Target identification and validation in drug discovery: the role of proteomics

Harnessing network pharmacology in drug discovery: an integrated approach

Traditional drug discovery approach is based on one drug-one target, that is associated with very lengthy timelines, high costs and very low success rates. Network pharmacology (NP) is a novel method of drug designing, that is based on a multiple-target approach. NP integrates systems such as biology, pharmacology and computational techniques to address the limitations of traditional methods of drug discovery. With help of mapping biological networks, it provides deep insights into biological molecules' interactions and enhances our understanding to the mechanism of drugs, polypharmacology and disease etiology. This review explores the theoretical framework of network pharmacology, discussing the principles and methodologies that enable the construction of drug-target and disease-gene networks. It highlights how data mining, bioinformatics tools and computational models are utilised to predict drug behaviour, repurpose existing drugs and identify novel therapeutic targets. Applications of network pharmacology in the treatment of complex diseases-such as cancer, neurodegenerative disorders, cardiovascular diseases and infectious diseases-are extensively covered, demonstrating its potential to identify multi-target drugs for multifaceted disease mechanisms. Despite the promising results, NP faces challenges due to incomplete and quality of biological data, computational complexities and biological system redundancy. It also faces regulatory challenges in drug approval, demanding revision in regulatory guidelines towards multi-target therapies. Advancements in AI and machine learning, dynamic network modelling and global collaboration can further enhance the efficacy of network pharmacology. This integrative approach has the potential to revolutionise drug discovery, offering new solutions for personalised medicine, drug repurposing and tackling the complexities of modern diseases.

Identification of potential geosmin-binding proteins in grass carp gill based on affinity responsive target stability and tandem mass tag proteomics

The escalating issue of water pollution, especially the accumulation of organic off-flavor pollutants, poses significant challenges. Geosmin, a typical off-flavor compound in aquatic environments, not only compromises the quality of aquatic products but also deters consumers. Its impact extends to aquatic organisms, with current research focusing on dose-response and ecotoxicity, while neglecting the molecular-level study of geosmin-binding proteins. This study employs an integrated approach combing affinity-responsive target stability in vitro, tandem mass tag proteomics in vivo, and molecular docking to identify geosmin-binding proteins in the gill tissue of grass carp (Ctenopharyngodon idella). ARTS analysis identified 56 proteins, predominantly membrane-associated proteins, such as catenin beta-1, annexin, and integrin beta. Proteomic analysis revealed 256 differentially expressed proteins in geosmin-exposure group, with 18 common proteins screened by in vivo and in vitro methods. Among these, annexin, cathepsin D, and interleukin-1 receptors were highlighted as potential geosmin targets, with annexin demonstrating the highest binding affinity in silico. This study provides a robust protocol integrating in vivo, in vitro, and in silico approaches to elucidate geosmin's target proteins in grass carp gill tissue, advancing our understanding of pollutant-biological interactions and enhancing environmental risk assessment accuracy.

Application of a high-throughput swarm-based deep neural network Algorithm reveals SPAG5 downregulation as a potential therapeutic target in adult AML

Gene‒gene interactions play pivotal roles in disease pathogenesis and are fundamental in the development of targeted therapeutics, particularly through the elucidation of oncogenic gene drivers in cancer. The systematic analysis of pathways and gene interactions is critical in the drug discovery process for various cancer subtypes. SPAG5, known for its role in spindle formation during cell division, has been identified as an oncogene in several cancers, although its specific impact on AML remains underexplored. This study leverages a high-throughput swarm-based deep neural network (SDNN) and transcriptomic data-an approach that enhances predictive accuracy and robustness through collective intelligence-to augment, model, and enhance the understanding of the TP53 pathway in AML cohorts. Our integrative systems biology approach identified SPAG5 as a uniquely downregulated driver in adult AML, underscoring its potential as a novel therapeutic target. The interaction of SPAG5 with key hub genes such as MDM2 and CDK1 not only reinforces its role in tumour suppression through negative regulation but also highlights its potential in moderating the phenotypic and genomic alterations associated with AML progression. This study of the role and interaction dynamics of SPAG5 sets the stage for future research aimed at developing targeted and personalized treatment approaches for AML, utilizing the capabilities of genetic interventions.

Rational Drug Design for Pseudomonas aeruginosa PqsA Enzyme: An in silico Guided Study to Block Biofilm Formation

Pseudomonas aeruginosa is an opportunistic gram-negative bacterium implicated in acute and chronic nosocomial infections and a leading cause of patient mortality. Such infections occur owing to biofilm formation that confers multidrug resistance and enhanced pathogenesis to the bacterium. In this study, we used a rational drug design strategy to inhibit the quorum signaling system of P. aeruginosa by designing potent inhibitory lead molecules against anthranilate-CoA ligase enzyme encoded by the pqsA gene. This enzyme produces autoinducers for cell-to-cell communication, which result in biofilm formation, and thus plays a pivotal role in the virulence of P. aeruginosa. A library of potential drug molecules was prepared by performing ligand-based screening using an available set of enzyme inhibitors. Subsequently, structure-based virtual screening was performed to identify compounds showing the best binding conformation with the target enzyme and forming a stable complex. The two hit compounds interact with the binding site of the enzyme through multiple short-range hydrophilic and hydrophobic interactions. Molecular dynamic simulation and MM-PBSA/GBSA results to calculate the affinity and stability of the hit compounds with the PqsA enzyme further confirmed their strong interactions. The hit compounds might be useful in tackling the resistant phenotypes of this pathogen.

Conformational transition of Acinetobacter baumannii KdsC enzyme and the role of magnesium in binding: An insight from comparative molecular dynamics simulation and its implications in novel antibiotics design

The 3-deoxy-d-manno-octulosonate 8-phosphate phosphatase (KdsC) catalyzes the hydrolysis of 3-deoxy-d-manno-octulosonate 8-phosphate (KDO 8-P) to 3-deoxy-d-manno-octulosonate (KDO) and inorganic phosphate in KDO biosynthesis pathway of Gram-negative bacteria lipopolysaccharide (LPS) hydrophobic lipid-A core. The essentiality of KDO for bacterial cell viability presents the possibility of its targeting to develop broad-spectrum antibacterial agents. In this study, a receptor based virtually screening method was put forward to identify novel lead inhibitory molecules for KdsC enzyme. Dynamics evaluation in solution revealed three complexes: Asinex-1197, Asinex-1705, and Asinex-1710 from Asinex antibacterial library as highly stable, involving conformational transition from open to close upon lead molecules binding and eloquent role of active pocket magnesium towards inhibitors binding and movements. Interconversion of local secondary structure elements in sequence region of Asp192-Asp208 covering motif β-turn, β-hairpin, and β-sheets is seen recurrently that could be in all likelihood of the pressure excreted on this region during closing conformation event or magnesium driven inhibitor adjustments. The binding free energy estimation predicted gas phase energy for all the three complexes dominating with major contribution from van der Waals energy (in case of Asinex-1705 and Asinex-1710) and balanced contributions of both electrostatic and van der Waals (in case of Asinex-1197). Key residues-scanning shortlisted Leu45, Asp185, Gy188, Arg231, and Lys255 as vital in the interaction network of magnesium and inhibitors at the binding site. Their crucial roles in net binding energy were reaffirmed via in silico site directed alanine scanning method. The filtered hits might be useful to further scaffolds addition and structural optimization to yield high affinity binders of KdsC enzyme, whose inhibition, in turn, will disrupt the outer membrane synthesis.

Computational Drug Repurposing: Current Trends

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Biomedical discovery has been reshaped upon the exploding digitization of data which can be retrieved from a number of sources, ranging from clinical pharmacology to cheminformatics-driven databases. Now, supercomputing platforms and publicly available resources such as biological, physicochemical, and clinical data, can all be integrated to construct a detailed map of signaling pathways and drug mechanisms of action in relation to drug candidates. Recent advancements in computer-aided data mining have facilitated analyses of ‘big data’ approaches and the discovery of new indications for pre-existing drugs has been accelerated. Linking gene-phenotype associations to predict novel drug-disease signatures or incorporating molecular structure information of drugs and protein targets with other kinds of data derived from systems biology provide great potential to accelerate drug discovery and improve the success of drug repurposing attempts. In this review, we highlight commonly used computational drug repurposing strategies, including bioinformatics and cheminformatics tools, to integrate large-scale data emerging from the systems biology, and consider both the challenges and opportunities of using this approach. Moreover, we provide successful examples and case studies that combined various in silico drug-repurposing strategies to predict potential novel uses for known therapeutics.

Andrographolide Sulfonate Attenuates Acute Lung Injury by Reducing Expression of Myeloperoxidase and Neutrophil-Derived Proteases in Mice

Andrographolide sulfonate (Andro-S), a sulfonation derivative of andrographolide, is known to be effective in treating inflammation-related diseases, while the underlying mechanisms and global protein alterations in response to Andro-S remain unknown. This study aimed to investigate the pharmacological effects and potential targets of Andro-S in a murine model of acute lung injury (ALI). ALI was induced by aerosolized lipopolysaccharide (LPS) exposure before treatment with Andro-S. Inflammatory state of each treatment group was determined by histological analysis and quantification of inflammatory markers. Differentially expressed proteins in lung tissues were identified by an iTRAQ-based quantitative proteomic approach and further confirmed by immunohistochemistry analysis. Administration of Andro-S alleviated LPS-induced histological changes in the lung and reduced the expression of inflammatory markers in serum, bronchoalveolar fluid and lung tissues. Proteomic analysis identified 31 differentially expressed proteins from a total of 2,234 quantified proteins in the lung. According to bioinformatics analysis, neutrophil elastase (ELANE), cathepsin G (CTSG) and myeloperoxidase (MPO), three neutrophil-derived proteases related to immune system process and defense responses to fungi were chosen as potential targets of Andro-S. Further immunohistochemistry analysis confirmed the inhibitory effects of Andro-S on LPS-induced ELANE, CTSG and MPO up-regulation. These results indicate that Andro-S suppressed the severity of LPS-induced ALI, possibly by attenuating the expression of and neutrophil-derived proteases.

Aptamers: novelty tools for cancer biology

Although the term ‘cancer’ was still over two thousand years away of being coined, the first known cases of the disease date back to about 3000BC, in ancient Egypt. Five thousand years later, still lacking a cure, it has become one of the leading causes of death, killing over half a dozen million people yearly. So far, monoclonal antibodies are the most successful immune-therapy tools when it comes to fighting cancer. The number of clinical trials that use them has been increasing steadily during the past few years, especially since the Food and Drug Administration greenlit the use of the first immune-checkpoint blockade antibodies. However, albeit successful, this approach does come with the cost of auto-inflammatory toxicity. Taking this into account, the development of new therapeutic reagents with low toxicity becomes evident, particularly ones acting in tandem with the tools currently at our disposal. Ever since its discovery in the early nineties, aptamer technology has been used for a wide range of diagnostic and therapeutic applications. With similar properties to those of monoclonal antibodies, such as high-specificity of recognition and high-affinity binding, and the advantages of being developed using in vitro selection procedures, aptamers quickly became convenient building blocks for the generation of multifunctional constructs. In this review, we discuss the steps involved in the in vitro selection process that leads to functional aptamers - known as Systematic Evolution of Ligands by Exponential Enrichment - as well as the most recent applications of this technology in diagnostic and treatment of oncological illnesses. Moreover, we also suggest ways to improve such use.

New technologies in computer-aided drug design: Toward target identification and new chemical entity discovery

In the postgenomic era, computer-aided drug design (CADD) has considerably extended its range of applications, spanning almost all stages in the drug discovery pipeline, from target identification to lead discovery, from lead optimization to preclinical or clinical trials. Two new technologies of CADD associated with target identification and new chemical entity discovery will be the focus of this review.

Li-he Zhang – School of Pharmaceutical Science, Peking University, Beijing, China

Kaixian Chen – Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China

Translational paradigms in pharmacology and drug discovery

The translational sciences represent the core element in enabling and utilizing the output from the biomedical sciences and to improving drug discovery metrics by reducing the attrition rate as compounds move from preclinical research to clinical proof of concept. Key to understanding the basis of disease causality and to developing therapeutics is an ability to accurately diagnose the disease and to identify and develop safe and effective therapeutics for its treatment. The former requires validated biomarkers and the latter, qualified targets. Progress has been hampered by semantic issues, specifically those that define the end product, and by scientific issues that include data reliability, an overt reductionistic cultural focus and a lack of hierarchically integrated data gathering and systematic analysis. A necessary framework for these activities is represented by the discipline of pharmacology, efforts and training in which require recognition and revitalization.

Computational drug discovery

Computational drug discovery is an effective strategy for accelerating and economizing drug discovery and development process. Because of the dramatic increase in the availability of biological macromolecule and small molecule information, the applicability of computational drug discovery has been extended and broadly applied to nearly every stage in the drug discovery and development workflow, including target identification and validation, lead discovery and optimization and preclinical tests. Over the past decades, computational drug discovery methods such as molecular docking, pharmacophore modeling and mapping, de novo design, molecular similarity calculation and sequence-based virtual screening have been greatly improved. In this review, we present an overview of these important computational methods, platforms and successful applications in this field.

Identification and validation of bioactive small molecule target through phenotypic screening

For effective bioactive small molecule discovery and development into new therapeutic drug, a systematic screening and target protein identification is required. Different from the conventional screening system, herein phenotypic screening in combination with multi-omics-based target identification and validation (MOTIV) is introduced. First, phenotypic screening provides visual effect of bioactive small molecules in the cell or organism level. It is important to know the effect on the cell or organism level since small molecules affect not only a single target but the entire cellular mechanism within a cell or organism. Secondly, MOTIV provides systemic approach to discover the target protein of bioactive small molecule. With the chemical genomics and proteomics approach of target identification methods, various target protein candidates are identified. Then network analysis and validations of these candidates result in identifying the biologically relevant target protein and cellular mechanism. Overall, the combination of phenotypic screening and MOTIV will provide an effective approach to discover new bioactive small molecules and their target protein and mechanism identification.

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.

Challenges of introducing new biomarker products for neuropsychiatric disorders into the market

There are many challenges associated with the discovery and development of serum-based biomarkers for psychiatric disorders such as schizophrenia. Here, we review these challenges from the point of view of psychiatrists, general practitioners, the regulatory agencies, and biomarker scientists. There is a general opinion in psychiatric medicine that improvements over the current subjective tests are essential. Despite this, there is a reluctance to accept that peripheral molecules can do the job any better. In addition, psychiatrists find it difficult to accept that peripheral molecules, such as those found in blood, can reflect what is happening in the brain. However, the regulatory health authorities now consider biomarkers as important for the future of drug development and have called for efforts to modernize methods, tools, and techniques for the purpose of developing more efficient and safer drugs. We also describe here the development of the first ever molecular blood test for schizophrenia, and its reception in the market place, as a case in point.

Two-dimensional gels for toxicological drug discovery applications

Two-dimensional gel electrophoresis (2DGE) continues to be a useful approach to study protein expression. Although liquid chromatographic and mass spectrometric approaches that overcome some of the limitations and labour intensity of 2DGE are increasingly popular, this electrophoretic approach still has exceptional relevance in toxicology. Despite the technical challenges, pharmacologists/toxicologists continue to use gel-based proteomics to assess the biological and health effects of chemical treatment and exposure. This brief review addresses the use of 2DGE-based proteomics in drug development and toxicology, emphasising its unique strengths and weaknesses, and considers recent developments in this strategy that have evolved to directly confront the issues of dynamic range and reproducibility that have previously limited the overall use of 2D electrophoresis.

Defining the role of pharmacology in the emerging world of translational research

Pharmacology is focused on studying the effects of endogenous agents and xenobiotics on tissue and organ function. Analysis of the concentration/response relationship is the foundation for these assessments as it provides quantifiable information on compound efficacy, potency, and, ultimately, side-effect liability and therapeutic index. Historically, pharmacology has been viewed as a unifying, hierarchically integrated, and technologically agnostic discipline. Besides being important in the development of new medications, pharmacological research has led to a better understanding of disease pathogenesis and progression. By defining the effects of compounds in vitro and in vivo, pharmacology has provided the means to validate, optimize, and advance new chemical entities (NCEs) to human testing. With the advent of molecular biology-based assay systems and a technology-driven (high-throughput screening, combinatorial chemistry, SNP mapping, systems biology) reductionistic focus, the integrated, hypothesis-driven pharmacological approach to drug discovery has been de-emphasized in recent years. This shift in research emphasis is now viewed by many as a major factor in the decline of new drug approvals and has led to various initiatives, the most notable being the Critical Path and Phase 0 clinical trial initiatives launched by the US Food and Drug Administration (FDA). These programs underscore the growing need for individuals trained in integrative pharmacology and having a background in molecular pharmacology to drive the drug discovery process and to fostering the translational research that is now considered vital for more rapidly identifying novel, more effective, and safer medications.

An historical overview of drug discovery

Drug Discovery in modern times straddles three main periods. The first notable period can be traced to the nineteenth century where the basis of drug discovery relied on the serendipity of the medicinal chemists. The second period commenced around the early twentieth century when new drug structures were found, which contributed for a new era of antibiotics discovery. Based on these known structures, and with the development of powerful new techniques such as molecular modelling, combinatorial chemistry, and automated high-throughput screening, rapid advances occurred in drug discovery towards the end of the century. The period also was revolutionized by the emergence of recombinant DNA technology, where it became possible to develop potential drugs target candidates. With all the expansion of new technologies and the onset of the "Omics" revolution in the twenty-first century, the third period has kick-started with an increase in biopharmaceutical drugs approved by FDA/EMEA for therapeutic use.

Proteomics investigation of protein expression changes in ouabain induced apoptosis in human umbilical vein endothelial cells

Ouabain is Na(+)/K(+)-ATPase inhibitor and an endogenous regulator of blood pressure, it has dual effect on vascular endothelial cells(VEC) cell growth and VEC apoptosis is contributed to vascular dysfunction involved in vascular remolding. However, the precise mechanisms of apoptosis induced by ouabain remained unclear. The objective of this study was to identify the differently expressed proteins involved in VEC apoptosis induced by ouabain in order to explore cellular and subcellular mechanisms related to ouabain actions. Human umbilical vein endothelial cells (HUVEC) were exposed to increasing concentrations (0.1 nM to 10 microM) of ouabain at 12-48 h intervals. Cell viability tests revealed that high concentrations of ouabain inhibited cell growth. Flow cytometry and caspase-3 activity analysis confirmed that apoptosis was primarily responsible for ouabain induced cell death. Two-dimensional electrophoresis in conjunction with mass spectrometry revealed that the ouabain-induced apoptosis was accompanied by regulated expression of programmed cell death protein 6, cytochrome C1, endothelin converting enzyme, claudin-1, reticulon-4, galectin-1, ras-related protein rab-11B, calnexin, profilin-1 and heat shock protein 60 (HSP60). Further study on cytochrome c and HSP60 demonstrated that levels of mitochondria and cytosol cytochrome c and HSP60 changed in response to ouabain treatment. Data showed that mitochondria proteins such as HSP60 interferes with HSP60-Bax interactions played an important role in ouabain induced apoptosis. These data bring new sights into physiological role for ouabain in VEC apoptosis and vascular remodeling, thus provide new strategies for new anti-cardiovascular disease drug development or the identification of biomarkers for vascular dysfunction in ouabain related hypertension.

Ovarian cancer in the proteomics era

Ovarian cancer presents a diagnostic challenge because of its subtle clinical presentation and elusive cell of origin. Two new technologies of proteomics have advanced the dissection of the underlying molecular signaling events and the proteomic characterization of ovarian cancer: mass spectrometry and protein array analysis. Mass spectrometry can provide a snapshot of a proteome in time and space, with sensitivity and resolution that may allow identification of the elusive "needle in the haystack" heralding ovarian cancer. Proteomic profiling of tumor tissue samples can survey molecular targets during treatment and quantify changes using reverse phase protein arrays generated from tumor samples captured by microdissection, lysed and spotted in serial dilutions for high-throughput analysis. This approach can be applied to identify the optimal biological dose of a targeted agent and to validate target to outcome link. The evolution of proteomic technologies has the capacity to advance rapidly our understanding of ovarian cancer at a molecular level and thus elucidate new directions for the treatment of this disease.

Fishing for anti-leishmania drugs: principles and problems

To date, there are no vaccines against any of the major parasitic diseases including leishmaniasis, and chemotherapy is the main weapon in our arsenal. Current drugs are toxic and expensive, and are losing their effectiveness due to parasite resistance. The availability of the genome sequence of two species of Leishmania, Leishmania major and Leishmania infantum, as well as that of Trypanosoma brucei and Trypanosoma cruzi should provide a cornucopia of potential new drug targets. Their exploitation will require a multi-disciplinary approach that includes protein structure and function and high throughput screening of random and directed chemical libraries, followed by in vivo testing in animals and humans. We outline the opportunities that are made possible by recent technologies, and potential problems that need to be overcome.

Proteomics in human cancer research

Proteomics is now widely employed in the study of cancer. Many laboratories are applying the rapidly emerging technologies to elucidate the underlying mechanisms associated with cancer development, progression, and severity in addition to developing drugs and identifying patients who will benefit most from molecular targeted compounds. Various proteomic approaches are now available for protein separation and identification, and for characterization of the function and structure of candidate proteins. In spite of significant challenges that still exist, proteomics has rapidly expanded to include the discovery of novel biomarkers for early detection, diagnosis and prognostication (clinical application), and for the identification of novel drug targets (pharmaceutical application). To achieve these goals, several innovative technologies including 2-D-difference gel electrophoresis, SELDI, multidimensional protein identification technology, isotope-coded affinity tag, solid-state and suspension protein array technologies, X-ray crystallography, NMR spectroscopy, and computational methods such as comparative and de novo structure prediction and molecular dynamics simulation have evolved, and are being used in different combinations. This review provides an overview of the field of proteomics and discusses the key proteomic technologies available to researchers. It also describes some of the important challenges and highlights the current pharmaceutical and clinical applications of proteomics in human cancer research.

Development and validation of a modular, extensible docking program: DOCK 5

We report on the development and validation of a new version of DOCK. The algorithm has been rewritten in a modular format, which allows for easy implementation of new scoring functions, sampling methods and analysis tools. We validated the sampling algorithm with a test set of 114 protein-ligand complexes. Using an optimized parameter set, we are able to reproduce the crystal ligand pose to within 2 A of the crystal structure for 79% of the test cases using our rigid ligand docking algorithm with an average run time of 1 min per complex and for 72% of the test cases using our flexible ligand docking algorithm with an average run time of 5 min per complex. Finally, we perform an analysis of the docking failures in the test set and determine that the sampling algorithm is generally sufficient for the binding pose prediction problem for up to 7 rotatable bonds; i.e. 99% of the rigid ligand docking cases and 95% of the flexible ligand docking cases are sampled successfully. We point out that success rates could be improved through more advanced modeling of the receptor prior to docking and through improvement of the force field parameters, particularly for structures containing metal-based cofactors.

Comparison of different separation technologies for proteome analyses: Isoform resolution as a prerequisite for the definition of protein biomarkers on the level of posttranslational modifications

In this article we evaluate methods used to reveal the molecular complexity, which is generated in biological samples by posttranslational modifications (PTM) of proteins. We show how distinct molecular differences on the level of phosphorylation sites in a single protein (ovalbumin) can be resolved with different success using 1D and 2D gel-electrophoresis and reversed-phase liquid chromatography (LC) with monolithic polystyrol-divinylbenzol (PS-DVB) columns for protein separation, and matrix-assisted laser desorption ionisation-time of flight mass spectrometry (MALDI-TOF MS) for protein identification. Phosphorylation site analysis was performed using enzymatic dephosphorylation in combination with differential peptide mass mapping. Liquid chromatography-MALDI-TOF MS coupling with subsequent on-target tryptic protein digestion turned out to be the fastest method tested but yielded low resolution for the analysis of PTM, whereas 2D gel-electrophoresis, due to its unique capability of resolving highly complex isoform pattern, turned out to be the most suitable method for this purpose. The evaluated methods complement one another and in connection with efficient technologies for differential and quantitative analysis, these approaches have the potential to reveal novel molecular details of protein biomarkers.

Tumour microenvironment - opinion: validating matrix metalloproteinases as drug targets and anti-targets for cancer therapy

The matrix metalloproteinases (MMPs) mediate homeostasis of the extracellular environment. They have multiple signalling activities that are commonly altered during tumorigenesis and that might serve as intervention points for anticancer drugs. However, there are many criteria to consider in validating MMPs as drug targets and for the development of MMP inhibitors. The inhibition of some MMPs could have pro-tumorigenic effects (making them anti-targets), counterbalancing the benefits of target inhibition. These effects might partially account for the failure of MMP inhibitors in clinical trials. What are the major challenges in MMP target validation and MMP-inhibitor-drug development?

Reengineering the pharmaceutical industry by crash-testing molecules

The recent decline in drug approvals and the increase in late-stage failures indicate that the ability to generate and screen large numbers of molecules has not improved the drug pipeline. Perhaps the pharmaceutical industry should follow the example of the automotive industry and agree upon a shared modeling language with vendors and academics to enable integration of predictive computational tools across the industry. This will then enable the virtual 'crash-testing' of drugs before synthesis, biological testing and, most importantly, clinical trials. This represents an ambitiously progressive approach using the models for simulating every stage of the drug discovery and development process. Combining the relevant computational algorithms into a grand unified model would enable prioritization of the best ideas before pursuing a discovery program, selecting a target or synthesizing a molecule. The successful application of these virtual crash-testing principles by any of its current proponents could revitalize the pharmaceutical industry so that failure is avoided.