Predictive Analytics: A Revolutionary Tool for Pharmaceutical Manufacturing

Dorota Owczarek - October 14, 2021

Predictive analytics is the machine learning process of using data to make predictions about future outcomes. Predictive analytics can be used in many industries, but it has become increasingly popular in the pharmaceutical industry. It is already revolutionizing drug discovery and development, clinical trials, drug distribution, and now the manufacturing process. It has the potential to help pharma manufacturers proactively identify production line optimization issues, recognize which batches are more likely to fail in development or bring about safety issues. This article will explore the field of predictive analytics and how it benefits pharmaceutical manufacturers.

What is Predictive Analytics?

Predictive analytics is a branch of statistics that focuses on predicting future events or outcomes based on historical data. Predictive analytics, as the phrase implies, relies on past information to make predictions about future results. In a broader sense, historical data is used to construct a mathematical model that reflects important trends. In terms of utilizing data, predictive analytics and forecasting are two different things, as the first one heavily relies on the insights found within the historical data. Predictive models are designed using past observations of the processes and conditions surrounding those events, including anything from equipment failures or weather changes to market trends and higher-level business decisions such as whether or not to open new locations in certain areas. A predictive model is then employed to forecast future trends and offer recommendations for what course to take.

Predictive analytics is currently catching a lot of attention, thanks to significant advancements in the technology behind it. Predictive analytics is no longer restricted to just one area of mathematics because of developments in machine learning and big data. Predictive analytics has been around for quite some time but has only recently gained significant influence. Organizations and business analysts are looking at many different predictive analytics techniques to obtain more insights.

How predictive analytics models are built and work to create predictions?

How predictive analytics models are built and work to create predictions?

Several different algorithms might be used for that purpose, yet the smaller subset of predictive analytics techniques is generally employed. These include:

  • regression analysis,
  • correlation analysis,
  • classification techniques,
  • segmentation techniques,
  • time series models,
  • deep learning technologies.

Why do we need predictive analytics, anyway? There are several reasons to consider. The most significant point is that today’s extremely competitive business environment demands businesses to rely on data rather than intuition or instinct. To obtain more precise results in the realm of consumer buying patterns, market situations, and so on, you’ll need data-driven insights. Predictive analytics is being used in a wide range of sectors for a variety of purposes.

How Are Pharmaceutical Companies Leveraging Big Data and Predictive Analytics?

The pharmaceutical industry is currently at the forefront of predictive analytics. How does this work practically? Pharmaceutical companies use advanced machine learning algorithms along with vast amounts of raw data to generate predictive models. These algorithms and mathematical equations crunch data across a variety of different variables or factors to forecast future outcomes, such as what’s the probability that certain drugs will fail in the research and development phase, which patient characteristics are likely to lead to adverse reactions when taking this particular drug, how quickly should we be able to produce medications before they expire?

Big data and predictive modeling are already revolutionizing several areas of the pharma landscape, including:

Drug Discovery and Development

Predictive analytics is playing a significant role in drug research and development processes. This technology can help pharma companies (and their research partners) identify and select promising therapeutic molecules with the potential to treat certain illnesses. To do this, predictive models in the drug discovery process take into account many different data points about each target - such as historical information on how that particular target behaves when interacting with other proteins, data on how that target behaved in previous experiments, and what types of drugs did or did not work before.

Pharmacovigilance and Drug Safety

Drug safety is a serious concern for any pharmaceutical company and can be extremely costly, especially if it means recalling batches of medication. There is also the potential that an adverse drug reaction (ADR) could lead to severe legal implications. Predictive analytics can help reduce these risks by identifying specific patient populations who might not tolerate certain drugs well or those at greater risk of experiencing an ADR.

Clinical Trials

Some of the biggest challenges in clinical trials are the time and cost of patient recruitment. Predictive analytics can be used to identify which patient populations have specific characteristics that might indicate they would fit in particular types of clinical trials. This is just one way in which predictive models can help reduce the number of test subjects needed for research purposes while also ensuring that only those who will fit will stay engaged till the end of the trial - resulting in more effective results with less wasted resources overall and shorter time to market of the developed drug.

Pharma Supply Chain

One of the most critical aspects of the pharmaceutical industry is to ensure that drugs are available for patients when they need them. Predictive analytics models help pharma companies better understand how demand will be impacted by various variables such as regional demographics or economic conditions - allowing drug manufacturers to make more efficient use of resources while also improving patient access to critical medicines to streamline the supply chain management.

Drug Distribution and Sales

Using advanced machine learning algorithms and predictive analytics models, pharma companies can forecast potential drug sales accurately. This allows for a more efficient distribution process overall through improved inventory management - which also helps reduce both the risks of over-stocking and having too few drugs available to meet demand. These mathematical models are used alongside historical data points about past sales trends in various regions by different customer segments (e.g., hospitals vs. pharmacies) and any other relevant variables that could contribute to future sales forecasts.

Predictive analytics can also help pharmaceutical companies identify and prioritize new sales opportunities. This is particularly useful in identifying the most profitable customer segments for a particular drug or medication, which could allow an organization to focus more on those with better potential for return on investment.

Pharmaceutical Marketing

In today’s world, pharmaceutical companies need to be as efficient as possible in advertising and marketing their products. This means that they must rely heavily on predictive analytics models during the early stages of product development - from identifying those patient populations most likely to benefit from a new drug all the way through every step of its life cycle. Customer Relationship Management (CRM) is also an essential aspect for any pharma company looking to improve customer relations while better understanding how customers interact with various aspects of their business operations throughout each phase or touchpoint within the overall sales process. Predictive modeling coupled with advanced analytics can track this information throughout the entire customer experience via numerous channels such as email campaigns, online ads, call center interactions, and more - giving pharma companies the insights needed to market their products properly.

Predictive Analytics for Pharmaceutical Manufacturing - How Artificial Intelligence Can Optimize Production Performance?

Pharmaceutical manufacturing businesses have struggled in adopting and benefiting from a slew of technological advancements that provide exciting new possibilities, suggest potential to improve resilience, and make it easier to achieve higher levels of quality in all operations. Because of their historic difficulty to accept change, they have well-established, usually complex, but proven manufacturing processes in place that they are understandably scared to disrupt. There has previously been a lot of resistance to more closely analyze manufacturing pain spots even after a technology has gone through the adoption cycle within similar industries or other companies and shown to provide value.

Drug manufacturing operations are striving for optimization, which can increase productivity, quality, and safety for the whole pharmaceutical industry. The efficient flow of manufacturing process data from the production line to the executive suite and back is dependent on a well-organized, modern data infrastructure. The time is right to change this thinking and start exploring Artificial Intelligence and Big Data technologies that can be applied to predictive maintenance of production equipment, automated inspection systems that provide early warning signals about possible malfunctions or damage before they lead to expensive downtime, and real-time control systems capable of maximizing manufacturing performance while minimizing waste and keeping up with required quality standards.

Pharmaceutical production efforts are extraordinarily complex and require significant planning to ensure success across every step from raw materials through end-product delivery. Lack of attention to detail at any point during this process can lead to severe consequences that impact both patients and financial performance - not only affecting your bottom line but potentially leading to legal implications down the road if things go wrong.

Build Manufacturing Intelligence for Real-time Predictions

Pharmaceutical companies use AI for asset performance management using advanced analytics to create manufacturing efficiencies and predictive maintenance systems that analyze production line patterns with batch-to-batch comparisons and provide anomaly alerts and warnings for batches that won’t meet the desired requirements.

Process analytical technology (PAT) can be used by pharmaceutical firms to monitor manufacturing and assess how variables like temperature and humidity may impact drug production and product outcomes. Pharmaceutical companies can reduce mistakes and boost yield with more data collection and artificial intelligence to uncover hidden patterns. Pharmaceutical industry leaders constantly invest in software that can store and analyze vast amounts of drug manufacturing data using advanced analytical techniques. The resulting real-time predictions and models solve a wide range of challenges.

Predictive analysis is a great choice to create insights from the manufacturing process. Usually, patterns responsible for deviations in batch quality and consistency are very complex. Temporal patterns appear over windows of time and multiple data points - not in a single snapshot. It is not uncommon that companies are not aware or even do not believe that their production data holds enough information to provide predictions for the future. Data from industrial ops is insight-rich and ubiquitous but underutilized. Fortunately, predictive modeling based on ML and deep learning comes here, enabling forecasting and making more informed decisions. Pooling minute-by-minute pressure and temperature readings, batch process control records, and other site data into one data lake would allow companies to get actionable insights from their production sites and plan optimization.

During the batch manufacturing process, several metrics can be tracked within each reactor including pressure, temperature, density, viscosity, and others. Time series data can provide insights that ML can extract to provide predictions, enabling early discovery of unanticipated behaviors, finding early warning signs, and creating reliable alarms that warn about possible quality issues for the final product earlier in the process.

During the batch manufacturing process, several metrics can be tracked within each reactor including pressure, temperature, density, viscosity, and others. Time series data can provide insights that ML can extract to provide predictions, enabling early discovery of unanticipated behaviors, finding early warning signs, and creating reliable alarms that warn about possible quality issues for the final product earlier in the process.

Predictive analytics helps manufacturers create manufacturing intelligence solutions to discover unexpected behaviors, notice early warning signs of quality issues, and develop reliable alarms for process engineers.

The result? Intelligent manufacturing with more accurate forecasts for improved production monitoring and planning, early warnings systems, less unplanned downtime due to fixable equipment settings – all leading to a better bottom line in terms of quality, consistency, reduced wastes, efficiency, and productivity.

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.

Smart manufacturing with predictive modeling enables production optimization through better throughput, quality, safety, and yield improvements. It is important to note that not only the predictive model is essential here, but the end-to-end custom implementation of a solution that interprets data, provides visualization, and custom automated actions to streamline pharmaceutical manufacturing.

Smart manufacturing with predictive modeling enables production optimization through better throughput, quality, safety, and yield improvements. It is important to note that not only the predictive model is essential here, but the end-to-end custom implementation of a solution that interprets data, provides visualization, and custom automated actions to streamline pharmaceutical manufacturing.

Continuous Compliance and Quality Assurance for the Pharmaceutical Industry

Predictive analytics has the potential to be a revolutionary tool for pharmaceutical companies in terms of continuous compliance and quality assurance. The ability to monitor, track, and quantify production operations on an ongoing basis can allow organizations to reduce costs while establishing clear guidance about how they should operate during each stage of APIs and drugs development.

The end goal of all this work is to produce the desired product correctly, which needs extensive planning for material procurement and batching methods that would be effective for different production steps like coating or lyophilization. Let’s consider just one action, such as tablet compression. It may require more than 30 parameters with corresponding tolerances, including pressure force levels on punches, the gap distance between holes, punch angle deviation from the ideal position, etc., suggesting how much effort goes into designing an efficient system capable of producing high-quality batches.

The final product quality and performance may differ across batches owing to various factors, including temperature changes, equipment condition, type and state of substrates, formulations, process step interactions, and mixing time constraints. The downtimes and delay in deliveries due to rejects can seriously hurt profitability and delivery speed.

Most of these parameters can be controlled by a system that continuously monitors the formula characteristics and notifies whenever the fluctuations indicate that the end batch will not match the set standards. In case of any deviations or changes that may pose a threat to final product quality, such an intelligent system can notify the operators before they start further processing. The earlier these issues are identified, the more corrective actions available for consideration at each stage or the cheaper it would be to get rid of the batch.

Planning Batch Manufacturing with Forecasting Models of Drugs Demand

Attaining a constant, full production is the goal of any manufacturing company in all situations. However, in the case of pharma companies where demand for particular active pharmaceutical ingredients or final therapeutics may go up or down significantly over time, achieving this objective becomes difficult. In such cases, it is advisable to plan batch production with forecasting models to calculate expected sales volume and help determine whether each proposed formula should be put into production and when.

Predictions of drug demand can be made by using statistical models that take into account past orders data, the number of patients suffering from a particular disease, prevalence and spread of this condition within the target population, existing treatment options on the market, cost-effectiveness evaluations for new drugs compared to other available therapies as well as their patent status. In cases where even one factor deviates from its historical trend or some unforeseeable event such as an epidemic or outbreak, it becomes challenging to predict future sales volume. However, when several factors are taken under consideration at once and combined with additional big data about the company’s past performance in managing production capacity for different products depending on actual demand forecasts, more accurate predictions can be obtained, which will allow companies to optimize inventory levels and reduce risk of suboptimal production.

Predicting drugs sales based on past patterns in drug demand to optimize the production process.

Predicting drugs sales based on past patterns in drug demand to optimize the production process.

Predictions can be made for each product separately, or they may concern the whole portfolio, which provides companies with an opportunity to optimize their investment decisions by using different scenarios and anticipating possible market changes. Forecasting models are also used in cases where various business objectives need to be fulfilled at once, such as ensuring that margins remain stable over time, maximizing profit margin on new drugs while taking into account cost-effectiveness analysis, or even just meeting demand without any delay when required quantities cannot be produced fast enough due to some unforeseen circumstances.

Predictive Maintenance for Pharmaceutical Production Line

Drugmakers are using artificial intelligence and data from IoT sensors to predict when equipment is at risk of failure, thereby averting costly and time-consuming unplanned shutdowns of pharmaceuticals production lines. Predictive analytics help manufacturers indicate when equipment needs cleaning or maintenance or is about to fail, eliminating costly manufacturing disruptions and prolonging service life.

Maintenance systems store plant equipment service dates and calibration settings. Building-management systems capture air pressure, temperature, and other readings in multiple locations at each plant, sampling by the minute that are transferred to digital format and analyzed with predictive technologies. Predictive maintenance technology can be applied to central equipment such as air and centrifugal compressors, boilers, pumps, and water purification systems. AI solutions can also be used for additional production and packaging equipment and tools such as autoclaves, bead mills, centrifuges, chillers, conveyors, granulators, plate dryers, or tablet presses.

Critical Benefits of Applying Predictive Analytics

Detect Patterns to Predict Performance

The main benefit of AI-driven improvements to the manufacturing process is faster, more accurate decision-making. Data flowing from production equipment enables a company to monitor and detect patterns that can be used as an early warning system for potential issues once analyzed by AI algorithms.

Improve Production Floor Operations

Predictive analytics play an essential role in the optimization of operations, processes, and equipment. The key benefit of applying predictive analysis software to manufacturing operations is providing real-time information on production line performance. The more available data, the easier it becomes for manufacturers to optimize production operations and improve overall output.

Reduce Costs

Using predictive analytics in pharmaceutical manufacturing also allows for a reduction in costs. With the support of AI, companies can improve productivity by streamlining processes so that more batches get completed with less effort. In addition, data collected from sales enables manufacturers to anticipate better future demands, which means they won’t have to spend as much on raw materials since they’ll be able to predict what’s needed based on upcoming demand levels accurately.

Less Waste

By utilizing AI-driven predictive analytics systems that provide real-time data about the manufacturing process and identify batches that with great possibility will not match the required quality can alarm production floor personnel to break the manufacturing process instead of waiting for the whole faulty batch processing to finish. Sensor-based, in-line quality inspection based on predictive modeling minimizes waste. It increases yield by detecting early process deviance, conducting root cause analysis, and making automatic corrections via closed control loops.

Boosting Product Quality

In the pharmaceuticals production process, various steps go into creating a quality end-product. AI-driven forecasting analysis can provide users with an improved ability to monitor and improve upon every step of this process, which will lead to higher quality products being produced. Artificial intelligence can predict whether the final product will meet set requirements based on real-time reads from the operation floor. Thanks to these tools, automated corrective actions can be applied to ensure product consistency.

Challenges in Predictive Analytics Implementation for Pharma Companies

Lack of Data Visibility

Limited data visibility in pharma production can make it challenging to derive meaningful insights from the production floor. It is still quite common that legacy manufacturing equipment lacks IoT sensors for monitoring key parameters. In other cases, data is trapped in siloed data lakes and cannot be easily analyzed. Along with other factors like inconsistent data collection practices and outdated technology infrastructure between departments, poor communication is another significant challenge impacting predictive analytics implementation within this industry today.

Technology Hurdles

Not all companies can quickly adopt new technologies like AI or IoT devices due to budget constraints and lack of resources. It is vital for companies that want to implement these types of solutions to find professional consulting and support from experts in implementing advanced technologies for their IT teams early on in the process so they can help identify any challenges before moving forward with implementation steps - this will save you time down the road if your solution needs a lot of modifications/additional work after going live.

Massive Expectations

Not all organizations that use predictive analytics will see the same level of results - it depends on your specific goals and desired outcomes. Companies need to be realistic about their expectations before moving forward with any solution, as this can save you a lot of time and frustration if things don’t go according to plan.

Lack of Appropriate Resources

Due to the complex nature of many production line operations today, having experienced staff who understand how AI-driven systems work is critical for success when implementing advanced technologies across multiple operations within manufacturing and maintenance. If your team doesn’t have experience in these types of solutions, then you should consider hiring an outside consulting firm as a must.

The Future of Pharmaceutical Manufacturing is Predictive and Prescriptive

The 4 types of data analytics from descriptive to prescriptive that not only provide insights but also foresight that help anticipate possible results and take specific actions.

The 4 types of data analytics from descriptive to prescriptive that not only provide insights but also foresight that help anticipate possible results and take specific actions.

Many pharmaceutical processes are run on a reactive basis, with plant personnel dealing with issues as they occur. Descriptive data analytics is reactive as it can answer the question ‘What happened?’ post factum. A better approach is to proactively predict problems before they occur, as this provides several benefits, including lower maintenance costs, increased uptime, and improved quality. As much as possible, production analytics should produce prescriptive corrective actions that are either automated or can be implemented with a minimum of time and effort.

Predictive analytics offers pharmaceutical manufacturers many benefits that directly impact the bottom line, including increased yields, reduced waste and rework, better utilization of equipment and workforce, and improved customer satisfaction and, therefore, revenues.

Predictive analytics offers an excellent opportunity for drug manufacturers who want their products produced faster with higher accuracy at lower costs - but only if they are willing to explore new technology solutions such as AI.

You won’t realize these advantages until you take action. Consider the financial benefits and ROI of applying these solutions to make a business case for it. Using accurate algorithms and comprehensive data analysis, your company can move from ad hoc decision-making to fact-based decision-making and strategy building. You’ll have even more insights at your disposal after you start, which can be used to improve your manufacturing processes another step further. There’s no need for you to build these capabilities yourself when there are already companies out there focusing on implementing custom machine learning solutions. Reach out to us. and optimize your manufacturing process with artificial intelligence.

References

Advanced analytics fuel tomorrow’s commercial strategy for drugs and devices - PwC

How and Why Pharmaceutical Manufacturers Are Applying Artificial Intelligence - Automation World

About the author

Dorota Owczarek

AI Product Lead & Design Thinking Facilitator

With over ten years of professional experience in designing and developing software, Dorota is quick to recognize the best ways to serve users and stakeholders by shaping strategies and ensuring their execution by working closely with engineering and design teams. She acts as a Product Leader, covering the ongoing AI agile development processes and operationalizing AI throughout the business.

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AI in Pharma

The pharmaceutical industry is one of the most regulated industries in the world. It's also one of the most expensive and challenging industries to work in. Pharma companies, like all other businesses, are looking for ways to reduce costs while improving quality and efficiency. This is where artificial intelligence comes into play!

Follow our article series to find out what are the benefits of AI in pharma and why this tech could be considered a game changer for the pharmaceutical sector.

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