Preclinical imaging focuses predominantly on estimating a parameter of interest, while clinical imaging is driven primarily by diagnostic efforts.
Preclinical imaging applications in drug development require multidisciplinary strategies and thoughtful planning. Imaging technology selection and quantitative analysis—the ability to extract numeric information from the image data—play primary roles.
Advancements and challenges in preclinical imaging were discussed by industry, academia, and government thought leaders at the recent GTC conference “Imaging in Drug Discovery and Development.”
Previously, a major preclinical image-processing bottleneck was manual segmentation of collected data, a slow process that did not provide enough relevant output information. Semiautomated or fully automated processing routines did not exist; a week of data collection translated to three weeks of processing.
According to inviCRO, its instrument-agnostic informatics protocols enable users to fully or semiautomatically segment regions of interest; store them in an accessible cloud-storage solution; create aggregate spreadsheets, or arrays of numbers, from the images to plot; and apply statistics, pharmacokinetic models, or automated reporting engines.
“We measure life one voxel at a time,” commented Jack Hoppin, Ph.D., co-founder and managing partner. “Datasets used to be 128 × 128 × 50. Now they are 1,000 × 1,000 × 1,000 × 70, or more. Creating a platform that can maintain and handle that data quantity is complicated.”
An interdisciplinary activity, imaging requires assembly of the right team of experts to maximize return on investment.
“Just defining the question you want to answer with the imaging data may be hard. How do you focus your efforts correctly to extract the best data amount without wasting time? You can spend a lot of time trying to automate things you should do manually, and vice versa.”
“Today, everyone acknowledges that imaging analytics, a technical, quantitative approach to data analysis, should be part of the workflow. You think through what your image-processing platform will be a priori—data management, processing, and reporting. It can be the most challenging aspect of the process,” concluded Dr. Hoppin.