The broader impact/commercial potential of this Small Business Technology Transfer (STTR) project will be to improve the safety testing of new drugs for approval by the FDA. By decreasing the time and costs associated with safety testing, the product will make all classes of new drugs safer, less expensive and available to patients sooner. All new drugs must demonstrate that they are safe. One common and critical point at which new candidate treatments fail is because they have the serious side effect of promoting sudden cardiac death through lethal arrhythmias. This product combines advanced biological techniques with advanced computing to develop a system that will enable pharma and biotech companies to more rapidly and accurately identify pro-arrhythmic drugs earlier in the development process, thus saving drug companies significant costs associated with drug development. Drugs that ultimately fail cardiac safety screening need to be eliminated as soon as possible from the development pipeline, and certainly pre-clinically. A drug that makes it to clinical trials before cardiac side effects are identified can result in significant wasted costs, in addition to the human cost. Conversely, a drug incorrectly eliminated also can be costly, both in terms of lost revenue and benefit to society.
This STTR Phase I is a proposal to improve the extraction of key data from experiments on the HERG ion channel and its interpretation through computational modeling in the new FDA CiPA initiative. Preclinical safety testing currently focuses on two interdependent questions: 1) Does the drug block the HERG channel? and 2) Does the drug prolong the action potential? The CiPA initiative proposes to integrate this process systematically, through screening of a defined set of cloned ion channels in high throughput systems and combing this with action potential modelling through the qNet index. The HERG channel is handled separately using a complex state-dependent block model that due to its complexity requires very difficult and time consuming manual measurements. This proposal will automate this process by using a real-time interface to computer model block and evaluate the information coming from voltage clamp experiments as they occur. As such it will be an artificial intelligence that will substitute for the judgement of a human experimenter by focusing only on protocols and exposure times that define the kinetics of a particular drug.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
