Altschuler and Wu develop new cell-screening approach to speed drug discovery

How do you discover new drugs against diseases such as cancer?

A commonly used modern method is to screen thousands of chemical compounds against genetically identical laboratory-grown cancer cells (cell lines), then track how those compounds alter the cells’ behavior or appearance (phenotype). For example, does exposure to a compound change the quantity, type, or location of protein molecules involved in biological processes (pathways) underlying cancer’s unchecked proliferation of abnormal cells?

This so-called high-content phenotypic screening tracks cellular changes by labeling proteins with fluorescent tags. The effect of a compound is assessed by combining microscopic images of those tags (fluorescence microscopy) with software programs that analyze the glowing patterns that emerge.

The goal is often to find potential new drug molecules that affect the targeted cells in ways similar but not identical to existing drugs. Thus they might work better against sub-types of the disease, cause fewer side effects, and offer options if cancer cells become resistant to initial treatments.

A new approach that could make such early stage screening for new drugs far more efficient is described in a paper published online December 14, 2015, in Nature Biotechnology, co-senior-authored by UCSF School of Pharmacy faculty members Steven Altschuler, PhD, and Lani Wu, PhD.

Their new method flips a key aspect of that standard approach for screening large libraries for potential drug leads. The latter uses cells prepared to report (reporter cells) on a particular treatment’s efficacy. For example, they will reveal via their fluorescent labeling that an introduced compound is inhibiting a pre-selected biomarker, such as a particular cellular protein increased in a cancer pathway.

“The problem is that screening results generally cannot be reused,” Wu says. “When you have a new biological target you want to hit with a drug, you have to go and screen the whole compound library again.”

Instead, the approach developed by Altschuler, Wu, and their co-authors began with nearly 100 variations on a cell line in which they fluorescently tagged different, randomly selected proteins involved a wide variety of biological activities. The scientists then treated those varied reporter cell lines with six classes of cancer drugs—that is, drugs which act on different pathways underlying the disease.

The drugs caused the cells to change their patterns of fluorescence in ways that software developed by the researchers—software similar in sophistication to that used for facial recognition—could analyze to determine the drug type and its cancer pathway target. The scientists found that one of their reporter cell lines could be used to distinguish between the six cancer drug classes with 94 percent accuracy.

To further test that cell line, which they dubbed an ORACL—Optimal Reporter cell lines for Annotating Compound Libraries—they screened more than 10,000 small molecules of unknown function from chemical libraries. They treated the cell line with the compounds and then used their software to analyze fluorescence patterns. After finding that more than 100 of those molecules matched the “training” cancer drugs’ patterns, they did experiments to further confirm that nearly all those matches affected the same bio-pathways.

In fact, using their ORACL cell line the researchers were also able to detect characteristic fluorescence and protein response patterns of other compounds that are either already in use or are in development to treat cancers.

“We were able to do one screen, one time, and fish out molecules that were in many diverse classes at once,” says Altschuler.

The researchers plan to further demonstrate and apply ORACLs’ potential for finding promising drug leads by using their method to annotate much larger compound libraries—that is, noting which cancer pathways each compound affects—thus speeding discovery of varied drug types with single-pass screening.

“These are really early steps in drug discovery,” says Altschuler. “We hope finding more high-quality compounds will make the later steps more efficient.”

Altschuler and Wu are faculty members in the School’s Department of Pharmaceutical Chemistry. Along with their co-authors, they conducted initial research on the ORACL approach while at the University of Texas Southwestern Medical Center.


Researchers Create Cellular “ORACLs” to Aid Drug Discovery

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