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The discovery of novel bromodomain inhibitors by fragment screening is complicated by the presence of dimethyl sulfoxide (DMSO), an acetyl-lysine mimetic, that can compromise the detection of low affinity fragments. We demonstrate surface plasmon resonance as a primary fragment screening approach for the discovery of novel bromodomain scaffolds, by describing a protocol to overcome the DMSO interference issue. We describe the discovery of several novel small molecules scaffolds that inhibit the bromodomains PCAF, BRD4, and CREBBP, representing canonical members of three out of the seven subfamilies of bromodomains. High-resolution crystal structures of the complexes of key fragments binding to BRD4(1), CREBBP, and PCAF were determined to provide binding mode data to aid the development of potent and selective inhibitors of PCAF, CREBBP, and BRD4.

Surface plasmon resonance (SPR) is one of the primary biophysical methods for the screening of low molecular weight ‘fragment’ libraries, due to its low protein consumption and ‘label-free’ methodology. SPR biosensor interaction analysis is employed to both screen and confirm the binding of compounds in fragment screening experiments, as it provides accurate information on the affinity and kinetics of molecular interactions. The most advanced application of the use of SPR for fragment screening is against membrane protein drug targets, such G-protein coupled receptors (GPCRs). Biophysical GPCR assays using SPR have been validated with pharmacological measurements approximate to cell-based methods, yet provide the advantage of biophysical methods in their ability to measure the weak affinities of low molecular weight fragments. A number of SPR fragment screens against GPCRs have now been disclosed in the literature. SPR fragment screening is proving versatile to screen both thermostabilised GPCRs and solubilised wild type receptors. In this chapter, we discuss the state-of-the-art in GPCR fragment screening by SPR and the technical considerations in performing such experiments.

The judicious application of ligand or binding efficiency metrics, which quantify the molecular properties required to obtain binding affinity for a drug target, is gaining traction in the selection and optimization of fragments, hits and leads. Retrospective analysis of recently marketed oral drugs shows that they frequently have highly optimized ligand efficiency values for their targets. Optimizing ligand efficiency metrics based on both molecular mass and lipophilicity, when set in the context of the specific target, has the potential to ameliorate the inflation of these properties that has been observed in current medicinal chemistry practice, and to increase the quality of drug candidates.

G-protein coupled receptors (GPCRs) are a class of drug targets of primary importance. However, receptor assays are based on measurement of either ligand displacement or downstream functional responses, rather than direct observation of ligand binding. Issues of allosteric modulation, probe dependence, and functional selectivity create challenges in selecting suitable assays formats. Therefore, a method that directly measures GPCR–ligand interactions, independent of binding site, probe, and signaling pathway would be a useful primary and orthogonal screening method. We have developed a GPCR biosensor assay protocol that offers the opportunity for high-throughput label-free screening that directly measures GPCR–ligand interactions. The biosensor-based direct screening method identifies the interaction of both orthosteric and allosteric ligands with solubilized, native GPCRs, in a label-free and cell-free environment, thus overcoming the limitations of indirect and displacement assay methods. We exemplify the method by the discovery of novel ligands for the chemokine receptor, CCR5, that are ligand efficient fragments.

Pandemic, epidemic and endemic infectious diseases are united by a common problem: how do we rapidly and cost-effectively identify potential pharmacological interventions to treat infections? Given the large number of emerging and neglected infectious diseases and the fact that they disproportionately afflict the poorest members of the global society, new ways of thinking are required to develop high productivity discovery systems that can be applied to a large number of pathogens. The growing availability of parasite genome data provides the basis for developing methods to prioritize, a priori potential drug targets and analyze the pharmacological landscape of an infectious disease. Thus the overall objective of infectious disease informatics is to enable the rapid generation of plausible, novel medical hypotheses of test-able pharmacological experiments, by uncovering undiscovered relationships in the wealth of biomedical literature and databases that were collected for other purposes. In particular our goal is to identify potential drug targets present in a pathogen genome and prioritize which pharmacological experiments are most likely to discover drug-like lead compounds rapidly against a pathogen (i.e. which specific compounds and drug targets should be screened, in which assays and where they can be sourced). An integral part of the challenge is the development and integration of methods to predict druggability, essentiality, synthetic lethality and polypharmocology in pathogen genomes, while simultaneously integrating the inevitable issues of chemical tractability and the potential for acquired drug resistance from the start.

The dominant paradigm in drug discovery is the concept of designing maximally selective ligands to act on individual drug targets. However, many effective drugs act via modulation of multiple proteins rather than single targets. Advances in systems biology are revealing a phenotypic robustness and a network structure that strongly suggests that exquisitely selective compounds, compared with multitarget drugs, may exhibit lower than desired clinical efficacy. This new appreciation of the role of polypharmacology has significant implications for tackling the two major sources of attrition in drug development--efficacy and toxicity. Integrating network biology and polypharmacology holds the promise of expanding the current opportunity space for druggable targets. However, the rational design of polypharmacology faces considerable challenges in the need for new methods to validate target combinations and optimize multiple structure-activity relationships while maintaining drug-like properties. Advances in these areas are creating the foundation of the next paradigm in drug discovery: network pharmacology.

G-protein coupled receptors (GPCRs) are the primary target class of currently marketed drugs, accounting for around a third of all drug targets of approved medicines. However, almost all the screening efforts for novel ligand discovery rely exclusively on cellular systems overexpressing the receptors. Current receptor assay systems are based on measurement of either ligand displacement or downstream functional responses, rather than direct observation of ligand binding. Issues of allosteric modulation, probe dependence, and functional selectivity create challenges in selecting suitable assay formats. Therefore, a method that directly measures GPCR-ligand interactions, independent of binding site, probe, and signaling pathway would be a useful primary and orthogonal screening method. An alternative ligand discovery strategy would be the direct measurement of GPCR-ligand interactions by label-free technologies, such as surface plasmon resonance (SPR). In this chapter, we summarize overview of the SPR technology and development of applications for detection of ligand binding to GPCRs using wild-type and thermostabilized receptors. We discuss the utilization of SPR as a biophysical screening method for fragment-based drug discovery for GPCRs. In particular, we show how SPR screening can detect both orthosteric and allosteric ligands with the appropriate experimental design.

Structure-based drug design is now used widely in modern medicinal chemistry. The application of structural biology to medicinal chemistry has heralded the "rational drug design" vision of discovering exquisitely selective ligands. However, recent advances in post-genomic biology are indicating that polypharmacology may be a necessary trait for the efficacy of many drugs, therefore questioning the "one drug, one target" assumption of current rational drug design. By combining advances in chemoinformatics and structural biology, it might be possible to rationally design the next generation of promiscuous drugs with polypharmacology.

An assessment of the number of molecular targets that represent an opportunity for therapeutic intervention is crucial to the development of post-genomic research strategies within the pharmaceutical industry. Now that we know the size of the human genome, it is interesting to consider just how many molecular targets this opportunity represents. We start from the position that we understand the properties that are required for a good drug, and therefore must be able to understand what makes a good drug target.

G-protein coupled receptors (GPCRs) are the primary target class of currently marketed drugs, accounting for about a quarter of all drug targets of approved medicines. However, almost all the screening efforts for novel ligand discovery rely exclusively on cellular systems overexpressing the receptors. An alternative ligand discovery strategy is a fragment-based drug discovery, where low molecular weight compounds, known as fragments, are screened as initial starting points for optimization. However, the screening of fragment libraries usually employs biophysical screening methods, and as such, it has not been routinely applied to membrane proteins. We present here a surface plasmon resonance biosensor approach that enables, cell-free, label-free, fragment screening that directly measures fragment interactions with wild-type GPCRs. We exemplify the method by the discovery of novel, selective, high affinity antagonists of human β2 adrenoceptor.

The clinical efficacy and safety of a drug is determined by its activity profile across many proteins in the proteome. However, designing drugs with a specific multi-target profile is both complex and difficult. Therefore methods to design drugs rationally a priori against profiles of several proteins would have immense value in drug discovery. Here we describe a new approach for the automated design of ligands against profiles of multiple drug targets. The method is demonstrated by the evolution of an approved acetylcholinesterase inhibitor drug into brain-penetrable ligands with either specific polypharmacology or exquisite selectivity profiles for G-protein-coupled receptors. Overall, 800 ligand-target predictions of prospectively designed ligands were tested experimentally, of which 75% were confirmed to be correct. We also demonstrate target engagement in vivo. The approach can be a useful source of drug leads when multi-target profiles are required to achieve either selectivity over other drug targets or a desired polypharmacology.

Publisher Summary The concept of “pharmacological space” provides a theoretical framework for navigating the apparently infinite number of choices in drug discovery. Pharmacological space attempts to chart the limits of chemical space, targets space, and disease space in order to reduce and systematize the search for new drugs in these spaces. Thus charting pharmacological space is an attempt to outline the areas where opportunities for new drugs may lie based on an extrapolation of the knowledge gained from experiments to date. This chapter outlines some of the recent theoretical arguments and empirical evidence for navigating target space and chemical space for drug discovery. Recent studies have also highlighted the relationship between high lipophilicity and the increased chance of toxicity via the increased target promiscuity of lipophilic molecules. The relationship between target class and the physicochemical properties of ligands is explored by calculating a set of physicochemical descriptors of hundreds of thousands of biologically active compounds across over a thousand proteins where the protein sequences assigned to each of the pharmacological targets were classified into gene families. The successful strategy of exploring pharmacological space in its widest sense is in contrast to drug discovery, which focuses on the narrow spectrum of a single disease or molecular target, but screens a wide sample of chemical space.

Large scale analysis of structure–activity relationships and screening data is enabling the emergence of chemogenomics approaches to understanding the relationships between chemical space and biological target space. Here, we present several example of how chemogenomics is enabling drug discovery by elucidating the relationship between the physico-chemical properties of molecular binding sites and ligands. A survey of the known landscape of proteins expressed from the human genome, for which leads have been discovered or by orthology are predicted to be druggable, present the pallet of drug targets for medicinal chemists. From this survey significant differences in the physical properties of ligands for different gene families are observed, with the ligands of several gene families are far from drug-like property space.

For the past decade, the number of molecular targets for approved drugs has been debated. Here, we reconcile apparently contradictory previous reports into a comprehensive survey, and propose a consensus number of current drug targets for all classes of approved therapeutic drugs. One striking feature is the relatively constant historical rate of target innovation (the rate at which drugs against new targets are launched); however, the rate of developing drugs against new families is significantly lower. The recent approval of drugs that target protein kinases highlights two additional trends: an emerging realization of the importance of polypharmacology, and also the power of a gene-family-led approach in generating novel and important therapies.

We present the global mapping of pharmacological space by the integration of several vast sources of medicinal chemistry structure-activity relationships (SAR) data. Our comprehensive mapping of pharmacological space enables us to identify confidently the human targets for which chemical tools and drugs have been discovered to date. The integration of SAR data from diverse sources by unique canonical chemical structure, protein sequence and disease indication enables the construction of a ligand-target matrix to explore the global relationships between chemical structure and biological targets. Using the data matrix, we are able to catalog the links between proteins in chemical space as a polypharmacology interaction network. We demonstrate that probabilistic models can be used to predict pharmacology from a large knowledge base. The relationships between proteins, chemical structures and drug-like properties provide a framework for developing a probabilistic approach to drug discovery that can be exploited to increase research productivity.

The success of many drug design projects is fundamentally limited by the nature of the target — the target’s “druggability”. Here we define “druggability” as the ability of a target to be modulated by potent, small “drug-like” molecules (which are often suitable for oral delivery). The physico-chemical properties of “drug-like” molecules are discussed below. In turn, the “druggability” of a target can be assessed from analysis of all the available sequence, structural and ligand information coupled with an understanding the structural and thermodynamic basis of protein-ligand interactions. Thus, early target assessment can be a powerful tool for portfolio management, directing resources towards “druggable” targets, which are more likely to deliver clinical candidates.

The susceptibility of organisms to chemical perturbation differs as a result of defenses that limit the permeation of small molecules. Screening for permeation, rather than bioactivity, to identify a priori organism-specific chemical space offers an intriguing approach to phenotypic assays and potentially addresses some fundamental challenges in drug discovery.

Computational methods that reliably predict the biological activities of compounds have long been sought. The validation of one such method suggests that in silico predictions for drug discovery have come of age.

Nat. Chem. Biol. 5, 441–447 (2009); published online 17 June 2009; corrected after print 17 June 2009 In the version of this article initially published, the phrase “Though MLI and its pilot phase budget and superficially modest productivity were subject to industrial criticism” originally cited reference 3, but should cite reference 2.