The OncoDxRx’s team expected the technique will improving the accuracy and reduce the time and cost of drug discovery and development, and accelerate precision medicine.

“Our transformative and exclusive platform can address the translational challenge from disease models to humans,” said OncoDxRx. “PGA uses functional genomics-inspired design and takes advantage of several recent advances in cell-free mRNA profiling, gene expression signatures, and in silico data computation.”

PGA provides a useful framework to take advantage of rich in vitro transcriptomic data for developing personalized clinical predictive power. The journey between identifying a potential therapeutic compound and FDA approval of a new drug can take well over a decade and cost upwards of a billion dollars.

Accurate and robust prediction of patient-specific responses to approved drugs is critical both for the expansion of safe and effective therapeutics, and also for selecting existing drugs for a specific patient.

However, it is unethical and infeasible to do early efficacy testing of drugs in humans directly. Cell or animal models are often used as a surrogate of the human body to evaluate the therapeutic effect of drug molecules. “In the early stage of drug discovery, cell lines and other in vitro models have been extensively applied to screen drug candidates,” OncoDxRx noted.

Unfortunately, the drug effect in a disease model may not correlate well with the drug efficacy and toxicity in human patients. This discrepancy is responsible for the high cost and low success rate of de novo drug discovery. And even for drugs that have been tested in clinical trials, patient responses to treatment can significantly vary. Moreover, it is often difficult to collect a large number of coherent patient data with drug treatment and response history to reliably predict which patient will benefit from the drug.

Developing a gene-to-drug mapping technology for predicting patient-specific clinical drug responses solely from in silico screens is challenging, PGA can otherwise provide a workaround to the problem of having sufficient patient data to establish a personalized model. Although many methods have been developed to utilize multiomics data overlay and fusion for predicting clinical responses, their performances are unreliable due to data incongruity and discrepancies.

PGA integrated relevant biomarkers involved in cellular oncogenic pathways as well as tumor microenvironment (TME) that were masked by noise and confounding factors and effectively alleviated the data-discrepancy problem. As a result, PGA test significantly improves accuracy and robustness over digital algorithm-based approaches in predicting patient-specific drug responses.

OncoDxRx further published their pilot trial of the PGA test to screen more than 700 cancer drugs for 30 cancer patients. The benefits of addition of PGA test are significantly improving clinical outcomes, suggesting the potential of PGA in drug-response prediction and further to develop effective personalized therapies.

OncoDxRx’ next milestone in advancing the technology’s application in drug discovery is to develop a way for PGA to reliably predict the effects of new drug candidates in human bodies.

Although current PGA technology is only applied to precision oncology here, it can be a standard prototype for other diseases. Overall, PGA provides a model framework to take advantage of rich in vitro wet-lab datasets and in silico dry-lab computation analyses for developing personalized clinical predictive power.

Read further at: https://www.mdpi.com/2673-7523/4/3/12