Precision Model for Anti-TNF Therapy Response Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current treatments for inflammatory bowel disease (IBD) using anti-TNF agents often result in primary non-response and secondary loss of response, leading to treatment failures and the need for medication changes or surgery in a significant proportion of patients.
Innovation Solution
Development of statistical models that predict patient response to anti-TNF therapy by combining parameters such as estimated clearance of the drug, genetic risk factors, serological markers, and patient wellbeing data to optimize treatment regimens, including dose modulation and agent selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If anti-TNF agents are administered to treat IBD, then disease symptoms are relieved, but treatment failures occur due to primary non-response and secondary loss of response
Solution Approach 1:
The patent applies preliminary action by measuring pharmacokinetic parameters (clearance, autoantibody levels) and genetic markers (HLA-DQA1*05 genotype) before initiating anti-TNF therapy. This pre-treatment assessment predicts patient response categories (definitive responder, probable responder, non-responder) and guides selection of appropriate anti-TNF agent and dosage, thereby improving treatment reliability before problems arise
Solution Approach 2:
The patent utilizes parameter changes by measuring clearance rate (CL/F) as a key pharmacokinetic parameter to predict treatment response. Patients are stratified based on clearance values (e.g., CL/F ≤ 0.3 mL/min/kg vs. CL/F > 0.3 mL/min/kg) and genetic parameters (presence/absence of HLA-DQA1*05 allele). These parameter measurements enable personalized treatment selection to overcome response variability
2Reliability
If standard anti-TNF therapy is used, then remission is achieved in some patients, but one-fifth of patients have no response at all (primary non-response)
Solution Approach 1:
The patent replaces the traditional trial-and-error mechanical approach with a predictive system based on pharmacokinetic modeling and genetic testing. By substituting empirical treatment selection with quantitative prediction based on clearance measurements and HLA-DQA1*05 genotyping, the system achieves more precise response prediction before treatment initiation
Solution Approach 2:
The patent implements feedback by measuring pharmacokinetic parameters (clearance, autoantibody formation) during treatment and using this information to predict future response. The model incorporates time-dependent changes in drug levels and immunogenicity to forecast whether patients will maintain response or experience loss of response, enabling proactive treatment adjustment
3Loss of time
If treatment is continued in non-responders, then treatment duration increases, but resources are wasted and patient quality of life deteriorates
Solution Approach 1:
The patent applies preliminary action by predicting treatment failure risk before it occurs through baseline pharmacokinetic and genetic assessment. Patients identified as probable non-responders (based on high clearance, HLA-DQA1*05 positivity, or other risk factors) are identified upfront, allowing clinicians to avoid prolonged ineffective treatment and redirect to alternative therapies sooner
Solution Approach 2:
The patent enables skipping of ineffective treatment phases by using predictive markers to identify patients who will not respond to anti-TNF therapy. Instead of proceeding through standard trial periods, high-risk patients are fast-tracked to alternative treatment modalities, reducing time loss and improving overall treatment efficiency
Data Source
AI summary
Provided are systems and methods for treating an immune-mediated inflammatory disease (e.g., inflammatory bowel disease) in a subject or selecting the subject for treatment, based on an estimated time to remission following induction of an anti-TNF therapy calculated by a patient-centric precision model.