Penn Pioneers a ‘One-Pot Platform’ to Promptly Produce mRNA Delivery Particles

by Nathi Magubane

Lipid nanoparticles present one of the most advanced drug delivery platforms to shuttle promising therapeutics such as mRNA but are limited by the time it takes to synthesize cationic lipids, a key component. Now, Michael Mitchell and his team at the School of Engineering and Applied Science have developed a faster way to make cationic lipids that are also more versatile, able to carry different kinds of treatments to target specific organs. (Image: iStock / Dr_Microbe)

Imagine a scenario where a skilled hacker must upload critical software to update a central server and thwart a potentially lethal virus from wreaking havoc across a vast computer network. The programmer, armed with the lifesaving code, must navigate through treacherous territory teeming with adversaries, and success hinges on promptly getting a safe, stealthy delivery vehicle that can place the hacker exactly where they need to be.

In the context of modern medicine, messenger RNA (mRNA) serves as the hacker, carrying genetic instructions to produce specific proteins within cells that can induce desired immune responses or sequester maladaptive cellular elements. Lipid nanoparticles (LNPs) are the stealthy delivery vehicles that transport these fragile mRNA molecules through the bloodstream to their target cells, overcoming the body’s defenses to deliver their payload safely and efficiently.

However, much like building an advanced stealth vehicle, the synthesis of cationic lipids—a type of lipid molecule that’s positively charged and a key component of LNPs—is often a time-consuming process, involving multiple steps of chemical synthesis and purification.

Now, Michael Mitchell and a team at the University of Pennsylvania have addressed this challenge with a novel approach that leverages a compound library fabrication technique known as “click-like chemistry” to create LNPs in a single, simple step. Their findings, published in the journal Nature Chemistry, show that this method not only speeds up the synthesis process but also presents a way to equip these delivery vehicles with a “GPS” to better target specific organs such as the liver, lungs, and spleen, potentially opening new avenues for treating a range of diseases that arise in these organs.

“We’ve developed what we call an amidine-incorporated degradable (AID) lipid, a uniquely structured biodegradable molecule,” Mitchell says. “Think of it as an easy-to-build custom mRNA vehicle with a body kit that informs its navigation system. By adjusting its shape and degradability, we can enhance mRNA delivery into cells in a safe manner. By adjusting the amount of the AID lipid that we incorporate into the LNP, we can also guide it to different organs in the body, much like programming different destinations into a GPS.”

First author Xuexiang Han, a former postdoctoral researcher in the Mitchell Lab, explains that their new approach allows the rapid creation of diverse lipid structures in just an hour, compared to the weekslong process traditionally required.

Read more in Penn Today.

Alison Pouch Wins 2024 Cardiac Center Innovation Award

Alison Pouch

Congratulations to Alison Pouch, Assistant Professor in Bioengineering in the School of Engineering and Applied Science, and in Radiology in the Perelman School of Medicine, on winning a 2024 Cardiac Center Innovation Award for scientific research from the Children’s Hospital of Philadelphia (CHOP)’s Philly Spin-In. Pouch’s study, titled “Systemic Semilunar Valve Mechanics and Simulated Repair in Congenital Heart Disease,” is a collaboration with Matthew Jolley, Assistant Professor of Anesthesiology and Critical Care at CHOP:

“Through biomechanical assessment, Drs. Matthew Jolley and Alison Pouch are leading an interdisciplinary CHOP-Penn team that plans to determine why current approaches to systemic semilunar valve (SSV) repair fail. They will also investigate methods to design improved repairs before going to the operating room by using computational simulation to iteratively optimize repair.

‘We believe that understanding biomechanics of abnormal SSVs and explorations of simulated repair will markedly improve our ability to characterize, risk stratify, and surgically treat SSV dysfunction, thereby improving long-term outcomes and quality of life in patients with SSV dysfunction,’ Dr. Jolley said.”

Pouch’s lab focuses on 3D/4D segmentation and modeling of heart valves in echocardiographic images with applications to surgical treatment of valvular regurgitation as part of the Penn Image Computing and Science Laboratory.

Read the full awards announcement in the CHOP Cornerstone Blog.

How to Learn About a World-class Double Bass? Give it a CT

by Darcy Lewis  

The instrument imaging team, from left: Philadelphia Orchestra bassist Duane Rosengard; Peter Noël, PhD, director of CT Research at the Perelman School of Medicine; luthier Zachary S. Martin; Leening Liu, a PhD student in Noël’s Laboratory of Advanced Computed Tomography Imaging; and Mark Kindig.

When you’re an expert in medical CT imaging, two things are bound to happen, says Peter Noël, PhD, associate professor of Radiology and director of CT Research at the Perelman School of Medicine. One: You develop an insatiable curiosity about the inner workings of all kinds of objects, including those unrelated to your research. And two: Both colleagues and complete strangers will ask for your help in imaging a wide variety of unexpected items.

Over the course of his career, in between managing his own research projects, Noël has imaged diverse objects ranging from animal skulls to tree samples from a German forest, all in the name of furthering scientific knowledge. But none has intrigued him as much as his current extracurricular project: the first known attempt to perform CT imaging of some of the world’s finest string basses. 

The goal is to crack the code on what makes a world-class instrument. This knowledge could both increase the ability to better care for masterworks built between the 17th and 19th centuries, as well as providing insights into refining the building of new ones, including possibly shifting from older, scarcer European wood to the use of sustainably harvested U.S. wood.

That’s why Noël and Leening Liu, a PhD student in Noël’s Laboratory of Advanced Computed Tomography Imaging, have found themselves volunteering to run the basses through a Penn CT scanner occasionally, when they’re not developing next-generation CT technology. 

“We always learn something out of projects like this … the more appealing part is that medical research can also be applied to non-medical things,” Noël said. “We have the opportunity to take what we learn in medicine and use it for something else—in this case, moving the arts forward.”

Read the full story in Penn Medicine News.

Peter Noël is Assistant Professor of Radiology in the Perelman School of Medicine and member of the Penn Bioengineering Graduate Group.

Leening Liu is a Ph.D. student in Bioengineering. She is a member of the Laboratory for Advanced Tomography Imaging (LACTI) with research interests including clinical applications of spectral CT and spectral CT thermometry.

2024 Graduate Awards for Bioengineering Students

Congratulations to the 2024 Bioengineering student recipients of the annual Penn Engineering Graduate Student Awards! The awardees were honored in a ceremony on May 15, 2024, hosted by Dean Vijay Kumar and graduate program faculty leadership.

Master’s Student Awards:
Elizabeth Brown – Outstanding Service
Tianyu Cai – Outstanding Research
Ekta Singh – Outstanding Service

PhD Student Awards:
Dimitris Boufidis – Outstanding Service
Katherine Mossburg – Outstanding Service
Kelsey Swingle – Outstanding Teaching

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.

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.

Brewing Brilliance

by Nathi Magubane & Ian Scheffler

Nader Engheta (left) and Firooz Aflatouni swap ideas over cups of tea.

According to Chinese legend, the first cup of tea was an accident. Shennong, a mythical emperor, boiled a pot of water, only for the wind to add a handful of leaves.

In Penn Engineering’s Department of Electrical and Systems Engineering (ESE), tea leaves likewise result in happy accidents.

Nader Engheta, H. Nedwill Ramsey Professor, regularly joins his colleague Firooz Aflatouni, associate professor and undergraduate chair in ESE, for a cup of tea in the latter’s office. “We talk about academic life,” says Engheta. “We talk about history, politics.” And, of course, science.

Engheta, who won the Benjamin Franklin Medal last year, is known for his groundbreaking contributions to the design of materials that interact with electromagnetic waves at tiny scales with unprecedented functionalities. More than a decade ago, the Department recruited Aflatouni, who specializes in the design of electronic and photonic chips, and Engheta became his mentor. “We come from different angles to the field of optics,” says Engheta.

Over tea, the two brew up new ideas. While perhaps not as directly inspired by teatime as James Watt, who famously experimented with kettles en route to inventing the steam engine, the pair nonetheless finds that ideas rise like the steam from their teacups. “It’s a pleasure to collaborate with Firooz,” says Engheta. “We love to see how we can bring our ideas together.” 

Read the full story in Penn Today.

Nader Engheta is H. Nedwill Ramsey Professor of Electrical and Systems Engineering at Penn Engineering, with secondary appointments in the departments of Bioengineering, Materials Science and Engineering, and Physics and Astronomy in the School of Arts & Sciences. Read more stories featuring Engheta in the BE Blog.

Karen Xu Honored with P.E.O. Scholar Award

Karen Xu, a 2024 doctoral graduate in Bioengineering at the University of Pennsylvania, is one of 100 doctoral students in the U. S. and Canada selected to receive a $25,000 Scholar Award from the P.E.O. Sisterhood. 

The P.E.O. Scholar Awards were established in 1991 to provide substantial merit-based awards for women of the United States and Canada who are pursuing a doctoral-level degree at an accredited college or university.  Scholar Awards recipients are a select group of women chosen for their high level of academic achievement and their potential for having a positive impact on society.

The P.E.O., founded January 21, 1869, at Iowa Wesleyan College, Mount Pleasant, Iowa, is a philanthropic educational organization dedicated to supporting higher education for women.  There are approximately 6,000 local chapters in the United States and Canada with nearly a quarter of a million active members.

Xu graduated summa cum laude with a B.S.E. in Biomedical Engineering from Duke University in 2018, after which she joined the M.D.-Ph.D. program at the University of Pennsylvania. She completed her Ph.D. in Bioengineering in spring 2024, funded by an NIH NRSA F30 fellowship, and is set to earn her M.D. in 2026. Under the mentorship of Jason Burdick, Bowman Endowed Professor in Chemical and Biological Engineering at the University of Colorado Boulder and Adjunct Professor in Bioengineering in Penn Engineering, and Robert Mauck, Mary Black Ralston Professor in Orthopaedic Surgery in the Perelman School of Medicine and in Bioengineering in Penn Engineering, her doctoral research has focused on engineering disease models to facilitate therapeutic discoveries. Her doctoral thesis involved the fabrication of hydrogels as tissue mimics to investigate how extracellular environments affect cell behaviors, thereby informing repair of dense connective tissues.

Beyond her research, Xu has taught with the Educational Pipeline Program at the Netter Center and the Perelman School of Medicine, where she hopes to inspire and support the next generation of healthcare workers and scientists.

Penn Bioengineering Junior Named 2024 Udall Scholar

by Louisa Shepard

Third-year undergraduate Joey Wu (Image: Courtesy of the Center for Undergraduate Research and Fellowships)

The University of Pennsylvania’s Joey Wu, a third-year student studying bioengineering and environmental science in the Vagelos Integrated Program in Energy Research (VIPER) program, has been named a 2024 Udall Scholar by the Udall Foundation. VIPER is a dual-degree program in the School of Engineering and Applied Science and School of Arts & Sciences.

Wu is among 55second-year and third-year students selected from 406 candidates nominated by 192colleges and universities nationwide. Scholars are recognized for leadership, public service, and commitment to issues related to the environment or to Native American nations. Each scholar will be awarded as much as $7,000.

A Taiwanese-American undergraduate scientist from Woodbury, Minnesota, Wu is the founder and international director of Waterroots, a nonprofit environmental education project that uses climate storytelling to combat water insecurity in more than 20 countries. Wu is a researcher in Penn Engineering’s McBride Lab, where he works as a plant specialist for a project that promotes environmental stability and sustainable agriculture. He is the deputy director of research for the nonprofit Climate Cardinals, a member of Penn’s Student Advisory Group for the Environment, and the North America representative for the Tunza Eco-Generation Ambassador program. Wu is a Clinton Global Initiative Scholar, a Duke Interfaith Climate Fellow, an IEEE Bio-X Scholar, a 2023 Millennium Fellow, and a 2024 UN ECOSOC Youth Delegate. In addition, he is a resident advisor in Penn’s Stouffer College House, as well as a Penn Engineering and a VIPER student ambassador.

Wu is the 10th student from Penn to be named a Udall Scholar since Congress established the foundation in 1992 to honor Morris and Stewart Udall for their impact on the nation’s environment, public lands, and natural resources and for their support of the rights and self-governance of American Indians and Alaska Natives.

Wu applied to the Udall Scholarship with the support of Penn’s Center for Undergraduate Research and Fellowships.

This story was originally posted in Penn Today.

The CiPD Partners with the Mack Institute for Innovation and Management to Develop Tooth-Brushing Robots

by Melissa Pappas

Left to right: Hong-Huy Tran, Chrissie Jaruchotiratanasakul, Manali Mahajan (Photo Courtesy of CiPD)

The Center for Innovation and Precision Dentistry (CiPD), a collaboration between Penn Engineering and Penn Dental Medicine, has partnered with Wharton’s Mack Institute for Innovation Management on a research project which brings robotics to healthcare. More specifically, this project will explore potential uses of nanorobot technology for oral health care. The interdisciplinary partnership brings together three students from different Penn programs to study the commercialization of a new technology that detects and removes harmful dental plaque.

“Our main goal is to bring together dental medicine and engineering for out-of-the-box solutions to address unresolved problems we face in oral health care,” says Hyun (Michel) Koo, Co-Founding Director of CiPD and Professor of Orthodontics. “We are focused on affordable solutions and truly disruptive technologies, which at the same time are feasible and translatable.”

Read the full story in Penn Engineering Today.

Michel Koo is a member of the Penn Bioengineering Graduate Group. Read more stories featuring Koo in the BE Blog.

To learn more about this interdisciplinary research, please visit CiPD.

This press release has been adapted from the original published by the Mack Institute for Innovation Management.