Saturday, December 10, 2022

Many drugs affect more than one target in the body: some drug designers are embracing “side effects” that were seen as inconvenient.

Drugs don’t always behave exactly as expected. While researchers may develop a drug to perform a specific function that may be tailored to a specific genetic profile, sometimes the drug may perform several other functions outside of its intended purpose.

This concept of drugs with multiple functions, called polypharmacology, can have unintended consequences. This is a common occurrence for cancer drugs in clinical trials that can have harmful side effects and toxicity in treatment.

But polypharmacology may in fact be the norm for most medications, not the exception. So instead of viewing a drug’s ability to perform many functions as a flaw, biomedical data scientists like myself and my colleagues in the lab believe it can be used to our advantage in designing drugs that address all of the complexity of biology.

Drugs often perform multiple tasks in cells

When scientists talk about drugs, they like to refer to their mechanism of action, or MOA, essentially exactly what a drug does when it enters the body. However, the official MOA for a drug may not include all the ways it can affect cells.

For example, the mechanism of action of a drug labeled VEGF inhibitor is to block the activity of a protein called VEGF, or vascular endothelial growth factor, in a cell. While VEGF plays an important role in creating new blood vessels, a process that is critical to the development of healthy tissue, it can also be a hallmark of cancer. Blocking VEGF can stop the formation of new blood vessels that supply nutrients to tumors and prevent the growth and spread of many types of cancer.

There are currently 14 drugs that inhibit the formation of new blood vessels approved in the US to treat cancer, and most target VEGF. You may be wondering why there are so many different drugs available if they all inhibit the same protein. The answer boils down to polypharmacology: while they all most likely work by blocking VEGF in some way, each probably has some other function that may be unique to that drug. That alternate function may cause side effects or only work under certain conditions.

VEGF belongs to a larger group of proteins called receptor tyrosine kinases, or RTKs, that are difficult to target individually. Many drugs that target one type of RTK, such as VEGF, also indiscriminately target other RTKs because they share a similar chemical structure, which can cause unwanted side effects.

For example, in 1999, scientists discovered that the infamous drug thalidomide for morning sickness also worked as a VEGF inhibitor to treat multiple myeloma, a type of blood cancer. This was a triumph for a drug that, just 70 years earlier, had been banned worldwide after causing serious miscarriages in an estimated 10,000 babies, not including miscarriages and stillbirths.

As in the case of thalidomide, a slight difference in chemical structure can make a big difference in how a drug affects the body.

Like thalidomide, many chemicals affect the body in many different ways, and their full mechanism of action is not yet fully understood. Even some approved drugs like lithium, acetaminophen, and many antidepressants still have an unclear MOA.

Perhaps the most famous example of the fluke of polypharmacology is Viagra, a drug originally developed for cardiovascular problems but later approved for erectile dysfunction. Interestingly, there is emerging evidence that Viagra also works as an activator of VEGF, which can help treat stroke or heart attack.

Taking advantage of polypharmacology

The problem is that when you take a drug with multiple functions, you can’t isolate one desired effect from all the others—you get them all at once. Researchers can react to polypharmacology in two ways. Scientists can try to design better drugs that target only one specific target. Alternatively, scientists can embrace the complexity of biology and try to take advantage of the multifaceted effects that drugs can offer.

Many existing drugs have unknown mechanisms that can be exploited as a strength, rather than a weakness. Researchers can use polypharmacology to repurpose existing drugs for other conditions, reducing the time and cost of developing new treatments. There is an entire industry of doctors and scientists currently trying to do exactly that. Chemists and drug designers are also deliberately designing multifunctional drugs to combat complex diseases such as cancer and type 2 diabetes, which may have multiple targets that may escape single-function treatments.

But to take advantage of the polypharmacology of existing drugs, researchers need a way to measure it. Chemists usually study the mechanisms of drugs through laborious experiments that test drugs one by one and do not always lead to conclusive answers. However, new experimental approaches, such as phenotypic drug screening, which measure the drug’s overall effect rather than trying to narrow down its mechanism of action, allow researchers to measure thousands of different drugs in a single experiment.

My colleagues and I use this approach to predict all the effects of specific drugs, using nothing more than images of cells. We collected 159 million snapshots of cells that react to more than 1,300 different drugs, and then applied a machine learning algorithm to identify important patterns in the images. Instead of teaching the algorithm to look for specific details, we allowed it to look for data in the images that would allow it to better predict how a cell would react to different types of drugs.

Machine learning can help predict how the chemical structure of any particular drug might affect the body.

Our model reused an approach called latent space arithmetic, originally developed with images of human faces, to predict drugs with polypharmacology. Just as the original algorithm could simulate the image of a man wearing glasses, we could simulate what a cell looks like when treated with a drug that has multiple mechanisms of action.

However, our model was far from perfect. Many drug mechanisms of action could not be well simulated, and we were limited by existing, probably incomplete, knowledge of how different drugs worked. Further work to demystify how different drug mechanisms affect cells in a broader context could help improve the prediction of all the potential functions of a drug, leading to more treatment possibilities for each compound.

I believe that embracing polypharmacology as an inevitable consequence of using drugs to treat disease can help researchers reimagine the drug discovery process. Could we design a drug that targets all the receptors going haywire in a specific patient’s tumor? Could we use artificial intelligence to simulate how a potential drug compound might look and behave in the body? Could polypharmacology really be the answer to precision medicine instead of one of its biggest challenges? A change in mindset could be the first step in answering these questions.

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