Pharmaceutical R&D teams are increasingly adopting high-throughput bioprocessing to screen and optimize drug candidates, particularly biologics. 

Enabled by recent advances in automation, labs can now conduct thousands of parallel experiments to identify promising drug candidates or optimize manufacturing processes. And to handle the volume and complexity of these experiments, many labs are turning to automated sampling and feedback loops that enable real-time decision-making and process optimization.

Delivering on these new capabilities requires software that keeps pace with the rapidly evolving demands of science, operations, and technology. This infrastructure is an investment that will determine the speed, reliability, and success of your biomanufacturing pipeline. Choosing the wrong software can lead to integration nightmares, data silos, and frustrated scientists. Choosing the right software can unlock exponential gains in efficiency, reproducibility, and innovation.

What is Bioprocessing Technology?

Bioprocessing technology is the use of living cells, enzymes, or biological components to create valuable products such as drugs, vaccines, and therapeutic proteins. It combines biology, engineering, and manufacturing principles to convert raw materials into complex biologics under controlled conditions. This technology is becoming increasingly common in modern pharmaceutical R&D, enabling scalable and consistent production of life-saving treatments while maintaining strict quality and regulatory standards throughout the development and manufacturing process.

Why is Bioprocess Technology Important?

Bioprocess technology is important because it enables the reliable, large-scale production of complex biologics such as vaccines, antibodies, and cell-based therapies. It ensures consistency, quality, and safety while meeting regulatory standards critical for patient care. As demand for biologics grows and therapies become more personalized, efficient bioprocessing shortens development timelines, lowers production costs, and accelerates access to life-saving treatments, making it a cornerstone of modern pharmaceutical innovation and manufacturing.

Why Bioprocessing Software Matters

In high-throughput labs, any steps that require scientists to manually collect samples, take measurements, or make adjustments quickly become a bottleneck. Software that can orchestrate real-time data collection from bioreactors, analyze trends, and automatically adjust parameters like pH, feed rate, or temperature removes these bottlenecks and enables labs to scale.

As experiment volume increases, labs must also meet regulatory requirements such as those defined by the United States Food and Drug Administration (FDA) and the Department of Agriculture (USDA), and the European Medicines Agency (EMA). The right bioprocessing platform will help streamline compliance at every stage of operation.

Common Challenges for High Throughput Bioprocessing Software

Despite the potential, many lab managers face real obstacles when trying to implement tools for automating bioprocessing sampling and feedback:

1. Data Silos

Instruments from different vendors often use proprietary formats or APIs, making integration into a unified system difficult.

2. Poor Interoperability

Sampling systems, sensors, and control software don’t always “speak the same language,” creating delays or requiring custom middleware.

3. Low Flexibility

Many legacy solutions are hardcoded for specific protocols, limiting the lab’s ability to adapt workflows on the fly.

4. Lack of Real-Time Processing

Some systems collect data in real time but cannot immediately process or respond to it, negating the benefits of automation.

5. Regulatory Compliance Gaps

Software that doesn’t support audit trails, robust user permissions, or other features of Good Laboratory Practices (GLP) can be a liability in a regulated environment.

Understanding these challenges helps frame the criteria that matter most when evaluating software options.

Must-Have Features and Functionality for Automation Software

When selecting software to automate sampling and feedback loops, lab managers should prioritize the following criteria:

1. End-to-End Integration Capabilities

The software must seamlessly interface with bioreactors, autosamplers, analytical instruments, and downstream analytics platforms. Application programming interfaces (APIs) are the “languages” that these systems use to communicate data between each other. Look for systems that support industry-standard APIs such as OPC-UA (Open Platform Communications Unified Architecture).

2. Real-Time Data Processing and Control

The system must be able to process sensor and sample data in real-time and trigger control responses. This includes adaptive control logic, dynamic thresholding, and machine-learning-based optimization routines.

3. Scalability

As your lab grows, the software must be ready to scale with it, from a few benchtop reactors to dozens of parallel fermenters or perfusion systems. Cloud-native platforms typically offer better flexibility and scalability than on-premise systems. Labs aiming to transition from small-scale development to GLP-ready production may benefit from integrating software with disposable bioreactor systems, which support leaner, more flexible manufacturing strategies.

4. User-Friendly Workflow Design

Lab scientists, not just software engineers, should be able to configure protocols, design feedback rules, and monitor processes. Visual workflow builders and intuitive dashboards can empower a wider user base and reduce onboarding time.

5. Data Integrity and Compliance

The software should support robust audit trails, versioning, e-signatures, and compliance with GLP standards. Built-in validation frameworks can reduce the burden on Quality Assurance (QA) teams.

6. Customizable Feedback Algorithms

Lab teams should be able to script or configure custom feedback rules using conditional logic or predictive models tailored to their unique workflows.

The Impact of the Right Software

Choosing the right bioprocessing solution can significantly accelerate discovery across your organization and your pipeline:

  • Shorter Development Cycles: Automated feedback loops can reduce iteration time from weeks to days, allowing R&D teams to get answers faster.
  • Higher Throughput: With better control and coordination, labs can run more experiments in parallel without additional headcount.
  • Better Process Understanding: Continuous data capture can provide real-time, granular insight into process performance, particularly when combined with technology such as digital twins.
  • Improved Reproducibility: Automating both sampling and feedback can reduce human error and increase the consistency of results across batches and operators.
  • Faster Scale-Up: Early detection of trends and optimal conditions can help de-risk transfer to pilot- and production-scale systems.

Towards Data-Driven Bioprocessing

Selecting the right software for automating sampling and feedback loops in high-throughput bioprocessing is a pivotal decision for any pharmaceutical R&D lab. It helps labs shift from manual experimentation to agile, data-driven bioprocessing. This foundation also sets the stage for AI-assisted design of experiments, model-predictive control, and continuous manufacturing.

This decision requires balancing technical capabilities, user needs, regulatory requirements, and long-term adaptability. When done right, it transforms not just the way work gets done, but also how quickly, intelligently, and reliably pharma teams can get life-saving drugs to market.