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http://www.aditecproject.eu/

A consortium that aims to accelerate the development of immunization technologies for the next generation of human vaccines. The goals are to characterize the mode of action and conduct comparative effectiveness studies of: adjuvants, vectors, formulations, delivery devices, routes of immunization, homologous and heterologous primeboost schedules, on vaccine efficacy. As part of these clinical trials, the consortium will also investigate the impact of host factors such as age, gender, genetics and pathologies. The consortium hopes to use insights gained from their projects to advance the development of next-generation vaccines, using tools such as standardized animal models to select promising immunization technologies. The intended outcome of this partnership is to improve the vaccine development process by advancing: basic research, new technology development, and clinical trial methods. Scientific objectives: # Development of adjuvants, vectors, formulations, and delivery devices # Selection of candidates, routes of immunization, and prime-boost combinations in animal models # Assessment of the impact of host factors in response to vaccination # Development of concepts and tools from human immunization # Development of concepts and tools to address regulatory and ethical issues posed by novel immunization technologies # Creation of an internationally recognized training program for translational immunology and vaccinology. Data is shared across the research partners within and between the different workstreams. Additionally, the consortium has plans to create a clinical database that combines phenotypic and clinical information to study the immune response to influenza vaccination at a population level, in an effort to advance studies into the effects of genetic background, gender, and disease on vaccine response.

Proper citation: Advanced Immunization Technologies (RRID:SCR_003741) Copy   

•   Source: SciCrunch Registry

http://www.abirisk.eu/

A consortium that seeks to provide an integrated approach to anti-drug immunization by evaluating immunogenicity in hemophilia A, multiple sclerosis, and inflammatory diseases, and exploring new tools for protein drug immunogenicity. The data collected will be pooled in a single immunogenicity databank and will be standardized and used to develop models of anti-drug antibodies. By examining the correlation between patient and clinical factors and the incidence of immunogenicity, it hopes to reduce the regulatory and resource burdens of immunogenicity testing. The objectives of the consortium are: # Access to large cohorts of patients treated with marketed biopharmaceutical products # Complementary expertise for anti-drug antibodies (ADA) assays; standardization and characterization of ADA # Novel integrated approaches to characterize anti-drug lymphocyte responses # Development and validation of innovative prediction tools # Collection and integration of immunogenicity-related data and clinical relevance of ADA ABIRISK is grouped into five working projects, which communicate with one another and provide each other with results and data for analysis. The five working projects are: ADA assay development and validation and cohort management; cellular characterization and mechanisms of the AD immune response; evaluation and development of technologies for predicting immunogenicity; establishment of database, data analyses and integration; and project management and communication.

Proper citation: ABIRISK (RRID:SCR_003740) Copy   

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http://www.oncotrack.eu/

An international consortium to develop and assess novel approaches to identify and characterize biological markers for colon cancer that will deepen the understanding of the variable make-up of tumors and how this affects the way patients respond to treatment. They will use cutting edge laboratory-based genome sequencing techniques coupled to novel computer modelling approaches to study both the biological heterogeneity of colon cancers (i.e. patient to patient variability) as well as tumor variation within the patient for example, by comparing primary tumors with metastases. This five year project brings together top scientists from European academic institutions offering a wide range of expertise, and partners them with pharmaceutical companies. The project is based on the premise that this genetic and epigenetic information, combined with a description of the molecular pathology of the tumor, will allow OncoTrack to generate a more accurate in-silico model of the cancer cell. This will facilitate the identification of predictive markers that can be used to guide the optimal therapy strategy at the level of the individual patient - and will also provide on-going prognostic guidance for the clinician. This project will not only advance understanding of the fundamental biology of colon cancers but will provide the means and approach for the identification of previously undetected biomarkers not only in the cancer under study, but potentially also in other solid cancers and, in doing so, open the door for personalized management of the oncology patient.

Proper citation: OncoTrack (RRID:SCR_003767) Copy   

•   Source: SciCrunch Registry

http://www.criver.com/

Commercial organism provider selling mice, rats and other model animals. American corporation specializing in a variety of pre-clinical and clinical laboratory services for the pharmaceutical, medical device and biotechnology industries. It also supplies assorted biomedical products and research and development outsourcing services for use in the pharmaceutical industry. (Wikipedia)

Proper citation: Charles River Laboratories (RRID:SCR_003792) Copy   

•   Source: SciCrunch Registry

http://c-path.org/programs/pkd/

Consortium to develop evidence supporting the use of imaging Total Kidney Volume (TKV) as a prognostic biomarker that predicts the progression of Autosomal Dominant Polycystic Kidney Disease (ADPKD) to select patients likely to respond to therapy into clinical trials. It aims to replace the currently used measurement of glomerular filtration rate (GFR). Scientists will use the data collected to develop a disease progression model that will evaluate the relationship between TKV and the known complications of ADPKD, including rate of loss of kidney function, hypertension, gross hematuria, kidney stones, urinary tract infections, development of end-stage renal disease, and mortality. These analyses will be used to support the regulatory qualification of TKV as an accepted measure for assessing the progression of ADPKD in clinical trials in which new therapies are tested. PKDOC has the following goals: # Develop standard clinical data elements and definitions that are specific to ADPKD # Create a database of aggregated data from existing multiple, longitudinal, and well-characterized research registries maintained over decades by the leading institutions in ADPKD clinical investigation # Advance and harmonize the missions of regulatory agencies by creating tools that help with the evaluation of new pharmaceutical compounds # Develop a quantitative disease progression model to examine the linkage between TKV and disease outcomes

Proper citation: Polycystic Kidney Disease Outcomes Consortium (RRID:SCR_003674) Copy   

•   Source: SciCrunch Registry

http://www.minituba.org

miniTUBA is a web-based modeling system that allows clinical and biomedical researchers to perform complex medical/clinical inference and prediction using dynamic Bayesian network analysis with temporal datasets. The software allows users to choose different analysis parameters (e.g. Markov lags and prior topology), and continuously update their data and refine their results. miniTUBA can make temporal predictions to suggest interventions based on an automated learning process pipeline using all data provided. Preliminary tests using synthetic data and laboratory research data indicate that miniTUBA accurately identifies regulatory network structures from temporal data. miniTUBA represents in a network view possible influences that occur between time varying variables in your dataset. For these networks of influence, miniTUBA predicts time courses of disease progression or response to therapies. minTUBA offers a probabilistic framework that is suitable for medical inference in datasets that are noisy. It conducts simulations and learning processes for predictive outcomes. The DBN analysis conducted by miniTUBA describes from variables that you specify how multiple measures at different time points in various variables influence each other. The DBN analysis then finds the probability of the model that best fits the data. A DBN analysis runs every combination of all the data; it examines a large space of possible relationships between variables, including linear, non-linear, and multi-state relationships; and it creates chains of causation, suggesting a sequence of events required to produce a particular outcome. Such chains of causation networks - are difficult to extract using other machine learning techniques. DBN then scores the resulting networks and ranks them in terms of how much structured information they contain compared to all possible models of the data. Models that fit well have higher scores. Output of a miniTUBA analysis provides the ten top-scoring networks of interacting influences that may be predictive of both disease progression and the impact of clinical interventions and probability tables for interpreting results. The DBN analysis that miniTUBA provides is especially good for biomedical experiments or clinical studies in which you collect data different time intervals. Applications of miniTUBA to biomedical problems include analyses of biomarkers and clinical datasets and other cases described on the miniTUBA website. To run a DBN with miniTUBA, you can set a number of parameters and constrain results by modifying structural priors (i.e. forcing or forbidding certain connections so that direction of influence reflects actual biological relationships). You can specify how to group variables into bins for analysis (called discretizing) and set the DBN execution time. You can also set and re-set the time lag to use in the analysis between the start of an event and the observation of its effect, and you can select to analyze only particular subsets of variables.

Proper citation: miniTUBA (RRID:SCR_003447) Copy   

•   Source: SciCrunch Registry

http://www.themmrf.org/research-programs/commpass-study/

A personalized medicine initiative to discover biomarkers that can better define the biological basis of multiple myeloma to help stratify patients. This effort hopes to obtain samples from approximately 1,000 multiple myeloma patients and follow them over time to identify how a patient's genetic profile is related to clinical progression and treatment response. As a partnership between 17 academic centers, 5 pharmaceuticals and the Department of Veterans Affairs, the goal of this eight year study is to create a database that can accelerate future clinical trials and personalized treatment strategies. MMRF's CoMMpass Study has the following goals: * Create a guide to which treatments work best for specific patient subgroups. * Share data with researchers to accelerate drug development for specific subtypes of multiple myeloma patients. In order to facilitate discoveries and development related to targeted therapies, the comprehensive data from CoMMpass is placed in an open-access research portal. The data will be part of the Multiple Myeloma Research Foundation's (MMRF) Personalized Medicine Platform combines CoMMpass data with those collected from MMRF's Genomics Initiative. It is hoped that the longitudinal data, combined with the annotated bio-specimens will help provide insights that can accelerate personalized therapies.

Proper citation: MMRF CoMMpass Study (RRID:SCR_003721) Copy   

•   Source: SciCrunch Registry

http://www.nncc-exam.org/

Organization that established credentialing mechanisms to promote patient safety and to improve the quality of care provided to nephrology patients. There is a diversity of examinations providing the opportunity for certification at various levels of education, experience, and areas of practice within nephrology nursing. All of the certification examinations are endorsed by American Nephrology Nurses'''' Association (ANNA). The Commission recognizes the value of education, administration, research, and clinical practice in fostering personal and professional growth and currently provides six examinations to validate clinical performance: * The Certified Dialysis Nurse examination * The Certified Dialysis LPN/LVN examination * The Certified Nephrology Nurse examination * The Certified Clinical Hemodialysis Technician * The Certified Clinical Hemodialysis Technician - Advanced * The Certified Nephrology Nurse - Nurse Practitioner

Proper citation: Nephrology Nursing Certification Commission (RRID:SCR_003994) Copy   

•   Source: SciCrunch Registry

http://www.imi.europa.eu/content/eu-aims

Consortium aiming to generate tools that will enhance understanding of autism spectrum disorders (ASD) and pave the way for the development of new, safe and effective treatments for use in both children and adults. For example, the team will gather samples from people bearing certain mutations associated with ASD; this will pave the way for the generation of cell lines that can be used to test treatments. Elsewhere, the researchers will advance the use of brain scans as a tool to boost ASD drug discovery and also identify which people with ASD might respond best to a given drug. The project will also create a pan-European network of clinical sites. As well as making it easier to run clinical trials, this network will create an interactive platform for those with ASD and professionals. By the end of the 5 year project they expect to provide novel validated cellular assays, animal models, new fMRI methods with dedicated analysis techniques, new PET radioligands, as well as new genetic and proteomic biomarkers for patient-segmentation or individual response prediction. They will provide a research network that can rapidly test new treatments in man. These tools should provide their EFPIA partners with an added competitive advantage in developing new drugs for ASD.

Proper citation: EU-AIMS (RRID:SCR_003861) Copy   

•   Source: SciCrunch Registry

http://earip.eu/

Consortium that convenes asthma experts from across Europe to define research gaps to reduce the impact of asthma. The project activities range from basic cell science research, to assessing and improving European healthcare systems. Their activities include workshops, prioritization exercises, consensus strategies, and the development and publication of a set of recommendations about what's needed to reduce asthma deaths and hospitalizations. The eventual goal is to have a comprehensive R&D roadmap for asthma. EARIP will target a number of asthma research areas to ensure a comprehensive overview of all current research strategies from across Europe is included in the project road map. These include: * Research into biological targets, aiming to discover new targets and better define the role of existing biological targets * Identify new systems, models and tools for phenotypic stratification * Develop better and more efficient healthcare systems across Europe * Define and develop new diagnostic tools * Assess and improve patient self-management systems and provide suggestions for how these can be developed * Identify how to establish a European Innovation Partnership (EIP) for the management of asthma * Establish a European research network of clinical asthma research facilities

Proper citation: EARIP (RRID:SCR_003854) Copy   

•   Source: SciCrunch Registry

http://www.transformproject.eu/portfolio-item/d6-2-clinical-research-information-model/

A clinical research information model for the integration of clinical research covering randomized clinical trials (RCT), case-control studies and database searches into the TRANSFoRm application development. TRANSFoRm clinical research is based on primary care data, clinical data and genetic data stored in databases and electronic health records and employs the principle of reusing primary care data, adapting data collection by patient reported outcomes (PRO) and eSource based Case Report Forms. CRIM was developed using the TRANSFoRm clinical use cases of GORD and Diabetes. Their use case driven approach consisted of three levels of modelling drawing heavily on the clinical research workflow of the use cases. Different available information models were evaluated for their usefulness to represent TRANSFoRm clinical research, including for example CTOM of caBIG, Primary Care Research Object Model (PRCOM) of ePCRN and BRIDG of CDISC. The PCROM model turned out to be the most suitable and it was possible to extend and modify this model with only 12 new information objects, 3 episode of care related objects and 2 areas to satisfy all requirements of the TRANSFoRm research use cases. Now the information model covers Good Clinical Practice (GCP) compliant research, as well as case control studies and database search studies, including the interaction between patient and GP (family doctor) during patient consultation, appointment, screening, patient recruitment and adverse event reporting.

Proper citation: TRANSFoRm Clinical Research Information Model (RRID:SCR_003889) Copy   

•   Source: SciCrunch Registry

http://www.transformproject.eu/

Project to develop a ''rapid learning healthcare system'' driven by advanced computational infrastructure that can improve both patient safety and the conduct and volume of clinical research in Europe. Three carefully chosen clinical ''use cases'' will drive, evaluate and validate the approach to the ICT (information and communications technology) challenges. The project will build on existing work at international level in clinical trial information models (BRIDG and PCROM), service-based approaches to semantic interoperability and data standards (ISO11179 and controlled vocabulary), data discovery, machine learning and electronic health records based on open standards (openEHR). TRANSFoRm will extend this work to interact with individual eHR systems as well as operate within the consultation itself providing both diagnostic support and support for the identification and follow up of subjects for research. The approach to system design will be modular and standards-based, providing services via a distributed architecture, and will be tightly linked with the user community. Four years of development and testing will end with a fifth year that will be dedicated to summative validation of the project deliverables in the Primary Care setting. In order to support patient safety in both clinical and research settings, significant ICT challenges need to be overcome in the areas of interoperability, common standards for data integration, data presentation, recording, scalability, and security., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: TRANSFoRm (RRID:SCR_003888) Copy   

•   Source: SciCrunch Registry

http://www.alzheimer-europe.org/Research/PharmaCog

Project aiming to tackle bottlenecks in Alzheimer''''s disease research and drug discovery by developing and validating new tools to test candidate drugs for the treatment of symptoms and disease in a faster and more sensitive way. They will provide the tools needed to define more precisely the potential of a drug candidate, reduce the development time of new medicines and thus accelerate the approvals of promising new medicines. By bringing together databases of previously conducted clinical trials and combining the results from blood tests, brain scans and behavioral tests, the scientists will develop a ''''signature'''' that gives more accurate information on the progression of the disease and the effect of candidate drugs than current methods do. The scientists will conduct parallel studies in laboratory models, healthy volunteers and patients in order to better predict good new drugs as early as possible. This will enable them, for instance, to find out how memory loss in Alzheimer''''s disease can be simulated in healthy volunteers, for example with sleep deprivation or drugs that temporarily affect the memory, in order to test the effect of candidate-medicines early in the drug development process.

Proper citation: PharmaCog (RRID:SCR_003878) Copy   

•   Source: SciCrunch Registry

https://sites.google.com/site/p2tconsortium/

A three-member pharmaceutical industry consortium that aims to provide a new platform to improve access to information about clinical trials for patients and providers. The platform aims to enhance the existing clinicaltrials.gov by providing more detailed and patient-friendly information about available trials and embedding a machine-readable target health profile to improve the ability of healthcare software to match individual health profiles with applicable clinical trials. Using clinicaltrials.gov as its foundation and Eli Lilly''''s Application Programming Interface (API), the consortium is focused on creating an open platform to make this data more amenable to patients and providers, as well as creating an opportunity to integrate a patient''''s electronic health record into the clinical trial matching service. This feature will allow patients to search for trials using their own Blue Button data. The following features are planned add-ons to clinicaltrials.gov: * Target Profile is a machine readable query, that can be executed against an electronic file (or record) with patient health data such as an Electronic Health Record (EHR), an Electronic Medical Record (EMR) or Personally Controlled Health Record (PCHR) * Augmented Content is public, IRB approved information about the study that has not been published on clinicaltrials.gov, and that is shared with / targeted for patients with a matching Target Profile. The following are the incremental goals of the consortium: * Advancement of the Lilly API platform to support read/write interaction and additional data objects and information. * The initial 3 sponsor organizations - Lilly, Pfizer and Novartis - will upload Target Profiles for a select set of clinical trials. A Target Profile is a machine interpretable description of the characteristics of patients who may qualify for that trial i.e. a query that can be executed against a patient''''s electronic health record or personal health record. Additionally, sponsors of clinical research studies will also be able to upload Augmented Content to the Lilly Platform to supplement information on clinicaltrials.gov with additional, patient-focused information about the study, e.g., a study brochure and practical information on how to contact investigational sites. * A matching service, developed by Corengi, will compare Target Profiles to a de-dentified personally controlled health record (PCHR), represented by patient''''s Blue Button Plus CCDA XML document. * Integration into a patient community platform from Avado for providing the patient PCHR and presenting the results of the match service. The patient will be able to explore the respective matching studies for additional information and next steps such as contacting a nearby investigator clinic or hospital. The first demo of the prototype was made available on June 2014, built on a database of anonymized patient health records from different clinical research studies sponsored by Lilly, Novartis, and Pfizer. Other website: http://portal.lillycoi.com/

Proper citation: Patients to Trials Consortium (RRID:SCR_003877) Copy   

•   Source: SciCrunch Registry

http://www.europeanlung.org/en/projects-and-research/projects/airprom/

Consortium focused on developing computer and physical models of the airway system for patients with asthma and chronic obstructive pulmonary disease (COPD). Developing accurate models will better predict how asthma and COPD develop, since current methods can only assess the severity of disease. They aim to bridge the gaps in clinical management of airways-based disease by providing reliable models that predict disease progression and the response to treatment for each person with asthma or COPD. A data management platform provides a secure and sustainable infrastructure that semantically integrates the clinical, physiological, genetic, and experimental data produced with existing biomedical knowledge from allied consortia and public databases. This resource will be available for analysis and modeling, and will facilitate sharing, collaboration and publication within AirPROM and with the broader community. Currently the AirPROM knowledge portal is only accessible by AirPROM partners.

Proper citation: AirPROM (RRID:SCR_003827) Copy   

•   Source: SciCrunch Registry

https://www.bioshare.eu/

A consortium of leading biobanks and international researchers from all domains of biobanking science to ensure the development of harmonized measures and standardized computing infrastructures enabling the effective pooling of data and key measures of life-style, social circumstances and environment, as well as critical sub-components of the phenotypes associated with common complex diseases. The overall aim is to build upon tools and methods available to achieve solutions for researchers to use pooled data from different cohort and biobank studies. This, in order to obtain the very large sample sizes needed to investigate current questions in multifactorial diseases, notably on gene-environment interactions. This aim will be achieved through the development of harmonization and standardization tools, implementation of these tools and demonstration of their applicability. BioSHaRE researchers are collaborating with P3G, the Global Alliance for Genomics and Health, IRDiRC (International Rare Diseases Research Consortium), H3Africa and other organizations on the development of an International Code of Conduct for Genomic and Health-Related Data Sharing. A draft version is available for external review. Generic documents have been prepared covering areas of biobanking that are of major importance. SOPs have been finalized for blood withdrawal (SOPWP5001blood withdrawal), manual blood processing (SOPWP5002blood processing), shipping of biosamples (SOPWP5003shipping) and withdrawal, processing and storage of urine samples (SOPWP5004urine).

Proper citation: BioSHaRE (RRID:SCR_003811) Copy   

•   Source: SciCrunch Registry

https://www.bigtencrc.org/

A consortium that aims to transform cancer research through collaborative oncology trials that leverage the scientific and clinical expertise of the Big Ten universities. The goal is to align the conduct of cancer research through collaborative, hypothesis-driven, highly translational oncology trials that leverage the scientific and clinical expertise. The clinical trials that will be developed will be linked to molecular diagnostics, enabling researchers to understand what drives the cancers to grow and what might be done to stop them from growing. The consortium also leverages geographical locations and existing relationships among the cancer centers. One of the consortium's goals is to harmonize contracts and scientific review processes to expedite clinical trials. The consortium will only focus on phase 0 to II trials because larger trials - even a randomized phase II trial - are difficult to conduct at a single cancer center.

Proper citation: Big Ten Cancer Research Consortium (RRID:SCR_004025) Copy   

•   Source: SciCrunch Registry

http://www.euadr-project.org/

Consortium that created the capability to detect Adverse Drug Response (ADR) signals by creating the infrastructure for large-scale monitoring of drug safety using electronic health records (EHR). The platform leverages EHR''''s comprising demographics, drug use and clinical data of over 30 million patients from several European countries. Special attention was given to patient groups that are not routinely involved in clinical trials, for ethical or practical reasons (e.g. pregnant women, elderly people, people using many drugs simultaneously, and children). This project also studies and compares a number of different techniques that all aim to detect unexpected or disproportional rates of events. The algorithms that they studied originate not only from the field of (pharmaco)epidemiology, but also from fields such as bio-terrorism, machine learning, and classical signal detection. EU-ADR specific objectives are: To detect events, To relate these events to drugs, To develop hypothesis that explain adverse events, To detect adverse events earlier, and To avoid false positives. The web-based platform is available at https://bioinformatics.ua.pt/euadr/ EU-ADR has contributed to the ability to conduct better drug safety studies based on the re-use of healthcare data. By facilitating the early detection of adverse drug reactions, but also providing key information on populations at risk, potential drug interactions, potential underlying mechanisms and intervening pathways in adverse events, etc., the project will allow for improved and more complete information to be available for drug and healthcare delivery, leading to increased patient safety and its associated cost savings. The EU-ADR system can be considered as a complementary tool to already existing pharamcovigilance systems. Should the system be widespread in the long term, it has the potential to contribute to the development of future electronic health record systems, insofar as the expected benefits of these IT tools are only fully attainable when EHRs develop themselves in consistency, richness and formats that allow them to be subject of such tools. In anticipation, EU-ADR has been designed to be modular and scalable, so that different EHR databases (other than those participating in the Consortium) can be progressively enlisted in the future, adopt the software for data extraction and therefore become susceptible of exploitation by the system, for maximum global effect.

Proper citation: EU-ADR (RRID:SCR_004028) Copy   

•   Source: SciCrunch Registry

http://www.cihr.gc.ca/e/46475.html

Consortium that will be the premier research hub for all aspects of research involving neurodegenerative diseases that affect cognition in aging - including Alzheimer's disease. They will promote high impact, inter-institutional and interdisciplinary collaboration through a pan-Canadian approach, and will position Canadian researchers to lead and participate in a new wave of national and international initiatives with congruent goals. The consortium focuses research into the basic mechanisms of neurodegenerative diseases, accelerating the development of tools that can be used to assist in the diagnosis and treatment of the diseases. The intended outcome of these tools is to improve the quality of life and services patients with neurodegenerative diseases. As part of the Canadian contribution to the International Collaborative Research Strategy for Alzheimer's disease, the consortium brings together Canadian government agencies (federal and provincial), foundations, pharmaceutical companies, philanthropists and international stakeholders to identify if there are common causes and risk factors to neurodegenerative diseases. The consortium is focused on three themes: * Primary Prevention aimed at preventing the disease from developing * Secondary Prevention focused on delaying the clinical manifestations of the already developing disease * Quality of Life designed for helping individuals, caregivers and the health system in the context of a clinically developed disease.

Proper citation: Canadian Consortium on Neurodegeneration in Aging (RRID:SCR_003846) Copy   

•   Source: SciCrunch Registry

http://clinicaltrials.gov/show/NCT01211678

A consortium evaluating a new biomarker screening test that might help identify patients with rheumatoid arthritis (RA) who are unlikely to benefit from anti-tumor necrosis factor-alpha (TNFalpha) medications. BATTER-UP will enroll around 1,000 patients being treated by one of several marketed anti-TNF RA drugs: Enbrel, Remicade, Humira, Simponi, or Cimzia. Through data analyses and predictive response modeling, the consortium aims to better understand which patients with RA will derive the greatest benefit from TNF inhibitors. The investigators in this observational study will attempt to validate an 8-gene biomarker set based on work by Biogen Idec researchers as likely to predict anti-TNF responsiveness in patients with RA. In preliminary results, the 8-gene biomarker set predicted with 89% accuracy individuals who did not reach European League Against Rheumatism (EULAR) Disease Activity Score (DAS)-28 good response after 14 weeks of treatment. The 8 genes included in the screen are CLTB, MXRA7, CXorf52, COL4A3BP, YIPF6, FAM44A, SFRS2, and PGK1. Biological samples and clinical outcome information will be used to confirm and extend the utility of previously published biomarkers that can predict response to anti-TNF agents. These data may also generate new hypotheses for further testing. The BATTER-UP samples and data will be established as a reference set for investigation of personalized medicine in RA. The study will be a resource of DNA and other biological materials that can be investigated for biomarkers in the future as new technologies arise.

Proper citation: Biomarkers of Anti-TNF Treatment Efficacy in Rheumatoid Arthritis - Unresponsive Populations (RRID:SCR_004019) Copy   

•   Source: SciCrunch Registry