Self-Trainable Neural Network for Biologic Particle Anomaly Detection
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Solution Overview
Problem
Current pharmaceutical manufacturing lacks real-time characterization and anomaly detection of drug substance particulates, leading to knowledge gaps, compliance risks, and inefficiencies due to inaccurate particle detection and characterization, particularly with translucent biologic particles, which are often misidentified or overlooked by conventional imaging software.
Innovation Solution
A self-trainable neural network system that includes a convolutional neural network for real-time identification of particulate-based anomalies in pharmaceutical products, allowing users to train and deploy AI models for particle classification and characterization, enabling accurate detection and reporting of particle morphologies and anomalies within images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional particle analysis software is used to detect biologic particles, then the system is simple and easy to operate, but the detection accuracy is poor because translucent particles are misidentified or overlooked
Solution Approach 1:
The patent replaces conventional mechanical/image processing-based particle analysis software with an artificial intelligence neural network system. This substitution enables the system to accurately detect translucent biologic particles by learning complex patterns from training data, overcoming the limitations of traditional algorithms that cannot distinguish these particles from background noise.
Solution Approach 2:
The neural network system is designed to be self-trainable, allowing users to provide their own training images and automatically train the model without requiring external assistance. This self-service capability enables continuous improvement of detection accuracy while maintaining system simplicity for end users.
2Measurement precision
If third-party outsourcing is used for biologic data analysis, then specialized expertise is accessed, but the turnaround time increases to two to four weeks and real-time detection is lost
Solution Approach 1:
The system enables pharmaceutical companies to perform particle analysis in-house using the self-trainable neural network, eliminating the need to outsource data to third parties. Users can train the model with their own data and immediately perform real-time analysis, reducing turnaround time from weeks to minutes while maintaining high accuracy through continuous model improvement.
Solution Approach 2:
The system allows for preliminary training of the neural network using representative biologic particle images before actual analysis begins. This pre-training phase prepares the model to accurately detect and characterize particles in real-time during manufacturing processes, eliminating delays associated with post-processing outsourcing.
3Quantity of substance
If only 1-2% of imaging data is exported to third parties for analysis, then data exchange costs are reduced, but holistic data optimization is inhibited and most valuable insights are lost
Solution Approach 1:
The in-house neural network system processes 100% of imaging data generated during manufacturing processes, eliminating the need to export data to third parties. This complete data utilization enables holistic optimization of particle characterization while reducing data exchange costs, as the system can analyze all images locally without external assistance.
Solution Approach 2:
The neural network system is designed to handle diverse biologic particle types and imaging conditions universally. By training the model on comprehensive datasets representing various particle morphologies and manufacturing scenarios, the system can accurately analyze all incoming images without requiring selective export to specialized third-party analysts.
Data Source
AI summary
System for analyzing anomalies in pharmaceuticals includes a server configured to host a neural network having an inference engine and a training engine, a database of images of in-process biologics; a first user interface module for displaying to a user particle morphologies in the images; a second user interface module for displaying to the user a training of the neural network; a third user interface module for displaying to the user an inference of images chosen by the neural network to fit selected criteria, wherein the neural network is a convolutional neural network, and training includes providing test images to the training engine to teach the neural network to recognize specific particle morphologies. The user provides images of the in-process biologics from the database, and the inference engine identifies anomalous particle morphologies in the user-provided images. A fourth user interface module provides a report about particle morphologies in the images.