The role of innovation in drug development

Inducing or enhancing protein-protein interaction to develop drugs: Molecular glues with various biological activity

Over the past two decades, molecular glues (MGs) have gradually attracted the attention of the pharmaceutical community with the advent of MG degraders such as IMiDs and indisulam. Such molecules degrade the target protein by promoting the interaction between the target protein and E3 ligase. In addition, as a chemical inducer, MGs promote the dimerization of homologous proteins and heterologous proteins to form ternary complexes, which have great prospects in regulating biological activities. This review focuses on the application of MGs in the field of drug development including protein-protein interaction (PPI) stability and protein degradation. We thoroughly analyze the structure of various MGs and the interactions between MGs and various biologically active molecules, thus providing new perspectives for the development of PPI stabilizers and new degraders.

Three Dimensional Bioprinting for Hepatic Tissue Engineering: From In Vitro Models to Clinical Applications

Fabrication of functional organs is the holy grail of tissue engineering and the possibilities of repairing a partial or complete liver to treat chronic liver disorders are discussed in this review. Liver is the largest gland in the human body and plays a responsible role in majority of metabolic function and processes. Chronic liver disease is one of the leading causes of death globally and the current treatment strategy of organ transplantation holds its own demerits. Hence there is a need to develop an in vitro liver model that mimics the native microenvironment. The developed model should be a reliable to understand the pathogenesis, screen drugs and assist to repair and replace the damaged liver. The three-dimensional bioprinting is a promising technology that recreates in vivo alike in vitro model for transplantation, which is the goal of tissue engineers. The technology has great potential due to its precise control and its ability to homogeneously distribute cells on all layers in a complex structure. This review gives an overview of liver tissue engineering with a special focus on 3D bioprinting and bioinks for liver disease modelling and drug screening.

Computational investigations on the dynamic binding effect of molecular tweezer CLR01 toward intrinsically disordered HIV-1 Nef

Intrinsically disordered proteins (IDPs) are highly flexible molecules that undergo disorder to order transition through their interaction with other molecules. IDPs play a vital role in several biological processes ranging from molecular recognition to several human diseases through the protein-protein interaction. The dynamic flexibility of IDPs and their implications in several human diseases enable these molecules to serve as novel therapeutic targets. However, the challenging task is to develop novel drugs against IDPs because of their lack of stable structures and the nature of high conformational flexibility. In this study, we have calculated the dynamic binding effect of the supramolecular tweezer CLR01 against the intrinsically disordered HIV-1 Nef by employing molecular docking and dynamics simulation approaches. From docking results, we predicted the strong binding affinity of the tweezer with the target residues of Nef. The docking results were further validated from the molecular dynamics simulation studies confirming the conformational stability of Nef upon tweezer binding. These findings provide useful insights into the development of potent inhibitors for targeting Nef protein functions.

A novel hybrid method named electron conformational genetic algorithm as a 4D QSAR investigation to calculate the biological activity of the tetrahydrodibenzazosines

To understand the structure-activity correlation of a group of tetrahydrodibenzazocines as inhibitors of 17β-hydroxysteroid dehydrogenase type 3, we have performed a combined genetic algorithm (GA) and four-dimensional quantitative structure-activity relationship (4D-QSAR) modeling study. The computed electronic and geometry structure descriptors were regulated as a matrix and named as electron-conformational matrix of contiguity (ECMC). A chemical property-based pharmacophore model was developed for series of tetrahydrodibenzazocines by EMRE software package. GA was employed to choose an optimal combination of parameters. A model has been developed for estimating anticancer activity quantitatively. All QSAR models were established with 40 compounds (training set), then they were considered for selective capability with additional nine compounds (test set). A statistically valid 4D-QSAR ( R training 2 = 0.856 , R test 2 = 0.851 and q² = 0.650) with good external set prediction was obtained.

In Silico Evaluation of Ibuprofen and Two Benzoylpropionic Acid Derivatives with Potential Anti-Inflammatory Activity

Inflammation is a complex reaction involving cellular and molecular components and an unspecific response to a specific aggression. The use of scientific and technological innovations as a research tool combining multidisciplinary knowledge in informatics, biotechnology, chemistry and biology are essential for optimizing time and reducing costs in the drug design. Thus, the integration of these in silico techniques makes it possible to search for new anti-inflammatory drugs with better pharmacokinetic and toxicological profiles compared to commercially used drugs. This in silico study evaluated the anti-inflammatory potential of two benzoylpropionic acid derivatives (MBPA and DHBPA) using molecular docking and their thermodynamic profiles by molecular dynamics, in addition to predicting oral bioavailability, bioactivity and toxicity. In accordance to our predictions the derivatives proposed here had the potential capacity for COX-2 inhibition in the human and mice enzyme, due to containing similar interactions with the control compound (ibuprofen). Ibuprofen showed toxic predictions of hepatotoxicity (in human, mouse and rat; toxicophoric group 2-arylacetic or 3-arylpropionic acid) and irritation of the gastrointestinal tract (in human, mouse and rat; toxicophoric group alpha-substituted propionic acid or ester) confirming the literature data, as well as the efficiency of the DEREK 10.0.2 program. Moreover, the proposed compounds are predicted to have a good oral bioavailability profile and low toxicity (LD50 < 700 mg/kg) and safety when compared to the commercial compound. Therefore, future studies are necessary to confirm the anti-inflammatory potential of these compounds.

Modulation of Disordered Proteins with a Focus on Neurodegenerative Diseases and Other Pathologies

Intrinsically disordered proteins (IDPs) do not have rigid 3D structures, showing changes in their folding depending on the environment or ligands. Intrinsically disordered proteins are widely spread in eukaryotic genomes, and these proteins participate in many cell regulatory metabolism processes. Some IDPs, when aberrantly folded, can be the cause of some diseases such as Alzheimer′s, Parkinson′s, and prionic, among others. In these diseases, there are modifications in parts of the protein or in its entirety. A common conformational variation of these IDPs is misfolding and aggregation, forming, for instance, neurotoxic amyloid plaques. In this review, we discuss some IDPs that are involved in neurodegenerative diseases (such as beta amyloid, alpha synuclein, tau, and the “IDP-like” PrP), cancer (p53, c-Myc), and diabetes (amylin), focusing on the structural changes of these IDPs that are linked to such pathologies. We also present the IDP modulation mechanisms that can be explored in new strategies for drug design. Lastly, we show some candidate drugs that can be used in the future for the treatment of diseases caused by misfolded IDPs, considering that cancer therapy has more advanced research in comparison to other diseases, while also discussing recent and future developments in this area of research. Therefore, we aim to provide support to the study of IDPs and their modulation mechanisms as promising approaches to combat such severe diseases.

Investigation of copper-cysteamine nanoparticles as a new photosensitizer for anti-hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a primary malignancy of the liver and occurs predominantly in patients with underlying chronic liver disease and cirrhosis. HCC is now the third leading cause of cancer deaths worldwide, with over 500,000 people affected. However, there is no complete effective (ideal) treatment for liver cancer yet, and the new methods are expected to be discovered. Herein, for the first time, we report the anti-HCC effects of copper-cysteamine nanoparticles (Cu-Cy NPs), a new type of photosensitizers. An in vitro 3-(4,5-dimethylthiazol- 2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay shows that Cu-Cy NPs could significantly reduce the activity of HepG2 cells at a very low dose after a short time of ultraviolet radiation. In addition, we found that cell death was induced by Cu-Cy NPs, which is associated with cellular apoptosis. This implied that apoptosis might be the main mechanism of the Cu-Cy's anti-HCC activity. Furthermore, we found that Cu-Cy NPs obviously inhibited the tumor growth in vivo. More interestingly, we found that the soluble Cu-Cy NPs were able to enter exosomes which were secreted by tumor cells, and exosomes could be used to deliver Cu-Cy NPs to target tumor cells. All these observations suggest that Cu-Cy NPs have a good potential for cancer treatment.

MicroRNA-small molecule association identification: from experimental results to computational models

Small molecule is a kind of low molecular weight organic compound with variety of biological functions. Studies have indicated that small molecules can inhibit a specific function of a multifunctional protein or disrupt protein-protein interactions and may have beneficial or detrimental effect against diseases. MicroRNAs (miRNAs) play crucial roles in cellular biology, which makes it possible to develop miRNA as diagnostics and therapeutic targets. Several drug-like compound libraries were screened successfully against different miRNAs in cellular assays further demonstrating the possibility of targeting miRNAs with small molecules. In this review, we summarized the concept and functions of small molecule and miRNAs. Especially, five aspects of miRNA functions were exhibited in detail with individual examples. In addition, four disease states that have been linked to miRNA alterations were summed up. Then, small molecules related to four important miRNAs miR-21, 122, 4644 and 27 were selected for introduction. Some important publicly accessible databases and web servers of the experimentally validated or potential small molecule-miRNA associations were discussed. Identifying small molecule targeting miRNAs has become an important goal of biomedical research. Thus, several experimental and computational models have been developed and implemented to identify novel small molecule-miRNA associations. Here, we reviewed four experimental techniques used in the past few years to search for small-molecule inhibitors of miRNAs, as well as three types of models of predicting small molecule-miRNA associations from different perspectives. Finally, we summarized the limitations of existing methods and discussed the future directions for further development of computational models.

The inadequacy of the reductionist approach in discovering new therapeutic agents against complex diseases

For more than 20 years, drug discovery has relied on two assumptions, i.e. (i) a therapeutic response can be triggered by modulating the activity of a single gene product, and (ii) a compound uncovered by its activity on a recombinant protein in vitro can perform its activity in vivo. Drug discovery operates accordingly by using the concepts of targets and pipelines. The target, such as a gene product, is the intended point of therapeutic intervention, and compounds that modulate its activity in vitro follow a series of downstream developments. This reductionist approach has developed due to advances in combinatorial chemistry, robotics, molecular biology, and genomics. The expectation of this approach is that the frequency of drug discovery will dramatically increase, while its associated cost would decrease. However, the frequency of new drug discovery has decreased, while the associated costs have surged. We performed a retrospective study that examined how successful development programs have led to marketed drugs for all indications except anti-infective and anti-neoplastic agents. We concluded that the target and pipeline paradigms are limited and are actually causing the drug development industry to collectively fail to meet the critical medical needs. Impact statement The initial scope of this investigation was to build the set of human genes that are presumed to be the therapeutic intervention points of US FDA-approved drugs, in all therapeutics areas but oncology. The prerequisite for this study was the establishment of the non-redundant set of all active pharmaceutical ingredients for these disease areas. Pertaining to complex diseases, the main observation was that there is not a single instance in the history of drug discovery, where a compound, initially selected by means of a biochemical assay, achieved a significant therapeutic response. The whole field of Drug R&D faces an unacceptable lack of new treatments to address unmet medical needs. The conclusion is that complex biological assays have to be designed for the primary selection of candidate therapeutics.

Bioinformatics and Translation Elongation

Codon usage depends on mutation bias, tRNA-mediated selection, and the need for high efficiency and accuracy in translation. One codon in a synonymous codon family is often strongly over-used, especially in highly expressed genes, which often leads to a high dN/dS ratio because dS is very small. Many different codon usage indices have been proposed to measure codon usage and codon adaptation. Sense codon could be misread by release factors and stop codons misread by tRNAs, which also contribute to codon usage in rare cases. This chapter outlines the conceptual framework on codon evolution, illustrates codon-specific and gene-specific codon usage indices, and presents their applications. A new index for codon adaptation that accounts for background mutation bias (Index of Translation Elongation) is presented and contrasted with codon adaptation index (CAI) which does not consider background mutation bias. They are used to re-analyze data from a recent paper claiming that translation elongation efficiency matters little in protein production. The reanalysis disproves the claim.

Bioinformatics and Drug Discovery

Bioinformatic analysis can not only accelerate drug target identification and drug candidate screening and refinement, but also facilitate characterization of side effects and predict drug resistance. High-throughput data such as genomic, epigenetic, genome architecture, cistromic, transcriptomic, proteomic, and ribosome profiling data have all made significant contribution to mechanismbased drug discovery and drug repurposing. Accumulation of protein and RNA structures, as well as development of homology modeling and protein structure simulation, coupled with large structure databases of small molecules and metabolites, paved the way for more realistic protein-ligand docking experiments and more informative virtual screening. I present the conceptual framework that drives the collection of these high-throughput data, summarize the utility and potential of mining these data in drug discovery, outline a few inherent limitations in data and software mining these data, point out news ways to refine analysis of these diverse types of data, and highlight commonly used software and databases relevant to drug discovery.

A comprehensive map of molecular drug targets

The success of mechanism-based drug discovery depends on the definition of the drug target. This definition becomes even more important as we try to link drug response to genetic variation, understand stratified clinical efficacy and safety, rationalize the differences between drugs in the same therapeutic class and predict drug utility in patient subgroups. However, drug targets are often poorly defined in the literature, both for launched drugs and for potential therapeutic agents in discovery and development. Here, we present an updated comprehensive map of molecular targets of approved drugs. We curate a total of 893 human and pathogen-derived biomolecules through which 1,578 US FDA-approved drugs act. These biomolecules include 667 human-genome-derived proteins targeted by drugs for human disease. Analysis of these drug targets indicates the continued dominance of privileged target families across disease areas, but also the growth of novel first-in-class mechanisms, particularly in oncology. We explore the relationships between bioactivity class and clinical success, as well as the presence of orthologues between human and animal models and between pathogen and human genomes. Through the collaboration of three independent teams, we highlight some of the ongoing challenges in accurately defining the targets of molecular therapeutics and present conventions for deconvoluting the complexities of molecular pharmacology and drug efficacy.

Computational modeling of drug response with applications to neuroscience

The development of novel high-throughput technologies has opened up the opportunity to deeply characterize patient tissues at various molecular levels and has given rise to a paradigm shift in medicine towards personalized therapies. Computational analysis plays a pivotal role in integrating the various genome data and understanding the cellular response to a drug. Based on that data, molecular models can be constructed that incorporate the known downstream effects of drug-targeted receptor molecules and that predict optimal therapy decisions. In this article, we describe the different steps in the conceptual framework of computational modeling. We review resources that hold information on molecular pathways that build the basis for constructing the model interaction maps, highlight network analysis concepts that have been helpful in identifying predictive disease patterns, and introduce the basic concepts of kinetic modeling. Finally, we illustrate this framework with selected studies related to the modeling of important target pathways affected by drugs.

Drug target miRNAs: chances and challenges

miRNAs, short ribonucleic acid gene regulators, are increasingly popular drug targets. Traditionally 'undruggable' proteins can be targeted via their miRNA gene regulators, enabling the treatment of diseases that, at present, seem impossible to cure. However, addressing miRNAs requires innovation at the level of drug discovery. This review article outlines the potential of miRNAs as drug targets, focuses on the challenges of developing miRNA-targeting drugs, and surveys new advances. The aim is to provide an orientation guide for scientists, as well business analysts, to help them navigate the jungle of different approaches in miRNA drug discovery.

Conducting clinical research in community mental health settings: Opportunities and challenges

Tremendous progress has been made in the past decade surrounding the underlying mechanisms and treatment of neuropsychiatric disease. Technological advancements and a broadened research paradigm have contributed to the understanding of the neurochemistry, brain function and brain circuitry involved in neuropsychiatric disorders. The predominant area of unmet medical need in the United States is major psychiatric disorders, and major depressive disorder is the leading cause of disability for ages 15-44. Total spending on research and development by the pharmaceutical industry has grown exponentially during the past decade, but fewer new molecular entities (NME) for the treatment of major psychiatric disorders have received regulatory approvals compared to other therapeutic areas. Though significant expansion has occurred during the “decade of the brain”, the translation of clinical trials outcomes into the community mental health setting is deficient. Randomized controlled trials (RCTs) have been the standard approach to clinical evaluation of the safety and efficacy of NMEs for the past 60 years; however, there are significant barriers and skepticism in the implementation of evidence-based outcomes into clinical practice. Recruitment of patients, shortages of experienced clinical researchers, regulatory requirements and later translation of outcomes into clinical practice are ever growing problems faced by investigators. The community mental health setting presents particular barriers in the replication of therapeutic outcomes from RCTs. The diagnostic complexity of major psychiatric diseases and the highly selective patient populations involved in clinical trials lend to the gap in translation from the “bench to the bedside”. The community mental health setting lends to a diverse patient population with numerous co-morbidities and environmental factors that are unaccounted in the average RCT. While we acknowledge the enormous complexity in developing novel and innovative treatments for major psychiatric disorders, we must continue to improve the translatability of clinical trials to real world settings. Progress has been rather slow but as the gap in treatment effectiveness is reduced, so will costs and barriers in community mental health.

The discovery of first-in-class drugs: origins and evolution

Analysis of the origins of new drugs approved by the US Food and Drug Administration (FDA) from 1999 to 2008 suggested that phenotypic screening strategies had been more productive than target-based approaches in the discovery of first-in-class small-molecule drugs. However, given the relatively recent introduction of target-based approaches in the context of the long time frames of drug development, their full impact might not yet have become apparent. Here, we present an analysis of the origins of all 113 first-in-class drugs approved by the FDA from 1999 to 2013, which shows that the majority (78) were discovered through target-based approaches (45 small-molecule drugs and 33 biologics). In addition, of 33 drugs identified in the absence of a target hypothesis, 25 were found through a chemocentric approach in which compounds with known pharmacology served as the starting point, with only eight coming from what we define here as phenotypic screening: testing a large number of compounds in a target-agnostic assay that monitors phenotypic changes. We also discuss the implications for drug discovery strategies, including viewing phenotypic screening as a novel discipline rather than as a neoclassical approach.

Yeast as a screening tool

The versatile genetic malleability of yeast, and the high degree of conservation between its cellular processes and those of human cells, have made it the model of choice for pioneering research in molecular and cell biology over the past four decades. These characteristics of yeast, taken together with technical advantages such as simple growth conditions, rapid cell division and the development of a wealth of genetic tools for analysis of biological functions, have expanded the application of yeast as screening tool to the field of drug discovery.:

Bioprospecting open reading frames for peptide effectors

Recent successes in the development of small-molecule antagonists of protein-protein interactions designed based on co-crystal structures of peptides bound to their biological targets confirm that short peptides derived from interacting proteins can be high-value ligands for pharmacologic validation of targets and for identification of druggable sites. Evolved sequence space is likely to be enriched for interacting peptides, but identifying minimal peptide effectors within genomic sequence can be labor intensive. Here we describe the use of incremental truncation to diversify genetic material on the scale of open reading frames into comprehensive libraries of constituent peptides. The approach is capable of generating peptides derived from both continuous and discontinuous sequence elements, and is compatible with the expression of free linear or backbone cyclic peptides, with peptides tethered to amino- or carboxyl-terminal fusion partners or with the expression of peptides displayed within protein scaffolds (peptide aptamers). Incremental truncation affords a valuable source of molecular diversity to interrogate the druggable genome or evaluate the therapeutic potential of candidate genes.

Targeting protein-protein interaction by small molecules

Protein-protein interactions (PPIs) are critical regulatory events in physiology and pathology, and they represent an important target space for pharmacological intervention. However, targeting PPIs with small molecules is challenging owing to the large surface area involved in protein-protein binding and the lack of obvious small-molecule-binding pockets at many protein-protein interfaces. Nonetheless, successful examples of small-molecule modulators of PPIs have been growing in recent years. This article reviews some of the recent advances in the discovery of small-molecule regulators of PPIs that involve key oncogenic proteins. Our discussion focuses on the three key modes of action for these small-molecule modulators: orthosteric inhibition, allosteric regulation, and interfacial binding/stabilization. Understanding the opportunities and challenges of these diverse mechanisms will help guide future efforts in developing small-molecule modulators against PPIs.

Interaction of herbal compounds with biological targets: a case study with berberine

Berberine is one of the main alkaloids found in the Chinese herb Huang lian (Rhizoma Coptidis), which has been reported to have multiple pharmacological activities. This study aimed to analyze the molecular targets of berberine based on literature data followed by a pathway analysis using the PANTHER program. PANTHER analysis of berberine targets showed that the most classes of molecular functions include receptor binding, kinase activity, protein binding, transcription activity, DNA binding, and kinase regulator activity. Based on the biological process classification of in vitro berberine targets, those targets related to signal transduction, intracellular signalling cascade, cell surface receptor-linked signal transduction, cell motion, cell cycle control, immunity system process, and protein metabolic process are most frequently involved. In addition, berberine was found to interact with a mixture of biological pathways, such as Alzheimer's disease-presenilin and -secretase pathways, angiogenesis, apoptosis signalling pathway, FAS signalling pathway, Hungtington disease, inflammation mediated by chemokine and cytokine signalling pathways, interleukin signalling pathway, and p53 pathways. We also explored the possible mechanism of action for the anti-diabetic effect of berberine. Further studies are warranted to elucidate the mechanisms of action of berberine using systems biology approach.

Improved synthesis strategy for peptide nucleic acids (PNA) appropriate for cell-specific fluorescence imaging

Progress in genomics and proteomics attended to the door for better understanding the recent rapid expanding complex research field of metabolomics. This trend in biomedical research increasingly focuses to the development of patient-specific therapeutic approaches with higher efficiency and sustainability. Simultaneously undesired adverse reactions are avoided. In parallel, the development of molecules for molecular imaging is required not only for the imaging of morphological structures but also for the imaging of metabolic processes like the aberrant expression of the cysteine protease cathepsin B (CtsB) gene and the activity of the resulting product associated with metastasis and invasiveness of malign tumors. Finally the objective is to merge imaging and therapy at the same level. The design of molecules which fulfil these responsibilities is pivotal and requires proper chemical methodologies. In this context our modified solid phase peptide chemistry using temperature shifts during synthesis is considered as an appropriate technology. We generated highly variable conjugates which consist of molecules useful as diagnostically and therapeutically active molecules. As an example the modular PNA products with the complementary sequence to the CtsB mRNA and additionally with a cathepsin B cleavage site had been prepared as functional modules for distinction of cell lines with different CtsB gene expression. After ligation to the modular peptide-based BioShuttle carrier, which was utilized to facilitate the delivery of the functional modules into the cells' cytoplasm, the modules were scrutinized.

Anti-Inflammatory Potential of Ethanolic Leaf Extract of Eupatorium adenophorum Spreng. Through Alteration in Production of TNF-α, ROS and Expression of Certain Genes

Search for a novel anti-inflammatory agent from a herbal source, such as Eupatorium adenophorum Spreng., a plant from the Eastern Himalayas, is of prime interest in the present investigation. Inflammation causes tissue destruction and development of diseases such as asthma, rheumatoid arthritis, and so forth. The ethanolic leaf extract of E. adenophorum (EEA) was administered intravenously and in other cases topically at the site of delayed type hypersensitivity (DTH) reaction in mouse foot paw induced with dinitrofluorobenzene. EEA can effectively inhibit DTH reaction and bring back normalcy to the paw much earlier than the controls. Efficacy of EEA on regulatory mechanisms for inflammation has also been considered. Intravenous administration of EEA increased the number of CD4⁺ T cells in spleen and tumor necrosis factor (TNF)-α in serum of DTH mice. Initially it was difficult to reconcile with the anti-inflammatory role of EEA and simultaneous induction of TNF-α, an established pro-inflammatory cytokine. EEA induces higher expression of TNF-α gene and amount of the cytokine in serum. We discussed the other role of TNF-α, its involvement in repairing tissue damage incurred in course of inflammatory reaction. EEA also induces TGF-β encoding a cytokine involved in tissue repair mechanism. EEA inhibits expression of another pro-inflammatory cytokine gene IL-1β and downregulates cycloxygenase 2 (COX2) gene responsible for metabolism of inflammatory mediators like prostaglandins. Furthermore, anti-inflammatory role of EEA is also revealed through its inhibition of hydroxyl radical generation. Notably EEA does not necessarily affect the expression of other inflammation-related genes such as IL-6, IL-10 and IKK. The present study reports and analyzes for the first time the anti-inflammatory property of the leaf extract of E. adenophorum.

Computational systems chemical biology

There is a critical need for improving the level of chemistry awareness in systems biology. The data and information related to modulation of genes and proteins by small molecules continue to accumulate at the same time as simulation tools in systems biology and whole body physiologically based pharmacokinetics (PBPK) continue to evolve. We called this emerging area at the interface between chemical biology and systems biology systems chemical biology (SCB) (Nat Chem Biol 3: 447-450, 2007).The overarching goal of computational SCB is to develop tools for integrated chemical-biological data acquisition, filtering and processing, by taking into account relevant information related to interactions between proteins and small molecules, possible metabolic transformations of small molecules, as well as associated information related to genes, networks, small molecules, and, where applicable, mutants and variants of those proteins. There is yet an unmet need to develop an integrated in silico pharmacology/systems biology continuum that embeds drug-target-clinical outcome (DTCO) triplets, a capability that is vital to the future of chemical biology, pharmacology, and systems biology. Through the development of the SCB approach, scientists will be able to start addressing, in an integrated simulation environment, questions that make the best use of our ever-growing chemical and biological data repositories at the system-wide level. This chapter reviews some of the major research concepts and describes key components that constitute the emerging area of computational systems chemical biology.

Systems biology approaches and tools for analysis of interactomes and multi-target drugs

Systems biology is essentially a proteomic and epigenetic exercise because the relatively condensed information of genomes unfolds on the level of proteins. The flexibility of cellular architectures is not only mediated by a dazzling number of proteinaceous species but moreover by the kinetics of their molecular changes: The time scales of posttranslational modifications range from milliseconds to years. The genetic framework of an organism only provides the blue print of protein embodiments which are constantly shaped by external input. Indeed, posttranslational modifications of proteins represent the scope and velocity of these inputs and fulfil the requirements of integration of external spatiotemporal signal transduction inside an organism. The optimization of biochemical networks for this type of information processing and storage results in chemically extremely fine tuned molecular entities. The huge dynamic range of concentrations, the chemical diversity and the necessity of synchronisation of complex protein expression patterns pose the major challenge of systemic analysis of biological models. One further message is that many of the key reactions in living systems are essentially based on interactions of moderate affinities and moderate selectivities. This principle is responsible for the enormous flexibility and redundancy of cellular circuitries. In complex disorders such as cancer or neurodegenerative diseases, which initially appear to be rooted in relatively subtle dysfunctions of multimodal physiologic pathways, drug discovery programs based on the concept of high affinity/high specificity compounds ("one-target, one-disease"), which has been dominating the pharmaceutical industry for a long time, increasingly turn out to be unsuccessful. Despite improvements in rational drug design and high throughput screening methods, the number of novel, single-target drugs fell much behind expectations during the past decade, and the treatment of "complex diseases" remains a most pressing medical need. Currently, a change of paradigm can be observed with regard to a new interest in agents that modulate multiple targets simultaneously, essentially "dirty drugs." Targeting cellular function as a system rather than on the level of the single target, significantly increases the size of the drugable proteome and is expected to introduce novel classes of multi-target drugs with fewer adverse effects and toxicity. Multiple target approaches have recently been used to design medications against atherosclerosis, cancer, depression, psychosis and neurodegenerative diseases. A focussed approach towards "systemic" drugs will certainly require the development of novel computational and mathematical concepts for appropriate modelling of complex data. But the key is the extraction of relevant molecular information from biological systems by implementing rigid statistical procedures to differential proteomic analytics.

Dynamic template-assisted strategies in fragment-based drug discovery

Fragment-based methods for drug discovery are increasingly popular because they provide drug leads with greater ligand efficiency than conventional high-throughput screening. However, established methods for fragment detection do not address the central question in fragment-based ligand discovery: how can a primary ligand be optimally extended by a secondary fragment? Dynamic screening methods solve this issue by using a protein target as a template for ligand assembly, thus yielding high-affinity binders from low-affinity fragments. This review summarizes recent work on dynamic screening methodology, which resulted in the development of several high-affinity binders for various targets. Strengths and limitations of the published approaches are discussed and possible contributions of dynamic screening methodology to the drug discovery process are highlighted.

Removing obstacles in neuroscience drug discovery: the future path for animal models

Despite great advances in basic neuroscience knowledge, the improved understanding of brain functioning has not yet led to the introduction of truly novel pharmacological approaches to the treatment of central nervous system (CNS) disorders. This situation has been partly attributed to the difficulty of predicting efficacy in patients based on results from preclinical studies. To address these issues, this review critically discusses the traditional role of animal models in drug discovery, the difficulties encountered, and the reasons why this approach has led to suboptimal utilization of the information animal models provide. The discussion focuses on how animal models can contribute most effectively to translational medicine and drug discovery and the changes needed to increase the probability of achieving clinical benefit. Emphasis is placed on the need to improve the flow of information from the clinical/human domain to the preclinical domain and the benefits of using truly translational measures in both preclinical and clinical testing. Few would dispute the need to move away from the concept of modeling CNS diseases in their entirety using animals. However, the current emphasis on specific dimensions of psychopathology that can be objectively assessed in both clinical populations and animal models has not yet provided concrete examples of successful preclinical-clinical translation in CNS drug discovery. The purpose of this review is to strongly encourage ever more intensive clinical and preclinical interactions to ensure that basic science knowledge gained from improved animal models with good predictive and construct validity readily becomes available to the pharmaceutical industry and clinical researchers to benefit patients as quickly as possible.

Generation of baculovirus vectors for the high-throughput production of proteins in insect cells

The baculovirus expression system is one of the most popular methods used for the production of recombinant proteins but has several complex steps which have proved inherently difficult to adapt to a multi-parallel process. We have developed a bacmid vector that does not require any form of selection pressure to separate recombinant virus from non-recombinant parental virus. The method relies on homologous recombination in insect cells between a transfer vector containing a gene to be expressed and a replication-deficient bacmid. The target gene replaces a bacterial replicon at the polyhedrin loci, simultaneously restoring a virus gene essential for replication. Therefore, only recombinant virus can replicate facilitating the rapid production of multiple recombinant viruses on automated platforms in a one-step procedure. Using this vector allowed us to automate the generation of multiple recombinant viruses with a robotic liquid handler and then rapidly screen infected insect cell supernatant for the presence of secreted proteins.

The design, structures and therapeutic potential of protein epitope mimetics

Using a biologically relevant peptide or protein structure as a starting point for lead identification represents one of the most powerful approaches in modern drug discovery. Here, we focus on the protein epitope mimetic (PEM) approach, where folded 3D structures of peptides and proteins are taken as starting points for the design of synthetic molecules that mimic key epitopes involved in protein-protein and protein-nucleic acid interactions. By transferring the epitope from a recombinant to a synthetic scaffold that can be produced by parallel combinatorial methods, it is possible to optimize target affinity and specificity as well as other drug-like ADMET properties. The PEM technology is a powerful tool for target validation, and for the development of novel PEM-based drugs.

Meta-basic estimates the size of druggable human genome

We present here the estimation of the upper limit of the number of molecular targets in the human genome that represent an opportunity for further therapeutic treatment. We select around approximately 6300 human proteins that are similar to sequences of known protein targets collected from DrugBank database. Our bioinformatics study estimates the size of 'druggable' human genome to be around 20% of human proteome, i.e. the number of the possible protein targets for small-molecule drug design in medicinal chemistry. We do not take into account any toxicity prediction, the three-dimensional characteristics of the active site in the predicted 'druggable' protein families, or detailed chemical analysis of known inhibitors/drugs. Instead we rely on remote homology detection method Meta-BASIC, which is based on sequence and structural similarity. The prepared dataset of all predicted protein targets from human genome presents the unique opportunity for developing and benchmarking various in silico chemo/bio-informatics methods in the context of the virtual high throughput screening.

Protein kinase CK2 as a druggable target

CK2 is probably the most pleiotropic Ser/Thr protein kinase with hundreds of endogenous substrates already known, which are implicated in a variety of cellular functions. At variance with most protein kinases whose activity is turned on only in response to specific stimuli, and whose genetic alterations often underlie pathological situations, CK2 is not susceptible to tight regulation and there are no mutations known to affect its constitutive activity. Nevertheless an abnormally high level of CK2 is invariably found in tumours, and solid arguments have accumulated suggesting that CK2 plays a global pro-survival function, which under special circumstances creates a cellular environment particularly favourable to the development and potentiation of the tumour phenotype. Therefore any strategy aimed at attenuating CK2 activity may represent a "master key" for the treatment of different neoplastic diseases. Waiting for the clarification of the epigenetic mechanisms promoting the rise of CK2 in cells predisposed to develop a tumour phenotype, a useful pharmacological aid can come from the improvement of a number of fairly potent and selective CK2 inhibitors already available.

SuperPred: drug classification and target prediction

The drug classification scheme of the World Health Organization (WHO) [Anatomical Therapeutic Chemical (ATC)-code] connects chemical classification and therapeutic approach. It is generally accepted that compounds with similar physicochemical properties exhibit similar biological activity. If this hypothesis holds true for drugs, then the ATC-code, the putative medical indication area and potentially the medical target should be predictable on the basis of structural similarity. We have validated that the prediction of the drug class is reliable for WHO-classified drugs. The reliability of the predicted medical effects of the compounds increases with a rising number of (physico-) chemical properties similar to a drug with known function. The web-server translates a user-defined molecule into a structural fingerprint that is compared to about 6300 drugs, which are enriched by 7300 links to molecular targets of the drugs, derived through text mining followed by manual curation. Links to the affected pathways are provided. The similarity to the medical compounds is expressed by the Tanimoto coefficient that gives the structural similarity of two compounds. A similarity score higher than 0.85 results in correct ATC prediction for 81% of all cases. As the biological effect is well predictable, if the structural similarity is sufficient, the web-server allows prognoses about the medical indication area of novel compounds and to find new leads for known targets.

Availability: the system is freely accessible at http://bioinformatics.charite.de/superpred. SuperPred can be obtained via a Creative Commons Attribution Noncommercial-Share Alike 3.0 License.

DrugBank: a knowledgebase for drugs, drug actions and drug targets

DrugBank is a richly annotated resource that combines detailed drug data with comprehensive drug target and drug action information. Since its first release in 2006, DrugBank has been widely used to facilitate in silico drug target discovery, drug design, drug docking or screening, drug metabolism prediction, drug interaction prediction and general pharmaceutical education. The latest version of DrugBank (release 2.0) has been expanded significantly over the previous release. With approximately 4900 drug entries, it now contains 60% more FDA-approved small molecule and biotech drugs including 10% more 'experimental' drugs. Significantly, more protein target data has also been added to the database, with the latest version of DrugBank containing three times as many non-redundant protein or drug target sequences as before (1565 versus 524). Each DrugCard entry now contains more than 100 data fields with half of the information being devoted to drug/chemical data and the other half devoted to pharmacological, pharmacogenomic and molecular biological data. A number of new data fields, including food-drug interactions, drug-drug interactions and experimental ADME data have been added in response to numerous user requests. DrugBank has also significantly improved the power and simplicity of its structure query and text query searches. DrugBank is available at http://www.drugbank.ca.

Crystal structure of a thermally stable rhodopsin mutant

We determined the structure of the rhodopsin mutant N2C/D282C expressed in mammalian cells; the first structure of a recombinantly produced G protein-coupled receptor (GPCR). The mutant was designed to form a disulfide bond between the N terminus and loop E3, which allows handling of opsin in detergent solution and increases thermal stability of rhodopsin by 10 deg.C. It allowed us to crystallize a fully deglycosylated rhodopsin (N2C/N15D/D282C). N15 mutations are normally misfolding and cause retinitis pigmentosa in humans. Microcrystallographic techniques and a 5 microm X-ray beam were used to collect data along a single needle measuring 5 microm x 5 microm x 90 microm. The disulfide introduces only minor changes but fixes the N-terminal cap over the beta-sheet lid covering the ligand-binding site, a likely explanation for the increased stability. This work allows structural investigation of rhodopsin mutants and shows the problems encountered during structure determination of GPCRs and other mammalian membrane proteins.

Using grid techniques for drug target identification

The completion of the sequencing of the human genome has opened an unprecedented opportunity in the discovery of novel drug targets for disease therapy. However, one of the major challenges facing the drug discovery community is the expanding of data and the need of large-scale computational power in a collaborative environment. Grid techniques can present an architectural framework that aims to provide access to heterogeneous resources in a secure, reliable and scalable manner across various administrative boundaries for drug discovery, which has been a promising strategy for solving large-scale problems in modern pharmaceutical R&D. In this review, we discuss the current applications of Grid technology in drug target protein identification process; and an overview of drug target discovery system architecture, focusing in particular on the data manager service system architecture is also proposed.

How many drug targets are there?

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

Drugs, their targets and the nature and number of drug targets

What is a drug target? And how many such targets are there? Here, we consider the nature of drug targets, and by classifying known drug substances on the basis of the discussed principles we provide an estimation of the total number of current drug targets.