Tag: chemical and biomolecular engineering

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Looking to AI to Solve Antibiotic Resistance

by Nathi Magubane

Cesar de la Fuente (left), Fangping Wan (center), and Marcelo der Torossian Torres (right). Fangping holds a 3D model of a unique ATP synthase fragment, identified by their lab’s deep learning model, APEX, as having potent antibiotic properties.

“Make sure you finish your antibiotics course, even if you start feeling better’ is a medical mantra many hear but ignore,” says Cesar de la Fuente of the University of Pennsylvania.

He explains that this phrase is, however, crucial as noncompliance could hamper the efficacy of a key 20th century discovery, antibiotics. “And in recent decades, this has led to the rise of drug-resistant bacteria, a growing global health crisis causing approximately 4.95 million deaths per year and threatens to make even common infections deadly,” he says.

De la Fuente, a Presidential Assistant Professor, and a team of interdisciplinary researchers have been working on biomedical innovations tackling this looming threat. In a new study, published in Nature Biomedical Engineering, they developed an artificial intelligence tool to mine the vast and largely unexplored biological data—more than 10 million molecules of both modern and extinct organisms— to discover new candidates for antibiotics.

“With traditional methods, it takes around six years to develop new preclinical drug candidates to treat infections and the process is incredibly painstaking and expensive,” de la Fuente says. “Our deep learning approach can dramatically reduce that time, driving down costs as we identified thousands of candidates in just a few hours, and many of them have preclinical potential, as tested in our animal models, signaling a new era in antibiotic discovery.” César de la Fuente holds a 3D model of a unique ATP synthase fragment, identified by his lab’s deep learning model, APEX, as having potent antibiotic properties. This molecular structure, resurrected from ancient genetic data, represents a promising lead in the fight against antibiotic-resistant bacteria.

These latest findings build on methods de la Fuente has been working on since his arrival at Penn in 2019. The team asked a fundamental question: Can machines be used to accelerate antibiotic discovery by mining the world’s biological information? He explains that this idea is based on the notion that biology, at its most basic level, is an information source, which could theoretically be explored with AI to find new useful molecules.

Read the full story in Penn Today.

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Largest-Ever Antibiotic Discovery Effort Uses AI to Uncover Potential Cures in Microbial Dark Matter

by Eric Horvath

Credit: Georgina Joyce

Almost a century ago, the discovery of antibiotics like penicillin revolutionized medicine by harnessing the natural bacteria-killing abilities of microbes. Today, a new study co-led by researchers at the Perelman School of Medicine at the University of Pennsylvania suggests that natural-product antibiotic discovery is about to accelerate into a new era, powered by artificial intelligence (AI).

The study, published in Cell, the researchers used a form of AI called machine learning to search for antibiotics in a vast dataset containing the recorded genomes of tens of thousands of bacteria and other primitive organisms. This unprecedented effort yielded nearly one million potential antibiotic compounds, with dozens showing promising activity in initial tests against disease-causing bacteria.

“AI in antibiotic discovery is now a reality and has significantly accelerated our ability to discover new candidate drugs. What once took years can now be achieved in hours using computers” said study co-senior author César de la Fuente, PhD, a Presidential Assistant Professor in Psychiatry, Microbiology, Chemistry, Chemical and Biomolecular Engineering, and Bioengineering.

Nature has always been a good place to look for new medicines, especially antibiotics. Bacteria, ubiquitous on our planet, have evolved numerous antibacterial defenses, often in the form of short proteins (“peptides”) that can disrupt bacterial cell membranes and other critical structures. While the discovery of penicillin and other natural-product-derived antibiotics revolutionized medicine, the growing threat of antibiotic resistance has underscored the urgent need for new antimicrobial compounds.

In recent years, de la Fuente and colleagues have pioneered AI-powered searches for antimicrobials. They have identified preclinical candidates in the genomes of contemporary humans, extinct Neanderthals and Denisovans, woolly mammoths, and hundreds of other organisms. One of the lab’s primary goals is to mine the world’s biological information for useful molecules, including antibiotics.

Read the full story in Penn Medicine News.

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Artificial Intelligence to Accelerate Antibiotic Discovery

Using AI for discovery of new antibiotics.

The growing threat of antimicrobial resistance demands innovative solutions in drug discovery. Scientists are turning to artificial intelligence (AI) and machine learning (ML) to accelerate the discovery and development of antimicrobial peptides (AMPs). These short strings of amino acids are promising for combating bacterial infections, yet transitioning them into clinical use has been challenging. Leveraging novel AI-driven models, researchers aim to overcome these obstacles, heralding a new era in antimicrobial therapy.

A new article in Nature Reviews Bioengineering illuminates the promises and challenges of using AI for antibiotic discovery. Cesar de la Fuente, Presidential Assistant Professor in Microbiology and Psychiatry in the Perelman School of Medicine, in Bioengineering and Chemical and Biomolecular Engineering in the School of Engineering and Applied Science, and Adjunct Assistant Professor in Chemistry in the School of Arts and Sciences, collaborated with James J. Collins, Termeer Professor of Medical Engineering and Science at MIT, to provide an introduction to this emerging field, outlining both its current limitations and its massive potential.

In the past five years, groundbreaking work in the de la Fuente Lab has dramatically accelerated the discovery of new antibiotics, reducing the timeline from years to mere hours. AI-driven approaches employed in his laboratory have already yielded numerous preclinical candidates, showcasing the transformative potential of AI in antimicrobial research and offering new potential solutions against currently untreatable infections.

Recent advancements in AI and ML are revolutionizing drug discovery by enabling the precise prediction of biomolecular properties and structures. By training ML models on high-quality datasets, researchers can accurately forecast the efficacy, toxicity and other crucial attributes of novel peptides. This predictive power expedites the screening process, identifying promising candidates for further evaluation in a fraction of the time required by conventional methods.

Traditional approaches to AMP development have encountered hurdles such as toxicity and poor stability. AI models help overcome these challenges by designing peptides with enhanced properties, improving stability, efficacy and safety profiles, and fast-tracking the peptides’ clinical application.

While AI-driven drug discovery has made significant strides, challenges remain. The availability of high-quality data is a critical bottleneck, necessitating collaborative efforts to curate comprehensive datasets to train ML models. Furthermore, ensuring the interpretability and transparency of AI-generated results is essential for fostering trust and wider adoption in clinical settings. However, the future is promising, with AI set to revolutionize antimicrobial therapy development and address drug resistance.

Integrating AI and ML into antimicrobial peptide development marks a paradigm shift in drug discovery. By harnessing these cutting-edge technologies, researchers can address longstanding challenges and accelerate the discovery of novel antimicrobial therapies. Continuous innovation in AI-driven approaches is likely to spearhead a new era of precision medicine, augmenting our arsenal against infectious diseases.

Read “Machine learning for antimicrobial peptide identification and design” in Nature Reviews Bioengineering.

The de la Fuente Lab uses use the power of machines to accelerate discoveries in biology and medicine. The lab’s current projects include using AI for antibiotic discovery, molecular de-extinction, reprogramming venom-derived peptides to discover new antibiotics, and developing low-cost diagnostics for bacterial and viral infections. Read more posts featuring de la Fuente’s work in the BE Blog.

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Episode 4 of Innovation & Impact: Exploring AI in Engineering

by Melissa Pappas

Susan Davidson, Cesar de la Fuente, Surbhi Goel and Chris Callison-Burch speak on AI in Engineering in episode 4 of the Innovation & Impact podcast.

With AI technologies finding their way into every industry, important questions must be considered by the research community: How can deep learning help identify new drugs? How can large language models disseminate information? Where and how are researchers using AI in their own work? And, how are humans anticipating and defending against potential harmful consequences of this powerful technology?

In this episode of Innovation & Impact, host Susan Davidson, Weiss Professor in Computer and Information Science (CIS), speaks with three Penn Engineering experts about leveraging AI to advance scientific discovery and methods to protect its users. Panelists include:

Chris Callison-Burch, Associate Professor in CIS, who researches the applications of large language models and AI tools in current and future real-world problems with a keen eye towards safety and ethical use of AI;  

Surbhi Goel, Magerman Term Assistant Professor in CIS, who works at the intersection of theoretical computer science and machine learning. Her focus on developing theoretical foundations for modern machine learning paradigms expands the possibilities of deep learning; and

Cesar de la Fuente, Presidential Assistant Professor in Bioengineering, Psychiatry and Microbiology with a secondary appointment in Chemical and Biomolecular Engineering, who leads research on technology in the medical field, using computers to find antibiotics in extinct organisms and identify pre-clinical candidates to advance drug discovery. 

Each episode of Penn Engineering’s Innovation & Impact podcast shares insight from leading experts at Penn and Penn Engineering on science, technology and medicine. 

Subscribe to the Innovation & Impact podcast on Apple Music, Spotify or your favorite listening platforms or find all the episodes on our Penn Engineering YouTube channel.

This story originally appeared in Penn Engineering Today.

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The Heart and Soul of Innovation: Noor Momin Harnesses the Immune System to Treat Heart Disease

by Ian Scheffler

Noor Momin, Stephenson Foundation Term Assistant Professor of Innovation

While growing up, Noor Momin, who joined the Department of Bioengineering in January as the Stephenson Foundation Term Assistant Professor of Innovation, imagined becoming a physician. Becoming a doctor seemed like a tangible way for someone interested in science to make a difference. Not until college did she realize the impact she could have as a bioengineer instead.

“I was taping microscope slides together,” Momin recalls of her initial experience as an undergraduate researcher at the University of Texas at Austin. “I didn’t even know what a Ph.D. was.”

It wasn’t until co-authoring her first paper, which explores how lipids, the water-repelling molecules that make up cell membranes (and also fats and oils), can switch between more fluid and less fluid arrangements, that Momin understood the degree to which bioengineering can influence medicine. “Someone could potentially use that paper for drug design,” Momin says.

Today, Momin’s research applies her molecular expertise to heart disease, which despite numerous advances in treatment — from coronary artery bypass surgery to cholesterol-lowering statins — remains the primary cause of mortality worldwide.

As Momin sees it, the conventional wisdom of treating the heart like a mechanical pump, whose pipes can be replaced or whose throughput can be treated to prevent clogging in the first place, overshadows the immune system’s critical role in the development of heart disease.

Read the full story in Penn Engineering Today.

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Penn Scientists Reflect on One Year of ChatGPT

by Erica Moser

René Vidal, at the podium, introduces the event “ChatGPT turns one: How is generative AI reshaping science?” Bhuvnesh Jain, left at the table, moderated the discussion with Sudeep Bhatia, Konrad Kording, Andrew Zahrt, and Nick Pangakis.

As a neuroscientist surveying the landscape of generative AI—artificial intelligence capable of generating text, images, or other media—Konrad Kording cites two potential directions forward: One is the “weird future” of political use and manipulation, and the other is the “power tool direction,” where people use ChatGPT to get information as they would use a drill to build furniture.

“I’m not sure which of those two directions we’re going but I think a lot of the AI people are working to move us into the power tool direction,” says Kording, a Penn Integrates Knowledge (PIK) University professor with appointments in the Perelman School of Medicine and School of Engineering and Applied Science. Reflecting on how generative AI is shifting the paradigm of science as a discipline, Kording said he thinks “it will push science as a whole into a much more collaborative direction,” though he has concerns about ChatGPT’s blind spots.

Kording joined three University of Pennsylvania researchers from the chemistry, political science, and psychology departments sharing their perspectives in the recent panel “ChatGPT turns one: How is generative AI reshaping science?” PIK Professor René Vidal opened the event, which was hosted by the School of Arts & Sciences’ Data Driven Discovery Initiative (DDDI), and Bhuvnesh Jain, physics and astronomy professor and co-faculty director of DDDI, moderated the discussion.

“Generative AI is moving so rapidly that even if it’s a snapshot, it will be very interesting for all of us to get that snapshot from these wonderful experts,” Jain said. OpenAI launched ChatGPT, a large language model (LLM)-based chatbot, on Nov. 30, 2022, and it rapidly ascended to ubiquity in news reports, faculty discussions, and research papers. Colin Twomey, interim executive director of DDDI, told Penn Today that it’s an open question as to how it will change the landscape of scientific research, and the` idea of the event was to solicit colleagues’ opinions on interesting directions in their fields.

Read the full story in Penn Today.

Konrad Paul Kording is Nathan Francis Mossell University Professor in Bioengineering and Computer and Information Science in Penn Engineering and in Neuroscience in the Perelman School of Medicine.

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The NEMO Prize Goes to Research Improving Soft-Tissue Transplant Surgeries

by Melissa Pappas

Daeyeon Lee (left), Oren Friedman (center) and Sergei Vinogradov (right)

Each year, the Nemirovsky Engineering and Medicine Opportunity (NEMO) Prize, funded by Penn Health-Tech, awards $80,000 to a collaborative team of researchers from the University of Pennsylvania’s Perelman School of Medicine and the School of Engineering and Applied Science for early-stage, interdisciplinary ideas.

This year, the NEMO Prize has been awarded to Penn Engineering’s Daeyeon Lee, Russel Pearce and Elizabeth Crimian Heuer Professor in Chemical and Biomolecular Engineering, Oren Friedman, Associate Professor of Clinical Otorhinolaryngology in the Perelman School of Medicine, and Sergei Vinogradov, Professor in the Department of Biochemistry and Biophysics in the Perelman School of Medicine and the Department of Chemistry in the School of Arts & Sciences. Together, they are developing a new therapy that improves the survival and success of soft-tissue grafts used in reconstructive surgery.

More than one million people receive soft-tissue reconstructive surgery for reasons such as tissue trauma, cancer or birth defects. Autologous tissue transplants are those where cells and tissue such as fat, skin or cartilage are moved from one part of a patient’s body to another. As the tissue comes from the patient, there is little risk of transplant rejection. However, nearly one in four autologous transplants fail due to tissue hypoxia, or lack of oxygen. When transplants fail the only corrective option is more surgery. Many techniques have been proposed and even carried out to help oxygenate soft tissue before it is transplanted to avoid failures, but current solutions are time consuming and expensive. Some even have negative side effects. A new therapy to help oxygenate tissue quickly, safely and cost-effectively would not only increase successful outcomes of reconstructive surgery, but could be widely applied to other medical challenges. 

The therapy proposed by this year’s NEMO Prize recipients is a conglomerate or polymer of microparticles that can encapsulate oxygen and disperse it in sustainable and controlled doses to specific locations over periods of time up to 72 hours. This gradual release of oxygen into the tissue from the time it is transplanted to the time it functionally reconnects to the body’s vascular system is essential to keeping the tissue alive. 

“The microparticle design consists of an oxygenated core encapsulated in a polymer shell that enables the sustained release of oxygen from the particle,” says Lee. “The polymer composition and thickness can be controlled to optimize the release rate, making it adaptable to the needs of the hypoxic tissue.” 

These life-saving particles are designed to be integrated into the tissue before transplantation. However, because they exist on the microscale, they can also be applied as a topical cream or injected into tissue after transplantation. 

“Because the microparticles are applied directly into tissues topically or by interstitial injection (rather than being administered intravenously), they surpass the need for vascular channels to reach the hypoxic tissue,” says Friedman. “Their micron-scale size combined with their interstitial administration, minimizes the probability of diffusion away from the injury site or uptake into the circulatory system. The polymers we plan to use are FDA approved for sustained-release drug delivery, biocompatible and biodegrade within weeks in the body, presenting minimal risk of side effects.”

The research team is currently testing their technology in fat cells. Fat is an ideal first application because it is minimally invasive as an injectable filler, making it versatile in remodeling scars and healing injury sites. It is also the soft tissue type most prone to hypoxia during transplant surgeries, increasing the urgency for oxygenation therapy in this particular tissue type.

Read the full story in Penn Engineering Today.

Daeyeon Lee and Sergei Vinogradov are members of the Penn Bioengineering Graduate Group.

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César de la Fuente Named ELHM Scholar by National Academy of Medicine

César de la Fuente, Ph.D.

César de la Fuente, Presidential Assistant Professor in Bioengineering, Psychiatry, Microbiology, and in Chemical and Biomolecular Engineering, has been selected as a 2023 Emerging Leaders in Health and Medicine (ELHM) Scholar by the National Academy of Medicine (NAM). With joint appointments in both Penn Engineering and the Perelman School of Medicine, de la Fuente works to combine human and machine intelligence to accelerate scientific discovery and develop useful tools and life-saving medicines.

NAM, founded in 1970, is an independent organization of professionals that advises the entire scientific community on critical health care issues. Each year, NAM chooses up to 10 new ELHM Scholars who are early-to-mid-career professionals from a wide range of health-related fields, including biomedical engineering, internal medicine, psychiatry, radiology and journalism to serve a three-year term.

“We are delighted that Dr. de la Fuente is receiving recognition from the National Academy of Medicine for his breakthrough contributions and exceptional leadership in the life sciences,” says Vijay Kumar, Nemirovsky Family Dean of Penn Engineering. “His pioneering work using computers to accelerate antibiotic discovery is extraordinary. We proudly celebrate his selection as part of this outstanding group of scholars.”

Read the full story in Penn Engineering Today.

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Harnessing Artificial Intelligence for Real Biological Advances—Meet César de la Fuente

by Eric Horvath

In an era peppered by breathless discussions about artificial intelligence—pro and con—it makes sense to feel uncertain, or at least want to slow down and get a better grasp of where this is all headed. Trusting machines to do things typically reserved for humans is a little fantastical, historically reserved for science fiction rather than science. 

Not so much for César de la Fuente, PhD, the Presidential Assistant Professor in Psychiatry, Microbiology, Chemical and Biomolecular Engineering, and Bioengineering in Penn’s Perelman School of Medicine and School of Engineering and Applied Science. Driven by his transdisciplinary background, de la Fuente leads the Machine Biology Group at Penn: aimed at harnessing machines to drive biological and medical advances. 

A newly minted National Academy of Medicine Emerging Leaders in Health and Medicine (ELHM) Scholar, among earning a host of other awards and honors (over 60), de la Fuente can sound almost diplomatic when describing the intersection of humanity, machines and medicine where he has made his way—ensuring multiple functions work together in harmony. 

“Biology is complexity, right? You need chemistry, you need mathematics, physics and computer science, and principles and concepts from all these different areas, to try to begin to understand the complexity of biology,” he said. “That’s how I became a scientist.”

Read the full story in Penn Medicine News.

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Center for Innovation & Precision Dentistry Positions Penn as a Leader in Engineering Health

by Devorah Fischler

Kathleen J. Stebe and Michel Koo urge “the academic community to adopt a coordinated approach uniting dental medicine and engineering to support research, training and entrepreneurship to address unmet needs and spur oral health care innovations.” (Image: Min Jun Oh and Seokyoung Yoon)

Penn’s Center for Innovation & Precision Dentistry (CiPD) is the first cross-disciplinary initiative in the nation to unite oral-craniofacial health sciences and engineering.

An institutional partnership formalizing the Center’s dual affiliation between the University of Pennsylvania School of Engineering and Applied Science and School of Dental Medicine makes CiPD unique.

In just two years since CiPD was founded, the outcomes of this newly conceived research partnership have proven its value: microrobots that clean teeth for people with limited mobility, a completely new understanding of bacterial physics in tooth decay, enzymes from plant chloroplasts that degrade plaque, promising futures for lipid nanoparticles in oral cancer treatment and new techniques and materials to restore nerves in facial reconstructive surgery.

In addition, CiPD is training the next generation of dentists, scientists and engineers through an NIH/NIDCR-sponsored postdoctoral training program as well as fellowships from industry.

The center’s Founding Co-Directors, Kathleen J. Stebe, Richer & Elizabeth Goodwin Professor in Chemical and Biomolecular Engineering, and Michel Koo, Professor of Orthodontics in Penn Dental Medicine, published an editorial in the Journal of Dental Research, planting a flag for CiPD’s mission and encouraging others to mirror its method.

The two urge “the academic community to adopt a coordinated approach uniting dental medicine and engineering to support research, training and entrepreneurship to address unmet needs and spur oral health care innovations.”

Read the full story in Penn Engineering Today.

Michel Koo is a member of the Penn Bioengineering Graduate Group.