Novel Phenotypic Outcomes Identified for a Public Collection of Approved Drugs from a Publicly Accessible Panel of Assays

Finding new analgesics: Computational pharmacology faces drug discovery challenges

Despite the worldwide prevalence and huge burden of pain, pain is an undertreated phenomenon. Currently used analgesics have several limitations regarding their efficacy and safety. The discovery of analgesics possessing a novel mechanism of action has faced multiple challenges, including a limited understanding of biological processes underpinning pain and analgesia and poor animal-to-human translation. Computational pharmacology is currently employed to face these challenges. In this review, we discuss the theory, methods, and applications of computational pharmacology in pain research. Computational pharmacology encompasses a wide variety of theoretical concepts and practical methodological approaches, with the overall aim of gaining biological insight through data acquisition and analysis. Data are acquired from patients or animal models with pain or analgesic treatment, at different levels of biological organization (molecular, cellular, physiological, and behavioral). Distinct methodological algorithms can then be used to analyze and integrate data. This helps to facilitate the identification of biological molecules and processes associated with pain phenotype, build quantitative models of pain signaling, and extract translatable features between humans and animals. However, computational pharmacology has several limitations, and its predictions can provide false positive and negative findings. Therefore, computational predictions are required to be validated experimentally before drawing solid conclusions. In this review, we discuss several case study examples of combining and integrating computational tools with experimental pain research tools to meet drug discovery challenges.

Novel Transmission-Blocking Antimalarials Identified by High-Throughput Screening of Plasmodium berghei Ookluc

Safe and effective malaria transmission-blocking chemotherapeutics would allow a community-level approach to malaria control and eradication efforts by targeting the mosquito sexual stage of the parasite life cycle. However, only a single drug, primaquine, is currently approved for use in reducing transmission, and drug toxicity limits its widespread implementation. To address this limitation in antimalarial chemotherapeutics, we used a recently developed transgenic Plasmodium berghei line, Ookluc, to perform a series of high-throughput in vitro screens for compounds that inhibit parasite fertilization, the initial step of parasite development within the mosquito. Screens of antimalarial compounds, approved drug collections, and drug-like molecule libraries identified 185 compounds that inhibit parasite maturation to the zygote form. Seven compounds were further characterized to block gametocyte activation or to be cytotoxic to formed zygotes. These were further validated in mosquito membrane-feeding assays using Plasmodium falciparum and P. vivax. This work demonstrates that high-throughput screens using the Ookluc line can identify compounds that are active against the two most relevant human Plasmodium species and provides a list of compounds that can be explored for the development of new antimalarials to block transmission.

Open Innovation in Medical and Pharmaceutical Research: A Literature Landscape Analysis

Open innovation in medical and pharmaceutical research has grown steadily over the last decade. However, the performance of the published literature in terms of the scientific impact and gaining social media attention remains largely unexplored. The scientific literature of open innovation was examined by means of bibliometric analyses to identify the most prolific authors, organizations, countries, journals, research areas, and recurring terms. By accessing the Web of Science Core Collection and Altmetric electronic databases, citation-related and Altmetric data were evaluated. Public-private partnerships and a selection of newly introduced potential novel drugs in the analyzed publications were identified. North America and Europe were the major literature contributors. Research outputs were mainly published in journals focused on business and economics, pharmacology and pharmacy, and engineering. Many pharmaceutical and biotechnological companies contributed to the analyzed publications, with higher mean citation counts and social media attention (Altmetric score) than nonindustry articles. Public-private partnerships fostered financial support, sharing of expertise and intellectual property, and research collaborations. In summary, open innovation might serve as a powerful strategy to both benefit the involved industry entities and accelerate the development of solutions and products for the betterment of human health.

A High-Throughput Assay for the Pancreatic Islet Beta-Cell Potassium Channel: Use in the Pharmacological Characterization of Insulin Secretagogues Identified from Phenotypic Screening

Phenotypic screening is a neoclassical approach for drug discovery. We conducted phenotypic screening for insulin secretion enhancing agents using INS-1E insulinoma cells as a model system for pancreatic beta-cells. A principal regulator of insulin secretion in beta-cells is the metabolically regulated potassium channel Kir6.2/SUR1 complex. To characterize hit compounds, we developed an assay to quantify endogenous potassium channel activity in INS-1E cells. We quantified ligand-regulated potassium channel activity in INS-1E cells using fluorescence imaging and thallium flux. Potassium channel activity was metabolically regulated and coupled to insulin secretion. The pharmacology of channel opening agents (diazoxide) and closing agents (sulfonylureas) was used to validate the applicability of the assay. A precise high-throughput assay was enabled, and phenotypic screening hits were triaged to enable a higher likelihood of discovering chemical matter with novel and useful mechanisms of action.

Challenges and Benefits of Repurposing Products for Use during a Radiation Public Health Emergency: Lessons Learned from Biological Threats and other Disease Treatments

The risk of a radiological or nuclear public health emergency is a major growing concern of the U.S. government. To address a potential incident and ensure that the government is prepared to respond to any subsequent civilian or military casualties, the U.S. Department of Health and Human Services and the Department of Defense have been charged with the development of medical countermeasures (MCMs) to treat the acute and delayed injuries that can result from radiation exposure. Because of the limited budgets in research and development and the high costs associated with bring promising approaches from the bench through advanced product development activities, and ultimately, to regulatory approval, the U.S. government places a priority on repurposing products for which there already exists relevant safety and other important information concerning their use in humans. Generating human data can be a costly and time-consuming process; therefore, the U.S. government has interest in drugs for which such relevant information has been established (e.g., products for another indication), and in determining if they could be repurposed for use as MCMs to treat radiation injuries as well as chemical and biological insults. To explore these possibilities, the National Institute of Allergy and Infectious Diseases (NIAID) convened a workshop including U.S. government, industry and academic subject matter experts, to discuss the challenges and benefits of repurposing products for a radiation indication. Topics covered included a discussion of U.S. government efforts (e.g. funding, stockpiling and making products available for study), as well unique regulatory and other challenges faced when repurposing patent protected or generic drugs. Other discussions involved lessons learned from industry on repurposing pre-license, pipeline products within drug development portfolios. This report reviews the information presented, as well as an overview of discussions from the meeting.

Novel approaches to develop community-built biological network models for potential drug discovery

INTRODUCTION

Hundreds of thousands of data points are now routinely generated in clinical trials by molecular profiling and NGS technologies. A true translation of this data into knowledge is not possible without analysis and interpretation in a well-defined biology context. Currently, there are many public and commercial pathway tools and network models that can facilitate such analysis. At the same time, insights and knowledge that can be gained is highly dependent on the underlying biological content of these resources. Crowdsourcing can be employed to guarantee the accuracy and transparency of the biological content underlining the tools used to interpret rich molecular data. Areas covered: In this review, the authors describe crowdsourcing in drug discovery. The focal point is the efforts that have successfully used the crowdsourcing approach to verify and augment pathway tools and biological network models. Technologies that enable the building of biological networks with the community are also described. Expert opinion: A crowd of experts can be leveraged for the entire development process of biological network models, from ontologies to the evaluation of their mechanistic completeness. The ultimate goal is to facilitate biomarker discovery and personalized medicine by mechanistically explaining patients' differences with respect to disease prevention, diagnosis, and therapy outcome.

In vivo phenotypic screening: clinical proof of concept for a drug repositioning approach

In vivo phenotypic screening and drug repositioning are strategies developed as alternatives to underperforming hypothesis-driven molecular target based drug discovery efforts. This article reviews examples of drugs identified by phenotypic observations and describes the use of the theraTRACE^®in vivo screening platform for finding and developing new indications for discontinued clinical compounds. Clinical proof-of-concept for the platform is exemplified by MLR-1023, a repositioned compound that has recently shown significant clinical efficacy in Type 2 diabetes patients. These findings validate an in vivo screening approach for drug development and underscore the importance of alternatives to target and mechanism based strategies that have failed to produce adequate numbers of new medicines.

Empowering pharmacoinformatics by linked life science data

With the public availability of large data sources such as ChEMBLdb and the Open PHACTS Discovery Platform, retrieval of data sets for certain protein targets of interest with consistent assay conditions is no longer a time consuming process. Especially the use of workflow engines such as KNIME or Pipeline Pilot allows complex queries and enables to simultaneously search for several targets. Data can then directly be used as input to various ligand- and structure-based studies. In this contribution, using in-house projects on P-gp inhibition, transporter selectivity, and TRPV1 modulation we outline how the incorporation of linked life science data in the daily execution of projects allowed to expand our approaches from conventional Hansch analysis to complex, integrated multilayer models.

Combine and conquer: challenges for targeted therapy combinations in early phase trials

Our increasing understanding of cancer biology has led to the development of molecularly targeted anticancer drugs. The full potential of these agents has not, however, been realised, owing to the presence of de novo (intrinsic) resistance, often resulting from compensatory signalling pathways, or the development of acquired resistance in cancer cells via clonal evolution under the selective pressures of treatment. Combinations of targeted treatments can circumvent some mechanisms of resistance to yield a clinical benefit. We explore the challenges in identifying the best drug combinations and the best combination strategies, as well as the complexities of delivering these treatments to patients. Recognizing treatment-induced toxicity and the inability to use continuous pharmacodynamically effective doses of many targeted treatments necessitates creative intermittent scheduling. Serial tumour profiling and the use of parallel co-clinical trials can contribute to understanding mechanisms of resistance, and will guide the development of adaptive clinical trial designs that can accommodate hypothesis testing, in order to realize the full potential of combination therapies.

A concise review on advances in development of small molecule anti-inflammatory therapeutics emphasising AMPK: An emerging target

Inflammatory diseases are complex, multi-factorial outcomes of evolutionarily conserved tissue repair processes. For decades, non-steroidal anti-inflammatory drugs and cyclooxygenase inhibitors, the primary drugs of choice for the management of inflammatory diseases, addressed individual targets in the arachidonic acid pathway. Unsatisfactory safety and efficacy profiles of the above have necessitated the development of multi-target agents to treat complex inflammatory diseases. Current anti-inflammatory therapies still fall short of clinical needs and the clinical trial results of multi-target therapeutics are anticipated. Additionally, new drug targets are emerging with improved understanding of molecular mechanisms controlling the pathophysiology of inflammation. This review presents an outline of small molecules and drug targets in anti-inflammatory therapeutics with a summary of a newly identified target AMP-activated protein kinase, which constitutes a novel therapeutic pathway in inflammatory pathology.

Translating Stem Cell Biology Into Drug Discovery

Pluripotent stem cell research has made extraordinary progress over the last decade. The robustness of nuclear reprogramming of somatic cells into induced pluripotent stem cells (iPSCs) has created entirely novel opportunities for drug discovery and personalized regenerative medicine. Patient- and disease-specific iPSCs can be expanded indefinitely and differentiated into relevant cell types of different organ systems. As the utilization of iPSCs is becoming a key enabling technology across various scientific disciplines, there are still important challenges that need to be addressed. Here we review the current state and reflect on the issues that the stem cell and translational communities are facing in bringing iPSCs closer to clinical application.

Open PHACTS computational protocols for in silico target validation of cellular phenotypic screens: knowing the knowns

Phenotypic screening is in a renaissance phase and is expected by many academic and industry leaders to accelerate the discovery of new drugs for new biology. Given that phenotypic screening is per definition target agnostic, the emphasis of in silico and in vitro follow-up work is on the exploration of possible molecular mechanisms and efficacy targets underlying the biological processes interrogated by the phenotypic screening experiments. Herein, we present six exemplar computational protocols for the interpretation of cellular phenotypic screens based on the integration of compound, target, pathway, and disease data established by the IMI Open PHACTS project. The protocols annotate phenotypic hit lists and allow follow-up experiments and mechanistic conclusions. The annotations included are from ChEMBL, ChEBI, GO, WikiPathways and DisGeNET. Also provided are protocols which select from the IUPHAR/BPS Guide to PHARMACOLOGY interaction file selective compounds to probe potential targets and a correlation robot which systematically aims to identify an overlap of active compounds in both the phenotypic as well as any kinase assay. The protocols are applied to a phenotypic pre-lamin A/C splicing assay selected from the ChEMBL database to illustrate the process. The computational protocols make use of the Open PHACTS API and data and are built within the Pipeline Pilot and KNIME workflow tools.

In silico methods for drug repurposing and pharmacology

Data in the biological, chemical, and clinical domains are accumulating at ever-increasing rates and have the potential to accelerate and inform drug development in new ways. Challenges and opportunities now lie in developing analytic tools to transform these often complex and heterogeneous data into testable hypotheses and actionable insights. This is the aim of computational pharmacology, which uses in silico techniques to better understand and predict how drugs affect biological systems, which can in turn improve clinical use, avoid unwanted side effects, and guide selection and development of better treatments. One exciting application of computational pharmacology is drug repurposing-finding new uses for existing drugs. Already yielding many promising candidates, this strategy has the potential to improve the efficiency of the drug development process and reach patient populations with previously unmet needs such as those with rare diseases. While current techniques in computational pharmacology and drug repurposing often focus on just a single data modality such as gene expression or drug-target interactions, we argue that methods such as matrix factorization that can integrate data within and across diverse data types have the potential to improve predictive performance and provide a fuller picture of a drug's pharmacological action. WIREs Syst Biol Med 2016, 8:186-210. doi: 10.1002/wsbm.1337 For further resources related to this article, please visit the WIREs website.

The development of high-content screening (HCS) technology and its importance to drug discovery

INTRODUCTION

High-content screening (HCS) was introduced about twenty years ago as a promising analytical approach to facilitate some critical aspects of drug discovery. Its application has spread progressively within the pharmaceutical industry and academia to the point that it today represents a fundamental tool in supporting drug discovery and development.

AREAS COVERED

Here, the authors review some of significant progress in the HCS field in terms of biological models and assay readouts. They highlight the importance of high-content screening in drug discovery, as testified by its numerous applications in a variety of therapeutic areas: oncology, infective diseases, cardiovascular and neurodegenerative diseases. They also dissect the role of HCS technology in different phases of the drug discovery pipeline: target identification, primary compound screening, secondary assays, mechanism of action studies and in vitro toxicology.

EXPERT OPINION

Recent advances in cellular assay technologies, such as the introduction of three-dimensional (3D) cultures, induced pluripotent stem cells (iPSCs) and genome editing technologies (e.g., CRISPR/Cas9), have tremendously expanded the potential of high-content assays to contribute to the drug discovery process. Increasingly predictive cellular models and readouts, together with the development of more sophisticated and affordable HCS readers, will further consolidate the role of HCS technology in drug discovery.

ARTIK-52 induces replication-dependent DNA damage and p53 activation exclusively in cells of prostate and breast cancer origin

The realization, that the androgen receptor (AR) is essential for prostate cancer (PC) even after relapse following androgen deprivation therapy motivated the search for novel types of AR inhibitors. We proposed that targeting AR expression versus its function would work in cells having either wild type or mutant AR as well as be independent of androgen synthesis pathways. Previously, using a phenotypic screen in androgen-independent PC cells we identified a small molecule inhibitor of AR, ARTIK-52. Treatment with ARTIK-52 caused the loss of AR protein and death of AR-positive, but not AR-negative, PC cells. Here we present data that ARTIK-52 induces degradation of AR mRNA through a mechanism that we were unable to establish. However, we found that ARTIK-52 is toxic to breast cancer (BC) cells expressing AR, although they were not sensitive to AR knockdown, suggesting an AR-independent mechanism of toxicity. Using different approaches we detected that ARTIK-52 induces replication-dependent double strand DNA breaks exclusively in cancer cells of prostate and breast origin, while not causing DNA damage, or any toxicity, in normal cells, as well as in non-PC and non-BC tumor cells, independent of their proliferation status. This amazing specificity, combined with such a basic mechanism of toxicity, makes ARTIK-52 a potentially useful tool to discover novel attractive targets for the treatment of BC and PC. Thus, phenotypic screening allowed us to identify a compound, whose properties cannot be predicted based on existing knowledge and moreover, uncover a barely known link between AR and DNA damage response in PC and BC epithelial cells.