Data Meets Drugs

Data Meets Drugs

New discoveries are changing the ways medicines are developed and tested

At the University of Chicago’s Pritzker School of Molecular Engineering, Savas Tay, PhD, is working on something that could change the game in terms of how drugs are developed and prescribed. They’re called organoids, and they may be the key to safely evaluating the toxicity and effectiveness of molecular compounds without having to test them on humans first. 

An organoid is a cluster of cells grown in a lab that resembles a real human organ. Think of it like 3D printing out of a test tube, with the end result being a piece of tissue with its cells organized as if it were the real deal. 

Researchers can analyze organoids to gain key insights into how our bodies turn cells into organs. And once the organoid is fully grown, they can bring it to its full potential: using it to revolutionize the way we think of drug testing and delivery. 

Optimizing drug development 

In order to bring a new drug to market, pharmaceutical companies in the U.S. need to follow a multiphase drug development pipeline. 

It starts with discovery, which involves identifying chemical entities for a potential therapy. A drug then goes to preclinical trials, which is when it’s tested in vitro (on a cell culture) and in vivo (in animals). If the drug proves effective and nontoxic, it heads to clinical trials, where it’s tested on humans. From there, if successful, it’s on to approval from the Food and Drug Administration. 

Unfortunately, only about 14% of drugs make it past clinical trials, according to a MIT study published in the journal Biostatistics. The others fail, either because they don’t work well in humans or because they have problematic side effects. 

The drug development process is long — it typically takes about seven to 13 years, says Tom Gao, PhD, an assistant professor of medicinal chemistry and pharmacognosy at the University of Illinois at Chicago College of Pharmacy. It’s also incredibly expensive: about $2 billion to $3 billion per drug, Gao says.

“We do these studies on cells and then on animals, and then we evaluate the drugs on humans. But although the human genome is very similar to these models, the outcome may be very different,” Gao says. “It’s one of the most expensive failures.” 

Here’s where organoids come in. Instead of testing new drugs on cell cultures, which are surprisingly poor indicators of toxicity and efficacy in humans, drug companies can test their drugs on these proxy organs. If all looks good, there’s a strong chance the drug will make it through the clinical trial phase. 

Testing drugs “can be done better, much more efficiently and more accurately on organoids rather than just regular cell cultures.”

Testing with organoids may eliminate the problem of discovering and testing new drugs only to have them fail in humans, says Tay, an associate professor and head of UChicago’s Tay Lab for Bioengineering and Systems Biology. Testing drugs “can be done better, much more efficiently and more accurately on organoids rather than just regular cell cultures,” he adds. 

Genetic clues

Organoids are one example of how advancements in data and data science could make the drug development pipeline cheaper, faster and more effective. But they’re not the only player on the field. Consider pharmacogenomics (PGx), a field of medicine that studies how your genetic makeup affects your body’s response to medications.

Some drugs, such as antidepressants, work well for some individuals, but in others they’re ineffective or cause significant side effects. Our genes have a role in determining how we metabolize drugs and who will have success with a particular type of medicine — information that PGx puts to use. 

PGx is most commonly used to personalize drug therapies. It involves looking at parts of a patient’s DNA to see how the patient will metabolize a drug, transport a drug through the bloodstream and bind it to receptors, says Mark Dunnenberger, PharmD, director of pharmacogenomics at NorthShore University HealthSystem. The optimal drug can then be picked for that patient. 

“If we can find changes in some of those places for any given patient, that will let us know whether a medication may not work for [them] or may cause side effects,” Dunnenberger says. 

Like organoids, PGx serves as a reliable drug-testing method, giving researchers a way to evaluate molecular compounds accurately without risk to actual humans. And similarly, PGx also has quite a lot of promise in terms of drug development.

Using PGx to better understand the genetics of potential diseases — and most importantly, what pathways lead to disease-driven genetic changes — can allow pharmaceutical companies to develop drugs that target the specific genetic properties of certain diseases, Dunnenberger says. The result is that drug companies can fast-track the discovery phase, using genetic data to develop drugs that are targeted to function in ways they already know will work. 

Putting data into action

There’s a caveat. Organoids and PGx both stand to significantly improve how drugs are discovered and tested, but they’re currently too expensive and time-consuming to go mainstream in the pharmaceutical realm. And pharma companies are too protective of their data to trust having their drugs tested off-site, so opportunities to expand on these innovations aren’t readily available yet.

That’s not to say that advancements in data science haven’t made their way into big pharma. Many companies are now using data analytics to better recruit patients for clinical trials, Gao says, selecting only patients who the drug is most likely to succeed with. That means less wasted time and money on failed trials and a quicker pipeline for drugs that have real potential to help patients.

Originally published in the Fall 2019/Winter 2020 issue.