ITC Data Modeling for Baseline Drift and Kinetic Analysis
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Solution Overview
Problem
Conventional isothermal titration calorimetry (ITC) methods are limited in their ability to accurately measure both thermodynamics and kinetics, have low throughput, are subject to baseline drift, and require long experiment times due to re-equilibration between injections, leading to unreliable data analysis.
Innovation Solution
A computer-implemented method using a model that includes a source term, buffer heat release term, reaction term, and baseline term to analyze ITC data, combined with machine learning models to improve data fitting and reduce subjectivity, allowing for simultaneous measurement of thermodynamics and kinetics with increased throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional ITC methods are used to measure thermodynamics, then thermodynamic parameters can be obtained, but kinetics measurement capability is lost and throughput is reduced
Solution Approach 1:
The patent merges thermodynamic and kinetic analysis into a unified framework by simultaneously fitting both the integral values and the complete time-resolved heat flow curves to a single binding model. This allows extraction of both thermodynamic parameters (Kd, ΔH, ΔS) and kinetic parameters (kon, koff) from the same experiment without requiring separate kinetic experiments.
Solution Approach 2:
The patent utilizes the time dimension of heat flow data that is inherently captured by the calorimeter but traditionally discarded or used only for qualitative assessment. By analyzing the temporal profile of heat release/absorption in addition to the integrated area, the method extracts kinetic information from a dimension (time) that was previously underutilized.
2Measurement precision
If the model is fitted to the integrals of the peaks, then thermodynamic properties can be estimated, but the number of data points is limited to the number of injections
Solution Approach 1:
The patent segments the heat flow curve into distinct phases (pre-injection baseline, injection phase, post-injection relaxation) and applies appropriate modeling to each segment. This allows the model to capture complex kinetic behavior while maintaining computational tractability by treating different temporal regions with tailored approaches.
Solution Approach 2:
The patent employs dynamic fitting where the model adapts to the actual shape and duration of each heat flow peak rather than assuming fixed patterns. The kinetic model parameters are optimized for each injection event based on the actual time-resolved data, allowing the system to handle variable injection conditions and extract maximum information from each data point.
3Measurement precision
If the system re-equilibrates between injections, then accurate peak integrals can be obtained, but experiment time increases and baseline drift risk increases
Solution Approach 1:
The patent eliminates the requirement for complete re-equilibration between injections by continuously analyzing the heat flow curve through the injection and relaxation phases. The kinetic model accounts for the ongoing binding processes during the relaxation phase, allowing the next injection to occur before full equilibrium is reached, thereby maintaining continuous data collection without sacrificing accuracy.
Solution Approach 2:
The patent applies baseline correction and model fitting procedures that account for drift and incomplete equilibration in advance of the actual measurement analysis. By incorporating baseline trends and relaxation kinetics into the fitting model beforehand, the method compensates for these effects rather than requiring their complete elimination through extended waiting periods.
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 method enables more accurate and efficient analysis of ITC data, reducing baseline drift and re-equilibration time, thereby enhancing the precision and speed of thermodynamic and kinetic measurements.
Implementation Method 1
The instrument measures the heat which is evolved or absorbed as a result of the addition of the analyte to the sample
Implementation Method 2
The isothermal titration calorimeter (ITC) is an isothermal device that keeps liquid in the sample cell and reference cell at an equal temperature
Data Source
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AI summary
A computer-implemented method of analysing isothermal titration calorimetry data is provided. The method comprises: obtaining isothermal titration calorimetry measurement data from analysis of one or more samples and fitting a model to the measurement data. The comprises an expression for each of: a source term modelling the injection of an analyte solution; a buffer heat release term modelling the heat released from the mixing of buffers during injection of an analyte solution; a reaction term modelling a reaction between an analyte and the sample; and a baseline term comprising a series of basis functions. The method further comprises determining a physical parameter of the sample from the model.