Biomarkers and methods for measuring and monitoring inflammatory disease activity
Inventors
Cavet, Guy L. • Shen, Yijing • Knowlton, Nicholas • Centola, Michael
Interested in licensing this patent?
MTEC can help explore whether this patent might be available for licensing for your application.
Assignees
Member
Oklahoma Medical Research FoundationFounded in 1946, this independent nonprofit biomedical research institute conducts basic, translational, and clinical research in critical areas such as heart disease, cancer, autoimmune, and neurodegenerative diseases. Its mission focuses on understanding biological mechanisms and advancing diagnostics and therapeutics. Activities include conducting clinical trials, managing a patent portfolio, commercializing biotechnologies, and supporting the biotech community. Research efforts are funded by grants and philanthropy, and the institute hosts advanced facilities, interdisciplinary research teams, and collaborations with academia and industry.
Founded in 1946, this independent nonprofit biomedical research institute conducts basic, translational, and clinical research in critical areas such as heart disease, cancer, autoimmune, and neurodegenerative diseases. Its mission focuses on understanding biological mechanisms and advancing diagnostics and therapeutics. Activities include conducting clinical trials, managing a patent portfolio, commercializing biotechnologies, and supporting the biotech community. Research efforts are funded by grants and philanthropy, and the institute hosts advanced facilities, interdisciplinary research teams, and collaborations with academia and industry.
Publication Number
US-11300575-B2
Publication Date
2022-04-12
Expiration Date
Abstract
Biomarkers useful for diagnosing and assessing inflammatory disease are provided, along with kits for measuring their expression. The invention also provides predictive models, based on the biomarkers, as well as computer systems, and software embodiments of the models for scoring and optionally classifying samples. The biomarkers include at least two biomarkers selected from the DAIMRK group and the score is a disease activity index (DAI).
Core Innovation
The invention provides biomarker-based Disease Activity Index (DAI) scores derived from protein level data measured in a blood sample and combined to generate a single index that tracks clinical disease activity determined from a reference population of confirmed rheumatoid arthritis patients. Representative DAI formulas include a 7-marker linear model, an 11-marker Curds-and-Whey Lasso model, and an explicit mapping formula DAI=((0.56*sqrt(PTJC)+0.28*sqrt(PSJC)+0.36*log(CRP/106+1)+(0.14*PPGHA)+0.96)*10.53)+1.
The invention discloses biomarker panels (DAIMRK/ALLMRK) comprising proteins selected from CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1 and VEGFA, and algorithms using multivariate and machine-learning approaches. A component-based formula predicts tender and swollen joint counts and patient global assessment, with a scaled 1–100 transformation, for inflammatory diseases exemplified by rheumatoid arthritis.
The disclosure enumerates alternative panel compositions including two- through six-marker sets and combinations, describes assay and implementation modalities [procedural detail omitted for safety], and claims computer-implemented systems and kits. Validation across clinical cohorts (BRASS, OMRF, Taylor) reports performance metrics and classifications enabling classification of disease activity, longitudinal tracking, and prediction of treatment response and radiographic progression.
Claims Coverage
Two independent claims (clm-00001 and clm-00012) are present. The following summarizes six main inventive features recited across the independent claims.
Immunoassay measurement of specified protein markers
Performing at least one immunoassay [procedural detail omitted for safety] on a blood sample to generate protein level data for at least four protein markers selected from CHI3L1; CRP; EGF; IL6; LEP; MMP1; MMP3; RETN; SAA1; TNFRSF1A; VCAM1; and VEGFA.
Disease activity index using explicit formula
Calculating a disease activity index score for the sample by combining the protein level data, wherein the disease activity index score tracks a clinical disease activity score determined from clinical data of a reference population of confirmed RA patients, and wherein the disease activity index score is ((0.56*sqrt(PTJC)+0.28*sqrt(PSJC)+0.36*log(CRP/106+1)+(0.14*PPGHA)+0.96)*10.53)+1.
Tracking clinical disease activity using statistical and machine-learning methods
Determining tracking of the clinical disease activity score by one or more of the recited methods including ANOVA, Bayesian networks, boosting and Ada-boosting, bootstrap aggregating or bagging, CART, boosted CART, Random Forest, RPART, Curds and Whey, Curds and Whey-Lasso, PCA, LDA, ELDA, DFA, factor rotation, Hidden Markov Models, kernel methods, linear regression, Forward Linear Stepwise Regression, LASSO, Elastic Net, glmnet, Logistic Regression, meta-learner, KNN, non-linear regression, neural networks, partial least square, shrunken centroids, sliced inverse regression, SPC regression, SVM, and RSVM.
Diagnosis or prognosis based on index threshold independent of comorbidities
Diagnosing or prognosing the subject as needing treatment for rheumatoid arthritis based on the protein level disease activity index score exceeding a reference value of the clinical disease activity score, wherein the diagnosis or prognosis is the same for subjects with and without comorbidities.
Administration of specified therapies following diagnosis
Administering a therapy to the subject diagnosed or prognosed as needing treatment, the therapy comprising one or more of administering a therapeutic compound selected from DMARDs, biologic DMARDs, non-steroidal anti-inflammatory drugs (NSAID's), and corticosteroids, and administering bariatric surgical intervention.
Applicability to subjects diagnosed or suspected of rheumatoid arthritis
Applying the method to a first subject previously diagnosed with rheumatoid arthritis, or suspected of having rheumatoid arthritis, by performing the immunoassay and calculating the protein level disease activity index score for such subjects.
The independent claims recite immunoassay-based measurement of specified multi-marker panels, computation of a defined protein-derived DAI mapped to clinical RA scores, tracking via enumerated statistical and machine-learning methods, clinical decisions based on index thresholds, specified therapeutic actions, and applicability to subjects diagnosed or suspected of RA.
Stated Advantages
Provides a Disease Activity Index that tracks clinical disease activity scores determined from clinical data of confirmed RA patients, including DAS28-CRP.
Enables diagnostic and prognostic use for identifying subjects needing treatment for rheumatoid arthritis.
Produces diagnoses or prognoses that are the same for subjects with and without comorbidities.
Supports longitudinal tracking of disease activity and discrimination of remission versus active disease.
Facilitates prediction of treatment response and prediction of radiographic progression.
Supplies explicit multivariate DAI formulas including a component-based formula and a scaled 1–100 transformation.
Reports validation performance metrics in clinical cohorts including correlations (r ≈ 0.57–0.60) and classification AUCs up to ≈0.91, and longitudinal correlations and prediction of radiographic progression.
Supports use for diagnosis, prognosis, monitoring, therapy selection and response assessment, population screening, and surrogate endpoints for trials.
Documented Applications
Diagnosis
Prognosis
Monitoring
Therapy selection and response assessment
Population screening
Surrogate endpoints for clinical trials
Classification of disease activity (remission versus active disease) and mapping to clinical disease activity scores such as DAS28-CRP.
Discrimination of rheumatoid arthritis patients versus controls.
Longitudinal tracking of disease activity across cohorts.
Prediction of treatment response.
Prediction of radiographic progression.
Interested in licensing this patent?