By Patrick McConville, Ph.D.
The pharmaceutical industry continues to actively adopt imaging technologies to accelerate the drug discovery process. Imaging methods such as magnetic resonance imaging (MRI), positron emission tomography (PET), single photon emission computed tomography (SPECT), computed tomography (CT), and in vivo optical imaging offer important and valuable improvements over traditional drug discovery methods.
These improvements include providing earlier and more highly predictive in vivo data, facilitating clinical translation, and yielding highly quantitative information from the more realistic models of disease that are becoming increasingly available.
However, success in a preclinical discovery imaging program goes beyond the initial steps of purchasing and installing the imaging equipment and hiring the skilled experts to run it. Imaging can be successful at advancing the drug discovery process only when a detailed and thoughtfully considered plan of action is soundly and strategically designed and applied.
One of the obstacles to achieving a significant ROI on imaging technology in the preclinical arena is optimization of the animal-to-animal imaging “throughput” without compromising data quality. Since the hourly cost of imaging is dominated by large depreciation costs, imaging throughput critically drives cost and ROI potential and also places limits on the timeline and scope of each study. An increase in animal-to-animal throughput is generally at the cost of resolution and sensitivity, one or both of which may already be limited, particularly in small animals where preclinical imaging is generally centered.
The converse is that state-of-the-art imaging hardware and protocols that enhance sensitivity or resolution can be translated to increased throughput. It is therefore critical in an imaging program to be able to rapidly install, develop, and validate the latest hardware and software advancements. Tools for automation of image data handling, backup, archiving, and postprocessing also have the potential for maximizing effective data throughput. What’s more, design of support systems for imaging — including animal preparation, anesthesia, and dosing apparatus — can critically affect the efficiency of image data generation and the quality and reproducibility of the image data itself and must be part of a larger plan in the installation and operation of in vivo imaging technologies.
A further obstacle to the optimal use of preclinical imaging is the increasingly high rates of data generation that have occurred in parallel with imaging technology advancements. Daily generation of gigabytes and even terabytes of data can mire the very process it was originally designed to accelerate. A network infrastructure that facilitates rapid data transfer, access, backup, and archiving must be a key part of the planning for an imaging program and not an afterthought. Facilitation of rapid and high-quality image reconstruction, segmentation, rendering, and quantification through state-of-the-art workstations and software packages are also needed. The lifetime of each image after acquisition and before final quantification heavily influences the effective throughput and timelines related to the imaging process.
Strategies must also be implemented for prevention of animal cross-contamination (biosecurity) and for personnel safety concerns. Strictly conforming to the highest level biosecurity and safety can end up costing as much as the imaging equipment itself. If not thoroughly and optimally planned, both biosecurity and safety also have the potential to limit the efficiency and power of the imaging protocols. Occupational health and safety issues are a particular concern for PET, SPECT, and CT, which involve ionizing radiation. Maximization of imaging throughput, personnel safety, and biosecurity can appear to create insurmountable conflicts. All are critically related to design and layout of the lab, vivarium, radiation shielding, and support systems for anesthesia, dosing, and animal handling.
Biosecurity is a particularly pressing issue for an imaging lab, especially for the specific pathogen-free environments generally required for oncology models. If not carefully considered, the imaging lab could be a weak link in the in vivo operations of a discovery organization. Again, design of the lab and implementation of support equipment and procedures for preventing animal-to-animal contamination and for decontamination of imaging equipment is a key consideration for a successful and high-quality imaging program.
The previously mentioned obstacles have the potential to decouple a state-of-the-art imaging center from delivering increased power and efficiency in the discovery process. However, these obstacles also have a key similarity — their solution. Without a doubt, the single greatest asset for a successful imaging program is experience, first in the experts running and driving the program, but also in the depth of experience across animal models, therapeutic strategies, and imaging probes and protocols. By bringing together imaging and discovery experts seamlessly, these requirements can be realized quickly and effectively, and in the process, limit potential obstacles. Preclinical discovery imaging can then yield maximal benefit in the efficiency and accuracy of the drug discovery process, as well as in the uniqueness of the data procured and the questions answered. Only when approached in this way can preclinical imaging accelerate the drug discovery process in the most cost-effective and efficient manner possible.
About The Author
Patrick McConville, Ph.D., is the director of imaging at Charles River Laboratories, Inc. He has more than a decade of experience helping clients accelerate drug development programs through the use of imaging technologies.