Bioprocessing 4.0 and the Benefits of Introducing AI to Biopharmaceutical Manufacturing

Dorota Owczarek - November 4, 2021

Every day, innovative biotherapeutic treatments are revolutionizing the lives of patients all around the world. They provide a strong defense against life-threatening illnesses such as cancer, diabetes, and rheumatoid arthritis. Manufacturers must develop and deliver these items in a fast-paced, ever-changing industry where quality, speed, and efficiency are crucial.

Bioprocesses are especially important for pharmaceutical development. Still, these processes are generally challenging to optimize due to their complexity, unsteady-state operation modes, and stochastic behaviors.

The manufacturing processes of all biotechnology and fermentation-related products require collecting and handling a vast quantity of online and offline data. The production and laboratory data vary due to multiple data sources from sensors, spectroscopic measurements, and process control systems. Bioprocess data consists of physical parameters like bioreactor conditions or control actions and chemical parameters like omics data, material input, and process output. Systematic collection, visualization, and analysis of these data can help improve and optimize a process from all aspects. Data analytics for the collected data is not easy as process data is unique due to its heterogeneous nature. This task has been made accessible due to advancements in machine learning and various data mining techniques. Solutions based on these emerging technologies and utilizing big data provide knowledge on how to maximize the production of biopharmaceuticals and insights on how to decrease manufacturing time and costs. This article will focus on the recent developments of bioprocesses thanks to artificial intelligence (AI) solutions, their applications, and their significance in biopharmaceutical manufacturing.

What Is Driving the Interest in Bioprocessing 4.0?

Bioprocesses are the heart of biotechnology and pharmaceuticals, as they provide the means to develop life-saving drugs for patients worldwide. As a result, there is significant interest in developing novel technologies that can improve these processes. Bioprocessing players from the pharmaceutical industry must innovate to remain competitive, but how can they safely and quickly take advantage of new technologies? The biopharmaceutical industry has responded by embracing the future with Bioprocessing 4.0 solutions that incorporate intelligent automation. Intelligent automation refers to a broad range of technological advancements in manufacturing settings, including robotics, artificial intelligence, machine learning, deep learning, big data analytics, and the Internet of Things (IoT).

At the level of preclinical research of drug discovery and development, such as target selection or cell engineering by rewiring their metabolism toward the production of a product or in clinical trials by running digital trials or assisting in patient recruitment, artificial intelligence-powered systems biology tools have proved to be critical. However, they may also be utilized in many other ways. For example, they might be used at the industrial level in processes related to upstream activities such as process design, monitoring, and control. They can reduce experimental work while increasing process robustness and intensity at the industrial level.

The biopharmaceutical sector strives to keep up with the rising demand and pricing for biotherapeutics by improving the manufacturing process performance. New instrumentation, sampling methods, and analyzers have been developed to monitor both upstream and downstream operations. These analytical tools, on the other hand, produce big, complicated datasets with multivariate interactions. The inherently intricate nature of these data makes extracting valuable and relevant information challenging.

Smart AI Use Could Benefit Biopharma Manufacturing

The introduction of AI solutions into bioprocessing has made it easier than ever before to collect data from various sources, allowing multiple stakeholders within an organization or across different organizations to utilize this information to improve their processes and product quality. Bioprocessing professionals have been using AI techniques such as pattern recognition, machine learning algorithms (e.g., multivariate data analysis, or more complex deep neural networks), text mining, natural language processing (NLP) along with other signal processing methods towards real-time process monitoring for understanding critical process parameters including mixing efficiency and cell growth rates at different stages of cell culture.

Monitoring and Control of Bioprocesses

Through the use of artificial intelligence, bioprocesses can be monitored and controlled. AI-based tools are being used to support process monitoring activities across different stages in a bioprocess, including upstream processes such as cell culture for production purposes or downstream processes where product purification is involved.

Post process quality testing
Traditional fixed process approach with post-process testing of quality results in variability in product quality and yield

Applying these technologies allows companies to achieve benefits in terms of quality, yield, productivity, cost reductions, and increased safety by providing more accurate data that would otherwise have been difficult to obtain without them. There are multiple applications that they can address when it comes to maintaining optimal conditions during growing cells at all phases throughout an entire process timeline from cultivation through harvest and beyond into purification steps involving chromatography systems that need precise control to achieve a high level of purity.

Sampling Methods and Analytics

Most biomanufacturing methods entail the growth of bacteria, implying a complex chemical, physical, and biological process. An accurate and consistent analytical technique is required to manage the process conditions throughout the biomanufacturing process (upstream, downstream, and product formulation).

Sampling positioning during manufacturing process
Sampling methods and their associated sensors and analyzers can be divided based on their position within the processing unit as on-line, in-line/in-situ, at-line, and off-line.
Key parameters sampling and tracking in biomanufacturing
Key parameters, sampling positioning, and tracking methods in biomanufacturing process with various sensors, spectroscopic measurements and microscopy

Various analytical techniques are used for the online and offline assessment of critical parameters in biomanufacturing processes, such as biomass, substrate, product, and by-product levels during cell cultivation. Bioprocessing monitoring covers different monitoring approaches, including in-line probes and fully automated online measurements systems with spectroscopic and mass spectrometry-based methods. It is not uncommon that bioreactor parameters are also tracked with additional procedures such as photometric sensors techniques, NMR spectroscopy, soft sensors, and biosensors. Offline analytical methods for biologics manufacturing include more complex techniques that are either expensive or hard to automate, like ELISA, electrophoresis, or the good old microscopy.

Quality by Design (QbD) with Process Analytical Technology (PAT)

The aim of manufacturing 4.0 factories is to improve sustainability by monitoring production in real-time. With real-time monitoring, automated control systems are expected to lower the number of faulty batches and cut maintenance expenses. As a result, the ability of biomanufacturing companies to automatically and adequately regulate their bioprocesses in an optimum condition is critical because it helps minimize manufacturing costs while boosting yields and maintaining product quality and uniformity.

With the advancement of analytical technologies, process sensors, and sophisticated data analysis tools, real-time monitoring of biopharmaceutical process development has evolved considerably in recent years. Robust Quality by Design (QbD) platforms for consistent product quality are becoming a must as biological medicines have grown in popularity over the last decade.

Quality by Design for process understanding and real time process monitoring
Quality by Design data-driven approach for process understanding and real-time process monitoring based on knowledge management, process sensors, and data analytics

Quality by Design is a process improvement method using predefined goals and emphasizing product and procedure knowledge and control based on solid science and quality assurance management. A thorough understanding of the development process is necessary for creating a QbD approach, in which proper analytical technologies and cyber-physical systems must be utilized to control processes and monitor critical process parameters (CPPs) and critical quality attributes (CQAs) at critical control points (CCPs).

Quality-by Design process improvement
Quality by Design approach and model validation workflow (adapted from Processes Accelerating Biologics Manufacturing by Modelling)

A CQA is a crucial indicator for the quality of the final product describing a physical, chemical, biological, or microbiological aspect or feature that should be within an acceptable range, distribution, or range to assure adequate product quality. A CPP is a process variable that influences CQAs, and the critical unit operations at which product quality attributes should be monitored are called CCPs.

Some CQAs are monitored in real-time; on the other hand, others may not be measured immediately or might be prohibitively expensive to do so in-line or at-line. In this situation, machine learning predictive models can be utilized to estimate these CQAs instead.

Predictive modeling for monitoring production parameters
Predictive modeling for monitoring production parameters

Adopting new sampling techniques and automated measurement methods may reduce time spent on process monitoring and control. Novel monitoring and control strategies such as Process Analytical Technology (PAT) tools improve and ensure product quality, especially in the pharmaceutical sector. It’s worth noting that a significant portion of PAT goals are generic in nature so that they may be applied to any biomanufacturing process regardless of industry.

Advanced Process Control with PAT CCPs CPPs and CQAs
PAT system for continuous monitoring, alerting, and feedback to provide consistency in product quality and quantity

Streamlining Natural Products Biomanufacturing With Artificial Intelligence Solutions

The ability to detect, identify, or analyze anomalies through real-time monitoring of processes coupled with anomalies detection, classification, or predictive analytics, is one of the primary benefits of introducing an Advanced Process Control (APC). Integration of process analytical sensor technologies, spectroscopic methods, data management, visualization, and sophisticated data analytics such as multivariate data analysis (MVDA), machine learning, and deep learning is required for advanced real-time monitoring of bioprocesses.

Data aggregation with omics analytics and AI to create cognitive monitoring system
Data aggregation from production line with omics analytics and AI to create a cognitive real-time monitoring system

Any unusual process action may be detected in real-time and recovered via feedback or feedforward control pathways to guarantee quality at the end product. Machine Learning algorithms are trained on historical data from a plant’s environment sensors to predict future values based on past performance patterns. Process monitoring and APC enable better understanding and consistency in product quality and quantity.

PAT Quality by Design
Advanced Process Control with PAT, CQAs, CCPs and CPPs for real-time monitoring to create Advanced Process Control system (APC)

Advanced data-driven modeling based on artificial intelligence solutions can also be used to comprehensively monitor and predict the entire biopharmaceutical manufacturing process (and not per single unit operation), followed by Advanced Process Control. This is especially important in continuous manufacturing, and the growing interest drives the development of AI solutions. Machine learning models trained on data sets from benchtop bioreactors could be used to automate the control of large-scale reactors and provide innovative pharmaceutical development based on continuous manufacturing.

Machine Learning for Biopharmaceutical Process Development

In the biopharmaceutical realm, some of the most difficult and least understood procedures exist. Bioprocesses on a commercial scale are typically very dynamic systems with some degree of variability and system irregularities. As a result, scaling such processes from the laboratory to an industrial scale and vice versa is not simple. The time and money needed for experiment-based product development are inefficient and expensive, and the experimental process development must be repeated as soon as the material system changes. A model-based implementation strategy may assist in process development for new goods and support a resource-efficient establishment.

Process Models

Process models that may convert process data into more useful information, assist decision-making, and support the development of digital and automated technologies are essential enablers in biopharmaceuticals production.

In an attempt to move from experimental toward a more model-based approach which includes computational models in bioprocess design, artificial intelligence has emerged as a powerful tool to improve productivity and product quality of upstream and downstream processing. This technology reduces both R&D costs and risks through improved speed, greater efficiency, increased reproducibility, and better results/outcomes.

Model based approach for process development and biomanufacturing improvements
Model based approach for process development and biomanufacturing improvements

Artificial intelligence has the potential to shorten process development in the biopharmaceutical industry significantly. AI-based process development modeling provides substantially higher precision and agreement with experimental data than do older modeling techniques.

Process development with predictive modeling and ML platform
Process development and continuous improvements with predictive modeling and ML platform

Upstream Processing

Upstream processing can be divided into the formulation and hydration part and the cell culture. Production is time-consuming, laborious, and costly in terms of reagents consumed. Scientists are trying to develop high throughput cell culture systems that provide a continuous stream of cells to improve manufacturing productivity.

Upstream and downstream processing
Upstream and downstream processing

Cell culture models are at the heart of bioprocess modeling. These are either based on experimental data or kinetic equations, which depend upon various factors such as time, temperature, and concentrations for different media components. All these parameters must be optimized during cell line development to maximize output while spending less energy and resources on higher biomass yield per liter volume with low metabolic cost.

ML algorithms can be used to build models that assist decision-making during new-product R&D by reducing both time and cost associated with experimentally based design methods. These models help us understand how cells grow and affect their growth rate under various conditions like pH levels, oxygen concentration, mixing status, etc.

Current single-use bioreactor technologies enable large-scale production with increased productivity through higher volumetric yields for downstream processing operations. Single-Use Technology (SUT) reduces both capital equipment/costs associated with cleaning & maintaining vessels between runs and personnel costs involved in their operation due to faster changeovers between batches which means more efficient utilization of process chemistries, thus reducing overall operational cost unit product produced.

Because current cell lines are developed for fed-batch high-throughout production, they may not be in top condition for perfusion cultures usage. When switching from batch-produced material to perfusion material, producers might notice significant alterations in impurity distributions, which might necessitate substantial changes in downstream development.

Downstream Processing

Following harvest collection comes downstream bioprocessing with the separation and purification processes and the final finishing of the product. Process modeling for downstream operations is a crucial step if a company is looking for improvements.

It is vital that downstream models handle a wide range of process conditions, including all the possible changes in pH and temperature levels along with other environmental parameters during production runs.

Modeling for downstream processing still has many challenges ahead before it can claim success at every step across different bioprocessing technologies being used today. Model development requires data science techniques, including experimental work on actual samples as well as computational methods such as deep learning through neural networks & machine learning algorithms so scientists can come up with more precise models based upon sound scientific principles rather than simply relying upon trial-and-error approaches or statistical correlations between variables observed over time.

Digital Bioprocess Twins: Statistic vs. Mechanistic Models

Process development is a complex task that requires an understanding of the mechanisms that govern cellular metabolism and regulatory networks and their interactions with culture environments.

Mechanistic Models

A mechanistic model comprises multiple scientific disciplines such as biology, mathematics & physics. These models are capable of addressing complex questions in drug discovery research based on metabolic engineering, including how cells metabolize sugars or other nutrients for energy production; what limits cell growth rate during batch cultivation; when do cells stop dividing due to nutrient depletion?

Statistical Models

The statistical modeling approach provides an alternative way to understand the behavior and the dynamics involved in production operations by taking measurements over time into account rather than considering data from different experimental conditions separately without any regard for other effects that might have shaped them.

Modeling and statistics approaches give a detailed overview of individual unit operations in biopharmaceutical processes. The machine learning approach constitutes statistic models that offer great opportunities but also significant challenges. Molecular understanding is just as crucial for refined biomanufacturing processes and root-cause analysis.

Hybrid modeling for omics with AI
Hybrid modeling based on real-time omics monitoring and AI tools (adapted from Nature Research)

Hybrid Models

Hybrid modeling is an approach that mixes machine learning models with mechanistic knowledge-based models. The advantages of these hybrid models extend beyond the additional costs that they necessitate. Increased process robustness and control can be combined with a more stringent bioprocess design due to fewer exploratory studies, which implies increased process safety. Additional efforts are made in addition to unit operations to develop digital bioprocess replicas, which are simple combinations of inoculum operation and process models from start to finish.

Digital Twins Based on Machine Learning Models Turn Data Into Deep Process Knowledge

Digital twins are information technologies that constitute the digital reproduction of a real-world entity or system. Digital twins combine data science, machine learning, and big data to establish virtual representations for physical assets in manufacturing environments & other industrial applications.

The concept of a digital twin is based on replication of fundamental properties (i.e., geometry) between what exists physically and their respective models; this enables monitoring by comparing each twin’s state against the others even though there might be some discrepancies due to measuring errors or inaccuracies associated with either one of them because they do not correspond entirely to reality since every model has its limits (for example resolution).

In bioprocessing, digital twins provide more and superior data and sound and scientific decision-making throughout the product lifecycle, making them preferable to experimental techniques. Therefore, they can serve as an early detection mechanism for changes in both systems' states despite any differences between actual values precisely. Early identification of risks may be made possible with the lowest costs.

Digital twins formed from mechanistic and statistical models are soft sensors that help enable a data-based approach with multiple model parameters for more effective biopharma process control and root-cause analysis when integrated into development laboratories or manufacturing facilities.

Improved productivity of upstream and downstream processes due to model-based control concepts are changing the role of clinical development for biopharmaceutical companies, with computational models being increasingly used for bioprocess development. Artificial intelligence has been identified as a key enabling technology that will increase speed and reduce costs by automating tedious tasks associated with product discovery, design optimization, predictive modeling, clinical trials design, and execution optimizing biomass generation through an automated selection of best process conditions among several alternatives.

Benefits of Machine Learning Solutions in the Biopharmaceutical Manufacturing Processes

Benefits-of-AI-in-bioprocessing.png

AI and ML in bioprocessing have multiple advantages, including:

  • Improved process oversight - Advanced analytics give faster and more accurate insights into processes.
  • Improved bioproducts quality - Better data means better decision-making and therefore enhanced process control and risk mitigation.
  • Streamlined production scheduling - Faster and improved knowledge management aids in better planning of production. Faulty batches can be stopped immediately.
  • Increased production capacity - Smart process development can be optimized for higher capacity.
  • Increased production efficiency - Predictive analytics and process development help high-throughput production by reducing waste and increasing yield.

Challenges of Introducing AI in Bioprocessing

The pharmaceutical companies continue experiencing difficulties with delivering efficient end-to-end AI-based solutions in bioprocessing, including:

  • Poor data quality - Low data quality is a significant obstacle in the context of data-driven biomanufacturing. * **Bioprocessing environments are complex and generate high data volumes that need to be preprocessed and analyzed by Data Scientists to produce better outcomes.
  • Low data quantity - Although biomanufacturing produces lots of data, it is not uncommon that the insights lay in data that is either not adequately collected or the company lacks the suitable infrastructure to handle large-scale datasets.
  • Infrastructure challenges - Maintainability of the biomanufacturing process is a challenge in itself. The industry requires new tools and strategies to support high-quality cell culture, upstream processing activities, including harvest operations, downstream purification steps & formulation efforts. Software that supports the operations is distributed and does not cover processes as a whole.
  • Lack of AI-skilled professionals - Only industry leaders, have R&D departments filled with Data Scientists and ML experts that can provide the needed knowledge on site. Other biopharmaceutical companies need to rely on partnerships and outsourcing AI teams for their projects. If your company is on the lookout for an AI development team, contact nexocode experts.

These challenges can be addressed effectively through digital transformation; however, this will require substantial changes throughout the biopharmaceutical industry’s drug discovery and process development journey. New technologies are being developed to address these needs, including AI-powered digital manufacturing that can capture more data and automate complex tasks while providing real-time feedback & alerts on process performance without human intervention.

Will Bioprocessing 4.0 Be a Reality Soon?

The digital transformation era has paved the way to the fourth industrial revolution in various sectors, including the Biopharma 4.0 initiative for biopharmaceutical manufacturing. One of the critical enablers for this digital transformation is recent advances in applying AI solutions. The examples from bioprocessing highlighted in the paragraphs above present a perspective on the current use cases, their prospects, and the possibilities AI presents to digitalization while simultaneously complying with regulatory requirements.

The need for an increased level of digitalization has been widely recognized across the biopharmaceutical industry due to its potential benefits, such as improvements in efficiency through reduced time, resources, delays, etc., and enhanced productivity, leading to increased revenue streams resulting from better decision-making capabilities. Leading professionals are already advocating for the fourth generation of bioprocessing. This new approach mixes deep learning models with mechanistic knowledge-based models. The advantages of these hybrid models extend beyond the additional costs that they necessitate. Thanks to predictive analytics, increased process robustness and control can be combined with a more stringent bioprocess design owing to a lowered number of exploratory studies, which implies increased process safety.

Biomanufacturers are still trying to find the perfect balance between robustness, process design, and product quality while minimizing risks regarding compliance requirements. AI models can help them reach their goal by providing a more data-driven approach for bioprocess optimization & control when integrated into process development and manufacturing sites.

References

Real-Time Process Monitoring in Biologics Development - American Pharmaceutical Review Towards a widespread adoption of metabolic modeling tools in biopharmaceutical industry: a process systems biology engineering perspective - Nature Research From Big Data to Precise Understanding: The Quest for Meaningful Information - BioProcess International Analytics for Modern Bioprocess Development - BioProcess International Process Integration of Precipitation in mAb Downstream Processing - Processes Accelerating Biologics Manufacturing by Modelling

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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