Open source drug discovery--a new paradigm of collaborative research in tuberculosis drug development

Molecular Property Diagnostic Suite for COVID-19 (MPDSCOVID-19): an open-source disease-specific drug discovery portal

Molecular Property Diagnostic Suite (MPDS) was conceived and developed as an open-source disease-specific web portal based on Galaxy. MPDSCOVID-19 was developed for COVID-19 as a one-stop solution for drug discovery research. Galaxy platforms enable the creation of customized workflows connecting various modules in the web server. The architecture of MPDSCOVID-19 effectively employs Galaxy v22.04 features, which are ported on CentOS 7.8 and Python 3.7. MPDSCOVID-19 provides significant updates and the addition of several new tools updated after six years. Tools developed by our group in Perl/Python and open-source tools are collated and integrated into MPDSCOVID-19 using XML scripts. Our MPDS suite aims to facilitate transparent and open innovation. This approach significantly helps bring inclusiveness in the community while promoting free access and participation in software development.

Availability & Implementation

The MPDSCOVID-19 portal can be accessed at https://mpds.neist.res.in:8085/.

Drug Repositioning: A Monetary Stratagem to Discover a New Application of Drugs

Drug repurposing, also referred to as drug repositioning or drug reprofiling, is a scientific approach to the detection of any new application for an already approved or investigational drug. It is a useful policy for the invention and development of new pharmacological or therapeutic applications of different drugs. The strategy has been known to offer numerous advantages over developing a completely novel drug for certain problems. Drug repurposing has numerous methodologies that can be categorized as target-oriented, drug-oriented, and problem-oriented. The choice of the methodology of drug repurposing relies on the accessible information about the drug molecule and like pharmacokinetic, pharmacological, physicochemical, and toxicological profile of the drug. In addition, molecular docking studies and other computer-aided methods have been known to show application in drug repurposing. The variation in dosage for original target diseases and novel diseases presents a challenge for researchers of drug repurposing in present times. The present review critically discusses the drugs repurposed for cancer, covid-19, Alzheimer's, and other diseases, strategies, and challenges of drug repurposing. Moreover, regulatory perspectives related to different countries like the United States (US), Europe, and India have been delineated in the present review.

Low-cost anti-mycobacterial drug discovery using engineered E. coli

Whole-cell screening for Mycobacterium tuberculosis (Mtb) inhibitors is complicated by the pathogen's slow growth and biocontainment requirements. Here we present a synthetic biology framework for assaying Mtb drug targets in engineered E. coli. We construct Target Essential Surrogate E. coli (TESEC) in which an essential metabolic enzyme is deleted and replaced with an Mtb-derived functional analog, linking bacterial growth to the activity of the target enzyme. High throughput screening of a TESEC model for Mtb alanine racemase (Alr) revealed benazepril as a targeted inhibitor, a result validated in whole-cell Mtb. In vitro biochemical assays indicated a noncompetitive mechanism unlike that of clinical Alr inhibitors. We establish the scalability of TESEC for drug discovery by characterizing TESEC strains for four additional targets.

In-Silico Tool for Predicting, Scanning, and Designing Defensins

Defensins are host defense peptides present in nearly all living species, which play a crucial role in innate immunity. These peptides provide protection to the host, either by killing microbes directly or indirectly by activating the immune system. In the era of antibiotic resistance, there is a need to develop a fast and accurate method for predicting defensins. In this study, a systematic attempt has been made to develop models for predicting defensins from available information on defensins. We created a dataset of defensins and non-defensins called the main dataset that contains 1,036 defensins and 1,035 AMPs (antimicrobial peptides, or non-defensins) to understand the difference between defensins and AMPs. Our analysis indicates that certain residues like Cys, Arg, and Tyr are more abundant in defensins in comparison to AMPs. We developed machine learning technique-based models on the main dataset using a wide range of peptide features. Our SVM (support vector machine)-based model discriminates defensins and AMPs with MCC of 0.88 and AUC of 0.98 on the validation set of the main dataset. In addition, we created an alternate dataset that consists of 1,036 defensins and 1,054 non-defensins obtained from Swiss-Prot. Models were also developed on the alternate dataset to predict defensins. Our SVM-based model achieved maximum MCC of 0.96 with AUC of 0.99 on the validation set of the alternate dataset. All models were trained, tested, and validated using standard protocols. Finally, we developed a web-based service "DefPred" to predict defensins, scan defensins in proteins, and design the best defensins from their analogs. The stand-alone software and web server of DefPred are available at https://webs.iiitd.edu.in/raghava/defpred.

Crowdsourcing in medical research: concepts and applications

Crowdsourcing shifts medical research from a closed environment to an open collaboration between the public and researchers. We define crowdsourcing as an approach to problem solving which involves an organization having a large group attempt to solve a problem or part of a problem, then sharing solutions. Crowdsourcing allows large groups of individuals to participate in medical research through innovation challenges, hackathons, and related activities. The purpose of this literature review is to examine the definition, concepts, and applications of crowdsourcing in medicine. This multi-disciplinary review defines crowdsourcing for medicine, identifies conceptual antecedents (collective intelligence and open source models), and explores implications of the approach. Several critiques of crowdsourcing are also examined. Although several crowdsourcing definitions exist, there are two essential elements: (1) having a large group of individuals, including those with skills and those without skills, propose potential solutions; (2) sharing solutions through implementation or open access materials. The public can be a central force in contributing to formative, pre-clinical, and clinical research. A growing evidence base suggests that crowdsourcing in medicine can result in high-quality outcomes, broad community engagement, and more open science.

Innovation in neglected tropical disease drug discovery and development

BACKGROUND

Neglected tropical diseases (NTDs) are closely related to poverty and affect over a billion people in developing countries. The unmet treatment needs cause high mortality and disability thereby imposing a huge burden with severe social and economic consequences. Although coordinated by the World Health Organization, various philanthropic organizations, national governments and the pharmaceutical industry have been making efforts in improving the situation, the control of NTDs is still inadequate and extremely difficult today. The lack of safe, effective and affordable medicines is a key contributing factor. This paper reviews the recent advances and some of the challenges that we are facing in the fight against NTDs.

MAIN BODY

In recent years, a number of innovations have demonstrated propensity to promote drug discovery and development for NTDs. Implementation of multilateral collaborations leads to continued efforts and plays a crucial role in drug discovery. Proactive approaches and advanced technologies are urgently needed in drug innovation for NTDs. However, the control and elimination of NTDs remain a formidable task as it requires persistent international cooperation to make sustainable progresses for a long period of time. Some currently employed strategies were proposed and verified to be successful, which involve both mechanisms of 'Push' which aims at cutting the cost of research and development for industry and 'Pull' which aims at increasing market attractiveness. Coupled to this effort should be the exercise of shared responsibility globally to reduce risks, overcome obstacles and maximize benefits. Since NTDs are closely associated with poverty, it is absolutely essential that the stakeholders take concerted and long-term measures to meet multifaceted challenges by alleviating extreme poverty, strengthening social intervention, adapting climate changes, providing effective monitoring and ensuring timely delivery.

CONCLUSIONS

The ongoing endeavor at the global scale will ultimately benefit the patients, the countries they are living and, hopefully, the manufacturers who provide new preventive, diagnostic and therapeutic products.

Structure based drug discovery for designing leads for the non-toxic metabolic targets in multi drug resistant Mycobacterium tuberculosis

Background

The problem of drug resistance and bacterial persistence in tuberculosis is a cause of global alarm. Although, the UN’s Sustainable Development Goals for 2030 has targeted a Tb free world, the treatment gap exists and only a few new drug candidates are in the pipeline. In spite of large information from medicinal chemistry to ‘omics’ data, there has been a little effort from pharmaceutical companies to generate pipelines for the development of novel drug candidates against the multi drug resistant Mycobacterium tuberculosis.

Methods

In the present study, we describe an integrated methodology; utilizing systems level information to optimize ligand selection to lower the failure rates at the pre-clinical and clinical levels. In the present study, metabolic targets (Rv2763c, Rv3247c, Rv1094, Rv3607c, Rv3048c, Rv2965c, Rv2361c, Rv0865, Rv0321, Rv0098, Rv0390, Rv3588c, Rv2244, Rv2465c and Rv2607) in M. tuberculosis, identified using our previous Systems Biology and data-intensive genome level analysis, have been used to design potential lead molecules, which are likely to be non-toxic. Various in silico drug discovery tools have been utilized to generate small molecular leads for each of the 15 targets with available crystal structures.

Results

The present study resulted in identification of 20 novel lead molecules including 4 FDA approved drugs (droxidropa, tetroxoprim, domperidone and nemonapride) which can be further taken for drug repurposing. This comprehensive integrated methodology, with both experimental and in silico approaches, has the potential to not only tackle the MDR form of Mtb but also the most important persister population of the bacterium, with a potential to reduce the failures in the Tb drug discovery.

Conclusion

We propose an integrated approach of systems and structural biology for identifying targets that address the high attrition rate issue in lead identification and drug development We expect that this system level analysis will be applicable for identification of drug candidates to other pathogenic organisms as well.

Electronic supplementary material

The online version of this article (10.1186/s12967-017-1363-9) contains supplementary material, which is available to authorized users.

Screening the Medicines for Malaria Venture Pathogen Box across Multiple Pathogens Reclassifies Starting Points for Open-Source Drug Discovery

Open-access drug discovery provides a substantial resource for diseases primarily affecting the poor and disadvantaged. The open-access Pathogen Box collection is comprised of compounds with demonstrated biological activity against specific pathogenic organisms. The supply of this resource by the Medicines for Malaria Venture has the potential to provide new chemical starting points for a number of tropical and neglected diseases, through repurposing of these compounds for use in drug discovery campaigns for these additional pathogens. We tested the Pathogen Box against kinetoplastid parasites and malaria life cycle stages in vitro Consequently, chemical starting points for malaria, human African trypanosomiasis, Chagas disease, and leishmaniasis drug discovery efforts have been identified. Inclusive of this in vitro biological evaluation, outcomes from extensive literature reviews and database searches are provided. This information encompasses commercial availability, literature reference citations, other aliases and ChEMBL number with associated biological activity, where available. The release of this new data for the Pathogen Box collection into the public domain will aid the open-source model of drug discovery. Importantly, this will provide novel chemical starting points for drug discovery and target identification in tropical disease research.

Data Intensive Genome Level Analysis for Identifying Novel, Non-Toxic Drug Targets for Multi Drug Resistant Mycobacterium tuberculosis

We report the construction of a novel Systems Biology based virtual drug discovery model for the prediction of non-toxic metabolic targets in Mycobacterium tuberculosis (Mtb). This is based on a data-intensive genome level analysis and the principle of conservation of the evolutionarily important genes. In the 1623 sequenced Mtb strains, 890 metabolic genes identified through a systems approach in Mtb were evaluated for non-synonymous mutations. The 33 genes showed none or one variation in the entire 1623 strains, including 1084 Russian MDR strains. These invariant targets were further evaluated for their experimental and in silico essentiality as well as availability of their crystal structure in Protein Data Bank (PDB). Along with this, targets for the common existing antibiotics and the new Tb drug candidates were also screened for their variation across 1623 strains of Mtb for understanding the drug resistance. We propose that the reduced set of these reported targets could be a more effective starting point for medicinal chemists in generating new chemical leads. This approach has the potential of fueling the dried up Tuberculosis (Tb) drug discovery pipeline.

An open source pharma roadmap

In an Essay, Matthew Todd and colleagues discuss an open source approach to drug development.

Open Source Drug Discovery: Highly Potent Antimalarial Compounds Derived from the Tres Cantos Arylpyrroles

The development of new antimalarial compounds remains a pivotal part of the strategy for malaria elimination. Recent large-scale phenotypic screens have provided a wealth of potential starting points for hit-to-lead campaigns. One such public set is explored, employing an open source research mechanism in which all data and ideas were shared in real time, anyone was able to participate, and patents were not sought. One chemical subseries was found to exhibit oral activity but contained a labile ester that could not be replaced without loss of activity, and the original hit exhibited remarkable sensitivity to minor structural change. A second subseries displayed high potency, including activity within gametocyte and liver stage assays, but at the cost of low solubility. As an open source research project, unexplored avenues are clearly identified and may be explored further by the community; new findings may be cumulatively added to the present work.

Open drug discovery for the Zika virus

The Zika virus (ZIKV) outbreak in the Americas has caused global concern that we may be on the brink of a healthcare crisis. The lack of research on ZIKV in the over 60 years that we have known about it has left us with little in the way of starting points for drug discovery. Our response can build on previous efforts with virus outbreaks and lean heavily on work done on other flaviviruses such as dengue virus. We provide some suggestions of what might be possible and propose an open drug discovery effort that mobilizes global science efforts and provides leadership, which thus far has been lacking. We also provide a listing of potential resources and molecules that could be prioritized for testing as in vitro assays for ZIKV are developed. We propose also that in order to incentivize drug discovery, a neglected disease priority review voucher should be available to those who successfully develop an FDA approved treatment. Learning from the response to the ZIKV, the approaches to drug discovery used and the success and failures will be critical for future infectious disease outbreaks.

Systems level mapping of metabolic complexity in Mycobacterium tuberculosis to identify high-value drug targets

BACKGROUND

The effectiveness of current therapeutic regimens for Mycobacterium tuberculosis (Mtb) is diminished by the need for prolonged therapy and the rise of drug resistant/tolerant strains. This global health threat, despite decades of basic research and a wealth of legacy knowledge, is due to a lack of systems level understanding that can innovate the process of fast acting and high efficacy drug discovery.

METHODS

The enhanced functional annotations of the Mtb genome, which were previously obtained through a crowd sourcing approach was used to reconstruct the metabolic network of Mtb in a bottom up manner. We represent this information by developing a novel Systems Biology Spindle Map of Metabolism (SBSM) and comprehend its static and dynamic structure using various computational approaches based on simulation and design.

RESULTS

The reconstructed metabolism of Mtb encompasses 961 metabolites, involved in 1152 reactions catalyzed by 890 protein coding genes, organized into 50 pathways. By accounting for static and dynamic analysis of SBSM in Mtb we identified various critical proteins required for the growth and survival of bacteria. Further, we assessed the potential of these proteins as putative drug targets that are fast acting and less toxic. Further, we formulate a novel concept of metabolic persister genes (MPGs) and compared our predictions with published in vitro and in vivo experimental evidence. Through such analyses, we report for the first time that de novo biosynthesis of NAD may give rise to bacterial persistence in Mtb under conditions of metabolic stress induced by conventional anti-tuberculosis therapy. We propose such MPG's as potential combination of drug targets for existing antibiotics that can improve their efficacy and efficiency for drug tolerant bacteria.

CONCLUSION

The systems level framework formulated by us to identify potential non-toxic drug targets and strategies to circumvent the issue of bacterial persistence can substantially aid in the process of TB drug discovery and translational research.

Emerging and reemerging neglected tropical diseases: a review of key characteristics, risk factors, and the policy and innovation environment

In global health, critical challenges have arisen from infectious diseases, including the emergence and reemergence of old and new infectious diseases. Emergence and reemergence are accelerated by rapid human development, including numerous changes in demographics, populations, and the environment. This has also led to zoonoses in the changing human-animal ecosystem, which are impacted by a growing globalized society where pathogens do not recognize geopolitical borders. Within this context, neglected tropical infectious diseases have historically lacked adequate attention in international public health efforts, leading to insufficient prevention and treatment options. This subset of 17 infectious tropical diseases disproportionately impacts the world's poorest, represents a significant and underappreciated global disease burden, and is a major barrier to development efforts to alleviate poverty and improve human health. Neglected tropical diseases that are also categorized as emerging or reemerging infectious diseases are an even more serious threat and have not been adequately examined or discussed in terms of their unique risk characteristics. This review sets out to identify emerging and reemerging neglected tropical diseases and explore the policy and innovation environment that could hamper or enable control efforts. Through this examination, we hope to raise awareness and guide potential approaches to addressing this global health concern.

Open source drug discovery - a limited tutorial

Open science is a new concept for the practice of experimental laboratory-based research, such as drug discovery. The authors have recently gained experience in how to run such projects and here describe some straightforward steps others may wish to take towards more openness in their own research programmes. Existing and inexpensive online tools can solve many challenges, while some psychological barriers to the free sharing of all data and ideas are more substantial.

The structure of Rv3717 reveals a novel amidase from Mycobacterium tuberculosis

Bacterial N-acetylmuramoyl-L-alanine amidases are cell-wall hydrolases that hydrolyze the bond between N-acetylmuramic acid and L-alanine in cell-wall glycopeptides. Rv3717 of Mycobacterium tuberculosis has been identified as a unique autolysin that lacks a cell-wall-binding domain (CBD) and its structure has been determined to 1.7 Å resolution by the Pt-SAD phasing method. Rv3717 possesses an α/β-fold and is a zinc-dependent hydrolase. The structure reveals a short flexible hairpin turn that partially occludes the active site and may be involved in autoregulation. This type of autoregulation of activity of PG hydrolases has been observed in Bartonella henselae amidase (AmiB) and may be a general mechanism used by some of the redundant amidases to regulate cell-wall hydrolase activity in bacteria. Rv3717 utilizes its net positive charge for substrate binding and exhibits activity towards a broad spectrum of substrate cell walls. The enzymatic activity of Rv3717 was confirmed by isolation and identification of its enzymatic products by LC/MS. These studies indicate that Rv3717, an N-acetylmuramoyl-L-alanine amidase from M. tuberculosis, represents a new family of lytic amidases that do not have a separate CBD and are regulated conformationally.

Biological diversity in the patent system

Biological diversity in the patent system is an enduring focus of controversy but empirical analysis of the presence of biodiversity in the patent system has been limited. To address this problem we text mined 11 million patent documents for 6 million Latin species names from the Global Names Index (GNI) established by the Global Biodiversity Information Facility (GBIF) and Encyclopedia of Life (EOL). We identified 76,274 full Latin species names from 23,882 genera in 767,955 patent documents. 25,595 species appeared in the claims section of 136,880 patent documents. This reveals that human innovative activity involving biodiversity in the patent system focuses on approximately 4% of taxonomically described species and between 0.8–1% of predicted global species. In this article we identify the major features of the patent landscape for biological diversity by focusing on key areas including pharmaceuticals, neglected diseases, traditional medicines, genetic engineering, foods, biocides, marine genetic resources and Antarctica. We conclude that the narrow focus of human innovative activity and ownership of genetic resources is unlikely to be in the long term interest of humanity. We argue that a broader spectrum of biodiversity needs to be opened up to research and development based on the principles of equitable benefit-sharing, respect for the objectives of the Convention on Biological Diversity, human rights and ethics. Finally, we argue that alternative models of innovation, such as open source and commons models, are required to open up biodiversity for research that addresses actual and neglected areas of human need. The research aims to inform the implementation of the 2010 Nagoya Protocol on Access to Genetic Resources and the Equitable Sharing of Benefits Arising from their Utilization and international debates directed to the governance of genetic resources. Our research also aims to inform debates under the Intergovernmental Committee on Intellectual Property and Genetic Resources, Traditional Knowledge and Folklore at the World Intellectual Property Organization.

Open-source approaches for the repurposing of existing or failed candidate drugs: learning from and applying the lessons across diseases

Repurposing has the objective of targeting existing drugs and failed, abandoned, or yet-to-be-pursued clinical candidates to new disease areas. The open-source model permits for the sharing of data, resources, compounds, clinical molecules, small libraries, and screening platforms to cost-effectively advance old drugs and/or candidates into clinical re-development. Clearly, at the core of drug-repurposing activities is collaboration, in many cases progressing beyond the open sharing of resources, technology, and intellectual property, to the sharing of facilities and joint program development to foster drug-repurposing human-capacity development. A variety of initiatives under way for drug repurposing, including those targeting rare and neglected diseases, are discussed in this review and provide insight into the stakeholders engaged in drug-repurposing discovery, the models of collaboration used, the intellectual property-management policies crafted, and human capacity developed. In the case of neglected tropical diseases, it is suggested that the development of human capital be a central aspect of drug-repurposing programs. Open-source models can support human-capital development through collaborative data generation, open compound access, open and collaborative screening, preclinical and possibly clinical studies. Given the urgency of drug development for neglected tropical diseases, the review suggests elements from current repurposing programs be extended to the neglected tropical diseases arena.

Enabling policy planning and innovation management through patent information and co-authorship network analyses: a study of tuberculosis in Brazil

Introduction

New tools and approaches are necessary to facilitate public policy planning and foster the management of innovation in countries' public health systems. To this end, an understanding of the integrated way in which the various actors who produce scientific knowledge and inventions in technological areas of interest operate, where they are located and how they relate to one another is of great relevance. Tuberculosis has been chosen as a model for the present study as it is a current challenge for Brazilian research and innovation.

Methodology

Publications about tuberculosis written by Brazilian authors were accessed from international databases, analyzed, processed with text searching tools and networks of coauthors were constructed and visualized. Patent applications about tuberculosis in Brazil were retrieved from the Brazilian National Institute of Industrial Property (INPI) and the European Patent Office databases, through the use of International Patent Classification and keywords and then categorized and analyzed.

Results/Conclusions

Brazilian authorship of articles about tuberculosis jumped from 1% in 1995 to 5% in 2010. Article production and patent filings of national origin have been concentrated in public universities and research institutions while the participation of private industry in the filing of Brazilian patents has remained limited. The goals of national patenting efforts have still not been reached, as up to the present none of the applications filed have been granted a patent. The analysis of all this data about TB publishing and patents clearly demonstrates the importance of maintaining the continuity of Brazil's production development policies as well as government support for infrastructure projects to be employed in transforming the potential of research. This policy, which already exists for the promotion of new products and processes that, in addition to bringing diverse economic benefits to the country, will also contribute to effective dealing with public health problems affecting Brazil and the World.

Open source drug discovery in practice: a case study

Background

Open source drug discovery offers potential for developing new and inexpensive drugs to combat diseases that disproportionally affect the poor. The concept borrows two principle aspects from open source computing (i.e., collaboration and open access) and applies them to pharmaceutical innovation. By opening a project to external contributors, its research capacity may increase significantly. To date there are only a handful of open source R&D projects focusing on neglected diseases. We wanted to learn from these first movers, their successes and failures, in order to generate a better understanding of how a much-discussed theoretical concept works in practice and may be implemented.

Methodology/Principal Findings

A descriptive case study was performed, evaluating two specific R&D projects focused on neglected diseases. CSIR Team India Consortium's Open Source Drug Discovery project (CSIR OSDD) and The Synaptic Leap's Schistosomiasis project (TSLS). Data were gathered from four sources: interviews of participating members (n = 14), a survey of potential members (n = 61), an analysis of the websites and a literature review. Both cases have made significant achievements; however, they have done so in very different ways. CSIR OSDD encourages international collaboration, but its process facilitates contributions from mostly Indian researchers and students. Its processes are formal with each task being reviewed by a mentor (almost always offline) before a result is made public. TSLS, on the other hand, has attracted contributors internationally, albeit significantly fewer than CSIR OSDD. Both have obtained funding used to pay for access to facilities, physical resources and, at times, labor costs. TSLS releases its results into the public domain, whereas CSIR OSDD asserts ownership over its results.

Conclusions/Significance

Technically TSLS is an open source project, whereas CSIR OSDD is a crowdsourced project. However, both have enabled high quality research at low cost. The critical success factors appear to be clearly defined entry points, transparency and funding to cover core material costs.

Open source drug discovery can be an influential model for discovering and developing new medicines and diagnostics for neglected diseases. It offers the opportunity to accelerate the discovery progress while keeping expenditures to a minimum by encouraging incremental contributions from volunteer scientists. Publishing raw data and results in the public domain is positive within the context of neglected diseases since it facilitates open collaboration while obviating the ability to patent any results. In this way it effectively de-links the research and development costs from the sales price of the end product, the new medicine or diagnostic. This case study demonstrates that implementations of the open source model can differ while still achieving the ultimate goal of obtaining high quality research at reduced costs. However, the importance of clearly defined entry points, transparency and funding are shared success factors. These findings present the practical challenges of implementing a theoretical concept and hopefully will assist other scientists in organizing future open source drug discovery projects.

Open Drug Discovery Teams: A Chemistry Mobile App for Collaboration

The Open Drug Discovery Teams (ODDT) project provides a mobile app primarily intended as a research topic aggregator of predominantly open science data collected from various sources on the internet. It exists to facilitate interdisciplinary teamwork and to relieve the user from data overload, delivering access to information that is highly relevant and focused on their topic areas of interest. Research topics include areas of chemistry and adjacent molecule-oriented biomedical sciences, with an emphasis on those which are most amenable to open research at present. These include rare and neglected diseases, and precompetitive and public-good initiatives such as green chemistry. The ODDT project uses a free mobile app as user entry point. The app has a magazine-like interface, and server-side infrastructure for hosting chemistry-related data as well as value added services. The project is open to participation from anyone and provides the ability for users to make annotations and assertions, thereby contributing to the collective value of the data to the engaged community. Much of the content is derived from public sources, but the platform is also amenable to commercial data input. The technology could also be readily used in-house by organizations as a research aggregator that could integrate internal and external science and discussion. The infrastructure for the app is currently based upon the Twitter API as a useful proof of concept for a real time source of publicly generated content. This could be extended further by accessing other APIs providing news and data feeds of relevance to a particular area of interest. As the project evolves, social networking features will be developed for organizing participants into teams, with various forms of communication and content management possible.

Crowd sourcing a new paradigm for interactome driven drug target identification in Mycobacterium tuberculosis

A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative 'Connect to Decode' (C2D) to generate the first and largest manually curated interactome of Mtb termed 'interactome pathway' (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach.

Global phenotypic screening for antimalarials

Malaria, a devastating infectious disease caused by Plasmodium spp., leads to roughly 655,000 deaths per year, mostly of African children. To compound the problem, drug resistance has emerged to all classical antimalarials and may be emerging for artemisinin-based combination therapies. To address the need for new antimalarials with novel mechanisms, several groups carried out phenotypic screening campaigns to identify compounds inhibiting growth of the blood stages of Plasmodium falciparum. In this review, we describe the characterization of these compounds, explore currently ongoing strategies to develop lead molecules, and endorse the concept of a "malaria box" of publicly accessible active compounds.