Not long ago, chromatography automation meant strip recorders and peristaltic pumps. Today, few people would consider this true automation and even fewer would settle for binders full of strip recorder paper reels. Automation is becoming intelligent and in the process is making our workflows smarter as well.
But how close is automation to being as smart as an experienced scientist? We spoke with academics, biotech R&D scientists, and industrial process engineers about the evolution of chromatography automation — where did it start, how far has it come, and what are its limits.
From list programming to click-and-drag software
Early fast protein liquid chromatography (FPLC) systems automated chromatography runs, but to set up a run meant climbing a steep learning curve. “It almost looked like you were programming code,” David Grabowski, Team Leader at R&D Systems, Inc. explains. “You programmed these things in a list orientation. You’ve got parentheses where you’re typing in different column volumes, and then you’ve got different variables for that block vs. your main block. And if you weren’t trained, it was very hard for people to use.” For a new employee or a student, being able to take advantage of automation meant learning two new skills: the basics of chromatography and how to program that FPLC.
But things have changed, says Edward Ha, Head of ADC bioconjugation at Igenica Inc. “People who have never done any protein purification, in under 20 minutes they’re off and going with a canned method that we have on the machine.” The software that was once a hurdle, an additional skill to acquire, is now a great equalizer that arguably makes a chromatography novice smarter.
Smarter and more streamlined workflows
Automation is also making our workflows smarter. Software packages with preprogrammed and customizable protocols not only give scientists instant basic chromatography know-how, but also allow multiple users to share one FPLC without having to reprogram their methods every single time.
“We have different functional units within Igenica” says Ha, “and they’ll have to purify different proteins using SEC, HIC, and affinity purifications like Nickel, Protein A, and Protein G, and it’s just been a godsend as far as ease of use.” Ha also credits autosamplers for streamlining his workflow, explaining that “getting an autosampler on the system was a huge achievement.”
Other advances, such as air sensors and automatic flow rate adjustment, now allow scientists to run columns overnight and unsupervised. Time that was previously spent programming runs or supervising a gravity column can now be spent more productively, says David Grabowski. “Extending your workday by running columns overnight lets you spend the day analyzing data and running your gel. You don’t have to spend time watching little blue lines go up and down.”
Not only does automation make Grabowski’s team more productive, it also reduces operator-introduced variability. “Being first to market is important.” explains Grabowski. “When we’re developing a protocol, we’re trying to eliminate as many variables as possible. By automating chromatography, we can totally eliminate that as one of the variables.”
Automation vs. experience
But automation in its present state cannot completely replace experience, points out Meng Guo Gi, a process engineer at Main Luck Pharmaceuticals. He praises features like buffer scouting and tandem chromatography for streamlining design of experiment (DOE) for his team, but he cautions that current automation “may not be suitable for unexpected conditions during process screening.”
Matthew Groves, an assistant professor at the University of Groningen, in The Netherlands, agrees, “From my point of view, once I’ve got a system set up and it’s expressing and crystallizing, then I can use automation to rerun my protocols to test a new compound. But when you play with a new protein, automation is less straightforward.”
What lies in the future?
But Groves thinks chromatography systems that can make intelligent decisions on our behalf are not too far off. “In ten years, an ideal automation experiment would be, I have my sample, I know it needs an ion exchange, so I load the loop. [The system] spends the first 10% of the sample screening through the pHs, establishes the best pH on a small scale, and then runs it on the large scale.”
So how close are we to an intelligent system that can identify optimal chromatography conditions for us? A first step towards allowing the scouting Groves is describing will be fast flow rates and small columns that are directly scalable to the large columns used for final purification.
Peak identification is another hurdle to full automation. “Chromatography data are not as computationally accessible,” says Groves, who developed “launch and leave” beamline software for crystallographers. ”You have to worry about peaks not splitting off nicely or getting shoulders, or asymmetry on things,” he adds. “But this is very much in the future. It’s kind of almost there.”
Finally, chromatography software needs to be able to identify the peak that contains the protein of interest. “For a nickel column,” says John Bruning, a lecturer at the University of Adelaide, in Australia. “I think it’s definitely possible because there’s one big peak and you generally take it all.” But things quickly get more complicated, he explains. “With a Q column, you have to think about which fractions to go with.”
Jeff Habel, Sr. Scientist in Protein Technologies R&D at Bio-Rad Laboratories, adds that this system of the future will also have to make some qualitative judgments. “If I’m a structural biologist, I may only want the first half of the peak because some contaminant eluting in the back half. If I’m an enzymologist, impurities may not bother me as much; I may want the entire peak.”
Unlike the peak-finding problem, this problem cannot be solved computationally. Fractions will have to be assayed to identify which contain the protein of interest. The FPLC UV detector could be used to track chromogenic proteins while automated gel loading and electrophoresis could be helpful for tracking contaminants. For other purification schemes automated testing of fractions for desired enzymatic activities may be required.
Although achieving these developments may seem like an insurmountable feat of engineering, similar problems have already been solved for small molecule and peptide chromatography. HPLCs can be coupled to mass or NMR spectrophotometers, permitting direct identification of the analyte, or to diode array and fluorescence detectors, allowing detection through postcolumn functionalization. Adapting similar techniques to protein identification can easily be envisioned.
“The system of the future, in terms of automation, will probably do exactly what Dr. Groves wants,” says Habel. “It will give you the best conditions for the first column, for the second column, for the third column, and then assemble them into a multidimensional chromatography method.” He’s hopeful that for many applications, chromatography will become like sample prep. “Being able to do the separation, the visualization of your protein, and even the quantitation to let you know how much there is, all in an automated fashion — that becomes a very interesting idea for the lab of the future.”
This level of automation, automation that is truly intelligent and able to replace human decision-making and intervention, may not lie in the immediate future, but the advances in automation seen in recent years suggest that the lab of the future that Groves and Habel envision may be a little closer than many would have believed possible at the beginning of this century.