Prospects for productivity

A survey of current trends in computational drug repositioning

Computational drug repositioning or repurposing is a promising and efficient tool for discovering new uses from existing drugs and holds the great potential for precision medicine in the age of big data. The explosive growth of large-scale genomic and phenotypic data, as well as data of small molecular compounds with granted regulatory approval, is enabling new developments for computational repositioning. To achieve the shortest path toward new drug indications, advanced data processing and analysis strategies are critical for making sense of these heterogeneous molecular measurements. In this review, we show recent advancements in the critical areas of computational drug repositioning from multiple aspects. First, we summarize available data sources and the corresponding computational repositioning strategies. Second, we characterize the commonly used computational techniques. Third, we discuss validation strategies for repositioning studies, including both computational and experimental methods. Finally, we highlight potential opportunities and use-cases, including a few target areas such as cancers. We conclude with a brief discussion of the remaining challenges in computational drug repositioning.

Towards tissue-specific pharmacology: insights from the calcium-sensing receptor as a paradigm for GPCR (patho)physiological bias

The calcium-sensing receptor (CaSR) is a widely expressed G protein-coupled receptor (GPCR) that mediates numerous tissue-specific functions. Its multiple ligands and diverse roles attest to the need for exquisite control over the signaling pathways that mediate its effects. 'Biased signaling' is the phenomenon by which distinct ligands stabilize preferred receptor signaling states. The CaSR is subject to biased signaling in response to its endogenous ligands. Interestingly, the 'natural' bias of the CaSR is altered in disease states, and small molecule drugs engender biased allosteric modulation of downstream signaling pathways. Thus, biased signaling from the CaSR also has important implications pathophysiologically and therapeutically. As outlined in this review, this novel paradigm extends to other GPCRs, making the CaSR a model for studies of ligand-biased signaling and for understanding how it may be used to foster selective drug activity in different tissues.

The Pharmaceutical Commons: Sharing and Exclusion in Global Health Drug Development

In the last decade, the organization of pharmaceutical research on neglected tropical diseases has undergone transformative change. In a context of perceived "market failure," the development of new medicines is increasingly handled by public-private partnerships. This shift toward hybrid organizational models depends on a particular form of exchange: the sharing of proprietary assets in general and of intellectual property rights in particular. This article explores the paradoxical role of private property in this new configuration of global health research and development. Rather than a tool to block potential competitors, proprietary assets function as a lever to attract others into risky collaborative ventures; instead of demarcating public and private domains, the sharing of property rights is used to increase the porosity of that boundary. This reimagination of the value of property is connected to the peculiar timescape of global health drug development, a promissory orientation to the future that takes its clearest form in the centrality of "virtual" business models and the proliferation of strategies of deferral. Drawing on the anthropological literature on inalienable possessions, we reconsider property's traditional exclusionary role and discuss the possibility that the new pharmaceutical "commons" proclaimed by contemporary global health partnerships might be the precursor of future enclosures.

A consumer adoption model for personalized medicine: an exploratory study

Purpose

– The purpose of this research is to identify factors linked to the potential acceptance of personalized medicine (PM) by consumers. Roger’s diffusion of innovation model (1995) and the work of Duguay et al. (2003) on transgenic biopharmaceuticals contributed to the development of the proposed conceptual model.

Design/methodology/approach

– The study design was an exploratory cross-sectional survey that used a Canadian national online panel of 307 respondents.

Findings

– The results suggest that the most important factors leading to consumer adoption of PM are knowledge, relative advantage and compatibility with existing values. The level of homophilus traits was negatively related to the acceptance of PM.

Originality/value

– Marketers will need to provide documented evidence of PM’s benefits over existing therapy based on improved efficacy and reduced side effects. Further, concerns about higher price, product distribution and drug reimbursement policies may limit its acceptance. This is the first study to examine the potential adoption and acceptance of PM by consumers.

Molecular modelling, synthesis, cytotoxicity and anti-tumour mechanisms of 2-aryl-6-substituted quinazolinones as dual-targeted anti-cancer agents

BACKGROUND AND PURPOSE

Our previous study demonstrated that 6-(pyrrolidin-1-yl)-2-(3-methoxyphenyl)quinazolin-4-one (HMJ38) was a potent anti-tubulin agent. Here, HMJ38 was used as a lead compound to develop more potent anti-cancer agents and to examine the anti-cancer mechanisms.

EXPERIMENTAL APPROACH

Using computer-aided drug design, 2-aryl-6-substituted quinazolinones (MJ compounds) were designed and synthesized by introducing substituents at C-2 and C-6 positions of HMJ38. The cytotoxicity of MJ compounds towards human cancer cells was examined by Trypan blue exclusion assay. Microtubule distribution was visualized using TubulinTracker(TM) Green reagent. Protein expression of cell cycle regulators and JNK was assessed by Western blot analysis.

KEY RESULTS

Compounds MJ65-70 exhibited strong anti-proliferative effects towards melanoma M21, lung squamous carcinoma CH27, lung non-small carcinoma H460, hepatoma Hep3B and oral cancer HSC-3 cells, with one compund MJ66 (6-(pyrrolidin-1-yl)-2-(naphthalen-1-yl)quinazolin-4-one) highly active against M21 cells (IC50 about 0.033 μM). Treatment of CH27 or HSC-3 cells with MJ65-70 resulted in significant mitotic arrest accompanied by increasing multiple asters of microtubules. JNK protein expression was involved in the MJ65-70-induced CH27 and M21 cell death. Consistent with the cell cycle arrest at G2/M phase, marked increases in cyclin B1 and Bcl-2 phosphorylation were also observed, after treatment with MJ65-70.

CONCLUSIONS AND IMPLICATION

MJ65-70 are dual-targeted, tubulin- and JNK-binding, anti-cancer agents and induce cancer cell death through up-regulation of JNK and interfering in the dynamics of tubulin. Our work provides a new strategy and mechanism for developing dual-targeted anti-cancer drugs, contributing to clinical anti-cancer drug discovery and application.

Proposing advancement criteria for efficient DMPK triage of new chemical entities

With the goal of refining our discovery DMPK workflow, we conducted a retrospective analysis on internal Celgene compounds by calculating the physicochemical properties and gathering data from several assays including solubility, rat and human liver S9 stability, Caco-2 permeability, and rat intravenous (iv.) and oral pharmacokinetics. Our analysis identified plasma clearance to be most statistically relevant for prediction of oral exposure. In rat, compounds with rat S9 stability of ≥70% at 60 min and a plasma clearance of ≤43 ml/min/kg had the greatest chance of achieving oral exposures above 3 µM.h. Compounds with the dual advantage of plasma clearance ≤43 ml/min/kg and Caco-2 permeability ≥8 × 10(-6) cm/s or efflux ratio ≤8 were highly likely to achieve those oral exposures. Implementation of these criteria leads to a significant increase in efficiency, good pharmacokinetic properties, cost savings and a reduction in the use of animals.

Systems biology, complexity, and the impact on antiepileptic drug discovery

The number of available anticonvulsant drugs increased in the period spanning over more than a century, amounting to the current panoply of nearly two dozen so-called antiepileptic drugs (AEDs). However, none of them actually prevents/reduces the post-brain insult development of epilepsy in man, and in no less than a third of patients with epilepsy, the seizures are not drug-controlled. Plausibly, the enduring limitation of AEDs' efficacy derives from the insufficient understanding of epileptic pathology. This review pinpoints the unbalanced reductionism of the analytic approaches that overlook the intrinsic complexity of epilepsy and of the drug resistance in epilepsy as the core conceptual flaw hampering the discovery of truly antiepileptogenic drugs. A rising awareness of the complexity of epileptic pathology is, however, brought about by the emergence of nonreductionist systems biology (SB) that considers the networks of interactions underlying the normal organismic functions and of SB-based systems (network) pharmacology that aims to restore pathological networks. By now, the systems pharmacology approaches of AED discovery are fairly meager, but their forthcoming development is both a necessity and a realistic prospect, explored in this review.

Drug-like properties: guiding principles for design - or chemical prejudice?

The concepts of 'drug-like' and 'lead-like' chemical properties are having a major influence on the selection of compounds for high-throughput screening, and in the design of lead generation libraries. Medicinal chemists are recycling 'privileged' drug-like structures, whilst aiming to seek optimal physical properties for oral delivery. This approach biases the chemical profiles of compound screening collections towards known structures. Novel library synthesis, creating new chemical classes to address intellectual property, toxicity issues, and less chemically tractable targets, though considered risky, is warranted.:

AMPK-derived peptides reduce blood glucose levels but lead to fat retention in the liver of obese mice

AMP-activated protein kinase (AMPK) is a regulator of energy balance at both the cellular and the whole-body levels. Direct activation of AMPK has been highlighted as a potential novel, and possibly safer, alternative to treat type II diabetes and obesity. In this study, we aimed to design and characterize novel peptides that mimic the αG region of the α2 AMPK catalytic domain to modulate its activity by inhibiting interactions between AMPK domains or other interacting proteins. The derived peptides were tested in vivo and in tissue culture. The computationally predicted structure of the free peptide with the addition of the myristoyl (Myr) or acetyl (Ac) moiety closely resembled the protein structure that it was designed to mimic. Myr-peptide and Ac-peptide activated AMPK in muscle cells and led to reduced adipose tissue weight, body weight, blood glucose levels, insulin levels, and insulin resistance index, as expected from AMPK activation. In addition, triglyceride, cholesterol, leptin, and adiponectin levels were also lower, suggesting increased adipose tissue breakdown, a result of AMPK activation. On the other hand, liver weight and liver lipid content increased due to fat retention. We could not find an elevated pAMPK:AMPK ratio in the liver in vivo or in hepatocytes ex vivo, suggesting that the peptide does not lead to AMPK activation in hepatocytes. The finding that an AMPK-derived peptide leads to the activation of AMPK in muscle cells and in adipose tissue and leads to reduced glucose levels in obese mice, but to fat accumulation in the liver, demonstrates the differential effect of AMPK modulation in various tissues.

Allosteric regulation of pathologic angiogenesis: potential application for angiogenesis-related blindness

Angiogenesis-related blindness (ARB) includes age-related macular degeneration, diabetic retinopathy, and retinopathy of prematurity, all of which are based on pathologic angiogenesis. Current treatment options such as surgery, laser photocoagulation, and steroid have shown limitations because they do not directly resolve the pathologic events in the retina. Furthermore, recently approved and developed therapeutic drugs only focus on direct inhibition of growth factors and suppression of downstream signaling molecules of activated receptor proteins by orthosteric ligands. In this regard, allosteric regulation of receptors and ligands can be a valuable mechanism in the development of novel drugs for ARB. In this review, we briefly address the clinical significance of ARB for further discussion on allosteric regulation of pathologic angiogenesis for ARB. Interestingly, key molecules in the pathogenesis of ARB can be targets for allosteric regulation, sharing characteristics as allosteric proteins. With investigation of allostery by introducing well-established models for allosteric proteins and currently published novel allosteric modulators, we discuss the potential of allosteric regulation for ARB. In conclusion, we hope that allosteric regulation of pathologic angiogenesis in ARB can open new opportunities for drug development.

Factors Related to Regulatory Approval of Late-Stage Development Compounds: Analysis of Japanese Pharmaceutical Company Activities, 1995-2007

Japanese pharmaceutical companies invest resources in their internal research and development (R&D) activities and in-licensing activities especially from Western companies, for the local market. The objective of this research is to investigate the fate of late-stage compounds developed by them and to identify company profiles and compound characteristics that could relate to regulatory approvability. Using publicly available information for late-stage compounds that were developed by Japanese companies in 1995-2007, logistic regression analysis was conducted to investigate the company characteristics and regulatory approval ratio, as well as compound characteristics and probability of approval, for late-stage development compounds. Compound approvability was correlated to the time when the compound was developed (ie, the approval ratio of compounds in phase 3 or later in 1995-1998 was lower than that in 2001 or later); also, in-licensed compounds from large pharmaceutical companies received a higher approval ratio. Company size and R&D expenses were not correlated to their approval ratio.

Predicting drug-target interactions using restricted Boltzmann machines

Motivation:In silico prediction of drug-target interactions plays an important role toward identifying and developing new uses of existing or abandoned drugs. Network-based approaches have recently become a popular tool for discovering new drug-target interactions (DTIs). Unfortunately, most of these network-based approaches can only predict binary interactions between drugs and targets, and information about different types of interactions has not been well exploited for DTI prediction in previous studies. On the other hand, incorporating additional information about drug-target relationships or drug modes of action can improve prediction of DTIs. Furthermore, the predicted types of DTIs can broaden our understanding about the molecular basis of drug action.

Results: We propose a first machine learning approach to integrate multiple types of DTIs and predict unknown drug-target relationships or drug modes of action. We cast the new DTI prediction problem into a two-layer graphical model, called restricted Boltzmann machine, and apply a practical learning algorithm to train our model and make predictions. Tests on two public databases show that our restricted Boltzmann machine model can effectively capture the latent features of a DTI network and achieve excellent performance on predicting different types of DTIs, with the area under precision-recall curve up to 89.6. In addition, we demonstrate that integrating multiple types of DTIs can significantly outperform other predictions either by simply mixing multiple types of interactions without distinction or using only a single interaction type. Further tests show that our approach can infer a high fraction of novel DTIs that has been validated by known experiments in the literature or other databases. These results indicate that our approach can have highly practical relevance to DTI prediction and drug repositioning, and hence advance the drug discovery process.

Availability: Software and datasets are available on request.

Contact:zengjy321@tsinghua.edu.cn

Supplementary information:Supplementary data are available at Bioinformatics online.

Translational science by public biotechnology companies in the IPO "class of 2000": the impact of technological maturity

The biotechnology industry plays a central role in the translation of nascent biomedical science into both products that offer material health benefits and creating capital growth. This study examines the relationship between the maturity of technologies in a characteristic life cycle and value creation by biotechnology companies. We examined the core technology, product development pipelines, and capitalization for a cohort of biotechnology companies that completed an IPO in 2000. Each of these companies was well financed and had core technologies on the leading edge of biological science. We found that companies with the least mature technologies had significantly higher valuations at IPO, but failed to develop products based on these technologies over the ensuing decade, and created less capital growth than companies with more mature technologies at IPO. The observation that this cohort of recently public biotechnology companies was not effective in creating value from nascent science suggests the need for new, evidence-based business strategies for translational science.

The protein kinase 2 inhibitor CX-4945 regulates osteoclast and osteoblast differentiation in vitro

Drug repositioning can identify new therapeutic applications for existing drugs, thus mitigating high R&D costs. The Protein kinase 2 (CK2) inhibitor CX-4945 regulates human cancer cell survival and angiogenesis. Here we found that CX-4945 significantly inhibited the RANKL-induced osteoclast differentiation, but enhanced the BMP2-induced osteoblast differentiation in a cell culture model. CX-4945 inhibited the RANKL-induced activation of TRAP and NFATc1 expression accompanied with suppression of Akt phosphorylation, but in contrast, it enhanced the BMP2-mediated ALP induction and MAPK ERK1/2 phosphorylation. CX-4945 is thus a novel drug candidate for bone-related disorders such as osteoporosis.

Network nonlinearities in drug treatment

Despite major achievements in the understanding of human disease, there is a general perception that the drug development industry has failed to meet the expectations that recent advances in biotechnology should drive. One of the potential sources of failure of many next generation drugs is that their targets are embedded in highly nonlinear signaling pathways and gene networks with multiple negative and positive feedback loops of regulation. There is increasing evidence that this complex network shapes the response to external perturbations in the form of drug treatment, originating bistability, hypersensitivity, robustness, complex dose-response curves or schedule dependent activity. This review focuses on the effect of nonlinearities on signaling and gene networks involved in human disease, using tools from Nonlinear Dynamics to discuss the implications and to overcome the effects of the nonlinearities on regulatory networks.

Anthelmintic discovery and development in the animal health industry

Most modern anthelmintics used against human pathogens have come from the animal health (AH) industry. Historically, new molecules were discovered empirically, but recent developments in genomic and screening technologies have significantly enhanced the opportunities for target-based identification of novel therapies. However, drug discovery and development is still a complicated and costly process with high attrition. Absence of a return in investment for tropical parasitic diseases makes it difficult for large pharmaceutical companies to justify seeking antiparasitics for less developed countries in isolation. A partnership in which there is a sharing of costs and leveraging of resources is one way forward and is reflected in the new paradigm of 'integrated drug discovery', where collaborations and networks of academic institutions and industry are working together towards the discovery of new treatments for tropical parasitic diseases.

The significance of non-human primates as preclinical models of human arthritic disease

BACKGROUND

The broad immunological gap between inbred SPF-raised strains of mice and rats and the diverse rheumatoid arthritis (RA) patient population limits the predictive value of the existing disease models for clinical success of new therapies, in particular for those using highly specific biologicals.

OBJECTIVE

This review argues that because of their closer immunological and physiological proximity to patients, disease models in non-human primates (NHPs) may bridge this gap and help reduce the failure of many (± 80%) new therapies in clinical trials. In various research areas, NHPs are an accepted intermediate between disease models in rodents and the ultimate introduction for clinical use in patients. However, with the exception of transplantation, this is not the case for immune-mediated inflammatory disorders, such as RA, although useful preclinical models are being developed.

METHOD

The validity and use of the rhesus monkey model of collagen-induced arthritis as a preclinical RA model is reviewed. The discussion comprises present genetic and immunological aspects, biomarkers, and an overview of published preclinical therapy evaluations.

CONCLUSION

It is time to consider the use of NHPs with a greater evolutionary proximity to humans as models for preclinical evaluation of new human-specific drugs for arthritic disease.

Troubleshooting and deconvoluting label-free cell phenotypic assays in drug discovery

INTRODUCTION

Central to drug discovery and development is to comprehend the target(s), potency, efficacy and safety of drug molecules using pharmacological assays. Owing to their ability to provide a holistic view of drug actions in native cells, label-free biosensor-enabled cell phenotypic assays have been emerging as new generation phenotypic assays for drug discovery. Despite the benefits associated with wide pathway coverage, high sensitivity, high information content, non-invasiveness and real-time kinetics, label-free cell phenotypic assays are often viewed to be a blackbox in the era of target-centric drug discovery.

METHODS

This article first reviews the biochemical and biological complexity of drug-target interactions, and then discusses the key characteristics of label-free cell phenotypic assays and presents a five-step strategy to troubleshooting and deconvoluting the label-free cell phenotypic profiles of drugs.

RESULTS

Drug-target interactions are intrinsically complicated. Label-free cell phenotypic signatures of drugs mirror the innate complexity of drug-target interactions, and can be effectively deconvoluted using the five-step strategy.

DISCUSSION

The past decades have witnessed dramatic expansion of pharmacological assays ranging from molecular to phenotypic assays, which is coincident with the realization of the innate complexity of drug-target interactions. The clinical features of a drug are defined by how it operates at the system level and by its distinct polypharmacology, ontarget, phenotypic and network pharmacology. Approaches to examine the biochemical, cellular and molecular mechanisms of action of drugs are essential to increase the efficiency of drug discovery and development. Label-free cell phenotypic assays and the troubleshooting and deconvoluting approach presented here may hold great promise in drug discovery and development.

Transcriptional data: a new gateway to drug repositioning?

Recent advances in computational biology suggest that any perturbation to the transcriptional programme of the cell can be summarised by a proper ‘signature’: a set of genes combined with a pattern of expression. Therefore, it should be possible to generate proxies of clinicopathological phenotypes and drug effects through signatures acquired via DNA microarray technology.

Gene expression signatures have recently been assembled and compared through genome-wide metrics, unveiling unexpected drug–disease and drug–drug ‘connections’ by matching corresponding signatures. Consequently, novel applications for existing drugs have been predicted and experimentally validated.

Here, we describe related methods, case studies and resources while discussing challenges and benefits of exploiting existing repositories of microarray data that could serve as a search space for systematic drug repositioning.

Rethinking leadership in drug discovery projects

Great efforts have been dedicated to rebuilding the engine of pharmaceutical R&D. However, one potential area of improvement has received limited attention in the literature and in practice: namely, leadership. In this article, we enrich the traditional views of leadership, which consider leadership a responsibility of a few centrally placed authorities, with the concept of distributed leadership. Distributed leadership reflects a group-based capability driven by everyday activities and the key scientific questions at hand. We identify three leadership challenges faced by R&D teams that could be addressed by implementing distributed leadership. Furthermore, we provide some suggestions as to how to foster distributed leadership in drug discovery projects.

Effective components screening and anti-myocardial infarction mechanism study of the Chinese medicine NSLF6 based on "system to system" mode

Background

Shuanglong formula (SLF), a Chinese medicine composed of panax ginseng and salvia miltiorrhiza exhibited significant effect in the treatment of myocardial infarction (MI) in clinical. Because of the complex nature and lack of stringent quality control, it's difficult to explain the action mechanism of SLF.

Method

In this study, we present a "system to system" (S2S) mode. Based on this mode, SLF was simplified successively through bioactivity-guided screening to achieve an optimized minimal phytochemical composition (new formula NSLF6) while maintaining its curative effect for MI.

Results

Pharmacological test combining with the study of systems biology show that NSLF6 has activity for treatment MI through synergistic therapeutic efficacies between total ginsenosides and total salvianolic acids via promoting cardiac cell regeneration and myocardial angiogenesis, antagonistic myocardial cell oxidative damage.

Conclusions

The present S2S mode may be an effective way for the discovery of new composite drugs from traditional medicines.

Third-generation sequencing techniques and applications to drug discovery

INTRODUCTION

There is an immediate need for functional and molecular studies to decipher differences between disease and 'normal' settings to identify large quantities of validated targets with the highest therapeutic utilities. Furthermore, drug mechanism of action and biomarkers to predict drug efficacy and safety need to be identified for effective design of clinical trials, decreasing attrition rates, regulatory agency approval process and drug repositioning. By expanding the power of genetics and pharmacogenetics studies, next-generation nucleic acid sequencing technologies have started to play an important role in all stages of drug discovery.

AREAS COVERED

This article reviews the first- and second-generation sequencing technologies (SGSTs) and challenges they pose to biomedicine. The article then focuses on the emerging third-generation sequencing technologies (TGSTs), their technological foundations and potential contributions to drug discovery.

EXPERT OPINION

Despite the scientific and commercial success of SGSTs, the goal of rapid, comprehensive and unbiased sequencing of nucleic acids has not been achieved. TGSTs promise to increase sequencing throughput and read lengths, decrease costs, run times and error rates, eliminate biases inherent in SGSTs and offer capabilities beyond nucleic acid sequencing. Such changes will have positive impact on all sequencing applications to drug discovery.

Systems biology impact on antiepileptic drug discovery

Systems biology (SB), a recent trend in bioscience research to consider the complex interactions in biological systems from a holistic perspective, sees the disease as a disturbed network of interactions, rather than alteration of single molecular component(s). SB-relying network pharmacology replaces the prevailing focus on specific drug-receptor interaction and the corollary of rational drug design of "magic bullets", by the search for multi-target drugs that would act on biological networks as "magic shotguns". Epilepsy being a multi-factorial, polygenic and dynamic pathology, SB approach appears particularly fit and promising for antiepileptic drug (AED) discovery. In fact, long before the advent of SB, AED discovery already involved some SB-like elements. A reported SB project aimed to find out new drug targets in epilepsy relies on a relational database that integrates clinical information, recordings from deep electrodes and 3D-brain imagery with histology and molecular biology data on modified expression of specific genes in the brain regions displaying spontaneous epileptic activity. Since hitting a single target does not treat complex diseases, a proper pharmacological promiscuity might impart on an AED the merit of being multi-potent. However, multi-target drug discovery entails the complicated task of optimizing multiple activities of compounds, while having to balance drug-like properties and to control unwanted effects. Specific design tools for this new approach in drug discovery barely emerge, but computational methods making reliable in silico predictions of poly-pharmacology did appear, and their progress might be quite rapid. The current move away from reductionism into network pharmacology allows expecting that a proper integration of the intrinsic complexity of epileptic pathology in AED discovery might result in literally anti-epileptic drugs.

The productivity crisis in pharmaceutical R&D

Advances in the understanding of the molecular basis of diseases have expanded the number of plausible therapeutic targets for the development of innovative agents in recent decades. However, although investment in pharmaceutical research and development (R&D) has increased substantially in this time, the lack of a corresponding increase in the output in terms of new drugs being approved indicates that therapeutic innovation has become more challenging. Here, using a large database that contains information on R&D projects for more than 28,000 compounds investigated since 1990, we examine the decline of R&D productivity in pharmaceuticals in the past two decades and its determinants. We show that this decline is associated with an increasing concentration of R&D investments in areas in which the risk of failure is high, which correspond to unmet therapeutic needs and unexploited biological mechanisms. We also investigate the potential variations in productivity with regard to the regional location of companies and find that although companies based in the United States and Europe differ in the composition of their R&D portfolios, there is no evidence of any productivity gap.

Exploiting drug-disease relationships for computational drug repositioning

Finding new uses for existing drugs, or drug repositioning, has been used as a strategy for decades to get drugs to more patients. As the ability to measure molecules in high-throughput ways has improved over the past decade, it is logical that such data might be useful for enabling drug repositioning through computational methods. Many computational predictions for new indications have been borne out in cellular model systems, though extensive animal model and clinical trial-based validation are still pending. In this review, we show that computational methods for drug repositioning can be classified in two axes: drug based, where discovery initiates from the chemical perspective, or disease based, where discovery initiates from the clinical perspective of disease or its pathology. Newer algorithms for computational drug repositioning will likely span these two axes, will take advantage of newer types of molecular measurements, and will certainly play a role in reducing the global burden of disease.

Drug discovery in Parkinson's disease-Update and developments in the use of cellular models

Parkinson's disease (PD) is the second most common neurodegenerative disorder and is characterized by the degeneration of dopaminergic (DA) neurons within the substantia nigra. Dopamine replacement drugs remain the most effective PD treatment but only provide temporary symptomatic relief. New therapies are urgently needed, but the search for a disease-modifying treatment and a definitive understanding of the underlying mechanisms of PD has been limited by the lack of physiologically relevant models that recapitulate the disease phenotype. The use of immortalized cell lines as in vitro model systems for drug discovery has met with limited success, since efficacy and safety too often fail to translate successfully in human clinical trials. Drug discoverers are shifting their focus to more physiologically relevant cellular models, including primary neurons and stem cells. The recent discovery of induced pluripotent stem (iPS) cell technology presents an exciting opportunity to derive human DA neurons from patients with sporadic and familial forms of PD. We anticipate that these human DA models will recapitulate key features of the PD phenotype. In parallel, high-content screening platforms, which extract information on multiple cellular features within individual neurons, provide a network-based approach that can resolve temporal and spatial relationships underlying mechanisms of neurodegeneration and drug perturbations. These emerging technologies have the potential to establish highly predictive cellular models that could bring about a desperately needed revolution in PD drug discovery.

The Alabama Drug Discovery Alliance: a collaborative partnership to facilitate academic drug discovery

The Alabama Drug Discovery Alliance is a collaboration between the University of Alabama at Birmingham and Southern Research Institute that aims to support the discovery and development of therapeutic molecules that address an unmet medical need. The alliance builds on the expertise present at both institutions and has the dedicated commitment of their respective technology transfer and intellectual property offices to guide any commercial opportunities that may arise from the supported efforts. Although most projects involve high throughput screening, projects at any stage in the drug discovery and development pathway are eligible for support. Irrespective of the target and stage of any project, well-functioning interdisciplinary teams are crucial to a project's progress. These teams consist of investigators with a wide variety of expertise from both institutions to contribute to the program's success.

Learning lessons from drugs that have recently entered the market

Which projects in the drug discovery field are most likely to be successful? In this article, I provide guidelines for answering this question by examining recent drug market entrants in detail, in particular their route of administration, trial design, novelty, therapeutic target and toxicities. I identify targets, trials and organizations as the key issues that are currently leading to the poor productivity in the pharmaceutical industry. Here, I outline some solutions and reasons for optimism, and suggest that the key determinants for success in drug discovery can be defined by studying recently launched drugs.

Label-free imaging and temporal signature in phenotypic cellular assays: a new approach to high-content screening

Some drug targets are not amenable to screening because of the lack of a practical or validated biological assay. Likewise, some screening assays may not be predictive of compound activity in a more disease-relevant scenario, or assay development may demand excessive allocation of resources (i.e., time, money or personnel) with limited knowledge of the actual tractability of the target. Label-free methodologies, implemented in microtiter plate format, may help address these issues and complement, simplify, or facilitate assays. Label-free biosensors, based on grating resonance or electrical impedance, are versatile platforms for detecting phenotypic changes in both engineered and native cells. Their non-invasive nature allows for the kinetic monitoring of multiple real-time cellular responses to external stimuli, as well as for the use of successive pharmacological challenges. The temporal signature recorded for a particular stimulus is characteristic of the cell type and the signaling pathway activated upon binding of a ligand to its receptor. Cellular label-free technology is an important technical advance in the study of functional pharmacological selectivity. Described in this overview are some of the hurdles encountered in modern drug discovery and the ways in which label-free technologies can be used to overcome these obstacles.

Specific chemotherapy of Chagas disease: relevance, current limitations and new approaches

A critical review of the development of specific chemotherapeutic approaches for the management of American Trypanosomiasis or Chagas disease is presented, including controversies on the pathogenesis of the disease, the initial efforts that led to the development of currently available drugs (nifurtimox and benznidazole), limitations of these therapies and novel approaches for the development of anti-Trypanosoma cruzi drugs, based on our growing understanding of the biology of this parasite. Among the later, the most promising approaches are ergosterol biosynthesis inhibitors such as posaconazole and ravuconazole, poised to enter clinical trials for chronic Chagas disease in the short term; inhibitors of cruzipain, the main cysteine protease of T. cruzi, essential for its survival and proliferation in vitro and in vivo; bisphosphonates, metabolic stable pyrophosphate analogs that have trypanocidal activity through the inhibition of the parasite's farnesyl-pyrophosphate synthase or hexokinase; inhibitors of trypanothione synthesis and redox metabolism and inhibitors of hypoxanthine-guanine phosphoribosyl-transferase, an essential enzyme for purine salvage in T. cruzi and related organisms. Finally, the economic and political challenges faced by development of drugs for the treatment of neglected tropical diseases, which afflict almost exclusively poor populations in developing countries, are analyzed and recent potential solutions for this conundrum are discussed.

Targeting breast cancer stem cells

The cancer stem cell (CSC) hypothesis postulates that tumors are maintained by a self-renewing CSC population that is also capable of differentiating into non-self-renewing cell populations that constitute the bulk of the tumor. Although, the CSC hypothesis does not directly address the cell of origin of cancer, it is postulated that tissue-resident stem or progenitor cells are the most common targets of transformation. Clinically, CSCs are predicted to mediate tumor recurrence after chemo- and radiation-therapy due to the relative inability of these modalities to effectively target CSCs. If this is the case, then CSC must be efficiently targeted to achieve a true cure. Similarities between normal and malignant stem cells, at the levels of cell-surface proteins, molecular pathways, cell cycle quiescence, and microRNA signaling present challenges in developing CSC-specific therapeutics. Approaches to targeting CSCs include the development of agents targeting known stem cell regulatory pathways as well as unbiased high-throughput siRNA or small molecule screening. Based on studies of pathways present in normal stem cells, recent work has identified potential "Achilles heals" of CSC, whereas unbiased screening provides opportunities to identify new pathways utilized by CSC as well as develop potential therapeutic agents. Here, we review both approaches and their potential to effectively target breast CSC.

Natural products in modern life science

With a realistic threat against biodiversity in rain forests and in the sea, a sustainable use of natural products is becoming more and more important. Basic research directed against different organisms in Nature could reveal unexpected insights into fundamental biological mechanisms but also new pharmaceutical or biotechnological possibilities of more immediate use. Many different strategies have been used prospecting the biodiversity of Earth in the search for novel structure-activity relationships, which has resulted in important discoveries in drug development. However, we believe that the development of multidisciplinary incentives will be necessary for a future successful exploration of Nature. With this aim, one way would be a modernization and renewal of a venerable proven interdisciplinary science, Pharmacognosy, which represents an integrated way of studying biological systems. This has been demonstrated based on an explanatory model where the different parts of the model are explained by our ongoing research. Anti-inflammatory natural products have been discovered based on ethnopharmacological observations, marine sponges in cold water have resulted in substances with ecological impact, combinatory strategy of ecology and chemistry has revealed new insights into the biodiversity of fungi, in depth studies of cyclic peptides (cyclotides) has created new possibilities for engineering of bioactive peptides, development of new strategies using phylogeny and chemography has resulted in new possibilities for navigating chemical and biological space, and using bioinformatic tools for understanding of lateral gene transfer could provide potential drug targets. A multidisciplinary subject like Pharmacognosy, one of several scientific disciplines bridging biology and chemistry with medicine, has a strategic position for studies of complex scientific questions based on observations in Nature. Furthermore, natural product research based on intriguing scientific questions in Nature can be of value to increase the attraction for young students in modern life science.

Designing focused chemical libraries enriched in protein-protein interaction inhibitors using machine-learning methods

Protein-protein interactions (PPIs) may represent one of the next major classes of therapeutic targets. So far, only a minute fraction of the estimated 650,000 PPIs that comprise the human interactome are known with a tiny number of complexes being drugged. Such intricate biological systems cannot be cost-efficiently tackled using conventional high-throughput screening methods. Rather, time has come for designing new strategies that will maximize the chance for hit identification through a rationalization of the PPI inhibitor chemical space and the design of PPI-focused compound libraries (global or target-specific). Here, we train machine-learning-based models, mainly decision trees, using a dataset of known PPI inhibitors and of regular drugs in order to determine a global physico-chemical profile for putative PPI inhibitors. This statistical analysis unravels two important molecular descriptors for PPI inhibitors characterizing specific molecular shapes and the presence of a privileged number of aromatic bonds. The best model has been transposed into a computer program, PPI-HitProfiler, that can output from any drug-like compound collection a focused chemical library enriched in putative PPI inhibitors. Our PPI inhibitor profiler is challenged on the experimental screening results of 11 different PPIs among which the p53/MDM2 interaction screened within our own CDithem platform, that in addition to the validation of our concept led to the identification of 4 novel p53/MDM2 inhibitors. Collectively, our tool shows a robust behavior on the 11 experimental datasets by correctly profiling 70% of the experimentally identified hits while removing 52% of the inactive compounds from the initial compound collections. We strongly believe that this new tool can be used as a global PPI inhibitor profiler prior to screening assays to reduce the size of the compound collections to be experimentally screened while keeping most of the true PPI inhibitors. PPI-HitProfiler is freely available on request from our CDithem platform website, www.CDithem.com.