Drug Discovery

Summary

Big Ideas Lab: Drug Discovery Episode Summary

Release Date: February 25, 2025
Hosted by: Mission.org
Featuring: Experts from Lawrence Livermore National Laboratory (LLNL)

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Introduction to Drug Discovery

The episode opens with a historical overview of pain relief methods, illustrating humanity's long-standing quest to alleviate ailments. From ancient practices using willow bark—rich in salicin, a precursor to modern aspirin—to the breakthrough development of aspirin by Bayer in 1915, the narrative sets the stage for understanding the complexities of drug discovery.

The Traditional Drug Development Pipeline

Drug development is a multifaceted process spanning approximately 10 to 15 years, comprising five critical stages:

1. Discovery and Development: Identifying a disease target and designing molecules to interact with it.
2. Preclinical and Clinical Research: Conducting extensive testing to ensure safety and efficacy.
3. FDA Review: Navigating regulatory approvals for market release.
4. Post-Market Safety Monitoring: Ongoing assessment of the drug's impact post-release.

Despite advancements, the initial discovery phase remains a significant bottleneck, often consuming up to five years.

LLNL's Innovative Approach to Accelerate Drug Discovery

Jim Brasi, Deputy Associate Director for Computing at LLNL, spearheads the lab's bioresilience initiative, integrating high-performance computing, machine learning, and AI to streamline the drug discovery process. Brasi emphasizes:

"The integration of computing and biology enables better predictive models and rapid countermeasure or drug development."
— Jim Brasi [05:39]

Felice Lightstone, a leader in LLNL's biochemical and biophysical systems group, elaborates on the precision of designing small molecule drugs:

"These would be drugs like you'd take in a pill form... it's a small molecule. It goes into your body and it finds its target... to show improvement of whatever disease you might have."
— Felice Lightstone [07:13]

Transforming Traditional Methods with Technology

Traditionally, drug discovery involved a random search through vast libraries of chemical compounds to find "hit molecules" that bind effectively to target proteins. This method is time-consuming and relies heavily on trial and error.

Jonathan Allen, an informatics scientist at LLNL, highlights the shift towards technology-driven discovery:

"Our goal is to de-risk the development of these molecules..."
— Jonathan Allen [09:45]

LLNL has partnered with the National Cancer Institute’s Frederick National Laboratory and BridgeBio Oncology Therapeutics to leverage supercomputing and machine learning. This collaboration facilitates running 10,000 simulations weekly, narrowing down potential candidates to a mere 20 molecules for synthesis.

Advancements in Supercomputing and AI

LLNL's state-of-the-art supercomputers, El Capitan and Tuolumne, are pivotal in this transformation. El Capitan, the world's fastest supercomputer, performs 1.7 exaflops—capable of executing 1.7 quintillion calculations per second. Its companion system, Tuolumne, operates at 288 petaflops and is dedicated to open science, including drug discovery.

"We're using high performance computing to try to solve biology problems... to design drugs in a faster way."
— Felice Lightstone [10:48]

Active Learning: Bridging Computational Models and Experimental Labs

One of the critical innovations is the active learning process, which creates a feedback loop between computational modeling and laboratory experiments. This system allows for the:

1. Specification of Experiments: Computational models determine necessary experiments to validate predictions or address uncertainties.
2. Automated Experimentation: Laboratories execute these experiments and return data to refine the models.
3. Iterative Improvement: Continuous updates enhance predictive accuracy, accelerating the convergence towards effective solutions.

"We can have this iteration... that allows us to actually converge to solutions faster."
— Jim Brasi [22:59]

Success Stories and Current Projects

LLNL's innovative methods have already demonstrated significant success:

• Reduced Synthesis: A recent study required synthesizing only 500-600 compounds, a 75% decrease compared to traditional methods.
• Clinical Trials: Two new molecular designs from LLNL are advancing into clinical trials, signaling potential breakthroughs in cancer treatment.

Jonathan Allen shares optimism about expanding the pipeline of FDA-approved drugs efficiently:

"I'm optimistic that we'll have a lot more therapeutic tools... in the next five to 10 years."
— Jonathan Allen [30:42]

Addressing Global Health Threats

LLNL's efforts extend beyond conventional drug discovery to counter bioterrorism and viral outbreaks. During the COVID-19 pandemic, the lab pivoted to develop antibodies swiftly, demonstrating the capability to redesign antibodies for viral variants within weeks—a process that previously took years.

"We quickly pivoted to focus on antibodies for viral infections... in just a few weeks."
— Jim Brasi [17:38]

Future Prospects and LLNL’s Vision

The ultimate goal at LLNL is to revolutionize drug discovery by harnessing unparalleled computational power and interdisciplinary collaboration. Felice Lightstone envisions breakthroughs, such as effective treatments targeting the notorious ras protein, implicated in 30% of all cancers.

"If we can actually find an effective drug that would target all of those proteins that are mutated... it would cure 30% of all human cancers."
— Felice Lightstone [27:52]

Join the Mission: Opportunities at LLNL

Throughout the episode, LLNL emphasizes its commitment to innovation and invites professionals across various disciplines to contribute to impactful projects. With 139 open positions ranging from scientists and engineers to IT experts and business professionals, LLNL offers a platform for individuals eager to make a tangible difference in advancing national security, energy solutions, and scientific frontiers.

"Your contributions are not just jobs, they're a chance to make an impact."
— LLNL Recruitment Segment [04:42]

Jim Brasi concludes with a forward-looking statement on LLNL's transformative potential in cancer treatment:

"If this works, this will be transformational for cancer treatment."
— Jim Brasi [29:13]

Conclusion

The "Drug Discovery" episode of Big Ideas Lab showcases Lawrence Livermore National Laboratory's pioneering efforts to expedite and enhance the drug development process through cutting-edge supercomputing and AI technologies. By bridging the gap between computational models and experimental validation, LLNL is poised to significantly reduce the time and cost associated with bringing new medications to market, potentially revolutionizing treatments for a myriad of diseases, including some of the most challenging cancers.

For those interested in contributing to this mission-driven work, LLNL invites you to explore current job opportunities at llnl.gov/careers.

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This summary encapsulates the key discussions, insights, and conclusions from the "Drug Discovery" episode of Big Ideas Lab, providing a comprehensive overview for those who haven't listened to the full episode.

Transcript

[Narrator]

When was the last time you had a headache? An ear splitting, skull pounding headache? What did you do? Probably massaged your temples, maybe dimmed the lights. And then you did what any normal sane person would do. Went outside, found the nearest willow tree and chewed its bark. Right. If you'd asked a physician 4,000 years ago, that's the exact prescription they would have given you. All across ancient Greece, Sumeria, Egypt and China, healers recommended willow leaves and bark to treat pain, fevers and inflammation. But if that doesn't sound tasty, you could brew it into a tea or more like hot bark water. This worked because willow bark contains a molecule called salicin, which causes a reaction in your body that eases pain. In the mid-1700s, a cleric of the Church of England, Edward Stone, rediscovered the willow's properties. But instead of simply gnawing on leaves, he took things a step further and dried the tree's bark for months, creating a powder. With it, he treated himself and several neighbors to varying success. Isolating and extracting the active ingredient salicin was the next great leap forward in 1826. After that, the development of the drug as we know it today began in earnest. But it wasn't until 1915, nearly 90 years later, that Bayer released over the counter aspirin, a small white pill that could banish a headache in 15 minutes. It almost seemed like magic. But its science and centuries of experimentation, research and testing. Nowadays, thankfully, creating new drugs doesn't take thousands of years, but it's still a long process, generally 15 years or more. The folks at Lawrence Livermore National Lab are hoping to change that. Using state of the art supercomputing, machine learning and AI technology, they hope to shorten the gap between research and discovery. It's a tall order, but they're already making huge strides in creating new medications, discovering cures and saving lives. Welcome to the Big Ideas Lab. Your weekly exploration inside Lawrence Livermore National Laboratory. Hear untold stories, meet boundary pushing pioneers, and get unparalleled access inside the gates. From national security challenges to computing revolutions, discover the innovations that are shaping tomorrow. Today, Lawrence Livermore National Laboratory is opening its doors to a new wave of talent. If you're driven by curiosity and a desire to solve complex challenges, the lab has a job opening for you. Currently, there are 139 open positions. These include opportunities in science, engineering, business administration and the skilled trades. From enhancing national security to pioneering new energy sources and advancing scientific frontiers, Lawrence Livermore National Laboratory is where you can make your mark on the world. Today's Open roles include lead power grid engineer, laser modeling, physicist, postdoctoral researcher, OCEC program leader, and chief data architect. But the list doesn't end there. Explore all available [e-mail] careers. Each opportunity comes with a comprehensive benefits package tailored to your lifestyle and future. Join a workplace that champions professional growth, fosters collaboration, inspires innovation, and drives the pursuit of excellence. If you are ready to contribute to work that matters, visit llnl.govcareers to explore all the current job listings. That's llnl.govcareers. your expertise could very well be the highlight of our next podcast interview.

[Jim Brasi]

Don't wait Drug discovery is the process of how we come up with a new medicine for a given purpose. So if we have a particular pathogen like SARS, CoV2, a virus, or a medical condition of some kind, what we want to do then is to find a molecule that could actually go into a person and restore their system to its normal operation.

[Narrator]

That's Jim Brasi.

[Jim Brasi]

My job title is Deputy Associate Director for Computing. I'm the overall lead on our bioresilience initiative at Lawrence Livermore. I coordinate across different bio projects at the lab, particularly in the areas where we're bringing together high performance computing and biology. That's the central theme of what we're doing, the integration of computing and biology to really enable better predictive models, better rapid countermeasure or drug development to make.

[Narrator]

It into your medicine cabinet, a drug has to travel a long, complex road. There are five stages of development. The first phase is discovery and development. Here, chemists and biologists decide what malady or disease they want to target and then design and formulate molecules to treat it. Phase 2 and 3 are preclinical and clinical research. This is where countless tests are run and safety for human consumption is determined. The next is FDA review. At this stage, hopefully the drug is effective and passes final reviews to be cleared for market. Once each of these steps has been completed, the medicine finally goes to patients and the fifth stage, post market safety monitoring. If it sounds like a complicated process, that's because it is. From start to finish, creating a new drug takes between 10 to 15 years. One of the biggest bottlenecks is at the first stage, Discovery and development. Forming a new drug can take up to five years. Lawrence Livermore wants to change that. But how?

[Felice Lightstone]

As a Department of Energy laboratory, we are interested in advancing and creating new technologies. The application of drug discovery and development is one of many areas that we can apply high performance computing.

[Narrator]

That was Felice Lightstone, a leader in the biochemical and biophysical systems group at the lab.

[Felice Lightstone]

When you say drug discovery or drug design, you are designing a small molecule which is a chemical entity designed to target a protein in exactly the same way each and every time it works. These would be drugs like you'd take in a pill form. You've got a headache, you take acetaminophen or Tylenol. It's a small molecule. It goes into your body and it finds its target, meaning where it's going to change some aspect of how your body functions. Usually it's a protein. You hope to change the function of that protein to show improvement of whatever disease you might have. It could be as simple as a headache. It could be complicated, like diabetes or metabolic syndrome or cancer.

[Narrator]

Felice and her team are doing some really exciting stuff with high performance computing in biology. But to appreciate their work, first we need to understand the traditional method of drug development.

[Jim Brasi]

Historically, drug discovery has really sort of used a random search. With experiments. You build up big libraries of chemical compounds of the types that you think might be applicable to a certain drug target.

[Narrator]

Think of the chemical compounds like puzzle pieces, and the targets as empty spaces in the puzzle.

[Felice Lightstone]

Once you have a target, you need to have what they call a hit molecule. That's any molecule that will bind to your protein target.

[Jim Brasi]

Then you test them against that particular molecular target and you see what works or what looks like it's close to working. Then you modify your set of chemicals. Then you do it again. Through this process of making and testing molecules, you eventually narrow in on molecules that could be effective in binding and neutralizing this target.

[Narrator]

They have to see if each puzzle piece or molecule will fit.

[Jim Brasi]

The problem with that approach is that it can take months for every one of those cycles to go through. You have to make molecules successfully. That's complicated. You need chemists to figure out the recipes. Then you have to actually do the testing and interpret the data and so on. You have to do quality control. It's a very time consuming process. That's how drug discovery has traditionally been done, and that's why it generally takes many years.

[Narrator]

At its core, the process now is still the same. Discover, make and test molecules.

[Jonathan Allen]

Our institution is more in the technology development and probably more on the discovery side of it. So our goal is to de risk the development of these molecules for the.

[Narrator]

Actual creation of molecules. Livermore has partnered with the National Cancer Institute's Frederick National Laboratory for cancer research and BridgeBio Oncology Therapeutics, a company looking for novel treatments for cancer. But how do they run 10,000 simulations a week? And narrow it down to only 20 molecules for BridgeBio to synthesize. This is where Livermore's supercomputers and machine learning come in.

[Jim Brasi]

There's really two trends in this that are important. One is our understanding of biology is getting better. Our approaches to simulating molecular interactions are getting better. Our computational power has grown so we can actually do physics based simulations. The other one is this data driven machine learning AI bringing those two together.

[Felice Lightstone]

You've heard about ChatGPT, that's a language model. But we're exploring different kinds of algorithms for machine learning. We get to use the biggest computers in the world. We're using high performance computing to try to solve biology problems. We start from the basic science where we're looking at new capabilities, actually trying to use the computers to make methods go faster. So how do we design drugs in a faster way?

[Jim Brasi]

The AI can predict new molecules in new regimes that we haven't seen, but they generally don't have enough data to drive them. We really get a lot of increased power in the predictive models by bringing those two streams of work together.

[Narrator]

In our analogy, the AI would be searching for puzzle pieces while computers run tests to see if they fit the missing space.

[Felice Lightstone]

The key here is that we move to a physics based simulation model and we can run about 10,000 simulations a week. We have a three dimensional model of every atom in that protein that we're targeting. We use computers to look at small molecules in that virtual space and then we design it so that that small molecule will bind tightly with the protein target.

[Narrator]

So instead of sifting through a box of millions of puzzle pieces by hand, they're using models to narrow down what piece is needed. A corner, edge, middle.

[Felice Lightstone]

But this way they can hone and target their experiments and there's hopefully an acceleration in the process and or a decrease in the cost.

[Narrator]

They saw huge success in a recent study.

[Felice Lightstone]

We only had to synthesize about 500 or 600 compounds. And it sounds still like a big number, But a traditional drug program runs anywhere from 2,000 to 5,000 compounds.

[Narrator]

That's a 75% decrease, which means drugs that might have taken years to develop can now be finished in a matter of months.

[Jonathan Allen]

My name is Jonathan Allen. I'm a informatics scientist at Lawrence Livermore National Laboratory.

[Narrator]

That means he works with data and computational problems to aid in biology research. His main focus now is small molecule drug discovery, but he's used high powered computing for other work as well.

[Jonathan Allen]

I'm thinking back to a previous project I worked on where we published a study that was a reanalysis of human microbiome samples, an instance where we were able to take advantage of unique computing facilities. We had access to this large cluster that allowed us to search every publicly available human microbiome sample against a very large database of microbial genomes to try to assess what actually was in these human microbiome samples.

[Narrator]

Every living person has microbes living in their body. These can be bacteria, viruses, and cells that contain DNA. Analyzing them gets scientists closer to developing new treatments for diseases both genetic and otherwise.

[Jonathan Allen]

What we actually ended up finding was there's a lot more human DNA in these samples than was previously recognized because we were able to pick up on more of the natural human genetic variation in the samples that hadn't been previously detected because it hadn't been searched for. It was on the order of like weeks that we were able to do thousands of samples which would otherwise take maybe months or a year to do.

[Narrator]

This acceleration is obviously an exciting prospect in drug development, but protecting public health doesn't stop at headaches or genetic diseases. Some of the greatest dangers to humanity lay in other threats like bioterrorism or viral outbreaks. Will we be ready for those Lawrence Livermore National Laboratory invites you to join a diverse team of professionals the Lab is currently hiring for a lead power grid engineer, a laser modeling physicist, postdoctoral researcher, an OCEC program leader, a chief data architect, and 139 other positions for scientists, engineers, IT experts, administrative and business professionals, welders, and more at Lawrence Livermore National Laboratory. Your contributions are not just jobs, they're a chance to make an impact. From strengthening US Security to leading the charge in revolutionary energy solutions and expanding the boundaries of scientific knowledge, the Lab values collaboration, innovation and excellence, offering a supportive workspace and comprehensive benefits to ensure your well being and secure your future. Seize the opportunity to help solve something monumental. Dive into the wide variety of job [e-mail] careers. This is your chance to join a team dedicated to a mission that matters. That's llnl.govcareers. your expertise might just be the spotlight in our next podcast interview. Don't delay.

[Jim Brasi]

Livermore has had a biology program since way back in the 1960s. Understanding genomics, bringing new technologies into biology, building detectors for biological defense against bio threats.

[Jonathan Allen]

A big one was this program called biowatch. Lawrence Livermore was responsible for contributing to a lot of the early technology.

[Narrator]

BioWatch was introduced in 2003. The system, managed by the Department of Homeland Security, monitors the air to detect possible bioterrorist attacks.

[Jonathan Allen]

The lab has this Two pronged focus in the space, which is around countermeasures, which is developing therapeutics for something that would be a biological threat. The flip side of that is also understanding what do we need to be developing countermeasures for? How can we be pre positioning ourselves so that we can rapidly respond to something that's an outbreak?

[Narrator]

From the Oval Office tonight, the President announced his boldest steps yet to combat the nationwide spread of the novel coronavirus.

[Jonathan Allen]

To keep new cases from entering our shores, we will be suspending all travel from Europe to the United States for the next 30 days.

[Jim Brasi]

The COVID 19 pandemic started. We quickly pivoted to focus on antibodies for viral infections, and that was extremely successful. We demonstrated that we could actually develop or redesign an antibody for a viral variant in just a few weeks using that, rather than taking a couple years to do this.

[Jonathan Allen]

RNA viruses are just a naturally concerning biological threat in general, and no better example of that than SARS CoV2. There's been a lot of activity at the DOE labs, Lawrence Livermore, as well as the others, in terms of bringing to bear all of these computational tools to develop various responses and countermeasures. We were able to engage in developing some small molecule drug discovery on some of the protein targets for SARS CoV2 and develop some potential candidates.

[Felice Lightstone]

We need to be ready for the next infectious disease that comes around. And I think that this is a national mission now that Lawrence Livermore can fulfill.

[Narrator]

Thankfully, the urgency of the pandemic has subsided, but the lab is using that same technology in other ways to protect Americans.

[Jim Brasi]

What we're doing now is designing antibodies to support warfighter protection. So if the DoD sends soldiers into a particular area of the world, they may be exposed to a virus. They want to have antibody therapies that they can give to those soldiers going in to protect them. They want to be able to develop these things rapidly. That's going very well.

[Narrator]

Lawrence Livermore is using computational power to speed up biology research. But almost everyone already has an extremely powerful computer in their pocket.

[Felice Lightstone]

Hello.

[Narrator]

A modern smartphone runs over 5000 times faster than one of the original 1980s supercomputers known as Cray 2.

[Felice Lightstone]

High performance computing is one of the cornerstones of Lawrence Livermore National Lab. Every three to five years, we'll get a new number one machine. Next is El Capitan, bigger and badder than any other machine that's on the planet Earth.

[Jim Brasi]

El Capitan, the fastest supercomputer in the world, about 1.7 exaflops. That means El Capitan can perform 1.7 quintillion math problems per second. Everything happens in a very short time span, but very complex physics going on. El Capitan is about 20 times faster than the lab's previous supercomputer, Sierra dies dedicated in 2018. What took days or weeks on Sierra, now just hours on El Capitan.

[Narrator]

El Capitan's unclassified companion system, Tuolamine, a 288 petaflop system using the same components as El Capitan, is currently the world's 10th most powerful supercomputer. It will be used for open science, including some of the team's drug discovery work. So how does Tuolumne compare to a smartphone? The device in your pocket operates at 2 to 2.5 teraflops, a measure of computing power.

[Felice Lightstone]

We're going to get 100 petaflops of dedicated time just for biology, and this probably will be the largest computing resource for biology in the world. Just for biology problems, a petaflop is.

[Narrator]

1000 times the power of one teraflop. And with a quick little math, that means Felice and her team will have over 50,000 times the computing power that.

[Felice Lightstone]

Your cell phone has for dedication to biology. That's never been achieved before.

[Narrator]

But they can't simply plug in the computer and put their feet up. These operations don't just happen automatically.

[Jonathan Allen]

Part of the challenge of these types of problems is that it's not always easy to formulate the problem to be solved on a large computing system. It can take time to think about how to structure the problem in such a way that it could be solved with a large computing resources.

[Narrator]

In other words, you have to know exactly which puzzle piece you're looking for and give the computer the parameters to find it. And once you find it, you still have to see if it fits.

[Jim Brasi]

One of the current bottlenecks is that it still takes quite a long time. Once we actually do the computational modeling, we still have to go out and make and test molecules with this. So what we're working on now is directly integrating our laboratories with these computational models. So the computational model can actually directly specify what experiment it needs to have done. Sometimes that's to validate what we're doing, or sometimes it's because the model is uncertain on what the prediction should be. In that case, what it can do is it can say, I really need more experimental data with this set of molecules, so can you go run those experiments for me? Then we can actually have automated systems in the laboratory which actually run those experiments. Bring that data back to the computational modeling systems. That system can update its models to improve their predictions, and then we keep going. And we can have this iteration then between computational modeling and experimental make test cycles and have this positive feedback between those two that allows us to actually converge to solutions faster. Yet we call that whole process active learning.

[Narrator]

It's finding, making, and testing puzzle pieces.

[Jim Brasi]

We still have lots of humans in that loop. We don't have an AI system going out and manufacturing molecules. There are humans in the loop doing that. But the system is really specifying exactly what it should be done.

[Felice Lightstone]

It also does take expertise. I don't want to minimize my team. They are a great team and they have the knowledge that it takes to recognize that certain parts of the drug molecule need interactions with certain parts of the protein molecule. And so by having that intuition or education, really can focus the simulations and the screening that we do as well.

[Narrator]

But even though Felice, Jim and Jonathan have streamlined the machine learning process, drug development can still come to a screeching halt during the second and third phases, clinical trials.

[Jonathan Allen]

The longer you get down that pipeline, going from initial discovery phase to actually clinical testing, you end up finding failures, particularly around safety, that can really slow down the whole pipeline. So one of the premises of the computational platform has been this idea that we want to try to evaluate these molecules up front as early in the discovery process as possible for all of these different criteria that go into making a good drug. The idea is that if we can initially explore different parts of chemical space that meet all of our design criteria, then we will save some time later on in terms of having to go back and start over again.

[Narrator]

Usually what causes a drug to fail at the clinical level is a safety concern.

[Jonathan Allen]

A lot of what we're trying to do is predict, number one, is whether this chemical will bind and affect the activity of the protein target primarily. But then secondarily, is it going to be safe in the human body? Small molecules that are associated with cardiotoxicity or liver toxicity and can typically make drug discovery fail later in the process as it gets closer to clinical trials.

[Narrator]

So by using AI to test these molecules before testing, Livermore is ensuring they don't have to go back to the drawing board multiple times. That's a great advantage. And they're applying the same techniques to address other drug problems, ones you're probably all too familiar with. Common side effects may include headache, dizziness.

[Felice Lightstone]

Dry mouth, fatigue, upset stomach, increased heart rate, mild rash.

[Jonathan Allen]

What can make small molecule drug discovery so challenging? There's a lot of things that we can ingest in the body that our body doesn't like or can make us have negative side effects.

[Felice Lightstone]

If you're taking a drug and it's in your bloodstream, you, want to make sure it hits its protein target and it doesn't go everywhere else in your body. This is where you would say you have side effects, and side effects are where it shouldn't be going. Like, you might feel nauseous, or you might have a rash or something like that, but you still want the drug to really work in where it's designed to work.

[Jonathan Allen]

We've been demonstrating this computational pipeline with this selective histamine inhibitor, A novel antihistamine that prevents drowsiness. The idea is to design a molecule that hits this histamine receptor target, but doesn't hit some of these other receptors that look like the target that have this negative side effect of drowsiness.

[Narrator]

That's just one example. Often, which targets companies are working on Is kept tightly under lock and key to prevent competition. But there's one protein that everyone in the drug development space Knows about.

[Felice Lightstone]

It's been called the undruggable target.

[Narrator]

It's the ras protein.

[Jim Brasi]

It's a protein that you actually need it. It actually initiates cell division, but when it's mutated, it can get stuck in an on state where it will just continually initiate cell division.

[Narrator]

And it's responsible for 30% of all cancers.

[Jim Brasi]

It's implicated as the triggering point in some of the worst and most untreatable cancers, like 90% of pancreatic cancer, Gastrointestinal and lung cancers.

[Narrator]

When a ras protein mutates, it doesn't know when to stop, Creating more cells, in this case, cancerous ones. To make matters worse, Some mutations Can also Also create resistance to cancer treatments, Making the disease even harder to combat.

[Felice Lightstone]

If we can actually find an effective drug that would target all of those proteins that are mutated, Then there's some vision that it would cure 30% of all human cancers.

[Narrator]

That would be revolutionary.

[Felice Lightstone]

You have to have an open mind that many things are possible. And the beautiful thing about lawrence livermore Is that if you really want to do something that is within the mission of the lab, you, can do it.

[Narrator]

And they've partnered with other research facilities that are just as passionate about developing a treatment. Working with frederick national laboratory, Lawrence livermore national laboratory has developed methods to model cancer cell interactions with proteins.

[Jim Brasi]

Some of that work has now led to work With a small company Called bridge bio.

[Narrator]

They're a pharmaceutical company Focused on addressing.

[Felice Lightstone]

Cancer Bridge is providing great chemists great insight in the cancer space and the ability to push and get to a new drug entity. Livermore is contributing the computational prowess of the Department of Energy labs and to show that we can develop new methods and new technologies. What that comes down to is the of people on both sides, it's the interaction at the human level. We have great experts in the computer space, we have great experts in the chemistry space and the ability to communicate and actually convince each other that we are able to do what we're able to do and produce new drugs.

[Jim Brasi]

We actually have two new molecular designs now that are coming from Foley's, Lightstone and her group that are going into clinical trials right now. And so that's very exciting. So if this works, this will be transformational for cancer treatment.

[Narrator]

Shortening the time between drug discovery and getting pills to patients is a lofty goal, but Felice, Jonathan, Jim and their incredible teams are tackling them head on.

[Jonathan Allen]

It's something like 750 FDA approved drugs right now that are available on the market. What I would love to see is our ability to expand that pipeline of molecules and therapeutics that can get towards FDA approval much more efficiently and more quickly and cheaply. I'm hoping we will have a whole set of new molecules that we can bring to bear on different disease targets.

[Narrator]

By using state of the art computing power and combining traditional chemistry and physics with machine learning and AI, they're already making huge strides in achieving that goal.

[Felice Lightstone]

In the last two years, we've had great success in being able to make a few drugs.

[Jim Brasi]

One was just FDA approved for the phase one trials and those will be starting up in the fall. These are small molecule drugs, so they go through the standard stages of clinical trials that will take up to a couple years to have real feedback on those.

[Jonathan Allen]

I'm optimistic that we'll have a lot more therapeutic tools in the toolbox, so to speak, to treat various diseases in the next five to 10 years.

[Narrator]

We've come a long way from the willow tree and the folks at Lawrence Livermore are confident that the lab's state of the art computing technologies will bring better, faster drugs in the next few years. Whether it's a headache, virus, genetic disease or cancer, no challenge is too great. Helping humanity, that's the priority. Lawrence Livermore National Laboratory is opening its doors to a new wave of talent. Whether you're a scientist, an IT professional, a welder, an administrative or business professional, or an engineer, Lawrence Livermore National Laboratory has an opportunity for you. From enhancing national security to pioneering new energy sources and advanced, advancing scientific frontiers. Lawrence Livermore National Laboratory is where you can make your mark on the world. Lawrence Livermore National Laboratory's culture is rooted in collaboration, innovation and the pursuit of excellence. We offer a work environment that supports your professional growth and a benefits package that looks after your well being and future. Are you ready to contribute to work that matters? Visit LLNL to explore current job openings and learn more about the application process. Don't miss the chance to be a part of a mission driven team working on projects that make the impossible possible. Visit llnl.govcareers now to view the current job listings. Remember, that's llnl.govcareers. your expertise could be the highlight of our next podcast interview. Don't wait, explore the possibilities today. Thank you for tuning in to Big Ideas Lab. If you loved what you heard, please let us know by leaving a rating and review. And if you haven't already, don't forget to hit the Follow or Subscribe button in your podcast app to keep up with our latest epis. Thanks for listening.