Biopharmaceutical Manufacturing Relies on Robust Data Analytics Tools for Process Optimization

Using the most updated data analytics methods and process modeling tools is essential for biopharma companies that want to develop robust products, meet regulatory requirements and manage quality control. Data analytics tools play an essential role at every stage of the biopharma product lifecycle – from R&D to process development to manufacturing – while helping to improve product design and decrease waste.

^{This article is posted on our Science Snippets Blog}

Biopharmaceutical companies that employ the most current data analytics methods are able to gain time-to-market advantages by shortening development and production times. Design of Experiments (DOE) and Multivariate Data Analysis (MVDA) help scientists and engineers achieve efficiencies by reducing the number of experiments needed to optimize production processes and create robust models. Real-time process monitoring helps keep processes optimized so that deviations don’t create bottlenecks and rework.

Data analytics is also integral to any digital transformation and biopharma 4.0 strategy. Being able to gain insights from connected process data helps create more robust pharmaceutical formulas, scale processes and manage real-time process control (both manually and with automation).

Within biopharma and pharmaceutical development, a number of data analytics tools play an important role in helping to create quality products, ensuring more consistent production processes and reducing the time to market.

Process Modeling Tools That Optimize Bioprocessing

In biopharma development and manufacturing, three key areas of data analytics play an essential role in developing robust processes and delivering high quality products to the market efficiently. These are:

• Design of Experiments (DOE)
• Multivariate Data Analysis (MVDA)
• Real-time process monitoring (RT-MVDA)

Let’s take a closer look at several Sartorius Data Analytics products that deliver this level of precision, compliance and process control for bioprocessing and pharmaceutical manufacturing.

Design of Experiments

Design of Experiments (DOE) software helps optimize process development and employ a Quality by Design (QbD) approach favored by regulators. DOE is the fastest and most cost-efficient way to design effective experiments, increase productivity, and tackle the toughest challenges in development and manufacturing.

MODDE^® DOE is the solution that makes it easy for non-data science experts to create and run statistically valid experiments that can shorten the product development timeline. That means production can be up-and-running faster and you get well-documented processes that follow regulatory-approved methods and create robust formulations that remain stable.

MODDE^® offers built-in guidance via wizards and quality measures that help users optimize experiments, so you get the most relevant and effective outcomes. With an efficient DOE approach to problem-solving, you can:

• Significantly reduce experimental costs  
• De-risk projects and increase clinical success rates
• Make the most of valuable samples, raw materials and human resources  
• Accelerate progress and time-to-market while staying within budget
• Achieve quality goals and satisfy QbD requirements

Multivariate Data Analysis (MVDA)

In the biopharmaceutical  industry, the processes involved with drug research and development and manufacturing all contribute to large volumes of data that are complex and difficult to analyze.  Multivariate data analysis is a way to analyze complex data effectively and create statistical models for bioprocesses that support scaling and batch-to-batch comparison investigations. MVDA techniques are also key elements for successful implementation of spectroscopy-based PAT solutions.

Process Analytical Technology (PAT) and other types of advanced models for process control are pivotal to support the industry trend towards continuous manufacturing. PAT also reduces regulatory compliance concerns, as the sort of documentation, data management and audit trails that PAT provides are what regulatory bodies are increasingly looking for to show compliance.

Sartorius SIMCA^® Multivariate Data Analysis software helps scientists gain insights from data, including using spectroscopy and other omics “big data.” SIMCA^® MVDA software provides a statistically relevant way to organize your data, visualize it and understand the relationships between different data points. It helps your team understand cause and effect, find outliers and look for deviations. Moreover, it shows how various data points from different sources could be influencing each other.

SIMCA^®  is the benchmark data analytics tool for scientists, engineers, researchers, product developers and others striving to gain information from large quantities of data.  As a premier data analytics tool for biopharma, SIMCA^® helps to:

• Spot important trends, clusters and “hidden gems” in the data
• Make data-driven decisions and implement QbD
• Enable faster troubleshooting
• Reduce the risk of costly downtime
• Create deployable multivariate calibration models for CQA predictions
• Save your organization time, money and resources

Real-Time Process Monitoring

Using advanced data analytics models in real time opens up a whole new world of possibilities for improving your manufacturing processes. Not only does real-time process monitoring provide a level of confidence in your process performance, but also it can help improve the overall quality of your production line output. Real-time analytics is also essential to make the move toward one of the key trends in the biopharmaceutical industry: continuous manufacturing.

SIMCA^®-online uses multivariate data analysis (MVDA) modeling to provide real-time process monitoring that gives operators insights to take corrective actions immediately when processes deviate and opens the door to predictive control. With SIMCA^®-online dashboards, operators can quickly see when processes are operating as they should or when deviations occur. The tool provides remote monitoring, fault detection, root cause analysis and corrective recommendations, and uses statistical process control and model predictive control methods.

Statistical Process Control (SPC) is a data analytics method that is particularly useful for quality control of batches. The technique uses a control chart that shows upper and lower warnings and action limits based on target values.

Model Predictive Control uses predictions to control and optimize a process and can be used to adjust the future values for critical quality attributes (CQAs) to optimize the direction a bioprocess is heading.

Real-time process monitoring with SIMCA^®-online helps you to:

• Minimize operational costs
• Increase confidence in your process performance
• Ensure more consistent product quality
• Maximize efficiency throughout operations

Embedded Solutions Streamline Bioprocess Analysis

Sartorius offers several lines of bioprocessing equipment that have data analytics integrated into their functions and offers an integrated data analytics platform for other instruments.

Embedding data analytics solutions into hardware or connecting directly with other applications allows manufacturers to development instruments that meet the growing need for robust process development, supporting PAT applications like RAMAN spectroscopy.

SIMCA^®-Q, is an embedded data analytics solution that simplifies and speeds instrument development, while offering users access to the gold standard of data analytics: multivariate data analysis (MVDA) using SIMCA.

Process modeling tools based on data analytics improve bioprocessing efficiency, quality and consistency.  Sartorius offers a range of best-in-class data analytics tools for DOE, MVDA, PAT and real-time process monitoring.