Myeloma cells producing monoclonal proteins of varying types, created by Scientific Animations under the Creative Commons Attributions-Share Alike International 4.0 License
Multiple myeloma is an incurable bone marrow cancer that kills over 100,000 people every year. Known for its quick and deadly spread, this disease is one of the most challenging to address. As these cancer cells move through different parts of the body, they mutate, outpacing possible treatments. People diagnosed with severe multiple myeloma that is resistant to chemotherapy typically survive for only three to six months. Innovative therapies are desperately needed to prevent the spread of this disease and provide a fighting chance for those who suffer from it.
Michael Mitchell, J. Peter and Geri Skirkanich Assistant Professor of Innovation in Bioengineering (BE), and Christian Figueroa-Espada, doctoral student in BE at the University of Pennsylvania School of Engineering and Applied Science, created an RNA nanoparticle therapy that makes it impossible for multiple myeloma to move and mutate. The treatment, described in their study published in PNAS, turns off a cancer-attracting function in blood vessels, disabling the pathways through which multiple myeloma cells travel.
By shutting down this “chemical GPS” that induces the migration of cancer cells, the team’s therapy stops the spread of multiple myeloma, helping to eliminate it altogether.
“Macrophages killing cancer cell” photographed by Susan Arnold.
By silencing the molecular pathway that prevents macrophages from attacking our own cells, Penn Engineers have manipulated these white blood cells to eliminate solid tumors.
Cancer remains one of the leading causes of death in the U.S. at over 600,000 deaths per year. Cancers that form solid tumors such as in the breast, brain or skin are particularly hard to treat. Surgery is typically the first line of defense for patients fighting solid tumors. But surgery may not remove all cancerous cells, and leftover cells can mutate and spread throughout the body. A more targeted and wholistic treatment could replace the blunt approach of surgery with one that eliminates cancer from the inside using our own cells.
Dennis Discher, Robert D. Bent Professor in Chemical and Biomolecular Engineering, Bioengineering, and Mechanical Engineering and Applied Mechanics, and postdoctoral fellow, Larry Dooling, provide a new approach in targeted therapies for solid tumor cancers in their study, published in Nature Biomedical Engineering. Their therapy not only eliminates cancerous cells, but teaches the immune system to recognize and kill them in the future.
“Due to a solid tumor’s physical properties, it is challenging to design molecules that can enter these masses,” says Discher. “Instead of creating a new molecule to do the job, we propose using cells that ‘eat’ invaders – macrophages.”
Macrophages, a type of white blood cell, immediately engulf and destroy – phagocytize – invaders such as bacteria, viruses, and even implants to remove them from the body. A macrophage’s innate immune response teaches our bodies to remember and attack invading cells in the future. This learned immunity is essential to creating a kind of cancer vaccine.
But, a macrophage can’t attack what it can’t see.
“Macrophages recognize cancer cells as part of the body, not invaders,” says Dooling. “To allow these white blood cells to see and attack cancer cells, we had to investigate the molecular pathway that controls cell-to-cell communication. Turning off this pathway – a checkpoint interaction between a protein called SIRPa on the macrophage and the CD47 protein found on all ‘self’ cells – was the key to creating this therapy.”
Multiple members in the biophysical engineering lab lead by Dennis Discher, including co-lead author and postdoctoral fellow and Penn Bioengineering alumnus Jason Andrechak and Bioengineering Ph.D. student Brandon Hayes, contributed to this study. The research was funded by grants from the National Heart, Lung, and Blood Institute and the National Cancer Institute, including the Physical Sciences Oncology Network, of the US National Institutes of Health.
They will conduct research, pursue graduate degrees, or teach English in Belgium, Brazil, Colombia, Denmark, Ecuador, Estonia, France, Germany, Guatemala, India, Israel, Latvia, Mexico, Nepal, New Zealand, the West Bank-Palestine territories, South Korea, Spain, Switzerland, Taiwan, and Thailand.
The Fulbright Program is the United States government’s flagship international educational exchange program, awarding grants to fund as long as 12 months of international experience.
Among the Penn Fulbright grant recipients for 2023-24 is Ella Atsavapranee, from Cabin John, Maryland, who graduated in May with a bachelor’s degree in bioengineering from the School of Engineering and Applied Science and a minor in chemistry from the College. She was offered a Fulbright to conduct research at the École Polytechnique Fédérale de Lausanne in Switzerland.
At Penn, Atsavapranee worked with Michael Mitchell, J. Peter and Geri Skirkanich Assistant Professor in Bioengineering, engineering lipid nanoparticles to deliver proteases that inhibit cancer cell proliferation. She has also worked with Shan Wang, Leland T. Edwards Professor in the School of Engineering and Professor of Electrical Engineering at Stanford University, using bioinformatics to discover blood biomarkers for cancer detection. To achieve more equitable health care, she worked with Lisa Shieh, Clinical Professor in Medicine at the Stanford School of Medicine, to evaluate an AI model that predicts risk of hospital readmission and study how room placement affects patient experience.
Outside of research, Atsavapranee spread awareness of ethical issues in health care and technology as editor-in-chief of the Penn Bioethics Journaland a teaching assistant for Engineering Ethics (EAS 2030). She was also a Research Peer Advisor for the Penn Center for Undergraduate Research & Fellowships (CURF), a student ambassador for the Office of Admissions, and a volunteer for Service Link, Puentes de Salud, and the Hospital of the University of Pennsylvania. She plans to pursue a career as a physician-scientist to develop and translate technologies that are more affordable and accessible to underserved populations.
Read the full list of Penn Fulbright grant recipients for 2023-24 in Penn Today.
Candida albicans is a species of yeast that is a normal part of the human microbiota but can also cause severe infections that pose a significant global health risk due to their resistance to existing treatments, so much so that the World Health Organization has highlighted this as a priority issue. The picture above shows a before (left) and after (right) fluorescence image of fungal biofilms being precisely targeted by nanozyme microrobots without bonding to or disturbing the tissue sample. (Image: Min Jun Oh and Seokyoung Yoon)
Infections caused by fungi, such as Candida albicans, pose a significant global health risk due to their resistance to existing treatments, so much so that the World Health Organization has highlighted this as a priority issue.
Although nanomaterials show promise as antifungal agents, current iterations lack the potency and specificity needed for quick and targeted treatment, leading to prolonged treatment times and potential off-target effects and drug resistance.
“Candida forms tenacious biofilm infections that are particularly hard to treat,” Koo says. “Current antifungal therapies lack the potency and specificity required to quickly and effectively eliminate these pathogens, so this collaboration draws from our clinical knowledge and combines Ed’s team and their robotic expertise to offer a new approach.”
The team of researchers is a part of Penn Dental’s Center for Innovation & Precision Dentistry, an initiative that leverages engineering and computational approaches to uncover new knowledge for disease mitigation and advance oral and craniofacial health care innovation.
For this paper, published in Advanced Materials, the researchers capitalized on recent advancements in catalytic nanoparticles, known as nanozymes, and they built miniature robotic systems that could accurately target and quickly destroy fungal cells. They achieved this by using electromagnetic fields to control the shape and movements of these nanozyme microrobots with great precision.
“The methods we use to control the nanoparticles in this study are magnetic, which allows us to direct them to the exact infection location,” Steager says. “We use iron oxide nanoparticles, which have another important property, namely that they’re catalytic.”
Other authors include Min Jun Oh, Alaa Babeer, Yuan Liu, Zhi Ren, Zhenting Xiang, Yilan Miao, and Chider Chen of Penn Dental; and David P. Cormode and Seokyoung Yoon of the Perelman School of Medicine. Cormode also holds a secondary appointment in Bioengineering.
This research was supported in part by the National Institute for Dental and Craniofacial Research (R01 DE025848, R56 DE029985, R90DE031532 and; the Basic Science Research Program through the National Research Foundation of Korea of the Ministry of Education (NRF-2021R1A6A3A03044553).
Neil Sheppard, Adjunct Associate Professor of Pathology and Laboratory Medicine in the Perelman School of Medicine, and David Mai, a Bioengineering graduate student in the School of Engineering and Applied Science, explained the findings of their recent study, which offered a potential strategy to improve T cell therapy in solid tumors, to the European biotech news website Labiotech.
Folding@home is led by Gregory Bowman, a Penn Integrates Knowledge Professor who has appointments in the Departments of Biochemistry and Biophysics in the Perelman School of Medicine and the Department of Bioengineering in the School of Engineering and Applied Science. (Image: Courtesy of Penn Medicine News)
Two heads are better than one. The ethos behind the scientific research project Folding@home is that same idea, multiplied: 50,000 computers are better than one.
Folding@home is a distributed computing project which is used to simulate protein folding, or how protein molecules assemble themselves into 3-D shapes. Research into protein folding allows scientists to better understand how these molecules function or malfunction inside the human body. Often, mutations in proteins influence the progression of many diseases like Alzheimer’s disease, cancer, and even COVID-19.
Penn is home to both the computer brains and human minds behind the Folding@home project which, with its network, forms the largest supercomputer in the world. All of that computing power continually works together to answer scientific questions such as what areas of specific protein implicated in Parkinson’s disease may be susceptible to medication or other treatment.
Using the network hub at Penn, Bowman and his team assign experiments to each individual computer which communicates with other computers and feeds info back to Philly. To date, the network is comprised of more than 50,000 computers spread across the world.
“What we do is like drawing a map,” said Bowman, explaining how the networked computers work together in a type of system that experts call Markov state models. “Each computer is like a driver visiting different places and reporting back info on those locations so we can get a sense of the landscape.”
Individuals can participate by signing up and then installing software to their standard personal desktop or laptop. Participants can direct the software to run in the background and limit it to a certain percentage of processing power or have the software run only when the computer is idle.
When the software is at work, it’s conducting unique experiments designed and assigned by Bowman and his team back at Penn. Users can play scientist and watch the results of simulations and monitor the data in real time, or they can simply let their computer do the work while they go about their lives.
Machine learning (ML) programs computers to learn the way we do – through the continual assessment of data and identification of patterns based on past outcomes. ML can quickly pick out trends in big datasets, operate with little to no human interaction and improve its predictions over time. Due to these abilities, it is rapidly finding its way into medical research.
People with breast cancer may soon be diagnosed through ML faster than through a biopsy. Those suffering from depression might be able to predict mood changes through smart phone recordings of daily activities such as the time they wake up and amount of time they spend exercising. ML may also help paralyzed people regain autonomy using prosthetics controlled by patterns identified in brain scan data. ML research promises these and many other possibilities to help people lead healthier lives.
But while the number of ML studies grow, the actual use of it in doctors’ offices has not expanded much past simple functions such as converting voice to text for notetaking.
The limitations lie in medical research’s small sample sizes and unique datasets. This small data makes it hard for machines to identify meaningful patterns. The more data, the more accuracy in ML diagnoses and predictions. For many diagnostic uses, massive numbers of subjects in the thousands would be needed, but most studies use smaller numbers in the dozens of subjects.
But there are ways to find significant results from small datasets if you know how to manipulate the numbers. Running statistical tests over and over again with different subsets of your data can indicate significance in a dataset that in reality may be just random outliers.
This tactic, known as P-hacking or feature hacking in ML, leads to the creation of predictive models that are too limited to be useful in the real world. What looks good on paper doesn’t translate to a doctor’s ability to diagnose or treat us.
These statistical mistakes, oftentimes done unknowingly, can lead to dangerous conclusions.
To help scientists avoid these mistakes and push ML applications forward, Konrad Kording, Nathan Francis Mossell University Professor with appointments in the Departments of Bioengineering and Computer and Information Science in Penn Engineering and the Department of Neuroscience at Penn’s Perelman School of Medicine, is leading an aspect of a large, NIH-funded program known as CENTER – Creating an Educational Nexus for Training in Experimental Rigor. Kording will lead Penn’s cohort by creating the Community for Rigor which will provide open-access resources on conducting sound science. Members of this inclusive scientific community will be able to engage with ML simulations and discussion-based courses.
“The reason for the lack of ML in real-world scenarios is due to statistical misuse rather than the limitations of the tool itself,” says Kording. “If a study publishes a claim that seems too good to be true, it usually is, and many times we can track that back to their use of statistics.”
Such studies that make their way into peer-reviewed journals contribute to misinformation and mistrust in science and are more common than one might expect.
Members of the inaugural cohort of fellows in the Center for Innovation and Precision Dentistry (CiPD)’s NIDCR T90/R90 Postdoctoral Training Program have been recognized for their research activities with fellows receiving awards from the American Association for Dental, Oral, and Craniofacial Research (AADOCR), the Society for Biomaterials, and the Osteology Foundation. All four of the honored postdocs are affiliated with Penn Bioengineering.
Zhi Ren
Zhi Ren won first place in the Fives-Taylor Award at the AADOCR Mini Symposium for Young Investigators. A postdoctoral fellow in the labs of Dr. Hyun (Michel) Koo at Penn Dental Medicine (and member of the Penn Bioengineering Graduate Group) and Dr. Kathleen Stebe of Penn Engineering, Dr. Ren’s research focuses on understanding how bacterial and fungal pathogens interact in the oral cavity to form a sticky plaque biofilm on teeth, which gives rise to severe childhood tooth decay that affects millions of children worldwide. In his award-winning study, titled “Interkingdom Assemblages in Saliva Display Group-Level Migratory Surface Mobility”, Dr. Ren discovered that bacteria and fungi naturally present in the saliva of toddlers with severe decay can form superorganisms able to move and rapidly spread on tooth surfaces.
Justin Burrell
Justin Burrell won second place in the AADOCR Hatton Competition postdoctoral category for his research. Dr. Burrell has been working with Dr. Anh Le in Penn Dental Medicine’s Department of Oral Surgery/Pharmacology and Dr. D. Kacy Cullen of Penn Medicine and Penn Bioengineering. Together, their interdisciplinary team of clinician-scientists, biologists, and neuroengineers have been developing novel therapies to expedite facial nerve regeneration and increase meaningful functional recovery.
Marshall Padilla
Marshall Padilla earned third place at the Society for Biomaterials Postdoctoral Recognition Award Competition for a project titled, “Branched lipid architecture improves lipid-nanoparticle-based mRNA delivery to the liver via enhanced endosomal escape”. Padilla was also a finalist in the AADOCR Hatton Award Competition, presenting on a separate project titled, “Lipid Nanoparticle Optimization for mRNA-based Oral Cancer Therapy”. Both projects employ lipid nanoparticles, the same delivery vehicles used in the mRNA COVID-19 vaccine technology. A postdoctoral fellow in the lab of Dr. Michael J. Mitchell of Penn’s Department of Bioengineering, Dr. Padilla’s research focuses on developing new ways to enhance the efficacy and safety of lipid nanoparticle technology and its applications in dentistry and biomedicine. He has been working in collaboration with Dr. Shuying (Sheri) Yang and Dr. Anh Le in Penn Dental Medicine.
Dennis Sourvanos
Dennis Sourvanos (GD’23, DScD’23) was the recipient of the Trainee Travel Grant award through the Osteology Foundation (Lucerne Switzerland). Dr. Sourvanos will be presenting his research related to medical dosimetry and tissue regeneration at the International Osteology Symposium in Barcelona, Spain (April 27th – 29th 2023). He also presented at the 2023 AADOCR/CADR Annual Meeting for his project titled, “Validating Head-and-Neck Human-Tissue Optical Properties for Photobiomodulation and Photodynamic Therapies.” Dr. Sourvanos has been working with Dr. Joseph Fiorellini in Penn Dental Medicine’s Department of Periodontics and Dr. Timothy Zhu in the Hospital of the University of Pennsylvania’s Department of Radiation Oncology and the Smilow Center for Translational Research (and member of the Penn Bioengineering Graduate Group).
Matthew Aronson and Alexandra Dumas are both members of the lab of Riccardo Gottardi, Assistant Professor in Pediatrics in the Perelman School of Medicine and in Bioengineering in the School of Engineering and Applied Science. Both presented their work at the recent 2023 SFB Annual Meeting and Exposition in San Diego, California in April 2023 and were honored with STAR Awards for their research.
The Gottardi Bioengineering and Biomaterials Laboratory studies treatment and function restoration for children with otolaryngologic disorders through the Children’s Hospital of Philadelphia (CHOP) in the Division of Otolaryngology.
Matthew Aronson
MatthewAronson is a third-year Ph.D. student in Bioengineering, an Ashton Fellow, and a NSF Fellow. His doctoral research focuses on studying pediatric airway diseases and disorders. More specifically, he is interested in how bacteria of the upper airway are responsible for the development and progression the disease subglottic stenosis, narrowing of the airway. In addition to understanding this devastating disease in the context of pediatric patients at CHOP, he also designed a novel drug-eluting endotracheal tube to deliver a selective antimicrobial peptide to function as a treatment modality for the prevention of the disease.
Alexandra Dumas
Alexandra Dumas is a rising fourth-year undergraduate in Bioengineering from Durban, South Africa. She is a PURM Fellow and a University Scholar. Her recent work in the Gottardi Lab focuses on using decellularized cartilage scaffolds to repair the meniscus and airway. After her undergraduate degree, she hopes to pursue a Ph.D. or M.D.-Ph.D. in bioengineering to pursue the design of new biomaterials for low-resource communities.
Read more stories featuring Gottardi and his team here.
Recipients of the 2023 President’s Innovation Prize, team Sonura, five bioengineering graduates from the School of Engineering and Applied Science, have created a device that filters out disruptive environmental noises for infants in neonatal intensive care units. Their beanie offers protection and fosters parental connection to newborns while also supporting their development.
Machines beeping and whirring in a rhythmic chorus, the droning hum of medical equipment, and the bustles of busy health care providers are the familiar sounds of an extended stay at a hospital. This cacophony can create a sense of urgency for medical professionals as they move about with focused determination, closely monitoring their patients, but for infants in neonatal intensive care units (NICU) this constant noise can be overwhelming and developmentally detrimental.
Enter Tifara Boyce, from New York City; Gabriela Cano, from Lawrenceville, New Jersey; Gabriella Daltoso, from Boise, Idaho; Sophie Ishiwari, from Chicago, and Caroline Magro, from Alexandria, Virginia, bioengineering graduates from the School of Engineering and Applied Science, who have created the Sonura Beanie. Their device filters out harmful noises for NICU infants while supporting cognitive and socioemotional development by allowing parents to send voice messages to their newborns.
The Sonura team members are recipients of the 2023 President’s Innovation Prize, which includes an award of $100,000 and an additional $50,000 living stipend per team member. The recent graduates will spend the year developing their product.
“The Penn engineers behind Sonura are determined to make a difference in the world,” says President Liz Magill. “They identified a substantial medical challenge that affects many parents and their newborn children. With the guidance of their mentors, they are taking key steps to address it and in doing so are improving the developmental prospects for children in the NICU. I am proud the University is able to support their important work.”
Prototype of the Sonura Beanie. (Image: Courtesy of the Sonura team)
She was particularly struck by the noisiness of the environment and considered the neurodevelopmental outcomes that may arise following long-term exposure to the harsh sounds at a critical developmental stage for infants. This concern prompted Magro to consult her team about potential solutions.
“I was really eager to tackle this problem because it bears some personal significance to me,” says Cano, who works on the device’s mobile application. “My sister was a NICU baby who was two months premature, so, when Caroline and I started talking about the issues a disruptive environment could cause, it seemed like the pieces of a puzzle started to come together.”
Machine learning (ML) programs computers to learn the way we do – through the continual assessment of data and identification of patterns based on past outcomes. ML can quickly pick out trends in big datasets, operate with little to no human interaction and improve its predictions over time. Due to these abilities, it is rapidly finding its way into medical research.
