Chemogenomic strategies to expand the bioactive chemical space

Progress on open chemoinformatic tools for expanding and exploring the chemical space

The concept of chemical space is a cornerstone in chemoinformatics, and it has broad conceptual and practical applicability in many areas of chemistry, including drug design and discovery. One of the most considerable impacts is in the study of structure-property relationships where the property can be a biological activity or any other characteristic of interest to a particular chemistry discipline. The chemical space is highly dependent on the molecular representation that is also a cornerstone concept in computational chemistry. Herein, we discuss the recent progress on chemoinformatic tools developed to expand and characterize the chemical space of compound data sets using different types of molecular representations, generate visual representations of such spaces, and explore structure-property relationships in the context of chemical spaces. We emphasize the development of methods and freely available tools focusing on drug discovery applications. We also comment on the general advantages and shortcomings of using freely available and easy-to-use tools and discuss the value of using such open resources for research, education, and scientific dissemination.

Preclinical validation of the small molecule drug quininib as a novel therapeutic for colorectal cancer

Colorectal cancer (CRC) is a leading cause of cancer deaths. Molecularly targeted therapies (e.g. bevacizumab) have improved survival rates but drug resistance ultimately develops and newer therapies are required. We identified quininib as a small molecule drug with anti-angiogenic activity using in vitro, ex vivo and in vivo screening models. Quininib (2-[(E)-2-(Quinolin-2-yl) vinyl] phenol), is a small molecule drug (molecular weight 283.75 g/mol), which significantly inhibited blood vessel development in zebrafish embryos (p < 0.001). In vitro, quininib reduced endothelial tubule formation (p < 0.001), cell migration was unaffected by quininib and cell survival was reduced by quininib (p < 0.001). Using ex vivo human CRC explants, quininib significantly reduced the secretions of IL-6, IL-8, VEGF, ENA-78, GRO-α, TNF, IL-1β and MCP-1 ex vivo (all values p < 0.01). Quininib is well tolerated in mice when administered at 50 mg/kg intraperitoneally every 3 days and significantly reduced tumour growth of HT-29-luc2 CRC tumour xenografts compared to vehicle control. In addition, quininib reduced the signal from a α_vβ₃ integrin fluorescence probe in tumours 10 days after treatment initiation, indicative of angiogenic inhibition. Furthermore, quininib reduced the expression of angiogenic genes in xenografted tumours. Collectively, these findings support further development of quininib as a novel therapeutic agent for CRC.

Counting on natural products for drug design

Natural products and their molecular frameworks have a long tradition as valuable starting points for medicinal chemistry and drug discovery. Recently, there has been a revitalization of interest in the inclusion of these chemotypes in compound collections for screening and achieving selective target modulation. Here we discuss natural-product-inspired drug discovery with a focus on recent advances in the design of synthetically tractable small molecules that mimic nature's chemistry. We highlight the potential of innovative computational tools in processing structurally complex natural products to predict their macromolecular targets and attempt to forecast the role that natural-product-derived fragments and fragment-like natural products will play in next-generation drug discovery.

Protein/peptide secondary structural mimics: design, characterization, and modulation of protein–protein interactions

This review discusses general aspects of novel artificial peptide secondary structure mimics for modulation of PPIs, their therapeutic applications and future prospects.

Phosphatase high-throughput screening assay design and selection

Phosphatases are a heterogeneous group of enzymes catalyzing dephosphorylation of diverse substrates ranging from small organic molecules to large phosphorylated multiprotein complexes. A wide variety of biochemical approaches for measuring phosphatase activity exists. Spectrophotometric methods utilizing artificial chromogenic, fluorogenic, and luminogenic substrates and taking advantage of the optical properties of dephosphorylated products are broadly used by research community. Another major assay type is based on quantitation of the second product of any phosphatase reactions, inorganic phosphate, using a variety of phosphate detection methods. Although, in theory, compatible with any phosphatase substrate, these assays often are unable to provide acceptable high-throughput screening adaptations of native phosphatase reactions. Conversely, phosphatase assays with artificial substrates frequently are incapable to mirror the intricacies of substrate binding and catalysis of the native reaction and, as a result, unable to deliver biologically relevant phosphatase modulators. Utilization of comprehensive phosphatase assay panels, employing honed biochemical assays and cell-based model systems, in conjunction with novel approaches for screening phosphatases may aid in identification of potent, selective, and biologically active phosphatase modulators.

Exploring chemical space: recent advances in chemistry

Recent advances and concepts for exploring chemical space are highlighted in this chapter and show how the synthetic chemical world meets the demand of making large and relevant collection of new molecules for analyzing the biological world more closely.

Chemogenomics of pyridoxal 5'-phosphate dependent enzymes

Pyridoxal 5'-phosphate (PLP) dependent enzymes comprise a large family that plays key roles in amino acid metabolism and are acquiring an increasing interest as drug targets. For the identification of compounds inhibiting PLP-dependent enzymes, a chemogenomics-based approach has been adopted in this work. Chemogenomics exploits the information coded in sequences and three-dimensional structures to define pharmacophore models. The analysis was carried out on a dataset of 65 high-resolution PLP-dependent enzyme structures, including representative members of four-fold types. Evolutionarily conserved residues relevant to coenzyme or substrate binding were identified on the basis of sequence-structure comparisons. A dataset was obtained containing the information on conserved residues at substrate and coenzyme binding site for each representative PLP-dependent enzyme. By linking coenzyme and substrate pharmacophores, bifunctional pharmacophores were generated that will constitute the basis for future development of small inhibitors targeting specific PLP-dependent enzymes.

Natural product-like synthetic libraries

There is a paucity of chemical matter suitably poised for effective drug development. Improving the quality and efficiency of research early on in the drug discovery process has been a long standing objective for the drug industry and improvements to the accessibility and quality of compound screening decks might have a significant and positive impact. In the absence of specific molecular information that can be modeled and used predicatively we are far from identifying which small molecules are most relevant to emerging biological targets such as protein-protein interactions. Natural products have been historically successful as an entry point for drug discovery and recently screening libraries are being synthesized to emulate natural product like features.

From in silico target prediction to multi-target drug design: current databases, methods and applications

Given the tremendous growth of bioactivity databases, the use of computational tools to predict protein targets of small molecules has been gaining importance in recent years. Applications span a wide range, from the 'designed polypharmacology' of compounds to mode-of-action analysis. In this review, we firstly survey databases that can be used for ligand-based target prediction and which have grown tremendously in size in the past. We furthermore outline methods for target prediction that exist, both based on the knowledge of bioactivities from the ligand side and methods that can be applied in situations when a protein structure is known. Applications of successful in silico target identification attempts are discussed in detail, which were based partly or in whole on computational target predictions in the first instance. This includes the authors' own experience using target prediction tools, in this case considering phenotypic antibacterial screens and the analysis of high-throughput screening data. Finally, we will conclude with the prospective application of databases to not only predict, retrospectively, the protein targets of a small molecule, but also how to design ligands with desired polypharmacology in a prospective manner.

Recent trends and observations in the design of high-quality screening collections

The design of a high-quality screening collection is of utmost importance for the early drug-discovery process and provides, in combination with high-quality assay systems, the foundation of future discoveries. Herein, we review recent trends and observations to successfully expand the access to bioactive chemical space, including the feedback from hit assessment interviews of high-throughput screening campaigns; recent successes with chemogenomics target family approaches, the identification of new relevant target/domain families, diversity-oriented synthesis and new emerging compound classes, and non-classical approaches, such as fragment-based screening and DNA-encoded chemical libraries. The role of in silico library design approaches are emphasized.

The role of computational methods in the identification of bioactive compounds

Computational methods play an ever increasing role in lead finding. A vast repertoire of molecular design and virtual screening methods emerged in the past two decades and are today routinely used. There is increasing awareness that there is no single best computational protocol and correspondingly there is a shift recommending the combination of complementary methods. A promising trend for the application of computational methods in lead finding is to take advantage of the vast amounts of HTS (High Throughput Screening) data to allow lead assessment by detailed systems-based data analysis, especially for phenotypic screens where the identification of compound-target pairs is the primary goal. Herein, we review trends and provide examples of successful applications of computational methods in lead finding.

Designing zebrafish chemical screens

The zebrafish is proving to be highly amenable to in vivo small molecule screening. With a growing number of screens successfully completed, a rich interface is being created between disciplines that have historically used zebrafish (e.g., embryology and genetics) and disciplines focused on small molecules (e.g., chemistry and pharmacology). Navigating this interface requires consideration of the unique demands of conducting high-throughput screening in vivo. In this chapter, we discuss design elements of successful zebrafish screens, established screening methods, and approaches for mechanism of action studies following discovery of novel small molecules. These methods are enabling the zebrafish to have an increasingly positive impact on biomedical research and drug development.

Challenges of antibacterial discovery revisited

The discovery of novel antibiotic classes has not kept pace with the growing threat of bacterial resistance. Antibiotic candidates that act at new targets or via distinct mechanisms have the greatest potential to overcome resistance; however, novel approaches are also associated with higher attrition and longer timelines. This uncertainty has contributed to the withdrawal from antibiotic programs by many pharmaceutical companies. Genomic approaches have not yielded satisfactory results, in part due to nascent knowledge about unprecedented molecular targets, the challenge of achieving antibacterial activity by lead optimization of enzyme inhibitors, and the limitations of compound screening libraries for antibacterial discovery. Enhanced diversity of compound screening banks, entry into new chemical space, and new screening technologies are currently being exploited to improve hit rates for antibacterial discovery. Antibacterial compound lead optimization faces hurdles associated with the high plasma exposures required for efficacy. Lead optimization would be enhanced by the identification of new antibiotic classes with improved tractability and by expanding the predictability of in vitro safety assays. Implementing multiple screening and target identification strategies is recommended for improving the likelihood of discovering new antibacterial compounds that address unmet needs.

Enhancements of screening collections to address areas of unmet medical need: an industry perspective

The past 20 years have witnessed an impressive expansion of the 'drug space'; defined as the intersection of the Medicinal Chemistry space and the Biologically Active space relevant in the quest for new treatments for disease. Despite the success of known lead discovery tactics, areas of unmet medical need are often linked to challenging or novel targets and are poorly served by current screening collections. A successful strategy to fill the gaps is to diversify the approaches taken in the enhancement of screening collections. Possible strategies include investments through proven methods, exploring areas of chemical space previously neglected (e.g. hydrophilic compounds, natural product mimics), and applying tactics to the lead discovery process that are complementary to HTS (e.g. fragment based screening or multidisciplinary team efforts to tackle new target classes).