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Here, we propose a broad concept of 'Clinical Outcome Pathways' (COPs), which are defined as a series of key molecular and cellular events that underlie therapeutic effects of drug molecules. We formalize COPs as a chain of the following events: molecular initiating event (MIE) → intermediate event(s) → clinical outcome. We illustrate the concept with COP examples both for primary and alternative (i.e., drug repurposing) therapeutic applications. We also describe the elucidation of COPs for several drugs of interest using the publicly accessible Reasoning Over Biomedical Objects linked in Knowledge-Oriented Pathways (ROBOKOP) biomedical knowledge graph-mining tool. We propose that broader use of COP uncovered with the help of biomedical knowledge graph mining will likely accelerate drug discovery and repurposing efforts.

Tyrosyl-DNA phosphodiesterase I (Tdp1) resolves topoisomerase I (Top1)-DNA adducts accumulated from natural DNA damage as well as from the action of certain anticancer drugs. Tdp1 catalyzes the hydrolysis of the phosphodiester bond between the catalytic tyrosine residue of topoisomerase I and the DNA 3′-phosphate. Only a limited number of weak inhibitors have been reported for Tdp1, and there is an unmet need to identify novel chemotypes through screening of chemical libraries. Herein, we present an easily configured, highly miniaturized, and robust Tdp1 assay using the AlphaScreen technology. Uninhibited enzyme reaction is associated with low signal, whereas inhibition leads to a gain of signal, making the present assay format especially attractive for automated large-collection high-throughput screening. We report the identification and initial characterization of four previously unreported inhibitors of Tdp1. Among them, suramin, NF449, and methyl-3,4-dephostatin are phosphotyrosine mimetics that may act as Tdp1 substrate decoys. We also report a novel biochemical assay using the SCAN1 Tdp1 mutant to study the mechanism of action of methyl-3,4-dephostatin. [Mol Cancer Ther 2009;8(1):240–8]

Supplementary Fig. S1 from Identification of phosphotyrosine mimetic inhibitors of human tyrosyl-DNA phosphodiesterase I by a novel AlphaScreen high-throughput assay

Supplemental Methods

Figure Legends for the Supplemental Data.

Supplementary Figure Legends 1-4 from Cardiac Glycosides Inhibit p53 Synthesis by a Mechanism Relieved by Src or MAPK Inhibition

Supplementary Figures 1-4 from Cardiac Glycosides Inhibit p53 Synthesis by a Mechanism Relieved by Src or MAPK Inhibition

Chronic lymphocytic leukemia (CLL) exhibits high remission rates after initial chemoimmunotherapy, but with relapses with treatment, refractory disease is the most common outcome, especially in CLL with the deletion of chromosome 11q or 17p. In addressing the need of treatments for relapsed disease, we report the identification of an existing U.S. Food and Drug Administration-approved small-molecule drug to repurpose for CLL treatment. Auranofin (Ridaura) is approved for use in treating rheumatoid arthritis, but it exhibited preclinical efficacy in CLL cells. By inhibiting thioredoxin reductase activity and increasing intracellular reactive oxygen species levels, auranofin induced a lethal endoplasmic reticulum stress response in cultured and primary CLL cells. In addition, auranofin displayed synergistic lethality with heme oxygenase-1 and glutamate-cysteine ligase inhibitors against CLL cells. Auranofin overcame apoptosis resistance mediated by protective stromal cells, and it also killed primary CLL cells with deletion of chromosome 11q or 17p. In TCL-1 transgenic mice, an in vivo model of CLL, auranofin treatment markedly reduced tumor cell burden and improved mouse survival. Our results provide a rationale to reposition the approved drug auranofin for clinical evaluation in the therapy of CLL. Cancer Res; 74(9); 2520–32. ©2014 AACR.

Supplemental Figure 1: Chemical structures of the molecules utilized in this study. Supplemental Table 1: Characteristics of the primary CLL cells utilized in these studies. Supplemental Figure 2: Apoptosis of CLL cells with AF and NAC. Supplemental Figure 3: AF effects on nurse-like cells (NLCs). Supplemental Table 2: Leukemia cell burden in TCL-1 mice pre and post treatment with AF. Supplemental Table 3: Gene expression changes in CLL cells after 8 hrs of AF treatment. Supplemental Table 4: Ingenuity Pathway Analysis of perturbed networks in Primary CLL cells after AF treatment. Supplemental Figure 4: IPA analysis of connected networks in CLL cells. Supplemental Figure 5: Synergy of AF with BSO or Zn-BG in CLL cells. Supplemental Figure 6: Hydrogen peroxide mediated effects on ROS, ER stress and apoptosis in CLL cells.

Clinical translation of human pluripotent stem cells (hPSCs) requires advanced strategies that ensure safe and robust long-term growth and functional differentiation. Pluripotent cells are capable of extensive self-renewal, yet remain highly sensitive to environmental perturbations in vitro, posing challenges to their therapeutic use. Here, we deployed innovative high-throughput screening strategies to identify a small molecule cocktail that dramatically improves viability of hPSCs and their differentiated progeny. The combination of Chroman 1, Emricasan, Polyamines, and Trans-ISRIB (CEPT) enhanced cell survival of genetically stable hPSCs by simultaneously blocking several stress mechanisms that otherwise compromise cell structure and function. CEPT provided strong improvements for several key applications in stem cell research, including routine cell passaging, cryopreservation of pluripotent and differentiated cells, embryoid body (EB) and organoid formation, single-cell cloning, and genome editing. Thus, CEPT represents a unique polypharmacology strategy for comprehensive cytoprotection, providing a new rationale for efficient and safe utilization of hPSCs. Conferring cell fitness by multi-target drug combinations may become a common approach in cryobiology, drug development, and regenerative medicine.

Personalized medicine remains an aspirational future state for the majority of diseases, but is the present state for rare diseases. As a result, the principles, practices, potential, and perils of personalized medicine are currently being realized in rare disease medicine, and we can look through the lens of rare disease at present and see the future for common disease personalized medicine. Personalized medicine endeavours to use in-depth genetic and environmental data to define a genotype–phenotype pairing that is based in definable physiological dysfunction and predicts treatment with a high likelihood of positive response. Rare diseases are defined and managed in just this way today. The experience of rare diseases suggests both exhilarating medical promise, and sobering operational and ethical realities, for personalized medicine of common diseases. Rare disease/personalized medicine can be just as effective as anticipated. But current medical systems, drug development, treatment, and payment paradigms are not well-suited to personalized medicine, and all will require adaptations for its promise to be realized.

The 11th International Conference on Rare Diseases and Orphan Drugs (ICORD), South Africa, included the Africa-Rare initiative launch and facilitated multi-stakeholder engagement in the challenges facing, and opportunities for, Africans living with rare diseases. The following ICORD Global Call to Action, developed in collaboration with the International Rare Diseases Research Consortium, synthesizes the outcomes of the deliberations and emphasizes the international collaborative efforts required to address the global effects of rare diseases on public health.

The Foundation for the National Institutes of Health (FNIH) Biomarkers Consortium (BC) is a public-private partnership that aims to facilitate drug development with biomarkers across a range of therapeutic areas. The BC is organized to address specific precompetitive biomarker projects, giving participating stakeholders a role in the design and conduct of projects and making the results freely public. Ultimately, the goals of the BC are to accelerate the development of new medicines, inform regulatory decision making, and improve patient care. Here, we describe how the BC works and briefly highlight its accomplishments. The BC has had many notable successful biomarker projects in the past 12 years, including I-SPY2, which has improved clinical trials and biomarker use for breast cancer, and an evidentiary framework for biomarker qualification. Recently, the BC has undergone a strategic expansion of its scope to include related drug development tools along the lines of the Biomarkers, Endpoints, and other Tools (BEST) resource.

The recent global pandemic caused by the new coronavirus SARS-CoV-2 presents an urgent need for new therapeutic candidates. While the importance of traditional in silico approaches such as QSAR in such efforts in unquestionable, these models fundamentally rely on structural similarity to infer biological activity and are thus prone to becoming trapped in the very nearby chemical spaces of already known ligands. For novel and unprecedented threats such as COVID-19 much faster and efficient paradigms must be devised to accelerate the identification of new chemical classes for rapid drug development. Here we report the development of a new biological activity-based modeling (BABM) approach that builds on the hypothesis that compounds with similar activity patterns tend to share similar targets or mechanisms of action. In BABM, compound activity profiles established on massive scale across multiple assays are used as signatures to predict compound activity in a new assay or against a new target. We first trained and validated this approach by identifying new antiviral lead candidates for Zika and Ebola based on data from ~0.5 million compounds screened against ~2,000 assays. BABM models were then applied to predict ~300 compounds not previously reported to have activity for SARS-CoV-2, which were then tested in a live virus assay with high (>30%) hit rates. The most potent compounds showed antiviral activities in the nanomolar range. These potent confirmed compounds have the potential to be further developed in novel chemical space into new anti-SARS-CoV-2 therapies. These results demonstrate unprecedented ability using BABM to predict novel structures as chemical leads significantly beyond traditional methods, and its application in rapid drug discovery response in a global public health crisis.

BackgroundThe majority of U.S. reports of COVID-19 clinical characteristics, disease course, and treatments are from single health systems or focused on one domain. Here we report the creation of the National COVID Cohort Collaborative (N3C), a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative U.S. cohort of COVID-19 cases and controls to date. This multi-center dataset supports robust evidence-based development of predictive and diagnostic tools and informs critical care and policy.Methods and FindingsIn a retrospective cohort study of 1,926,526 patients from 34 medical centers nationwide, we stratified patients using a World Health Organization COVID-19 severity scale and demographics; we then evaluated differences between groups over time using multivariable logistic regression. We established vital signs and laboratory values among COVID-19 patients with different severities, providing the foundation for predictive analytics. The cohort included 174,568 adults with severe acute respiratory syndrome associated with SARS-CoV-2 (PCR >99% or antigen ConclusionsThis is the first description of an ongoing longitudinal observational study of patients seen in diverse clinical settings and geographical regions and is the largest COVID-19 cohort in the United States. Such data are the foundation for ML models that can be the basis for generalizable clinical decision support tools. The N3C Data Enclave is unique in providing transparent, reproducible, easily shared, versioned, and fully auditable data and analytic provenance for national-scale patient-level EHR data. The N3C is built for intensive ML analyses by academic, industry, and citizen scientists internationally. Many observational correlations can inform trial designs and care guidelines for this new disease.

Altres ajuts: This work and the IRDiRC Scientific Secretariat are supported by the European FP7 contract, "SUPPORT-IRDiRC" (No 305207). H.L. receives funding from the European Commission under FP7 through NeurOmics (No 305121) and RD-Connect (No 305444). C.P.A. contributed to this work in his capacity as Chair of the IRDiRC Consortium Assembly, not as Director of the NCATS. The remaining authors declare that they have no competing interests. The International Rare Diseases Research Consortium (IRDiRC) has agreed on IRDiRC Policies and Guidelines, following extensive deliberations and discussions in 2012 and 2013, as a first step towards improving coordination of research efforts worldwide. The 25 funding members and 3 patient umbrella organizations (as of early 2013) of IRDiRC, a consortium of research funders that focuses on improving diagnosis and therapy for rare disease patients, agreed in Dublin, Ireland in April 2013 on the Policies and Guidelines that emphasize collaboration in rare disease research, the involvement of patients and their representatives in all relevant aspects of research, as well as the sharing of data and resources. The Policies and Guidelines provide guidance on ontologies, diagnostics, biomarkers, patient registries, biobanks, natural history, therapeutics, models, publication, intellectual property, and communication. Most IRDiRC members-currently nearly 50 strong-have since incorporated its policies in their funding calls and some have chosen to exceed the requirements laid out, for instance in relation to data sharing. The IRDiRC Policies and Guidelines are the first, detailed agreement of major public and private funding organizations worldwide to govern rare disease research, and may serve as a template for other areas of international research collaboration. While it is too early to assess their full impact on research productivity and patient benefit, the IRDiRC Policies and Guidelines have already contributed significantly to improving transparency and collaboration in rare disease research.

The NIH Virtual SARS-CoV-2 Antiviral Summit, held on 6 November 2020, was organized to provide an overview on the status and challenges in developing antiviral therapeutics for coronavirus disease 2019 (COVID-19), including combinations of antivirals. Scientific experts from the public and private sectors convened virtually during a live videocast to discuss severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) targets for drug discovery as well as the preclinical tools needed to develop and evaluate effective small-molecule antivirals. The goals of the Summit were to review the current state of the science, identify unmet research needs, share insights and lessons learned from treating other infectious diseases, identify opportunities for public-private partnerships, and assist the research community in designing and developing antiviral therapeutics. This report includes an overview of therapeutic approaches, individual panel summaries, and a summary of the discussions and perspectives on the challenges ahead for antiviral development.

A challenge not only to our ability to carry on vital research despite lab closures, but also to our capacity to respond to the urgent research needs of COVID-19 itself – its diagnosis, characterization, treatment, prevention, and sequelae, while at the same time supporting the increase clinical needs as well as enhanced public health response. Leaders of the CTSA hubs across the country managed and directed critical COVID-19 response efforts at their institutions and quickly shared their experiences, observations, and best practices on medical and operational adaptations to the pandemic through a network-wide communication mechanism established by the CTSA Center for Leading Innovation and Collaboration (CLIC). Clinical trials were initiated rapidly, many of which were facilitated by accelerated IRB review that was made possible by SMART IRB and IREx. [...]in perhaps, the most ambitious EHR project ever undertaken, COVID-19 patient data were rapidly collected, harmonized, organized, and made available in a secure environment in just a few months, involving over 1000 people from the NCATS’ informative program, the Center for Data to Health (CD2H), virtually every CTSA hub, many Institutional Development Award Network for Clinical and Translational Research institutions/organizations (IDeA-CTRs), and NCATS.

[Image: see text] Since 2009, the Tox21 project has screened ∼8500 chemicals in more than 70 high-throughput assays, generating upward of 100 million data points, with all data publicly available through partner websites at the United States Environmental Protection Agency (EPA), National Center for Advancing Translational Sciences (NCATS), and National Toxicology Program (NTP). Underpinning this public effort is the largest compound library ever constructed specifically for improving understanding of the chemical basis of toxicity across research and regulatory domains. Each Tox21 federal partner brought specialized resources and capabilities to the partnership, including three approximately equal-sized compound libraries. All Tox21 data generated to date have resulted from a confluence of ideas, technologies, and expertise used to design, screen, and analyze the Tox21 10K library. The different programmatic objectives of the partners led to three distinct, overlapping compound libraries that, when combined, not only covered a diversity of chemical structures, use-categories, and properties but also incorporated many types of compound replicates. The history of development of the Tox21 “10K” chemical library and data workflows implemented to ensure quality chemical annotations and allow for various reproducibility assessments are described. Cheminformatics profiling demonstrates how the three partner libraries complement one another to expand the reach of each individual library, as reflected in coverage of regulatory lists, predicted toxicity end points, and physicochemical properties. ToxPrint chemotypes (CTs) and enrichment approaches further demonstrate how the combined partner libraries amplify structure–activity patterns that would otherwise not be detected. Finally, CT enrichments are used to probe global patterns of activity in combined ToxCast and Tox21 activity data sets relative to test-set size and chemical versus biological end point diversity, illustrating the power of CT approaches to discern patterns in chemical–activity data sets. These results support a central premise of the Tox21 program: A collaborative merging of programmatically distinct compound libraries would yield greater rewards than could be achieved separately.

Organs-on-chips (OoCs), also known as microphysiological systems or ‘tissue chips’ (the terms are synonymous), have attracted substantial interest in recent years owing to their potential to be informative at multiple stages of the drug discovery and development process. These innovative devices could provide insights into normal human organ function and disease pathophysiology, as well as more accurately predict the safety and efficacy of investigational drugs in humans. Therefore, they are likely to become useful additions to traditional preclinical cell culture methods and in vivo animal studies in the near term, and in some cases replacements for them in the longer term. In the past decade, the OoC field has seen dramatic advances in the sophistication of biology and engineering, in the demonstration of physiological relevance and in the range of applications. These advances have also revealed new challenges and opportunities, and expertise from multiple biomedical and engineering fields will be needed to fully realize the promise of OoCs for fundamental and translational applications. This Review provides a snapshot of this fast-evolving technology, discusses current applications and caveats for their implementation, and offers suggestions for directions in the next decade. Organs-on-chips (OoCs) could be useful at various stages of drug discovery and development, providing insight regarding human organ physiology in both normal and disease contexts, as well as accurately predicting developmental drug safety and efficacy. This Review discusses the advances that have enabled OoCs to demonstrate physiological relevance, and the challenges and opportunities that need to be tackled to tap the full potential of OoC utility for translational research.

Objective COVID-19 poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. Methods The Clinical and Translational Science Award (CTSA) Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. Organized in inclusive workstreams, in two months we created: legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. Discussion The N3C has demonstrated that a multi-site collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multi-organizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19. LAY SUMMARY COVID-19 poses societal challenges that require expeditious data and knowledge sharing. Though medical records are abundant, they are largely inaccessible to outside researchers. Statistical, machine learning, and causal research are most successful with large datasets beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many clinical centers to reveal patterns in COVID-19 patients. To create N3C, the community had to overcome technical, regulatory, policy, and governance barriers to sharing patient-level clinical data. In less than 2 months, we developed solutions to acquire and harmonize data across organizations and created a secure data environment to enable transparent and reproducible collaborative research. We expect the N3C to help save lives by enabling collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care needs and thereby reduce the immediate and long-term impacts of COVID-19.

Most rare diseases still lack approved treatments despite major advances in research providing the tools to understand their molecular basis, as well as legislation providing regulatory and economic incentives to catalyse the development of specific therapies. Addressing this translational gap is a multifaceted challenge, for which a key aspect is the selection of the optimal therapeutic modality for translating advances in rare disease knowledge into potential medicines, known as orphan drugs. With this in mind, we discuss here the technological basis and rare disease applicability of the main therapeutic modalities, including small molecules, monoclonal antibodies, protein replacement therapies, oligonucleotides and gene and cell therapies, as well as drug repurposing. For each modality, we consider its strengths and limitations as a platform for rare disease therapy development and describe clinical progress so far in developing drugs based on it. We also discuss selected overarching topics in the development of therapies for rare diseases, such as approval statistics, engagement of patients in the process, regulatory pathways and digital tools.

In recent years, artificial intelligence (AI)/machine learning (ML; a subset of AI) have become increasingly important to the biomedical research community. These technologies, coupled to big data and cheminformatics, have tremendous potential to improve the design of novel therapeutics and to provide safe and effective drugs to patients. A National Center for Advancing Translational Sciences (NCATS) program called A Specialized Platform for Innovative Research Exploration (ASPIRE) leverages advances in AI/ML, automated synthetic chemistry, and high-throughput biology, and seeks to enable translation and drug development by catalyzing exploration of biologically active chemical space. Here we discuss the opportunities and challenges surrounding the application of AI/ML to the exploration of novel biologically relevant chemical space as part of ASPIRE.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Failures in the current paradigm for drug development have resulted in soaring research and development costs and reduced numbers of new drug approvals. Over 90% of new drug programs fail, the majority terminated at the level of Phase 2/3 clinical trials, largely because of efficacy failures or unexplained toxicity. A recent workshop brought together members from research institutions, regulatory agencies, industry, academia, and nongovernmental organizations to discuss how existing programs could be better applied to understanding human biology and improving drug discovery. Recommendations include increased emphasis on human relevance, better access and curation of data, and improved interdisciplinary and international collaboration.

Drug repurposing is an effective approach to identify active drugs with known toxicity profiles for rare cancers such as ACC. The objective of this study was to determine the anticancer activity of combination treatment for ACC from previously identified candidate agents using quantitative high-throughput screening (qHTS). In this study, we evaluated the anticancer activity of flavopiridol and carfilzomib in three ACC cell lines in vitro and in vivo. Human ACC samples were analyzed for drug-target analysis, and cancer-related pathway arrays were used to identify biomarkers of treatment response. Because flavopiridol is a potent cyclin-dependent kinase (CDK) inhibitor, we found significantly higher CDK1 and CDK2 mRNA expression in three independent cohorts human ACC (p

The Assay Guidance Manual (AGM) is an eBook of best practices for the design, development, and implementation of robust assays for early drug discovery. Initiated by pharmaceutical company scientists, the manual provides guidance for designing a "testing funnel" of assays to identify genuine hits using high-throughput screening (HTS) and advancing them through preclinical development. Combined with a workshop/tutorial component, the overall goal of the AGM is to provide a valuable resource for training translational scientists.

In vitro assay data have recently emerged as a potential alternative to traditional animal toxicity studies to aid in the prediction of adverse effects of chemicals on humans. Here we evaluate the data generated from a battery of quantitative high-throughput screening (qHTS) assays applied to a large and diverse collection of chemicals, including approved drugs, for their capacity in predicting human toxicity. Models were built with animal in vivo toxicity data, in vitro human cell-based assay data, as well as in combination with chemical structure and/or drug-target information to predict adverse effects observed for drugs in humans. Interestingly, we found that the models built with the human cell-based assay data performed close to those of the models based on animal in vivo toxicity data. Furthermore, expanding the biological space coverage of assays by including additional drug-target annotations was shown to significantly improve model performance. We identified a small set of targets, which, when added to the current suite of in vitro human cell-based assay data, result in models that greatly outperform those built with the existing animal toxicity data. Assays can be developed for this set of targets to screen compounds for construction of robust models for human toxicity prediction.