The escalating cost of discovering new biopharmaceutical therapies is a well-known and long-standing problem. Recent estimates by the Tufts Center for the Study of Drug Development put the cost of developing and getting approval for a new drug just shy of $3 billion1.
The underlying buildup of costs, causes of inflation, and ultimate accuracy of the total price tag for a new molecular entity are subjects of ongoing discussion and debate2, but the fact that directionally costs continue to grow is generally accepted as fact.
Something less widely known and appreciated is that many of the biopharmaceutical companies working to develop new therapies are some of the most sophisticated and progressive adopters of robotics and automation technology.
A number of the biggest, most acclaimed biopharmaceutical companies in the world have been rigorously committed to using technology to advance productivity and thus contain discovery costs for some time. While costs ultimately have been growing, it is hard to imagine how much more they might have grown without the substantial commitment discovery organizations have made to robotics and automation.
Laboratory robotics and automation drive value in a few fundamental ways, but typically not in the ways most people initially imagine. Contrary to common perception, robots are generally not faster than people on any given task. However, they are relentless. They do not stop to answer the phone, take a bio-break, or reflect upon what they are doing over the weekend.
As such, even though they are not fundamentally faster than a human, their unit output per time period is often higher due to the elimination of inactive time. This is the first source of increased productivity from robotics.
In addition to being relentless, robotic systems also are capable of parallel processing far in excess of anything a human scientist could coordinate. Imagine needing to keep track of exactly how long a plate has been in an incubator, while also tracking plates in a liquid handler, a centrifuge, and a thermocycler.
Even the best human multi-taskers have very low limits of mental juggling capacity compared to automation. Proper high-end automation scheduling software can track a nearly limitless set of activities happening in concert. This enables a single system to be simultaneously managing multiple sequential steps in any given scientific workflow concurrently, which further increases overall experimental productivity.
As Novartis notes in their use of automation, running samples by hand has a potential output of around 30-40 per day, where robotic systems can approach output in the hundreds of thousands per day3.
Robotic systems also significantly increase process accuracy and quality of data capture. When you instruct a robot to heat a plate for exactly 128 seconds, it never accidentally heats it for 145 seconds because it was talking to a co-worker.
Thus, as you generate experimental data, a robotic system can be relied upon to have generated it with precision, and to have recorded its process parameters in ways that could be hard for a human, particularly in volume.
AstraZeneca is another good example of a company pioneering ways to generate higher experimental output and accuracy through laboratory automation4. Using flexible, modular, and collaborative robotics, the systems developed at their Cambridge, U.K. facility can test up to 300,000 compounds per day. It is difficult to imagine how many humans would be required to achieve this level of output, but suffice it to say it is a very, very large number of people and subject to a substantial amount of data errors.
The use of robotics and automation thereby greatly increases the number of experimental possibilities and associated results per unit time, without simultaneously increasing required headcount.
AstraZeneca furthers the advantages of their investment in these technologies through a strategy of open innovation5 with other companies. This increases the utilization levels of the system, and thus further attacks the cost of discovery as evidenced in their recent partnership with Charles River6.
Robotic and automation technology advancement is also happening at an astonishing pace. Notably, the advent of collaborative robotics is rapidly changing both how automation can be approached and expanding the range of applications where automation sensibly can be deployed. This is because traditional robotic systems based on industrial robot technologies require guarding and protection to ensure the robot does not potentially injure someone in its area.
Industrial robots are highly effective, but demand complete separation from people to be safe. By contrast, a collaborative robot can sense the presence of a person, modulate its speed as needed, and safely stop if it comes in contact with a person.
Using collaborative robots in laboratory automation thus has a variety of benefits.
First, system sizes can be reduced as guarding is eliminated, which adds to productivity per dollar or square foot of lab space.
Second, methods can be used where robots and people collaborate to perform the work. In places where humans are better than robots at certain tasks, they can safely work alongside each other, enabling new and more complex workflows to be automated.
Third, robotics can be made mobile and more flexible as the fixed nature of requisite tooling and guarding are no longer needed. Today, robots can be placed on mobile carts, coupled and uncoupled from larger systems modularly, or even wheeled down the hall to different workspaces.
Finally, and perhaps most importantly, the combination of the above benefits is opening up automation to a wide range of potential applications previously not suited for industrial robot technology.
Although drug discovery costs have increased and represent an ongoing challenge in developing new therapies, without the substantial investment and commitment to automation and robotics from the major research organizations globally, they would just be that much higher.
As new technology continues to develop, the contribution of robotics and automation to controlling development costs and increasing scientific productivity can only be expected to strengthen.
About the Author
Peter Harris is the Chief Executive Officer of HighRes Biosolutions, with offices in Beverly, MA, Manchester, U.K., and Shanghai, China. The company designs and builds laboratory automation systems, dynamic scheduling software, and lab automation instruments to help accelerate and streamline discovery for pharmaceutical, biotech, and academic research clients.
This story can also be found in the March 2018 issue of Pharmaceutical Processing.