Improving success rates for lead generation using affinity binding technologies

Affinity technologies have been applied at several stages of the drug discovery process, ranging from target identification and purification to the identification of preclinical candidates. The detection of ligand-macromolecule interactions in lead discovery is the best studied and most powerful of these techniques. Although affinity methods have been in widespread use for about a decade, only recently have many reports emerged on their utility. Primary affinity screens of large libraries of small molecules or fragments have begun to produce results for challenging targets. Furthermore, in secondary assays affinity methods are opening new avenues to tackle important medicinal chemistry tasks.     

DNA-encoded chemical libraries

The discovery and development of novel drugs for the multitude of targets originating from functional genomic research is a challenging task. While antibodies can nowadays be raised against virtually any given target using phage-display methodologies, a similar "selection/amplification" approach for the facile discovery of low-molecular weight compounds capable of specific binding to protein targets of choice has so far been lacking. The development of DNA-encoded chemical libraries, combined with suitable selection and high-throughput sequencing strategies, holds promises to fill this gap. Here, we review the latest developments in the field of DNA-encoded chemical libraries, commenting on the challenges and opportunities for the different experimental strategies in this rapidly evolving research area, which may gain importance for the future drug discovery process.     

Fragment-based activity space: smaller is better

Fragment-based drug discovery has the potential to supersede traditional high throughput screening based drug discovery for molecular targets amenable to structure determination. This is because the chemical diversity coverage is better accomplished by a fragment collection of reasonable size than by larger HTS collections. Furthermore, fragments have the potential to be efficient target binders with higher probability than more elaborated drug-like compounds. The selection of the fragment screening technique is driven by sensitivity and throughput considerations, and we advocate in the present article the use of high concentration bioassays in conjunction with NMR-based hit confirmation. Subsequent ligand X-ray structure determination of the fragment ligand in complex with the target protein by co-crystallisation or crystal soaking can focus on confirmed binders.     

A loop-mimetic inhibitor of the HCV-NS3 protease derived from a minibody

We have been interested for some time in establishing a strategy for deriving lead compounds from macromolecule ligands such as minibody variants. A minibody is a minimized antibody variable domain whose two loops are amenable to combinatorial mutagenesis. This approach can be especially useful when dealing with 'difficult' targets. One such target is the NS3 protease of hepatitis C virus (HCV), a human pathogen that is believed to infect about 100 million individuals worldwide and for which an effective therapy is not yet available. Based on known inhibitor specificity (residues P6-P1) of NS3 protease, we screened a number of minibodies from our collection and we were able to identify a competitive inhibitor of this enzyme. We thus validated an aspect of recognition by HCV NS3 protease, namely that an acid anchor is necessary for inhibitor activity. In addition, the characterization of the minibody inhibitor led to the synthesis of a constrained hexapeptide mimicking the bioactive loop of the parent macromolecule. The cyclic peptide is a lead compound prone to rapid optimization through solid phase combinatorial chemistry. We therefore confirmed that the potential of turning a protein ligand into a low molecular weight active compound for lead discovery is achievable and can complement more traditional drug discovery approaches.     

Fragment-based lead discovery: a chemical update

Fragment-based lead discovery constructs drug leads from small molecular fragments. In theory, this is a highly efficient method for drug discovery, and the technique has become enormously popular in the past few years. In this review, I describe how a variety of approaches in fragment-based lead discovery--including NMR, X-ray crystallography, mass spectrometry, functional screening, and in silico screening--have produced drug leads. Although the examples show that the technique can reliably generate potent molecules, there is still much work to be done to maintain the efficiency of molecules' binding affinities as fragments are linked, expanded, and otherwise improved.     

Fragment-based screening for inhibitors of PDE4A using enthalpy arrays and X-ray crystallography

Fragment-based screening has typically relied on X-ray or nuclear magnetic resonance methods to identify low-affinity ligands that bind to therapeutic targets. These techniques are expensive in terms of material and time, so it useful to have a higher throughput method to reliably prescreen a fragment library to identify a subset of compounds for structural analysis. Calorimetry provides a label-free method to assay binding and enzymatic activity that is unaffected by the spectroscopic properties of the sample. Conventional microcalorimetry is hampered by requiring large quantities of reagents and long measurement times. Nanocalorimeters can overcome these limitations of conventional isothermal titration calorimetry. Here we have used enthalpy arrays, which are arrays of nanocalorimeters, to perform an enzyme activity-based fragment screen for competitive inhibitors of phosphodiesterase 4A (PDE4A). Several inhibitors with K ( I ) <2 mM were identified and moved to X-ray crystallization trials. Although the co-crystals did not yield high-resolution data, evidence of binding was observed, and the chemical structures of the hits were consistent with motifs of known PDE4 inhibitors. This study shows how array calorimetry can be used as a prescreening method for fragment-based lead discovery with enzyme targets and provides a list of candidate fragments for inhibition of PDE4A.     

Affinity-based ranking of ligands for DPP-4 from mixtures

Affinity-based selection strategies have recently emerged as a complement to traditional high throughput screening for the rapid discovery of lead compounds for the large number of protein targets emerging from--omics technologies. Herein, we describe a method for the ranking of mixtures of ligands by affinity selection and apply it to rank order a set of inhibitors for the enzyme dipeptidyl peptidase IV.     

Design and combinatorial synthesis of a novel kinase-focused library using click chemistry-based fragment assembly

Fragment-based lead discovery is a new approach for lead generation that has emerged in the past decade. Because the initial fragments identified in the fragment screening typically show weak binding affinity, an intensive medicinal chemistry effort would be required to grow initial fragments into a potential lead compound. Here we demonstrate a kinase focused evolved fragment (KFEF) library, constructed by click chemistry-based fragment assembly, that is a valuable source of kinase inhibitors. This combinatorial assembly of two fragments, kinase-privileged alkyne fragments and diversified azide fragments, by two cycloaddition reactions shows a unique potential for the one-step synthesis of structurally diverse evolved fragments. The screening of this triazole-based KFEF library allowed the rapid identification of potent lead candidates for FLT3 and GSK3β kinase.     

Binding-Site Assessment by Virtual Fragment Screening

The accurate prediction of protein druggability (propensity to bind high-affinity drug-like small molecules) would greatly benefit the fields of chemical genomics and drug discovery. We have developed a novel approach to quantitatively assess protein druggability by computationally screening a fragment-like compound library. In analogy to NMR-based fragment screening, we dock ∼11000 fragments against a given binding site and compute a computational hit rate based on the fraction of molecules that exceed an empirically chosen score cutoff. We perform a large-scale evaluation of the approach on four datasets, totaling 152 binding sites. We demonstrate that computed hit rates correlate with hit rates measured experimentally in a previously published NMR-based screening method. Secondly, we show that the in silico fragment screening method can be used to distinguish known druggable and non-druggable targets, including both enzymes and protein-protein interaction sites. Finally, we explore the sensitivity of the results to different receptor conformations, including flexible protein-protein interaction sites. Besides its original aim to assess druggability of different protein targets, this method could be used to identifying druggable conformations of flexible binding site for lead discovery, and suggesting strategies for growing or joining initial fragment hits to obtain more potent inhibitors.     

Process biology: integrated genomics and bioinformatics tools for improved target assessment

The availability of the human genome sequence has greatly increased the number of potential drug targets in recent years. As a result, the way targets are assessed has become crucial to the success of the drug discovery process. Unfortunately, the traditional methods relied on by the pharmaceutical industry to identify and validate targets are too slow and labor-intensive to be useful in the current environment. One solution to this dilemma is to adopt a new paradigm, which we call Process Biology. Process Biology integrates genomics and bioinformatics tools into automated, optimized modules that can be applied readily to a wide range of biological questions. By using organizational principles not usually applied to biological experiments, Process Biology can significantly impact target assessment and assist in decision-making throughout the drug discovery process.     

Experimental validation of a fragment library for lead discovery using SPR biosensor technology

A new fragment library for lead discovery has been designed and experimentally validated for use in surface plasmon resonance (SPR) biosensor-based screening. The 930 compounds in the library were selected from 4.6 million commercially available compounds using a series of physicochemical and medicinal chemistry filters. They were screened against 3 prototypical drug targets: HIV-1 protease, thrombin and carbonic anhydrase, and a nontarget: human serum albumin. Compound solubility was not a problem under the conditions used for screening. The high sensitivity of the sensor surfaces allowed the detection of interactions for 35% to 97% of the fragments, depending on the target protein. None of the fragments was promiscuous (i.e., interacted with a stoichiometry ≥5:1 with all 4 proteins), and only 2 compounds dissociated slowly from all 4 proteins. The use of several targets proved valuable since several compounds would have been disqualified from the library on the grounds of promiscuity if fewer target proteins had been used. The experimental procedure allowed an efficient evaluation and exploration of the new fragment library and confirmed that the new library is suitable for SPR biosensor-based screening.     

Strategies and methods in the identification of antagonists of protein-protein interactions

The identification of antagonists of protein-protein interactions is a critical challenge to the pharmaceutical industry. The selection of a protein target, which is amenable to antagonism, is the first of many decisions that determine the success of these efforts. In addition, the definition of strategies and the development and application of methodologies appropriate to that target will be vital to the success of efforts to identify antagonists of a protein-protein interaction. An analysis of current approaches to the identification of lead molecules demonstrates that a search for competitors of a known binder is the basis of traditional screening as well as more modern approaches based on structure activity relationship (SAR) by nuclear magnetic resonance (NMR), molecular fragments, rational design, and tethering. The latter methods employ a structural perspective, throughout the discovery and optimization of a lead, to provide the practitioner with some control over the success of the process.     

A mouse model-based screening platform for the identification of immune activating compounds such as natural products for novel cancer immunotherapies

The therapy of cancer continues to be a challenge aggravated by the evolution of resistance against current medications. As an alternative for the traditional tripartite treatment options of surgery, radiation and chemotherapy, immunotherapy is gaining increasing attention due to the opportunity of more targeted approaches. Promising targets are antigen-presenting cells which drive innate and adaptive immune responses. The discovery and emergence of new drugs and lead structures can be inspired by natural products which comprise many highly bioactive molecules. The development of new drugs based on natural products is hampered by the current lack of guidelines for screening these structures for immune activating compounds. In this work, we describe a phenotypic preclinical screening pipeline for first-line identification of promising natural products using the mouse as a model system. Favorable compounds are defined to be non-toxic to immune target cells, to show direct anti-tumor effects and to be immunostimulatory at the same time. The presented screening pipeline constitutes a useful tool and aims to integrate immune activation in experimental approaches early on in drug discovery. It supports the selection of natural products for later chemical optimization, direct application in in vivo mouse models and clinical trials and promotes the emergence of new innovative drugs for cancer treatment.     

Chemical genetic approaches to probing cell death

Chemical genetics has arisen as a tool for the discovery of pathways and proteins in mammalian systems. This approach, comprising small-molecule screening combined with biochemical and genomic target identification methods, enables one to assess which proteins are involved in regulating a particular phenotype. Applied to cell death, this strategy can reveal novel targets and pathways regulating the demise of mammalian cells. Numerous diseases have been linked to the loss of regulation of cell death. Defining the mechanisms governing cell death in these diseases might lead to the discovery of therapeutic agents and targets and provide a richer understanding of the mortality of living systems. Recent advances include the discovery of novel small molecules regulating cell death pathways -- necrostatin and erastin -- as well as the elucidation of the mechanism of death induced in cancer cells by the cytotoxic agent Apratoxin A.     

Depth and Nature of Giant Petroleum Discoveries Through Time as an Indicator of Resource Depletion

A striking increase of the depth of giant petroleum discoveries in the past 15 years coincides with a shift to discoveries in subsalt plays that require more challenging exploration and drilling. Technological advances have facilitated these changes, but technological advances alone could not have induced these changes in petroleum exploration on a planet in which shallow and less challenging targets remained in abundance like that of previous decades. Instead, the trends toward greater discovery depths and more challenging plays suggest that most of the conventional petroleum accumulations of the kind that fueled the global economic system of the 1900s have already been found.     

cnnAlpha: Protein disordered regions prediction by reduced amino acid alphabets and convolutional neural networks

Intrinsically disordered regions (IDR) play an important role in key biological processes and are closely related to human diseases. IDRs have great potential to serve as targets for drug discovery, most notably in disordered binding regions. Accurate prediction of IDRs is challenging because their genome wide occurrence and a low ratio of disordered residues make them difficult targets for traditional classification techniques. Existing computational methods mostly rely on sequence profiles to improve accuracy which is time consuming and computationally expensive. This article describes an ab initio sequence-only prediction method—which tries to overcome the challenge of accurate prediction posed by IDRs—based on reduced amino acid alphabets and convolutional neural networks (CNNs). We experiment with six different 3-letter reduced alphabets. We argue that the dimensional reduction in the input alphabet facilitates the detection of complex patterns within the sequence by the convolutional step. Experimental results show that our proposed IDR predictor performs at the same level or outperforms other state-of-the-art methods in the same class, achieving accuracy levels of 0.76 and AUC of 0.85 on the publicly available Critical Assessment of protein Structure Prediction dataset (CASP10). Therefore, our method is suitable for proteome-wide disorder prediction yielding similar or better accuracy than existing approaches at a faster speed.     

Progress in functional genomics approaches to antifungal drug target discovery

Antifungal drug discovery is starting to benefit from the enormous advances in the genomics field, which have occurred in the past decade. As traditional drug screening on existing targets is not delivering the long-awaited potent antifungals, efforts to use novel genetics and genomics-based strategies to aid in the discovery of novel drug targets are gaining increased importance. The current paradigm in antifungal drug target discovery focuses on basically two main classes of targets to evaluate: genes essential for viability and virulence or pathogenicity factors. Here we report on recent advances in genetics and genomics-based technologies that will allow us not only to identify and validate novel fungal drug targets, but hopefully in the longer run also to discover potent novel therapeutic agents. Fungal pathogens have typically presented significant obstacles when subjected to genetics, but the creativity of scientists in the anti-infectives field and the cross-talk with scientists in other areas is now yielding exciting new tools and technologies to tackle the problem of finding potent, specific and non-toxic antifungal therapeutics.     

Molecular diversity in the context of leadlikeness: compound properties that enable effective biochemical screening

Molecular diversity is of vital importance in drug screening in general and for the discovery and development of new pharmacophores in particular. Biochemical screening is a powerful tool for pharmacophore development given understanding of the properties of a good lead compound operating in the biochemical environment. The properties of leadlikeness have evolved to accommodate the artificial conditions of a biochemical assay. Accordingly, the properties of leadlikeness that are suited for screening at protein targets biochemically are different and complementary to the properties of druglikeness used to guide the selection of good compounds studied biologically in cellular studies and animal models. The benefits of leadlikeness in the biochemical screening arena (including fragment-based screening and co-crystallization studies) are described here and recommendations are forwarded for the generation of leadlike molecular diversity. Chemically stable low molecular weight 'minimalist' compounds (or fragments) with dense heteroatom substitution and variable conformational constraint are promoted as conceptually superior compounds for biochemical screening.     

Drug discovery in academia

Drug discovery and development is generally done in the commercial rather than the academic realm. Drug discovery involves target discovery and validation, lead identification by high-throughput screening, and lead optimization by medicinal chemistry. Follow-up preclinical evaluation includes analysis in animal models of compound efficacy and pharmacology (ADME: administration, distribution, metabolism, elimination) and studies of toxicology, specificity, and drug interactions. Notwithstanding the high-cost, labor-intensive, and non-hypothesis-driven aspects of drug discovery, the academic setting has a unique and expanding niche in this important area of investigation. For example, academic drug discovery can focus on targets of limited commercial value, such as third-world and rare diseases, and on the development of research reagents such as high-affinity inhibitors for pharmacological "gene knockout" in animal models ("chemical genetics"). This review describes the practical aspects of the preclinical drug discovery process for academic investigators. The discovery of small molecule inhibitors and activators of the cystic fibrosis transmembrane conductance regulator is presented as an example of an academic drug discovery program that has yielded new compounds for physiology research and clinical development. high-throughput screening; drug development; pharmacology; fluorescence; cystic fibrosis transmembrane conductance regulator