Combinatorial Optimization for Drug Dosage via Response Surface Modeling

Solution Overview

Problem

Current methods for optimizing complex systems, such as drug combinations for clinical trials, face challenges due to discontinuity between in vitro and in vivo results, high labor and cost, and inefficiencies in identifying effective drug-dosage combinations, often resulting in labor-intensive and costly animal testing with potential toxicity risks.

Innovation Solution

A combinatorial optimization method involving multi-dimensional fitting and response surface modeling to identify optimized input parameter combinations, allowing for reduced in vivo testing and direct identification of effective drug-dosage combinations, leveraging a low-order function representation of complex system responses.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional in vitro drug combination methods are used, then drug efficacy can be初步 validated, but the results do not reliably predict in vivo effectiveness due to ADME discontinuity

Engineering Contradiction:

Improvepredictive reliability of drug combination efficacyVSAvoidcomplexity of translation from in vitro to in vivo

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent performs preliminary in vivo pharmacokinetic studies to establish ADME parameters before conducting efficacy studies. This preliminary action allows the system to account for absorption, distribution, metabolism, and excretion characteristics upfront, enabling better prediction of in vivo effectiveness from in vitro results and reducing the discontinuity between preclinical and clinical stages.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent systematically varies drug dosage parameters and combines them with ADME parameters to create a comprehensive pharmacokinetic-pharmacodynamic model. By changing and optimizing these parameters together, the system bridges the gap between in vitro efficacy and in vivo effectiveness, allowing reliable prediction of clinical outcomes from preclinical data.

Inventive Principle:

Principle #35Parameter changes

2Manufacturing precision

If comprehensive in vivo testing of all drug combination dosages is conducted, then optimized drug combinations can be identified, but labor and costs increase significantly

Engineering Contradiction:

Improveprecision of drug dosage optimizationVSAvoidefficiency of drug combination screening

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The patent replaces extensive mechanical in vivo testing with a mathematical pharmacokinetic-pharmacodynamic model. By substituting physical experimentation with computational modeling based on ADME parameters and dose-response relationships, the system achieves precise drug dosage optimization without the labor and cost burden of comprehensive in vivo screening.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent performs preliminary in vivo pharmacokinetic studies to establish ADME parameters before conducting efficacy studies. This preliminary action allows the system to account for absorption, distribution, metabolism, and excretion characteristics upfront, enabling better prediction of in vivo effectiveness from in vitro results and reducing the discontinuity between preclinical and clinical stages.

Inventive Principle:

Principle #10Preliminary action

3Reliability

If multiple drug combinations are tested in vivo to ensure safety and efficacy, then reliable treatment protocols can be established, but the number of animal tests and potential toxicity risks increase

Engineering Contradiction:

Improvereliability of treatment protocolVSAvoidtoxicity risk in animal testing

Core Design Contradiction:

ReliabilityVSObject-affected harmful factors

Solution Approach 1:

The patent performs preliminary in vivo pharmacokinetic studies to establish ADME parameters before conducting efficacy studies. This preliminary action allows the system to account for absorption, distribution, metabolism, and excretion characteristics upfront, enabling better prediction of in vivo effectiveness from in vitro results and reducing the discontinuity between preclinical and clinical stages.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces extensive mechanical in vivo testing with a mathematical pharmacokinetic-pharmacodynamic model. By substituting physical experimentation with computational modeling based on ADME parameters and dose-response relationships, the system achieves precise drug dosage optimization without the labor and cost burden of comprehensive in vivo screening.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20210358636A1Rapid identification of optimized combinations of input parameters for a complex system

Publication Date: 2021.11.18 RGT UNIV OF CALIFORNIA

AI summary

Multiple tests of a complex system are conducted by applying varying combinations of input parameters from a pool of input parameters. Results of the tests are fitted into a model of the complex system by using multi-dimensional fitting. Using the model of the complex system, identification is made of at least one optimized combination of input parameters to yield a desired response of the complex system.