Agency: European Commission | Branch: FP7 | Program: CP-IP | Phase: HEALTH.2010.2.3.2-1 | Award Amount: 16.70M | Year: 2011
The More Medicines for Tuberculosis (MM4TB) consortium evolved from the highly successful FP6 project, New Medicines for TB (NM4TB), that delivered a candidate drug for clinical development two years ahead of schedule. Building on these firm foundations and exploiting its proprietary pharmacophores, MM4TB will continue to develop new drugs for TB treatment. An integrated approach will be implemented by a multidisciplinary team that combines some of Europes leading academic TB researchers with two major pharmaceutical companies and four SMEs, all strongly committed to the discovery of anti-infective agents. MM4TB will use a tripartite screening strategy to discover new hits in libraries of natural products and synthetic compounds, while concentrating on both classical and innovative targets that have been pharmacologically validated. Whole cell screens will be conducted against Mycobacterium tuberculosis using in vitro and ex vivo models for active growth, latency and intracellular infection. Hits that are positive in two or more of these models will then be used for target identification using functional genomics technologies including whole genome sequencing and genetic complementation of resistant mutants, yeast three hybrid, click chemistry and proteomics. Targets thus selected will enter assay development, structure determination, fragment-based and rational drug design programs; functionally related targets will be found using metabolic pathway reconstruction. Innovative techniques, based on microfluidics and array platforms, will be used for hit ranking, determining rates of cidality and confirming mechanism of action. Medicinal chemistry will convert leads to molecules with drug-like properties for evaluation of efficacy in different animal models and late preclinical testing.
Ekins S.,Collaborative Drug Discovery, Inc. |
Clark A.M.,Molecular Materials Informatics Inc. |
Sarker M.,SRI International
Journal of Cheminformatics | Year: 2013
Background: An increasing number of researchers are focused on strategies for developing inhibitors of Mycobacterium tuberculosis (Mtb) as tuberculosis (TB) drugs. Results: In order to learn from prior work we have collated information on molecules screened versus Mtb and their targets which has been made available in the Collaborative Drug Discovery (CDD) database. This dataset contains published data on target, essentiality, links to PubMed, TBDB, TBCyc (which provides a pathway-based visualization of the entire cellular biochemical network) and human homolog information. The development of mobile cheminformatics apps could lower the barrier to drug discovery and promote collaboration. Therefore we have used this set of over 700 molecules screened versus Mtb and their targets to create a free mobile app (TB Mobile) that displays molecule structures and links to the bioinformatics data. By input of a molecular structures and performing a similarity search within the app we can infer potential targets or search by targets to retrieve compounds known to be active. Conclusions: TB Mobile may assist researchers as part of their workflow in identifying potential targets for hits generated from phenotypic screening and in prioritizing them for further follow-up. The app is designed to lower the barriers to accessing this information, so that all researchers with an interest in combatting this deadly disease can use it freely to the benefit of their own efforts. © 2013 Ekins et al.; licensee Chemistry Central Ltd.
Litterman N.K.,Collaborative Drug Discovery, Inc. |
Ekins S.,Collaborative Drug Discovery, Inc.
Drug Discovery Today | Year: 2015
Stem cell research is at an important juncture: despite significant potential for human health and several countries with key initiatives to expedite commercialization, there are gaps in capturing and exploiting the results of past and current research. Here, we propose a concerted plan that could be taken to foster a more collaborative approach and ensure that all research efforts can be leveraged across the community. The creation of a definitive centralized database repository, or at least harmonized data repositories, for stem cell groups in academia and industry, enabling secure selective sharing of data when needed, could provide the core structure that is sought globally and protect intellectual property. The development of minimum information about stem cell experiments (MIASCE) could be key to this development. © 2014 Elsevier B.V. All rights reserved.
Collaborative Drug Discovery, Inc. | Date: 2015-07-07
A process for identifying, accurately selecting, and storing scientific data that is present in textual formats. The process includes providing scientific data located in a text document and searching the text document using a computer and selecting a plurality of key words and phrases using an algorithm. The selected key words and phrases are matched with a plurality of semantic definitions and a plurality of semantic definition-key words and phrase pairs are created. The created plurality of semantic definition-key words and phrase pairs are displayed to a user via a computer user interface and the user selects which of the created plurality of semantic definition-key words and phrase pairs are accurate. The process also includes storing the selected and accurate semantic definition-key words and phrase pairs in computer memory.
Agency: Department of Health and Human Services | Branch: | Program: STTR | Phase: Phase II | Award Amount: 997.18K | Year: 2012
DESCRIPTION (provided by applicant): We are witnessing the growing menace of both increasing cases of drug-sensitive and drug-resistant Mycobacterium tuberculosis (Mtb) strains and the challenge to produce the first new tuberculosis (TB) drug in well over40 years. The TB community, having invested in extensive high-throughput screening efforts, is faced with the question of how to optimally leverage this data in order to move from a hit to a lead to a clinical candidate and potentially a new drug. Complementing this approach, yet conducted on a much smaller scale, cheminformatic techniques have been leveraged. We suggest these computational approaches should be more optimally integrated in a workflow with experimental approaches to accelerate TB drug discovery. This Small Business Technology Transfer Phase II project entitled Identification of novel therapeutics for tuberculosis combining cheminformatics, diverse databases and logic-based pathway analysis describes the development of software that willfacilitate new drug discovery efforts for Mycobacterium tuberculosis (TB) and the progression of molecules discovered with it as mimics for substrates of enzymes and their in vivo essential genes. In phase 1 we illustrated the concept of loosely marrying the cheminformatic and pathways database that resulted in two compounds as proposed mimics of 2 D-fructose 1,6 bisphosphate with activity against Mtb (MIC 20 and 40mg/ml). In phase II via an API we will link the knowledge in CDD, SRI and other databases andtools seamlessly. A researcher will be able to investigate molecules, targets, pathways and then select metabolites or other molecules for pharmacophore analysis, scoring with TB machine learning models and ADME and drug-likeness assessment from within one interface. This tool will be used to aid the identification of novel therapeutics for tuberculosis and be useful for hypotheses testing, knowledge sharing, data archiving, data mining and drug discovery. We will make CDD into a mobile application such that the generalized workflow in this project can be performed anywhere. We present promising preliminary work which resulted in two active compounds, that suggests phase II support of the mimic strategy to identify compounds of interest for TB would be a viable strategy. This proposal balances software development, database development and drug discovery activities in order to achieve our goals. We expect this product could be quickly applied to other infectious diseases which have a great societal impact and as a stretch goal we will endeavor to demonstrate this. PUBLIC HEALTH RELEVANCE: We propose to develop an integrated system to facilitate new drug discovery efforts for TB using novel logical inference techniques developed by scientists at SRI International, linked with knowledge which has been assembled by the curation of diverse biological data types and computational prediction by Collaborative Drug Discovery (CDD). This tool will be used to aid the identification of novel therapeutics for tuberculosis by combining cheminformatics, diverse databases and logic-based pathway analysis. We will demonstrate the utility of the tool (available also as a mobile application) and overall workflow ourselves by discovering and developing compounds for TB and other infectious diseases.
Agency: Department of Health and Human Services | Branch: National Institutes of Health | Program: SBIR | Phase: Phase II | Award Amount: 1.49M | Year: 2015
DESCRIPTION provided by applicant Collaborative Drug Discovery Inc CDD proposes to create an innovative software module that will help biologists to quickly and easily encode their plain text biological assay protocols into formats suitable for computational processing The software will enable scientists engaged in early stage drug discovery to automatically identify sort and compare datasets across research groups and to efficiently and properly document experimental procedures In order to encourage adoption the software will integrate seamlessly into preclinical data management platforms such as CDDandapos s prioritize intuitive ease of use by scientists who are not informatics experts harmonize with existing laboratory workflows minimize the extra effort of annotation and deliver clear and immediate benefits to the user as part of an integrated experience This combination of new capabilities and extreme ease of use will accelerate translational drug discovery efforts by empowering software platforms that bridge the divide between biologists and medicinal chemists to apply sophisticated tools long available on the chemistry side for the first time also to the biological side and thus across both domains Existing software can already easily connect screening results to chemical structures This new platform will further connect these data to the purpose and methodology of the screens Specific aims for Phase are to Complete development of the novel annotation platform that interactively encodes assay protocols using an expressive ontology The software components will be designed to be modular flexible robust and versatile so that they can also be incorporated into other types of platforms such as websites ELNs and LIMS Train the software on a broad corpus of assay protocols andgt so it is ready for widespread use Develop the capability for the platform to train itself through continued use to a improve its performance for end users and b assist bioinformatics specialists to maintain and extend the underlying ontologies Document a quantitative improvement in annotation accuracy compared with fully automated approaches Deploy the core technology as a free web based service to scientists e g in collaboration with PubChem Develop applications that utilize th core technology to deliver immediate benefits to end users as described in more detail in Section I B of the Research Strategy and thereby promote adoption PUBLIC HEALTH RELEVANCE The proposed project will create novel computational tools that will help researchers to translate new experimental discoveries into the development of novel and improved drugs against a wide range of diseases These tools will particularly benefit networks of researchers working on diseases that leading pharmaceutical companies have largely ignored because they are not perceived as highly profitable opportunities despite the fact that in many cases they afflict millions of people
Agency: Department of Health and Human Services | Branch: | Program: STTR | Phase: Phase I | Award Amount: 255.22K | Year: 2013
DESCRIPTION (provided by applicant): Identification and Validation of Targets of Phenotypic High Throughput Screening Hits for Chagas Disease Project Summary Nearly 10 million people in Latin America are infected with the eukaryotic parasite Trypanosoma cruzi, the causative agent of Chagas disease. The World Health Organization (WHO) classifies Chagas disease as a neglected tropical disease, but Chagas disease is gaining recognition as an emerging infection in the United States where an estimated 300,000 people may be infected. Unfortunately, there are no FDA approved treatments for Chagas disease and treatments used outside the U.S. have toxic side effects. New therapeutics for Chagas disease are desperately needed. However, few promising drug candidates have advanced to the clinic and the existing drug development pipeline lacks target diversity. In order to facilitate and catalyze the identification of novel therapeutics for Chagas disease, Collaborative Drug Discovery and SRI propose to develop and validate a new combined computational- systems biology approach that predicts metabolic enzyme targets of phenotypic screening hits. The proposed methodology will be developed and validated for Chagas disease (Phase I) and expanded to develop a prototype research tool to support target prediction and validation for phenotypic screening hits from multiple diseases (Phase II). More specifically, in Phase I CDD and SRI will (i) develop a novel approach that using computational methods to identify parasite metabolites structurally mimicked by high throughput screening (HTS) hits and bioinformatics analyses of metabolic pathways to ultimately predict the target of hits, (ii) apply the approach to HTS hits for Chagas disease compiled from over 300,000 compounds tested against T. cruzi in the literature and public HTS datasets compiled in CDD's public database, and (iii) conduct preliminary experiments to validate predicted target-compound pairs. In Phase II, CDD and SRI will conduct more extensive experimental validationof predictions and apply the drug target prediction methodologies to additional neglected tropical diseases to demonstrate the broader utility of the approach. Ultimately, CDD will develop a software module that automates workflow and facilitates sharingof bioinformatics and chemiformatic data between CDD's software platform and external bioinformatics databases such as the SRI BioCyc database. This module is one of a suite of proposed modules addressing aspects of the drug discovery process that will beintegrated and commercialized along with CDD's existing drug discovery software platform. PUBLIC HEALTH RELEVANCE PUBLIC HEALTH RELEVANCE: Nearly 10 million people in Latin America and 300,000 people in the United States are believed to be infected with the eukaryotic parasite Trypanosoma cruzi, the causative agent of Chagas disease; new drugs to treat Chagas disease are desperately needed. In order to identify novel drug targets for Chagas disease and accelerate drug development, Collaborative Drug Discovery (CDD) and SRI propose to develop a computational approach to help scientists predict the drug targets of small molecules that kill the parasite. These methods will eventually be expanded to predict novel drug targets for multiple diseases andserve as the basis for a software module that can be commercialized through CDD's existing drug discovery software.
Agency: Department of Health and Human Services | Branch: National Institutes of Health | Program: SBIR | Phase: Phase II | Award Amount: 1.40M | Year: 2013
DESCRIPTION Collaborative Drug Discovery Inc CDD will create a novel web based software platform that enables scientists to work together effectively to discover and improve new drug leads by sharing computational predictions based on open source descriptors and models for the first time without needing to reveal underlying chemical structures and biodata It will create the first practical system of bio computational analysis across distributed datasets with different owners while respecting data privacy By lowering this key barrier to collaboration the platform will accelerate the pre clinical drug discovery pipeline Research aimed at neglected diseases and orphan indications will especially benefit because they often rely on the loosely affiliated efforts of academic investigators non profit foundations government laboratories and small biotechnology firms andquot extra pharmaandquot entities Such efforts typically lack not only the resources but also the integrated workflows of discovery projects conducted at large pharmaceutical companies within which data can be shared freely across departments The project will for the first time enable researchers focused on neglected diseases and orphan indications to effectively exploit bio computational tools such as virtual screening and ADME Tox predictions which are now considered to be standard and indispensible components of early discovery workflows within large pharma It will also make it easier for these extra pharma researchers to collaborate with large pharma and benefit from large pharmaandapos s significant investment accumulating large high quality datasets In Phase II of this SBIR project CDD will Create a stand alone platform based entirely on open source technologies that enables researchers to share models share predictions from models and create models from distributed heterogeneous QSAR data all without needing to divulge the underlying training sets Develop approaches that enable scientists who are not computational chemists to exploit the technology A series of user interfaces will automate and intelligently guide the user to create or exploit models and assist the user to visualize domains of applicability interpret results and understand their limitations The integrated platforms will enable scientists to seamlessly create share and execute computational models leveraging private data vaults with or without sharing the underlying training data Validate the platform by a developing a suite of at least five ADME Tox and physicochemical property models based on open source descriptors and data obtained from commercial ADME vendors as well as public data from PubChem ChEMBL and other sources b securely making available a series of sophisticated pre competitive ADME Tox models provided by large pharmaceutical companies and c demonstrating that col laboratory can utilize the platform on their own without relying on a computational chemist to discover and advance TB drug leads with good ADME Tox properties PUBLIC HEALTH RELEVANCE The proposed project will create novel computational tools that will help researchers to accelerate the discovery of new and improved drugs against a wide range of diseases These tools will particularly benefit researchers working on diseases that leading pharmaceutical companies have largely ignored because they are not perceived as highly profitable opportunities despite the fact that in many cases they afflict millions of peopl
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 150.00K | Year: 2011
DESCRIPTION (provided by applicant): Collaborative Drug Discovery, Inc. (CDD) proposes to create a novel web-based software platform that enables scientists to work together effectively to discover and improve new drug leads, yet with the option not to reveal chemical structures to each other. It will create the first practical system of biocomputational analysis across distributed datasets with different owners, while respecting data privacy. By lowering this key barrier to collaboration, the platform willaccelerate the pre-clinical drug discovery pipeline. Research aimed at neglected diseases and orphan indications will especially benefit, because they often rely on the loosely affiliated efforts of academic investigators, non-profit foundations, government laboratories, and small biotechnology firms ( extra-pharma entities). Such efforts typically lack not only the resources but also the integrated workflows of discovery projects conducted at large pharmaceutical companies (within which data can be shared freely across departments). The project will for the first time enable researchers focused on neglected diseases and orphan indications to effectively exploit biocomputational tools such as virtual screening and ADME/Tox predictions, which are now considered to be standard and indispensible components of early discovery workflows within large pharma. It will also make it easier for these extra-pharma researchers to collaborate with large pharma and benefit from large pharma's significant investment accumulating large high-quality datasets. In Phase 1 of the proposed SBIR, CDD will leverage ongoing collaborations to prove the feasibility and value of the approach with prospective potency predictions in advance of experimental confirmation. Key collaborators include Prof. Carl Nathan at Weill Cornell Medical College, Dr. Clifton Barry, III, at NIAID, and Allen Casey at the Infectious Disease Research Institute (IDRI). Their groups will serve as an experimental test bed for the project. They all have ongoingscreening programs to discover compounds active against tuberculosis (TB). Specific aims for Phase 1 include: 1. Demonstrate the value to the collaborating screening centers of creating computational TB screening models derived from distributed, heterogeneous collections of data and exploiting the models prospectively to filter and prioritize the molecules scheduled to be screened. Validate the hypothesis that by selecting subsets enriched with active compounds, the centers can efficiently explore more ofchemical space than would otherwise be possible with limited resources. 2. Develop initial standards for specifying models (including purpose, inputs, outputs, algorithms, descriptor types, domain of applicability and other parameters necessary for presentation, interpretation, and exchange) that will form the outline for more comprehensive software prototypes that CDD will iteratively develop, deploy, test and validate in Phase 2. PUBLIC HEALTH RELEVANCE: The proposed project will create novel computational tools that will help researchers to accelerate the discovery of new and improved drugs against a wide range of diseases. These tools will particularly benefit researchers working on diseases that leading pharmaceutical companies have largely ignored because they are not perceived as highly profitable opportunities, despite the fact that in many cases they afflict millions of people.