Systems biology in drug discovery

The hope of the rapid translation of 'genes to drugs' has foundered on the reality that disease biology is complex, and that drug development must be driven by insights into biological responses. Systems biology aims to describe and to understand the operation of complex biological systems and ultimately to develop predictive models of human disease. Although meaningful molecular level models of human cell and tissue function are a distant goal, systems biology efforts are already influencing drug discovery. Large-scale gene, protein and metabolite measurements ('omics') dramatically accelerate hypothesis generation and testing in disease models. Computer simulations integrating knowledge of organ and system-level responses help prioritize targets and design clinical trials. Automation of complex primary human cell-based assay systems designed to capture emergent properties can now integrate a broad range of disease-relevant human biology into the drug discovery process, informing target and compound validation, lead optimization, and clinical indication selection. These systems biology approaches promise to improve decision making in pharmaceutical development.     

Modeling opportunities in comparative oncology for drug development

Successful development of novel cancer drugs depends on well-reasoned scientific drug discovery, rigorous preclinical development, and carefully conceived clinical trials. Failure in any of these steps contributes to poor rates of approval for new drugs to treat cancer. As technological and scientific advances have opened the door to a variety of novel approaches to cancer drug discovery and development, preclinical models that can answer questions about the activity and safety of novel therapies are increasingly necessary. The advance of a drug to clinical trials based on information from preclinical models presupposes that the models convey informative data for future use in human patients with cancer. The study of novel cancer drugs using in vitro models is highly controllable, reproducible, relatively inexpensive, and linked to high throughput. However, these models fail to reproduce many of the complex features of human cancer. Mouse models address some of these limitations but have important biological differences from human cancer. The integration of studies using pet dogs with spontaneously occurring tumors as models in the development path can answer questions not adequately addressed in conventional models and is therefore gaining attention and interest in drug development communities. The study of novel cancer drugs in dogs with naturally occurring tumors allows drug assessment in a cancer that shares many fundamental features with the human cancer condition, and thus provides an opportunity to answer questions that inform the cancer drug development path in ways not possible in more conventional models.     

Understanding drugs and diseases by systems biology?

Systems biology aims to provide a holistic and in many cases dynamic picture of biological function and malfunction, in case of disease. Technology developments in the generation of genome-wide datasets and massive improvements in computer power now allow to obtain new insights into complex biological networks and to copy nature by computing these interactions and their kinetics and by generating in silico models of cells, tissues and organs. The expectations are high that systems biology will pave the way to the identification of novel disease genes, to the selection of successful drug candidates—that do not fail in clinical studies due to toxicity or lack of human efficacy—and finally to a more successful discovery of novel therapeutics. However, further research is necessary to fully unleash the potential of systems biology. Within this review we aim to highlight the most important and promising top-down and bottom-up systems biology applications in drug discovery.     

Activity- and reactivity-based proteomics: Recent technological advances and applications in drug discovery

Activity-based protein profiling (ABPP) is recognized as a powerful and versatile chemoproteomic technology in drug discovery. Central to ABPP is the use of activity-based probes to report the activity of specific enzymes or reactivity of amino acid types in complex biological systems. Over the last two decades, ABPP has facilitated the identification of new drug targets and discovery of lead compounds in human and infectious disease. Furthermore, as part of a sustained global effort to illuminate the druggable proteome, the repertoire of target classes addressable with activity-based probes has vastly expanded in recent years. Here, we provide an overview of ABPP and summarise the major technological advances with an emphasis on probe development.     

The impact of whole genome sequence data on drug discovery--a malaria case study.

BACKGROUND: Identification and validation of a drug discovery target is a prominent step in drug development. In the post-genomic era it is possible to reevaluate the association of a gene with a specific biological function to see if a homologous gene can subsume this role. This concept has special relevance to drug discovery in human infectious diseases, like malaria. A trophozoite cysteine protease (falcipain-1) from the papain family, thought to be responsible for the degradation of erythrocyte hemoglobin, has been considered a promising target for drug discovery efforts owing to the antimalarial activity of peptide based covalent cysteine protease inhibitors. This led to the development of non-peptidic non-covalent inhibitors of falcipain-1 and their characterization as antimalarials. It is now clear from sequencing efforts that the malaria genome contains more than one cysteine protease and that falcipain-1 is not the most important contributor to hemoglobin degradation. Rather, falcipain-2 and falcipain-3 appear to account for the majority of cysteine hemoglobinase activity in the plasmodium trophozoite. MATERIALS AND METHODS: We have modeled the falcipain-2 cysteine protease from one of the major human malaria species, Plasmodium falciparum and compared it to our original work on falcipain-1. As with falcipain-1, computa-tional screening of the falcipain-2 active site was conducted using DOCK. Using structural superpositions within the protease family and evolutionary analysis of substrate specificity sites, we focused on the commonalities and the protein specific features to direct our drug discovery effort. RESULTS: Since 1993, the size of the Available Chemicals Directory had increased from 55313 to 195419 unique chemical structures. For falcipain-2, eight inhibitors were identified with IC50's against the enzyme between 1 and 7 microM. Application of three of these inhibitors to infected erythrocytes cured malaria in culture, but parasite death did not correlate with food vacuole abnormalities associated with the activity of mechanistic inhibitors of cysteine proteases like the epoxide E64. CONCLUSIONS: Using plasmodial falcipain proteases, we show how a protein family perspective can influence target discovery and inhibitor design. We suspect that parallel drug discovery programs where a family of targets is considered, rather than serial programs built on a single therapeutic focus, will become the dominant industrial paradigm. Economies of scale in assay development and in compound synthesis are expected owing to the functional and structural features of individual family members. One of the remaining challenges in post-genomic drug discovery is that inhibitors of one target are likely to show some activity against other family members. This lack of specificity may lead to difficulties in functional assignments and target validation as well as a complex side effect profile.     

Automated high-content live animal drug screening using C. elegans expressing the aggregation prone serpin α1-antitrypsin Z

The development of preclinical models amenable to live animal bioactive compound screening is an attractive approach to discovering effective pharmacological therapies for disorders caused by misfolded and aggregation-prone proteins. In general, however, live animal drug screening is labor and resource intensive, and has been hampered by the lack of robust assay designs and high throughput work-flows. Based on their small size, tissue transparency and ease of cultivation, the use of C. elegans should obviate many of the technical impediments associated with live animal drug screening. Moreover, their genetic tractability and accomplished record for providing insights into the molecular and cellular basis of human disease, should make C. elegans an ideal model system for in vivo drug discovery campaigns. The goal of this study was to determine whether C. elegans could be adapted to high-throughput and high-content drug screening strategies analogous to those developed for cell-based systems. Using transgenic animals expressing fluorescently-tagged proteins, we first developed a high-quality, high-throughput work-flow utilizing an automated fluorescence microscopy platform with integrated image acquisition and data analysis modules to qualitatively assess different biological processes including, growth, tissue development, cell viability and autophagy. We next adapted this technology to conduct a small molecule screen and identified compounds that altered the intracellular accumulation of the human aggregation prone mutant that causes liver disease in α1-antitrypsin deficiency. This study provides powerful validation for advancement in preclinical drug discovery campaigns by screening live C. elegans modeling α1-antitrypsin deficiency and other complex disease phenotypes on high-content imaging platforms.     

Drug discovery of the future: the implications of the human genome project

The elucidation of the 3.2-gigabase human genome will have various impacts on drug discovery. The number of drug targets will increase by at least one order of magnitude and target validation will become a high-throughput process. To benefit from these opportunities, a theory-based integration of the vast amount of new biological data into models of biological systems is called for. The skills and knowledge required for genome-based drug discovery of the future go beyond the traditional competencies of the pharmaceutical industry. Cooperation with biotechnology firms and research institutions during drug discovery and development will become even more important.     

Predictive markers in early research and companion diagnostic developments in oncology

Predictive biomarkers are discovered and used in oncology research to formulate hypotheses aimed at the identification of patients benefiting from specific therapeutic intervention(s). They pave the way to the development of companion diagnostic tests which are tools readily implemented in the clinic and serve to qualify a patient for treatment with a particular targeted drug or the continued use of a particular drug, thus maximizing the benefit to risk ratio of the medical intervention to the patient. Predictive biomarkers are defined by biological characteristics of the patient's or tumor status that can be measured objectively and correlated with clinical outcome: these can be molecular, cellular or biochemical features. Predictive markers need extensive analytical validation - specific for the tool utilized for their assessment - as well as rigorous clinical qualification in the context of the drug treatment for which they define clinical utility. The process of companion diagnostic development is a highly interdisciplinary and complex one, driven by key crucial milestones and accompanying the same and typical process of a whole drug discovery and development continuum, from marker discovery and validation, assay development, clinical qualification until test approval and commercialization.     

Chemical proteomics and its application to drug discovery

The completion of the human genome sequencing project has provided a flood of new information that is likely to change the way scientists approach the study of complex biological systems. A major challenge lies in translating this information into new and better ways to treat human disease. The multidisciplinary science of chemical proteomics can be used to distill this flood of new information. This approach makes use of synthetic small molecules that can be used to covalently modify a set of related enzymes and subsequently allow their purification and/or identification as valid drug targets. Furthermore, such methods enable rapid biochemical analysis and small-molecule screening of targets thereby accelerating the often difficult process of target validation and drug discovery.     

Proteomics in the post-genome age.

The genome sequencing effort has helped spawn the burgeoning field of proteomics. This review article examines state-of-the-art proteomics methods that are helping change the discovery paradigm in a variety of biological disciplines and, in particular, protein biochemistry. The review discusses both classical and novel methods to perform high-throughput qualitative and quantitative "global" as well as targeted proteome analysis of complex biological systems. From a drug discovery standpoint, the synergy between genomics and proteomics will help elucidate disease mechanisms, identify novel drug targets, and identify surrogate biomarkers that could be used to conduct clinical trials.     

Metabolic network modeling and simulation for drug targeting and discovery

Systems biology has greatly contributed toward the analysis and understanding of biological systems under various genotypic and environmental conditions on a much larger scale than ever before. One of the applications of systems biology can be seen in unraveling and understanding complicated human diseases where the primary causes for a disease are often not clear. The in silico genome-scale metabolic network models can be employed for the analysis of diseases and for the discovery of novel drug targets suitable for treating the disease. Also, new antimicrobial targets can be discovered by analyzing, at the systems level, the genome-scale metabolic network of pathogenic microorganisms. Such applications are possible as these genome-scale metabolic network models contain extensive stoichiometric relationships among the metabolites constituting the organism's metabolism and information on the associated biophysical constraints. In this review, we highlight applications of genome-scale metabolic network modeling and simulations in predicting drug targets and designing potential strategies in combating pathogenic infection. Also, the use of metabolic network models in the systematic analysis of several human diseases is examined. Other computational and experimental approaches are discussed to complement the use of metabolic network models in the analysis of biological systems and to facilitate the drug discovery pipeline.     

Illuminating drug discovery with biological pathways

Systems biology promises to impact significantly on the drug discovery process. One of its ultimate goals is to provide an understanding of the complete set of molecular mechanisms describing an organism. Although this goal is a long way off, many useful insights can already come from currently available information and technology. One of the biggest challenges in drug discovery today is the high attrition rate: many promising candidates prove ineffective or toxic owing to a poor understanding of the molecular mechanisms of biological systems they target. A "systems" approach can help identify pathways related to a disease and can suggest secondary effects of drugs that might cause these problems and thus ultimately improve the drug discovery pipeline.     

Towards a molecular characterisation of pathological pathways

The dominant conceptual reductionism in drug discovery has resulted in many promising drug candidates to fail during the last clinical phases, mainly due to a lack of knowledge about the patho-physiological pathways they are acting on. Consequently, to increase the revenues of the drug discovery process, we need to improve our understanding of the molecular mechanisms underlying complex cellular processes and consider each potential drug target in its full biological context. Here, we review several strategies that combine computational and experimental techniques, and suggest a systems pathology approach that will ultimately lead to a better comprehension of the molecular bases of disease.     

Sensitive cell viability assay for use in drug screens and for studying the mechanism of action of drugs in Dictyostelium discoideum

In order to increase the effectiveness of Dictyostelium discoideum as a lead genetic model for drug discovery, a luminescence-based assay has been adapted and standardized for sensitive and rapid cell viability measurements. The applicability of the assay was demonstrated by measuring the cytotoxicity of several drugs in wild-type and mutant cells. The robustness and ease of the assay demonstrate that it can be used in high-throughput applications such as drug or mutant screens. Conclusions from these studies are applicable to evaluating cell viability assays in other systems as well.     

Development of a high-throughput screening method for LIM kinase 1 using a luciferase-based assay of ATP consumption

Kinases are attractive drug targets because of the central roles they play in signal transduction pathways and human diseases. Their well-formed adenosine triphosphate (ATP)-binding pockets make ideal targets for small-molecule inhibitors. For drug discovery purposes, many peptide-based kinase assays have been developed that measure substrate phosphorylation using fluorescence-based readouts. However, for some kinases these assays may not be appropriate. In the case of the LIM kinases (LIMK), an inability to phosphorylate peptide substrates resulted in previous high-throughput screens (HTS) using radioactive labeling of recombinant cofilin protein as the readout. We describe the development of an HTS-compatible assay that measures relative ATP levels using luciferase-generated luminescence as a function of LIMK activity. The assay was inexpensive to perform, and proof-of-principle screening of kinase inhibitors demonstrated that compound potency against LIMK could be determined; ultimately, the assay was used for successful prosecution of automated HTS. Following HTS, the secondary assay format was changed to obtain more accurate measures of potency and mechanism of action using more complex (and expensive) assays. The luciferase assay nonetheless provides an inexpensive and reliable primary assay for HTS that allowed for the identification of LIMK inhibitors to initiate discovery programs for the eventual treatment of human diseases.     

Cover Image,Volume 116, Number 9, September 2019

The new and rapid advancement in the complexity of biologics drug discovery has been driven by a deeper understanding of biological systems combined with innovative new therapeutic modalities, paving the way to breakthrough therapies for previously intractable diseases. These exciting times in biomedical innovation require the development of novel technologies to facilitate the sophisticated, multifaceted, high-paced workflows necessary to support modern large molecule drug discovery. A high-level aspiration is a true integration of “lab-on-a-chip” methods that vastly miniaturize cellulmical experiments could transform the speed, cost, and success of multiple workstreams in biologics development. Several microscale bioprocess technologies have been established that incrementally address these needs, yet each is inflexibly designed for a very specific process thus limiting an integrated holistic application. A more fully integrated nanoscale approach that incorporates manipulation, culture, analytics, and traceable digital record keeping of thousands of single cells in a relevant nanoenvironment would be a transformative technology capable of keeping pace with today's rapid and complex drug discovery demands. The recent advent of optical manipulation of cells using light-induced electrokinetics with micro- and nanoscale cell culture is poised to revolutionize both fundamental and applied biological research. In this review, we summarize the current state of the art for optical manipulation techniques and discuss emerging biological applications of this technology. In particular, we focus on promising prospects for drug discovery workflows, including antibody discovery, bioassay development, antibody engineering, and cell line development, which are enabled by the automation and industrialization of an integrated optoelectronic single-cell manipulation and culture platform. Continued development of such platforms will be well positioned to overcome many of the challenges currently associated with fragmented, low-throughput bioprocess workflows in biopharma and life science research.     

Using aptamers as capture reagents in bead-based assay systems for diagnostics and hit identification

Most applications of xMAP (Luminex) bead-based assay technology in diagnostics and drug discovery use immobilized antigens or antibodies. Here the authors describe the development of novel assay systems in which synthetic oligonucleotides that specifically bind and inhibit other biomolecules--so-called aptamers--are directly immobilized on beads. The robustness, specificity, and sensitivity of aptamer-based assays were demonstrated in a test system that detected human alpha-thrombin in serum samples. xMAP technology was also adapted to competitive screening formats where an aptamer/protein complex was disrupted by a functionally analogous competitor. The results indicate that such assays are excellently suited for diagnostic applications or drug screening, where aptamers serve as competitive binding probes for the identification of small-molecule hits. These methods should be transferable to a large number of applications because specific aptamers can be rapidly generated for almost any protein target.     

Application of mass spectrometry technologies for the discovery of low-molecular weight modulators of enzymes and protein-protein interactions

In recent years, mass spectrometry has gained widespread use as an assay and screening technology in drug discovery because it enables sensitive, label-free detection of low-molecular weight modulators of biomolecules as well as sensitive and accurate detection of high-molecular weight modifications of biomolecules. Electrospray and matrix-assisted laser desorption ionization are the most widely used ionization techniques to identify chemical compounds interfering with enzymatic function, receptor-ligand binding or molecules modulating a protein-protein interaction of interest. Mass spectrometry based techniques are no longer restricted to screening in biochemical assay systems but have now become also applicable to imaging of biomolecules and chemical compounds in cell-based assay systems and even in highly complex tissue sections.     

Synthetic biology advancing clinical applications

The 'omics' era, with its identification of genetic and protein components, has combined with systems biology, which provided insights into network structures, to set the stage for synthetic biology, an emerging interdisciplinary life science that uses engineering principles. By capitalizing on an iterative design cycle that involves molecular and computational biology tools to assemble functional designer devices from a comprehensive catalogue of standardized biological components with predictable functions, synthetic biology has significantly advanced our understanding of complex control dynamics that program living systems. Such insights, collected over the past decade, are priming a variety of synthetic biology-inspired biomedical applications that have the potential to revolutionize drug discovery and production technologies, as well as treatment strategies for infectious diseases and metabolic disorders.     

Analysis of multiple compound–protein interactions reveals novel bioactive molecules

The discovery of novel bioactive molecules advances our systems‐level understanding of biological processes and is crucial for innovation in drug development. For this purpose, the emerging field of chemical genomics is currently focused on accumulating large assay data sets describing compound–protein interactions (CPIs). Although new target proteins for known drugs have recently been identified through mining of CPI databases, using these resources to identify novel ligands remains unexplored. Herein, we demonstrate that machine learning of multiple CPIs can not only assess drug polypharmacology but can also efficiently identify novel bioactive scaffold‐hopping compounds. Through a machine‐learning technique that uses multiple CPIs, we have successfully identified novel lead compounds for two pharmaceutically important protein families, G‐protein‐coupled receptors and protein kinases. These novel compounds were not identified by existing computational ligand‐screening methods in comparative studies. The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.