Tag: postdoc

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Measuring Chaos: Using Machine Learning to Satisfy Our Need to Know

by Melissa Pappas

How do we measure chaos and why would we want to? Together, Penn engineers Dani S. Bassett, J. Peter Skirkanich Professor in Bioengineering and in Electrical and Systems Engineering, and postdoctoral researcher Kieran Murphy leverage the power of machine learning to better understand chaotic systems, opening doors for new information analyses in both theoretical modeling and real-world scenarios.

Humans have been trying to understand and predict chaotic systems such as weather patterns, the movement of planets and population ecology for thousands of years. While our models have continued to improve over time, there will always remain a barrier to perfect prediction. That’s because these systems are inherently chaotic. Not in the sense that blue skies and sunshine can turn into thunderstorms and torrential downpours in a second, although that does happen, but in the sense that mathematically, weather patterns and other chaotic systems are governed by physics with nonlinear characteristics. 

“This nonlinearity is fundamental to chaotic systems,” says Murphy. “Unlike linear systems, where the information you start with to predict what will happen at timepoints in the future stays consistent over time, information in nonlinear systems can be both lost and generated through time.”

Like a game of telephone where information from the original source gets lost as it travels from person to person while new words and phrases are added to fill in the blanks, outcomes in chaotic systems become harder to predict as time passes. This information decay thwarts our best efforts to accurately forecast the weather more than a few days out.

“You could put millions of probes in the atmosphere to measure wind speed, temperature and precipitation, but you cannot measure every single atom in the system,” says Murphy. “You must have some amount of uncertainty, which will then grow, and grow quickly. So while a prediction for the weather in a few hours might be fairly accurate, that growth in uncertainty over time makes it impossible to predict the weather a month from now.”

In their recent paper published in Physical Review Letters, Murphy and Bassett applied machine learning to classic models of chaos, physicists’ reproductions of chaotic systems that do not contain any external noise or modeling imperfections, to design a near-perfect measurement of chaotic systems to one day improve our understanding of systems including weather patterns. 

“These controlled systems are testbeds for our experiments,” says Murphy. “They allow us to compare with theoretical predictions and carefully evaluate our method before moving to real-world systems where things are messy and much less is known. Eventually, our goal is to make ‘information maps’ of real-world systems, indicating where information is created and identifying what pieces of information in a sea of seemingly random data are important.” 

Read the full story in Penn Engineering Today.

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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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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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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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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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Precision Pulmonary Medicine: Penn Engineers Target Lung Disease with Lipid Nanoparticles

by Ian Scheffler

Penn Engineers have developed a way to target lung diseases, including lung cancer, with lipid nanoparticles (LNPs). (wildpixel via Getty Images)

Penn Engineers have developed a new means of targeting the lungs with lipid nanoparticles (LNPs), the miniscule capsules used by the Moderna and Pfizer-BioNTech COVID-19 vaccines to deliver mRNA, opening the door to novel treatments for pulmonary diseases like cystic fibrosis. 

In a paper in Nature Communications, Michael J. Mitchell, Associate Professor in the Department of Bioengineering, demonstrates a new method for efficiently determining which LNPs are likely to bind to the lungs, rather than the liver. “The way the liver is designed,” says Mitchell, “LNPs tend to filter into hepatic cells, and struggle to arrive anywhere else. Being able to target the lungs is potentially life-changing for someone with lung cancer or cystic fibrosis.”

Previous studies have shown that cationic lipids — lipids that are positively charged — are more likely to successfully deliver their contents to lung tissue. “However, the commercial cationic lipids are usually highly positively charged and toxic,” says Lulu Xue, a postdoctoral fellow in the Mitchell Lab and the paper’s first author. Since cell membranes are negatively charged, lipids with too strong a positive charge can literally rip apart target cells.  

Typically, it would require hundreds of mice to individually test the members of a “library” of LNPs — chemical variants with different structures and properties — to find one with a low charge that has a higher likelihood of delivering a medicinal payload to the lungs.

Instead, Xue, Mitchell and their collaborators used what is known as “barcoded DNA” (b-DNA) to tag each LNP with a unique strand of genetic material, so that they could inject a pool of LNPs into just a handful of animal models. Then, once the LNPs had propagated to different organs, the b-DNA could be scanned, like an item at the supermarket, to determine which LNPs wound up in the lungs. 

Read the full story in Penn Engineering Today.

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A Moonshot for Obesity: New Molecules, Inspired by Space Shuttles, Advance Lipid Nanoparticle Delivery for Weight Control

by Ian Scheffler

Like space shuttles using booster rockets to breach the atmosphere, lipid nanoparticles (LNPs) equipped with the new molecule more successfully deliver medicinal payloads. (Love Employee via Getty Images)

Inspired by the design of space shuttles, Penn Engineering researchers have invented a new way to synthesize a key component of lipid nanoparticles (LNPs), the revolutionary delivery vehicle for mRNA treatments including the Pfizer-BioNTech and Moderna COVID-19 vaccines, simplifying the manufacture of LNPs while boosting their efficacy at delivering mRNA to cells for medicinal purposes.

In a paper in Nature Communications, Michael J. Mitchell, Associate Professor in the Department of Bioengineering, describes a new way to synthesize ionizable lipidoids, key chemical components of LNPs that help protect and deliver medicinal payloads. For this paper, Mitchell and his co-authors tested delivery of an mRNA drug for treating obesity and gene-editing tools for treating genetic disease. 

Previous experiments have shown that lipidoids with branched tails perform better at delivering mRNA to cells, but the methods for creating these molecules are time- and cost-intensive. “We offer a novel construction strategy for rapid and cost-efficient synthesis of these lipidoids,” says Xuexiang Han, a postdoctoral student in the Mitchell Lab and the paper’s co-first author. 

Read the full story in Penn Engineering Today.

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“Switchable” Bispecific Antibodies Pave Way for Safer Cancer Treatment

by Nathi Magubane

Bispecific T cell engagers are emerging as a powerful class of immunotherapy to treat cancer but are sometimes hindered by unwanted outcomes, such as on-target, off-tumor toxicity; cytokine release syndrome; and neurotoxicity. Now, researchers Penn researchers have developed a novel “switchable” bispecific T cell engager that mitigates these negative effects by co-opting a drug already approved by the FDA. (Image: iStock / CIPhotos)

In the ever-evolving battle against cancer, immunotherapy presents a turning point. It began with harnessing the body’s immune system to fight cancer, a concept rooted more than a century ago but only gaining significant momentum in recent years. Pioneering this shift were therapies like CAR T cell therapy, which reprograms a patient’s T cells to attack cancer cells. Within this domain, bispecific T cell engagers, or bispecific antibodies, have emerged as effective treatments for many blood-borne cancers in the clinic and are being evaluated for solid tumor therapy.

These antibodies simultaneously latch onto both a cancer cell and a T cell, effectively bridging the gap between the two. This proximity triggers the T cells to unleash their lethal arsenal, thereby killing the cancer cells. However, bispecific T cell engagers, like many cancer therapies, face hurdles such as cell-specific targeting limitations, known as on-target off-tumor toxicity, which means the tumor is correctly targeted but so are other healthy cells in the body, leading to healthy tissue damage. Moreover, bispecific antibodies may also lead to immune system overactivation, a precursor for cytokine release syndrome (CRS), and neurotoxicity.

Now, researchers led by Michael Mitchell of the University of Pennsylvania have found a way to circumvent many of these deleterious effects by developing a bispecific T cell nanoengager that is equipped with an “off switch.” Their findings are published in Nature Biomedical Engineering.

“We’re excited to show that bispecific antibodies can be tweaked in a way that allows us to tap into their powerful cancer-killing potential without inducing toxicity to healthy tissues,” says Mitchell, associate professor of bioengineering at Penn’s School of Engineering and Applied Science. “This new controllable drug-delivery mechanism, which we call switchable bispecific T cell nanoengagers, or SiTEs, adds this switchable component to the antibody via administering an FDA-approved small-molecule drug, amantadine.”

Read the full story in Penn Today.

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Researchers Breathe New Life into Lung Repair

by Nathi Magubane

Image: iStock/Mohammed Haneefa Nizamudeen

In the human body, the lungs and their vasculature can be likened to a building with an intricate plumbing system. The lungs’ blood vessels are the pipes essential for transporting blood and nutrients for oxygen delivery and carbon dioxide removal. Much like how pipes can get rusty or clogged, disrupting normal water flow, damage from respiratory viruses, like SARS-CoV-2 or influenza, can interfere with this “plumbing system.”

In a recent study, researchers looked at the critical role of vascular endothelial cells in lung repair. Their work, published in Science Translational Medicine, was led by Andrew Vaughan of the University of Pennsylvania’s School of Veterinary Medicine and shows that, by using techniques that deliver vascular endothelial growth factor alpha (VEGFA) via lipid nanoparticles (LNPs), that they were able to greatly enhance modes of repair for these damaged blood vessels, much like how plumbers patch sections of broken pipes and add new ones.

“While our lab and others have previously shown that endothelial cells are among the unsung heroes in repairing the lungs after viral infections like the flu, this tells us more about the story and sheds light on the molecular mechanisms at play,” says Vaughan, assistant professor of biomedical sciences at Penn Vet. “Here we’ve identified and isolated pathways involved in repairing this tissue, delivered mRNA to endothelial cells, and consequently observed enhanced recovery of the damaged tissue. These findings hint at a more efficient way to promote lung recovery after diseases like COVID-19.”

They found VEGFA’s involvement in this recovery, while building on work in which they used single cell RNA sequencing to identify transforming growth factor beta receptor 2 (TGFBR2) as a major signaling pathway. The researchers saw that when TGFBR2 was missing it stopped the activation of VEGFA. This lack of signal made the blood vessel cells less able to multiply and renew themselves, which is vital for the exchange of oxygen and carbon dioxide in the tiny air sacs of the lungs.

“We’d known there was a link between these two pathways, but this motivated us to see if delivering VEGFA mRNA into endothelial cells could improve lung recovery after disease-related injury,” says first author Gan Zhao, a postdoctoral researcher in the Vaughan Lab.

The Vaughan Lab then reached out to Michael Mitchell of the School of Engineering and Applied Science, whose lab specializes in LNPs, to see if delivery of this mRNA cargo would be feasible.

“LNPs have been great for vaccine delivery and have proven incredibly effective delivery vehicles for genetic information. But the challenge here was to get the LNPs into the bloodstream without them heading to the liver, which is where they tend to congregate as its porous structure lends favor to substances passing from the blood into hepatic cells for filtration,” says Mitchell, an associate professor of bioengineering at Penn Engineering and a coauthor of the paper. “So, we had to devise a way to specifically target the endothelial cells in the lungs.”

Lulu Xue, a postdoctoral researcher in the Mitchell Lab and a co-first author of the paper, explains that they engineered the LNP to have an affinity for lung endothelial cells, this is known as extra hepatic delivery, going beyond the liver.

Read the full story in Penn Today.

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Bioengineers on the Brink of Breaching Blood-brain Barrier

by Nathi Magubane

From left: Emily Han, Rohan Palanki, Jacqueline Li, Michael Mitchell, Dongyoon Kim, and Marshall Padilla of Penn Engineering.

Imagine the brain as an air traffic control tower, overseeing the crucial and complex operations of the body’s ‘airport.’ This tower, essential for coordinating the ceaseless flow of neurological signals, is guarded by a formidable layer that functions like the airport’s security team, diligently screening everything and everyone, ensuring no unwanted intruders disrupt the vital workings inside.

However, this security, while vital, comes with a significant drawback: sometimes, a ‘mechanic’—in the form of critical medication needed for treating neurological disorders—is needed inside the control tower to fix arising issues. But if the security is too stringent, denying even these essential agents entry, the very operations they’re meant to protect could be jeopardized.

Now, researchers led by Michael Mitchell of the University of Pennsylvania are broaching this long-standing boundary in biology, known as the blood-brain barrier, by developing a method akin to providing this mechanic with a special keycard to bypass security. Their findings, published in the journal Nano Letters, present a model that uses lipid nanoparticles (LNPs) to deliver mRNA, offering new hope for treating conditions like Alzheimer’s disease and seizures—not unlike fixing the control tower’s glitches without compromising its security.

“Our model performed better at crossing the blood-brain barrier than others and helped us identify organ-specific particles that we later validated in future models,” says Mitchell, associate professor of bioengineering at Penn’s School of Engineering and Applied Science, and senior author on the study. “It’s an exciting proof of concept that will no doubt inform novel approaches to treating conditions like traumatic brain injury, stroke, and Alzheimer’s.”

Read the full story in Penn Today.