Of all therapeutic areas, new drugs for cancer are the least likely to succeed in clinical trials and become marketed drugs. In 2020, the United States was projected to have 18.1 million cancer survivors and cancer care costs of $157.77 Billion. More is spent on research and development (R&D) for drugs to treat cancer than any other disease area. Cancer drugs cost an average of $2.771 Billion to develop.  Unfortunately, oncology drugs in the R&D pipeline have the lowest clinical approval rate, 6.7%. The other 93.3% of drugs that enter clinical trials for cancer patients did not work, and the failure rate is the worse than treatments for any other type of disease. If a platform for drug discovery can identify higher-quality drugs to enter clinical trials, the improved chances of those drugs to succeed should bring more effective drugs to the market more rapidly and lower cost.

An effective medicine must attach well to its target. Generally, the most effective medicines can remain on their target for a long time and keep working for the patient. The best drugs usually have a long residence time on the drug target. By contrast, an ineffective drug falls off the target too soon (and then fails to stick back rapidly onto the target); that drug will be degraded, removed from the body and stop working. To get one marketed drug, 5,000-10,000 chemical compounds must be screened. A bottleneck is due to state-of-the-art laboratory technologies that have only a limited capacity for testing kinetics due to low throughput (only 8 tests per instrument) and high cost, so few drug designs are ever tested for kinetics. In a drug-discovery campaign, the vast majority of chemical compounds are never tested for ability to bind onto the target rapidly and persistently to work a long time, so many promising compounds are completely missed.

The goal of this Small Business Innovation Research project was to develop and demonstrate the concept for an advanced platform technology (instrument and chemical reagents) for the high-throughput kinetic analysis of small molecule drugs binding to drug targets. Hardware and operating software for a new detection technology were designed, built and tested. Four major design iterations of the detection system were evaluated with an overall 5000x improvement in sensitivity. The complete prototype is a highly parallel system that analyzes 384 tests simultaneously in two alternative acquisition modes. An advanced software algorithm was developed to enable the highest frequency mode of data acquisition. The system was sensitive and could detect binding ≤205 pM using the assay chemistry. The research project for assay chemistry pivoted to focus the assay on competitive inhibitors. The assay detected binding to a class of drug targets called kinases, that are important drug targets for treating cancer and diseases of the immune system. The new assay detected specific binding to a key component of a drug target: the catalytic domain of the human kinase called BRAF. To demonstrate a higher level of performance, the assay also detected binding to a complete drug target, the full-length kinase ABL1. Measuring binding rates will allow Medicinal Chemists in pharmaceutical companies to explore many more designs of chemical compounds to find better medicines. A futuristic application for the technology developed in this project will enable chemists to find superior drugs that can take advantage of the changing shape of the drug target. Next-generation drugs will bind to a target causing a certain change in the target shape (large conformational change due to an induced fit). These Next-Gen medicines will work better by being locked onto the target and show a longer duration of action in cancer patients. The energy barriers that hold these drugs in place on the target will also be measurable using the Capienda system. Thus, Medicinal Chemists who can exploit the energy landscape for binding in the drug pocket should be able to find new medicines with next-generation effectiveness.

The Broader Impacts of this SBIR project are in two areas. The first area of Broader Impact is in Artificial Intelligence (AI) for drug discovery. Capienda Biotech is in discussions with AI companies. They require laboratory data, which will improve the software and find virtual drugs that function closer to the way real drugs would work on target. The Capienda system would provide new laboratory data such as on-rates and off-rates for drugs engaging their target. The next level in AI drug discovery will exploit the dynamics of the target shape and optimize for the energy barrier that holds a Next-Gen drug on target. The second area of Broader Impact was employing an undergraduate Engineering Intern. She learned C# programming language, wrote software for multi-threaded high-speed data acquisition, the Graphical User Interface for instrument control and results display. She also produced CAD models and 3-D printed components for the prototype.

Last Modified: 10/19/2023
Modified by: Mark A Bernard