Trial by Fire

The Philippines was under attack by a killer.

The entire world knew about this killer. Everyone despised it. The evidence of its misdoings was undeniable. Yet it was impossible to bring this killer to justice. After all, it wasn’t human.

It was dengue fever.

Dengue fever is a sometimes fatal disease caused by the dengue virus and spread by mosquitoes2. In places like the Philippines, it can be a leading cause of death2.

But there was promise. A new vaccine, creatively named “Dengvaxia,” had the potential to prevent dengue fever.

The Philippines went all-in on a vaccination campaign. Eventually, it reached over a hundred thousand Filipino children3. Everything was going well.

Until it wasn’t.

The human body fights off disease using a variety of mechanisms. One of the important ones is the antibody, which marks germs for destruction and makes it hard for the germs to wreak havoc4. After catching the disease for the first time or being vaccinated, the body prepares itself to make antibodies more efficiently next time. Ordinarily, this would be good — but, as it turns out, dengue virus uses antibodies to transport itself throughout the body, in a phenomenon known as antibody-dependent enhancement5. A study released in November 2017 showed that Dengvaxia can also stimulate antibody-dependent enhancement3.

In other words: In people who hadn’t caught dengue fever before, the vaccine could actually make the disease worse.

And then the world burned!

Well, it didn’t literally burn, but things got really bad. First of all, the vast majority of those 100 000 kids were now at a higher risk for dengue complications. And while that’s bad enough, things got worse — because of fear. The scandal gained a lot of publicity, leading to some very concerned — and, in many cases, angry — parents. These parents decided to simply not vaccinate their kids at all. This is a recipe for disaster. That disaster struck in 2019, when a measles outbreak hit. The unvaccinated kids were barely protected against the disease. It spread like wildfire throughout the Philippines, infecting 23 563 people and killing 338 of them6.

And the worst part? Based on the original clinical trial data for Dengvaxia, one scientist suspected that the vaccine might have disastrous effects — but that data wasn’t detailed enough to actually prove his suspicions3. Had the data been more thorough, this entire chain of unfortunate events could have been avoided.

The moral of the story is that we can’t get rid of clinical trials. We can’t sacrifice their accuracy. People’s lives are at stake. When we make clinical trials faster, we need to make sure they’re still accurate.

Clinical trials are bogged down by paperwork and logistical challenges. The first step in making trials faster would be to get rid of some of these. Paperwork should be reduced. Some of the questionnaires given to prospective participants can potentially be redesigned to improve expediency7. The integration of efficient technology and medical record systems can make things even faster.

I, however, am far more interested in shortening the actual trial, using scientific means. In most cases, obtaining a sizable number of participants is easy. The problem is that these participants need to be monitored over a long time. It’s usually pretty clear if a medicine or vaccine works immediately. But it’s trickier to observe long-term effects, like those that Dengvaxia caused. To observe those, the only real solution is to sit and wait.

Or is there a better way?

We might find hope in the world of organoids.

Organoids are basically microscopic blobs of cells that act like various organs8. A lung organoid, for example, would have cells found in a lung8. They would be arranged in such a way that if you were to look at it under a microscope, it would look like a very tiny lung — from a microanatomy perspective, that is8. The catch is that, of course, it isn’t a real lung. It’s a microscopic blob. It’s not quite alive, either — it needs constant external support for its cells to live.

Making an organoid may seem difficult.

It turns out, though, that organoids can be made easily with the help of stem cells. These are cells that can later mature into a variety of other types of cells. In the case of organoids, the stem cells can be coaxed into growing and maturing in a pattern that produces an organoid. (This explanation is a bit oversimplified, but it’s all that’s needed here.)

And here’s the important part — since organoids are so similar to regular organs, we can use them to test medicines. This is especially useful in studying long-term effects. If you want to test the effects of, for instance, repeated disease in humans, you would just need to wait for data on people who get sick repeatedly — but if you have organoids, you can subject them to repeated disease without having to deal with ethical issues.

So organoids can help us speed up trials without sacrificing rigor. This can be especially useful in times of crisis, like a major disease outbreak.

But in the future, we’ll have even more powerful tools.

In the land of biology, some of the most important molecules are proteins. They are the workhorses of the cell, doing basically everything that needs to be done.

Proteins are made based on instructions found in DNA. (Also very oversimplified.) The DNA (and a few other factors, but mostly the DNA) dictates the sequence of amino acids, which are the building blocks of proteins, that are to be used to make a given protein. But proteins are more complicated than that — what they can do relies on their three-dimensional shape, not just the linear amino acid sequence9. These 3D shapes form because of chemical interactions involving amino acids, their environment, and other amino acids in the chain9.

Today, a main focus of microbiology is trying to figure out the rules behind “protein folding,” as it is called. If we know these rules, we can simulate the shape of any given protein. And that would be a huge accomplishment.

If we know the shapes of all of these proteins, we can work on discovering their functions and how they interact with each other. More on-topic for this article, though, we would be able to see how drugs interact with proteins. If medicines interact with human proteins that they’re not supposed to hang out with, that could be an indicator of an unanticipated side effect. These simulated interactions can also reveal weaknesses in medicines, like those in Dengvaxia.

And we can simulate even more things.

So far, we’ve been focusing on trials for medicines. But an equally important point is making medicines. After all, making medicines faster means that they can get to trials faster, which in turn means that they can become available faster.

Here, again, I’ll consider the example of the COVID-19 pandemic, where a new virus emerged out of the blue. (You can read about how it emerged — and why it’s so dangerous — here.) It’s these new pathogens that can be very dangerous.

To fight these, we need to predict what these new pathogens could be. Obviously, this is no trivial task — after all, they don’t exist yet. But they need to come from somewhere.

And, in fact, they do: new pathogens evolve gradually from old ones. Evolution isn’t completely deterministic, but there are some general trends — pathogens that can spread easily and that infect many types of cells have a competitive advantage.

Today, we already survey animals and the environment for germs that could prove to be dangerous. But we’ll need to do this more. And we can go a step further — by simulating slight random changes to these pathogens, we can see what types of dangerous diseases can emerge in the future. We can then investigate if some of these new diseases have the potential to be very dangerous. Using our growing knowledge of proteins and where they are found in the body, we can get the basic idea of what regions these new pathogens will affect, which goes a long way in determining how much they spread and how deadly they might be. Then, we can start identifying potential medicines. Obviously, it would be extraordinarily difficult — not to mention extraordinarily useless and impractical — if we try to develop new medicines for each and every one of these potential diseases. But we can pinpoint which existing medicines might work and which parts of the theoretical pathogens might be good targets for new medicines. At this phase, we can also start to plan out some basic logistics for testing these medicines, like which patients should be included in the trials. Now, it’s just a waiting game.

The vast majority of the theoretical pathogens will never appear, but our work was general. We didn’t look for medicines tailor-made to fight one, very specific disease; we looked at medicines that could fight a variety of vaguely similar diseases. This generalized approach means that our effort isn’t wasted, and it’s also more useful — after all, diseases change, and we need general solutions that can fight diseases even when they change. When some of the pathogens earmarked as “potentially dangerous” actually appears, then we can use the knowledge we collected to immediately start trials for potential medicines and start developing new medicines or vaccines.

Once medicines are developed, though, you need to manufacture them. Clinical trials need rapid medicine manufacturing — and, more importantly, medicines need to be manufactured before you can actually give them to people.

And that’s another area that needs improvement.

Antibiotics kill bacteria, so bacteria making antibiotics might sound preposterous. But such strange creatures exist! For instance, the antibiotic streptomycin comes from the bacterium Streptomyces griseus10. (Incidentally, this bacterium has been so useful in discovering new medicines that it’s actually been made the official microbe of the state of New Jersey.)

This opens up the idea of looking for new medicines out in the wild. Perhaps useful medications are produced by bacteria, or fungi, or plants, or other animals.

But this also entails something else: Bacteria can be used to make things. And these things can be medicines.

The field of biosynthesis is still new, but it is promising. Researchers are using clever techniques to make bacteria mass-produce a variety of molecules11. As these techniques grow more advanced, we’ll be able to better synthesize more and more medicines. Already, we can use genetic engineering to take advantage of cells’ protein-building mechanisms so that we can make “custom” proteins, with whatever amino acid sequence we want. Some of these can even have “upgraded” functionality, using artificially-made parts — like special amino acids or other small chemicals — that can be assembled by cells12. These biologically-inspired molecules are already proving useful in medicine13. As we learn more about proteins, we might be able to create protein “factories” that can churn out a wide variety of other, non-biological molecules, as well. And if we can use life to make medicines, why not go further? We can give benign bacteria the ability to make medicines, and then give those bacteria directly to people. This way, we don’t have to worry as much about handling limited supplies of delicate medicines.

Thus, biosynthesis can give us the ability to make more varied medicines more efficiently. This, in turn, will help make trials faster and will help get medicines to more people, both of which are very good.

All of these technologies can help streamline the process of developing medicines. Some are a bit out there, but there are some things that we can do right now. They may take time, they may take money, and they may take effort. But they improve the world, help us understand science, and — most importantly — save lives. And if that’s not worth it, what is?

This article is dedicated to the Persian polymath Ibn Sina, also called Avicenna. He lived from the late tenth century to the early eleventh, and explored topics from philosophy to astronomy to medicine. In one of his major works, the encyclopedia The Canon of Medicine, he set forth principles for effective clinical trials14. He said, for example, that medicines for one disease should also be tested in patients with a different disease. Modern scientists would say that this provides a kind of “control group,” which isn’t supposed to provide results — instead, it serves as a baseline against which we can compare the results from a “regular” trial. Our standards for control groups have since changed, but nonetheless, they remain an essential concept. Avicenna also stressed reproducibility — after all, if a finding can’t be repeated, it was probably a fluke. Although modern clinical trials would only emerge much later, the basic idea of an effective trial was pioneered by various ancient and medieval scholars, such as Avicenna.

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13. Fosgerau K, Hoffmann T. Peptide therapeutics: current status and future directions. Drug Discovery Today. 2015;20(1):122–128. https://www.sciencedirect.com/science/article/pii/S1359644614003997. doi:10.1016/j.drudis.2014.10.003.

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I am not a scientist, doctor, or a professional in any field. The content of this article merely expresses my personal views, opinions, and visions for the future. This content is not intended for use as professional advice on any matter.