Open access high throughput drug discovery in the public domain: a Mount Everest in the making

In Vivo Acute Toxicity Studies of Novel Anti-Melanoma Compounds Downregulators of hnRNPH1/H2

Despite the recent advances in melanoma therapy, the need for new targets and novel approaches to therapy is urgent. We previously reported melanoma actives that work via binding and downregulating spliceosomal proteins hnRNPH1 and H2. Given the lack of knowledge about the side effects of using spliceosomal binders in humans, an acute toxicity study was conducted to evaluate these compounds in mice. Male and female mice were treated with compounds 2155-14 and 2155-18 at 50 mg/kg/day via subcutaneous injections, and the clinical signs of distress were monitored for 21 days and compared with control mice. Additionally, the effect of the leads on blood chemistry, blood cell counts, and organs was evaluated. No significant changes were observed in the body weight, blood cell count, blood chemistry, or organs of the mice following the compound treatment. The results show that our compounds, 2155-14 and 2155-18, are not toxic for the study period of three weeks.

Imitating Hypoxia and Tumor Microenvironment with Immune Evasion by Employing Three Dimensional in vitro Cellular Models: Impressive Tool in Drug Discovery

The heterogeneous tumor microenvironment is exceptionally perplexing and not wholly comprehended. Different multifaceted alignments lead to the generation of oxygen destitute situations within the tumor niche that modulate numerous intrinsic tumor microenvironments. Disentangling these communications is vital for scheming practical therapeutic approaches that can successfully decrease tumor allied chemotherapy resistance by utilizing the innate capability of the immune system. Several research groups have concerned with a protruding role for oxygen metabolism along with hypoxia in the immunity of healthy tissue. Hypoxia in addition to hypoxia-inducible factors (HIFs) in the tumor microenvironment plays an important part in tumor progression and endurance. Although numerous hypoxia-focused therapies have shown promising outcomes both in vitro and in vivo these outcomes have not effectively translated into clinical preliminaries. Distinctive cell culture techniques have utilized as an in vitro model for tumor niche along with tumor microenvironment and proficient in more precisely recreating tumor genomic profiles as well as envisaging therapeutic response. To study the dynamics of tumor immune evasion, three-dimensional (3D) cell cultures are more physiologically important to the hypoxic tumor microenvironment. Recent research has revealed new information and insights into our fundamental understanding of immune systems, as well as novel results that have been established as potential therapeutic targets. There are a lot of patented 3D cell culture techniques which will be highlighted in this review. At present notable 3D cell culture procedures in the hypoxic tumor microenvironment, discourse open doors to accommodate both drug repurposing, advancement, and divulgence of new medications and will deliberate the 3D cell culture methods into standard prescription disclosure especially in the field of cancer biology which will be discussing here.

Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery

The accumulation of massive data in the plethora of Cheminformatics databases has made the role of big data and artificial intelligence (AI) indispensable in drug design. This has necessitated the development of newer algorithms and architectures to mine these databases and fulfil the specific needs of various drug discovery processes such as virtual drug screening, de novo molecule design and discovery in this big data era. The development of deep learning neural networks and their variants with the corresponding increase in chemical data has resulted in a paradigm shift in information mining pertaining to the chemical space. The present review summarizes the role of big data and AI techniques currently being implemented to satisfy the ever-increasing research demands in drug discovery pipelines.

Chemical Screening of Nuclear Receptor Modulators

Nuclear receptors are ligand-inducible transcriptional factors that control multiple biological phenomena, including proliferation, differentiation, reproduction, metabolism, and the maintenance of homeostasis. Members of the nuclear receptor superfamily have marked structural and functional similarities, and their domain functionalities and regulatory mechanisms have been well studied. Various modulators of nuclear receptors, including agonists and antagonists, have been developed as tools for elucidating nuclear receptor functions and also as drug candidates or lead compounds. Many assay systems are currently available to evaluate the modulation of nuclear receptor functions, and are useful as screening tools in the discovery and development of new modulators. In this review, we cover the chemical screening methods for nuclear receptor modulators, focusing on assay methods and chemical libraries for screening. We include some recent examples of the discovery of nuclear receptor modulators.

Natural Products as Modulators of Sirtuins

Natural products have been used for the treatment of human diseases since ancient history. Over time, due to the lack of precise tools and techniques for the separation, purification, and structural elucidation of active constituents in natural resources there has been a decline in financial support and efforts in characterization of natural products. Advances in the design of chemical compounds and the understanding of their functions is of pharmacological importance for the biomedical field. However, natural products regained attention as sources of novel drug candidates upon recent developments and progress in technology. Natural compounds were shown to bear an inherent ability to bind to biomacromolecules and cover an unparalleled chemical space in comparison to most libraries used for high-throughput screening. Thus, natural products hold a great potential for the drug discovery of new scaffolds for therapeutic targets such as sirtuins. Sirtuins are Class III histone deacetylases that have been linked to many diseases such as Parkinson`s disease, Alzheimer’s disease, type II diabetes, and cancer linked to aging. In this review, we examine the revitalization of interest in natural products for drug discovery and discuss natural product modulators of sirtuins that could serve as a starting point for the development of isoform selective and highly potent drug-like compounds, as well as the potential application of naturally occurring sirtuin inhibitors in human health and those in clinical trials.

Identification of a 2,4-diaminopyrimidine scaffold targeting Trypanosoma brucei pteridine reductase 1 from the LIBRA compound library screening campaign

The LIBRA compound library is a collection of 522 non-commercial molecules contributed by various Italian academic laboratories. These compounds have been designed and synthesized during different medicinal chemistry programs and are hosted by the Italian Institute of Technology. We report the screening of the LIBRA compound library against Trypanosoma brucei and Leishmania major pteridine reductase 1, TbPTR1 and LmPTR1. Nine compounds were active against parasitic PTR1 and were selected for cell-based parasite screening, as single agents and in combination with methotrexate (MTX). The most interesting TbPTR1 inhibitor identified was 4-(benzyloxy)pyrimidine-2,6-diamine (LIB_66). Subsequently, six new LIB_66 derivatives were synthesized to explore its Structure-Activity-Relationship (SAR) and absorption, distribution, metabolism, excretion and toxicity (ADMET) properties. The results indicate that PTR1 has a preference to bind inhibitors, which resemble its biopterin/folic acid substrates, such as the 2,4-diaminopyrimidine derivatives.

Prediction of drug combination effects with a minimal set of experiments

High-throughput drug combination screening provides a systematic strategy to discover unexpected combinatorial synergies in pre-clinical cell models. However, phenotypic combinatorial screening with multi-dose matrix assays is experimentally expensive, especially when the aim is to identify selective combination synergies across a large panel of cell lines or patient samples. Here we implemented DECREASE, an efficient machine learning model that requires only a limited set of pairwise dose-response measurements for accurate prediction of drug combination synergy and antagonism. Using a compendium of 23,595 drug combination matrices tested in various cancer cell lines, and malaria and Ebola infection models, we demonstrate how cost-effective experimental designs with DECREASE capture almost the same degree of information for synergy and antagonism detection as the fully-measured dose-response matrices. Measuring only the diagonal of the matrix provides an accurate and practical option for combinatorial screening. The open-source web-implementation enables applications of DECREASE to both pre-clinical and translational studies.

Quality Control of Quantitative High Throughput Screening Data

Quantitative high throughput screening (qHTS) experiments can generate 1000s of concentration-response profiles to screen compounds for potentially adverse effects. However, potency estimates for a single compound can vary considerably in study designs incorporating multiple concentration-response profiles for each compound. We introduce an automated quality control procedure based on analysis of variance (ANOVA) to identify and filter out compounds with multiple cluster response patterns and improve potency estimation in qHTS assays. Our approach, called Cluster Analysis by Subgroups using ANOVA (CASANOVA), clusters compound-specific response patterns into statistically supported subgroups. Applying CASANOVA to 43 publicly available qHTS data sets, we found that only about 20% of compounds with response values outside of the noise band have single cluster responses. The error rates for incorrectly separating true clusters and incorrectly clumping disparate clusters were both less than 5% in extensive simulation studies. Simulation studies also showed that the bias and variance of concentration at half-maximal response (AC50 ) estimates were usually within 10-fold when using a weighted average approach for potency estimation. In short, CASANOVA effectively sorts out compounds with "inconsistent" response patterns and produces trustworthy AC50 values.

Linguistic measures of chemical diversity and the "keywords" of molecular collections

Computerized linguistic analyses have proven of immense value in comparing and searching through large text collections (“corpora”), including those deposited on the Internet – indeed, it would nowadays be hard to imagine browsing the Web without, for instance, search algorithms extracting most appropriate keywords from documents. This paper describes how such corpus-linguistic concepts can be extended to chemistry based on characteristic “chemical words” that span more than traditional functional groups and, instead, look at common structural fragments molecules share. Using these words, it is possible to quantify the diversity of chemical collections/databases in new ways and to define molecular “keywords” by which such collections are best characterized and annotated.

Early Probe and Drug Discovery in Academia: A Minireview

Drug discovery encompasses processes ranging from target selection and validation to the selection of a development candidate. While comprehensive drug discovery work flows are implemented predominantly in the big pharma domain, early discovery focus in academia serves to identify probe molecules that can serve as tools to study targets or pathways. Despite differences in the ultimate goals of the private and academic sectors, the same basic principles define the best practices in early discovery research. A successful early discovery program is built on strong target definition and validation using a diverse set of biochemical and cell-based assays with functional relevance to the biological system being studied. The chemicals identified as hits undergo extensive scaffold optimization and are characterized for their target specificity and off-target effects in in vitro and in animal models. While the active compounds from screening campaigns pass through highly stringent chemical and Absorption, Distribution, Metabolism, and Excretion (ADME) filters for lead identification, the probe discovery involves limited medicinal chemistry optimization. The goal of probe discovery is identification of a compound with sub-µM activity and reasonable selectivity in the context of the target being studied. The compounds identified from probe discovery can also serve as starting scaffolds for lead optimization studies.

NMR-Fragment Based Virtual Screening: A Brief Overview

Fragment-based drug discovery (FBDD) using NMR has become a central approach over the last twenty years for development of small molecule inhibitors against biological macromolecules, to control a variety of cellular processes. Yet, several considerations should be taken into account for obtaining a therapeutically relevant agent. In this review, we aim to list the considerations that make NMR fragment screening a successful process for yielding potent inhibitors. Factors that may govern the competence of NMR in fragment based drug discovery are discussed, as well as later steps that involve optimization of hits obtained by NMR-FBDD.

Data Mining and Computational Modeling of High-Throughput Screening Datasets

We are now seeing the benefit of investments made over the last decade in high-throughput screening (HTS) that is resulting in large structure activity datasets entering public and open databases such as ChEMBL and PubChem. The growth of academic HTS screening centers and the increasing move to academia for early stage drug discovery suggests a great need for the informatics tools and methods to mine such data and learn from it. Collaborative Drug Discovery, Inc. (CDD) has developed a number of tools for storing, mining, securely and selectively sharing, as well as learning from such HTS data. We present a new web based data mining and visualization module directly within the CDD Vault platform for high-throughput drug discovery data that makes use of a novel technology stack following modern reactive design principles. We also describe CDD Models within the CDD Vault platform that enables researchers to share models, share predictions from models, and create models from distributed, heterogeneous data. Our system is built on top of the Collaborative Drug Discovery Vault Activity and Registration data repository ecosystem which allows users to manipulate and visualize thousands of molecules in real time. This can be performed in any browser on any platform. In this chapter we present examples of its use with public datasets in CDD Vault. Such approaches can complement other cheminformatics tools, whether open source or commercial, in providing approaches for data mining and modeling of HTS data.

Scaffold Diversity of Fungal Metabolites

Many drug discovery projects rely on commercial compounds to discover active leads. However, current commercial libraries, with mostly synthetic compounds, access a small fraction of the possible chemical diversity. Natural products, in contrast, possess a vast structural diversity and have proven to be an outstanding source of new drugs. Several chemoinformatic analyses of natural products have demonstrated their diversity and structural complexity. However, to our knowledge, the scaffold content and structural diversity of fungal secondary metabolites have never been studied. Herein, the scaffold diversity of 223 fungal metabolites was measured and compared to the diversity of approved drugs and commercial libraries for HTS containing natural, synthetic, and semi-synthetic compounds. In addition, the global diversity of the fungal isolates was assessed and compared to other reference data sets using Consensus Diversity Plots, a chemoinformatic tool recently developed. It was concluded that fungal secondary metabolites are cyclic systems with few ramifications and more diverse than the commercial libraries with natural products and semi-synthetic compounds. The fungal metabolites data set was one of the most structurally diverse, containing a large proportion of different and unique scaffolds not found in the other compound data sets including ChEMBL. Therefore, fungal metabolites offer a rich source of molecules suited for identifying diverse candidates for drug discovery.

PubChem BioAssay: A Decade's Development toward Open High-Throughput Screening Data Sharing

High-throughput screening (HTS) is now routinely conducted for drug discovery by both pharmaceutical companies and screening centers at academic institutions and universities. Rapid advance in assay development, robot automation, and computer technology has led to the generation of terabytes of data in screening laboratories. Despite the technology development toward HTS productivity, fewer efforts were devoted to HTS data integration and sharing. As a result, the huge amount of HTS data was rarely made available to the public. To fill this gap, the PubChem BioAssay database (https://www.ncbi.nlm.nih.gov/pcassay/) was set up in 2004 to provide open access to the screening results tested on chemicals and RNAi reagents. With more than 10 years’ development and contributions from the community, PubChem has now become the largest public repository for chemical structures and biological data, which provides an information platform to worldwide researchers supporting drug development, medicinal chemistry study, and chemical biology research. This work presents a review of the HTS data content in the PubChem BioAssay database and the progress of data deposition to stimulate knowledge discovery and data sharing. It also provides a description of the database’s data standard and basic utilities facilitating information access and use for new users.

Dynamic Compound-Dependent Acoustic Transfer to Investigate Inhibitor Reversibility

Automated mechanism of action studies are introducing the need for tailored compound delivery, which can be challenging for standard compound management procedures. Jump dilution assays investigating inhibitor reversibility require compound delivery at specific volumes to assay specific concentrations of 10 × IC₅₀ for each inhibitor. Creating custom-made source plates with unique compound concentrations to dispense a uniform single volume can be prohibitively slow. A broadly applicable tool that enables on-the fly dispensing of variable amounts of stock concentrations was developed using the Acoustic Transfer System (ATS). The Dynamic Transfer Modification Program (DTMP) is an integrated LabVIEW program used to automate customized volume transfers from each well based on compound identity within a given source plate. A jump dilution investigating the time-dependent inhibition of the enzyme dipeptidyl peptidase-4 (DPP4) with multiple inhibitors is described here to demonstrate the delivery of specific volumes of various compounds in a high-throughput manner. The ability to automate this process allows for the characterization of inhibitor reversibility earlier in the drug discovery process, resulting in better informed lead candidate selection.

Screening of Conditionally Reprogrammed Patient-Derived Carcinoma Cells Identifies ERCC3-MYC Interactions as a Target in Pancreatic Cancer

PURPOSE

Even when diagnosed prior to metastasis, pancreatic ductal adenocarcinoma (PDAC) is a devastating malignancy with almost 90% lethality, emphasizing the need for new therapies optimally targeting the tumors of individual patients.

EXPERIMENTAL DESIGN

We first developed a panel of new physiologic models for study of PDAC, expanding surgical PDAC tumor samples in culture using short-term culture and conditional reprogramming with the Rho kinase inhibitor Y-27632, and creating matched patient-derived xenografts (PDX). These were evaluated for sensitivity to a large panel of clinical agents, and promising leads further evaluated mechanistically.

RESULTS

Only a small minority of tested agents was cytotoxic in minimally passaged PDAC cultures in vitro Drugs interfering with protein turnover and transcription were among most cytotoxic. Among transcriptional repressors, triptolide, a covalent inhibitor of ERCC3, was most consistently effective in vitro and in vivo causing prolonged complete regression in multiple PDX models resistant to standard PDAC therapies. Importantly, triptolide showed superior activity in MYC-amplified PDX models and elicited rapid and profound depletion of the oncoprotein MYC, a transcriptional regulator. Expression of ERCC3 and MYC was interdependent in PDACs, and acquired resistance to triptolide depended on elevated ERCC3 and MYC expression. The Cancer Genome Atlas analysis indicates ERCC3 expression predicts poor prognosis, particularly in CDKN2A-null, highly proliferative tumors.

CONCLUSIONS

This provides initial preclinical evidence for an essential role of MYC-ERCC3 interactions in PDAC, and suggests a new mechanistic approach for disruption of critical survival signaling in MYC-dependent cancers. Clin Cancer Res; 22(24); 6153-63. ©2016 AACR.

Consensus Diversity Plots: a global diversity analysis of chemical libraries

Background

Measuring the structural diversity of compound databases is relevant in drug discovery and many other areas of chemistry. Since molecular diversity depends on molecular representation, comprehensive chemoinformatic analysis of the diversity of libraries uses multiple criteria. For instance, the diversity of the molecular libraries is typically evaluated employing molecular scaffolds, structural fingerprints, and physicochemical properties. However, the assessment with each criterion is analyzed independently and it is not straightforward to provide an evaluation of the “global diversity”.

Results

Herein the Consensus Diversity Plot (CDP) is proposed as a novel method to represent in low dimensions the diversity of chemical libraries considering simultaneously multiple molecular representations. We illustrate the application of CDPs to classify eight compound data sets and two subsets with different sizes and compositions using molecular scaffolds, structural fingerprints, and physicochemical properties.

Conclusions

CDPs are general data mining tools that represent in two-dimensions the global diversity of compound data sets using multiple metrics. These plots can be constructed using single or combined measures of diversity. An online version of the CDPs is freely available at: https://consensusdiversityplots-difacquim-unam.shinyapps.io/RscriptsCDPlots/.Graphical Abstract

Using cultured endothelial cells to study endothelial barrier dysfunction: Challenges and opportunities

Despite considerable progress in the understanding of endothelial barrier regulation and the identification of approaches that have the potential to improve endothelial barrier function, no drug- or stem cell-based therapy is presently available to reverse the widespread vascular leak that is observed in acute respiratory distress syndrome (ARDS) and sepsis. The translational gap suggests a need to develop experimental approaches and tools that better mimic the complex environment of the microcirculation in which the vascular leak develops. Recent studies have identified several elements of this microenvironment. Among these are composition and stiffness of the extracellular matrix, fluid shear stress, interaction of endothelial cells (ECs) with pericytes, oxygen tension, and the combination of toxic and mechanic injurious stimuli. Development of novel cell culture techniques that integrate these elements would allow in-depth analysis of EC biology that closely approaches the (patho)physiological conditions in situ. In parallel, techniques to isolate organ-specific ECs, to define EC heterogeneity in its full complexity, and to culture patient-derived ECs from inducible pluripotent stem cells or endothelial progenitor cells are likely to advance the understanding of ARDS and lead to development of therapeutics. This review 1) summarizes the advantages and pitfalls of EC cultures to study vascular leak in ARDS, 2) provides an overview of elements of the microvascular environment that can directly affect endothelial barrier function, and 3) discusses alternative methods to bridge the gap between basic research and clinical application with the intent of improving the translational value of present EC culture approaches.

Microscopy-Based High-Content Screening

Image-based screening is used to measure a variety of phenotypes in cells and whole organisms. Combined with perturbations such as RNA interference, small molecules, and mutations, such screens are a powerful method for gaining systematic insights into biological processes. Screens have been applied to study diverse processes, such as protein-localization changes, cancer cell vulnerabilities, and complex organismal phenotypes. Recently, advances in imaging and image-analysis methodologies have accelerated large-scale perturbation screens. Here, we describe the state of the art for image-based screening experiments and delineate experimental approaches and image-analysis approaches as well as discussing challenges and future directions, including leveraging CRISPR/Cas9-mediated genome engineering.

DNA-Encoded Solid-Phase Synthesis: Encoding Language Design and Complex Oligomer Library Synthesis

The promise of exploiting combinatorial synthesis for small molecule discovery remains unfulfilled due primarily to the “structure elucidation problem”: the back-end mass spectrometric analysis that significantly restricts one-bead-one-compound (OBOC) library complexity. The very molecular features that confer binding potency and specificity, such as stereochemistry, regiochemistry, and scaffold rigidity, are conspicuously absent from most libraries because isomerism introduces mass redundancy and diverse scaffolds yield uninterpretable MS fragmentation. Here we present DNA-encoded solid-phase synthesis (DESPS), comprising parallel compound synthesis in organic solvent and aqueous enzymatic ligation of unprotected encoding dsDNA oligonucleotides. Computational encoding language design yielded 148 thermodynamically optimized sequences with Hamming string distance ≥ 3 and total read length <100 bases for facile sequencing. Ligation is efficient (70% yield), specific, and directional over 6 encoding positions. A series of isomers served as a testbed for DESPS’s utility in split-and-pool diversification. Single-bead quantitative PCR detected 9 × 10⁴ molecules/bead and sequencing allowed for elucidation of each compound’s synthetic history. We applied DESPS to the combinatorial synthesis of a 75 645-member OBOC library containing scaffold, stereochemical and regiochemical diversity using mixed-scale resin (160-μm quality control beads and 10-μm screening beads). Tandem DNA sequencing/MALDI-TOF MS analysis of 19 quality control beads showed excellent agreement (<1 ppt) between DNA sequence-predicted mass and the observed mass. DESPS synergistically unites the advantages of solid-phase synthesis and DNA encoding, enabling single-bead structural elucidation of complex compounds and synthesis using reactions normally considered incompatible with unprotected DNA. The widespread availability of inexpensive oligonucleotide synthesis, enzymes, DNA sequencing, and PCR make implementation of DESPS straightforward, and may prompt the chemistry community to revisit the synthesis of more complex and diverse libraries.

Finding new scaffolds of JAK3 inhibitors in public database: 3D-QSAR models & shape-based screening

The STAT/JAK3 pathway is a well-known therapeutic target in various diseases (ex. rheumatoid arthritis and psoriasis). The therapeutic advantage of JAK3 inhibition motivated to find new scaffolds with desired DMPK. For the purpose, in silico high-throughput sieves method is developed consisting of a receptor-guided three-dimensional quantitative structure-activity relationship study and shape-based virtual screening. We developed robust and predictive comparative molecular field analysis (q (2) = 0.760, r (2) = 0.915) and comparative molecular similarity index analysis (q (2) = 0.817, r (2) = 0.981) models and validated these using a test set, which produced satisfactory predictions of 0.925 and 0.838, respectively.

Computational prediction and validation of an expert's evaluation of chemical probes

In a decade with over half a billion dollars of investment, more than 300 chemical probes have been identified to have biological activity through NIH funded screening efforts. We have collected the evaluations of an experienced medicinal chemist on the likely chemistry quality of these probes based on a number of criteria including literature related to the probe and potential chemical reactivity. Over 20% of these probes were found to be undesirable. Analysis of the molecular properties of these compounds scored as desirable suggested higher pKa, molecular weight, heavy atom count, and rotatable bond number. We were particularly interested whether the human evaluation aspect of medicinal chemistry due diligence could be computationally predicted. We used a process of sequential Bayesian model building and iterative testing as we included additional probes. Following external validation of these methods and comparing different machine learning methods, we identified Bayesian models with accuracy comparable to other measures of drug-likeness and filtering rules created to date.

Metadata Standard and Data Exchange Specifications to Describe, Model, and Integrate Complex and Diverse High-Throughput Screening Data from the Library of Integrated Network-based Cellular Signatures (LINCS)

The National Institutes of Health Library of Integrated Network-based Cellular Signatures (LINCS) program is generating extensive multidimensional data sets, including biochemical, genome-wide transcriptional, and phenotypic cellular response signatures to a variety of small-molecule and genetic perturbations with the goal of creating a sustainable, widely applicable, and readily accessible systems biology knowledge resource. Integration and analysis of diverse LINCS data sets depend on the availability of sufficient metadata to describe the assays and screening results and on their syntactic, structural, and semantic consistency. Here we report metadata specifications for the most important molecular and cellular components and recommend them for adoption beyond the LINCS project. We focus on the minimum required information to model LINCS assays and results based on a number of use cases, and we recommend controlled terminologies and ontologies to annotate assays with syntactic consistency and semantic integrity. We also report specifications for a simple annotation format (SAF) to describe assays and screening results based on our metadata specifications with explicit controlled vocabularies. SAF specifically serves to programmatically access and exchange LINCS data as a prerequisite for a distributed information management infrastructure. We applied the metadata specifications to annotate large numbers of LINCS cell lines, proteins, and small molecules. The resources generated and presented here are freely available.

Overcoming scientific and structural bottlenecks in antibacterial discovery and development

Antibiotic resistance is becoming an increasing threat, with too few novel antibiotics coming to market to replace those lost due to resistance development. Efforts by the pharmaceutical industry to screen for and design novel antibacterials have not been successful, with several companies minimizing or closing down their antibacterial research units, leading to a loss of skills and know-how. At the same time, antibiotic innovation in academia is not filling the void due to misaligned incentive structures and lack of vital knowledge of drug discovery. The scientific and structural difficulties in discovering new antibiotics have only begun to be appreciated in the latest years. Part of the problem has been a paradigm shift within both industry and academia to focus on 'rational' drug development with an emphasis on single targets and high-throughput screening of large chemical libraries, which may not be suited to target bacteria. The very particular aspects of 'targeting an organism inside another organism' have not been given enough attention. In this paper, researcher interviews have complemented literature studies to delve deeper into the specifics of the different scientific and structural barriers, and some potential solutions are offered.

Identification of verrucarin a as a potent and selective steroid receptor coactivator-3 small molecule inhibitor

Members of the steroid receptor coactivator (SRC) family are overexpressed in numerous types of cancers. In particular, steroid receptor coactivator 3 (SRC-3) has been recognized as a critical coactivator associated with tumor initiation, progression, recurrence, metastasis, and chemoresistance where it interacts with multiple nuclear receptors and other transcription factors to enhance their transcriptional activities and facilitate cross-talk between pathways that stimulate cancer progression. Because of its central role as an integrator of growth signaling pathways, development of small molecule inhibitors (SMIs) against SRCs have the potential to simultaneously disrupt multiple signal transduction networks and transcription factors involved in tumor progression. Here, high-throughput screening was performed to identify compounds able to inhibit the intrinsic transcriptional activities of the three members of the SRC family. Verrucarin A was identified as a SMI that can selectively promote the degradation of the SRC-3 protein, while affecting SRC-1 and SRC-2 to a lesser extent and having no impact on CARM-1 and p300 protein levels. Verrucarin A was cytotoxic toward multiple types of cancer cells at low nanomolar concentrations, but not toward normal liver cells. Moreover, verrucarin A was able to inhibit expression of the SRC-3 target genes MMP2 and MMP13 and attenuated cancer cell migration. We found that verrucarin A effectively sensitized cancer cells to treatment with other anti-cancer drugs. Binding studies revealed that verrucarin A does not bind directly to SRC-3, suggesting that it inhibits SRC-3 through its interaction with an upstream effector. In conclusion, unlike other SRC SMIs characterized by our laboratory that directly bind to SRCs, verrucarin A is a potent and selective SMI that blocks SRC-3 function through an indirect mechanism.

Bufalin is a potent small-molecule inhibitor of the steroid receptor coactivators SRC-3 and SRC-1

Virtually all transcription factors partner with coactivators that recruit chromatin remodeling factors and interact with the basal transcription machinery. Coactivators have been implicated in cancer cell proliferation, invasion, and metastasis, including the p160 steroid receptor coactivator (SRC) family composed of SRC-1 (NCOA1), SRC-2 (TIF2/GRIP1/NCOA2), and SRC-3 (AIB1/ACTR/NCOA3). Given their broad involvement in many cancers, they represent candidate molecular targets for new chemotherapeutics. Here, we report on the results of a high-throughput screening effort that identified the cardiac glycoside bufalin as a potent small-molecule inhibitor for SRC-3 and SRC-1. Bufalin strongly promoted SRC-3 protein degradation and was able to block cancer cell growth at nanomolar concentrations. When incorporated into a nanoparticle delivery system, bufalin was able to reduce tumor growth in a mouse xenograft model of breast cancer. Our work identifies bufalin as a potentially broad-spectrum small-molecule inhibitor for cancer.

Robotic implementation of assays: tissue-nonspecific alkaline phosphatase (TNAP) case study

Laboratory automation and robotics have "industrialized" the execution and completion of large-scale, enabling high-capacity and high-throughput (100 K-1 MM/day) screening (HTS) campaigns of large "libraries" of compounds (>200 K-2 MM) to complete in a few days or weeks. Critical to the success these HTS campaigns is the ability of a competent assay development team to convert a validated research-grade laboratory "benchtop" assay suitable for manual or semi-automated operations on a few hundreds of compounds into a robust miniaturized (384- or 1,536-well format), well-engineered, scalable, industrialized assay that can be seamlessly implemented on a fully automated, fully integrated robotic screening platform for cost-effective screening of hundreds of thousands of compounds. Here, we provide a review of the theoretical guiding principles and practical considerations necessary to reduce often complex research biology into a "lean manufacturing" engineering endeavor comprising adaption, automation, and implementation of HTS. Furthermore we provide a detailed example specifically for a cell-free in vitro biochemical, enzymatic phosphatase assay for tissue-nonspecific alkaline phosphatase that illustrates these principles and considerations.

Shifting from the single to the multitarget paradigm in drug discovery

Increasing evidence that several drug compounds exert their effects through interactions with multiple targets is boosting the development of research fields that challenge the data reductionism approach. In this article, we review and discuss the concepts of drug repurposing, polypharmacology, chemogenomics, phenotypic screening and high-throughput in vivo testing of mixture-based libraries in an integrated manner. These research fields offer alternatives to the current paradigm of drug discovery, from a one target-one drug model to a multiple-target approach. Furthermore, the goals of lead identification are being expanded accordingly to identify not only 'key' compounds that fit with a single-target 'lock', but also 'master key' compounds that favorably interact with multiple targets (i.e. operate a set of desired locks to gain access to the expected clinical effects).

A multibody atomic statistical potential for the prediction of enzyme-inhibitor binding energy

Accurate prediction of enzyme-inhibitor binding energy has the capacity to speed drug design and chemical genomics efforts by helping to narrow the focus of experiments. Here a non-redundant set of three hundred high-resolution crystallographic enzyme-inhibitor structures was compiled for analysis, complexes with known binding energies (ΔG) based on the availability of experimentally determined inhibition constants (ki). Additionally, a separate set of over 1400 diverse high-resolution macromolecular crystal structures was collected for the purpose of creating an all-atom knowledge-based statistical potential, via application of the Delaunay tessellation computational geometry technique. Next, two hundred of the enzyme-inhibitor complexes were randomly selected to develop a model for predicting binding energy, first by tessellating structures of the complexes as well as the enzymes without their bound inhibitors, then by using the statistical potential to calculate a topological score for each structure tessellation. We derived as a predictor of binding energy an empirical linear function of the difference between topological scores for a complex and its isolated enzyme. A correlation coefficient (r) of 0.79 was obtained for the experimental and calculated ΔG values, with a standard error of 2.34 kcal/mol. Lastly, the model was evaluated with the held-out set of one hundred complexes, for which structure tessellations were performed in order to calculate topological score differences, and binding energy predictions were generated from the derived linear function. Calculated binding energies for the test data also compared well with their experimental counterparts, displaying a correlation coefficient of r= 0.77 with a standard error of 2.50 kcal/mol.

High-throughput screening for small-molecule modulators of inward rectifier potassium channels

Specific members of the inward rectifier potassium (Kir) channel family are postulated drug targets for a variety of disorders, including hypertension, atrial fibrillation, and pain. For the most part, however, progress toward understanding their therapeutic potential or even basic physiological functions has been slowed by the lack of good pharmacological tools. Indeed, the molecular pharmacology of the inward rectifier family has lagged far behind that of the S4 superfamily of voltage-gated potassium (Kv) channels, for which a number of nanomolar-affinity and highly selective peptide toxin modulators have been discovered. The bee venom toxin tertiapin and its derivatives are potent inhibitors of Kir1.1 and Kir3 channels, but peptides are of limited use therapeutically as well as experimentally due to their antigenic properties and poor bioavailability, metabolic stability and tissue penetrance. The development of potent and selective small-molecule probes with improved pharmacological properties will be a key to fully understanding the physiology and therapeutic potential of Kir channels. The Molecular Libraries Probes Production Center Network (MLPCN) supported by the National Institutes of Health (NIH) Common Fund has created opportunities for academic scientists to initiate probe discovery campaigns for molecular targets and signaling pathways in need of better pharmacology. The MLPCN provides researchers access to industry-scale screening centers and medicinal chemistry and informatics support to develop small-molecule probes to elucidate the function of genes and gene networks. The critical step in gaining entry to the MLPCN is the development of a robust target- or pathway-specific assay that is amenable for high-throughput screening (HTS). Here, we describe how to develop a fluorescence-based thallium (Tl(+)) flux assay of Kir channel function for high-throughput compound screening. The assay is based on the permeability of the K(+) channel pore to the K(+) congener Tl(+). A commercially available fluorescent Tl(+) reporter dye is used to detect transmembrane flux of Tl(+) through the pore. There are at least three commercially available dyes that are suitable for Tl(+) flux assays: BTC, FluoZin-2, and FluxOR. This protocol describes assay development using FluoZin-2. Although originally developed and marketed as a zinc indicator, FluoZin-2 exhibits a robust and dose-dependent increase in fluorescence emission upon Tl(+) binding. We began working with FluoZin-2 before FluxOR was available and have continued to do so. However, the steps in assay development are essentially identical for all three dyes, and users should determine which dye is most appropriate for their specific needs. We also discuss the assay's performance benchmarks that must be reached to be considered for entry to the MLPCN. Since Tl(+) readily permeates most K(+) channels, the assay should be adaptable to most K(+) channel targets.

CellProfiler and KNIME: open source tools for high content screening

High content screening (HCS) has established itself in the world of the pharmaceutical industry as an essential tool for drug discovery and drug development. HCS is currently starting to enter the academic world and might become a widely used technology. Given the diversity of problems tackled in academic research, HCS could experience some profound changes in the future, mainly with more imaging modalities and smart microscopes being developed. One of the limitations in the establishment of HCS in academia is flexibility and cost. Flexibility is important to be able to adapt the HCS setup to accommodate the multiple different assays typical of academia. Many cost factors cannot be avoided, but the costs of the software packages necessary to analyze large datasets can be reduced by using Open Source software. We present and discuss the Open Source software CellProfiler for image analysis and KNIME for data analysis and data mining that provide software solutions which increase flexibility and keep costs low.

A rare variant in the osteoarthritis-associated locus GDF5 is functional and reveals a site that can be manipulated to modulate GDF5 expression

Osteoarthritis (OA) is a polygenic disease characterized by cartilage loss, with the single-nucleotide polymorphism (SNP) rs143383 (C/T) influencing OA susceptibility across a range of ethnic groups. The SNP resides within the 5′-UTR of the growth and differentiation factor 5 gene (GDF5), with the OA-associated T-allele mediating reduced GDF5 expression. As GDF5 codes for a cartilage anabolic protein, this reduced expression may explain why the T-allele of rs143383 is an OA risk factor. Our deep sequencing of GDF5 identified a C/A transversion located −41 bp relative to the gene's transcription start site. This promoter variant is predicted to affect transcription factor binding and it may therefore highlight a regulatory site that could be exploited to manipulate GDF5 expression and alleviate the detrimental effect mediated by the T-allele of rs143383. Here, we describe our functional assessment of the −41 bp variant. Using reporter constructs we demonstrated that the transversion leads to increased gene expression to such a degree that the A-allele is able to compensate for the reduced expression mediated by the T-allele of rs143383. Using electrophoretic mobility shift assays we identified YY1 as a trans-acting factor that differentially binds to the alleles of the −41 bp variant, with more avid binding to allele A. Knockdown of YY1 led to a significant reduction in GDF5 expression, supporting YY1 as a GDF5 activator. In conclusion, we demonstrated that the −41 bp variant is functional and we have identified a regulatory region of GDF5 that can be exploited to overcome the OA genetic deficit mediated by the T-allele of rs143383.

MScreen: an integrated compound management and high-throughput screening data storage and analysis system

High-throughput screening (HTS) has historically been used by the pharmaceutical industry to rapidly test hundreds of thousands of compounds to identify potential drug candidates. More recently, academic groups have used HTS to identify new chemical probes or small interfering RNA (siRNA) that can serve as experimental tools to examine the biology or physiology of novel proteins, processes, or interactions. HTS presents a significant challenge with the vast and complex nature of data generated. This report describes MScreen, a Web-based, open-source cheminformatics application for chemical library and siRNA plate management, primary HTS and dose-response data handling, structure search, and administrative functions. Each project in MScreen can be secured with passwords or shared in an open-information environment that enables collaborators to easily compare data from many screens, providing a useful means to identify compounds with desired selectivity. Unique features include compound, substance, mixture, and siRNA plate creation and formatting; automated dose-response fitting and quality control (QC); and user, target, and assay method administration. MScreen provides an effective means to facilitate HTS information handling and analysis in the academic setting so that users can efficiently view their screening data and evaluate results for follow-up.

Collaboration versus outsourcing: the need to think outside the box

As has been widely reviewed elsewhere, the pharmaceutical industry is experiencing an ‘innovation deficit’ as evidenced by the decline in new chemical entity output. This decline, compounded by increased costs and regulatory requirements highlights the need to significantly revise strategic options across the drug-discovery spectrum. Within such revision(s), much of the focus has been on outsourcing to reduce, or at least contain, costs, but if the underlying predominance of ‘closed collaborations’ is not challenged to allow better use of combined knowledge and, thus, move towards a more genuine collaborative process then a ‘numbers only’ approach will not bring medium-to-long-term survival. There are many problems to confront in evolving new sustainable strategies, a real need to think differently exists and should to be cultivated. This article reviews current outsourcing and collaboration strategies to provide a perspective on how great knowledge sharing could help revise the drug-discovery process.

BioAssay Ontology (BAO): a semantic description of bioassays and high-throughput screening results

BACKGROUND

High-throughput screening (HTS) is one of the main strategies to identify novel entry points for the development of small molecule chemical probes and drugs and is now commonly accessible to public sector research. Large amounts of data generated in HTS campaigns are submitted to public repositories such as PubChem, which is growing at an exponential rate. The diversity and quantity of available HTS assays and screening results pose enormous challenges to organizing, standardizing, integrating, and analyzing the datasets and thus to maximize the scientific and ultimately the public health impact of the huge investments made to implement public sector HTS capabilities. Novel approaches to organize, standardize and access HTS data are required to address these challenges.

RESULTS

We developed the first ontology to describe HTS experiments and screening results using expressive description logic. The BioAssay Ontology (BAO) serves as a foundation for the standardization of HTS assays and data and as a semantic knowledge model. In this paper we show important examples of formalizing HTS domain knowledge and we point out the advantages of this approach. The ontology is available online at the NCBO bioportal http://bioportal.bioontology.org/ontologies/44531.

CONCLUSIONS

After a large manual curation effort, we loaded BAO-mapped data triples into a RDF database store and used a reasoner in several case studies to demonstrate the benefits of formalized domain knowledge representation in BAO. The examples illustrate semantic querying capabilities where BAO enables the retrieval of inferred search results that are relevant to a given query, but are not explicitly defined. BAO thus opens new functionality for annotating, querying, and analyzing HTS datasets and the potential for discovering new knowledge by means of inference.