Cutting-Edge Approaches to Target Identification and Validation

Pharma uses a host of both tried-and-true and innovative techniques to find and validate new drug targets. Many of these were seen in academic research labs and journals long before they made their way into the drug discovery pipeline, or were migrated from other fields. Here we look at some technologies, both old and new—like CRISPR screening, single-cell analysis, RNA-seq, spatial transcriptomics, and other modern tools—that are gaining traction in the search for new drug targets.

Drug discovery and target identification go hand in hand. Similarly, identification and validation sometimes blend into each other, or bounce back and forth between each other, and drugs and targets rejected for one indication may be repurposed for another.

Steven Corsello, an oncologist-scientist at Stanford University, takes two approaches to identify new drug targets in cancer. The first, he says, is to start with a target-centric approach, for example as part of “large-scale projects such as the Cancer Dependency Map, where investigators are seeking to collect a large set of cancer models that can be grown in the lab, and then systematically interrupt the function of every gene using CRISPR/Cas9 technology.” They then use that information to look for new cancer vulnerabilities, starting from the targets.

His group, though, focuses its efforts mainly on a phenotypic (or cell-based) drug discovery approach. Here they start with cancer models and systematically test compounds of interest, looking for activity profiles—for example inhibiting growth. “And then we take those compounds back to the lab and figure out how they’re working.” They do not know the target in advance, but instead are relying on the target’s phenotype, and information about which models are, for example, inhibited. This may allow them to form hypotheses about predictive biomarkers. “Then we typically perform functional genomic experiments with the compounds to try to figure out the mediators of the compound sensitivity and resistance. In some cases, that’s pointed us toward direct molecular targets,” Corsello explains.

CRISPR screening

CRISPR screens are versatile—they can be used to find effective compounds as well as identify drug targets.

To find drivers of a drug response, “we start with a drug that has an interesting or promising anti-cancer activity with an unknown molecular target,” Corsello notes. They then introduce Cas9 protein, as well as a pooled library of guide RNAs that target every gene in the genome, into their cellular cancer model, and look for genetic alterations that drive sensitivity or resistance to the drug. The screen works with both gene knockouts and activation technologies.

Knockout and knockdown screens can also be performed using more established technologies such as small interfering RNA (siRNA) and short hairpin RNA (shRNA), for example. “Oftentimes that’s looking for targets in a particular pathology or pathway,” points out Daniel LaBarbera, Director of the Center for Drug Discovery at the University of Colorado.

CRISPR screens are also being used in conjunction with other technologies such as multiplex tissue imaging and spatial transcriptomics, for example, to analyze the cellular location of affected genes.

Phenotypic discovery

Phenotypic drug discovery is a target- and hypothesis-agnostic way to capture a cell’s response to treatment, typically using a variety of phenotypes such as viability and changes in gene expression, as its readout.

The concept is not new, and has been enjoying a resurgence in popularity for the past decade or so. What is new are the scale of the experiments and “the information-rich cellular readouts of drug activity that are now available,” Corsello points out. “One can potentially generate a reference dataset of drug effects on each of those readouts, and then come back to that information in the future.”

Cell painting is a multiplex assay using fluorescently tagged antibodies to label cellular components. The painting can be imaged along with other parameters such as surface texture and smoothness—“These are just a few examples of thousands of parameters that can be captured and analyzed by powerful artificial intelligence informatics to decipher the cellular changes that I can’t even comprehend,” explains LaBarbera.

Spatial biology

Spatial biology has been making a huge splash. Nature Methods named it—specifically spatially resolved transcriptomics—as 2020’s Method of the Year “for its ability to provide valuable insights into the biology of cells and tissues while retaining information about spatial context.”

There are now a host of players—established companies, start-ups, and academics—in the spatial biology space, offering a choice of ways to explore not only the landscape of RNA expression in tissue, but other molecules, such as the proteome and metabolome as well.

“Now you can ask, How is it expressed? What are the neighboring cells? What effect do the genes in the neighboring cells have?,” points out Vidyodhaya (Vidya) Sundaram, VP of Business Development at the CRO BioChain.

Some platforms, such as those that rely on RNA sequencing (RNA-seq), excel at large, unbiased experiments, and work well for discovery, she says. Others focus on a smaller number of markers, making them a good tool for biomarker validation.

Single cells

There are now a host of techniques that examine ‘omes not as a collection of cells but as single cells. Researchers can then identify sub-populations, such as stroma and a variety of immune cells, within a bigger population of cells that make up a tumor, for example.

“They would then develop some sort of single-cell ‘omics to try and identify the population of interest that they can exploit either as a model or for drug discovery,” explains LaBarbera. Powerful bioinformatics can then help deconvolute the potential molecular targets involved in regulating those cell populations.

Researchers are also enthusiastic about the advent of patient-derived models in phenotypic drug discovery, which may be more reminiscent of actual tumors or diseased tissue inside of patients.

And Corsello is also excited about co-culture systems—for example, those “looking at the effects of stromal cells, or immune cells, in combination with tumor cells”—which have the potential to capture additional biology that may be hard to read out just by growing models one at a time on their own.