Personalized Biologic Dosing via Pharmacokinetic Feedback
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Solution Overview
Problem
Current treatments for immune-mediated inflammatory diseases often fail to achieve optimal biologic drug concentrations, leading to ineffective treatment outcomes due to variability in patient pharmacokinetics and autoantibody production, resulting in increased healthcare costs and treatment inefficacy.
Innovation Solution
A method involving the analysis of biological samples to quantify biologic drug levels, autoantibodies, and albumin, followed by the adjustment of drug doses and inter-dose intervals based on likelihood calculations using algorithms like Naive Bayes or Metropolis Hastings, to achieve a pre-specified threshold concentration, potentially switching to alternative drugs like small molecule inhibitors if necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard dosing regimens are used for biologic drugs, then treatment coverage is broad and simple to administer, but treatment effectiveness is reduced due to variability in patient pharmacokinetics and autoantibody production
Solution Approach 1:
The patent applies parameter changes by adjusting drug dosage and dosing interval based on measured pharmacokinetic parameters (drug concentration, autoantibody levels, albumin levels) for each patient. This transforms the fixed standard dosing regimen into a dynamic, personalized dosing strategy that adapts to individual patient characteristics, thereby improving treatment effectiveness while managing complexity through systematic measurement and calculation protocols
Solution Approach 2:
The patent implements feedback mechanisms by measuring drug concentrations, autoantibody levels, and albumin levels in patient samples, then using these measurements to determine subsequent dosing decisions. The system calculates the probability of achieving therapeutic concentrations and uses this feedback information to adjust future dosing, creating a closed-loop control system that continuously optimizes treatment based on actual patient response
2Reliability
If higher doses or more frequent dosing are administered to ensure therapeutic concentrations, then treatment effectiveness improves, but healthcare costs and treatment burden increase
Solution Approach 1:
The patent uses parameter changes to optimize dosing by calculating the probability of achieving therapeutic concentrations based on measured pharmacokinetic parameters. Instead of universally increasing doses, the system adjusts dosage and dosing interval specific to each patient's measured parameters, achieving therapeutic goals while minimizing unnecessary resource consumption in patients who already achieve adequate concentrations
Solution Approach 2:
The patent applies partial action by administering only the necessary dose to achieve therapeutic concentrations for each patient rather than using fixed high doses for all. The system determines the minimum effective dosing strategy based on individual patient pharmacokinetics, avoiding excessive medication use in patients who achieve therapeutic levels with standard or reduced dosing
3Productivity
If personalized dosing based on pharmacokinetic analysis is implemented, then treatment effectiveness and resource optimization improve, but measurement and calculation complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the personalized dosing process into distinct measurable components: drug concentration measurement, autoantibody level measurement, albumin level measurement, probability calculation, and dosing decision. This segmentation transforms a complex holistic problem into manageable discrete steps that can be systematically executed and monitored
Solution Approach 2:
The patent uses intermediaries in the form of standardized measurement protocols and calculation algorithms that bridge the gap between raw patient data and dosing decisions. These intermediaries include defined assays for measuring pharmacokinetic parameters and established probability calculation methods, which simplify the translation from complex measurements to actionable dosing recommendations
Data Source
AI summary
Provided herein are systems and methods for optimizing a biological therapy regimen for a subject. The subject may be a patient diagnosed with an immune mediated inflammatory disease. In some embodiments, the systems and methods may involve inputting patient data into a model to forecast a drug concentration level in a patient and establish a dosing regimen for maintaining a pre-specified threshold drug concentration level in the patient. The pre-specified threshold may be a target concentration level for effective treatment of the immune mediated inflammatory disease in the patient.