Better Data May Speed Path to Drug Trials

By Iva Fedorka

An expert in measurement science has led his research team at Purdue University to develop an electronic signal filter that may help to more accurately determine the results of biological and chemical analyses.

FDA Approval

Data is key to drug discovery, medication development, and efficacy determinations. Pharmaceutical companies in particular make major scientific decisions based on the results of chemical and biological testing. Variations in or uncertainty about those measurements can increase both patient health risks and financial risks to the companies themselves.

According to the Food and Drug Administration, it can take 10 to 15 years or longer to move a drug from discovery to the point of public consumption. This electronic filter may allow for better and more exact measurements earlier in the drug development stage, which would shorten the time required to move a drug to the clinical trials phase.

Filter Development

The data filter was developed by Garth Simpson, professor of analytical and physical chemistry at Purdue's College of Science during his work with the Merck-Purdue Center for Measurement Science. This industrial-academic partnership was formed in 2017 to focus on technologies to improve drug discovery and delivery.

"This center provides real-world test beds for validating emerging technology related to chemical measurements," Simpson said. "Our latest development is this novel filter design for digital deconvolution that helps us remove timing artifacts arising from the response function of the instrument we are using for data acquisition."

Analytical data may contain millions of data points, so Simpson and his team used mathematical formulas to analyze and organize the information into more useable formats for researchers and drug developers.

"This center provides real-world test beds for validating emerging technology related to chemical measurements."

Light Detection Instruments

The practical measurement of an electronic event is a combination of the actual event and the capabilities of the measuring instrument. Simpson found that most algorithms used to correct for the instrument’s response require a significant understanding of how the instrument itself operates.

Many instruments used to measure concentrations depend upon a light emission that produces an electronic signal. These include (but are not limited to) photon counting (PC), chromatography, super resolution imaging, fluorescence imaging, mass spectrometry, and scintillation counting. Each instrument uses an integral algorithm to determine which of the light emissions are coming from the actual sample, and which ones are stray or background interference.

Improved Data Analysis

In contrast, "Our digital filter approach only requires that a user have the data," Simpson said. "Our filter and algorithm then use non-negative matrix factorization over short sections of data to allow the analysis of data sets that are too large to be characterized by other conventional approaches."

The new Purdue filter can be used for microscopy, chromatography, and triboluminescence measurements. These techniques are often used in the early stages of drug development to identify the molecules that demonstrate the greatest potential for further testing and success.

Simpson is working with Purdue University’s Office of Technology Commercialization to patent the new filter, and his research team hopes to find researchers and partners who are interested in licensing the software.

Purdue University is currently celebrating its sesquicentennial in a year-long celebration of achievements and advancements that showcase it as an intellectual center that can help solve real-world problems.

The technology is published in the March 25 edition of Analytical Chemistry.