A crowdsourcing evaluation of the NIH chemical probes

Four disruptive strategies for removing drug discovery bottlenecks

Drug discovery is shifting focus from industry to outside partners and, in the process, creating new bottlenecks. Technologies like high throughput screening (HTS) have moved to a larger number of academic and institutional laboratories in the USA, with little coordination or consideration of the outputs and creating a translational gap. Although there have been collaborative public-private partnerships in Europe to share pharmaceutical data, the USA has seemingly lagged behind and this may hold it back. Sharing precompetitive data and models may accelerate discovery across the board, while finding the best collaborators, mining social media and mobile approaches to open drug discovery should be evaluated in our efforts to remove drug discovery bottlenecks. We describe four strategies to rectify the current unsustainable situation.

Synthesis and characterization of tricarbonyl-Re/Tc(I) chelate probes targeting the G protein-coupled estrogen receptor GPER/GPR30

The discovery of the G protein-coupled estrogen receptor GPER (also GPR30) and the resulting development of selective chemical probes have revealed new aspects of estrogen receptor biology. The potential clinical relevance of this receptor has been suggested from numerous studies that have identified GPER expression in breast, endometrial, ovarian and other cancers. Thus GPER can be considered a candidate biomarker and target for non-invasive imaging and therapy. We have designed and synthesized a series of organometallic tricarbonyl-rhenium complexes conjugated to a GPER-selective small molecule derived from tetrahydro-3H-cyclopenta[c]quinoline. The activity and selectivity of these chelates in GPER-mediated signaling pathways were evaluated. These results demonstrate that GPER targeting characteristics depend strongly on the structure of the chelate and linkage. Ethanone conjugates functioned as agonists, a 1,2,3-triazole spacer yielded an antagonist, and derivatives with increased steric volume exhibited decreased activities. Promising GPER selectivity was observed, as none of the complexes interacted with the nuclear estrogen receptors. Radiolabeling with technetium-99m in aqueous media was efficient and gave radioligands with high radiochemical yields and purity. These chelates have favorable physicochemical properties, show excellent stability in biologically relevant media, exhibit receptor specificity and are promising candidates for continuing development as diagnostic imaging agents targeting GPER expression in cancer.

Identifying mechanism-of-action targets for drugs and probes

Notwithstanding their key roles in therapy and as biological probes, 7% of approved drugs are purported to have no known primary target, and up to 18% lack a well-defined mechanism of action. Using a chemoinformatics approach, we sought to "de-orphanize" drugs that lack primary targets. Surprisingly, targets could be easily predicted for many: Whereas these targets were not known to us nor to the common databases, most could be confirmed by literature search, leaving only 13 Food and Drug Administration-approved drugs with unknown targets; the number of drugs without molecular targets likely is far fewer than reported. The number of worldwide drugs without reasonable molecular targets similarly dropped, from 352 (25%) to 44 (4%). Nevertheless, there remained at least seven drugs for which reasonable mechanism-of-action targets were unknown but could be predicted, including the antitussives clemastine, cloperastine, and nepinalone; the antiemetic benzquinamide; the muscle relaxant cyclobenzaprine; the analgesic nefopam; and the immunomodulator lobenzarit. For each, predicted targets were confirmed experimentally, with affinities within their physiological concentration ranges. Turning this question on its head, we next asked which drugs were specific enough to act as chemical probes. Over 100 drugs met the standard criteria for probes, and 40 did so by more stringent criteria. A chemical information approach to drug-target association can guide therapeutic development and reveal applications to probe biology, a focus of much current interest.

Open PHACTS: semantic interoperability for drug discovery

Open PHACTS is a public-private partnership between academia, publishers, small and medium sized enterprises and pharmaceutical companies. The goal of the project is to deliver and sustain an 'open pharmacological space' using and enhancing state-of-the-art semantic web standards and technologies. It is focused on practical and robust applications to solve specific questions in drug discovery research. OPS is intended to facilitate improvements in drug discovery in academia and industry and to support open innovation and in-house non-public drug discovery research. This paper lays out the challenges and how the Open PHACTS project is hoping to address these challenges technically and socially.

The Challenges of Multi-Target Lead Optimization

During multi-target lead optimization, medicinal chemists must address three principal challenges. First, the various desired activities must be adequately balanced and the activity profile of any active metabolites must also be considered. Second, undesired activities associated with side effects need to be removed. Third, a pharmacokinetic profile appropriate for the desired route of administration, usually oral, must be obtained. Given the extent of these challenges, the need for a high quality lead compound is paramount.

Compound prioritization methods increase rates of chemical probe discovery in model organisms

Preselection of compounds that are more likely to induce a phenotype can increase the efficiency and reduce the costs for model organism screening. To identify such molecules, we screened ~81,000 compounds in Saccharomyces cerevisiae and identified ~7500 that inhibit cell growth. Screening these growth-inhibitory molecules across a diverse panel of model organisms resulted in an increased phenotypic hit-rate. These data were used to build a model to predict compounds that inhibit yeast growth. Empirical and in silico application of the model enriched the discovery of bioactive compounds in diverse model organisms. To demonstrate the potential of these molecules as lead chemical probes, we used chemogenomic profiling in yeast and identified specific inhibitors of lanosterol synthase and of stearoyl-CoA 9-desaturase. As community resources, the ~7500 growth-inhibitory molecules have been made commercially available and the computational model and filter used are provided.

Distributed human intelligence for colonic polyp classification in computer-aided detection for CT colonography

PURPOSE

To assess the diagnostic performance of distributed human intelligence for the classification of polyp candidates identified with computer-aided detection (CAD) for computed tomographic (CT) colonography.

MATERIALS AND METHODS

This study was approved by the institutional Office of Human Subjects Research. The requirement for informed consent was waived for this HIPAA-compliant study. CT images from 24 patients, each with at least one polyp of 6 mm or larger, were analyzed by using CAD software to identify 268 polyp candidates. Twenty knowledge workers (KWs) from a crowdsourcing platform labeled each polyp candidate as a true or false polyp. Two trials involving 228 KWs were conducted to assess reproducibility. Performance was assessed by comparing the area under the receiver operating characteristic curve (AUC) of KWs with the AUC of CAD for polyp classification.

RESULTS

The detection-level AUC for KWs was 0.845 ± 0.045 (standard error) in trial 1 and 0.855 ± 0.044 in trial 2. These were not significantly different from the AUC for CAD, which was 0.859 ± 0.043. When polyp candidates were stratified by difficulty, KWs performed better than CAD on easy detections; AUCs were 0.951 ± 0.032 in trial 1, 0.966 ± 0.027 in trial 2, and 0.877 ± 0.048 for CAD (P = .039 for trial 2). KWs who participated in both trials showed a significant improvement in performance going from trial 1 to trial 2; AUCs were 0.759 ± 0.052 in trial 1 and 0.839 ± 0.046 in trial 2 (P = .041).

CONCLUSION

The performance of distributed human intelligence is not significantly different from that of CAD for colonic polyp classification.

Identification of Simple Compounds with Microtubule-Binding Activity That Inhibit Cancer Cell Growth with High Potency

We analyzed more than 1 million small molecules with the goal of finding simple synthetic compounds that potently inhibit cancer cell growth. We identified three such compounds with unknown mechanisms of action. Subsequent studies revealed that all three of these small molecules target microtubules. These three scaffolds can serve as templates for developing new microtubule-targeted agents, overcoming the limits of existing microtubule-inhibiting drugs derived from complex natural products.

Of possible cheminformatics futures

For over a decade, cheminformatics has contributed to a wide array of scientific tasks from analytical chemistry and biochemistry to pharmacology and drug discovery; and although its contributions to decision making are recognized, the challenge is how it would contribute to faster development of novel, better products. Here we address the future of cheminformatics with primary focus on innovation. Cheminformatics developers often need to choose between "mainstream" (i.e., accepted, expected) and novel, leading-edge tools, with an increasing trend for open science. Possible futures for cheminformatics include the worst case scenario (lack of funding, no creative usage), as well as the best case scenario (complete integration, from systems biology to virtual physiology). As "-omics" technologies advance, and computer hardware improves, compounds will no longer be profiled at the molecular level, but also in terms of genetic and clinical effects. Among potentially novel tools, we anticipate machine learning models based on free text processing, an increased performance in environmental cheminformatics, significant decision-making support, as well as the emergence of robot scientists conducting automated drug discovery research. Furthermore, cheminformatics is anticipated to expand the frontiers of knowledge and evolve in an open-ended, extensible manner, allowing us to explore multiple research scenarios in order to avoid epistemological "local information minimum trap".

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

High throughput screening (HTS) facilitates screening large numbers of compounds against a biochemical target of interest using validated biological or biophysical assays. In recent years, a significant number of drugs in clinical trails originated from HTS campaigns, validating HTS as a bona fide mechanism for hit finding. In the current drug discovery landscape, the pharmaceutical industry is embracing open innovation strategies with academia to maximize their research capabilities and to feed their drug discovery pipeline. The goals of academic research have therefore expanded from target identification and validation to probe discovery, chemical genomics, and compound library screening. This trend is reflected in the emergence of HTS centers in the public domain over the past decade, ranging in size from modestly equipped academic screening centers to well endowed Molecular Libraries Probe Centers Network (MLPCN) centers funded by the NIH Roadmap initiative. These centers facilitate a comprehensive approach to probe discovery in academia and utilize both classical and cutting-edge assay technologies for executing primary and secondary screening campaigns. The various facets of academic HTS centers as well as their implications on technology transfer and drug discovery are discussed, and a roadmap for successful drug discovery in the public domain is presented. New lead discovery against therapeutic targets, especially those involving the rare and neglected diseases, is indeed a Mount Everestonian size task, and requires diligent implementation of pharmaceutical industry's best practices for a successful outcome.

Ultrafast shape recognition: method and applications

Molecular shape complementarity is widely recognized as a key indicator of biological activity. Unfortunately, efficient computation of shape similarity is challenging, which severely limits the potential of shape-based virtual screening. Ultrafast shape recognition (USR) is a recent shape similarity technique that is characterized by its extremely high speed of operation. Here we review important methodological aspects for the optimal application of USR as well as its first applications to medicinal chemistry problems. These applications already include several particularly successful prospective virtual screens, which shows the important role that USR can play in identifying bioactive molecules to be used as chemical probes and potentially as starting points for the drug-discovery process.

Mining small-molecule screens to repurpose drugs

Repurposing and repositioning drugs--discovering new uses for existing and experimental medicines-is an attractive strategy for rescuing stalled pharmaceutical projects, finding treatments for neglected diseases, and reducing the time, cost and risk of drug development. As this strategy emerged, academic researchers began performing high-throughput screens (HTS) of small molecules--the type of experiments once exclusively conducted in industry--and making the data from these screens available to all. Several methods can mine this data to inform repurposing and repositioning efforts. Despite these methods' limitations, it is hopeful that they will accelerate the discovery of new uses for known drugs, but this hope has not yet been realized.

Characterizing the diversity and biological relevance of the MLPCN assay manifold and screening set

The NIH Molecular Libraries Initiative (MLI), launched in 2004 with initial goals of identifying chemical probes for characterizing gene function and druggability, has produced PubChem, a chemical genomics knowledgebase for fostering translation of basic research into new therapeutic strategies. This paper assesses progress toward these goals by evaluating MLI target novelty and propensity for undergoing biochemically or therapeutically relevant modulations and the degree of chemical diversity and biogenic bias inherent in the MLI screening set. Our analyses suggest that while MLI target selection has not yet been fully optimized for biochemical diversity, it covers biologically interesting pathway space that complements established drug targets. We find the MLI screening set to be chemically diverse and to have greater biogenic bias than comparable collections of commercially available compounds. Biogenic enhancements such as incorporation of more metabolite-like chemotypes are suggested.

Qualitative pharmacology in a quantitative world: diminishing value in the drug discovery process

Preclinical characterization of new chemical entities (NCEs) in terms of efficacy, safety and their pharmacokinetic (PK), pharmacodynamic and pharmaceutical properties, is key to advancing appropriate compounds to clinical trials. The use of high throughput synthetic and screening methodologies has frequently led to NCE characterization becoming highly reductionistic, to the extent that compounds are often selected without adequate characterization. Classical, null hypothesis-based approaches involving the use of concentration/dose response curves and antagonists have been replaced by more qualitative approaches that limit NCE characterization. The return to a more integrated, hierarchical and pharmacologically driven approach will aid in ensuring that the NCEs advanced to clinical status are better understood, strengthening the process and predictivity of the translational approach in drug discovery.

Connecting the virtual world of computers to the real world of medicinal chemistry

Drug discovery involves the simultaneous optimization of chemical and biological properties, usually in a single small molecule, which modulates one of nature's most complex systems: the balance between human health and disease. The increased use of computer-aided methods is having a significant impact on all aspects of the drug-discovery and development process and with improved methods and ever faster computers, computer-aided molecular design will be ever more central to the discovery process.

Structurally sophisticated octahedral metal complexes as highly selective protein kinase inhibitors

The generation of synthetic compounds with exclusive target specificity is an extraordinary challenge of molecular recognition and demands novel design strategies, in particular for large and homologous protein families such as protein kinases with more than 500 members. Simple organic molecules often do not reach the necessary sophistication to fulfill this task. Here, we present six carefully tailored, stable metal-containing compounds in which unique and defined molecular geometries with natural-product-like structural complexity are constructed around octahedral ruthenium(II) or iridium(III) metal centers. Each of the six reported metal compounds displays high selectivity for an individual protein kinase, namely GSK3α, PAK1, PIM1, DAPK1, MLCK, and FLT4. Although being conventional ATP-competitive inhibitors, the combination of the unusual globular shape and rigidity characteristics, of these compounds facilitates the design of highly selective protein kinase inhibitors. Unique structural features of the octahedral coordination geometry allow novel interactions with the glycine-rich loop, which contribute significantly to binding potencies and selectivities. The sensitive correlation between metal coordination sphere and inhibition properties suggests that in this design, the metal is located at a "hot spot" within the ATP binding pocket, not too close to the hinge region where globular space is unavailable, and at the same time not too far out toward the solvent where the octahedral coordination sphere would not have a significant impact on potency and selectivity. This study thus demonstrates that inert (stable) octahedral metal complexes are sophisticated structural scaffolds for the design of highly selective chemical probes.

Design, engineering and utility of biotic games

Games are a significant and defining part of human culture, and their utility beyond pure entertainment has been demonstrated with so-called 'serious games'. Biotechnology--despite its recent advancements--has had no impact on gaming yet. Here we propose the concept of 'biotic games', i.e., games that operate on biological processes. Utilizing a variety of biological processes we designed and tested a collection of games: 'Enlightenment', 'Ciliaball', 'PAC-mecium', 'Microbash', 'Biotic Pinball', 'POND PONG', 'PolymerRace', and 'The Prisoner's Smellemma'. We found that biotic games exhibit unique features compared to existing game modalities, such as utilizing biological noise, providing a real-life experience rather than virtual reality, and integrating the chemical senses into play. Analogous to video games, biotic games could have significant conceptual and cost-reducing effects on biotechnology and eventually healthcare; enable volunteers to participate in crowd-sourcing to support medical research; and educate society at large to support personal medical decisions and the public discourse on bio-related issues.

The Interweaving of Cheminformatics and HTS

The aim of this chapter is to describe the stages of early drug discovery that can be assisted by techniques commonly used in the field of cheminformatics. In fact, cheminformatics tools can be applied all the way from the design of compound libraries and the analysis of HTS results, to the discovery of functional relationships between compounds and their targets.

Selective inhibition of BET bromodomains

Epigenetic proteins are intently pursued targets in ligand discovery. So far, successful efforts have been limited to chromatin modifying enzymes, or so-called epigenetic 'writers' and 'erasers'. Potent inhibitors of histone binding modules have not yet been described. Here we report a cell-permeable small molecule (JQ1) that binds competitively to acetyl-lysine recognition motifs, or bromodomains. High potency and specificity towards a subset of human bromodomains is explained by co-crystal structures with bromodomain and extra-terminal (BET) family member BRD4, revealing excellent shape complementarity with the acetyl-lysine binding cavity. Recurrent translocation of BRD4 is observed in a genetically-defined, incurable subtype of human squamous carcinoma. Competitive binding by JQ1 displaces the BRD4 fusion oncoprotein from chromatin, prompting squamous differentiation and specific antiproliferative effects in BRD4-dependent cell lines and patient-derived xenograft models. These data establish proof-of-concept for targeting protein-protein interactions of epigenetic 'readers', and provide a versatile chemical scaffold for the development of chemical probes more broadly throughout the bromodomain family.

Exploiting PubChem for Virtual Screening

IMPORTANCE OF THE FIELD: PubChem is a public molecular information repository, a scientific showcase of the NIH Roadmap Initiative. The PubChem database holds over 27 million records of unique chemical structures of compounds (CID) derived from nearly 70 million substance depositions (SID), and contains more than 449,000 bioassay records with over thousands of in vitro biochemical and cell-based screening bioassays established, with targeting more than 7000 proteins and genes linking to over 1.8 million of substances. AREAS COVERED IN THIS REVIEW: This review builds on recent PubChem-related computational chemistry research reported by other authors while providing readers with an overview of the PubChem database, focusing on its increasing role in cheminformatics, virtual screening and toxicity prediction modeling. WHAT THE READER WILL GAIN: These publicly available datasets in PubChem provide great opportunities for scientists to perform cheminformatics and virtual screening research for computer-aided drug design. However, the high volume and complexity of the datasets, in particular the bioassay-associated false positives/negatives and highly imbalanced datasets in PubChem, also creates major challenges. Several approaches regarding the modeling of PubChem datasets and development of virtual screening models for bioactivity and toxicity predictions are also reviewed. TAKE HOME MESSAGE: Novel data-mining cheminformatics tools and virtual screening algorithms are being developed and used to retrieve, annotate and analyze the large-scale and highly complex PubChem biological screening data for drug design.

Synthetic toxicology: where engineering meets biology and toxicology

This article examines the implications of synthetic biology (SB) for toxicological sciences. Starting with a working definition of SB, we describe its current subfields, namely, DNA synthesis, the engineering of DNA-based biological circuits, minimal genome research, attempts to construct protocells and synthetic cells, and efforts to diversify the biochemistry of life through xenobiology. Based on the most important techniques, tools, and expected applications in SB, we describe the ramifications of SB for toxicology under the label of synthetic toxicology. We differentiate between cases where SB offers opportunities for toxicology and where SB poses challenges for toxicology. Among the opportunities, we identified the assistance of SB to construct novel toxicity testing platforms, define new toxicity-pathway assays, explore the potential of SB to improve in vivo biotransformation of toxins, present novel biosensors developed by SB for environmental toxicology, discuss cell-free protein synthesis of toxins, reflect on the contribution to toxic use reduction, and the democratization of toxicology through do-it-yourself biology. Among the identified challenges for toxicology, we identify synthetic toxins and novel xenobiotics, biosecurity and dual-use considerations, the potential bridging of toxic substances and infectious agents, and do-it-yourself toxin production.

PubChem as a public resource for drug discovery

PubChem is a public repository of small molecules and their biological properties. Currently, it contains more than 25 million unique chemical structures and 90 million bioactivity outcomes associated with several thousand macromolecular targets. To address the potential utility of this public resource for drug discovery, we systematically summarized the protein targets in PubChem by function, 3D structure and biological pathway. Moreover, we analyzed the potency, selectivity and promiscuity of the bioactive compounds identified for these biological targets, including the chemical probes generated by the NIH Molecular Libraries Program. As a public resource, PubChem lowers the barrier for researchers to advance the development of chemical tools for modulating biological processes and drug candidates for disease treatments.

Cellular phone-based image acquisition and quantitative ratiometric method for detecting cocaine and benzoylecgonine for biological and forensic applications

Here we describe the first report of using low-cost cellular or web-based digital cameras to image and quantify standardized rapid immunoassay strips as a new point-of-care diagnostic and forensics tool with health applications. Quantitative ratiometric pixel density analysis (QRPDA) is an automated method requiring end-users to utilize inexpensive (∼ $1 USD/each) immunotest strips, a commonly available web or mobile phone camera or scanner, and internet or cellular service. A model is described whereby a central computer server and freely available IMAGEJ image analysis software records and analyzes the incoming image data with time-stamp and geo-tag information and performs the QRPDA using custom JAVA based macros (http://www.neurocloud.org). To demonstrate QRPDA we developed a standardized method using rapid immunotest strips directed against cocaine and its major metabolite, benzoylecgonine. Images from standardized samples were acquired using several devices, including a mobile phone camera, web cam, and scanner. We performed image analysis of three brands of commercially available dye-conjugated anti-cocaine/benzoylecgonine (COC/BE) antibody test strips in response to three different series of cocaine concentrations ranging from 0.1 to 300 ng/ml and BE concentrations ranging from 0.003 to 0.1 ng/ml. This data was then used to create standard curves to allow quantification of COC/BE in biological samples. Across all devices, QRPDA quantification of COC and BE proved to be a sensitive, economical, and faster alternative to more costly methods, such as gas chromatography-mass spectrometry, tandem mass spectrometry, or high pressure liquid chromatography. The limit of detection was determined to be between 0.1 and 5 ng/ml. To simulate conditions in the field, QRPDA was found to be robust under a variety of image acquisition and testing conditions that varied temperature, lighting, resolution, magnification and concentrations of biological fluid in a sample. To determine the effectiveness of the QRPDA method for quantifying cocaine in biological samples, mice were injected with a sub-locomotor activating dose of cocaine (5 mg/kg; i.p.) and were found to have detectable levels of COC/BE in their urine (160.6 ng/ml) and blood plasma (8.1 ng/ml) after 15–30 minutes. By comparison rats self-administering cocaine in a 4 hour session obtained a final BE blood plasma level of 910 ng/ml with an average of 62.5 infusions. It is concluded that automated QRPDA is a low-cost, rapid and highly sensitive method for the detection of COC/BE with health, forensics, and bioinformatics application and the potential to be used with other rapid immunotest strips directed at several other targets. Thus, this report serves as a general reference and method describing the use of image analysis of lateral flow rapid test strips.

Discovery of selective probes and antagonists for G-protein-coupled receptors FPR/FPRL1 and GPR30

Recent technological advances in flow cytometry provide a versatile platform for high throughput screening of compound libraries coupled with high-content biological testing and drug discovery. The G protein-coupled receptors (GPCRs) constitute the largest class of signaling molecules in the human genome with frequent roles in disease pathogenesis, yet many examples of orphan receptors with unknown ligands remain. The complex biology and potential for drug discovery within this class provide strong incentives for chemical biology approaches seeking to develop small molecule probes to facilitate elucidation of mechanistic pathways and enable specific manipulation of the activity of individual receptors. We have initiated small molecule probe development projects targeting two distinct families of GPCRs: the formylpeptide receptors (FPR/FPRL1) and G protein-coupled estrogen receptor (GPR30). In each case the assay for compound screening involved the development of an appropriate small molecule fluorescent probe, and the flow cytometry platform provided inherently biological rich assays that enhanced the process of identification and optimization of novel antagonists. The contributions of cheminformatics analysis tools, virtual screening, and synthetic chemistry in synergy with the biomolecular screening program have yielded valuable new chemical probes with high binding affinity, selectivity for the targeted receptor, and potent antagonist activity. This review describes the discovery of novel small molecule antagonists of FPR and FPRL1, and GPR30, and the associated characterization process involving secondary assays, cell based and in vivo studies to define the selectivity and activity of the resulting chemical probes.

Chemical space: missing pieces in cheminformatics

Cheminformatics is at a turning point, the pharmaceutical industry benefits from using the various methods developed over the last twenty years, but in our opinion we need to see greater development of novel approaches that non-experts can use. This will be achieved by more collaborations between software companies, academics and the evolving pharmaceutical industry. We suggest that cheminformatics should also be looking to other industries that use high performance computing technologies for inspiration. We describe the needs and opportunities which may benefit from the development of open cheminformatics technologies, mobile computing, the movement of software to the cloud and precompetitive initiatives.

Probing the probes: fitness factors for small molecule tools

Chemical probes for interrogating biological processes are of considerable current interest. Cell permeable small molecule tools have a major role in facilitating the functional annotation of the human genome, understanding both physiological and pathological processes, and validating new molecular targets. To be valuable, chemical tools must satisfy necessary criteria and recent publications have suggested objective guidelines for what makes a useful chemical probe. Although recognizing that such guidelines may be valuable, we caution against overly restrictive rules that may stifle innovation in favor of a "fit-for-purpose" approach. Reviewing the literature and providing examples from the cancer field, we recommend a series of "fitness factors" to be considered when assessing chemical probes. We hope this will encourage innovative chemical biology research while minimizing the generation of poor quality and misleading biological data, thus increasing understanding of the particular biological area, to the benefit of basic research and drug discovery.

Highly efficient synthesis and characterization of the GPR30-selective agonist G-1 and related tetrahydroquinoline analogs

The GPR30 agonist probe G-1 and structural analogs were efficiently synthesized using multicomponent or stepwise Sc(III)-catalyzed aza-Diels-Alder cyclization. Optimization of solvent and reaction temperature provided enhanced endo-diastereoselectivity.

Drugging the PI3 kinome: from chemical tools to drugs in the clinic

The phosphatidylinositide 3-kinase (PI3K) pathway is very commonly activated in a wide range of human cancers and is a major driving force in oncogenesis. One of the class I lipid kinase members of the PI3K family, p110alpha, is probably the most commonly mutated kinase in the human genome. Alongside genetic, molecular biological, and biochemical studies, chemical inhibitors have been extremely helpful tools in understanding the role of PI3K enzymes in signal transduction and downstream physiological and pathological processes, and also in validating PI3Ks as therapeutic targets. Although they have been valuable in the past, the early and still frequently employed inhibitors, wortmannin and LY294002, have significant limitations as chemical tools. Here, we discuss the case history of the discovery and properties of an increasingly used chemical probe, the pan-class I PI3K and mammalian target of rapamycin (mTOR) inhibitor PI-103 (a pyridofuropyrimidine), and its very recent evolution into the thienopyrimidine drug GDC-0941, which exhibits excellent oral anticancer activity in preclinical models and is now undergoing phase I clinical trials in cancer patients. We also illustrate the impact of structural biology on the design of PI3K inhibitors and on the interpretation of their effects. The challenges and outlook for drugging the PI3 kinome are discussed in the more general context of the role of structural biology and chemical biology in innovative drug discovery.