PHARMACEUTICAL MANUFACTURING SYSTEM AND METHOD THEREOF

Abstract

An AI-optimized pharmaceutical manufacturing system 101, optimized with the use of machine learning algorithms, 102, concerning optimization concerning efficiency and quality of drug production; it may comprise a real-time process monitoring 103, predictive maintenance 104, and adaptive control mechanisms 105. It makes use of a central AI processor 106, where data may be analyzed by utilizing multiple sensors 107, strategically located on the manufacturing line. The system optimizes production parameters 108, predicts and prevents equipment failures 109, and ensures consistent drug quality 110. Such innovation significantly reduces production costs, minimizes batch rejections, and accelerates time-to-market for pharmaceutical products 111.

Specification

Description:The present invention has been particularly shown, and described concerning certain preferred embodiment, and specific features thereof.

The following description includes the preferred best mode of one embodiment of the present invention. It is clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications, and embodiments thereto. Therefore, the present description should be seen as illustrative, and not limiting.

While the invention is susceptible to various modifications, and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the spirit, and scope of the invention as defined in the claims.

In any embodiment described herein, the open-ended terms "comprising," "comprises," and the like (which are synonymous with "including," "having" and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of," consists essentially of," and the like or the respective closed phrases "consisting of," "consists of, the like.

As used herein, the singular forms "a," "an," and "the" designate both the singular and the plural, unless expressly stated to designate the singular only.

The following is a step-by-step description of the invention, detailing the components, and their functionalities mentioned below:

1. Sensor Network (107):
The foundation of the system is a comprehensive network of advanced sensors (107) strategically placed throughout the manufacturing line. These sensors continuously monitor various parameters critical to the drug production process, including:

- Temperature sensors (112): Monitor heat levels at different stages of production.
- Pressure sensors (113): Track pressure in reactors and other vessels.
- pH sensors (114): Measure acidity or alkalinity of solutions.
- Chemical composition sensors (115): Analyze the makeup of materials in real-time.
- Flow rate sensors (116): Monitor the movement of liquids and gases.
- Viscosity sensors (117): Measure the thickness and flow properties of liquids.
- Particle size analyzers (118): Assess the size distribution of particulate matter.

Each sensor is equipped with wireless communication capabilities, allowing for real-time data transmission to the central AI processor.

2. Central AI Processor (106):

The system is a powerful central AI processor 106 that receives and analyzes data from the sensor network. This processor employs state-of-the-art machine learning algorithms 102 to interpret the incoming data and make intelligent decisions. Key features of the AI processor include:

- Data preprocessing module (119): Cleans and normalizes incoming sensor data.
- Pattern recognition algorithms (120): Identify trends and anomalies in process parameters.
- Predictive modeling engine (121): Forecasts future process states and potential issues.
- Decision-making module (122): Determines optimal actions based on current and predicted states.
- Learning module (123): Continuously improves system performance through experience.

The AI processor operates on a high-performance computing platform capable of processing vast amounts of data in real-time.

3. Adaptive Control Mechanisms (105):
Based on insights from the AI processor, the system implements adaptive control mechanisms 105 to optimize the manufacturing process continuously. These mechanisms include:

? Automated adjustment of process parameters (124): Dynamically modifies settings such as temperature, pressure, and flow rates.
? Feedback control loops (125): Ensures that process adjustments achieve desired outcomes.
? Multi-variable optimization algorithms (126): Balances multiple process parameters simultaneously for overall optimal performance.

4. Predictive Maintenance System (104):
To minimize downtime and ensure equipment reliability, the invention includes a sophisticated predictive maintenance system 104. This system:

- Monitors equipment performance metrics (127): Tracks factors like vibration, temperature, and power consumption.
- Analyzes historical maintenance data (128): Identifies patterns that precede equipment failures.
- Predicts potential failures (129): Uses machine learning to forecast when maintenance will be needed.
- Schedules proactive maintenance (130): Automatically generates maintenance schedules to prevent breakdowns.

5. Quality Control Module (110):
Ensuring consistent drug quality is a primary focus of the invention. The quality control module 110 works in tandem with the sensor network and AI processor to:

- Monitor critical quality attributes (131): Continuously assess factors that impact drug efficacy and safety.
- Perform real-time product analysis (132): Uses spectroscopic and other analytical techniques to evaluate product characteristics.
- Implement statistical process control (133): Applies advanced statistical methods to detect and correct quality deviations.
- Generate compliance reports (134): Automatically create documentation for regulatory purposes.

6. User Interface and Visualization System (135):
To facilitate human oversight and decision-making, the invention includes an intuitive user interface and visualization system 135. This component:

- Displays real-time process data (136): Presents key manufacturing metrics in easy-to-understand formats.
- Visualizes predictive insights (137): Shows forecasted trends and potential issues.
- Provides interactive dashboards (138): Allows users to explore data and scenarios.
- Offers decision support tools (139): Suggests actions based on AI recommendations.

7. Data Storage and Management System (140):
To support long-term analysis and regulatory compliance, the invention includes a strong data storage and management system 140. This system:

- Securely stores historical process data (141): Maintains a comprehensive record of all manufacturing runs.
- Implements data encryption and access controls (142): Ensures data integrity and confidentiality.
- Provides data retrieval and analysis tools (143): Facilitates retrospective analysis and audits.

8. Integration and Communication Module (144):

To ensure continuous operation within existing manufacturing environments, the invention includes an integration and communication module 144. This module:

- Interfaces with existing manufacturing execution systems (145): Allows for smooth data exchange with other factory systems.
- Implements standardized communication protocols (146): Ensures compatibility with a wide range of equipment and software.
- Provides APIs for third-party integrations (147): Allows for extensibility and customization.

Operation of the Invention:
The AI-optimized pharmaceutical manufacturing system works this way:
1. A sensor network 107 continuously collects the data at various points throughout the entire manufacturing process.
2. Such data is transmitted in real-time to the central AI processor 106.
3. The AI processor analyses incoming data with machine learning algorithms 102), recognizes patterns, predicts future states, and determines optimal actions.
4. The analysis of the AI shows the adaptive mechanisms of control 105 concerning real time and parameters in the process 108, that keep changing and adjusting so as to be within the optimal boundaries.
5. At the same time, predictive systems of maintenance 104 monitor equipment health and schedule accordingly maintenance activities.
6. The quality control module 110 will continuously assess product quality, adjusting accordingly, and flagging deviations.
7. All the activities and results are brought out in the user interface 135, which allows for human review and inputs, if called upon.
8. Data is securely kept and managed for future access and regulatory compliance.

This approach ensures that the entire manufacturing process is optimized continuously. This results in higher efficiency, consistent quality, and lower costs.

Method of Performing the Invention
The optimal implementation of the AI-optimized pharmaceutical manufacturing system involves the following steps and considerations:

1. Sensor Deployment:
- Conduct a thorough analysis of the manufacturing process to identify critical measurement points.
- Install high-precision sensors 107 at these points, ensuring coverage of all key process parameters.
- Use wireless sensors where possible to minimize disruption to existing infrastructure.

2. AI Processor Setup:
- Deploy the central AI processor 106 on a high-performance computing cluster with redundancy for reliability.
- Implement a distributed computing architecture to handle large volumes of real-time data processing.
- Use GPU acceleration for machine learning algorithms to enhance processing speed.

3. Machine Learning Model Development:
- Begin with pre-trained models for common pharmaceutical processes.
- It utilizes model predictive control (MPC) algorithms for complicated, multivariable processes.
- Reinforcement learning techniques can be applied to improve the optimization of the control strategy over time.

4. Adaptive Control Implementation:
- Develop detailed process models for each unit operation in the manufacturing line.
- Implement model predictive control (MPC) algorithms for complex, multi-variable processes.
- Use reinforcement learning techniques to optimize control strategies over time.

5. Predictive Maintenance Integration:
- Collect historical maintenance data and equipment specifications.
- Develop equipment-specific degradation models.
- Implement variance detection algorithms to identify unusual equipment behavior.

6. Quality Control Enhancement:
- PAT, or in-line analytical technologies, must be integrated to provide real-time product quality.
- Techniques of multivariate statistical process control (MSPC) should be implemented.
- Develop AI-driven image analysis for visual inspection tasks.

7. User Interface Design:
- Create role-specific dashboards for operators, supervisors, and managers.
- Implement responsive design for accessibility across different devices.
- Include customizable alerts and notifications for critical events.

8. Data Management and Security:
- Implement a scalable data lake architecture for efficient storage and retrieval.
- Use blockchain technology for immutable record-keeping of critical process data.
- Implement end-to-end encryption and role-based access controls.

9. System Integration:
- Develop custom adapters for integration with existing manufacturing execution systems (MES) and enterprise resource planning (ERP) systems.
- Implement OPC UA (Open Platform Communications Unified Architecture) for standardized industrial communication.

10. Validation and Compliance:
- Develop a comprehensive validation master plan by GAMP 5 guidelines.
- Implement continuous validation techniques to maintain compliance with evolving regulations.
- Create automated audit trail and reporting functionalities.

11. Training and Change Management:
- Create an all-encompassing training program for operators, engineers, and management.
- Roll it out in phases, starting with non-critical processes.
- Form a team that is cross-functional and capable of managing the transition process, and helping to solve arising problems.
, Claims:1. An AI-optimized pharmaceutical manufacturing system, comprising:
? a sensor network 107 for real-time monitoring of manufacturing process parameters;
? a central AI processor 106 employing machine learning algorithms 102 for data analysis and decision-making;
? adaptive control mechanisms 105 for dynamic adjustment of process parameters 108;
? a predictive maintenance system 104 for equipment health monitoring and failure prediction;
? a quality control module 110 for ensuring consistent drug quality;
? a user interface and visualization system 135 for displaying process data and insights;
? a data storage and management system 140 for secure data handling and analysis; and
? an integration and communication module 144 for interfacing with existing manufacturing systems;
wherein the system continuously optimizes the pharmaceutical manufacturing process based on real-time data analysis and predictive insights.

2. A method for AI-optimized pharmaceutical manufacturing as claimed in claim 1, comprising the steps of:
? collecting real-time data from a sensor network 107 throughout the manufacturing process;
? analyzing the collected data using machine learning algorithms 102 in a central AI processor 106;
? dynamically adjusting process parameters 108 using adaptive control mechanisms 105 based on AI insights;
? predicting and preventing equipment failures using a predictive maintenance system 104;
? continuously monitoring and ensuring product quality using a quality control module 110;
? displaying process data and insights through a user interface 135;
? storing and managing data securely for analysis and compliance; and
? integrating with existing manufacturing systems using an integration and communication module 144;
wherein the method enables continuous optimization of the pharmaceutical manufacturing process.
3. The system as claimed in claim 1, wherein the sensor network 107 comprises temperature sensors 112, pressure sensors 113, pH sensors 114, chemical composition sensors 115, flow rate sensors 116, viscosity sensors 117, and particle size analyzers 118.
4. The system as claimed in claim 1, wherein the central AI processor 106 includes a data preprocessing module 119, pattern recognition algorithms 120, a predictive modeling engine 121, a decision-making module 122, and a learning module 123.
5. The system as claimed in claim 1, wherein the adaptive control mechanisms 105 include automated adjustment of process parameters 124, feedback control loops 125, and multi-variable optimization algorithms 126.
6. The system as claimed in claim 1, wherein the predictive maintenance system 104 monitors equipment performance metrics 127, analyzes historical maintenance data 128, predicts potential failures 129, and schedules proactive maintenance 130.
7. The system as claimed in claim 1, wherein the quality control module 110 monitors critical quality attributes 131, performs real-time product analysis 132, implements statistical process control 133, and generates compliance reports 134.
8. The system as claimed in claim 1, wherein the user interface and visualization system 135 displays real-time process data 136, visualizes predictive insights 137, provides interactive dashboards 138, and offers decision support tools 139.
9. The system as claimed in claim 1, wherein the data storage and management system 140 securely stores historical process data 141, implements data encryption and access controls 142, and provides data retrieval and analysis tools 143.
10. The system as claimed in claim 1, wherein the integration and communication module 144 interfaces with existing manufacturing execution systems 145, implements standardized communication protocols 146, and provides APIs for third-party integrations 147.

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