How to Build a Resilient Active Pharmaceutical Ingredient Production Process — Artificial Intelligence in API Manufacturing
The active pharmaceutical ingredient (API) production process is a complex and delicate one. Any disruption can lead to a loss in revenue, and even product recalls. This is why it’s essential for manufacturers to build a resilient API production process that can withstand disruptions. The process must be handled carefully to maintain the quality and safety of the API products. This blog post will discuss how artificial intelligence can be used in API manufacturing to improve production efficiency and quality.
How Are APIs Manufactured?
The manufacturing of APIs can be divided into two main categories: synthetic and biologic, depending on the final compound produced:
- chemical synthesis of APIs
- biological processes (for fermentation-derived and cell line-based APIs)
The two processes are entirely different, segmenting the market of API production into synthetic chemical API and biological API.
In the case of synthetic APIs, a chemical reaction is used to produce the active ingredient. The most common method for producing synthetic APIs is through a process called crystallization. In this process, a dissolved active pharmaceutical ingredient is purified by being precipitated out of the solution as a solid crystal.
Biologic APIs are produced using biological processes such as fermentation or cell culture. These methods involve using microorganisms or cells to create the active ingredients. Biologic APIs tend to be more expensive and complex than synthetic ones.
How and Why API Manufacturers Are Applying Artificial Intelligence?
There are several reasons why API manufacturers are applying artificial intelligence to their production processes. Some of the key benefits of using AI in API manufacturing include:
- Improved efficiency and quality
- Increased throughput
- Reduced costs
- Improved product stability
Let’s take a closer look at each of these benefits in more detail.
Improved Efficiency and Quality
Using artificial intelligence in API manufacturing can improve the process’s efficiency and quality. With machine learning, it is possible to predict problems before they occur, which can help to prevent disruptions from happening. In addition, AI can be used for data analysis to identify trends and patterns that may not be visible to the human eye or apply more straightforward analytical methods. This can help to improve product quality by detecting problems with product purity, stability, and other chemical parameters.
Increased Throughput
Throughput is the amount of product produced in a given time. By using artificial intelligence and advanced analytics, manufacturers can improve throughput by identifying process bottlenecks and providing insights on altering the manufacturing process for better yields (e.g., faster reaction times or greater purity). This can help to reduce production times and increase output. In addition, AI can be used for predictive maintenance, which can help to prevent equipment failures and minimize downtime.
Reduced Costs
Artificial intelligence can also help pharmaceutical companies reduce the costs of API manufacturing. With machine learning, it is possible to optimize the process and make better use of resources (optimizing raw materials used or reducing waste and decreasing energy consumption and operating time).
Improved Product Stability
API products must meet specific stability requirements to ensure safety and effectiveness. Using artificial intelligence in production can help to improve product stability by detecting and correcting any errors early on. This can help ensure that the final product’s active pharmaceutical ingredients are not affected by changes in the manufacturing process.
API manufacturers are starting to apply artificial intelligence (AI) in their production processes. There are several potential applications of AI in API manufacturing, including:
- AI in research and development
- Predictive maintenance
- Predictive manufacturing
- Image recognition and analysis on the production line
Each of these applications can improve the efficiency and quality of the API production process. Let’s take a closer look at each one.
Artificial Intelligence in Research and Development
Pharmaceutical companies have been using artificial intelligence to improve their operations when it comes to R&D activities. One good example of applying machine learning is to use it for process development.
Process modeling is a technique that has been used to develop predictive models of different reaction conditions. This approach can help identify better process conditions and reduce the number of experiments needed for development. In addition, AI can also be used for target identification during the early discovery stages.
Machine learning algorithms are effective in identifying active molecules from large data sets. This can help to improve hit rates during drug discovery and reduce the amount of time and resources needed for lead identification.
Overall, it is clear that artificial intelligence can play an essential role in improving pharmaceutical research and development activities. By using machine learning algorithms, companies can achieve better results while reducing costs and time requirements.
Predictive Manufacturing Processes
Predictive manufacturing is a type of AI that uses machine learning algorithms to predict how a production process will behave under different conditions. Using predictive manufacturing, manufacturers can identify potential problems before they occur and take corrective action. This can help to improve efficiency and quality while reducing costs.
In addition, pharma companies can use predictive manufacturing for process control. In this scenario, the machine learning algorithm is used to predict the state of a drug manufacturing process at any given time. This information can be immediately used to adjust the process directly as needed. By using predictive control, manufacturers can ensure that products are produced within specifications and minimize the risk of failure.
One application of predictive manufacturing is in the area of batch processing of active ingredients. Variations in the process can affect the quality of the final product. In addition, it can be challenging to detect and correct errors when using this approach.
Predictive manufacturing can help to address these problems by providing feedback control and process monitoring. By using predictive models, manufacturers can get real-time information on how the process performs. In this scenario, the machine learning algorithm predicts the output of a given process based on past data. This information can be used to adjust the parameters of the process as needed. This approach can help to optimize throughput and ensure that products meet quality requirements.
API manufacturers need to ensure that their products meet quality requirements and that the yields from continuous manufacturing are kept on the desired level. They need a way to intensify the processes. One way to do this is by constantly monitoring the process and identifying how changing environment parameters will affect the final product. Similarly, artificial intelligence solutions can make predictions applicable in continuous manufacturing to monitor active pharmaceutical ingredients (APIs) during production.
However, for this approach to be practical, manufacturers need access to real-time data. It is too costly, though, to run processes with altered parameters and without a promise of getting quality products at the end. This is where machine learning can be used to make predictions about the process. By using AI models, manufacturers can get a better understanding of how the process is performing and identify any changes that need to be addressed — in silico first and only when the AI model has high certainty run the actual manufacturing process.
Waste Reduction — Minimize Batch Losses
One of the biggest challenges faced by API manufacturers is minimizing batch losses; it can be incredibly costly in biopharmaceutical production. These can be caused by several factors, including errors in the complexity of the manufacturing process, contamination, and product degradation. The manufacturing of APIs is a complex process, and even a tiny process variation can result in the loss of an entire batch. To prevent this, pharmaceutical companies need to continuously monitor their production processes to ensure that they are running smoothly.
Manufacturers have traditionally used a variety of approaches to try to minimize batch losses. These include Statistical Process Control (SPC), which uses historical data, and Multivariate Data Analysis (MVDA) to identify trends in the manufacturing process. This information can then be used to decide how to adjust the process parameters.
However, SPC and multivariate analytics have limitations in identifying small changes in pharmaceuticals production that can lead to significant variations in product quality. In addition, it can be challenging to detect and correct errors when using this approach.
To address these limitations, manufacturers from the pharmaceutical industry are increasingly turning to artificial intelligence solutions. These can be used to create models to identify the relationships between process parameters and product quality. This information can then be used to decide how to adjust the process parameters.
AI models can learn from data and detect patterns that are not detectable by humans. In addition, they can adapt as the manufacturing process changes, allowing them to maintain a high degree of accuracy in predicting product quality. AI software applied for waste reduction also plays a critical role in the environmental impact of drug production.
Related case study: APIs Production Process Predictive Monitoring To improve the current repeatable batch production processes, a producer of active pharmaceutical ingredients approached us to implement AI models and utilize predictive modeling.
Our challenge? Building a model that analyzes real-time data streams from the production process and identifies potential outliers that may lead to deterioration of quality, based on historical data. The benefits are improved effectiveness, predictability, and efficiency of manufacturing operations and yields. Read more about this case study.
Quality Assurance
To improve quality assurance, some manufacturers are turning to machine learning solutions. These can be used to develop models that automatically predict potential problems resulting in product alterations. In addition, they can be used to identify the root cause of defects to be corrected.
In traditional approaches to manufacturing, a fixed process relies on post-process testing of quality to ensure the product is within specification. Unfortunately, for complex procedures, a fixed process does not guarantee a desired quality and quantity.
Active Pharmaceutical Ingredients manufacturing follows a prescribed set of recipes. The recipe is determined by the active pharmaceutical ingredient and the ingredients and process parameters used to produce it. To ensure that the quality of the API meets specifications, manufacturers use a variety of quality control tests at different points in the production process.
However, these tests can be time-consuming and expensive, and they do not always identify all defects in the product. In addition, even if a fault is identified, it can be challenging to determine what caused it and how to correct it.
Machine learning solutions can be used to develop models that can automatically predict product defects. In addition, they can be used to identify the root cause of the problem. Machine learning solutions are a great way to develop models that can automatically predict product anomalies and help with root cause analysis with prescriptive feedback control. This will help speed up the process of finding and fixing these errors, ultimately leading to a more resilient API production process that ensures consistency in product quality and quantity.
Streamlining Production Schedule
Predictive manufacturing can also optimize and streamline production schedules. AI models can help identify the products that are most likely to experience a delay in production so that the necessary steps can be taken to ensure that they are produced on time and meet demand. This helps reduce the risk of delays in product delivery, which can impact customer satisfaction and business operations.
Predictive Maintenance
Another application of AI in pharmaceutical manufacturing is predictive maintenance. In this scenario, machine learning algorithms predict when a piece of equipment will fail. By using predictive maintenance, companies can schedule repairs before equipment fails, thus improving efficiency minimizing downtime and repair costs. In addition, it can also help to extend the life of machines and equipment.
It is difficult to detect small changes in equipment performance in many cases until it becomes a problem. Predictive maintenance uses machine learning algorithms to analyze data from sensors on machines. This data can be used to build models of how the equipment will perform under different conditions. The Industry Internet of Things (IIoT) is another way to apply artificial intelligence in maintenance activities. The IIoT is a network of connected devices that can be used to collect and transmit data. This data can then be used to improve operations and make decisions about optimizing the process and reducing the lifecycle maintenance costs.
Manufacturers need access to historical data on equipment failures for predictive maintenance to be effective. The production line that has been in operation for several years assures that the company has significant historical information on failures, repairs, and scheduled maintenance actions. The machine learning algorithm can use this data to build a model of how equipment behaves over time. The data will also be used to improve the accuracy of the predictions made by the machine learning algorithm.
Sensor and Visual Inspection for Quality Assurance
Another application of AI in API manufacturing is image recognition and analysis on the production line. This approach can be used to identify defects in products or contamination in the process environment. In addition, it can also be used for quality control purposes.
In this scenario, images of products are taken as they are produced and analyzed using machine learning algorithms. This data is then used to build models that can detect defects in the product.
The use of AI-based image recognition and analysis can help identify defects that may not be detectable by traditional quality control tests. In addition, it can also help speed up the process of identifying and correcting these defects. Using AI-based image recognition and analysis, companies can ensure that their products meet the highest quality standards.
Benefits of Introducing AI to APIs Manufacturing
There are many benefits of introducing AI to APIs production. Some of these benefits include:
- Faster process development for new drugs
- Increased resilience to disruptions in production
- Improved production process
- Opportunity to increase production yields
- Reduced risk of product defects and contamination
- Improved quality control
- Faster identification and correction of defects
- Streamlined production schedule
- Reduced production losses and wasted raw material
- Reduced downtime and maintenance costs
These benefits contribute to a more resilient pharmaceutical production process that is less likely to experience disruptions or problems. In addition, it also helps improve business efficiency by reducing the amount of time and resources needed for quality assurance activities. This, in turn, results in higher quality products that meet the highest standards.
Resilient Future for Pharmaceutical Manufacturers with Innovative Technologies
The use of AI in API production can help improve efficiency, reduce errors and improve quality. By using predictive maintenance, image recognition and analysis, and other AI-based methods, companies can make their API production process more resilient to disruptions and problems. This, in turn, results in higher quality products that meet the highest standards. AI-powered active ingredient production can help improve efficiency, reduce errors and improve quality.
As the industry continues to evolve, manufacturers need to adopt new technologies that will help them stay competitive. The use of AI software in API production is one such technology that can help manufacturers stay ahead of the curve. With its ability to improve efficiency, reduce risk and improve quality, AI adoption offers several advantages that can benefit pharmaceutical companies across all stages of pharmaceutical development, starting with drug discovery, clinical trials, process development, and finally, manufacturing operations. In addition to these benefits, AI can also help pharmaceutical manufacturers prepare for the future.
Like companies from other industries, pharma enterprises need to rethink their manufacturing approach and supply chain management. Drug manufacturers should look into their operations as their current offshore production, and just-in-time system are now being reevaluated. These strategies have recently proved not to be fully resilient.
Active Pharmaceutical Ingredient production is a complex process that requires a high degree of precision and reliability. Nexocode’s AI experts can help companies from the pharma industry to implement solutions that will improve the resilience of their API production process. Our solutions can help reduce the risk of product defects and contamination, improve quality control, and speed up identifying and correcting defects. In addition, our AI experts can help pharmaceutical companies prepare for the future by implementing new technologies that will help them stay competitive. Contact us today to learn more about our AI-based solutions for drug production.
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Originally published at https://nexocode.com on February 5, 2022.
