probed. Distinguishing the results described in this study from those obtained with affinity-based methods, the observed association between biospectra and biological response profile similarity remains intact even upon removal of putative drug targets from the dataset (four dopaminergic [D1/D2/D3/D4] and two adrenergic [alpha1 and alpha2] receptors). These findings indicate that biospectra analysis provides an unbiased new tool for forecasting structure-response relationships and for translating broad biological effect information into chemical structure design.

Biological spectra analysis: Linking biological activity profiles to molecular structure.

Establishing quantitative relationships between molecular structure and broad biological effects has been a longstanding challenge in science. Currently, no method exists for forecasting broad biological activity profiles of medicinal agents even within narrow boundaries of structurally similar molecules. Starting from the premise that biological activity results from the capacity of small organic molecules to modulate the activity of the proteome, we set out to investigate whether descriptor sets could be developed for measuring and quantifying this molecular property. Using a 1,567-compound database, we show that percent inhibition values, determined at single high drug concentration in a battery of in vitro assays representing a cross section of the proteome, provide precise molecular property descriptors that identify the structure of molecules. When broad biological activity of molecules is represented in spectra form, organic molecules can be sorted by quantifying differences between biological spectra. Unlike traditional structure-activity relationship methods, sorting of molecules by using biospectra comparisons does not require knowledge of a molecule's putative drug targets. To illustrate this finding, we selected as starting point the biological activity spectra of clotrimazole and tioconazole because their putative target, lanosterol demethylase (CYP51), was not included in the bioassay array. Spectra similarity obtained through profile similarity measurements and hierarchical clustering provided an unbiased means for establishing quantitative relationships between chemical structures and biological activity spectra. This methodology, which we have termed biological spectra analysis, provides the capability not only of sorting molecules on the basis of biospectra similarity but also of predicting simultaneous interactions of new molecules with multiple proteins.

Substituted 6-phenyl-pyridin-2-ylamines: selective and potent inhibitors of neuronal nitric oxide synthase.

The synthesis and nNOS and eNOS activity of 6-(4-(dimethylaminoalkyl)-/6-(4-(dimethylaminoalkoxy)-5-ethyl-2-methoxyphenyl)-pyridin-2-ylamines and 6-(4-(dimethylaminoalkyl)-/6-(4-(dimethylaminoalkoxy)-2,5-dimethoxyphenyl)-pyridin-2-ylamines 1-8 are described. These compounds are potent inhibitors of the human nNOS isoform.

Structure-activity relationships of potent, selective inhibitors of neuronal nitric oxide synthase based on the 6-phenyl-2-aminopyridine structure.

The synthesis and structure-activity relationships of a series of 6-phenyl-2-aminopyridines that potently and selectively inhibit the neuronal isoform of nitric oxide synthase (nNOS) are described. Compound 14bi from this series exhibits potent in vivo activity in harmaline-induced cGMP formation in rat cerebellum, a functional model of nNOS inhibition, and in the PCP-induced hypermotility model in the rat. These results suggest that 14bi may be a useful reagent for evaluating potential therapeutic applications of nNOS inhibitors in the central nervous system.