What Current Trends in Data Analytics Mean for Bioprocessing

Data Analytics

Mar 22, 2022 | 6 min read

In certain ways, trends around the broader field of data analytics and biopharma are converging. As data analytics becomes more integral to biopharma process development and production, the trends appearing across the broader data analytics field become more engrained and visible within bioprocessing.

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Data analytics has long been the cornerstone of digital transformation and the move toward biopharma 4.0. Biopharma manufacturing continues to evolve, due to the need to reduce costs and time to market, as well as from regulatory pressure.

The broader trends in data analytics can be seen across all industries and will certainly be felt in biopharma. Some of the key insights here, include:

Data analytics is supporting and accelerating the need for rapid transitions, whether for market changes, new opportunities or because of the global pandemic.
Digital transformation and artificial intelligence (AI) have been accelerated by market disruptions like COVID-19.
Change acceleration has become necessary for survival. The sign of a resilient and disruption-proof organization is the ability to create decision flows using data analytics.
Using graph-based techniques to understand connections from diverse sets of data (from multiple sources) is essential.
Distributed everything — data, people and devices — is accelerating, so being able to connect information digitally has become imperative.

Biopharma Trends Supported by Data Analytics

Modeling techniques from data analytics can help improve process control and monitoring in biopharma production. A number of current trends are supported by data analytics. These include:

Platform Processes
Many of today’s largest blockbuster drugs are well-established monoclonal antibodies (mAb)s–laboratory-produced molecules engineered to serve as substitute antibodies¬– which, like mRNA, benefit from manufacturing platforms based on data analytics.

Biosimilars
Ensuring a biosimilar meets the critical quality attributes (CQA) of the original biologic is a major challenge. Optimizing production at full scale is impractical, which makes a quality by design (QbD) approach using a reliable scale down model of the process an attractive alternative.

Process Intensification
As the biologics industry continues to mature, more organizations will need to intensify their biomanufacturing and bioprocesses. Undergoing intensification means high-throughput data collection, management and analysis capabilities, which can then help feed future data modeling activities, as well.

CDMO Expansion
Effective process development is essential for achieving cost-effective CDMO operations. The most successful CDMOs have identified strategies for completing projects efficiently and effectively while incorporating QbD approaches that provide increased process understanding and lead to optimal bioprocesses.

Personalized Medicine
Biopharmaceuticals are increasingly targeting smaller patient populations, which introduces challenges. With smaller production volumes, comes the need to use smart factory concepts such as continuous manufacturing.

Artificial Intelligence
AI can be used in many ways to make production more efficient with faster output and less waste. The application of data analytics is the first step toward AI.

Trends in Bioprocessing Development and Manufacturing

Some of the top trends in the bioprocessing development space are related to digitalization and process efficiency. These include:

In-Silico Experimentation. The hype around this disruptive technology in the pharma and biotech industries is real. In-silico-based tools can be integrated in both process development and manufacturing scenarios to reduce experimentation and risk.
High-throughput Process Development. This trend involves the miniaturization, automation, and parallelization of process development activities in order to create a systematic approach for a time- and resource-efficient workflow. The idea of creating digital twins is a big part of this concept right now.
Continuous Bioprocessing. The trends toward high-throughput process development and QbD reflect an overall push toward being able to operate in a continuous fashion. The intensification of both upstream and downstream operations will require higher levels of control during PD and presents new scale-up challenges.
Model Predictive Control. Using data analytics to create forecasting models for processes and process parameters not only lets you see what is happening in your processes as they are running, but also be able to predict the outcomes. Regulators encourage biopharma manufacturers to shift from traditional batch/start-stop processing to continuous manufacturing and use Multivariate Statistical Process Control (MVSPC).
Quality by Design (QbD). In addition to creating more robust formulas and well-documented processes, following a QbD approach can help ease regulatory compliance and ensure a stable process for long-term production optimization.
Real-Time Monitoring. Real-time process monitoring helps to ensure consistent product quality, maximizes operation efficiency, reduces risks from process deviations, and minimizes operational costs.
Spectroscopy. Using Raman spectroscopy in biomanufacturing is an effective way to apply Process Analytical Technology (PAT) and monitor bioreactor analyte concentrations, like glucose, lactate, glutamate, and glutamine in a sample. PAT tools, like Raman, can become even more effective when the analyzers themselves are integrated within the bioreactor system.

Digital Transformation Underpins Bioprocessing Trends

The move toward biopharma 4.0 is the underlying thread of many biopharma digital analytics trends today. Making the transition to a digitally mature company means looking at processes, updating equipment and training your team on new ways of working. Advanced data analytics tools, such as SIMCA^®, MODDE^® and SIMCA^®-online, are an essential part of any digital transformation plan.

Trends Affecting Bioprocessing

Read more about these and other trends affecting bioprocessing, including issues such as COVID-19 impact on development timelines, monoclonal antibodies (mAbs), new therapeutic approaches, antimicrobial resistance (AMR) and other issues affecting biopharma development and manufacturing in our report: Trends in Biopharma: How Technology Is Impacting Bioprocessing.