The Future of Pharmaceutical Manufacturing Process: Artificial Intelligence

One of the most important industries in the world is pharmaceutical manufacturing. This industry has been a major contributor to society for decades, and it will continue to be for many more years into the future. It’s hard to imagine what life would be like without drugs that cure diseases and help people live healthier lives. As technology advances, we can do things with artificial intelligence (AI) that were once only dreams of science fiction movies. We have already seen AI breakthroughs as far as drug discovery goes; now, companies are working on developing AI-powered drug manufacturing plants, which could change how this industry operates forever. This article will discuss how artificial intelligence will change pharmaceutical manufacturing as we know it!

How Pharmaceutical Manufacturing Process Works?

The drug production process is a long and complex one, but it’s important to know the basics of how this industry operates. It is the process of industrial-scale synthesis of pharmaceuticals as part of the pharmaceutical industry. The pharmaceutical manufacturing process is broken down into a series of steps, and each step has an impact on the efficacy and consistency of the final product.

The pharmaceutical production process starts with the identification of a drug and its development. The company develops the active pharmaceutical ingredient (API) and then goes through clinical trials to ensure it’s safe for humans before moving on to commercialization. Next come patenting, formulation, production scale-up (manufacturing), quality testing/control, packaging functions like labeling and marketing.

The main objective of pharmaceutical manufacturing is a continuous supply of APIs of consistent quality. The active pharmaceutical ingredient development process consists of stages: chemical transformations or purifications that may require isolated or nonisolated intermediates. These comprise numerous unit operations, which are executed according to set limits. They are concerned with the synthetic route selection for the drug substance and product, the mechanism of API impurities formation, and the suitability of the optimization studies. Making a compound drug involves mixing various chemicals in exact proportions, often as powders that are then pressed into pills or other shapes. All activities are constantly monitored during the laboratory pilot scale to commercial scale. The sole production consists of the following unit operations:

• Formulation and pre-formulation development,
• Powder feeding in continuous manufacturing,
• Mixing Active Pharmaceutical Ingredients with excipients,
• Milling or granulating,
• Hot melt extrusion,
• Compressing the powder blend to form tablets or capsules,
• Packaging and labeling pharmaceuticals for distribution.

The pharmaceutical company must produce the drug in a high enough quantity to be profitable. So, they need to make sure that each production step is efficient and scalable. If it’s not, then there will be waste or cost overruns in downstream operations, which could jeopardize profit margins and safety. Read more about AI in Chemicals here.

What is Artificial Intelligence?

Artificial Intelligence is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using logical rules to reach approximate or definite conclusions), and self-correction. The Artificial Intelligence field includes various branches: artificial neural networks, machine learning, data science, expert systems, and hybrid systems. They have been widely used in various applications for the pharmaceutical industry, including modeling, process control, pharmacovigilance automation, prediction systems, fault detection, pharma supply chain management, and data management.

How AI Will Impact Pharmaceutical Manufacturing in the Future?

So what does Artificial Intelligence mean for pharmaceutical manufacturing? It means more than just having machines do factory work; it literally changes how we make medicine. AI will bring efficiency to every step of the process from research and development through manufacturing and distribution, meaning more affordable, safe, and advanced medicine for everyone.

The Benefits of AI for Drug Manufacturing

Cheaper Production by Using Predictive Analytics to Optimize Resource Utilization

To ensure that production is both efficient and scalable, pharmaceutical manufacturers need to optimize their manufacturing processes. Artificial intelligence can help by providing a third-party perspective on how the drug process should operate and suggesting changes in equipment design for maximum efficiencies. For example, artificial intelligence could analyze data from previous batches of drugs produced at a factory to identify ways to reduce costs and increase production.

AI can also be used to predict the optimal production schedule for a drug. This is based on inventory levels, current demand, and the factory’s capacity - all factors that are readily available with today’s digital information technology systems. The best manufacturing pipeline configuration will minimize waste and optimize output so that there is an ample supply of drugs when needed.

A successful AI project needs to transfer data from different sources to provide accurate predictions about how much raw materials or parts need to be ordered at any given time and what equipment should be allocated where throughout the plant during setup and operations. Artificial Intelligence provides valuable insight into what would otherwise have been unknown variables by finding patterns within vast amounts of data which helps pharmaceutical manufacturers implement good manufacturing practices and produce medicines efficiently and cost-effectively.

Active Pharmaceutical Ingredients Manufacturing

The production of APIs is often conducted by large-scale manufacturers who distribute drug products to pharmacies and hospitals. Even though APIs are typically relatively small molecules, they account for over 50% of all drugs on the market. Many product properties such as API concentration in a dosage form and the mechanical strength of the dosage form have to be kept within predefined specifications, so obtaining control of such properties is needed to ensure the quality and safety of each produced tablet and dose. The difficulty in controlling the product quality properties often relies on controlling variation, that the raw material properties may change over time or that the supplier of the raw material is changed.

AI processes can help produce more accurate dosing as well as improved standardization across batches for APIs. Companies can use machine learning to capture historical batch performances for the real-time optimization of critical process parameters to achieve optimal quality output. Another way that this technology helps pharmaceutical companies meet demand is through its ability to reduce hold times between stages, which will allow them to maintain their supply chain without experiencing any delays due to manual processing or paperwork mistakes along the process.

Related case study: APIs Production Process Predictive MonitoringTo improve the current repeatable production processes, a producer of active pharmaceutical ingredients approached us to implement AI models and utilize predictive modeling.Our challenge? Building a system 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 production processes. Read more about this case study.

Improving Continuous Pharmaceutical Manufacturing

In pharmaceutical production, the goal is to manufacture a product that meets quality standards at every stage of production. AI provides manufacturers with an opportunity to optimize their processes and identify opportunities for improvement in continuous manufacturing – where products are continuously manufactured on a single line without interruption or stoppage.

AI solutions can be used as part of process control and as tools for monitoring equipment performance measurements, such as temperature and pressure levels, checking cleaning procedures against factory specs, and identifying possible contamination sources based on sensor readings from sensors embedded throughout the facility. This capability enables pharmaceutical industry organizations to monitor specifically detailed parameters related to personnel compliance like handwashing frequency by capturing images through cameras installed over sink areas or ladders.

Making the Most Of the Batch Manufacturing Process

Not every pharmaceutical is produced in continuous pharmaceutical manufacturing processes. Instead, they are manufactured in batches with multiple steps to finish a single product. After each step, there may be a “hold time” when production stops while samples are analyzed offline for quality during the batch manufacturing process. The process can be complex and lengthy, with significant delays between steps. Sometimes during these hold times between steps of the manufacturing process, the material may be stored or transported to complete the designated step elsewhere. This can cause weeks or months of delay for some active ingredients that are sensitive to environmental degradation. Due to the process complexity and multiple prone-to error steps, batch drug production can benefit from applying AI to parts of the process.

Manufacturing batches with AI means better standardization. In the pharmaceutical manufacturing process, many steps have to be taken for a product to make it from start to finish. These tasks include things like checking and recording temperatures or pressures at different points of production as well as monitoring equipment performance status such as running diagnostics on an instrument. AI can help reduce inefficiencies and errors by incorporating automation for parts of the production process while still maintaining human supervision. This will allow pharmaceutical companies to more effectively meet supply chain demands that are required for end-to-end quality control, such as reducing hold times between stages, improving flexibility during transportation and storage processes, and minimizing errors resulting from manual processing or paperwork mistakes way.

Increased Quality Assurance and Regulatory Compliance

The pharmaceutical industry faces increased scrutiny from regulatory authorities, as well as the public. With artificial intelligence, pharma manufacturers can monitor activities on a company-wide scale and detect any deviations that could lead to quality issues or breaches in compliance.

AI is particularly powerful for providing insights into manufacturing processes where human labor has traditionally been involved—for example, controlling raw materials’ quality and how it impacts the end product. AI-assisted quality assurance improves process development by spotting defects in raw materials before they enter a production line.

AI-assisted quality assurance improves pharmaceutical production, for instance, by spotting defects in raw materials before they enter a production line. With drug manufacturers needing to meet more stringent regulatory requirements and consumer expectations demanding greater consistency with their products, this technology can help them achieve both goals. Furthermore, some drug manufacturers are now using IoT technologies coupled with AI-based computer vision on the production lines to spot faulty products or packaging in real-time—giving them an edge over competitors and helping increase customer satisfaction levels as well.

Predictive Maintenance for Pharmaceutical Production Line

From the factory floor perspective, AI can detect issues with equipment and proactively manage compliance efforts. AI-powered predictive maintenance solutions can analyze the performance of pharmaceutical production lines and provide early warning when equipment begins to wear down or require repairs. They also offer significant cost savings in terms of reduced downtime, which is typically one of the biggest expenses for manufacturers. This capability will become even more important as new regulations are phased in that mandate increased levels of product quality assurance at all stages of manufacturing.

AI could also help pharmaceutical manufacturers respond more quickly to changing customer demands for new drugs or unexpected safety events, such as when a drug is recalled because of potential side effects. AI can analyze large amounts of data in a short period of time — much faster than humans without the aid of artificial intelligence.

Planning Production Based on Purchase Orders and Customer Needs

If companies are using AI to optimize their pharmaceutical manufacturing, they can use it to plan production based on Purchase Orders and customer needs. The companies need to take into account the number of products they have available as well as what will be needed to keep up with demand for these medicines.

AI software can monitor how much product a company has left over at any given time so that delivery can stay on schedule. It also allows them to know when inventory is about to run out before it reaches critical levels, which means there will not be any interruption in supply if something goes wrong such as damage or theft. This makes planning more efficient since they do not have to worry about having enough materials beforehand, meaning less waste and fewer disruptions due to stockouts or overstocking.

Optimizing Drug Discovery and Development

Artificial Intelligence software is also able to help optimize drug development. This is something that pharmaceutical companies are already looking into, and it may very well be the future of medicine manufacturing. AI can speed up research by providing a constant supply of new data from study participants, progress reports on clinical trials, a better understanding of side effects or adverse reactions when people take different drugs together, which means they will not have to wait as long before launching their product. It has the potential to make pharmaceuticals more efficient in general because there will be fewer errors and wasted time spent on redundant work so employees can concentrate on other tasks such as improving formulations or making sure patients get what they need. You can read more about AI in drug discovery and development in our recent article .

Challenges to Implementing AI Into Pharma Manufacturing Processes

Achieving the benefits of AI-based manufacturing future requires a vision and an understanding of the extant regulatory, technical, and logistical barriers to realizing it. There are several potential challenges for pharmaceutical manufacturers who want to implement artificial intelligence into drug production processes:

AI Implementation Costs

AI will not be an instant fix, and it will require significant investments. Defining and understanding how to measure the ROI of implementing AI early on is critical.

Lack of Time to Develop AI-Based Systems

The pharmaceutical industry is already having trouble keeping up with the demands for new drugs and safety recalls; they may not have enough resources to devote more time to developing artificial intelligence software.

Lack of AI Expertise Within Pharma It Teams

There are not enough people trained in AI, and the pharma industry is suffering from a skills shortage. It is hard to find enough talent to fill positions due to rapid growth and increased competition for skilled workers. When it comes to artificial intelligence, pharmaceutical companies will find themselves struggling with workforce shortages even more than other sectors of society.

Lack of a Clear Understanding of How Best to Use and Incorporate Artificial Intelligence Technologies Into Current Systems Between Pharma Executives

There is a lack of understanding about where and how this technology should fit into drug development and manufacturing. CEOs and pharma decision-makers may have wrong expectations as they do not know the current technical possibilities. What type of analytic method is needed? How long will it take before an ROI can be seen? What level of expertise do you need to have on staff already?

Lack of Normalized Data

Some companies, although they seem to be already after the digital transformation stage, still do not gather or gather in an unnormalized way crucial data that can be used to feed AI. AI must be fed data and lots of it in order to produce the desired results, which may require pharmaceutical manufacturers to redesign their systems and data pipelines or change how they store information about production processes.

Siloed Legacy Infrastructure

Although the pharmaceutical sector boasts access to a wealth of data, the data is often siloed or exists as tacit information, rendering it a challenge to unlock its full potential. In many instances, this is because the legacy infrastructure does not connect different parts of the pharmaceutical enterprise.

Regulatory Compliance and Safety Issues for the Pharmaceutical Industry

One major challenge the pharmaceutical industry faces when introducing AI or automation into their process is ensuring compliance with regulatory standards related to data security, safety-critical systems engineering requirements, and FDA 21.

Legal Issues Related to Pharmaceutical Development and Manufacturing Process

There are legal challenges that need to be addressed before implementing AI by drug manufacturers, such as data privacy or intellectual property ownership rights.

Different Countries’ Regulations Regarding the Use of Artificial Intelligence in Pharma Manufacturing Processes

Some countries ban or limit the use of AI, while others do not have any restrictions at all. In order to adopt this technology, there needs to be global agreement on how and when artificial intelligence will be used in the production process.

How Can You Prepare Your Company for the Future of Artificial Intelligence in Pharma Production?

The pharmaceutical industry is relatively new to artificial intelligence. However, the use of AI in manufacturing processes can be a game-changer for pharma organizations and their production process. AI adoption for drug manufacturers means increased efficiency, quality control, cost reduction, and more. The best way to prepare your company for this future is by investing time into research on how you would want it applied within your organization, as well as understanding current AI capabilities. This will help ensure that your company will have an advantage over competitors because you were able to start early adoption.

Prepare yourself now! - There are many challenges that need to be addressed before implementing AI into drug production processes. The most important one is getting a shared understanding of what value AI can bring to your organization, what the AI implementation process looks like, and how it can be approached in an agile, iterative way. If you’re willing to take the first step in AI adoption, take a look at our AI Design Sprint offering - workshops that help uncover AI opportunities and design solutions in just two days. With nexocode’s help, pharmaceutical and biotech companies receive efficient and sustainable solutions that enable them to make reliable plans for many years to come.

Conclusions

Drug manufacture is a complex task and one that requires careful planning in order to ensure all processes are monitored for efficiency. These days, some pharmaceutical companies have been questioning whether they should upgrade their systems by integrating artificial intelligence into the production chain, while others are already investing heavily in AI in Pharma solutions.

There are many challenges that need to be addressed before implementing AI into drug production processes, but nexocode’s offer - starting with AI Design Sprint workshops, through building AI Proof of Concept, and then moving to production implementation - should give pharmaceutical executives enough information to make reliable plans for years. We have significant experience in supporting our clients in implementing AI solutions for pharma, so make sure to reach out to us!