Autoantibody Detection for FSGS Recurrence Prediction

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Solution Overview

Problem

Current methods fail to accurately predict and manage the recurrence of Focal Segmental Glomerulosclerosis (FSGS) in kidney transplant patients, leading to high risks of graft loss and renal function deterioration, with existing biomarkers being non-specific and ineffective in pre-transplant risk stratification and treatment.

Innovation Solution

Development of methods and compositions that utilize specific autoantibodies such as CD40, PTPRO, CGB5, FAS, P2RY11, SNRPB2, APOL2, CCL19, MYLK, and RXRA to predict FSGS recurrence by detecting increased binding in biological samples, and administering blocking factors or antibodies to prevent autoantibody binding, thereby mitigating disease progression.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If existing biomarkers such as suPAR are used for prediction, then some correlation with FSGS recurrence is achieved, but the biomarkers are non-specific and have low accuracy

Engineering Contradiction:
Improveprediction accuracyVSAvoidnon-specificity of biomarkers
Core Design Contradiction:
Measurement precisionVSObject-affected harmful factors

Solution Approach 1:

The patent segments the prediction approach by identifying and measuring multiple distinct autoantibodies (anti-CD40, anti-PTPRT, anti-CGB5, anti-FAS, anti-P2RY11, anti-SNRPB2, anti-APOL2, anti-CCL19, anti-MYLK, anti-RXRA) rather than relying on a single non-specific biomarker like suPAR. This segmentation allows for more specific and accurate prediction of FSGS recurrence and native FSGS risk

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

The patent applies local quality by focusing on specific autoantibodies that target particular podocyte proteins (CD40, PTPRT, CGB5, FAS, P2RY11, SNRPB2, APOL2, CCL19, MYLK, RXRA). Each autoantibody provides localized, specific information about different aspects of podocyte injury and FSGS pathogenesis, improving overall prediction accuracy

Inventive Principle:
Principle #3Local quality

2Reliability

If pre-transplant risk stratification is performed, then treatment decisions can be improved, but current methods lack accuracy in predicting FSGS recurrence and native FSGS

Engineering Contradiction:
Improverisk stratification reliabilityVSAvoidprediction accuracy
Core Design Contradiction:
ReliabilityVSMeasurement precision

Solution Approach 1:

The patent performs preliminary action by measuring autoantibody levels in pre-transplant serum samples to identify patients at high risk for FSGS recurrence or native FSGS before kidney transplantation occurs. This allows for pre-transplant risk stratification and informed treatment decisions, improving reliability of risk assessment

Inventive Principle:
Principle #10Preliminary action

Solution Approach 2:

The patent uses feedback by comparing autoantibody measurement results against established thresholds or reference ranges to determine risk categories. This feedback mechanism enables automated risk stratification and guides subsequent treatment decisions, enhancing both reliability and precision

Inventive Principle:
Principle #23Feedback

3Adaptability or versatility

If autoantibodies are targeted for treatment, then specific treatment paradigms can be developed, but the complexity of identifying and targeting multiple autoantibodies increases

Engineering Contradiction:
Improvetreatment customizationVSAvoidtreatment complexity
Core Design Contradiction:
Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent segments the treatment approach by targeting specific autoantibodies (anti-CD40, anti-PTPRT, anti-CGB5, anti-FAS, anti-P2RY11, anti-SNRPB2, anti-APOL2, anti-CCL19, anti-MYLK, anti-RXRA) that are identified through measurement. This segmentation allows for customized treatment paradigms tailored to each patient's specific autoantibody profile, improving adaptability while managing complexity through systematic identification

Inventive Principle:
Principle #1Segmentation

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

The approach allows for accurate prediction of FSGS recurrence with high accuracy (>80%) and effective treatment paradigms that improve graft function and survival by targeting specific autoantibodies, reducing proteinuria and podocyte injury.

Implementation Method 1

contacting a biological sample from the individual with a binding agent; and detecting the binding of the binding agent to at least one autoantibody in the sample

Methodology Applied
Scientific EffectAntibody binding:

Data Source

PatentUS10317401B2Methods and compositions for the prediction and treatment of focal segmental glomerulosclerosis
Publication Date: 2019.06.11 SARWAL MINNIE
  • US10317401B2 patent drawing
  • US10317401B2 patent drawing
  • US10317401B2 patent drawing

AI summary

Provided herein are methods and compositions for the prediction and treatment of focal segmental glomerulosclerosis and other proteinuric renal diseases such as native FSGS, minimal change disease, glomerular nephritis, membrano-proliferative glomerular nephritis (membranous), or IgA glomerular nephritis (membranous).