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.

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.

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.

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.

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.

Torin 2 Derivative, NCATS-SM3710, Has Potent Multistage Antimalarial Activity through Inhibition of P. falciparum Phosphatidylinositol 4-Kinase (Pf PI4KIIIß).

Drug resistance is a constant threat to malaria control efforts making it important to maintain a good pipeline of new drug candidates. Of particular need are compounds that also block transmission by targeting sexual stage parasites. Mature sexual stages are relatively resistant to all currently used antimalarials except the 8-aminoquinolines that are not commonly used due to potential side effects. Here, we synthesized a new Torin 2 derivative, NCATS-SM3710 with increased aqueous solubility and specificity for Plasmodium and demonstrate potent in vivo activity against all P. berghei life cycle stages. NCATS-SM3710 also has low nanomolar EC50s against in vitro cultured asexual P. falciparum parasites (0.38 ± 0.04 nM) and late stage gametocytes (5.77 ± 1 nM). Two independent NCATS-SM3710/Torin 2 resistant P. falciparum parasite lines produced by growth in sublethal Torin 2 concentrations both had genetic changes in PF3D7_0509800, annotated as a phosphatidylinositol 4 kinase (Pf PI4KIIIß). One line had a point mutation in the putative active site (V1357G), and the other line had a duplication of a locus containing Pf PI4KIIIß. Both lines were also resistant to other Pf PI4K inhibitors. In addition NCATS-SM3710 inhibited purified Pf PI4KIIIß with an IC50 of 2.0 ± 0.30 nM. Together the results demonstrate that Pf PI4KIIIß is the target of Torin 2 and NCATS-SM3710 and provide new options for potent multistage drug development.

Design, Synthesis, and Biological Evaluation of Quinazolin-4-one-Based Hydroxamic Acids as Dual PI3K/HDAC Inhibitors.

A series of quinazolin-4-one based hydroxamic acids was rationally designed and synthesized as novel dual PI3K/HDAC inhibitors by incorporating an HDAC pharmacophore into a PI3K inhibitor (Idelalisib) via an optimized linker. Several of these dual inhibitors were highly potent (IC50 < 10 nM) and selective against PI3Kγ, δ and HDAC6 enzymes and exhibited good antiproliferative activity against multiple cancer cell lines. The lead compound 48c, induced necrosis in several mutant and FLT3-resistant AML cell lines and primary blasts from AML patients, while showing no cytotoxicity against normal PBMCs, NIH3T3, and HEK293 cells. Target engagement of PI3Kδ and HDAC6 by 48c was demonstrated in MV411 cells using the cellular thermal shift assay (CETSA). Compound 48c showed good pharmacokinetics properties in mice via intraperitoneal (ip) administration and provides a means to examine the biological effects of inhibiting these two important enzymes with a single molecule, either in vitro or in vivo.

Targeting AML-associated FLT3 mutations with a type I kinase inhibitor.

Tyrosine kinase domain (TKD) mutations contribute to acquired resistance to FMS-like tyrosine kinase 3 (FLT3) inhibitors used to treat FLT3-mutant acute myeloid leukemia (AML). We report a cocrystal structure of FLT3 with a type I inhibitor, NCGC1481, that retained potent binding and activity against FLT3 TKD and gatekeeper mutations. Relative to the current generation of advanced FLT3 inhibitors, NCGC1481 exhibited superior antileukemic activity against the common, clinically relevant FLT3-mutant AML cells in vitro and in vivo.

Identification of Multiple Cryptococcal Fungicidal Drug Targets by Combined Gene Dosing and Drug Affinity Responsive Target Stability Screening.

UNLABELLED: Cryptococcus neoformans is a pathogenic fungus that is responsible for up to half a million cases of meningitis globally, especially in immunocompromised individuals. Common fungistatic drugs, such as fluconazole, are less toxic for patients but have low efficacy for initial therapy of the disease. Effective therapy against the disease is provided by the fungicidal drug amphotericin B; however, due to its high toxicity and the difficulty in administering its intravenous formulation, it is imperative to find new therapies targeting the fungus. The antiparasitic drug bithionol has been recently identified as having potent fungicidal activity. In this study, we used a combined gene dosing and drug affinity responsive target stability (GD-DARTS) screen as well as protein modeling to identify a common drug binding site of bithionol within multiple NAD-dependent dehydrogenase drug targets. This combination genetic and proteomic method thus provides a powerful method for identifying novel fungicidal drug targets for further development. IMPORTANCE: Cryptococcosis is a neglected fungal meningitis that causes approximately half a million deaths annually. The most effective antifungal agent, amphotericin B, was developed in the 1950s, and no effective medicine has been developed for this disease since that time. A key aspect of amphotericin B's effectiveness is thought to be because of its ability to kill the fungus (fungicidal activity), rather than just stop or slow its growth. The present study utilized a recently identified fungicidal agent, bithionol, to identify potential fungicidal drug targets that can be used in developing modern fungicidal agents. A combined protein and genetic analysis approach was used to identify a class of enzymes, dehydrogenases, that the fungus uses to maintain homeostasis with regard to sugar nutrients. Similarities in the drug target site were found that resulted in simultaneous inhibition and killing of the fungus by bithionol. These studies thus identify a common, multitarget site for antifungal development.

Gene Expression Patterns Associated With Histopathology in Toxic Liver Fibrosis.

Toxic industrial chemicals induce liver injury, which is difficult to diagnose without invasive procedures. Identifying indicators of end organ injury can complement exposure-based assays and improve predictive power. A multiplexed approach was used to experimentally evaluate a panel of 67 genes predicted to be associated with the fibrosis pathology by computationally mining DrugMatrix, a publicly available repository of gene microarray data. Five-day oral gavage studies in male Sprague Dawley rats dosed with varying concentrations of 3 fibrogenic compounds (allyl alcohol, carbon tetrachloride, and 4,4'-methylenedianiline) and 2 nonfibrogenic compounds (bromobenzene and dexamethasone) were conducted. Fibrosis was definitively diagnosed by histopathology. The 67-plex gene panel accurately diagnosed fibrosis in both microarray and multiplexed-gene expression assays. Necrosis and inflammatory infiltration were comorbid with fibrosis. ANOVA with contrasts identified that 51 of the 67 predicted genes were significantly associated with the fibrosis phenotype, with 24 of these specific to fibrosis alone. The protein product of the gene most strongly correlated with the fibrosis phenotype PCOLCE (Procollagen C-Endopeptidase Enhancer) was dose-dependently elevated in plasma from animals administered fibrogenic chemicals (P < .05). Semiquantitative global mass spectrometry analysis of the plasma identified an additional 5 protein products of the gene panel which increased after fibrogenic toxicant administration: fibronectin, ceruloplasmin, vitronectin, insulin-like growth factor binding protein, and α2-macroglobulin. These results support the data mining approach for identifying gene and/or protein panels for assessing liver injury and may suggest bridging biomarkers for molecular mediators linked to histopathology.

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.

Systems level analysis and identification of pathways and networks associated with liver fibrosis.

Toxic liver injury causes necrosis and fibrosis, which may lead to cirrhosis and liver failure. Despite recent progress in understanding the mechanism of liver fibrosis, our knowledge of the molecular-level details of this disease is still incomplete. The elucidation of networks and pathways associated with liver fibrosis can provide insight into the underlying molecular mechanisms of the disease, as well as identify potential diagnostic or prognostic biomarkers. Towards this end, we analyzed rat gene expression data from a range of chemical exposures that produced observable periportal liver fibrosis as documented in DrugMatrix, a publicly available toxicogenomics database. We identified genes relevant to liver fibrosis using standard differential expression and co-expression analyses, and then used these genes in pathway enrichment and protein-protein interaction (PPI) network analyses. We identified a PPI network module associated with liver fibrosis that includes known liver fibrosis-relevant genes, such as tissue inhibitor of metalloproteinase-1, galectin-3, connective tissue growth factor, and lipocalin-2. We also identified several new genes, such as perilipin-3, legumain, and myocilin, which were associated with liver fibrosis. We further analyzed the expression pattern of the genes in the PPI network module across a wide range of 640 chemical exposure conditions in DrugMatrix and identified early indications of liver fibrosis for carbon tetrachloride and lipopolysaccharide exposures. Although it is well known that carbon tetrachloride and lipopolysaccharide can cause liver fibrosis, our network analysis was able to link these compounds to potential fibrotic damage before histopathological changes associated with liver fibrosis appeared. These results demonstrated that our approach is capable of identifying early-stage indicators of liver fibrosis and underscore its potential to aid in predictive toxicity, biomarker identification, and to generally identify disease-relevant pathways.

Characterization of chemically induced liver injuries using gene co-expression modules.

Liver injuries due to ingestion or exposure to chemicals and industrial toxicants pose a serious health risk that may be hard to assess due to a lack of non-invasive diagnostic tests. Mapping chemical injuries to organ-specific damage and clinical outcomes via biomarkers or biomarker panels will provide the foundation for highly specific and robust diagnostic tests. Here, we have used DrugMatrix, a toxicogenomics database containing organ-specific gene expression data matched to dose-dependent chemical exposures and adverse clinical pathology assessments in Sprague Dawley rats, to identify groups of co-expressed genes (modules) specific to injury endpoints in the liver. We identified 78 such gene co-expression modules associated with 25 diverse injury endpoints categorized from clinical pathology, organ weight changes, and histopathology. Using gene expression data associated with an injury condition, we showed that these modules exhibited different patterns of activation characteristic of each injury. We further showed that specific module genes mapped to 1) known biochemical pathways associated with liver injuries and 2) clinically used diagnostic tests for liver fibrosis. As such, the gene modules have characteristics of both generalized and specific toxic response pathways. Using these results, we proposed three gene signature sets characteristic of liver fibrosis, steatosis, and general liver injury based on genes from the co-expression modules. Out of all 92 identified genes, 18 (20%) genes have well-documented relationships with liver disease, whereas the rest are novel and have not previously been associated with liver disease. In conclusion, identifying gene co-expression modules associated with chemically induced liver injuries aids in generating testable hypotheses and has the potential to identify putative biomarkers of adverse health effects.

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).

Identifying cytochrome p450 functional networks and their allosteric regulatory elements.

Cytochrome P450 (CYP) enzymes play key roles in drug metabolism and adverse drug-drug interactions. Despite tremendous efforts in the past decades, essential questions regarding the function and activity of CYPs remain unanswered. Here, we used a combination of sequence-based co-evolutionary analysis and structure-based anisotropic thermal diffusion (ATD) molecular dynamics simulations to detect allosteric networks of amino acid residues and characterize their biological and molecular functions. We investigated four CYP subfamilies (CYP1A, CYP2D, CYP2C, and CYP3A) that are involved in 90% of all metabolic drug transformations and identified four amino acid interaction networks associated with specific CYP functionalities, i.e., membrane binding, heme binding, catalytic activity, and dimerization. Interestingly, we did not detect any co-evolved substrate-binding network, suggesting that substrate recognition is specific for each subfamily. Analysis of the membrane binding networks revealed that different CYP proteins adopt different membrane-bound orientations, consistent with the differing substrate preference for each isoform. The catalytic networks were associated with conservation of catalytic function among CYP isoforms, whereas the dimerization network was specific to different CYP isoforms. We further applied low-temperature ATD simulations to verify proposed allosteric sites associated with the heme-binding network and their role in regulating metabolic fate. Our approach allowed for a broad characterization of CYP properties, such as membrane interactions, catalytic mechanisms, dimerization, and linking these to groups of residues that can serve as allosteric regulators. The presented combined co-evolutionary analysis and ATD simulation approach is also generally applicable to other biological systems where allostery plays a role.

Rapid countermeasure discovery against Francisella tularensis based on a metabolic network reconstruction.

In the future, we may be faced with the need to provide treatment for an emergent biological threat against which existing vaccines and drugs have limited efficacy or availability. To prepare for this eventuality, our objective was to use a metabolic network-based approach to rapidly identify potential drug targets and prospectively screen and validate novel small-molecule antimicrobials. Our target organism was the fully virulent Francisella tularensis subspecies tularensis Schu S4 strain, a highly infectious intracellular pathogen that is the causative agent of tularemia and is classified as a category A biological agent by the Centers for Disease Control and Prevention. We proceeded with a staggered computational and experimental workflow that used a strain-specific metabolic network model, homology modeling and X-ray crystallography of protein targets, and ligand- and structure-based drug design. Selected compounds were subsequently filtered based on physiological-based pharmacokinetic modeling, and we selected a final set of 40 compounds for experimental validation of antimicrobial activity. We began screening these compounds in whole bacterial cell-based assays in biosafety level 3 facilities in the 20th week of the study and completed the screens within 12 weeks. Six compounds showed significant growth inhibition of F. tularensis, and we determined their respective minimum inhibitory concentrations and mammalian cell cytotoxicities. The most promising compound had a low molecular weight, was non-toxic, and abolished bacterial growth at 13 µM, with putative activity against pantetheine-phosphate adenylyltransferase, an enzyme involved in the biosynthesis of coenzyme A, encoded by gene coaD. The novel antimicrobial compounds identified in this study serve as starting points for lead optimization, animal testing, and drug development against tularemia. Our integrated in silico/in vitro approach had an overall 15% success rate in terms of active versus tested compounds over an elapsed time period of 32 weeks, from pathogen strain identification to selection and validation of novel antimicrobial compounds.

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.

Fungal bis-Naphthopyrones as Inhibitors of Botulinum Neurotoxin Serotype A.

An in silico screen of the NIH Molecular Library Small Molecule Repository (MLSMR) of ∼350000 compounds and confirmatory bioassays led to identification of chaetochromin A (1) as an inhibitor of botulinum neurotoxin serotype A (BoNT A). Subsequent acquisition and testing of analogues of 1 uncovered two compounds, talaroderxines A (2) and B (3), with improved activity. These are the first fungal metabolites reported to exhibit BoNT/A inhibitory activity.

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.