Have you ever wondered how long it took for that pill you took this morning to reach your hands? First, they do extensive research in laboratories. Then they test it on animals. But before they can be approved for use in humans, they have to test it on humans. This is what we call a clinical trial. It's a very complicated and expensive process.
Simply put, what is a clinical trial?
In simple terms, a clinical trial works like this: Researchers recruit patients with the disease that the newly developed drug is targeting. These volunteers are then randomly divided into two groups.
1. The first group is given the new drug to be tested.
2. The second group , the ``Control Group'', is given a placebo that has no active ingredient and looks exactly like the drug. We call this ``Placebo''.
If, at the end of this trial, the symptoms of those who took the new drug have improved more than those who took the placebo, then this proves that the new drug is effective.
One of the biggest challenges in such a trial is finding enough patients who are exactly the right fit for the trial. Sometimes even doctors don't know which trials are right for their patients. Also, the characteristics of the patients who want to participate may not match the requirements of the trial. However, artificial intelligence (AI) could make this task much easier.
Meet your "Digital Twin"
Imagine that you have your own computer model, that is, a digital copy of yourself. That is what is called a ``Digital Twin''. These are just ``Computer Models'' that simulate a real-world object or system on a computer. But when viewed numerically, they behave exactly like the real thing. The best example is when an oxygen tank on the Apollo 13 spacecraft exploded, NASA helped repair it with a ``Digital Twin'' of the same spacecraft built on Earth.
Now, scientists can create a ``Digital Twin`` of a human being, given enough ``data``. This uses an artificial intelligence technology called ``Machine Learning``. Simply put, this involves giving a computer program a large amount of data and letting it learn from it.
Digital Twins of patients are created by training these Machine Learning models using data from previous clinical trials and each patient's personal medical records. The model can then predict how a patient's health will change over the course of the trial if they are given a placebo. This means that a computer-generated control group is created for that patient.
Now let's see how this works. Let's say someone named "Samadhi" is selected to be in the group that receives a new drug. Samadhi's ``Digital Twin,'' a computer model, is in the control group. The model predicts what would happen if Samadhi were given a ``Placebo'' instead of the actual drug. Ultimately, the difference between Samadhi's actual response to the drug and what the model predicted would happen if he took the ``Placebo'' is a measure of how effective the drug is for Samadhi.
One of the biggest advantages of this method is that almost everyone who participates in a clinical trial has the opportunity to receive the new drug, because the ``placebo'' is given to their digital twin.
Let's look at this table to clearly see the benefits of using ``Digital Twins'' instead of traditional control teams.
| Characteristic | Traditional method | Digital Twin Method |
|---|---|---|
| Control Group | Consisting of real people. They get a (Placebo). | Consists of computer models (Digital Twins). |
| Advantage for patients | There is a 50% chance of getting a placebo. That means you can skip the new drug. | Many people have the opportunity to try out the real medicine. |
| Number of volunteers needed | More people are needed (for both teams). | The test can be conducted with a small number of people. |
| Time and cost | Very high. | Can be significantly reduced. |
Even though this technology is good, aren't there problems?
Although this technology holds great promise for the future, it has not yet been widely used. There may be good reasons for this. As Dr. Daniel Neal, an expert in ``(Machine Learning),'' points out, these models rely on large amounts of data. Also, it is very difficult to obtain high-quality data from individuals.
"People often self-report information about things like their diet and exercise habits. People don't always tell the truth. They tend to overstate the amount of exercise they do and understate the amount of junk food they eat."
Another issue is the rare, unexpected side effects that a drug can cause. These are often not covered by the model designed for the control group.
But Dr. Neal's biggest fear is that this predictive model is based on "business as usual." Imagine a big, unexpected event like the COVID-19 pandemic that changes everyone's behavior and makes people sick. "Control models don't take this into account," he says. Then, because of these events that the control group didn't take into account, the final results of the experiment could be completely different.
So can this really be used? What will the future hold?
Eric Topol, an expert on the use of digital technology in healthcare, thinks the idea is great, but the time is not yet right. "I don't think clinical trials will change in the near future. Because it requires multiple layers of data, beyond just health records, such as genome sequences, gut microbiome, environmental data."
According to Charles Fisher, founder of Unlearn.AI, a pioneer in this technology, there are already solutions to the problems of data privacy and bias. "Privacy is easy. We only work with data that has already been anonymized."
When it comes to bias, he says that although the issue has not been fully resolved, it is not relevant to the results of the study. Because the studies are randomized, any bias in the data should not affect the results. But some experts disagree. They say that because you are comparing a real person to a computer model, any errors in the model can directly affect the results .
However, Unlearn.AI is already working with pharmaceutical companies to design clinical trials for neurological diseases such as Alzheimer's, Parkinson's, and multiple sclerosis. They chose these diseases as a good place to start because they have more data than others. Fisher says that if the method can one day be applied to all diseases, it could significantly reduce the time it takes for new drugs to come to market.
Take-Home Message
- A digital twin is a computer model of a real patient used in clinical trials testing new drugs.
- This allows these digital models to be used instead of using real people for the control group.
- A major advantage of this method is that more participants in the trial have the opportunity to receive the actual drug being tested.
- However, challenges remain, such as the difficulty of obtaining high-quality data and the inability to model unexpected events.
- Although this technology holds great promise for the future of medicine, it is still in its infancy. If you have any concerns or doubts, it is important to talk to your doctor .
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