Forecasting System for Inhalable Protein Particle Optimization
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Solution Overview
Problem
Current particle engineering techniques fail to produce stable and aerosol-performing inhalable monoclonal antibody (mAb) powders due to stress-induced structural changes and aggregation, limiting their use in inhalable protein therapeutics.
Innovation Solution
A forecasting computing system that optimizes atomization settings during spray drying processes by using predictive modeling techniques to determine suitable particle size and stability, incorporating design generation, stability assessment, and visualization modules to identify optimal formulation conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of moving object
If stress-inducing processes are used to reduce particle size for deep lung delivery, then aerosol performance is improved, but protein structural changes and aggregation occur
Solution Approach 1:
The patent applies parameter changes by systematically varying atomization parameters (gas flow rate, liquid flow rate, temperature, pressure) to achieve optimal particle size without excessive stress. The forecasting system models how different parameter combinations affect both particle size and protein stability, enabling selection of conditions that reduce particle volume while maintaining structural integrity.
Solution Approach 2:
The patent employs preliminary action through the forecasting computing system that predicts optimal atomization settings before actual particle production. By pre-determining the best parameter combinations using predictive models, the system prevents protein damage before it occurs during the atomization process, rather than attempting to correct issues after particle formation.
2Volume of moving object
If conventional particle engineering techniques are used to produce inhalable powders, then particle size is reduced, but protein aggregation increases
Solution Approach 1:
The patent implements feedback through the forecasting computing system that uses predictive models to evaluate how atomization parameters affect protein aggregation. The system continuously refines parameter selections based on predicted aggregation outcomes, adjusting settings to minimize harmful aggregation while achieving the desired particle size for inhalation.
Solution Approach 2:
The patent replaces conventional mechanical particle size reduction methods (which cause aggregation) with a controlled atomization process guided by predictive modeling. Instead of using mechanical force to break down particles, the system uses precisely controlled fluid dynamics and phase changes to form particles of the desired size without excessive mechanical stress that causes aggregation.
3Speed
If spray drying is used to produce inhalable mAb powders, then aerosol performance can be improved, but protein stability deteriorates
Solution Approach 1:
The patent applies dynamics by making the atomization process adjustable and adaptable through real-time parameter control. The forecasting system enables dynamic optimization of atomization conditions, allowing the process to be tuned for each specific protein and application. This dynamic control ensures that aerosol performance and protein stability requirements are met simultaneously by adjusting parameters during the process.
4Volume of moving object
If deep lung delivery is achieved through small particle size, then therapeutic efficacy is improved, but manufacturing complexity increases
Solution Approach 1:
The patent applies universality through the forecasting computing system that serves multiple functions: predicting particle size, estimating protein stability, optimizing atomization parameters, and guiding formulation development. This multi-functional tool consolidates what would otherwise require multiple separate analytical systems and expert judgments, reducing overall manufacturing complexity while enabling precise control of particle size for deep lung delivery.
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 system enables the production of stable, inhalable protein particles with improved aerosol performance and reduced protein aggregation, facilitating targeted lung delivery and enhanced therapeutic efficacy for chronic lung diseases.
Implementation Method 1
A forecasting computing system that optimizes atomization settings during spray drying processes by using predictive modeling techniques to determine suitable particle size and stability
Data Source
AI summary
A forecasting modeling computing system includes a processors and a memory including a set of computer-executable instructions that, when executed by the processor, cause the forecasting modeling computing system to receive design parameters, determine a predicted median particle size, identify a predictive quadratic model, and display a response surface visualization. A computer-implemented method includes receiving design parameters, determining a predicted median particle size, identifying a predictive quadratic model; and display a response surface visualization.