Duxin Sun, University of Michigan and Christian Macedonia, University of Michigan

The potential of using artificial intelligence in drug discovery and development has sparked both excitement and skepticism among scientists, investors and the general public.

“Artificial intelligence is taking over drug development,” claim some companies and researchers. Over the past few years, interest in using AI to design drugs and optimize clinical trials has driven a surge in research and investment. AI-driven platforms like AlphaFold, which won the 2024 Nobel Prize for its ability to predict the structure of proteins and design new ones, showcase AI’s potential to accelerate drug development.

AI in drug discovery is “nonsense,” warn some industry veterans. They urge that “AI’s potential to accelerate drug discovery needs a reality check,” as AI-generated drugs have yet to demonstrate an ability to address the 90% failure rate of new drugs in clinical trials. Unlike the success of AI in image analysis, its effect on drug development remains unclear.

We have been following the use of AI in drug development in our work as a pharmaceutical scientist in both academia and the pharmaceutical industry and as a former program manager in the Defense Advanced Research Projects Agency, or DARPA. We argue that AI in drug development is not yet a game-changer, nor is it complete nonsense. AI is not a black box that can turn any idea into gold. Rather, we see it as a tool that, when used wisely and competently, could help address the root causes of drug failure and streamline the process.

Most work using AI in drug development intends to reduce the time and money it takes to bring one drug to market – currently 10 to 15 years and US$1 billion to $2 billion. But can AI truly revolutionize drug development and improve success rates?

AI in drug development

Researchers have applied AI and machine learning to every stage of the drug development process. This includes identifying targets in the body, screening potential candidates, designing drug molecules, predicting toxicity and selecting patients who might respond best to the drugs in clinical trials, among others.

Between 2010 and 2022, 20 AI-focused startups discovered 158 drug candidates, 15 of which advanced to clinical trials. Some of these drug candidates were able to complete preclinical testing in the lab and enter human trials in just 30 months, compared with the typical 3 to 6 years. This accomplishment demonstrates AI’s potential to accelerate drug development.

On the other hand, while AI platforms may rapidly identify compounds that work on cells in a Petri dish or in animal models, the success of these candidates in clinical trials – where the majority of drug failures occur – remains highly uncertain.

Unlike other fields that have large, high-quality datasets available to train AI models, such as image analysis and language processing, the AI in drug development is constrained by small, low-quality datasets. It is difficult to generate drug-related datasets on cells, animals or humans for millions to billions of compounds. While AlphaFold is a breakthrough in predicting protein structures, how precise it can be for drug design remains uncertain. Minor changes to a drug’s structure can greatly affect its activity in the body and thus how effective it is in treating disease.

Survivorship bias

Like AI, past innovations in drug development like computer-aided drug design, the Human Genome Project and high-throughput screening have improved individual steps of the process in the past 40 years, yet drug failure rates haven’t improved.

Most AI researchers can tackle specific tasks in the drug development process when provided with high-quality data and particular questions to answer. But they are often unfamiliar with the full scope of drug development, reducing challenges into pattern recognition problems and refinement of individual steps of the process. Meanwhile, many scientists with expertise in drug development lack training in AI and machine learning. These communication barriers can hinder scientists from moving beyond the mechanics of current development processes and identifying the root causes of drug failures.

Current approaches to drug development, including those using AI, may have fallen into a survivorship bias trap, overly focusing on less critical aspects of the process while overlooking major problems that contribute most to failure. This is analogous to repairing damage to the wings of aircraft returning from the battle fields in World War II while neglecting the fatal vulnerabilities in engines or cockpits of the planes that never made it back. Researchers often overly focus on how to improve a drug’s individual properties rather than the root causes of failure.

The current drug development process operates like an assembly line, relying on a checkbox approach with extensive testing at each step of the process. While AI may be able to reduce the time and cost of the lab-based preclinical stages of this assembly line, it is unlikely to boost success rates in the more costly clinical stages that involve testing in people. The persistent 90% failure rate of drugs in clinical trials, despite 40 years of process improvements, underscores this limitation.

Addressing root causes

Drug failures in clinical trials are not solely due to how these studies are designed; selecting the wrong drug candidates to test in clinical trials is also a major factor. New AI-guided strategies could help address both of these challenges.

Currently, three interdependent factors drive most drug failures: dosage, safety and efficacy. Some drugs fail because they’re too toxic, or unsafe. Other drugs fail because they’re deemed ineffective, often because the dose can’t be increased any further without causing harm.

We and our colleagues propose a machine learning system to help select drug candidates by predicting dosage, safety and efficacy based on five previously overlooked features of drugs. Specifically, researchers could use AI models to determine how specifically and potently the drug binds to known and unknown targets, the level of these targets in the body, how concentrated the drug becomes in healthy and diseased tissues, and the drug’s structural properties.

These features of AI-generated drugs could be tested in what we call phase 0+ trials, using ultra-low doses in patients with severe and mild disease. This could help researchers identify optimal drugs while reducing the costs of the current “test-and-see” approach to clinical trials.

While AI alone might not revolutionize drug development, it can help address the root causes of why drugs fail and streamline the lengthy process to approval.

Duxin Sun, Associate Dean for Research, Charles Walgreen Jr. Professor of Pharmacy and Pharmaceutical Sciences, University of Michigan and Christian Macedonia, Adjunct Professor in Pharmaceutical Sciences, University of Michigan

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Colleen E. Reid, University of Colorado Boulder

Three years ago, on Dec. 30, 2021, a wind-driven wildfire raced through two communities just outside Boulder, Colorado. In the span of about eight hours, more than 1,000 homes and businesses burned.

The fire left entire blocks in ash, but among them, pockets of houses survived, seemingly untouched. The owners of these homes may have felt relief at first. But fire damage can be deceiving, as many soon discovered.

When wildfires like the Marshall Fire reach the wildland-urban interface, they are burning both vegetation and human-made materials. Vehicles and buildings burn, along with all of the things inside them – electronics, paint, plastics, furniture.

Research shows that when human-made materials like these burn, the chemicals released are different from what is emitted when just vegetation burns. The smoke and ash can blow under doors and around windows in nearby homes, bringing in chemicals that stick to walls and other indoor surfaces and continue off-gassing for weeks to months, particularly in warmer temperatures.

In a new study, my colleagues and I looked at the health effects people experienced when they returned to still-standing homes after the Marshall Fire. We also created a checklist for people to use after urban wildfires in the future to help them protect their health and reduce their risks when they return to smoke-damaged homes.

Tests in homes found elevated metals and VOCs

In the days after the Marshall Fire, residents quickly reached out to nearby scientists who study wildfire smoke and health risks at the University of Colorado Boulder and area labs. People wanted to know what was in the ash and causing the lingering smells inside their homes.

In homes we were able to test, my colleagues found elevated levels of metals and PAHs – polycyclic aromatic hydrocarbons – in the ash. We also found elevated VOCs – volatile organic compounds – in airborne samples. Some VOCs, such as dioxins, benzene, formaldehyde and PAHs, can be toxic to humans. Benzene is a known carcinogen.

People wanted to know whether the chemicals that got into their homes that day could harm their health.

At the time, we could find no information about physical health implications for people who have returned to smoke-damaged homes after a wildfire. To look for patterns, we surveyed residents affected by the fire six months, one year and two years afterward.

Symptoms 6 months after the fire

Even six months after the fire, we found that many people were reporting symptoms that aligned with health risks related to smoke and ash from fires.

More than half (55%) of the people who responded to our survey reported that they were experiencing at least one symptom six months after the blaze that they attributed to the Marshall Fire. The most common symptoms reported were itchy or watery eyes (33%), headache (30%), dry cough (27%), sneezing (26%) and sore throat (23%).

All of these symptoms, as well as having a strange taste in one’s mouth, were associated with people reporting that their home smelled differently when they returned to it one week after the fire.

Many survey respondents said that the smells decreased over time. Most attributed the improvement in smell to the passage of time, cleaning surfaces and air ducts, replacing furnace filters, and removing carpet, textiles and furniture from the home. Despite this, many still had symptoms.

We found that living near a large number of burned structures was associated with these health symptoms. For every 10 additional destroyed buildings within 820 feet (250 meters) of a person’s home, there was a 21% increase in headaches and a 26% increase in having a strange taste in their mouth.

These symptoms align with what could be expected from exposure to the chemicals that we found in the ash and measured in the air inside the few smoke-damaged homes that we were able to study in depth.

Lingering symptoms and questions

There are a still a lot of unanswered questions about the health risks from smoke- and ash-damaged homes.

For example, we don’t yet know what long-term health implications might look like for people living with lingering gases from wildfire smoke and ash in a home.

We found a significant decline in the number of people reporting symptoms one year after the fire. However, 33% percent of the people whose homes were affected still reported at least one symptom that they attributed to the fire. About the same percentage also reported at least one symptom two years after the fire.

We also could not measure the level of VOCs or metals that each person was exposed to. But we do think that reports of a change in the smell of a person’s home one week after the fire demonstrates the likely presence of VOCs in the home. That has health implications for people whose homes are exposed to smoke or ash from a wildfire.

Tips to protect yourself after future wildfires

Wildfires are increasingly burning homes and other structures as more people move into the wildland-urban interface, temperatures rise and fire seasons lengthen.

It can be confusing to know what to do if your home is one that survives a wildfire nearby. To help, my colleagues and I put together a website of steps to take if your home is ever infiltrated by smoke or ash from a wildfire.

Here are a few of those steps:

• When you’re ready to clean your home, start by protecting yourself. Wear at least an N95 (or KN95) mask and gloves, goggles and clothing that covers your skin.

• Vacuum floors, drapes and furniture. But avoid harsh chemical cleaners because they can react with the chemicals in the ash.

• Clean your HVAC filter and ducts to avoid spreading ash further. Portable air cleaners with carbon filters can help remove VOCs.

A recent scientific study documents how cleaning all surfaces within a home can reduce reservoirs of VOCs and lower indoor air concentrations of VOCs.

Given that we don’t know much yet about the health harms of smoke- and ash-damaged homes, it is important to take care in how you clean so you can do the most to protect your health.

Colleen E. Reid, Associate Professor of Geography, University of Colorado Boulder

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Christopher Palma, Penn State

If you have visited an island like one of the Hawaiian Islands, Tahiti or Easter Island, also known as Rapa Nui, you may have noticed how small these land masses appear against the vast Pacific Ocean. If you’re on Hawaii, the nearest island to you is more than 1,000 miles (1,600 kilometers) away, and the coast of the continental United States is more than 2,000 miles (3,200 kilometers) away. To say these islands are secluded is an understatement.

For me, watching the movie “Moana” in 2016 was eye-opening. I knew that Polynesian people traveled between a number of Pacific islands, but seeing Moana set sail on a canoe made me realize exactly how small those boats are compared with what must have seemed like an endless ocean. Yet our fictional hero went on this journey anyway, like the countless real-life Polynesian voyagers upon which she is based.

As an astronomer, I have been teaching college students and visitors to our planetarium how to find stars in our sky for more than 20 years. As part of teaching appreciation for the beauty of the sky and the stars, I want to help people understand that if you know the stars well, you can never get lost.

U.S. Navy veterans learned the stars in their navigation courses, and European cultures used the stars to navigate, but the techniques of Polynesian wayfinding shown in Moana brought these ideas to a very wide audience.

The movie Moana gave me a new hook – pun not intended – for my planetarium shows and lessons on how to locate objects in the night sky. With “Moana 2” out now, I am excited to see even more astronomy on the big screen and to figure out how I can build new lessons using the ideas in the movie.

The North Star

Have you ever found the North Star, Polaris, in your sky? I try to spot it every time I am out observing, and I teach visitors at my shows to use the “pointer stars” in the bowl of the Big Dipper to find it. These two stars in the Big Dipper point you directly to Polaris.

If you are facing Polaris, then you know you are facing north. Polaris is special because it is almost directly above Earth’s North Pole, and so everyone north of the equator can see it year-round in exactly the same spot in their sky.

It’s a key star for navigation because if you measure its height above your horizon, that tells you how far you are north of Earth’s equator. For the large number of people who live near 40 degrees north of the equator, you will see Polaris about 40 degrees above your horizon.

If you live in northern Canada, Polaris will appear higher in your sky, and if you live closer to the equator, Polaris will appear closer to the horizon. The other stars and constellations come and go with the seasons, though, so what you see opposite Polaris in the sky will change every month.

You can use all of the stars to navigate, but to do that you need to know where to find them on every night of the year and at every hour of the night. So, navigating with stars other than Polaris is more complicated to learn.

Maui’s fishhook

At the end of June, around 11 p.m., a bright red star might catch your eye if you look directly opposite from Polaris. This is the star Antares, and it is the brightest star in the constellation Scorpius, the Scorpion.

If you are a “Moana” fan like me and the others in my family, though, you may know this group of stars by a different name – Maui’s fishhook.

If you are in the Northern Hemisphere, Scorpius may not fully appear above your horizon, but if you are on a Polynesian island, you should see all of the constellation rising in the southeast, hitting its highest point in the sky when it is due south, and setting in the southwest.

Astronomers and navigators can measure latitude using the height of the stars, which Maui and Moana did in the movie using their hands as measuring tools.

The easiest way to do this is to figure out how high Polaris is above your horizon. If you can’t see it at all, you must be south of the equator, but if you see Polaris 5 degrees (the width of three fingers at arm’s length) or 10 degrees above your horizon (the width of your full fist held at arm’s length), then you are 5 degrees or 10 degrees north of the equator.

The other stars, like those in Maui’s fishhook, will appear to rise, set and hit their highest point at different locations in the sky depending on where you are on the Earth.

Polynesian navigators memorized where these stars would appear in the sky from the different islands they sailed between, and so by looking for those stars in the sky at night, they could determine which direction to sail and for how long to travel across the ocean.

Today, most people just pull out their phones and use the built-in GPS as a guide. Ever since “Moana” was in theaters, I see a completely different reaction to my planetarium talks about using the stars for navigation. By accurately showing how Polynesian navigators used the stars to sail across the ocean, Moana helps even those of us who have never sailed at night to understand the methods of celestial navigation.

The first “Moana” movie came out when my son was 3 years old, and he took an instant liking to the songs, the story and the scenery. There are many jokes about parents who dread having to watch a child’s favorite over and over again, but in my case, I fell in love with the movie too.

Since then, I have wanted to thank the storytellers who made this movie for being so careful to show the astronomy of navigation correctly. I also appreciated that they showed how Polynesian voyagers used the stars and other clues, such as ocean currents, to sail across the huge Pacific Ocean and land safely on a very small island thousands of miles from their home.

Christopher Palma, Teaching Professor, Department of Astronomy & Astrophysics, Penn State

This article is republished from The Conversation under a Creative Commons license. Read the original article.