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Evaluation of Candidate Biomarkers of Type 1 Diabetes via the Core for Assay Validation.

Speake C, Odegard JM - Biomark Insights (2015)

Bottom Line: In this model, the CAV facilitates the validation of candidate assay methods as well as qualification of proposed biomarkers for a specific clinical use in well-characterized patients.We describe here a CAV-driven pilot project aimed at identifying biomarkers that predict the rate of decline in beta cell function after diagnosis.This strategy could be a model for other collaborative biomarker development efforts in and beyond T1D.

View Article: PubMed Central - PubMed

Affiliation: Diabetes Clinical Research Program, Benaroya Research Institute, Seattle, WA, USA.

ABSTRACT
Recognizing an increasing need for biomarkers that predict clinical outcomes in type 1 diabetes (T1D), JDRF, a major funding organization for T1D research, recently instituted the Core for Assay Validation (CAV) to accelerate the translation of promising assays from discovery to clinical implementation via a process of coordinated evaluation of biomarkers. In this model, the CAV facilitates the validation of candidate assay methods as well as qualification of proposed biomarkers for a specific clinical use in well-characterized patients. We describe here a CAV-driven pilot project aimed at identifying biomarkers that predict the rate of decline in beta cell function after diagnosis. In a formalized pipeline, candidate assays are first assessed for general rationale, technical precision, and biological associations in a cross-sectional cohort. Those with the most favorable characteristics are then applied to placebo arm subjects of T1D intervention trials to assess their predictive correlation with beta cell function. We outline a go/no-go process for advancing candidate assays in a defined qualification pipeline that also allows for the discovery of novel predictive biomarker combinations. This strategy could be a model for other collaborative biomarker development efforts in and beyond T1D.

No MeSH data available.


Related in: MedlinePlus

Validation pipeline for C-peptide prediction project. A priori knowledge of the assay, results from blinded replicate testing, and biological variability data from a cross-sectional T1D cohort will be assessed for each assay. These three criteria will be combined into a single scorecard for each assay as indicated by the dashed lines. Highest ranking assays will receive samples from recent-onset T1D clinical trials.
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f1-bmi-suppl.4-2015-019: Validation pipeline for C-peptide prediction project. A priori knowledge of the assay, results from blinded replicate testing, and biological variability data from a cross-sectional T1D cohort will be assessed for each assay. These three criteria will be combined into a single scorecard for each assay as indicated by the dashed lines. Highest ranking assays will receive samples from recent-onset T1D clinical trials.

Mentions: The C-peptide Prediction Project represents the first iteration of a biomarker qualification pipeline that could be applied to multiple clinical questions. The pipeline is outlined in Figure 1 and contains three initial steps: (1) a priori knowledge of the assay, (2) results from blinded replicate testing, and (3) biological variability data from a cross-sectional T1D cohort. These three criteria will be combined into a single scorecard for each assay, which will be used to select assays ready to receive well-characterized samples from clinical trials. The specific criteria for go/no-go decisions at each step in the process are described below. The ultimate goal of this work is to qualify biomarkers for correlation with future C-peptide decline, ideally culminating in Food and Drug Administration (FDA) recognition of their utility as a Drug Development Tool suitable for use in both industrial and academic clinical settings. The C-peptide Prediction Project represents the first major step in this pipeline. In general, the process focuses on biomarker qualification, that is, identification of the biological meaning and clinical utility of the characteristic(s) being measured. In the long term, we expect that assays with solid clinical correlations will undergo full assay validation process that is appropriate for commercialization and use as a Drug Development Tool.


Evaluation of Candidate Biomarkers of Type 1 Diabetes via the Core for Assay Validation.

Speake C, Odegard JM - Biomark Insights (2015)

Validation pipeline for C-peptide prediction project. A priori knowledge of the assay, results from blinded replicate testing, and biological variability data from a cross-sectional T1D cohort will be assessed for each assay. These three criteria will be combined into a single scorecard for each assay as indicated by the dashed lines. Highest ranking assays will receive samples from recent-onset T1D clinical trials.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4589091&req=5

f1-bmi-suppl.4-2015-019: Validation pipeline for C-peptide prediction project. A priori knowledge of the assay, results from blinded replicate testing, and biological variability data from a cross-sectional T1D cohort will be assessed for each assay. These three criteria will be combined into a single scorecard for each assay as indicated by the dashed lines. Highest ranking assays will receive samples from recent-onset T1D clinical trials.
Mentions: The C-peptide Prediction Project represents the first iteration of a biomarker qualification pipeline that could be applied to multiple clinical questions. The pipeline is outlined in Figure 1 and contains three initial steps: (1) a priori knowledge of the assay, (2) results from blinded replicate testing, and (3) biological variability data from a cross-sectional T1D cohort. These three criteria will be combined into a single scorecard for each assay, which will be used to select assays ready to receive well-characterized samples from clinical trials. The specific criteria for go/no-go decisions at each step in the process are described below. The ultimate goal of this work is to qualify biomarkers for correlation with future C-peptide decline, ideally culminating in Food and Drug Administration (FDA) recognition of their utility as a Drug Development Tool suitable for use in both industrial and academic clinical settings. The C-peptide Prediction Project represents the first major step in this pipeline. In general, the process focuses on biomarker qualification, that is, identification of the biological meaning and clinical utility of the characteristic(s) being measured. In the long term, we expect that assays with solid clinical correlations will undergo full assay validation process that is appropriate for commercialization and use as a Drug Development Tool.

Bottom Line: In this model, the CAV facilitates the validation of candidate assay methods as well as qualification of proposed biomarkers for a specific clinical use in well-characterized patients.We describe here a CAV-driven pilot project aimed at identifying biomarkers that predict the rate of decline in beta cell function after diagnosis.This strategy could be a model for other collaborative biomarker development efforts in and beyond T1D.

View Article: PubMed Central - PubMed

Affiliation: Diabetes Clinical Research Program, Benaroya Research Institute, Seattle, WA, USA.

ABSTRACT
Recognizing an increasing need for biomarkers that predict clinical outcomes in type 1 diabetes (T1D), JDRF, a major funding organization for T1D research, recently instituted the Core for Assay Validation (CAV) to accelerate the translation of promising assays from discovery to clinical implementation via a process of coordinated evaluation of biomarkers. In this model, the CAV facilitates the validation of candidate assay methods as well as qualification of proposed biomarkers for a specific clinical use in well-characterized patients. We describe here a CAV-driven pilot project aimed at identifying biomarkers that predict the rate of decline in beta cell function after diagnosis. In a formalized pipeline, candidate assays are first assessed for general rationale, technical precision, and biological associations in a cross-sectional cohort. Those with the most favorable characteristics are then applied to placebo arm subjects of T1D intervention trials to assess their predictive correlation with beta cell function. We outline a go/no-go process for advancing candidate assays in a defined qualification pipeline that also allows for the discovery of novel predictive biomarker combinations. This strategy could be a model for other collaborative biomarker development efforts in and beyond T1D.

No MeSH data available.


Related in: MedlinePlus