Prophylactic treatment with the c-Abl inhibitor, neurotinib, diminishes neuronal damage and the convulsive state in pilocarpine-induced mice

The molecular mechanisms underlying seizure generation remain elusive, yet they are crucial for developing effective treatments for epilepsy. The current study shows that inhibiting c-Abl tyrosine kinase prevents apoptosis, reduces dendritic spine loss, and maintains N-methyl-d-aspartate (NMDA) receptor subunit 2B (NR2B) phosphorylated in in vitro models of excitotoxicity. Pilocarpine-induced status epilepticus (SE) in mice promotes c-Abl phosphorylation, and disrupting c-Abl activity leads to fewer seizures, increases latency toward SE, and improved animal survival. Currently, clinically used c-Abl inhibitors are non-selective and have poor brain penetration. The allosteric c-Abl inhibitor, neurotinib, used here has favorable potency, selectivity, pharmacokinetics, and vastly improved brain penetration. Neurotinib-administered mice have fewer seizures and improved survival following pilocarpine-SE induction. Our findings reveal c-Abl kinase activation as a key factor in ictogenesis and highlight the impact of its inhibition in preventing the insurgence of epileptic-like seizures in rodents and humans.

An Orally Efficacious Thyrotropin Receptor Ligand Inhibits Growth and Metastatic Activity of Thyroid Cancers

CONTEXT

Thyroid-stimulating hormone (or thyrotropin) receptor (TSHR) could be a selective target for small molecule ligands to treat thyroid cancer (TC).

OBJECTIVE

We report a novel, orally efficacious ligand for TSHR that exhibits proliferation inhibitory activity against human TC in vitro and in vivo, and inhibition of metastasis in vivo.

DESIGN

A35 (NCATS-SM4420; NCGC00241808) was selected from a sub-library of >200 TSHR ligands. Cell proliferation assays including BrdU incorporation and WST-1, along with molecular docking studies were done. In vivo activity of A35 was assessed in TC cell-derived xenograft (CDX) models with immunocompromised (NSG) mice. FFPE sections of tumor and lung tissues were observed for the extent of cell death and metastasis.

RESULTS

A35 was shown to stimulate cAMP production in some cell types by activating TSHR but not in TC cells, MDA-T32 and MDA-T85. A35 inhibited proliferation of MDA-T32 & MDA-T85 in vitro and in vivo, and pulmonary metastasis of MDA-T85F1 in mice. In vitro, A35 inhibition of proliferation was reduced by a selective TSHR antagonist. Inhibition of CDX tumor growth without decreases in mouse weights and liver function showed A35 to be efficacious without apparent toxicity. Lastly, A35 reduced levels of Ki67 in the tumors and metastatic markers in lung tissues.

CONCLUSION

We conclude that A35 is a TSHR-selective inhibitor of TC cell proliferation and metastasis, and suggest that A35 may be a promising lead drug candidate for the treatment of differentiated thyroid cancer in humans.

A patient safety knowledge graph supporting vaccine product development

BACKGROUND

Knowledge graphs are well-suited for modeling complex, unstructured, and multi-source data and facilitating their analysis. During the COVID-19 pandemic, adverse event data were integrated into a knowledge graph to support vaccine safety surveillance and nimbly respond to urgent health authority questions. Here, we provide details of this post-marketing safety system using public data sources. In addition to challenges with varied data representations, adverse event reporting on the COVID-19 vaccines generated an unprecedented volume of data; an order of magnitude larger than adverse events for all previous vaccines. The Patient Safety Knowledge Graph (PSKG) is a robust data store to accommodate the volume of adverse event data and harmonize primary surveillance data sources.

METHODS

We designed a semantic model to represent key safety concepts. We built an extract-transform-load (ETL) data pipeline to parse and import primary public data sources; align key elements such as vaccine names; integrated the Medical Dictionary for Regulatory Activities (MedDRA); and applied quality metrics. PSKG is deployed in a Neo4J graph database, and made available via a web interface and Application Programming Interfaces (APIs).

RESULTS

We import and align adverse event data and vaccine exposure data from 250 countries on a weekly basis, producing a graph with 4,340,980 nodes and 30,544,475 edges as of July 1, 2022. PSKG is used for ad-hoc analyses and periodic reporting for several widely available COVID-19 vaccines. Analysis code using the knowledge graph is 80% shorter than an equivalent implementation written entirely in Python, and runs over 200 times faster.

CONCLUSIONS

Organizing safety data into a concise model of nodes, properties, and edge relationships has greatly simplified analysis code by removing complex parsing and transformation algorithms from individual analyses and instead managing these centrally. The adoption of the knowledge graph transformed how the team answers key scientific and medical questions. Whereas previously an analysis would involve aggregating and transforming primary datasets from scratch to answer a specific question, the team can now iterate easily and respond as quickly as requests evolve (e.g., "Produce vaccine-X safety profile for adverse event-Y by country instead of age-range").

Validation of a Natural Language Machine Learning Model for Safety Literature Surveillance

INTRODUCTION

As part of routine safety surveillance, thousands of articles of potential interest are manually triaged for review by safety surveillance teams. This manual triage task is an interesting candidate for automation based on the abundance of process data available for training, the performance of natural language processing algorithms for this type of cognitive task, and the small number of safety signals that originate from literature review, resulting in its lower risk profile. However, deep learning algorithms introduce unique risks and the validation of such models for use in Good Pharmacovigilance Practice remains an open question.

OBJECTIVE

Qualifying an automated, deep learning approach to literature surveillance for use at AstraZeneca.

METHODS

The study is a prospective validation of a literature surveillance triage model, comparing its real-world performance with that of human surveillance teams working in parallel. The biggest risk in modifying this triage process is missing a safety signal (resulting in model false negatives) and hence model recall is the main evaluation metric considered.

RESULTS

The model demonstrates consistent global performance from training through testing, with recall rates comparable to that of existing surveillance teams. The model is accepted for use specifically for those products where non-inferiority to the manual process is rigorously demonstrated.

CONCLUSION

Characterizing model performance prospectively, under real-world conditions, allows us to thoroughly examine model consistency and failure modes, qualifying it for use in our surveillance processes. We also identify potential future improvements and recognize the opportunity for the community to collaborate on this shared task.

Discovery of small molecule inhibitors that effectively disrupt IQGAP1-Cdc42 interaction in breast cancer cells

The small GTPase Cdc42 is an integral component of the cytoskeleton, and its dysregulation leads to pathophysiological conditions, such as cancer. Binding of Cdc42 to the scaffold protein IQGAP1 stabilizes Cdc42 in its active form. The interaction between Cdc42 and IQGAP1 enhances migration and invasion of cancer cells. Disrupting this association could impair neoplastic progression and metastasis; however, no effective means to achieve this has been described. Here, we screened 78,500 compounds using a homogeneous time resolved fluorescence-based assay to identify small molecules that disrupt the binding of Cdc42 to IQGAP1. From the combined results of the validation assay and counter-screens, we selected 44 potent compounds for cell-based experiments. Immunoprecipitation and cell viability analysis rendered four lead compounds, namely NCGC00131308, NCGC00098561, MLS000332963 and NCGC00138812, three of which inhibited proliferation and migration of breast carcinoma cells. Microscale thermophoresis revealed that two compounds bind directly to Cdc42. One compound reduced the amount of active Cdc42 in cells and effectively impaired filopodia formation. Docking analysis provided plausible models of the compounds binding to the hydrophobic pocket adjacent to the GTP binding site of Cdc42. In conclusion, we identified small molecules that inhibit binding between Cdc42 and IQGAP1, which could potentially yield chemotherapeutic agents.

Real-Time Cellular Thermal Shift Assay to Monitor Target Engagement

Determining a molecule's mechanism of action is paramount during chemical probe development and drug discovery. The cellular thermal shift assay (CETSA) is a valuable tool to confirm target engagement in cells for a small molecule that demonstrates a pharmacological effect. CETSA directly detects biophysical interactions between ligands and protein targets, which can alter a protein's unfolding and aggregation properties in response to thermal challenge. In traditional CETSA experiments, each temperature requires an individual sample, which restricts throughput and requires substantial optimization. To capture the full aggregation profile of a protein from a single sample, we developed a prototype real-time CETSA (RT-CETSA) platform by coupling a real-time PCR instrument with a CCD camera to detect luminescence. A thermally stable Nanoluciferase variant (ThermLuc) was bioengineered to withstand unfolding at temperatures greater than 90 °C and was compatible with monitoring target engagement events when fused to diverse targets. Utilizing well-characterized inhibitors of lactate dehydrogenase alpha, RT-CETSA showed significant correlation with enzymatic, biophysical, and other cell-based assays. A data analysis pipeline was developed to enhance the sensitivity of RT-CETSA to detect on-target binding. RT-CETSA technology advances capabilities of the CETSA method and facilitates the identification of ligand-target engagement in cells, a critical step in assessing the mechanism of action of a small molecule.

Discovery and Optimization of Pyrrolopyrimidine Derivatives as Selective Disruptors of the Perinucleolar Compartment, a Marker of Tumor Progression toward Metastasis

The perinucleolar compartment (PNC) is a dynamic subnuclear body found at the periphery of the nucleolus. The PNC is enriched with RNA transcripts and RNA-binding proteins, reflecting different states of genome organization. PNC prevalence positively correlates with cancer progression and metastatic capacity, making it a useful marker for metastatic cancer progression. A high-throughput, high-content assay was developed to identify novel small molecules that selectively reduce PNC prevalence in cancer cells. We identified and further optimized a pyrrolopyrimidine series able to reduce PNC prevalence in PC3M cancer cells at submicromolar concentrations without affecting cell viability. Structure-activity relationship exploration of the structural elements necessary for activity resulted in the discovery of several potent compounds. Analysis of in vitro drug-like properties led to the discovery of the bioavailable analogue, metarrestin, which has shown potent antimetastatic activity with improved survival in rodent models and is currently being evaluated in a first-in-human phase 1 clinical trial.

Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science

Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph‐based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these “knowledge graphs” (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open‐access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open‐source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object‐oriented classification and graph‐oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.

Progress toward a universal biomedical data translator

Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based "Translator" system capable of integrating existing biomedical data sets and "translating" those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system's architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems.

RXFP2 Small Molecule Agonists: Potential Therapeutics for Osteoporosis

Osteoporosis is a chronic bone disease characterized by decreased bone mass and increased risk of developing fractures, predominantly observed in the elderly. Osteoporosis affects approximately 10 million people in the US, and the number is expected to increase exponentially as the elderly population continues to grow. The pathophysiological cause of the disease is a decrease in the activity of the bone-forming cells (osteoblasts) that alters bone remodeling in favor of bone resorption, leading to a decrease in bone mass. Recent studies identified the G protein-coupled receptor (GPCR) for insulin-like 3 peptide (INSL3), relaxin family peptide receptor 2 (RXFP2), as an attractive target for the treatment of osteoporosis. The goal of this study is to develop small molecule agonists of RXFP2, expressed in osteoblast cells, to promote bone growth and counteract the deleterious effects of osteoporosis. Currently, the most effective available treatment for osteoporosis is an expensive hormone therapy that requires daily injections. We aim to create drugs that are stable and can be delivered orally. Several low molecular weight compounds were identified as agonists of the RXFP2 receptor using a high-throughput screen of the NCATS small molecule library. The screening assay measured cAMP response in RXFP2-transfected HEK293T cells. An extensive structure-activity relationship campaign resulted in highly potent and efficient full RXFP2 agonists. The selectivity and specificity of the compounds for human and mouse RXFP2 was shown using orthogonal cAMP assays, counter-screening against the related relaxin receptor RXFP1, and a GPCRome screen using the PRESTO-Tango assay. The drug candidates were further tested in primary human osteoblasts to demonstrate that they promote mineralization by measuring hydroxyapatite deposition in the cell matrix. We showed that the compounds had low cytotoxicity in various cell types. Using a series of RXFP2/RXFP1 chimeric receptors, we established that the compounds are allosteric agonists of the RXFP2 receptor and identified the GPCR transmembrane domains as the specific region for compound interaction. The RXFP2 agonist with the highest activity in vitro was selected for pharmacokinetics (PK) profiling in mice, showing optimal oral bioavailability and bone exposure. An efficacy study using 2-month-old WT female mice treated orally with 10 mg/kg of compound 3 times a week for 8 weeks showed a significant increase of the vertebral trabecular number and thickness by micro-CT analysis compared to vehicle treated controls. We expect that the further characterization of these compounds may lead to the development of a new class of cost-effective drugs for the treatment of osteoporosis and other diseases associated with bone loss.

c-Abl Activation Linked to Autophagy-Lysosomal Dysfunction Contributes to Neurological Impairment in Niemann-Pick Type A Disease

Niemann-Pick type A (NPA) disease is a fatal lysosomal neurodegenerative disorder caused by the deficiency in acid sphingomyelinase (ASM) activity. NPA patients present severe and progressive neurodegeneration starting at an early age. Currently, there is no effective treatment for this disease and NPA patients die between 2 and 3 years of age. NPA is characterized by an accumulation of sphingomyelin in lysosomes and dysfunction in the autophagy-lysosomal pathway. Recent studies show that c-Abl tyrosine kinase activity downregulates autophagy and the lysosomal pathway. Interestingly, this kinase is also activated in other lysosomal neurodegenerative disorders. Here, we describe that c-Abl activation contributes to the mechanisms of neuronal damage and death in NPA disease. Our data demonstrate that: 1) c-Abl is activated in-vitro as well as in-vivo NPA models; 2) imatinib, a clinical c-Abl inhibitor, reduces autophagy-lysosomal pathway alterations, restores autophagy flux, and lowers sphingomyelin accumulation in NPA patient fibroblasts and NPA neuronal models and 3) chronic treatment with nilotinib and neurotinib, two c-Abl inhibitors with differences in blood-brain barrier penetrance and target binding mode, show further benefits. While nilotinib treatment reduces neuronal death in the cerebellum and improves locomotor functions, neurotinib decreases glial activation, neuronal disorganization, and loss in hippocampus and cortex, as well as the cognitive decline of NPA mice. Our results support the participation of c-Abl signaling in NPA neurodegeneration and autophagy-lysosomal alterations, supporting the potential use of c-Abl inhibitors for the clinical treatment of NPA patients.

High-Throughput Cellular Thermal Shift Assay Using Acoustic Transfer of Protein Lysates

Cellular thermal shift assay (CETSA) is a valuable method to confirm target engagement within a complex cellular environment, by detecting changes in a protein's thermal stability upon ligand binding. The classical CETSA method measures changes in the thermal stability of endogenous proteins using immunoblotting, which is low-throughput and laborious. Reverse-phase protein arrays (RPPAs) have been demonstrated as a detection modality for CETSA; however, the reported procedure requires manual processing steps that limit throughput and preclude screening applications. We developed a high-throughput CETSA using an acoustic RPPA (HT-CETSA-aRPPA) protocol that is compatible with 96- and 384-well microplates from start-to-finish, using low speed centrifugation to remove thermally destabilized proteins. The utility of HT-CETSA-aRPPA for guiding structure-activity relationship studies was demonstrated for inhibitors of lactate dehydrogenase A. Additionally, a collection of kinase inhibitors was screened to identify compounds that engage MEK1, a clinically relevant kinase target.

How to Illuminate the Druggable Genome Using Pharos

Pharos is an integrated web-based informatics platform for the analysis of data aggregated by the Illuminating the Druggable Genome (IDG) Knowledge Management Center, an NIH Common Fund initiative. The current version of Pharos (as of October 2019) spans 20,244 proteins in the human proteome, 19,880 disease and phenotype associations, and 226,829 ChEMBL compounds. This resource not only collates and analyzes data from over 60 high-quality resources to generate these types, but also uses text indexing to find less apparent connections between targets, and has recently begun to collaborate with institutions that generate data and resources. Proteins are ranked according to a knowledge-based classification system, which can help researchers to identify less studied "dark" targets that could be potentially further illuminated. This is an important process for both drug discovery and target validation, as more knowledge can accelerate target identification, and previously understudied proteins can serve as novel targets in drug discovery. Two basic protocols illustrate the levels of detail available for targets and several methods of finding targets of interest. An Alternate Protocol illustrates the difference in available knowledge between less and more studied targets. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Search for a target and view details Alternate Protocol: Search for dark target and view details Basic Protocol 2: Filter a target list to get refined results.

Discovery and Optimization of a 4-Aminopiperidine Scaffold for Inhibition of Hepatitis C Virus Assembly

The majority of FDA-approved HCV therapeutics target the viral replicative machinery. An automated high-throughput phenotypic screen identified several small molecules as potent inhibitors of hepatitis C virus replication. Here, we disclose the discovery and optimization of a 4-aminopiperidine (4AP) scaffold targeting the assembly stages of the HCV life cycle. The original screening hit (1) demonstrates efficacy in the HCVcc assay but does not show potency prior to or during viral replication. Colocalization and infectivity studies indicate that the 4AP chemotype inhibits the assembly and release of infectious HCV. Compound 1 acts synergistically with FDA-approved direct-acting antiviral compounds Telaprevir and Daclatasvir, as well as broad spectrum antivirals Ribavirin and cyclosporin A. Following an SAR campaign, several derivatives of the 4AP series have been identified with increased potency against HCV, reduced in vitro toxicity, as well as improved in vitro and in vivo ADME properties.

Validating ADME QSAR Models Using Marketed Drugs

Problems with drug ADME are responsible for many clinical failures. By understanding the ADME properties of marketed drugs and modeling how chemical structure contributes to these inherent properties, we can help new projects reduce their risk profiles. Kinetic aqueous solubility, the parallel artificial membrane permeability assay (PAMPA), and rat liver microsomal stability constitute the Tier I ADME assays at the National Center for Advancing Translational Sciences (NCATS). Using recent data generated from in-house lead optimization Tier I studies, we update quantitative structure-activity relationship (QSAR) models for these three endpoints and validate in silico performance against a set of marketed drugs (balanced accuracies range between 71% and 85%). Improved models and experimental datasets are of direct relevance to drug discovery projects and, together with the prediction services that have been made publicly available at the ADME@NCATS web portal (https://opendata.ncats.nih.gov/adme/), provide important tools for the drug discovery community. The results are discussed in light of our previously reported ADME models and state-of-the-art models from scientific literature.Graphical Abstract[Figure: see text].

Mannose receptor (CD206) activation in tumor-associated macrophages enhances adaptive and innate antitumor immune responses

Solid tumors elicit a detectable immune response including the infiltration of tumor-associated macrophages (TAMs). Unfortunately, this immune response is co-opted into contributing toward tumor growth instead of preventing its progression. We seek to reestablish an antitumor immune response by selectively targeting surface receptors and endogenous signaling processes of the macrophage subtypes driving cancer progression. RP-182 is a synthetic 10-mer amphipathic analog of host defense peptides that selectively induces a conformational switch of the mannose receptor CD206 expressed on TAMs displaying an M2-like phenotype. RP-182-mediated activation of this receptor in human and murine M2-like macrophages elicits a program of endocytosis, phagosome-lysosome formation, and autophagy and reprograms M2-like TAMs to an antitumor M1-like phenotype. In syngeneic and autochthonous murine cancer models, RP-182 suppressed tumor growth, extended survival, and was an effective combination partner with chemo- or immune checkpoint therapy. Antitumor activity of RP-182 was also observed in CD206^high patient-derived xenotransplantation models. Mechanistically, via selective reduction of immunosuppressive M2-like TAMs, RP-182 improved adaptive and innate antitumor immune responses, including increased cancer cell phagocytosis by reprogrammed TAMs.

CATMoS: Collaborative Acute Toxicity Modeling Suite

BACKGROUND

Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals.

OBJECTIVES

The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg).

METHODS

An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches.

RESULTS

The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results.

DISCUSSION

CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495.

Global Substance Registration System: consistent scientific descriptions for substances related to health

The US Food and Drug Administration (FDA) and the National Center for Advancing Translational Sciences (NCATS) have collaborated to publish rigorous scientific descriptions of substances relevant to regulated products. The FDA has adopted the global ISO 11238 data standard for the identification of substances in medicinal products and has populated a database to organize the agency's regulatory submissions and marketed products data. NCATS has worked with FDA to develop the Global Substance Registration System (GSRS) and produce a non-proprietary version of the database for public benefit. In 2019, more than half of all new drugs in clinical development were proteins, nucleic acid therapeutics, polymer products, structurally diverse natural products or cellular therapies. While multiple databases of small molecule chemical structures are available, this resource is unique in its application of regulatory standards for the identification of medicinal substances and its robust support for other substances in addition to small molecules. This public, manually curated dataset provides unique ingredient identifiers (UNIIs) and detailed descriptions for over 100 000 substances that are particularly relevant to medicine and translational research. The dataset can be accessed and queried at https://gsrs.ncats.nih.gov/app/substances.

TCRD and Pharos 2021: mining the human proteome for disease biology

In 2014, the National Institutes of Health (NIH) initiated the Illuminating the Druggable Genome (IDG) program to identify and improve our understanding of poorly characterized proteins that can potentially be modulated using small molecules or biologics. Two resources produced from these efforts are: The Target Central Resource Database (TCRD) (http://juniper.health.unm.edu/tcrd/) and Pharos (https://pharos.nih.gov/), a web interface to browse the TCRD. The ultimate goal of these resources is to highlight and facilitate research into currently understudied proteins, by aggregating a multitude of data sources, and ranking targets based on the amount of data available, and presenting data in machine learning ready format. Since the 2017 release, both TCRD and Pharos have produced two major releases, which have incorporated or expanded an additional 25 data sources. Recently incorporated data types include human and viral-human protein-protein interactions, protein-disease and protein-phenotype associations, and drug-induced gene signatures, among others. These aggregated data have enabled us to generate new visualizations and content sections in Pharos, in order to empower users to find new areas of study in the druggable genome.

546. Capturing Clinician’s Experiences Repurposing Drugs to Inform Future Studies During COVID-19

Background

CURE ID is an internet-based repository developed collaboratively by FDA and NCATS/NIH, with the support of WHO and IDSA. It encourages clinicians globally to share novel uses of existing drugs for patients with difficult-to-treat infections. It is designed to serve as a rapid communication platform for healthcare providers during an outbreak, providing for systematic case-sharing, discussion, and the latest literature. Besides case reports, CURE ID offers a discussion platform for clinicians, disease-specific clinical trials curated from clincialtrials.gov, and a newsfeed that shows relevant journal articles and news related to COVID-19 and other infectious diseases.

Methods

The CURE ID team extracted individual case reports on patient-level treatments and outcomes of COVID-19 infection from the published literature and gathered clinician-submitted cases through the electronic case report form. Additionally, CURE ID partnered with the University of Pennsylvania’s CORONA database to further populate the CURE ID database with published cases.

Results

As of submission, lopinavir-Ritonavir (n=51) was the most commonly reported drug used. The following were also reported: hydroxychloroquine (n=31), azithromycin (n=28), arbidol (n=22), interferon alfa-2B (n=18), moxifloxacin (n=18), methylprednisolone (n=17), ivermectin (n=14), lopinavir (n=12), oseltamivir (n=12). The other drugs reported were danoprevir-ritonavir, intravenous immunoglobulins, interferon, interferon alfa, and tocilizumab. CURE ID currently includes more than 150 detailed COVID case reports of 65 repurposed drugs. We expect case reporting for specific drugs to be dynamic and additional data to accrue. Updated results will be presented.

Conclusion

Several drugs are being repurposed to treat COVID-19. CURE ID gives clinicians an opportunity to share their treatment experiences and discuss their questions with a global community of healthcare providers. By utilizing the CURE ID platform, in conjunction with data gathered from other registries, observational studies and clinical trials, hypotheses can be generated that may inform future clinical trials and ultimately, potentially find safe and effective treatments for this deadly disease.

Disclosures

All Authors: No reported disclosures

Fluoxazolevir inhibits hepatitis C virus infection in humanized chimeric mice by blocking viral membrane fusion

Fluoxazolevir is an aryloxazole-based entry inhibitor of hepatitis C virus (HCV). We show that fluoxazolevir inhibits fusion of HCV with hepatic cells by binding HCV envelope protein 1 to prevent fusion. Nine of ten fluoxazolevir resistance-associated substitutions are in envelope protein 1, and four are in a putative fusion peptide. Pharmacokinetic studies in mice, rats and dogs revealed that fluoxazolevir localizes to the liver. A 4-week intraperitoneal regimen of fluoxazolevir in humanized chimeric mice infected with HCV genotypes 1b, 2a or 3 resulted in a 2-log reduction in viraemia, without evidence of drug resistance. In comparison, daclatasvir, an approved HCV drug, suppressed more than 3 log of viraemia but is associated with the emergence of resistance-associated substitutions in mice. Combination therapy using fluoxazolevir and daclatasvir cleared HCV genotypes 1b and 3 in mice. Fluoxazolevir combined with glecaprevir and pibrentasvir was also effective in clearing multidrug-resistant HCV replication in mice. Fluoxazolevir may be promising as the next generation of combination drug cocktails for HCV treatment.

Retrospective assessment of rat liver microsomal stability at NCATS: data and QSAR models

Hepatic metabolic stability is a key pharmacokinetic parameter in drug discovery. Metabolic stability is usually assessed in microsomal fractions and only the best compounds progress in the drug discovery process. A high-throughput single time point substrate depletion assay in rat liver microsomes (RLM) is employed at the National Center for Advancing Translational Sciences. Between 2012 and 2020, RLM stability data was generated for ~ 24,000 compounds from more than 250 projects that cover a wide range of pharmacological targets and cellular pathways. Although a crucial endpoint, little or no data exists in the public domain. In this study, computational models were developed for predicting RLM stability using different machine learning methods. In addition, a retrospective time-split validation was performed, and local models were built for projects that performed poorly with global models. Further analysis revealed inherent medicinal chemistry knowledge potentially useful to chemists in the pursuit of synthesizing metabolically stable compounds. In addition, we deposited experimental data for ~ 2500 compounds in the PubChem bioassay database (AID: 1508591). The global prediction models are made publicly accessible ( https://opendata.ncats.nih.gov/adme ). This is to the best of our knowledge, the first publicly available RLM prediction model built using high-quality data generated at a single laboratory.

An integrative knowledge graph for rare diseases, derived from the Genetic and Rare Diseases Information Center (GARD)

Background

The Genetic and Rare Diseases (GARD) Information Center was established by the National Institutes of Health (NIH) to provide freely accessible consumer health information on over 6500 genetic and rare diseases. As the cumulative scientific understanding and underlying evidence for these diseases have expanded over time, existing practices to generate knowledge from these publications and resources have not been able to keep pace. Through determining the applicability of computational approaches to enhance or replace manual curation tasks, we aim to both improve the sustainability and relevance of consumer health information, but also to develop a foundational database, from which translational science researchers may start to unravel disease characteristics that are vital to the research process.

Results

We developed a meta-ontology based integrative knowledge graph for rare diseases in Neo4j. This integrative knowledge graph includes a total of 3,819,623 nodes and 84,223,681 relations from 34 different biomedical data resources, including curated drug and rare disease associations. Semi-automatic mappings were generated for 2154 unique FDA orphan designations to 776 unique GARD diseases, and 3322 unique FDA designated drugs to UNII, as well as 180,363 associations between drug and indication from Inxight Drugs, which were integrated into the knowledge graph. We conducted four case studies to demonstrate the capabilities of this integrative knowledge graph in accelerating the curation of scientific understanding on rare diseases through the generation of disease mappings/profiles and pathogenesis associations.

Conclusions

By integrating well-established database resources, we developed an integrative knowledge graph containing a large volume of biomedical and research data. Demonstration of several immediate use cases and limitations of this process reveal both the potential feasibility and barriers of utilizing graph-based resources and approaches to support their use by providers of consumer health information, such as GARD, that may struggle with the needs of maintaining knowledge reliant on an evolving and growing evidence-base. Finally, the successful integration of these datasets into a freely accessible knowledge graph highlights an opportunity to take a translational science view on the field of rare diseases by enabling researchers to identify disease characteristics, which may play a role in the translation of discover across different research domains.

Chlorcyclizine Inhibits Viral Fusion of Hepatitis C Virus Entry by Directly Targeting HCV Envelope Glycoprotein 1

Chlorcyclizine (CCZ) is a potent hepatitis C virus (HCV) entry inhibitor, but its molecular mechanism is unknown. Here, we show that CCZ directly targets the fusion peptide of HCV E1 and interferes with the fusion process. Generation of CCZ resistance-associated substitutions of HCV in vitro revealed six missense mutations in the HCV E1 protein, five being in the putative fusion peptide. A viral fusion assay demonstrated that CCZ blocked HCV entry at the membrane fusion step and that the mutant viruses acquired resistance to CCZ's action in blocking membrane fusion. UV cross-linking of photoactivatable CCZ-diazirine-biotin in both HCV-infected cells and recombinant HCV E1/E2 protein demonstrated direct binding to HCV E1 glycoprotein. Mass spectrometry analysis revealed that CCZ cross-linked to an E1 sequence adjacent to the putative fusion peptide. Docking simulations demonstrate a putative binding model, wherein CCZ binds to a hydrophobic pocket of HCV E1 and forms extensive interactions with the fusion peptide.

Discovery, Optimization, and Characterization of ML417: A Novel and Highly Selective D3 Dopamine Receptor Agonist

To identify novel D₃ dopamine receptor (D3R) agonists, we conducted a high-throughput screen using a β-arrestin recruitment assay. Counterscreening of the hit compounds provided an assessment of their selectivity, efficacy, and potency. The most promising scaffold was optimized through medicinal chemistry resulting in enhanced potency and selectivity. The optimized compound, ML417 (20), potently promotes D3R-mediated β-arrestin translocation, G protein activation, and ERK1/2 phosphorylation (pERK) while lacking activity at other dopamine receptors. Screening of ML417 against multiple G protein-coupled receptors revealed exceptional global selectivity. Molecular modeling suggests that ML417 interacts with the D3R in a unique manner, possibly explaining its remarkable selectivity. ML417 was also found to protect against neurodegeneration of dopaminergic neurons derived from iPSCs. Together with promising pharmacokinetics and toxicology profiles, these results suggest that ML417 is a novel and uniquely selective D3R agonist that may serve as both a research tool and a therapeutic lead for the treatment of neuropsychiatric disorders.

Validation and Characterization of Five Distinct Novel Inhibitors of Human Cytomegalovirus

The critical consequences of human cytomegalovirus (HCMV) infection in the transplant population and in congenitally infected infants, the limited treatment options for HCMV, and the rise of resistant mutants toward existing therapies has fueled the search for new anti-HCMV agents. A pp28-luciferase recombinant HCMV was used as a reporter system for high-throughput screening of HCMV inhibitors. Approximately 400 000 compounds from existing libraries were screened. Subsequent validation assays using resynthesized compounds, several virus strains, and detailed virology assays resulted in the identification of five structurally unique and selective HCMV inhibitors, active at sub to low micromolar concentrations. Further characterization revealed that each compound inhibited a specific stage of HCMV replication. One compound was also active against herpes simplex virus (HSV1 and HSV2), and another compound was active against Epstein-Barr virus (EBV). Drug combination studies revealed that all five compounds were additive with ganciclovir or letermovir. Future studies will focus on optimization of these new anti-HCMV compounds along with mechanistic studies.

Sunitinib promotes myogenic regeneration and mitigates disease progression in the mdx mouse model of Duchenne muscular dystrophy

Duchenne muscular dystrophy (DMD) is a lethal, muscle degenerative disease causing premature death of affected children. DMD is characterized by mutations in the dystrophin gene that result in a loss of the dystrophin protein. Loss of dystrophin causes an associated reduction in proteins of the dystrophin glycoprotein complex, leading to contraction-induced sarcolemmal weakening, muscle tearing, fibrotic infiltration and rounds of degeneration and failed regeneration affecting satellite cell populations. The α7β1 integrin has been implicated in increasing myogenic capacity of satellite cells, therefore restoring muscle viability, increasing muscle force and preserving muscle function in dystrophic mouse models. In this study, we show that a Food and Drug Administration (FDA)-approved small molecule, Sunitinib, is a potent α7 integrin enhancer capable of promoting myogenic regeneration by stimulating satellite cell activation and increasing myofiber fusion. Sunitinib exerts its regenerative effects via transient inhibition of SHP-2 and subsequent activation of the STAT3 pathway. Treatment of mdx mice with Sunitinib demonstrated decreased membrane leakiness and damage owing to myofiber regeneration and enhanced support at the extracellular matrix. The decreased myofiber damage translated into a significant increase in muscle force production. This study identifies an already FDA-approved compound, Sunitinib, as a possible DMD therapeutic with the potential to treat other muscular dystrophies in which there is defective muscle repair.

Evaluating kratom alkaloids using PHASE

Kratom is a botanical substance that is marketed and promoted in the US for pharmaceutical opioid indications despite having no US Food and Drug Administration approved uses. Kratom contains over forty alkaloids including two partial agonists at the mu opioid receptor, mitragynine and 7-hydroxymitragynine, that have been subjected to the FDA's scientific and medical evaluation. However, pharmacological and toxicological data for the remaining alkaloids are limited. Therefore, we applied the Public Health Assessment via Structural Evaluation (PHASE) protocol to generate in silico binding profiles for 25 kratom alkaloids to facilitate the risk evaluation of kratom. PHASE demonstrates that kratom alkaloids share structural features with controlled opioids, indicates that several alkaloids bind to the opioid, adrenergic, and serotonin receptors, and suggests that mitragynine and 7-hydroxymitragynine are the strongest binders at the mu opioid receptor. Subsequently, the in silico binding profiles of a subset of the alkaloids were experimentally verified at the opioid, adrenergic, and serotonin receptors using radioligand binding assays. The verified binding profiles demonstrate the ability of PHASE to identify potential safety signals and provide a tool for prioritizing experimental evaluation of high-risk compounds.

Anxiolytic Drug FGIN-1-27 Ameliorates Autoimmunity by Metabolic Reprogramming of Pathogenic Th17 Cells

Th17 cells are critical drivers of autoimmune diseases and immunopathology. There is an unmet need to develop therapies targeting pathogenic Th17 cells for the treatment of autoimmune disorders. Here, we report that anxiolytic FGIN-1-27 inhibits differentiation and pathogenicity of Th17 cells in vitro and in vivo using the experimental autoimmune encephalomyelitis (EAE) model of Th17 cell-driven pathology. Remarkably, we found that the effects of FGIN-1-27 were independent of translocator protein (TSPO), the reported target for this small molecule, and instead were driven by a metabolic switch in Th17 cells that led to the induction of the amino acid starvation response and altered cellular fatty acid composition. Our findings suggest that the small molecule FGIN-1-27 can be re-purposed to relieve autoimmunity by metabolic reprogramming of pathogenic Th17 cells.

Small-molecule activation of lysosomal TRP channels ameliorates Duchenne muscular dystrophy in mouse models

Duchenne muscular dystrophy (DMD) is a devastating disease caused by mutations in dystrophin that compromise sarcolemma integrity. Currently, there is no treatment for DMD. Mutations in transient receptor potential mucolipin 1 (ML1), a lysosomal Ca²⁺ channel required for lysosomal exocytosis, produce a DMD-like phenotype. Here, we show that transgenic overexpression or pharmacological activation of ML1 in vivo facilitates sarcolemma repair and alleviates the dystrophic phenotypes in both skeletal and cardiac muscles of mdx mice (a mouse model of DMD). Hallmark dystrophic features of DMD, including myofiber necrosis, central nucleation, fibrosis, elevated serum creatine kinase levels, reduced muscle force, impaired motor ability, and dilated cardiomyopathies, were all ameliorated by increasing ML1 activity. ML1-dependent activation of transcription factor EB (TFEB) corrects lysosomal insufficiency to diminish muscle damage. Hence, targeting lysosomal Ca²⁺ channels may represent a promising approach to treat DMD and related muscle diseases.

Agonist-specific voltage-dependent gating of lysosomal two-pore Na+ channels

Mammalian two-pore-channels (TPC1, 2; TPCN1, TPCN2) are ubiquitously- expressed, PI(3,5)P2-activated, Na+-selective channels in the endosomes and lysosomes that regulate luminal pH homeostasis, membrane trafficking, and Ebola viral infection. Whereas the channel activity of TPC1 is strongly dependent on membrane voltage, TPC2 lacks such voltage dependence despite the presence of the presumed 'S4 voltage-sensing' domains. By performing high-throughput screening followed by lysosomal electrophysiology, here we identified a class of tricyclic anti-depressants (TCAs) as small-molecule agonists of TPC channels. TCAs activate both TPC1 and TPC2 in a voltage-dependent manner, referred to as Lysosomal Na+ channel Voltage-dependent Activators (LyNa-VAs). We also identified another compound which, like PI(3,5)P2, activates TPC2 independent of voltage, suggesting the existence of agonist-specific gating mechanisms. Our identification of small-molecule TPC agonists should facilitate the studies of the cell biological roles of TPCs and can also readily explain the reported effects of TCAs in the modulation of autophagy and lysosomal functions.

Novel Consensus Architecture To Improve Performance of Large-Scale Multitask Deep Learning QSAR Models

Advances in the development of high-throughput screening and automated chemistry have rapidly accelerated the production of chemical and biological data, much of them freely accessible through literature aggregator services such as ChEMBL and PubChem. Here, we explore how to use this comprehensive mapping of chemical biology space to support the development of large-scale quantitative structure-activity relationship (QSAR) models. We propose a new deep learning consensus architecture (DLCA) that combines consensus and multitask deep learning approaches together to generate large-scale QSAR models. This method improves knowledge transfer across different target/assays while also integrating contributions from models based on different descriptors. The proposed approach was validated and compared with proteochemometrics, multitask deep learning, and Random Forest methods paired with various descriptors types. DLCA models demonstrated improved prediction accuracy for both regression and classification tasks. The best models together with their modeling sets are provided through publicly available web services at https://predictor.ncats.io .

The NCATS Pharmaceutical Collection: a 10-year update

The National Center for Advancing Translational Sciences (NCATS) Pharmaceutical Collection (NPC), a comprehensive collection of clinically approved drugs, was made a public resource in 2011. Over the past decade, the NPC has been systematically profiled for activity across an array of pathways and disease models, generating an unparalleled amount of data. These data have not only enabled the identification of new repurposing candidates with several in clinical trials, but also uncovered new biological insights into drug targets and disease mechanisms. This retrospective provides an update on the NPC in terms of both successes and lessons learned. We also report our efforts in bringing the NPC up-to-date with drugs approved in recent years.

Metarrestin, a perinucleolar compartment inhibitor, effectively suppresses metastasis

Metastasis remains a leading cause of cancer mortality due to the lack of specific inhibitors against this complex process. To identify compounds selectively targeting the metastatic state, we used the perinucleolar compartment (PNC), a complex nuclear structure associated with metastatic behaviors of cancer cells, as a phenotypic marker for a high-content screen of over 140,000 structurally diverse compounds. Metarrestin, obtained through optimization of a screening hit, disassembles PNCs in multiple cancer cell lines, inhibits invasion in vitro, suppresses metastatic development in three mouse models of human cancer, and extends survival of mice in a metastatic pancreatic cancer xenograft model with no organ toxicity or discernable adverse effects. Metarrestin disrupts the nucleolar structure and inhibits RNA polymerase (Pol) I transcription, at least in part by interacting with the translation elongation factor eEF1A2. Thus, metarrestin represents a potential therapeutic approach for the treatment of metastatic cancer.

SAT-035 Small Molecule Allosteric Agonist of Relaxin Receptor Ml290 Demonstrates Antifibrotic Properties in Liver Fibrosis

Fibrosis is an underlying cause of cirrhosis and hepatic failure resulting in end stage liver disease with limited pharmacological therapeutic options. The beneficial effects of relaxin peptide treatment have been demonstrated previously in clinically relevant animal models of liver disease. However, the low stability of the recombinant relaxin peptide in vivo requires continuous intravenous delivery for chronic therapeutic application. We have recently identified a first series of small molecule allosteric biased agonists of the human relaxin receptor RXFP1 which showed efficacy similar to relaxin in several functional assays in vitro. Here we investigated the therapeutic effects of small molecule RXFP1 agonist on activated hepatic stellate cells, the main source of excessive collagen production in liver fibrosis. We have demonstrated that RXFP1 expression is increased in fibrotic mouse liver, specifically in activated hepatic stellate cells. The lead compound, ML290, was selected based on its effects on the expression of the genes involved in fibrosis in primary human stellate cells. RNA-Seq analysis of TGFβ1-activated LX-2 hepatic stellate cells showed that about 500 genes were misregulated by ML290. Gene Ontology analysis demonstrated that ML290 treatment primarily affects extracellular matrix remodeling and cytokine signaling, with expression profiles indicating an antifibrotic effect of ML290. ML290 treatment of human liver organoids with lipopolysaccharide-induced fibrotic phenotype resulted in dramatic reduction of type I collagen. The pharmacokinetics of ML290 in mice after multiple injections demonstrated its high stability in vivo, as evidenced by the sustained concentrations of compound in the liver. The ML290 treatment of mice expressing human RXFP1 gene with carbon tetrachloride-induced liver fibrosis resulted in significantly reduced collagen content, alpha-smooth muscle actin expression and cell proliferation around portal ducts, and the decrease of pro-fibrotic genes’ expression. In summary, ML290, the small molecule agonist of relaxin receptor, has anti-fibrotic effects in liver fibrosis. Funding source: NIH/NIDDK

The NCATS BioPlanet - An Integrated Platform for Exploring the Universe of Cellular Signaling Pathways for Toxicology, Systems Biology, and Chemical Genomics

Chemical genomics aims to comprehensively define, and ultimately predict, the effects of small molecule compounds on biological systems. Chemical activity profiling approaches must consider chemical effects on all pathways operative in mammalian cells. To enable a strategic and maximally efficient chemical profiling of pathway space, we have created the NCATS BioPlanet, a comprehensive integrated pathway resource that incorporates the universe of 1,658 human pathways sourced from publicly available, manually curated sources, which have been subjected to thorough redundancy and consistency cross-evaluation. BioPlanet supports interactive browsing, retrieval, and analysis of pathways, exploration of pathway connections, and pathway search by gene targets, category, and availability of corresponding bioactivity assay, as well as visualization of pathways on a 3-dimensional globe, in which the distance between any two pathways is proportional to their degree of gene component overlap. Using this resource, we propose a strategy to identify a minimal set of 362 biological assays that can interrogate the universe of human pathways. The NCATS BioPlanet is a public resource, which will be continually expanded and updated, for systems biology, toxicology, and chemical genomics, available at http://tripod.nih.gov/bioplanet/.

Identification, design and synthesis of novel pyrazolopyridine influenza virus nonstructural protein 1 antagonists

Nonstructural protein 1 (NS1) plays a crucial function in the replication, spread, and pathogenesis of influenza virus by inhibiting the host innate immune response. Here we report the discovery and optimization of novel pyrazolopyridine NS1 antagonists that can potently inhibit influenza A/PR/8/34 replication in MDCK cells, rescue MDCK cells from cytopathic effects of seasonal influenza A strains, reverse NS1-dependent inhibition of IFN-β gene expression, and suppress the slow growth phenotype in NS1-expressing yeast. These pyrazolopyridines will enable researchers to investigate NS1 function during infection and how antagonists can be utilized in the next generation of treatments for influenza infection.

A large scale high-throughput screen identifies chemical inhibitors of phosphatidylinositol 4-kinase type II alpha

The minor phospholipid, phosphatidylinositol 4-phosphate (PI4P), is emerging as a key regulator of lipid transfer in ER-membrane contact sites. Four different phosphatidylinositol 4-kinase (PI4K) enzymes generate PI4P in different membrane compartments supporting distinct cellular processes, many of which are crucial for the maintenance of cellular integrity but also hijacked by intracellular pathogens. While type III PI4Ks have been targeted by small molecular inhibitors, thus helping decipher their importance in cellular physiology, no inhibitors are available for the type II PI4Ks, which hinders investigations into their cellular functions. Here, we describe the identification of small molecular inhibitors of PI4K type II alpha (PI4K2A) by implementing a large scale small molecule high-throughput screening. A novel assay was developed that allows testing of selected inhibitors against PI4K2A in intact cells using a bioluminescence resonance energy transfer approach adapted to plate readers. The compounds disclosed here will pave the way to the optimization of PI4K2A inhibitors that can be used in cellular and animal studies to better understand the role of this enzyme in both normal and pathological states.

Identification of Chemotype Agonists for Human Resolvin D1 Receptor DRV1 with Pro-Resolving Functions

Resolution of acute inflammation is governed, in part, by specialized pro-resolving mediators, including lipoxins, resolvins, protectins, and maresins. Among them, resolvin D1 (RvD1) exhibits potent pro-resolving functions via activating human resolvin D1 receptor (DRV1/GPR32). RvD1 is a complex molecule that requires challenging organic synthesis, diminishing its potential as a therapeutic. Therefore, we implemented a high-throughput screening of small-molecule libraries and identified several chemotypes that activated recombinant DRV1, represented by NCGC00120943 (C1A), NCGC00135472 (C2A), pMPPF, and pMPPI. These chemotypes also elicited rapid impedance changes in cells overexpressing recombinant DRV1. With human macrophages, they each stimulated phagocytosis of serum-treated zymosan at concentrations comparable with that of RvD1, the endogenous DRV1 ligand. In addition, macrophage phagocytosis of live E. coli was significantly increased by these chemotypes in DRV1-transfected macrophages, compared with mock-transfected cells. Taken together, these chemotypes identified by unbiased screens act as RvD1 mimetics, exhibiting pro-resolving functions via interacting with human DRV1.

Pharmacokinetic evaluation of the PNC disassembler metarrestin in wild-type and Pdx1-Cre;LSL-KrasG12D/+;Tp53R172H/+ (KPC) mice, a genetically engineered model of pancreatic cancer

PURPOSE

Metarrestin is a first-in-class small molecule clinical candidate capable of disrupting the perinucleolar compartment, a subnuclear structure unique to metastatic cancer cells. This study aims to define the pharmacokinetic (PK) profile of metarrestin and the pharmacokinetic/pharmacodynamic relationship of metarrestin-regulated markers.

METHODS

PK studies included the administration of single or multiple dose of metarrestin at 3, 10, or 25 mg/kg via intravenous (IV) injection, gavage (PO) or with chow to wild-type C57BL/6 mice and KPC mice bearing autochthonous pancreatic tumors. Metarrestin concentrations were analyzed by UPLC-MS/MS. Pharmacodynamic assays included mRNA expression profiling by RNA-seq and qRT-PCR for KPC mice.

RESULTS

Metarrestin had a moderate plasma clearance of 48 mL/min/kg and a large volume of distribution of 17 L/kg at 3 mg/kg IV in C57BL/6 mice. The oral bioavailability after single-dose (SD) treatment was > 80%. In KPC mice treated with SD 25 mg/kg PO, plasma AUC0-∞ of 14400 ng h/mL, Cmax of 810 ng/mL and half-life (t1/2) of 8.5 h were observed. At 24 h after SD of 25 mg/kg PO, the intratumor concentration of metarrestin was high with a mean value of 6.2 µg/g tissue (or 13 µM), well above the cell-based IC50 of 0.4 µM. At multiple dose (MD) 25 mg/kg/day PO in KPC mice, mean tissue/plasma AUC0-24h ratio for tumor, spleen and liver was 37, 30 and 31, respectively. There was a good linear relationship of dosage to AUC0-24h and C24h. AUC0-24h MD to AUC0-24h SD ratios ranged from two for liver to five for tumor indicating additional accumulation in tumors. Dose-dependent normalization of FOXA1 and FOXO6 mRNA expression was observed in KPC tumors.

CONCLUSIONS

Metarrestin is an effective therapeutic candidate with a favorable PK profile achieving excellent intratumor tissue levels in a disease with known poor drug delivery.

Advancing precision medicine with personalized drug screening

Personalized drug screening (PDS) of approved drug libraries enables rapid development of specific small-molecule therapies for individual patients. With a multidisciplinary team including clinicians, researchers, ethicists, informaticians and regulatory professionals, patient treatment can be optimized with greater efficacy and fewer adverse effects by using PDS as an approach to find remedies. In addition, PDS has the potential to rapidly identify therapeutics for a patient suffering from a disease without an existing therapy. From cancer to bacterial infections, we review specific maladies addressed with PDS campaigns. We predict that PDS combined with personal genomic analyses will contribute to the development of future precision medicine endeavors.

Optimization of the first small-molecule relaxin/insulin-like family peptide receptor (RXFP1) agonists: Activation results in an antifibrotic gene expression profile

A dose responsive quantitative high throughput screen (qHTS) of >350,000 compounds against a human relaxin/insulin-like family peptide receptor (RXFP1) transfected HEK293 cell line identified 2-acetamido-N-phenylbenzamides 1 and 3 with modest agonist activity. An extensive structure-activity study has been undertaken to optimize the potency, efficacy, and physical properties of the series, resulting in the identification of compound 65 (ML-290), which has excellent in vivo PK properties with high levels of systemic exposure. This series, exemplified by 65, has produced first-in-class small-molecule agonists of RXFP1 and is a potent activator of anti-fibrotic genes.

Identification of Positive Allosteric Modulators of the D1 Dopamine Receptor That Act at Diverse Binding Sites

The D₁ dopamine receptor is linked to a variety of neuropsychiatric disorders and represents an attractive drug target for the enhancement of cognition in schizophrenia, Alzheimer disease, and other disorders. Positive allosteric modulators (PAMs), with their potential for greater selectivity and larger therapeutic windows, may represent a viable drug development strategy, as orthosteric D₁ receptor agonists possess known clinical liabilities. We discovered two structurally distinct D₁ receptor PAMs, MLS6585 and MLS1082, via a high-throughput screen of the NIH Molecular Libraries program small-molecule library. Both compounds potentiate dopamine-stimulated G protein- and β-arrestin-mediated signaling and increase the affinity of dopamine for the D₁ receptor with low micromolar potencies. Neither compound displayed any intrinsic agonist activity. Both compounds were also found to potentiate the efficacy of partial agonists. We tested maximally effective concentrations of each PAM in combination to determine if the compounds might act at separate or similar sites. In combination, MLS1082 + MLS6585 produced an additive potentiation of dopamine potency beyond that caused by either PAM alone for both β-arrestin recruitment and cAMP accumulation, suggesting diverse sites of action. In addition, MLS6585, but not MLS1082, had additive activity with the previously described D₁ receptor PAM "Compound B," suggesting that MLS1082 and Compound B may share a common binding site. A point mutation (R130Q) in the D₁ receptor was found to abrogate MLS1082 activity without affecting that of MLS6585, suggesting this residue may be involved in the binding/activity of MLS1082 but not that of MLS6585. Together, MLS1082 and MLS6585 may serve as important tool compounds for the characterization of diverse allosteric sites on the D₁ receptor as well as the development of optimized lead compounds for therapeutic use.