Piero Zucchelli
When it comes to new drugs, speed to market is critical. The pressure to discover promising new treatment options, like immunotherapy or precision medicine, to address the 18 million new cases of cancer the World Health Organization is estimating for this year has pharma companies racing against the clock.
While these personalized options offer hope to many patients, they also come with a hefty price tag — some of which can cost more than $400,000 per patient per year. For instance, in 2017 Novartis brought the first immunotherapy drug available in the U.S., Kymriah, to market, costing an incredible $475,000 as a single treatment drug. It’s a model that’s clearly not sustainable for the long-term.
The high cost of these drugs has developers pursuing lower-cost treatments and generic alternatives to brand name drugs. To produce a lower costing drug, pharma companies need to boost laboratory efficiency and cut costs. At the core of this initiative lies automation.
The Reproducibility Crisis
Reproducibility is fundamental to any scientific experiment on three scales: within the lab, interlaboratory and across experiments. When people are relied upon for repetitive, manual tasks, human error is bound to produce wildly varying results due to things such as miscalibrated pipettes or transcription mistakes.
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In fact, many in the scientific community believe there’s a reproducibility crisis. A recent Nature survey found that more than 70 percent of researchers have tried and failed to reproduce another scientist’s experiments, and more than half have failed to reproduce their own experiments.
Reproducibility is critical when it comes to guaranteeing the integrity of the data in drug development experiments, such as those used to inform clinical trials. Unfortunately, it’s not uncommon for researchers to push a drug to clinical trial based on their experiments, only to later find out that the preliminary tests were invalid due to a lack of reproducibility or accuracy issues. These issues, usually stemming from human error, can cost upwards of $1.4 billion, depending on when the flaws are identified. Perhaps more importantly, failed clinical trials caused by a lack of reproducibly and precision can lead to costly delays.
The Solution
Scientific and lay communities alike agree that drug development should be less expensive and more effective, and lab automation has been proven to be a viable solution. Automated technologies nearly eliminate the possibility for human error, increasing reproducibility — leading to conclusions that can be trusted. New technologies like intelligent pipettes that can sense, control and track the liquids being used, while simultaneously increasing development speed and data accuracy, resulting in lower costs and better outcomes for patients.
Automation in the lab can also shorten the time between phases without necessarily requiring that more laboratory technicians are hired. Liquid handling robots, for example can run pipetting operations fully unattended, without compromising — and, in fact, increasing — the reliability of data. With robots and expert systems supervising the data production without biases, lab technicians are free to focus on more value-added functions such as data analysis.
Automation is making great strides in the drug discovery arena, enabling humans to collect, edit and modify the data contained in DNA, which has been uncharted territory until recent years. These tools, combined with human intelligence have the power to eventually enable a thorough understanding of the way a living organism works, with the possibility of changing it.
If pharmaceutical companies truly want to succeed when it comes to drug discovery and development — in terms of speed to market and affordable treatment options for patients — technology will need to play a significant role. Lab automation has the power to remove the repeatability issue from the equation, while cutting the costs of drug development by reducing time, errors and labor costs — ultimately bringing lifesaving drugs to market faster.
