Category: issues

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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.

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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.

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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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How “Invitations” from Penn Medicine Restored Mammogram Completion Rates

by Frank Otto

The first few waves of COVID-19 slowed life across the United States, affecting everything from attending school to eating out for dinner and going on vacation. Segments of health care were also affected: Services that were not considered immediately crucial to fighting the virus were slowed or stopped during the pandemic’s first wave.  

But once Penn Medicine invited patients back to resume normal health care—including preventive care, like screenings for disease—there was some lag in numbers. 

“As we opened up to routine outpatient care, screening rates for situations when patients didn’t have symptoms were not returning back to normal,” said Mitchell Schnall, MD, PhD, FACR, a professor of Radiology, now the senior vice president for Data and Technology Solutions at Penn Medicine, and then the head of a team focused on the “resurgence” efforts to ease patients back into outpatient care. “Although a short delay in health screening is likely not going to cause long-term health problems, we were concerned whether screening rates would stay lower and lead to a long-term impact.”  

Read the full story in Penn Medicine News.

Mitchell Schnall is a member of the Penn Bioengineering Graduate Group.

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Different Brain Structures in Females Lead to More Severe Cognitive Deficits After Concussion Than Males

by Kelsey Geesler

Top: Axons in female and male subject brains Bottom: damaged axons in male and female brains after injury (Credit: Penn Medicine)

Important brain structures that are key for signaling in the brain are narrower and less dense in females, and more likely to be damaged by brain injuries, such as concussion. Long-term cognitive deficits occur when the signals between brain structures weaken due to the injury. The structural differences in male and female brains might explain why females are more prone to concussions and experience longer recovery from the injury than their male counterparts, according to a preclinical study led by the Perelman School of Medicine at the University of Pennsylvania, published this week in Acta Neuropathologica.

Each year, approximately 50 million individuals worldwide suffer a concussion, also referred to as mild traumatic brain injury (TBI). However, there is nothing “mild” about this condition for the more than 15 percent of individuals who suffer persisting cognitive dysfunction, which includes difficulty concentrating, learning and remembering new information, and making decisions.

Although males make up the majority of emergency department visits for concussion, this has been primarily attributed to their greater exposure to activities with a risk of head impacts compared to females. In contrast, it has recently been observed that female athletes have a higher rate of concussion and appear to have worse outcomes than their male counterparts participating in the same sport.

“Clinicians have observed for a long time that females suffer from concussion at higher rates than males in the same sports, and that they take longer to recover cognitive function, but couldn’t explain the underlying mechanisms of this phenomenon,” said senior author Douglas Smith, MD, a professor of Neurosurgery and director of Penn’s Center for Brain Injury and Repair. “The variances in brain structures of females and males not only illuminate why this disparity exists, but also exposes biomarkers, such as axon protein fragments, that can be measured in the blood to determine injury severity, monitor recovery, and eventually help identify and develop treatments that help patients repair these damaged structures and restore cognitive function.”

Read the full story in Penn Medicine News.

Douglas H. Smith is a member of the Penn Bioengineering Graduate Group.

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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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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.

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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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Study Reveals Inequities in Access to Transformative CAR T Cell Therapy

Image: iStock/PeopleImages

Patients being treated for B-cell non-Hodgkin’s Lymphoma (NHL) who are part of minority populations may not have equal access to cutting-edge CAR T cell therapies, according to a new analysis led by researchers from the Perelman School of Medicine and published in NEJM Evidence.

CAR T cell therapy is a personalized form of cancer therapy that was pioneered at Penn Medicine and has brought hope to thousands of patients who had otherwise run out of treatment options. Six different CAR T cell therapies have been approved since 2017 for a variety of blood cancers, including B-cell NHL that has relapsed or stopped responding to treatment. Image: iStock/PeopleImages

“CAR T cell therapy represents a major leap forward for blood cancer treatment, with many patients living longer than ever before, but its true promise can only be realized if every patient in need has access to these therapies,” says lead author Guido Ghilardi, a postdoctoral fellow in the laboratory of senior author Marco Ruella, an assistant professor of hematology-oncology and scientific director of the Lymphoma Program. “From the scientific perspective, we’re constantly working in the laboratory to make CAR T cell therapy work better, but we also want to make sure that when a groundbreaking treatment like this becomes available, it reaches all patients who might be able to benefit.”

Read the full story in Penn Medicine News.

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

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A Return to Jamaica Brings Seven Student-Invented Devices to Help People and Wildlife

by Melissa Pappas

Students test the GaitMate harness and structure as a tool to help recovering patients walk.

Penn students have been building their knowledge and hands-on experience in places all over the world through Penn Global Seminars. Last May, “Robotics and Rehabilitation” brought Penn students back to the tropical island of Jamaica to collaborate with local university students and make an impact on recovery and quality of life for patients in Kingston and beyond. 

Course leaders Camillo Jose (CJ) Taylor, Raymond S. Markowitz President’s Distinguished Professor in Computer and Information Science (CIS), and Michelle J. Johnson, Associate Professor of Physical Medicine and Rehabilitation at the Perelman School of Medicine and Associate Professor in Bioengineering (BE) and Mechanical Engineering and Applied Mechanics (MEAM) at Penn Engineering, brought the first cohort of students to the island in 2019. 

“CJ and I are both Jamaicans by birth,” says Johnson. “We were both excited to introduce the next generation of engineers to robotics, rehabilitation and the process of culturally sensitive design in a location that we are personally connected to.” 

As they built relationships with colleagues at the University of West Indies, Mona (UWI, Mona) and the University of Technology, Jamaica (UTECH), both Johnson and Taylor worked to tie the goals of the course to the location.

“In the initial iteration of the course, our goal was to focus on the applications of robotics to rehabilitation in a developing country where it is necessary to create solutions that are cost effective and will work in under-resourced settings,” says Taylor. 

Taylor and Johnson wanted to make the course a regular offering, however, due to COVID-related travel restrictions, it wasn’t until last spring that they were able to bring it back. But when they did, they made up for lost time and expanded the scope of the course to include solving health problems for both people and the environment.

“While we started with a focus on people, we realized that the health and quality of life of a community is also impacted by the health of the environment,” says Taylor. “Jamaica has rich terrestrial and marine ecosystems, but those resources need to be monitored and regulated. We ventured into developing robotics tools to make environmental monitoring more effective and cost-friendly.”

One of those student-invented tools was a climate survey drone called “BioScout.” 

“Our aim was to create a drone to monitor the ecosystem and wildlife in Jamaica,” says Rohan Mehta, junior in Systems Science and Engineering. “We wanted to help researchers and rangers who need to monitor wildlife and inspect forest sectors without entering and disturbing territories, but there were no available drones that met all of the following criteria necessary for the specific environment: affordable, modular, water-resistant and easy to repair. So we made our own.”

Another team of students created a smart buoy to reduce overfishing. The buoy was equipped with an alarm that goes off when fishermen get too close to a no-fishing zone.

Five other student teams dove into projects aligned to the original goals of the course. Their devices addressed patients’ decreased mobility due to diabetes, strokes and car accidents. These projects were sponsored by the Sir John Golding Rehabilitation Center.

One of which, the GaitMate, was engineered to help stroke patients who had lost partial muscle control regain their ability to walk.  

“We developed a device that supports a patient’s weight and provides sensory feedback to help correct their form and gait as they walk on a treadmill, ultimately enhancing the recovery process and providing some autonomy to the patient,” says Taehwan Kim, senior in BE. “The device is also relatively cheap and simple, making it an option for a wide variety of physical therapy needs in Jamaica and other countries.”

Read the full story in Penn Engineering Today.