Background A biomarker is a defined characteristic that is measured as an indicator of normal biological processes, pathogenic processes, or responses to an exposure or intervention, including therapeutic interventions. Biomarker modalities are diverse, and can include ‘omics, imaging, behavioral, digital and physiologic endpoints, as well as composite biomarkers or biomarker signatures. Because the measurement of the biomarker is integral to defining the biomarker, it is necessary to describe the biomarker in terms of its biological source or matrix, measurable features, and the analytic method used to measure it. Biomarkers are critical to the discovery and development of therapeutics and can serve a variety of functions such as verifying therapeutic target engagement, improving trial design by patient stratification, and facilitating clinical care decisions. Despite the active pace of discovery of novel biomarker candidates, few biomarkers progress beyond discovery to analytical validation and clinical practice, and robust, well-validated biomarkers for use in Phase II and Phase III clinical trials remain scant. Thus, there is a critical need to advance validation of biomarkers to improve public health, particularly for disorders of the nervous system where advancing therapeutics from discovery to the market is notoriously challenging. This PAR is intended to address the gap in biomarker validation by encouraging rigorous analytical validation of the biomarker detection method. Analytical Validation is defined as the process of establishing that the performance characteristics of the measurement(s) are acceptable in terms of the sensitivity, specificity, accuracy, precision, and other relevant performance characteristics using a specified technical protocol (which may include sample collection and standardization procedures). The level of analytical rigor that is necessary depends upon the characteristics of the biomarker, the detection technology, and the intended category(ies) of biomarker (diagnostic, prognostic, predictive, pharmacodynamic/response, monitoring, safety, or susceptibility/risk) within the proposed context of use(s). Analytical validation establishes the measurement's technical performance so that final clinical validation can be established. Applicants with a detection method that has already been analytically validated for its intended Context of Use may apply directly to the companion PAR, Clinical Validation of a Candidate Biomarker for Neurological or Neuromuscular Disorders (U01 - Clinical Trial Optional) which addresses retrospective and/or prospective clinical validation of candidate biomarkers for use in clinical trials and/or clinical practice. Research Objectives Applications to this PAR must propose to conduct analytical validation of a biomarker or biomarker signature that already has a well-defined proof of concept and biological rationale. Premise and proof of concept must include evidence that the biomarker/biomarker signature has been tested in an appropriate clinical population, using either prospective or retrospective data or samples and shows sufficient sensitivity and specificity to warrant additional investment. In addition, applications to this PAR must include evidence that the detection method for the biomarker has been developed and subjected to initial evaluation of precision and analytical sensitivity. The application should clearly describe how the proposed study plans to optimize and standardize the detection method, as well as clearly define and rigorously test the analytic and pre-analytic variables to ensure broad and reliable clinical use across multiple sites. This funding opportunity uses a cooperative agreement, milestone driven mechanism that enables significant input from NIH staff to assist investigators with preparing and evaluating their analytical validation strategy. The Analytical Validation PARs and companion Clinical Validation PARs are designed to enable the production of a package of evidence needed for FDA Biomarker Qualification Submission, which is encouraged but not required as part of the application process. For more information on the Biomarker Qualification program see: https://www.fda.gov/Drugs/DevelopmentApprovalProcess/DrugDevelopmentToolsQualificationProgram/BiomarkerQualificationProgram/default.htm Entry Criteria Entry Criteria should include the following: Within NINDS mission: The project should focus on detection methods where the biomarker is likely to be used as a tool in the diagnosis, treatment, or prevention of diseases and conditions within the NINDS mission Context of Use: The intended Context of Use (COU) should be identified and applications should include a statement with the heading “Context of Use” that fully and clearly describes the way the biomarker is expected to be used. Considerations involved in defining the context of use should include the biomarker category (susceptibility/risk, diagnostic, monitoring, prognostic, predictive, pharmacodynamic/response, or safety), biomarker modality, the method of detection and the clinical population characteristics. The COU is critical for determining the experimental design and level of analytical validation required, therefore it should be carefully considered and clearly defined. Context of use statements are discussed extensively in the Framework for Defining Evidentiary Criteria for Biomarker Qualification developed by the Biomarkers Consortium: https://fnih.org/sites/default/files/final/pdf/Evidentiary%20Criteria%20Framework%20Final%20Version%20Oct%2020%202016.pdf Evidence of preliminary validation of the biomarker: Preliminary data illustrating that the biomarker reflects the intended pathophysiology and/or clinical endpoint appropriate for the Context of Use are required. Evidence should include a preliminary estimate of the clinical sensitivity and specificity and data illustrating the detection method has been developed and subjected to initial evaluation of precision and sensitivity at a minimum. Application Characteristics: A strong justification for use of the proposed detection method and biomarker including clinical and/or research unmet need should be included. Feasibility, including potential added clinical burden and cost should also be directly addressed. The status of the existing detection method and the plan for its optimization in clinical laboratories or point of care settings should be described. The project should include analytical validation in multiple testing sites. If a single site is proposed, a scientific justification should be included. Milestones describing the metrics for evaluating analytical validation to assess success in achieving each of the research plan’s objectives must be included. The Biomarker or biomarker signature should be described using the FDA-NIH Biomarker Working Group BEST (Biomarkers, EndpointS, and other Tools) Resource, available from: https://www.ncbi.nlm.nih.gov/books/NBK326791/. Biomarker categories include: monitoring biomarkers to track the success of a therapeutic intervention or disease progression diagnostic biomarkers for detecting clinical manifestation of disease prognostic biomarkers for predicting outcomes predictive biomarkers for determining responders and non-responders to a therapeutic intervention pharmacodynamic/response biomarkers for demonstrating therapeutic target engagement safety biomarkers to indicate the likelihood, presence, or extent of an adverse effect, and susceptibility/risk biomarkers that indicate the potential for developing a disease or medical condition in an individual who does not currently have a clinically apparent disease. Although an individual indicator may be useful as more than one category of biomarker (i.e. both diagnostic and monitoring) the type of evidence required to validate it depends on the biomarker category specified; for example, a monitoring biomarker would need to demonstrate robustness in a longitudinal study design whereas a diagnostic may be evaluated with a cross sectional design. Thus, defining the biomarker category(ies) is essential to developing the experimental design and analytical performance necessary to validate it. The research strategy should clearly describe how the application will utilize a rigorous design, execution, and interpretation of the proposed studies. NINDS encourages investigators, whenever possible, to address these elements directly in their applications. Analytical Validation should include the following metrics with use of FDA guidance standards appropriate for the Context of Use Accuracy Precision Analytical sensitivity Analytical specificity including interfering substances or signals Reportable range of test results for the test system Reference intervals (normal values) with controls and calibrators Harmonization of analytical performance of the detection method to be performed in multiple laboratories Establishment of appropriate quality control and improvement procedures Any other performance characteristics necessary for establishing calibration and control procedures Responsiveness Criteria Responsive studies include analytical validation of biomarker detection methods that indicate pharmacodynamic responses to therapeutics, predict efficacy or safety response as well as those for diagnostic, prognostic and disease progression or risk/susceptibility detection. Applications Not Responsive to this FOA Non-responsive studies include those seeking to develop therapeutic agents or devices as the primary intent, as well as those seeking to answer specific questions about clinical efficacy, effectiveness, and/or clinical management as the primary intent. Studies using animal models are non-responsive. Studies that fail to include milestones are also considered non-responsive. Non-responsive applications will be administratively withdrawn without review. Collaborations Multi-disciplinary collaboration among scientific investigators, developers, clinicians, statisticians, consultants, and clinical laboratory staff must be an integral part of the application. Projects proposed for this PAR should utilize multi-site design as applicable, with standardized data stewardship to ensure that data are reusable and accessible. Investigators are encouraged to form collaborations with individuals knowledgeable in the FDA qualification process as well as those familiar with the process of analytical validation, including statistical design and analysis experts. Leveraging Existing Resources Applicants should leverage existing research resources for their studies. Such resources may include clinical biospecimen samples from the NINDS Human Biospecimen and Data Repository (BioSEND; https://biosend.org/) or other existing biospecimen, imaging and data repositories. Leveraging the research resources from advocacy groups, private research foundations, academic institutions, other government agencies and the NIH Intramural program are also encouraged. Studies are also encouraged to leverage the resources of ongoing clinical trials supported through other Federal or private funds. Project Milestones A project timeline with annual milestones must be included in the application (see Section IV). Milestones should describe project decision points with results-driven, quantitative metrics for go/no-go decision making throughout the funding period. The NIH Program Official will contact the applicant to discuss the proposed milestones prior to the award. The Program Officer and Scientific Officer will discuss with the Program Director(s)/Principal Investigator(s) any recommended changes to the research plan or suggestions from peer review, and the plan will be revised as appropriate prior to the award. Reproducibility and Data Sharing To improve reproducibility and community uptake, investigators are expected to share code/scripts, analytic tools/statistical models, protocols/processes and metadata before the end of the project’s period of performance. The resource sharing plan should describe where information and data will be shared, including where the original controlled datasets exist and how to request access to them. Investigators should incorporate plans for sharing and dissemination of the data, protocols, and any analytical methods in their research sharing plan and project timeline. Budgets should reflect any costs associated with these efforts. Information on many available NIH supported or frequently used repositories is available at: https://www.nlm.nih.gov/NIHbmic/nih_data_sharing_repositories.html Applicants collecting biofluid samples from prospectively enrolled study participants are encouraged to share samples through the NINDS biomarker repository, BioSEND (https://biosend.org/ ) to provide the broader scientific community with a data resource for hypothesis generation and test validation. Applicants should contact BioSEND to incorporate sharing plans and cost in their application. Pre-Application Consultation Under this Cooperative Agreement mechanism, NINDS Program Staff will have substantial communication and involvement with researchers in decision making prior to award and during the conduct of the study to provide oversight of data and safety monitoring, ensure the timely completion of the proposed studies and to maximize the positive impact of the studies on upcoming clinical trials. Applicants are strongly encouraged to consult with NINDS Program Staff early on during the planning for an application. This early contact will provide an opportunity to discuss and clarify NINDS policies and guidelines, including the scope of the project relative to the NINDS mission and intent of this PAR. These discussions can also provide any needed clarification regarding development of an appropriate timeline and milestone plan. Funding Considerations Applications within the top scoring meritorious range will be considered for funding. Within that range, priority may be placed on applications that fill a critical program gap to ensure biomarker development that is reflective of the breadth of disorders and conditions within NINDS’s mission. Additionally, priority will be given to biomarkers that: 1) address an unmet medical need, 2) are supported by a strong biological rationale, 3) include a carefully designed plan for performance evaluation, 4) include a plan for standardization of samples and data collection for use in validation and 5) provide a strong justification for the utility of the biomarker in the clinical setting or the added value in a clinical research design.