Laquinimod arrests experimental autoimmune encephalomyelitis by activating the aryl hydrocarbon receptor.

Laquinimod is an oral drug currently being evaluated for the treatment of relapsing, remitting, and primary progressive multiple sclerosis and Huntington's disease. Laquinimod exerts beneficial activities on both the peripheral immune system and the CNS with distinctive changes in CNS resident cell populations, especially astrocytes and microglia. Analysis of genome-wide expression data revealed activation of the aryl hydrocarbon receptor (AhR) pathway in laquinimod-treated mice. The AhR pathway modulates the differentiation and function of several cell populations, many of which play an important role in neuroinflammation. We therefore tested the consequences of AhR activation in myelin oligodendrocyte glycoprotein (MOG)-induced experimental autoimmune encephalomyelitis (EAE) using AhR knockout mice. We demonstrate that the pronounced effect of laquinimod on clinical score, CNS inflammation, and demyelination in EAE was abolished in AhR-/- mice. Furthermore, using bone marrow chimeras we show that deletion of AhR in the immune system fully abrogates, whereas deletion within the CNS partially abrogates the effect of laquinimod in EAE. These data strongly support the idea that AhR is necessary for the efficacy of laquinimod in EAE and that laquinimod may represent a first-in-class drug targeting AhR for the treatment of multiple sclerosis and other neurodegenerative diseases.

Benzofuran Derivatives as Potent, Orally Active S1P1 Receptor Agonists: A Preclinical Lead Molecule for MS.

We have discovered novel benzofuran-based S1P1 agonists with excellent in vitro potency and selectivity. 1-((4-(5-Benzylbenzofuran-2-yl)-3-fluorophenyl)methyl) azetidine-3-carboxylic acid (18) is a potent S1P1 agonist with >1000× selectivity over S1P3. It demonstrated a good in vitro ADME profile and excellent oral bioavailability across species. Dosed orally at 0.3 mg/kg, 18 significantly reduced blood lymphocyte counts 24 h postdose and demonstrated efficacy in a mouse EAE model of relapsing MS.

Discovery of a Potent, S1P3-Sparing Benzothiazole Agonist of Sphingosine-1-Phosphate Receptor 1 (S1P1).

Optimization of a benzofuranyl S1P1 agonist lead compound (3) led to the discovery of 1-(3-fluoro-4-(5-(2-fluorobenzyl)benzo[d]thiazol-2-yl)benzyl)azetidine-3-carboxylic acid (14), a potent S1P1 agonist with minimal activity at S1P3. Dosed orally at 0.3 mg/kg, 14 significantly reduced blood lymphocyte counts 24 h postdose and attenuated a delayed type hypersensitivity (DTH) response to antigen challenge.

Small molecule correctors of F508del-CFTR discovered by structure-based virtual screening.

Folding correctors of F508del-CFTR were discovered by in silico structure-based screening utilizing homology models of CFTR. The intracellular segment of CFTR was modeled and three cavities were identified at inter-domain interfaces: (1) Interface between the two Nucleotide Binding Domains (NBDs); (2) Interface between NBD1 and Intracellular Loop (ICL) 4, in the region of the F508 deletion; (3) multi-domain interface between NBD1:2:ICL1:2:4. We hypothesized that compounds binding at these interfaces may improve the stability of the protein, potentially affecting the folding yield or surface stability. In silico structure-based screening was performed at the putative binding-sites and a total of 496 candidate compounds from all three sites were tested in functional assays. A total of 15 compounds, representing diverse chemotypes, were identified as F508del folding correctors. This corresponds to a 3% hit rate, ~tenfold higher than hit rates obtained in corresponding high-throughput screening campaigns. The same binding sites also yielded potentiators and, most notably, compounds with a dual corrector-potentiator activity (dual-acting). Compounds harboring both activity types may prove to be better leads for the development of CF therapeutics than either pure correctors or pure potentiators. To the best of our knowledge this is the first report of structure-based discovery of CFTR modulators.

G Protein-Coupled Receptors: target-based in silico screening.

In silico (or virtual) screening has become a common practice in current computer-aided drug design efforts. However, application to hit discovery in the G Protein-Coupled Receptors (GPCRs) arena was until recently hampered by the paucity of crystal structures available for this important class of pharmaceutical targets, forcing practitioners in the field to rely on GPCR models derived either ab initio or through homology modeling approaches. In this work we describe the EPIX in silico screening workflow which consists of the following stages: (1) Target modeling; (2) Preparation of screening library; (3) Docking; (4) Binding mode selection; (5) Scoring; (6) Consensus scoring and (7) Selection of virtual hits. This workflow was applied to the virtual screening of 13 GPCRs (5 biogenic amine receptors, 5 peptide receptors, 1 lipid receptor, 1 purinergic receptor and 1 cannabinoid receptor). Hit rates vary between 4% and 21% with higher hit rates usually obtained for biogenic amines and lower hits rates for peptide receptors. These data are analyzed in the context of the available experimental information (i.e., mutational data), the model (i.e., binding site location, and type of interactions) and the screening library. Specific examples are discussed in more detail as well as the future directions and challenges of this approach to in silico screening.

SeleX-CS: a new consensus scoring algorithm for hit discovery and lead optimization.

Identifying active compounds (hits) that bind to biological targets of pharmaceutical relevance is the cornerstone of drug design efforts. Structure based virtual screening, namely, the in silico evaluation of binding energies and geometries between a protein and its putative ligands, has emerged over the past few years as a promising approach in this field. The success of the method relies on the availability of reliable 3-dimensional (3D) structures of the target protein and its candidate ligands (the screening library), a reliable docking method that can fit the different ligands into the protein's binding site, and an accurate scoring function that can rank the resulting binding modes in accord with their binding affinities. This last requirement is arguably the most difficult to meet due to the complexity of the binding process. A potential solution to this so-called scoring problem is the usage of multiple scoring functions in an approach known as consensus scoring. Several consensus scoring methods were suggested in the literature and have generally demonstrated an improved ranking of screening libraries relative to individual scoring functions. Nevertheless, current consensus scoring strategies suffer from several shortcomings, in particular, strong dependence on the initial parameters and an incomplete treatment of inactive compounds. In this work we present a new consensus scoring algorithm (SeleX-Consensus Scoring abbreviated to SeleX-CS) specifically designed to address these limitations: (i) A subset of the initial set of the scoring functions is allowed to form the consensus score, and this subset is optimized via a Monte Carlo/Simulated Annealing procedure. (ii) Rank redundancy between the members of the screening library is removed. (iii) The method explicitly considers the presence of inactive compounds. The new algorithm was applied to the ranking of screening libraries targeting two G-protein coupled receptors (GPCR). Excellent enrichment factors were obtained in both cases: For the cannabinoid receptor 1 (CB1), SeleX-CS outperformed the best single score and afforded an enrichment factor of 41 at 1% of the screening library compared with the best single score value of 15 (GOLD_Fitness). For the chemokine receptor type 2 (CCR2) SeleX-CS afforded an enrichment factor of 72 (again at 1% of the screening library) once more outperforming any single score (enrichment factor of 20 by GSCORE). Moreover, SeleX-CS demonstrated success rates of 67% (CCR2) and 73% (CB1) when applied to ranking an external test set. In both cases, the new algorithm also afforded good derichment of inactive compounds (i.e., the ability to push inactive compounds to the bottom of the ranked library). The method was then extended to rank a lead optimization series targeting the Kv4.3 potassium ion channel, resulting in a Spearman's correlation coefficient, p = 0.63 (n = 40), between the SeleX-CS-based rank and the actual pKi values. These results suggest that SeleX-CS is a powerful method for ranking screening libraries in the lead discovery phase and also merits consideration as a lead optimization tool.

VRX-03011, a novel 5-HT4 agonist, enhances memory and hippocampal acetylcholine efflux.

Recent evidence suggests that 5-hydroxytryptamine (5-HT)(4) receptor activity enhances cognition and provides neuroprotection. Here we report the effects of VRX-03011, a novel partial 5-HT(4) agonist, that is both potent (K(i) approximately 30 nM) and highly selective (K(i) > 5 microM for all other 5-HT receptors tested). In separate experiments, rats received VRX-03011 (0.1-10 mg/kg i.p.) 30 min prior to spontaneous alternation testing in a no-delay or a 30-s delay condition. VRX-03011 (1, 5 and 10 mg/kg, but not 0.1 mg/kg) significantly enhanced delayed spontaneous alternation performance while none of the doses enhanced performance in the no-delay test. VRX-03011 (1 and 5 mg/kg) concomitantly enhanced hippocampal acetylcholine output and delayed spontaneous alternation scores compared to that of vehicle controls, but had no effect on hippocampal acetylcholine release under a resting condition. Moreover, suboptimal doses of VRX-03011 and the acetylcholinesterase inhibitor galanthamine combined to enhance memory. VRX-03011 also regulated amyloid precursor protein (APP) metabolism by inducing a concentration-dependent increase in the non-amyloidogenic soluble form of APP (sAPPalpha) with an EC(50) approximately 1--10 nM. VRX-03011 had no effect on contractile properties in guinea pig ileum or colon preparations with an EC(50) > 10 microM and did not alter rat intestinal transit at doses up to 10 mg/kg. These findings suggest that VRX-03011 may represent a novel treatment for Alzheimer's disease that reduces cognitive impairments and provides neuroprotection without gastrointestinal side effects.

An integrated in silico 3D model-driven discovery of a novel, potent, and selective amidosulfonamide 5-HT1A agonist (PRX-00023) for the treatment of anxiety and depression.

We report the discovery of a novel, potent, and selective amidosulfonamide nonazapirone 5-HT1A agonist for the treatment of anxiety and depression, which is now in Phase III clinical trials for generalized anxiety disorder (GAD). The discovery of 20m (PRX-00023), N-{3-[4-(4-cyclohexylmethanesulfonylaminobutyl)piperazin-1-yl]phenyl}acetamide, and its backup compounds, followed a new paradigm, driving the entire discovery process with in silico methods and seamlessly integrating computational chemistry with medicinal chemistry, which led to a very rapid discovery timeline. The program reached clinical trials within less than 2 years from initiation, spending less than 6 months in lead optimization with only 31 compounds synthesized. In this paper we detail the entire discovery process, which started with modeling the 3D structure of 5-HT1A using the PREDICT methodology, and then performing in silico screening on that structure leading to the discovery of a 1 nM lead compound (8). The lead compound was optimized following a strategy devised based on in silico 3D models and realized through an in silico-driven optimization process, rapidly overcoming selectivity issues (affinity to 5-HT1A vs alpha1-adrenergic receptor) and potential cardiovascular issues (hERG binding), leading to a clinical compound. Finally we report key in vivo preclinical and Phase I clinical data for 20m tolerability, pharmacokinetics, and pharmacodynamics and show that these favorable results are a direct outcome of the properties that were ascribed to the compound during the rational structure-based discovery process. We believe that this is one of the first examples for a Phase III drug candidate that was discovered and optimized, from start to finish, using in silico model-based methods as the primary tool.

PREDICT modeling and in-silico screening for G-protein coupled receptors.

G-protein coupled receptors (GPCRs) are a major group of drug targets for which only one x-ray structure is known (the nondrugable rhodopsin), limiting the application of structure-based drug discovery to GPCRs. In this paper we present the details of PREDICT, a new algorithmic approach for modeling the 3D structure of GPCRs without relying on homology to rhodopsin. PREDICT, which focuses on the transmembrane domain of GPCRs, starts from the primary sequence of the receptor, simultaneously optimizing multiple 'decoy' conformations of the protein in order to find its most stable structure, culminating in a virtual receptor-ligand complex. In this paper we present a comprehensive analysis of three PREDICT models for the dopamine D2, neurokinin NK1, and neuropeptide Y Y1 receptors. A shorter discussion of the CCR3 receptor model is also included. All models were found to be in good agreement with a large body of experimental data. The quality of the PREDICT models, at least for drug discovery purposes, was evaluated by their successful utilization in in-silico screening. Virtual screening using all three PREDICT models yielded enrichment factors 9-fold to 44-fold better than random screening. Namely, the PREDICT models can be used to identify active small-molecule ligands embedded in large compound libraries with an efficiency comparable to that obtained using crystal structures for non-GPCR targets.

G protein-coupled receptors: in silico drug discovery in 3D.

The application of structure-based in silico methods to drug discovery is still considered a major challenge, especially when the x-ray structure of the target protein is unknown. Such is the case with human G protein-coupled receptors (GPCRs), one of the most important families of drug targets, where in the absence of x-ray structures, one has to rely on in silico 3D models. We report repeated success in using ab initio in silico GPCR models, generated by the predict method, for blind in silico screening when applied to a set of five different GPCR drug targets. More than 100,000 compounds were typically screened in silico for each target, leading to a selection of <100 "virtual hit" compounds to be tested in the lab. In vitro binding assays of the selected compounds confirm high hit rates, of 12-21% (full dose-response curves, Ki < 5 microM). In most cases, the best hit was a novel compound (New Chemical Entity) in the 1- to 100-nM range, with very promising pharmacological properties, as measured by a variety of in vitro and in vivo assays. These assays validated the quality of the hits as lead compounds for drug discovery. The results demonstrate the usefulness and robustness of ab initio in silico 3D models and of in silico screening for GPCR drug discovery.

Modeling the 3D structure of GPCRs: advances and application to drug discovery.

G protein-coupled receptors (GPCRs) are membrane-embedded proteins responsible for signal transduction; these receptors are, therefore, among the most important pharmaceutical drug targets. In the absence of X-ray structures, there have been numerous attempts to model the three-dimensional (3D) structure of GPCRs. In this review, the current status of GPCR modeling is evaluated, highlighting recent progress made in rhodopsin-based homology modeling and de novo modeling technology. Assessment of recent rhodopsin-based homology modeling studies indicates that, despite significant progress, these models do not yield hit rates that are sufficiently high for in silico screening (10 to 40% when screening for known binders). In contrast, the PREDICT modeling algorithm, which is independent of the rhodopsin structure, has now been fully validated in the context of drug discovery. PREDICT models are successfully used for drug discovery, yielding excellent hit rates (85 to 100% when screening for known binders), leading to the discovery of nanomolar-range new chemical entities for a variety of GPCR targets. Thus, 3D models of GPCRs should now allow the use of productive structure-based approaches for drug discovery.