Biotechnology, Imaging & Drug Development

The broad goal of the reorganized Biotechnology, Imaging, and Drug Development (BIDD) Program is to utilize novel computational, chemical, and engineering-based approaches to detect, diagnose, and treat cancer. Two pre-existing programs, Molecular Diagnostics & Therapeutics and Onco-imaging & Biotechnology, were both savvy in developing technologies from design to the startup company stage. Reorganizing these two programs into BIDD not only capitalizes on the broad expertise of the two initial programs, but also provides the economy of scale for developing a broader intellectual property start-up and Phase I clinical trials infrastructure.

Strongly interdisciplinary, BIDD integrates research in chemistry, physics, optics, engineering, biology, and medicine to develop new approaches to target the cancer problem. New ideas begin when members meet at Program meetings, “matchmaker service” meetings, or interdisciplinary seminars. Nascent project ideas are then conceptualized with input from chemists, physicists, and engineers. Biologists then provide input as to how to validate the technologies in model systems and tissues. Finally, successful ideas are often spun out into startups, after input from the investment community, and are eventually tested in clinical trials with input/guidance from clinicians. Numerous devices and drugs have reached the startup company stage and are proceeding to clinical testing.

The BIDD leadership, composed of chemical biologist Brian Paegel, PhD, engineer-physicist Thomas Milner, PhD, and medical-scientist Anand Ganesan, MD, PhD, facilitates this development process by:

1. Building interdisciplinary teams comprising clinicians, physical scientists, and biologists to help vet new ideas and concepts
2. Identifying necessary expertise and connections to the Cancer Center’s Shared Resources
3. Facilitating chemistry, physics, and engineering technology development by making the right connections, identifying the appropriate funding sources, and providing guidance on the most appropriate interdisciplinary journals in which to publish new findings
4. Assisting in the design of validation experiments using the appropriate models (animal models, human tissues through the Experimental Tissue Resource (ETR), and ex vivo tissues)
5. Implementing the latest deep learning approaches to continually improve the developed device, technology, or drug
6. Providing guidance and input so projects can overcome the IND hurdle
7. Designing the appropriate clinical trials to test developed drugs and devices so the technology can make its way to the bedside