Optimizing Phytochemical Plasma Tmax via Physicochemical Modeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods fail to optimize the bioavailability of phytochemicals in dietary plants, as they do not effectively predict the time it takes for these compounds to reach maximum concentration in blood plasma, which is crucial for maximizing health benefits related to chronic disease prevention and inflammation regulation.
Innovation Solution
A method using mathematical models to calculate the time (Tmax) it takes for plant metabolites to reach maximum concentration in plasma based on physicochemical data such as molecular mass, lipophilicity, and Polar Surface Area, allowing for the optimization of plant extract formulations to match target absorption times for enhanced bioavailability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ad hoc uses of dietary plants are employed, then simplicity of formulation is maintained, but bioavailability and health benefits are not maximized
Solution Approach 1:
The patent applies parameter changes by using mathematical models to calculate Tmax (time to maximum plasma concentration) based on physicochemical parameters such as molecular mass, lipophilicity (log P), and polar surface area (PSA). By changing the formulation parameters to match target absorption times, the patent optimizes bioavailability and health benefits without requiring overly complex formulation processes
Solution Approach 2:
The patent replaces traditional trial-and-error formulation methods with a mathematical modeling approach. Instead of relying on empirical testing and mechanical adjustment of formulations, the system uses computational models to predict absorption kinetics and optimize formulations based on calculated Tmax values
2Loss of time
If traditional dietary approaches are used, then ease of consumption is maintained, but timing of phytochemical absorption is not optimized
Solution Approach 1:
The patent applies preliminary action by calculating the Tmax for each phytochemical component before finalizing the formulation. This advance calculation allows the formulation to be designed with pre-determined absorption characteristics, ensuring that phytochemicals reach peak concentration at the desired time without requiring complex consumption protocols
Solution Approach 2:
The patent uses mathematical models that replicate in vivo absorption kinetics through in silico calculations. By creating a computational copy of the absorption process, the system can predict and optimize timing without requiring extensive clinical trials or complex consumption instructions
3Measurement precision
If plant extracts with varying physicochemical properties are used, then diversity of phytochemicals is achieved, but prediction of absorption time becomes difficult
Solution Approach 1:
The patent applies universality by developing a mathematical model that can handle diverse phytochemicals with varying physicochemical properties through a unified approach. The model uses general parameters (molecular mass, log P, PSA) that apply across different plant extracts and metabolite types, enabling precise absorption time prediction while maintaining versatility in handling diverse phytochemical compositions
Data Source
AI summary
Generating an optimised formulation for a foodstuff, comprises receiving at a processor physicochemical data pertaining to one or more plant extracts and operating the processor to calculate a time, Tmax, representing a time to reach a maximum concentration in plasma of at least one metabolite of the one or more plant extracts by applying the received physicochemical data to a mathematical model stored in memory. The processor is then operated to identify ones of the metabolites for which the calculated Tmax substantially corresponds with a target absorption time for the foodstuff; and to generate an optimised formulation for the foodstuff comprising the one or more plant extracts containing the identified metabolites. The physicochemical data include one or more of molecular mass (M), liphophilicity (log P) and Polar Surface Area (PSA).