Rare disease variant curation from literature: assessing gaps with creatine transport deficiency in focus.

BACKGROUND: Approximately 4-8% of the world suffers from a rare disease. Rare diseases are often difficult to diagnose, and many do not have approved therapies. Genetic sequencing has the potential to shorten the current diagnostic process, increase mechanistic understanding, and facilitate research on therapeutic approaches but is limited by the difficulty of novel variant pathogenicity interpretation and the communication of known causative variants. It is unknown how many published rare disease variants are currently accessible in the public domain. RESULTS: This study investigated the translation of knowledge of variants reported in published manuscripts to publicly accessible variant databases. Variants, symptoms, biochemical assay results, and protein function from literature on the SLC6A8 gene associated with X-linked Creatine Transporter Deficiency (CTD) were curated and reported as a highly annotated dataset of variants with clinical context and functional details. Variants were harmonized, their availability in existing variant databases was analyzed and pathogenicity assignments were compared with impact algorithm predictions. 24% of the pathogenic variants found in PubMed articles were not captured in any database used in this analysis while only 65% of the published variants received an accurate pathogenicity prediction from at least one impact prediction algorithm. CONCLUSIONS: Despite being published in the literature, pathogenicity data on patient variants may remain inaccessible for genetic diagnosis, therapeutic target identification, mechanistic understanding, or hypothesis generation. Clinical and functional details presented in the literature are important to make pathogenicity assessments. Impact predictions remain imperfect but are improving, especially for single nucleotide exonic variants, however such predictions are less accurate or unavailable for intronic and multi-nucleotide variants. Developing text mining workflows that use natural language processing for identifying diseases, genes and variants, along with impact prediction algorithms and integrating with details on clinical phenotypes and functional assessments might be a promising approach to scale literature mining of variants and assigning correct pathogenicity. The curated variants list created by this effort includes context details to improve any such efforts on variant curation for rare diseases.

Transcriptomic profiling in canines and humans reveals cancer specific gene modules and biological mechanisms common to both species.

Understanding relationships between spontaneous cancer in companion (pet) canines and humans can facilitate biomarker and drug development in both species. Towards this end we developed an experimental-bioinformatic protocol that analyzes canine transcriptomics data in the context of existing human data to evaluate comparative relevance of canine to human cancer. We used this protocol to characterize five canine cancers: melanoma, osteosarcoma, pulmonary carcinoma, B- and T-cell lymphoma, in 60 dogs. We applied an unsupervised, iterative clustering method that yielded five co-expression modules and found that each cancer exhibited a unique module expression profile. We constructed cancer models based on the co-expression modules and used the models to successfully classify the canine data. These canine-derived models also successfully classified human tumors representing the same cancers, indicating shared cancer biology between canines and humans. Annotation of the module genes identified cancer specific pathways relevant to cells-of-origin and tumor biology. For example, annotations associated with melanin production (PMEL, GPNMB, and BACE2), synthesis of bone material (COL5A2, COL6A3, and COL12A1), synthesis of pulmonary surfactant (CTSH, LPCAT1, and NAPSA), ribosomal proteins (RPL8, RPS7, and RPLP0), and epigenetic regulation (EDEM1, PTK2B, and JAK1) were unique to melanoma, osteosarcoma, pulmonary carcinoma, B- and T-cell lymphoma, respectively. In total, 152 biomarker candidates were selected from highly expressing modules for each cancer type. Many of these biomarker candidates are under-explored as drug discovery targets and warrant further study. The demonstrated transferability of classification models from canines to humans enforces the idea that tumor biology, biomarker targets, and associated therapeutics, discovered in canines, may translate to human medicine.

Benserazide racemate and enantiomers induce fetal globin gene expression in vivo: Studies to guide clinical development for beta thalassemia and sickle cell disease.

Increased expression of developmentally silenced fetal globin (HBG) reduces the clinical severity of ß-hemoglobinopathies. Benserazide has a relatively benign safety profile having been approved for 50 years in Europe and Canada for Parkinson's disease treatment. Benserazide was shown to activate HBG gene transcription in a high throughput screen, and subsequent studies confirmed fetal hemoglobin (HbF) induction in erythroid progenitors from hemoglobinopathy patients, transgenic mice containing the entire human ß-globin gene (ß-YAC) and anemic baboons. The goal of this study is to evaluate efficacies and plasma exposure profiles of benserazide racemate and its enantiomers to select the chemical form for clinical development. Intermittent treatment with all forms of benserazide in ß-YAC mice significantly increased proportions of red blood cells expressing HbF and HbF protein per cell with similar pharmacokinetic profiles and with no cytopenia. These data contribute to the regulatory justification for development of the benserazide racemate. Additionally, dose ranges and frequencies required for HbF induction using racemic benserazide were explored. Orally administered escalating doses of benserazide in an anemic baboon induced γ-globin mRNA up to 13-fold and establish an intermittent dose regimen for clinical studies as a therapeutic candidate for potential treatment of ß-hemoglobinopathies.

Pharmacological clearance of misfolded rhodopsin for the treatment of RHO-associated retinitis pigmentosa.

Rhodopsin mutation and misfolding is a common cause of autosomal dominant retinitis pigmentosa (RP). Using a luciferase reporter assay, we undertook a small-molecule high-throughput screening (HTS) of 68, 979 compounds and identified nine compounds that selectively reduced the misfolded P23H rhodopsin without an effect on the wild type (WT) rhodopsin protein. Further, we found five of these compounds, including methotrexate (MTX), promoted P23H rhodopsin degradation that also cleared out other misfolded rhodopsin mutant proteins. We showed MTX increased P23H rhodopsin degradation via the lysosomal but not the proteasomal pathway. Importantly, one intravitreal injection (IVI) of 25 pmol MTX increased electroretinogram (ERG) response and rhodopsin level in the retinae of RhoP23H/+ knock-in mice at 1 month of age. Additionally, four weekly IVIs increased the photoreceptor cell number in the retinae of RhoP23H/+ mice compared to vehicle control. Our study indicates a therapeutic potential of repurposing MTX for the treatment of rhodopsin-associated RP.

Discovery of 3-(4-sulfamoylnaphthyl)pyrazolo[1,5-a]pyrimidines as potent and selective ALK2 inhibitors.

The pyrazolo[1,5-a]pyrimidine LDN-193189 is a potent inhibitor of activin receptor-like kinase 2 (ALK2) but is nonselective for highly homologous ALK3 and shows only modest kinome selectivity. Herein, we describe the discovery of a novel series of potent and selective ALK2 inhibitors by replacing the quinolinyl with a 4-(sulfamoyl)naphthyl, yielding ALK2 inhibitors that exhibit not only excellent discrimination versus ALK3 but also high kinome selectivity. In addition, the optimized compound 23 demonstrates good ADME and in vivo pharmacokinetic properties.

Novel lead structures with both Plasmodium falciparum gametocytocidal and asexual blood stage activity identified from high throughput compound screening.

BACKGROUND: Blocking malaria transmission is an important step in eradicating malaria. In the field, transmission requires the production of sexual stage Plasmodium parasites, called gametocytes, which are not effectively killed by the commonly used anti-malarials allowing individuals to remain infectious after clearance of asexual parasites. METHODS: To identify new gametocytocidal compounds, a library of 45,056 compounds with diverse structures was screened using a high throughput gametocyte viability assay. The characteristics of active hits were further evaluated against asexual stage parasites in a growth inhibition assay. Their cytotoxicity were tested against mammalian cells in a cytotoxicity assay. The chemical scaffold similarity of active hits were studied using scaffold cluster analysis. RESULTS: A set of 23 compounds were identified and further confirmed for their activity against gametocytes. All the 23 confirmed compounds possess dual-activities against both gametocytes responsible for human to mosquito transmission and asexual parasites that cause the clinical symptoms. Three of these compounds were fourfold more active against gametocytes than asexual parasites. Further cheminformatic analysis revealed three sets of novel scaffolds, including highly selective 4-1H-pyrazol-5-yl piperidine analogs. CONCLUSIONS: This study revealed important new structural scaffolds that can be used as starting points for dual activity anti-malarial drug development.

A systems biology strategy to identify molecular mechanisms of action and protein indicators of traumatic brain injury.

The multifactorial nature of traumatic brain injury (TBI), especially the complex secondary tissue injury involving intertwined networks of molecular pathways that mediate cellular behavior, has confounded attempts to elucidate the pathology underlying the progression of TBI. Here, systems biology strategies are exploited to identify novel molecular mechanisms and protein indicators of brain injury. To this end, we performed a meta-analysis of four distinct high-throughput gene expression studies involving different animal models of TBI. By using canonical pathways and a large human protein-interaction network as a scaffold, we separately overlaid the gene expression data from each study to identify molecular signatures that were conserved across the different studies. At 24 hr after injury, the significantly activated molecular signatures were nonspecific to TBI, whereas the significantly suppressed molecular signatures were specific to the nervous system. In particular, we identified a suppressed subnetwork consisting of 58 highly interacting, coregulated proteins associated with synaptic function. We selected three proteins from this subnetwork, postsynaptic density protein 95, nitric oxide synthase 1, and disrupted in schizophrenia 1, and hypothesized that their abundance would be significantly reduced after TBI. In a penetrating ballistic-like brain injury rat model of severe TBI, Western blot analysis confirmed our hypothesis. In addition, our analysis recovered 12 previously identified protein biomarkers of TBI. The results suggest that systems biology may provide an efficient, high-yield approach to generate testable hypotheses that can be experimentally validated to identify novel mechanisms of action and molecular indicators of TBI.

Exploiting large-scale drug-protein interaction information for computational drug repurposing.

BACKGROUND: Despite increased investment in pharmaceutical research and development, fewer and fewer new drugs are entering the marketplace. This has prompted studies in repurposing existing drugs for use against diseases with unmet medical needs. A popular approach is to develop a classification model based on drugs with and without a desired therapeutic effect. For this approach to be statistically sound, it requires a large number of drugs in both classes. However, given few or no approved drugs for the diseases of highest medical urgency and interest, different strategies need to be investigated. RESULTS: We developed a computational method termed "drug-protein interaction-based repurposing" (DPIR) that is potentially applicable to diseases with very few approved drugs. The method, based on genome-wide drug-protein interaction information and Bayesian statistics, first identifies drug-protein interactions associated with a desired therapeutic effect. Then, it uses key drug-protein interactions to score other drugs for their potential to have the same therapeutic effect. CONCLUSIONS: Detailed cross-validation studies using United States Food and Drug Administration-approved drugs for hypertension, human immunodeficiency virus, and malaria indicated that DPIR provides robust predictions. It achieves high levels of enrichment of drugs approved for a disease even with models developed based on a single drug known to treat the disease. Analysis of our model predictions also indicated that the method is potentially useful for understanding molecular mechanisms of drug action and for identifying protein targets that may potentiate the desired therapeutic effects of other drugs (combination therapies).

Synthesis and evaluation of Strychnos alkaloids as MDR reversal agents for cancer cell eradication.

Natural products represent the fourth generation of multidrug resistance (MDR) reversal agents that resensitize MDR cancer cells overexpressing P-glycoprotein (Pgp) to cytotoxic agents. We have developed an effective synthetic route to prepare various Strychnos alkaloids and their derivatives. Molecular modeling of these alkaloids docked to a homology model of Pgp was employed to optimize ligand-protein interactions and design analogues with increased affinity to Pgp. Moreover, the compounds were evaluated for their (1) binding affinity to Pgp by fluorescence quenching, and (2) MDR reversal activity using a panel of in vitro and cell-based assays and compared to verapamil, a known inhibitor of Pgp activity. Compound 7 revealed the highest affinity to Pgp of all Strychnos congeners (Kd=4.4µM), the strongest inhibition of Pgp ATPase activity, and the strongest MDR reversal effect in two Pgp-expressing cell lines. Altogether, our findings suggest the clinical potential of these synthesized compounds as viable Pgp modulators justifies further investigation.

Quatramer™ encapsulation of dual-targeted PI3-Kδ/HDAC6 inhibitor, HSB-510, suppresses growth of breast cancer.

Multiple studies have shown that the progression of breast cancer depends on multiple signaling pathways, suggesting that therapies with multitargeted anticancer agents will offer improved therapeutic benefits through synergistic effects in inhibiting cancer growth. Dual-targeted inhibitors of phosphoinositide 3-kinase (PI3-K) and histone deacetylase (HDAC) have emerged as promising cancer therapy candidates. However, poor aqueous solubility and bioavailability limited their efficacy in cancer. The present study investigates the encapsulation of a PI3-Kδ/HDAC6 dual inhibitor into hybrid block copolymers (polylactic acid-methoxy polyethylene glycol; polylactic acid-polyethylene glycol-polypropylene glycol-polyethylene glycol-polylactic acid) (HSB-510) as a delivery system to target PI3-Kδ and HDAC6 pathways in breast cancer cells. The prepared HSB-510 showed an average diameter of 96 ± 3 nm, a zeta potential of -17 ± 2 mV, and PDI of ˂0.1 with a slow and sustained release profile of PI3-Kδ/HDAC6 inhibitors in a nonphysiological buffer. In vitro studies with HSB-510 have demonstrated substantial growth inhibition of breast cancer cell lines, MDA-MB-468, SUM-149, MCF-7, and Ehrlich ascites carcinoma (EAC) as well as downregulation of phospho-AKT, phospho-ERK, and c-Myc levels. Importantly, bi-weekly treatment of Balb/c wild-type mice harboring EAC cells with HSB-510 at a dose of 25 mg/kg resulted in significant tumor growth inhibition. The treatment with HSB-510 was without any significant effect on the body weights of the mice. These results demonstrate that a novel Quatramer encapsulation of a PI3-Kδ/HDAC6 dual inhibitor (HSB-510) represents an approach for the successful targeting of breast cancer and potentially other cancer types.

Transcriptional profiling of canine osteosarcoma identifies prognostic gene expression signatures with translational value for humans.

Canine osteosarcoma is increasingly recognized as an informative model for human osteosarcoma. Here we show in one of the largest clinically annotated canine osteosarcoma transcriptional datasets that two previously reported, as well as de novo gene signatures devised through single sample Gene Set Enrichment Analysis (ssGSEA), have prognostic utility in both human and canine patients. Shared molecular pathway alterations are seen in immune cell signaling and activation including TH1 and TH2 signaling, interferon signaling, and inflammatory responses. Virtual cell sorting to estimate immune cell populations within canine and human tumors showed similar trends, predominantly for macrophages and CD8+ T cells. Immunohistochemical staining verified the increased presence of immune cells in tumors exhibiting immune gene enrichment. Collectively these findings further validate naturally occurring osteosarcoma of the pet dog as a translationally relevant patient model for humans and improve our understanding of the immunologic and genomic landscape of the disease in both species.

Reserpine maintains photoreceptor survival in retinal ciliopathy by resolving proteostasis imbalance and ciliogenesis defects.

Ciliopathies manifest from sensory abnormalities to syndromic disorders with multi-organ pathologies, with retinal degeneration a highly penetrant phenotype. Photoreceptor cell death is a major cause of incurable blindness in retinal ciliopathies. To identify drug candidates to maintain photoreceptor survival, we performed an unbiased, high-throughput screening of over 6000 bioactive small molecules using retinal organoids differentiated from induced pluripotent stem cells (iPSC) of rd16 mouse, which is a model of Leber congenital amaurosis (LCA) type 10 caused by mutations in the cilia-centrosomal gene CEP290. We identified five non-toxic positive hits, including the lead molecule reserpine, which maintained photoreceptor development and survival in rd16 organoids. Reserpine also improved photoreceptors in retinal organoids derived from induced pluripotent stem cells of LCA10 patients and in rd16 mouse retina in vivo. Reserpine-treated patient organoids revealed modulation of signaling pathways related to cell survival/death, metabolism, and proteostasis. Further investigation uncovered dysregulation of autophagy associated with compromised primary cilium biogenesis in patient organoids and rd16 mouse retina. Reserpine partially restored the balance between autophagy and the ubiquitin-proteasome system at least in part by increasing the cargo adaptor p62, resulting in improved primary cilium assembly. Our study identifies effective drug candidates in preclinical studies of CEP290 retinal ciliopathies through cross-species drug discovery using iPSC-derived organoids, highlights the impact of proteostasis in the pathogenesis of ciliopathies, and provides new insights for treatments of retinal neurodegeneration.

Leber congenital amaurosis (LCA) is an inherited disease that affects the eyes and causes sight loss in early childhood, which generally gets worse over time. Individuals with this condition have genetic mutations that result in the death of light-sensitive cells, known as photoreceptors, in a region called the retina at the back of the eye. Patients carrying a genetic change in the gene CEP290 account for 20-25% of all LCA. At present, treatment options are only available for a limited number of patients with LCA. One option is to use small molecules as drugs that may target or bypass the faulty processes within the eye to help the photoreceptors survive in many different forms of LCA and other retinal diseases. However, over 90% of new drug candidates fail the first phase of clinical trials for human diseases. This in part due to the candidates having been developed using cell cultures or animal models that do not faithfully reflect how the human body works. Recent advances in cell and developmental biology are now enabling researchers to use stem cells derived from humans to grow retina tissues in a dish in the laboratory. These tissues, known as retinal organoids, behave in a more similar way to retinas in human eyes than those of traditional animal models. However, the methods for making and maintaining human retinal organoids are time-consuming and labor-intensive, which has so far limited their use in the search for new therapies. To address this challenge, Chen et al. developed a large-scale approach to grow retinal organoids from rd16 mutant mice stem cells (which are a good model for LCA caused by mutations to CEP290) and used the photoreceptors from these organoids to screen over 6,000 existing drugs for their ability to promote the survival of photoreceptors. The experiments found that the drug reserpine, which was previously approved to treat high blood pressure, also helped photoreceptors to survive in the diseased organoids. Reserpine also had a similar effect in retinal organoids derived from human patients with LCA and in the rd16 mice themselves. Further experiments suggest that reserpine may help patients with LCA by partially restoring a process by which the body destroys and recycles old and damaged proteins in the cells. The next steps following on from this work will be to perform further tests to demonstrate that this use of reserpine is safe to enter clinical trials as a treatment for LCA and other similar eye diseases.

Locally weighted learning methods for predicting dose-dependent toxicity with application to the human maximum recommended daily dose.

Toxicological experiments in animals are carried out to determine the type and severity of any potential toxic effect associated with a new lead compound. The collected data are then used to extrapolate the effects on humans and determine initial dose regimens for clinical trials. The underlying assumption is that the severity of the toxic effects in animals is correlated with that in humans. However, there is a general lack of toxic correlations across species. Thus, it is more advantageous to predict the toxicological effects of a compound on humans directly from the human toxicological data of related compounds. However, many popular quantitative structure-activity relationship (QSAR) methods that build a single global model by fitting all training data appear inappropriate for predicting toxicological effects of structurally diverse compounds because the observed toxicological effects may originate from very different and mostly unknown molecular mechanisms. In this article, we demonstrate, via application to the human maximum recommended daily dose data that locally weighted learning methods, such as k-nearest neighbors, are well suited for predicting toxicological effects of structurally diverse compounds. We also show that a significant flaw of the k-nearest neighbor method is that it always uses a constant number of nearest neighbors in making prediction for a target compound, irrespective of whether the nearest neighbors are structurally similar enough to the target compound to ensure that they share the same mechanism of action. To remedy this flaw, we proposed and implemented a variable number nearest neighbor method. The advantages of the variable number nearest neighbor method over other QSAR methods include (1) allowing more reliable predictions to be achieved by applying a tighter molecular distance threshold and (2) automatic detection for when a prediction should not be made because the compound is outside the applicable domain.

QSAR classification model for antibacterial compounds and its use in virtual screening.

As novel and drug-resistant bacterial strains continue to present an emerging health threat, the development of new antibacterial agents is critical. This includes making improvements to existing antibacterial scaffolds as well as identifying novel ones. The aim of this study is to apply a Bayesian classification QSAR approach to rapidly screen chemical libraries for compounds predicted to have antibacterial activity. Toward this end we assembled a data set of 317 known antibacterial compounds as well as a second data set of diverse, well-validated, non-antibacterial compounds from 215 PubChem Bioassays against various bacterial species. We constructed a Bayesian classification model using structural fingerprints and physicochemical property descriptors and achieved an accuracy of 84% and precision of 86% on an independent test set in identifying antibacterial compounds. To demonstrate the practical applicability of the model in virtual screening, we screened an independent data set of ~200k compounds. The results show that the model can screen top hits of PubChem Bioassay actives with accuracy up to ~76%, representing a 1.5-2-fold enrichment. The top screened hits represented a mixture of both known antibacterial scaffolds as well as novel scaffolds. Our study suggests that a well-validated Bayesian classification QSAR approach could compliment other screening approaches in identifying novel and promising hits. The data sets used in constructing and validating this model have been made publicly available.

Exploring polypharmacology using a ROCS-based target fishing approach.

Polypharmacology has emerged as a new theme in drug discovery. In this paper, we studied polypharmacology using a ligand-based target fishing (LBTF) protocol. To implement the protocol, we first generated a chemogenomic database that links individual protein targets with a specified set of drugs or target representatives. Target profiles were then generated for a given query molecule by computing maximal shape/chemistry overlap between the query molecule and the drug sets assigned to each protein target. The overlap was computed using the program ROCS (Rapid Overlay of Chemical Structures). We validated this approach using the Directory of Useful Decoys (DUD). DUD contains 2950 active compounds, each with 36 property-matched decoys, against 40 protein targets. We chose a set of known drugs to represent each DUD target, and we carried out ligand-based virtual screens using data sets of DUD actives seeded into DUD decoys for each target. We computed Receiver Operator Characteristic (ROC) curves and associated area under the curve (AUC) values. For the majority of targets studied, the AUC values were significantly better than for the case of a random selection of compounds. In a second test, the method successfully identified off-targets for drugs such as rimantadine, propranolol, and domperidone that were consistent with those identified by recent experiments. The results from our ROCS-based target fishing approach are promising and have potential application in drug repurposing for single and multiple targets, identifying targets for orphan compounds, and adverse effect prediction.

Rottlerin inhibits La Crosse virus-induced encephalitis in mice and blocks release of replicating virus from the Golgi body in neurons.

La Crosse virus (LACV) is a mosquito-borne orthobunyavirus that causes approximately 60 to 80 hospitalized pediatric encephalitis cases in the United States yearly. The primary treatment for most viral encephalitis, including LACV, is palliative care, and specific antiviral therapeutics are needed. We screened the National Center for Advancing Translational Sciences library of 3,833 FDA-approved and bioactive small molecules for the ability to inhibit LACV-induced death in SH-SY5Y neuronal cells. The top three hits from the initial screen were validated by examining their ability to inhibit virus-induced cell death in multiple neuronal cell lines. Rottlerin consistently reduced LACV-induced death by 50% in multiple human and mouse neuronal cell lines with an effective concentration of 0.16-0.69 µg ml-1 depending on cell line. Rottlerin was effective up to 12 hours post-infection in vitro and inhibited virus particle trafficking from the Golgi apparatus to trans-Golgi vesicles. In human inducible pluripotent stem cell-derived cerebral organoids, rottlerin reduced virus production by one log and cell death by 35% compared with dimethyl sulfoxide-treated controls. Administration of rottlerin in mice by intraperitoneal or intracranial routes starting at 3 days post-infection decreased disease development by 30-50%. Furthermore, rottlerin also inhibited virus replication of other pathogenic California serogroup orthobunyaviruses (Jamestown Canyon and Tahyna virus) in neuronal cell lines.

Modeling G protein-coupled receptors for structure-based drug discovery using low-frequency normal modes for refinement of homology models: application to H3 antagonists.

G Protein-Coupled Receptors (GPCRs) are integral membrane proteins that play important role in regulating key physiological functions, and are targets of about 50% of all recently launched drugs. High-resolution experimental structures are available only for very few GPCRs. As a result, structure-based drug design efforts for GPCRs continue to rely on in silico modeling, which is considered to be an extremely difficult task especially for these receptors. Here, we describe Gmodel, a novel approach for building 3D atomic models of GPCRs using a normal mode-based refinement of homology models. Gmodel uses a small set of relevant low-frequency vibrational modes derived from Random Elastic Network model to efficiently sample the large-scale receptor conformation changes and generate an ensemble of alternative models. These are used to assemble receptor-ligand complexes by docking a known active into each of the alternative models. Each of these is next filtered using restraints derived from known mutation and binding affinity data and is refined in the presence of the active ligand. In this study, Gmodel was applied to generate models of the antagonist form of histamine 3 (H3) receptor. The validity of this novel modeling approach is demonstrated by performing virtual screening (using the refined models) that consistently produces highly enriched hit lists. The models are further validated by analyzing the available SAR related to classical H3 antagonists, and are found to be in good agreement with the available experimental data, thus providing novel insights into the receptor-ligand interactions.

Identification of a new class of small molecule C5a receptor antagonists.

C5a is a terminal product of the complement cascade that activates and attracts inflammatory cells including granulocytes, mast cells and macrophages via a specific GPCR, the C5a receptor (C5aR). Inhibition of C5a/C5aR interaction has been shown to be efficacious in several animal models of autoimmune diseases, including RA, SLE and asthma. This account reports the discovery of a new class of C5aR antagonists through high-throughput screening. The lead compounds in this series are selective and block C5a binding, C5a-promoted calcium flux in human neutrophils with nanomolar potency.

Benzimidazole- and indole-substituted 1,3'-bipyrrolidine benzamides as histamine H3 receptor antagonists.

Using a focused screen of biogenic amine compounds we identified a novel series of H(3)R antagonists. A preliminary SAR study led to reduction of MW while increasing binding affinity and potency. Optimization of the physical properties of the series led to (S)-6n, with improved brain to plasma exposure and efficacy in both water intake and novel object recognition models.