When asked about the biggest challenge in today’s bioprocessing, Sandy Williams, PhD, a biomedical engineering expert at Scientific Bioprocessing (SBI) in Pittsburgh who focuses on regenerative medicine, says, “The biggest concern we have heard from the industry is that bioprocessing is undersampled. While scaled-up upstream processes are well monitored and controlled, small-scale upstream cultures and downstream bioprocessing appear to be largely undersampled and poorly monitored.”

Scientists at SBI make tools that address these problems.

In all forms of medical manufacturing, cost enters the equation, and expense seems like a potential cause of the undersampling in bioprocessing, but not so according to Williams. “It’s not the cost so much, but the type of technology and how compatible it is with single-use technologies and some aspects of bioprocessing,” she says.

Information processing

Information processing makes up part of the technology needed to improve sampling, and that’s easy and inexpensive enough. “The cost of processing power has plummeted,” says John Moore, president of SBI. “So, you have the processing power and now you need the data.”

Scientific Bioprocessing developer’s kit
Scientific Bioprocessing developer’s kit is one way to get started in collecting more data from bioprocessing.

In bioprocessing, those data come from sensors of dissolved oxygen, pH, and so on. That’s why SBI offers postage-stamp size sensors that can be put all over a single-use vessel.

“Ultimately, sensors are a tool of paramount importance without which the bioprocess would be a black box,” Williams says.

The combined processing power and sensor-collected data can then be put to use in bioprocessing.

“Critical process parameters can be monitored and/or controlled with sensors to ensure that critical quality attribute values are reached and maintained,” Williams explains. “Predictive modeling and machine learning have opened up new ways of using sensor data by making connections to functional outcomes.”

Then, combining artificial-intelligence algorithms with data from sensors helps bioprocess engineers improve a process.

In such a technical field, any aspect going undersampled seems unexpected, especially given that patients and physicians depend on the products. In the years ahead, undersampling should fade into bioprocessing’s past.

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