Automation’s advances and widespread integration into the scientific workflow can reduce drug R&D time from the usual 15 years to five, according to a study released by Frost & Sullivan. “Robotics already is meeting most relevant needs in drug discovery. Its impact in the coming few years will be remarkable,” says Cecilia Van Cauwenberghe, associate fellow and industry analyst, TechVision at Frost & Sullivan. “Robotics will make pharmaceutical processes significantly more cost-and time-effective and allow precise, real-time documentation of every task. That, in turn, contributes to process optimization.”
All of this happens by transferring mundane tasks to robots, thus freeing scientists for more creative and innovative work.
When automation first entered the pharmaceutical industry in the 1980s, it was seen as a way to improve precision and replace repetitive R&D tasks. With the emergence of high-throughput screening and the sequencing of the complete human genome in 2003, automated systems evolved to cope with increasingly complex tasks, such as large-scale DNA sequencing, single nucleotide polymorphism (SNP) analysis, and a number of variations and insert-deletions (INDEL) determination.
While some processes, including active ingredient development, haven’t been widely automated, most of the needed gains will come from new and emerging advances in automation.
BACTEVO — 100 MILLION ASSAYS PER HOUR
Bactevo, a new drug development company formed to develop therapeutics targeting rare and untreatable diseases, has developed its own automation advancement. Its Totally Integrated Medicines Engine (TIME), announced in late April 2017, performs on-the-fly chemical synthesis and screens 100 million phenotypic assays per hour while simultaneously performing drug-dose response and ADMET assays. TIME may cut drug development time for Bactevo by at least five years.
“TIME was designed to work on minute amounts of reagents or single cells to enable ultrahigh-throughput screening on patient-derived samples, thereby bringing researchers closer to the disease they are working to cure,” says Alex Alanine, Ph.D., COO. The approach is part of Bactevo’s philosophy that lead generation and screening should be redesigned.
“By automating individual components of serial processes, pharmaceutical researchers inevitably removed themselves from the disease by increasing throughput, which led them further from the fundamental disease pathology,” Alanine says. “High-throughput screening techniques tend to reduce a disease to its relationship with a single protein. That, however, probably doesn’t reflect the true state of the disease. Most diseases can’t be reduced to a single mechanism of action or cause of onset.” Screening, therefore, should embrace the still-evolving system’s biology approach. TIME enables that.
ASTRAZENECA AND NICOLA-B
In early 2017, AstraZeneca launched its drug discovery robot, NiCoLA-B, which is capable of screening 40 million compounds per year. “That’s three times faster than previous high-throughput screening robots, at half the size,” Van Cauwenberghe points out. It even adjusts itself to the presence of people in the lab.
The NiCoLA-B robot will be deployed into the new AstraZeneca global R&D headquarters at the Cambridge biomedical campus where it will be shared with AstraZeneca’s partners in the Open Innovation Initiative, Cancer Research U.K. and the Medical Research Council.
“The Open Innovation Initiative is designed to develop robots capable of replicating many of the simpler decisions made by scientists during experiments, thereby improving machine intelligence,” Van Cauwenberghe elaborates. “The NiCoLA-B robot can be programmed to detect process imperfections in ongoing runs so it can take particular actions at the right moments.”
BAYER’S MILLION-MOLECULE OCTOPUS
Automation also is crucial as tests continue to be miniaturized. “Microreaction mixtures involve many tasks that can be performed only by robots because humans lack the visual acuity and dexterity to carry out such experimental formats,” Van Cauwenberghe says.
Bayer’s Million-Molecule Octopus is a prime example. The mechanical device screens one million substances daily in Bayer’s Wuppertal, Germany, facility. This ultrahigh- throughput system screens the pharmacokinetic and pharmacodynamics properties of chemical agents to find new, potentially disruptive therapeutic products. “The same workload would have taken an entire century using robots developed 20 years ago,” Van Cauwenberghe says.
Alex Alanine, Ph.D., COO, Bactevo
The benefit, aside from speed, is the ability of the Octopus to carry out completely new experimental designs. As she says, “It comprehends different combinable modules that can be integrated into the high-throughput system using new methods on demand. It also can be coupled with a second robot to optimize the preparation of the reactions, all harmonized through novel computer systems.”
The Million Molecule Octopus uses 1,536-well microtiter plates, testing substances first on isolated proteins and then on living cells using luminescence- or fluorescence- based measurement methods. A similar system is used in Bayer’s Berlin lab. In Cologne, a robotic system is being set up to test and optimize the binding characteristics of more than 10,000 antibodies.
EMBRACE PARADIGM-SHIFTING OPPORTUNITIES
Using automation to reduce drug-development time requires more than merely automating some processes. Instead, Alanine says, “Achieving such a dramatic reduction in drug-development time also requires a different way of conducting R&D.”
Drug-development companies need to complete the paradigm shift to direct-to-patient clinical recruitment using the internet and social media and to access real-time patient data from wearable clinical and consumer devices. “That will allow studies to form around a more focused set of patients, which should reduce the time frame considerably,” Alanine says. Embracing more efficient participant recruitment and patient-monitoring strategies will transform the way clinical trials are run and could pare five years from the R&D timeline.
The final piece of the integrated automation picture calls for incorporating advanced analytics. “Without powerful analytics engines, it’s impossible for humans to intelligently survey the vast quantities of data produced by ultrahigh-throughput screening to extract trends and patterns,” Alanine says. Machine learning allows a faster and more precise science that reduces subjectivity in experiments.
CONVERGENCE IS LINKED TO AUTOMATION
Today’s drug-development environment has been called a medical Renaissance. If the actuality lives up to predictions, that’s because of the convergence of automated tools that speed up scientific advancements.
“The dramatic changes that laboratory automation has made during the past decade have revolutionized life sciences research and most especially drug discovery and testing procedures,” Van Cauwenberghe says. As the convergence of other technologies (e.g., nanotechnology, materials science, electronics, synthetic biology, molecular self-assembly, high-resolution imaging, software development, and data analytics) accelerates, it becomes feasible to automate, miniaturize, and streamline drug-development processes to help them reach peak efficiency.
As that happens, scientists at all levels can devote more time to design and analysis and less to repetitive tasks, making the five-year drug development timeline Frost & Sullivan predicts feasible today.