drug discovery Archives - Sanford Burnham Prebys

Tag: drug discovery

Institute News

Protein superfamily crucial to the immune system experiences Broadway-style revival

AuthorGreg Calhoun
Date

November 19, 2024

Tumor necrosis factor-α (TNF), shown in pink and tan, is depicted in a complex with an antibody called golimumab, pictured in slate gray
Tumor necrosis factor-α (TNF), shown in pink and tan, is part of a large family of signaling proteins known to play a key role in developing and coordinating the immune system. Here, TNF is depicted in a complex with an antibody called golimumab, pictured in slate gray. Golimumab is used as a drug to treat rheumatoid arthritis and other autoimmune disorders, which it does by smothering the receptors on TNF that initiate inflammation.

More than 25 years after targeting a member of this superfamily of proteins led to groundbreaking treatments for several autoimmune diseases including rheumatoid arthritis and Crohn’s disease, San Diego scientists note a resurgence of interest in research to find related new drug candidates.

In 1998, the same year “Footloose” debuted on Broadway, REMICADE® (infliximab) was approved by the FDA for the treatment of Crohn’s disease. This was the first monoclonal antibody ever used to treat a chronic condition, and it upended the treatment of Crohn’s disease.

Research published in February 2024 demonstrated better outcomes for patients receiving infliximab or similar drugs right after diagnosis rather than in a “step up” fashion after trying other more conservative treatments such as steroids.

Infliximab and ENBREL® (etanercept) — also approved in 1998 to treat rheumatoid arthritis — were the first FDA-approved tumor necrosis factor-α (TNF) inhibitors. TNF is part of a large family of signaling proteins known to play a key role in developing and coordinating the immune system.

The early success of infliximab and etanercept generated excitement among researchers and within the pharmaceutical industry at the possibility of targeting other members of this protein family. They were interested in finding new protein-based (biologics) drugs to alter inflammation that underlies the destructive processes in autoimmune diseases.

As “Footloose” made it back to Broadway in 2024 for the first time since its initial run, therapies targeting the TNF family are in the midst of their own revival. Carl Ware, PhD, a professor in the Immunity and Pathogenesis Program at Sanford Burnham Prebys, and collaborators at the La Jolla Institute for Immunology and biotechnology company Inhibrx, report in Nature Reviews Drug Discovery that there is a resurgence of interest and investment in these potential treatments.

“Many of these signaling proteins or their associated receptors are now under clinical investigation,” said Ware. “This includes testing the ability to target them to treat autoimmune and inflammatory diseases, as well as cancer.”

Today, there are seven FDA-approved biologics that target TNF family members to treat autoimmune and inflammatory diseases. There also are three biologics and two chimeric antigen receptor (CAR)-T cell-based therapies targeting TNF members for the treatment of cancer. This number is poised to grow as Ware and his colleagues report on the progress of research and many clinical trials to test new drugs in this field and repurpose currently approved drugs for additional diseases.

“The anticipation levels are high as we await the results of the clinical trials of these first-, second- and — in some cases — third-generation biologics,” said Ware.

Ware and his coauthors also weighed in on the challenges that exist as scientists and drug companies develop therapies targeting the TNF family of proteins, as well as opportunities presented by improvements in technology, computational analysis and clinical trial design.

Portrait of Carl Ware

Carl Ware, PhD, is a professor in the Immunity and Pathogenesis Program at Sanford Burnham Prebys.

“There are still many hurdles to get over before we truly realize the potential of these drugs,” noted Ware. “This includes the creation of more complex biologics that can engage several different proteins simultaneously, and the identification of patient subpopulations whose disease is more likely to depend on the respective proteins being targeted.

“It will be important for researchers to use computational analysis of genetics, biomarkers and phenotypic traits, as well as animal models that mimic these variables. This approach will likely lead to a better understanding of disease mechanisms for different subtypes of autoimmune conditions, inflammatory diseases, and cancer, enabling us to design better clinical trials where teams can identify the appropriate patients for each drug.”

Institute News

The implastic nature of plastic culture

AuthorScott LaFee
Date

November 4, 2024

Cultured cancer cells from the “immortal” HeLa human line. Image courtesy of Matthew Daniels, Wellcome Collection.
Cultured cancer cells, like these from the “immortal” HeLa human line, have proven crucial to basic cancer research. But do they accurately capture the look and behavior of living tumor cells? Kevin Tharp says no, hindering effective drug development. Image courtesy of Matthew Daniels, Wellcome Collection.

There is an art (and science) to creating cell culture models that reflect the complexities of disease. Such models have long been indispensable to parsing the underlying mechanisms of pathology and to preclinical drug discovery.

But art, writes Kevin Tharp, PhD, assistant professor in the Cancer Metabolism and Microenvironment Program, doesn’t always imitate life — at least not when it comes to finding effective cancer therapeutics.

“Just like a machine-learning algorithm trained on irrelevant datasets, efforts to discover anticancer therapeutics are limited by the models we use,” Tharp writes in the British Journal of Pharmacology. “Our drug discovery pipeline works incredibly well but is applied to models that poorly recapitulate in vivo physiology. This may be why drug discovery approaches efficiently identify drugs that work in the context tested and yet often fail to translate into clinical success.”

It’s a case of there’s no place like home. Cancer cell models are cultured on plastic in two-dimensions with limited or no diversity of neighbors. Cancer cells in vivo reside in three dimensions, with dynamic and complex interactions with neighboring cells and surroundings, i.e., the tumor microenvironment.

It’s like growing up on Disneyland’s Main Street versus a real-world urban city. Cultured cancer cells simply don’t look or behave exactly the same as cancer cells in an actual  tumor. Nor do the investigational molecules being tested as potential therapies.

Tharp suggests a multi-pronged approach: Initially culture target cells using conventional methods, then transfer the cells to new culture formats that enforce distinct, non-genomic cytoskeleton architectures and expression patterns that more closed mimic real life.

Institute News

How AI can make drug discovery faster, better and cheaper

AuthorMichael R. Jackson, PhD
Date

September 30, 2024

image derived from cryo-electron microscopy of SBI-4710, a small molecule inhibitor discovered at Sanford Burnham Prebys
An image derived from cryo-electron microscopy of SBI-4710, a small molecule inhibitor discovered at Sanford Burnham Prebys (in white) bound into the glutaryl-CoA binding pocket of the GCDH protein, a cancer drug target. Residing in a nearby pocket is flavin adenine dinucleotide (orange), a co-factor. The image was generated by a team of researchers, including Merve Demir, PhD; Jianhua Zhao, PhD; Eduard Sergienko, PhD; Steve Olson, PhD and Ze’ev Ronai, PhD, who are collaborating to develop a first-in-class drug to inhibit this enzyme as a therapeutic for the treatment of melanoma. Generating a detailed structural understanding of how an inhibitor binds to a target enables an AI-driven process of compound optimization, greatly accelerating the drug development process.

In an essay, Michael R. Jackson, PhD, senior vice president for drug discovery and development at Sanford Burnham Prebys, explains.

Apart from the occasional moment of serendipity, the development of first-in-class drugs has always been more grind than grand, requiring as much as a decade and hundreds of millions of dollars to bring a new medicine to market. Most drug discovery efforts never reach that goal.

The more we learn about the molecular details of life — the previously unseen and unknown biology of different molecules and how they interact in health and disease — the more complex we realize it is, leaving much uncertainty as to what to target with a drug and how best to achieve desired results.

Indeed, the overall success rate of discovering new drugs, especially small molecules, has not dramatically improved over the past 20 years. While incremental advances have occurred, considerable risk and uncertainty remains in every step of the process.

Artificial intelligence (AI) and related advances are poised to change this reality, and rapidly. They are reshaping almost every stage of the drug discovery process, from identifying drug targets and simulating molecular interactions to designing drugs de novo (entirely from scratch) and accurately predicting which are most likely succeed before actual testing or clinical trials.

AI promises transformational progress in discovering drug. We can work faster, cheaper and more efficiently.

Perhaps the most impactful step to be improved is the selection of which molecule (typically a protein) to target with a drug. In a marriage of medical informatics and bioinformatics, data scientists are using AI to merge huge multi-omic datasets to reveal the mechanisms of disease, and which targets should be drugged. Downstream of this critical decision are three stages of drug development all of which seem destined to be revamped by AI:

First, for small molecule drugs we need to find a chemical that interacts with the selected drug target in a way that prevents, inhibits or erases a disease or its symptoms. Traditionally, this might entail screening 500,000 or more random chemicals in the hope that a few will bind (so called hits) that can be further developed into a drug.

Technologies like cryo-electron microscopy now allow us to visualize the three-dimensional structure of biomolecules alone or in complexes. We can see at the molecular level precisely how a chemical, found in a screen, fits into a protein target, not unlike a key into a lock or a jigsaw piece into a puzzle.

Exactly how a chemical binds informs on whether it inhibits, promotes or alters the function of the drug target. It can help medicinal chemists optimize the fit of the bound chemical.

With that information, emerging artificial intelligence tools can tap into and help make sense of vast, ever-growing databases, then suggest the most promising chemicals, which are similar to screening hits but can be calculated to fit the pocket better.

And in a huge step, AI- driven processes can be deployed to identify completely new binding chemicals that are chemically different from screening hits. This is achieved by a process called “in silico docking,” in which the fit of billions of different chemicals is calculated. A massively parallel computational effort is required to accomplish this scale of activity. It was not achievable until the advent of AI chips.

This is research driven by calculated hypothesis, not educated guesswork, and it happens in silico, meaning through computer modeling and simulation. It’s all virtual, compressing years of work into months, weeks or days. AI and machine learning processes have put this stage of the drug discovery process on steroids.

Second, drugs need to have other properties beyond simply binding to their target so that they can be taken as once-a-day pills, safe as well as efficacious. Recent advances in deep learning techniques allow the drug like properties of a chemical to be more accurately predicted by a computer. As this can be done very rapidly and before a chemical is made, it allows a medicinal chemist to focus on making only those compounds that have properties suitable to be a drug. While predicting drug properties is not new, AI has greatly enhanced predictive power, impacting the pace and success rates.

Third, human testing can be much more precise. Designed drugs can be refined to meet extremely specific medical needs. You have data to show which drug candidates are most likely to be effective for different types of patients and diseases and in combination with other drugs. As a result, clinical trials can be more focused, shorter and less costly. Remedies can get to patients who need them faster.

All of this happens universally. Most data is shared. Used effectively, AI informs everybody’s work, though human ingenuity and innovation remain critical. Scientists still need to interpret the data and make ensure that hypotheses are rigorously tested.

The future of drug discovery and development is simply bigger and better with AI. Researchers aren’t limited to what they’ve discovered or learned alone or in their labs. They now have tools to explore and exploit boundless troves of data and knowledge generated by the entire scientific enterprise.

Progress and achievement won’t come without bumps and glitches, of course. There are fundamental issues to address, such as access to the enormous computing powers and resources necessary to effectively use AI, new imaging technologies and other tools. Researchers, labs and institutions unable or unwilling to embrace these technologies may be left behind.

Going all in on AI isn’t just the smart choice. It’s the only choice.

Programming in a Petri Dish, an 8-part series

How artificial intelligence, machine learning and emerging computational technologies are changing biomedical research and the future of health care

Institute News

Acceleration by automation

AuthorGreg Calhoun
Date

September 5, 2024

Programming in a Petri Dish - AI series graphic

Increases in the scale and pace of research and drug discovery are being made possible by robotic automation of time-consuming tasks that must be repeated with exhaustive exactness.

Humans have long been fascinated by automata, objects that can or appear to move and act of their own volition. From the golems of Jewish folklore to Pinocchio and Frankenstein’s Creature—among the subjects of many other tales—storytellers have long explored the potential consequences of creating beings that range from obedient robots to sentient saboteurs.

While the power of our imagination preceded the available technology for such feats of automation, many scientists and engineers throughout history succeeded in creating automata that were as amusing as they were examples of technical mastery. Three doll automata made by inventor Pierre Jaquet-Droz traveled around the world to delight kings and emperors by writing, drawing and playing music, and they now fascinate visitors to the Musée d’Art et d’Histoire of Neuchâtel, Switzerland.

While these more whimsical machinations can be found in collections from the House on the Rock in Spring Green, Wis., to the Hermitage Museum in Saint Petersburg, Russia, applications in certain forms of labor have made it so more modern automation is located in factories and workshops. There is no comparing the level of automation at research institutions to that of many manufacturing facilities more than 110 years since the introduction of the assembly line, nor should there be given the differing aims. However, the mechanization of certain tasks in the scientific process has been critical to increasing the accessibility of the latest biomedical research techniques and making current drug discovery methods possible.

researcher at work in Prebys Center

As a premier drug discovery center, the Conrad Prebys Center for Chemical Genomics team is well-versed in using automation to enable the testing of hundreds of thousands of chemicals to find new potential medicines.

“Genomic sequencing has become a very important procedure for experiments in many labs,” says Ian Pass, PhD, director of High-Throughput Screening at the Conrad Prebys Center for Chemical Genomics (Prebys Center) at Sanford Burnham Prebys. “Looking back just 20-30 years, the first sequenced human genome required the building of a robust international infrastructure and more than 12 years of active research. Now, with how we’ve refined and automated the process, I could probably have my genome sampled and sequenced in an afternoon.”

While many tasks in academic research labs require hands-on manipulation of pipettes, petri dishes, chemical reagents and other tools of the trade, automation has been a major factor enabling omics and other methods that process and sequence hundreds or thousands of samples to capture incredible amounts of information in a single experiment. Many of these sophisticated experiments would be simply too labor-intensive and expensive to conduct by hand.

Where some of the automation of yore would play a tune, enact a puppet show or tell a vague fortune upon inserting a coin, scientists now prepare samples for instruments equipped with advanced robotics, precise fluid handling technologies, cameras and integrated data analysis capabilities. Automation in liquid handling has enabled one of the biggest steps forward as it allows tests to be miniaturized. This not only results in major cost savings, but also it allows experiments to have many replicas, generating very high-quality, reliable data. These characteristics in data are a critical underpinning for ensuring the integrity of the scientific community’s findings and maintaining the public’s trust.

Ian Pass headshot

Ian Pass, PhD, is the director of High-Throughput Screening at the Conrad Prebys Center for Chemical Genomics.

“At their simplest, many robotic platforms amount to one or more arms that have a grip that can be programmed to move objects around,” explains Pass. “If a task needs to be repeated just a few times, then it probably isn’t worth the effort to deploy a robot. But, once that step needs to be repeated thousands of times at precise intervals, and handled the exact same way each time, then miniaturization and automation are the answers.”

As a premier drug discovery center, the Prebys Center team is well-versed in using automation to enable the testing of hundreds of thousands of chemicals to find new potential medicines. The center installed its first robotics platform, affectionately called “big yellow,” in the late 2000s to enable what is known as ultra-high-throughput screening (uHTS). Between 2009 and 2014, this robot was the workhorse for completing over 100 uHTS of a large chemical library. It generated tens of millions of data points as part of an initiative funded by the National Institutes of Health (NIH) called the Molecular Libraries Program that involved more than 50 research institutions across the US. The output of the program was the identification of hundreds of chemical probes that have been used to accelerate drug discovery and launch the field of chemical biology.

“Without automation, we simply couldn’t have done this,” says Pass. “If we were doing it manually, one experiment at a time, we’d still be on the first screen.”

Over the past 10 years the Center has shifted focus from discovering chemical probes to discovering drugs. Fortunately, much of the process is the same, but the scale of the experiments is even bigger, with screens of over 750,000 chemicals. To screen such large libraries, highly miniaturized arrays are used in which 1536 tests are conducted in parallel. Experiments are miniaturized to such an extent that hand pipetting is not possible and acoustic dispensing (i.e. sound waves) are used to precisely move the tiny amounts of liquid in a touchless, tipless automated process. In this way, more than 250,000 tests can be accomplished in a single day, allowing chemicals that bind to the drug target to be efficiently identified. Once the Prebys Center team identifies compounds that bind, these prototype drugs are then improved by the medicinal chemistry team, ultimately generating drugs with properties suitable for advancing to phase I clinical trials in humans.

Within the last year, the Prebys Center has retired “big yellow” and replaced it with three acoustic dispensing enabled uHTS robotic systems using 1536 well high-density arrays that can run fully independently.

“We used to use big yellow for just uHTP library screening, but now, with the new line up of robots, we use them for everything in the lab we can,” notes Pass. “It has really changed how we use automation to support and accelerate our science. Having multiple systems allows us to run simultaneous experiments and avoid scheduling conflicts. It also allows us to stay operational if one of the systems requires maintenance.”

One of the many drug discovery projects at the Prebys Center focuses on the national epidemic of opioid addiction. In 2021, fentanyl and other synthetic opioids accounted for nearly 71,000 of 107,000 fatal drug overdoses in the U.S. By comparison, in 1999 drug-involved overdose deaths totaled less than 20,000 among all ages and genders.

Like other addictive substances, opioids are intimately related to the brain’s dopamine-based reward system. Dopamine is a neurotransmitter that serves critical roles in memory, movement, mood and attention. Michael Jackson, PhD, senior vice president of Drug Discovery and Development at the Prebys Center and co-principal investigator Lawrence Barak, MD, PhD, at Duke University, have been developing a completely new class of drugs that works by targeting a receptor on neurons called neurotensin 1 receptor or NTSR1, that regulates dopamine release.

The researchers received a $6.3 million award from NIH and the National Institute on Drug Abuse (NIDA) in 2023 to advance their addiction drug candidate, called SBI-810, to the clinic. SBI-810 is an improved version of SBI-533, which previously had been shown to modulate NTSR1 signaling and demonstrated robust efficacy in mouse models of addiction without adverse side effects.

Michael Jackson profile photo

Michael Jackson, PhD, is the senior vice president of Drug Discovery and Development at the Conrad Prebys Center for Chemical Genomics.

Prebys Center researchers at work

The funding from the NIH and NIDA will be used to complete preclinical studies and initiate a Phase 1 clinical trial to evaluate safety in humans.

“The novel mechanism of action and broad efficacy of SBI-810 in preclinical models hold the promise of a truly new, first-in-class treatment for patients affected by addictive behaviors,” says Jackson.

Programming in a Petri Dish, an 8-part series

How artificial intelligence, machine learning and emerging computational technologies are changing biomedical research and the future of health care

  • Part 1 – Using machines to personalize patient care. Artificial intelligence and other computational techniques are aiding scientists and physicians in their quest to prescribe or create treatments for individuals rather than populations.
  • Part 2 – Objective omics. Although the hypothesis is a core concept in science, unbiased omics methods may reduce attachments to incorrect hypotheses that can reduce impartiality and slow progress.
  • Part 3 – Coding clinic. Rapidly evolving computational tools may unlock vast archives of untapped clinical information—and help solve complex challenges confronting health care providers.
  • Part 4 – Scripting their own futures. At Sanford Burnham Prebys Graduate School of Biomedical Sciences, students embrace computational methods to enhance their research careers.
  • Part 5 – Dodging AI and computational biology dangers. Sanford Burnham Prebys scientists say that understanding the potential pitfalls of using AI and other computational tools to guide biomedical research helps maximize benefits while minimizing concerns.
  • Part 6 – Mapping the human body to better treat disease. Scientists synthesize supersized sets of biological and clinical data to make discoveries and find promising treatments.
  • Part 7 – Simulating science or science fiction? By harnessing artificial intelligence and modern computing, scientists are simulating more complex biological, clinical and public health phenomena to accelerate discovery.
  • Part 8 – Acceleration by automation. Increases in the scale and pace of research and drug discovery are being made possible by robotic automation of time-consuming tasks that must be repeated with exhaustive exactness.
Institute News

Simulating science or science fiction? 

AuthorGreg Calhoun
Date

August 27, 2024

Programming in a Petri Dish - AI series graphic

By harnessing artificial intelligence and modern computing, scientists are simulating more complex biological, clinical and public health phenomena to accelerate discovery.

While scientists have always employed a vast set of methods to observe the immense worlds among and beyond our solar system, in our planet’s many ecosystems, and within the biology of Earth’s inhabitants, the public’s perception tends to reduce this mosaic to a single portrait.

A Google image search will reaffirm that the classic image of the scientist remains a person in a white coat staring intently at a microscope or sample in a beaker or petri dish. Many biomedical researchers do still use their fair share of glassware and plates while running experiments. These scientists, however, now often need advanced computational techniques to analyze the results of their studies, expanding the array of tools researchers must master to push knowledge forward. For every scientist pictured pipetting, we should imagine others writing code or sending instructions to a supercomputer.

In some cases, scientists are testing whether computers can be used to simulate the experiments themselves. Computational tools such as generative artificial intelligence (AI) may be able to help scientists improve data inputs, create scenarios and generate synthetic data by simulating biological processes, clinical outcomes and public health campaigns. Advances in simulation one day might help scientists more quickly narrow in on promising results that can be confirmed more efficiently through real-world experiments.

“There are many different types of simulation in the life sciences,” says Kevin Yip, PhD, professor in the Cancer Genome and Epigenetics Program at Sanford Burnham Prebys and director of the Bioinformatics Shared Resource. “Molecular simulators, for example, have been used for a long time to show how certain molecules will change their shape and interact with other molecules.”

“One of the most successful examples is in structural biology with the program AlphaFold, which is used to predict protein structures and interactions,” adds Yip. “This program was built on a very solid foundation of actual experiments determining the structures of many proteins. This is something that other fields of science can work to emulate, but in most other cases simulation continues to be a work in progress rather than a trusted technique.”

In the Sanford Burnham Prebys Conrad Prebys Center for Chemical Genomics (Prebys Center), scientists are using simulation-based techniques to more effectively and efficiently find new potential drugs.

Click to Play VideoNanome Virtual Reality demonstration

To expedite their drug discovery and optimization efforts, the Prebys Center team uses a suite of computing tools to run simulations that model the fit between proteins and potential drugs, how long it will take for drugs to break down in the body, and the likelihood of certain harmful side effects, among other properties.

“In my group, we know what the proteins of interest look like, so we can simulate how certain small molecules would fit into those proteins to try and design ones that fit really well,” says Steven Olson, PhD, executive director of Medicinal Chemistry at the Prebys Center. In addition to fit, Olson and team look for drugs that won’t be broken down too quickly after being taken.

“That can be the difference between a once-a-day drug and one you have to take multiple times a day, and we know that patients are less likely to take the optimal prescribed dose when it is more than once per day,” notes Olson. 

Steven Olson, PhD, profile photo

Steven Olson, PhD, is the executive director of Medicinal Chemistry at the Prebys Center.

“We can use computers now to design drugs that stick around and achieve concentrations that are pharmacologically effective and active. What the computers produce are just predictions that still need to be confirmed with actual experiments, but it is still incredibly useful.”

In one example, Olson is working with a neurobiologist at the University of California Santa Barbara and an x-ray crystallographer at the University of California San Diego on new potential drugs for Alzheimer’s disease and other forms of dementia.

“This protein called farnesyltransferase was a big target for cancer drug discovery in the 1990s,” explains Olson. “While targeting it never showed promise in cancer, my collaborator showed that a farnesyltransferase inhibitor stopped proteins from aggregating in the brains of mice and creating tangles, which are a pathological hallmark of Alzheimer’s.”

“We’re working together to make drugs that would be safe enough and penetrate far enough into the brain to be potentially used in human clinical trials. We’ve made really good progress and we’re excited about where we’re headed.”

To expedite their drug discovery and optimization efforts, Olson’s team uses a suite of computing tools to run simulations that model the fit between proteins and potential drugs, how long it will take for drugs to break down in the body, and the likelihood of certain harmful side effects, among other properties. The Molecular Operating Environment program is one commercially available application that enables the team to visualize candidate drugs’ 3D structures and simulate interactions with proteins. Olson and his collaborators can manipulate the models of their compounds even more directly in virtual reality by using another software application known as Nanome. DeepMirror is an AI tool that helps predict the potency of new drugs while screening for side effects, while StarDrop uses learning models to enable the team to design drugs that aren’t metabolized too quickly or too slowly.

Steven Olson et al using VR in Prebys Center

The Prebys Center team demonstrates how the software application known as Nanome allows scientists to manipulate the models of potential drug compounds directly in virtual reality.

“In addition, there are certain interactions that can only be understood by modeling with quantum mechanics,” Olson notes. “We use a program called Gaussian for that, and it is so computationally intense that we have to run it over the weekend and wait for the results.”

“We use these tools to help us visualize the drugs, make better plans and give us inspiration on what we should make. They also can help explain the results of our experiments. And as AI improves, it’s helping us to predict side effects, metabolism and all sorts of other properties that previously you would have to learn by trial and error.”

While simulation is playing an active and growing role in drug discovery, Olson continues to see it as complementary to the human expertise required to synthesize new drugs and put predictions to the test with actual experiments.

“The idea that we’re getting to a place where we can simulate the entire drug design process, that’s science fiction,” says Olson. “Things are evolving really fast right now, but I think in the future you’re still going to need a blend of human brainpower and computational brainpower to design drugs.”

Programming in a Petri Dish, an 8-part series

How artificial intelligence, machine learning and emerging computational technologies are changing biomedical research and the future of health care

  • Part 1 – Using machines to personalize patient care. Artificial intelligence and other computational techniques are aiding scientists and physicians in their quest to prescribe or create treatments for individuals rather than populations.
  • Part 2 – Objective omics. Although the hypothesis is a core concept in science, unbiased omics methods may reduce attachments to incorrect hypotheses that can reduce impartiality and slow progress.
  • Part 3 – Coding clinic. Rapidly evolving computational tools may unlock vast archives of untapped clinical information—and help solve complex challenges confronting health care providers.
  • Part 4 – Scripting their own futures. At Sanford Burnham Prebys Graduate School of Biomedical Sciences, students embrace computational methods to enhance their research careers.
  • Part 5 – Dodging AI and computational biology dangers. Sanford Burnham Prebys scientists say that understanding the potential pitfalls of using AI and other computational tools to guide biomedical research helps maximize benefits while minimizing concerns.
  • Part 6 – Mapping the human body to better treat disease. Scientists synthesize supersized sets of biological and clinical data to make discoveries and find promising treatments.
  • Part 7 – Simulating science or science fiction? By harnessing artificial intelligence and modern computing, scientists are simulating more complex biological, clinical and public health phenomena to accelerate discovery.
  • Part 8 – Acceleration by automation. Increases in the scale and pace of research and drug discovery are being made possible by robotic automation of time-consuming tasks that must be repeated with exhaustive exactness.
Institute News

Presenting The Conrad Prebys Foundation fellows

AuthorMiles Martin
Date

May 15, 2023

Prebys fellows group photo

Thanks to a generous grant from The Conrad Prebys Foundation, a diverse group of early-career researchers will gain hands-on experience in drug discovery and translational medicine.

A new educational program at Sanford Burnham Prebys has welcomed a diverse group of early-career scientists to learn how to transform research discoveries into treatments for human diseases. The program was made possible by a generous grant from The Conrad Prebys Foundation as part of its mission to increase the diversity of San Diego’s biomedical workforce.

“Our mission at The Conrad Prebys Foundation is to create an inclusive, equitable and dynamic future for all San Diegans,” says Grant Oliphant, CEO at The Conrad Prebys Foundation. “San Diego is one of the top areas in the country for biomedical research, and we’re pleased to partner with Sanford Burnham Prebys to help strengthen the pipeline of diverse talent in life sciences research.”

Graduate students and postdoctoral fellows selected for the program will complete projects at the Institute’s Conrad Prebys Center for Chemical Genomics (Prebys Center), the nation’s leading nonprofit drug discovery center. The Prebys Center specializes in finding new medicines for diseases with a substantial unmet medical need in order to develop better therapies. 

“Thank you to The Conrad Prebys Foundation. I am beyond grateful for their support,” says predoctoral Prebys fellow Michael Alcaraz, who will complete his project on the links between aging and brain disease with Professor Peter D. Adams, PhD, and Steven Olson, PhD, executive director of Medicinal Chemistry at the Prebys Center. 

To help fulfill the Foundation’s mission, Sanford Burnham Prebys students and postdocs from historically underrepresented groups were encouraged to apply for the new program.

“Promoting diversity in the biomedical workforce is a founding principle of our educational program,” says Alessandra Sacco, PhD, vice dean and associate dean of Student Affairs in the Graduate School of Biomedical Sciences at Sanford Burnham Prebys. Sacco will oversee the new program alongside Dean Guy Salvesen, PhD, and Professor Michael Jackson, PhD

“Working actively to train people from all backgrounds gives opportunities to people who may not otherwise have had them—and it also improves the quality of the research itself,” she adds.

“Translational research is one of the biggest priorities in biomedicine right now because it’s how we turn discoveries into actual medicines,” says Sacco. “This program gives students and postdocs an opportunity to build the skills they need for translational research jobs in academia or industry.”

The fellowship will culminate in a final symposium next spring, where the fellows will present their research to their peers and to the wider community. 

“I’m looking forward to gaining more experience and making my contribution to the translational science at the Prebys Center,” says predoctoral Prebys fellow Merve Demir, who will complete a structural biochemistry project with Assistant Professor Jianhua Zhao, PhD, and Eduard Sergienko, PhD, director of Assay Development at the Prebys Center. 

The full list of fellows includes:
 

Postdoctoral Fellows

– Karina Barbosa Guerra [Deshpande Lab, Ed Sergienko co-mentor]
“SGF29 as a novel therapeutic target in AML”
 
– Merve Demir [Zhao Lab, Ed Sergienko co-mentor]
“Structural studies of MtCK and GCDH enzyme drug targets”
 
– Jerry Tyler DeWitt [Haricharan Lab, TC Chung co-mentor]
“Investigating the unique molecular landscape of ER+ breast cancer in black women” 
 
– Alicia Llorente Lope [Emerling Lab, Ian Pass co-mentor]
“Exploring PI5P4Kγ as a novel molecular vulnerability of therapy-resistant breast cancer” 
 
– Van Giau Vo [Huang Lab, TC Chung co-mentor]
“Identifying enhancers of SNX27 to promote neuroprotective pathways in Alzheimer’s disease and Down Syndrome”
 
– Xiuqing Wei [Puri Lab, Anne Bang co-mentor]
“Selective targeting of a pathogenetic IL6-STAT3 feedforward loop activated during denervation and cancer cachexia”

 

Predoctoral Fellows

– Michael Alexander Alcaraz [Adams Lab, Steven Olson co-mentor]
“Activating the NAMPT-NAD+ axis in senescence to target age-associated disease”
 
– Shea Grenier Davis [Commisso Lab, Steven Olson co-mentor]
“Examining PIKfyve as a potential therapeutic target in pancreatic cancer” 
 
– Patrick Hagan [Cosford Lab, Ian Pass co-mentor]
“Discovery and development of novel ATG13 degrading compounds that inhibit autophagy and treat non-small-cell lung cancer”
 
– Texia Loh [Wang Lab, Ed Sergienko co-mentor]
“Investigating the role of HELLS in mediating resistance to PARP Inhibition in small-cell lung cancer”
 
– Michaela Lynott [Colas Lab, TC Chung co-mentor]
“Identification of small molecules inhibiting ATF7IP-SETDB1 interacting complex to improve cardiac reprogramming efficiency”
 
– Tatiana Moreno [Kumsta Lab, Anne Bang co-mentor]
“Identifying TFEB/HLH-30 regulators to modulate autophagy in age-related diseases”
 
– Utkarsha Paithane [Bagchi Lab, TC Chung co-mentor]
“Identification of small-molecule enhancers of Honeybadger, a novel RAS/MAPK inhibitor” 
 

Institute News

Mining “junk DNA” reveals a new way to kill cancer cells

AuthorMonica May
Date

February 11, 2021

DNA strand and Cancer Cell Oncology Research Concept 3D rendering, abstract background

Scientists unearth a previously unknown vulnerability for cancer and a promising drug candidate that leverages the approach

Scientists at Sanford Burnham Prebys have uncovered a drug candidate, called F5446, that exposes ancient viruses buried in “junk DNA” to selectively kill cancer cells. Published in the journal Cell, the proof-of-concept study reveals a previously unknown Achilles’ heel for cancer that could lead to treatments for deadly breast, brain, colon and lung cancers.

“We found within ‘junk DNA’ a mechanism to stimulate an immune response to cancer cells, while also causing tumor-specific DNA damage and cell death,” says Charles Spruck, PhD, assistant professor in the National Cancer Institute (NCI)-designated Cancer Center and senior author of the study. “This is a very new field of research, with only a handful of papers published, but this has the potential to be a game-changer in terms of how we treat cancer.”

Since the human genome was fully sequenced in 2003, scientists have learned that our DNA is filled with some very strange stuff—including mysterious, noncoding regions dubbed “junk DNA.” These regions are silenced for a reason—they contain the genomes of ancient viruses and other destabilizing elements. An emerging area of cancer research called “viral mimicry” aims to activate these noncoding regions and expose the ancient viruses to make it appear that a cancer cell is infected. The hypothesis is that the immune system will then be triggered to destroy the tumor.

A one-two punch to cancer

In the study, Spruck and his team set out to find the molecular machinery that silences “junk DNA” in cancer cells. Using sophisticated molecular biology techniques, they found that a protein called FBXO44 is key to this process. Blocking this protein caused the noncoding sections of DNA to unwind—but not for long.

“When we revealed noncoding regions, which aren’t meant to be expressed, this caused DNA breakage. This told the cell that something is deeply wrong, and it committed suicide,” explains Spruck. “At the same time, the DNA of the ancient virus was exposed, so the immune system was recruited to the area and caused more cell death. So, we really delivered a one-two punch to cancer.”

The scientists then showed that a drug that targets the FBXO44 pathway, called F5446, shrank tumors in mice with breast cancer. The drug also improved the survival of mice with breast cancer that were resistant to anti-PD-1 treatment, an immunotherapy that is highly effective but often stops working over time. Additional studies in cells grown in a lab dish showed that the drug stops the growth of other tumors, including brain, colon and lung cancers.

The scientists also conducted many experiments to show that this silencing mechanism only occurs in cancer cells, not regular cells. Analysis of patient tumor databases confirmed that FBXO44 is overproduced in many cancers and correlated with worse outcomes—further indicating that a drug that inhibits this protein would be beneficial.

Moving the research toward people

As a next step, the scientists are working with the Conrad Prebys Center for Chemical Genomics to design an FBXO44 pathway-inhibiting drug that is more potent and selective than F5446. This state-of-the-art drug discovery facility is located at Sanford Burnham Prebys.

“Now that we have a compound that works, medicinal chemists can make modifications to the drug so we have a greater chance of success when we test it in people,” says Jia Zack Shen, PhD, staff scientist at Sanford Burnham Prebys and co-first author of the study. “Our greatest hope is that this approach will be a safe and effective pan-cancer drug, which maybe one day could even replace toxic chemotherapy.”

 

Institute News

Sanford Burnham Prebys hosts top life-science VC firm to learn the secrets of getting funded

AuthorMonica May
Date

February 26, 2019

SBP Start-up Seminar

There’s no way around it: Developing medicine is costly. The average drug takes about $2.6 billion to develop through FDA approval, according to the Tufts Center for the Study of Drug Development. With a price tag that high, securing venture capital (VC) funding is critical for turning a scientist’s discovery into reality.

This month, Kirsten Leute of Osage University Partners (OUP), a top life-science VC firm, spoke to SBP scientists about best practices and common pitfalls when making a VC pitch. 

Anjali Gupta, a graduate student in the laboratory of Karen Ocorr, PhD, assistant professor at SBP, attended the presentation and explained why the insights are so valuable. 

“Drug discovery is a complex and expensive process. It’s important to understand how bench science can be translated into successful products—in this case, potentially life-saving medicines,” says Gupta. “At SBP, scientists are investigating the underlying causes of rare, debilitating diseases and looking for cures for cancer, heart failure, Alzheimer’s disease and more by discovering novel therapeutic targets, signaling pathways, and mechanisms. By knowing the funding options and strategies available, we can make more informed decisions about our discoveries and increase the probability of developing our research into medicines for patients who need these treatments.” 

Below are seven tips Leute shared to help scientists navigate building a start-up and getting it funded. 

  1. It’s all about the team. VC firms look for an investable management team. Is this the leadership team’s first start-up? Or are they serial entrepreneurs? What skills do they have, and which do they lack? Being a novice entrepreneur isn’t a funding deal breaker—but you may want to consider supplementing your team with experienced partners. 
  2. Pick your partners wisely. The most important relationship decision you make in your life is choosing your significant other. The second most important? Finding your business partners. Many founders bring in colleagues who work down the hall—or even neighbors. But it’s most important to know how you work with one another. Can you argue respectfully? Do you trust that you all have the company’s best interest in mind? 
  3. Do your homework. Before you approach a VC firm, make sure you know its investment focus. Does it specialize in pre-clinical or late-stage assets? Does it lead investments or follow on? In recent years, we’ve seen more crossover investing: firms that invest in a company prior to an initial public offering (IPO). A firm’s focus may shift over time, so make sure to stay up to speed. 
  4. Contact the right person. At most firms, each individual has a specialty, such as immunotherapy. So, if you have an immunotherapy product, make sure you’re contacting the individual who works in that area—and not the person who focuses on robotics, for example. 
  5. Mind your budget. VC firms see a lot of pitches. They can spot it if you haven’t budgeted enough—or are budgeting too much—for an activity. If you’re a first-time entrepreneur, it’s best to hire a consultant or find a mentor who can help (see the resources below).
  6. Ask for advice. VC firms love giving advice, which can help strengthen your ultimate pitch for funding. This will also stimulate their interest in your company. Your institute’s technology transfer office may also have helpful resources and connections. 
  7. Show all your cards—even the negative ones. VC firms hate surprises. Make sure you address any concerns or risks up front. The truth always comes out eventually. 

Ready to get your discovery funded? Below are additional resources and reading materials. 

Resources: 

Reading: 

  • Life Sci VC: blog about all things biotech venture capital by scientist turned early-stage venture capitalist Bruce Booth 
  • The Long Run: veteran biotech reporter Luke Timmerman conducts in-depth interviews with biotech newsmakers 
  • Nature’s bioentrepreneur: practical advance and guidance for starting a biotech company 
Institute News

Florida Translational Research Program funding re-fuels drug discovery collaborations with leading research institutions

AuthorDeborah Robison
Date

January 18, 2017

robotic arm at the Prebys Center in Lake Nona

Reinstatement of Florida Translational Research Program (FTRP) funding has provided scientists at Florida universities and medical research institutes with renewed access to the world-class drug discovery technology housed within Sanford Burnham Prebys Medical Discovery Institute at Lake Nona (SBP). The FTRP offers investigators the chance to work with drug discovery experts to translate their research advances into potential new medicines. The facility’s high-tech resources, including high-throughput robotics that screen tens of thousands of chemical compounds per day, combined with expert advice from faculty that have decades of experience in the pharmaceutical industry, make for powerful collaborations that benefit the statewide life science industry.

Funded by the state of Florida and administered by SBP, the program’s most recent call for proposals netted 16 projects—some new and some ongoing—from all Florida universities with biomedical research programs, including the University of Florida, Florida State, Florida International University, University of Central Florida, University of South Florida and University of Miami, as well as the Mayo Clinic and Moffitt Cancer Center.  

All projects focus on major unmet medical needs: aggressive cancers, Alzheimer’s, diabetes, heart disease and drug-resistant infections. While some teams are testing drug libraries to find compounds with desired properties, others are refining active compounds for potency and specificity. The collaborations aim to identify drug candidates with clinical benefits such as reducing tumor size, halting aggressive breast cancer metastasis, reducing inflammation in diseased brains or treating antibiotic-resistant pathogens.

“Our intent is to replicate success stories like that of Pamela McLean, associate professor of neuroscience at the Mayo Clinic,” says Layton Smith, PhD, director of drug discovery at SBP’s Lake Nona campus. “The initial results from her FTRP project led to her receiving the biggest grant ever awarded by the Michael J. Fox Foundation. Similarly, our work with Kirk Conrad, professor of physiology at the University of Florida on a potential heart failure drug has attracted the interest of a major pharmaceutical company.”

“Our approach to collaborative drug discovery has brought more research funding to the state,” adds Smith. “But more important, our work may lead to new therapeutics that reduce the burden of disease around the world.”