César de la Fuente, PhD, Presidential Assistant Professor in Bioengineering, Chemical and Biomolecular Engineering, Psychiatry, and Microbiology, was featured in the Philadelphia Business Journal’s Class of 2021 “40 Under 40” list. Currently focused on antibiotic discovery, creating tools for microbiome engineering, and low-cost diagnostics, de le Fuente pioneered the world’s first computer-designed antibiotic with efficacy in animal models.
De la Fuente was previously included in the AIChE’s “35 Under 35” list in 2020 and most recently published his work demonstrating a rapid COVID-19 diagnostic test which delivers highly accurate results within four minutes.
Read “40 Under 40: Philadelphia Business Journal’s complete Class of 2021” here.
Read other BE blog posts featuring Dr. de la Fuente here.
César de la Fuente, Presidential Assistant Professor in Bioengineering, Chemical and Biomolecular Engineering, Microbiology, and Psychiatry, was the inaugural recipient of the Nemirovsky Engineering and Medicine Opportunity (NEMO) Prize from Penn Health-Tech in 2020 for his low-cost, rapid COVID test. Now with promising results recently published in the journal Matter (showing 90 percent accuracy in as little as four minutes), Penn Health-Tech caught up with de la Fuente to discuss his experience over the past year:
“How did [your project] evolve in the past year?
‘We started with one prototype and now have three entirely different prototypes for the test. Two use electrochemistry, and we are now working on a new technology that uses calorimetry. With calorimetry, when the cotton swabs are exposed to the virus, they change color. This means users are able to see if they’re affected by a virus through a simple color change, making it more of a visual detection method.'”
Even as COVID-19 vaccinations are being rolled out, testing for active infections remains a critical tool in fighting the pandemic. Existing rapid tests that can directly detect the virus rely on reverse transcription polymerase chain reaction (RT-PCR), a common genetic assay that nevertheless requires trained technicians and lab space to conduct.
Alternative testing methods that can be scaled up and deployed in places where those are in short supply are therefore in high demand.
Penn researchers have now demonstrated such a method, which senses the virus by measuring the change in an electrical signal when a piece of the SARS-CoV-2 virus binds to a biosensor in their device, which they call RAPID 1.0.
The work, published in the journal Matter, was led by César de la Fuente, a Presidential Assistant Professor who has appointments in Engineering’s departments of Chemical and Biomolecular Engineering, and Bioengineering, as well as in Psychiatry and Microbiology in the Perelman School of Medicine.
“Prior to the pandemic, our lab was working on diagnostics for bacterial infections. But then, COVID-19 hit. We felt a responsibility to use our expertise to help—and the diagnostic space was ripe for improvements,” de la Fuente said. “We feel strongly about the health inequities witnessed during the pandemic, with testing access and the vaccine rollout, for example. We believe inexpensive diagnostic tests like RAPID could help bridge some of those gaps.”
The RAPID technology uses electrochemical impedance spectroscopy (EIS), which transforms the binding event between the SARS-CoV-2 viral spike protein and its receptor in the human body, the protein ACE2 (which provides the entry point for the coronavirus to hook into and infect human cells), into an electrical signal that clinicians and technicians can detect. That signal allows the test to discriminate between infected and healthy human samples. The signal can be read through a desktop instrument or a smartphone.
A recent Penn Medicine blog post surveys the efforts across Penn and the Perelman School of Medicine to develop novel says to detect SARS-CoV-2 and features several Department of Bioengineering faculty and Graduate Group members, including César de la Fuente, Presidential Assistant Professor in Psychiatry, Microbiology, and Bioengineering; Arupa Ganguly, Professor in Genetics; A.T. Charlie Johnson, Rebecca W. Bushnell Professor in Physics and Astronomy; Lyle Ungar, Professor in Computer and Information Science; and Ping Wang, Associate Professor in Pathology and Laboratory Medicine.
Read “We’ll Need More than Vaccines to Vanquish the Virus: New COVID-19 Testing Technology at Penn” by Melissa Moodyin Penn Medicine News.
The lab of Penn’s César de la Fuente sits at the interface of machines and biology, with much of its work focused on innovative treatments for infectious disease. When COVID-19 appeared, de la Fuente and his colleagues turned their attention to building a paper-based biosensor that could quickly determine the presence of SARS-CoV-2 particles from saliva and from samples from the nose and back of the throat. The initial iteration, called DETECT 1.0, provides results in four minutes with nearly 100% accuracy.
Clinical trials for the diagnostic began Jan. 5, with the goal of collecting 400 samples—200 positive for COVID-19, 200 negative—from volunteers who also receive a RT-PCR or “reverse transcription polymerase chain reaction” test. This will provide a comparison set against which to measure the biosensor to determine whether the results the researchers secured at the bench hold true for samples tested in real time. De la Fuente expects the trial will take about a month.
If all goes accordingly, he hopes these portable rapid breath tests could play a part in monitoring the COVID status of faculty, students, and staff around Penn.
Taking on COVID-19 research in this fashion made sense for this lab. “We’re the Machine Biology Group, and we’re interested in existing and emerging pathogens,” says de la Fuente, who has appointments in the Perelman School of Medicine and School of Engineering and Applied Science. “In this case, we’re using a machine to rapidly detect SARS-CoV-2.”
To this point in the pandemic, most SARS-CoV-2 diagnostics have used RT-PCR. Though effective, the technique requires significant space and trained workers to employ, and it is costly and takes hours or days to provide results. De la Fuente felt there was potential to create something inexpensive, quicker, and, perhaps most importantly, scalable.
César de la Fuente a Presidential Assistant Professor in the Perelman School of Medicine’s departments of Psychiatry and Microbiology and Engineering’s department of Bioengineering, has racked up accolades for his innovative, computational approach to discovering new antibiotics.
Now, in his most recent study, de la Fuente has shown how these vital drugs might be derived from wasp venom.
The study, published in The Proceedings of the National Academy of Sciences, involved altering a highly toxic small protein from a common Asian wasp species, Vespula lewisii, the Korean yellow-jacket wasp. The alterations enhanced the molecule’s ability to kill bacterial cells while greatly reducing its ability to harm human cells. In animal models, de la Fuente and his colleagues showed that this family of new antimicrobial molecules made with these alterations could protect mice from otherwise lethal bacterial infections.
There is an urgent need for new drug treatments for bacterial infections, as many circulating bacterial species have developed a resistance to older drugs. The U.S. Centers for Disease Control & Prevention has estimated that each year nearly three million Americans are infected with antibiotic-resistant microbes and more than 35,000 die of them. Globally the problem is even worse: Sepsis, an often-fatal inflammatory syndrome triggered by extensive bacterial infection, is thought to have accounted for about one in five deaths around the world as recently as 2017.
“New antibiotics are urgently needed to treat the ever-increasing number of drug-resistant infections, and venoms are an untapped source of novel potential drugs. We think that venom-derived molecules such as the ones we engineered in this study are going to be a valuable source of new antibiotics,” says de la Fuente.
De la Fuente and his team started with a small protein, or “peptide,” called mastoparan-L, a key ingredient in the venom of Vespula lewisii wasps. Mastoparan-L-containing venom is usually not dangerous to humans in the small doses delivered by wasp stings, but it is quite toxic. It destroys red blood cells, and triggers a type of allergic/inflammatory reaction that in susceptible individuals can lead to a fatal syndrome called anaphylaxis—in which blood pressure drops and breathing becomes difficult or impossible.
Mastoparan-L (mast-L) also is known for its moderate toxicity to bacterial species, making it a potential starting point for engineering new antibiotics. But there are still some unknowns, including how to enhance its anti-bacterial properties, and how to make it safe for humans.
“The AIChE 35 Under 35 Award was founded to recognize young chemical engineers who have achieved greatness in their fields,” reads the 2020 award announcement. “The winners are a group of driven, engaged, and socially active professionals, representing the breadth and diversity that chemical engineering exemplifies.”
De la Fuente was named in the list’s “Bioengineering” category for his his lab’s work in machine biology. Their goal is to develop computer-made tools and medicines that will combat antibiotic resistance. De la Fuente has already been featured on several other young innovators lists, including MIT Technology Review’s 35 under 35 and GEN’s Top 10 under 40, both in 2019. His research in antibiotic resistance has been profiled in Penn Today and Penn Engineering Today, and he was recently awarded Penn Health-Tech’s inaugural NEMO Prize for his proposal to develop paper-based COVID diagnostic system that could capture viral particles on a person’s breath.
In addition to being named on the 2020 list, the honorees will receive a $500 prize and will be celebrated at the 2020 AIChE Annual Meeting this November.
Learn more about de la Fuente’s pioneering research on his lab website.
The paper-based tests could be integrated directly into facemasks and provide instant results at testing sites.
When Penn Health-Tech announced its Nemirovsky Engineering and Medicine Opportunity, or NEMO Prize, in February, the center’s researchers could only begin to imagine the impact the looming COVID-19 pandemic was about to unleash. But with the promise of $80,000 to support early-stage ideas at the intersection of engineering and medicine, the contest quickly sparked a winning innovation aimed at combating the crisis.
Judges from the University of Pennsylvania’s School of Engineering and Applied Sciences and Perelman School of Medicine awarded its first NEMO Prize to César de la Fuente, PhD, who proposed a paper-based COVID diagnostic system that could capture viral particles on a person’s breath, then give a result in a matter of seconds when taken to a testing site.
Similar tests for bacteria cost less than a dollar each to make. De la Fuente, a Presidential Assistant Professor in the departments of Psychiatry, Microbiology, and Bioengineering, is aiming to make COVID tests at a similar price point and with a smaller footprint so that they could be directly integrated into facemasks, providing further incentive for their regular use.
“Wearing a facemask is vital to containing the spread of COVID because, before you know you’re sick, they block your virus-carrying droplets so those droplets can’t infect others,” de la Fuente says. “What we’re proposing could eventually lead to a mask that can be infected by the virus and let you know that you’re infected, too.”
De la Fuente’s expertise is in synthetic biology and molecular-scale simulations of disease-causing viruses and bacteria. Having such fine-grained computational models of these microbes’ binding sites allow de la Fuente to test them against massive libraries of proteins, seeing which bind best. Other machine learning techniques can then further narrow down the minimum molecular structures responsible for binding, resulting in functional protein fragments that are easier to synthesize and manipulate.
The spike-shaped proteins that give coronaviruses their crown-like appearance and name bind to a human receptor known as ACE2. De la Fuente and his colleagues are now aiming to characterize the molecular elements and environmental factors that would allow for the most precise, reliable detection of the virus.
Once hailed as medical miracles, antibiotics are losing their effectiveness due to the rapid increase of bacterial immunity.
Researchers are scrambling to keep up with evolution, and they are currently exploring how machine learning can be applied to microbiology to develop more effective treatments.
In the past, researchers have studied bacteria behavior and used their findings to work against the natural patterns of bacterial life. In the 1980s, computer-assisted screening methods helped researchers in their efforts but few developments surfaced from their work. It seemed that there were no new antibiotics to be found using traditional methods, and pharmaceutical companies stepped away from funding antibiotic development in favor of more profitable drugs used to treat chronic conditions. But a new field of research shows a way forward, thanks to the massive advances in computing that have occurred over the intervening decades.
Among the pioneering researchers in this field is César de La Fuente, Presidential Assistant Professor in Psychiatry, Microbiology and Bioengineering. De La Fuente is accelerating the discovery of new antibiotics with his Drug Repurposing Hub, a library of more than 6,000 compounds that is using machine learning algorithms to seek out possible solutions for human disease. With his compound library, de La Fuente is able to examine drugs already approved by the FDA and hunt for new, more effective applications.
In addition to this work, de La Fuente and his colleagues are interested in using machine learning to innovate drug design itself. His lab uses a machine learning platform to generate new molecules in silico and perform experiments on them. Once the results of the experiments come in, they are fed back into the computer so the machine learning platform can continuously learn and improve its findings from the data.
In a recent interview with Katherine Harmon Courage in Quanta Magazine, de La Fuente said:
“The hypothesis is that nature has run out of inspiration in terms of providing us with new antibiotics. That’s why we think that machines … could diversify natural molecules to convert them to synthetic versions that would be much more effective.”
Positive results in first-in-U.S. trial of CRISPR-edited immune cells
Genetically editing a cancer patient’s immune cells using CRISPR/Cas9 technology, then infusing those cells back into the patient appears safe and feasible based on early data from the first-ever clinical trial to test the approach in humans in the United States. Researchers from the Abramson Cancer Center have infused three participants in the trial thus far—two with multiple myeloma and one with sarcoma—and have observed the edited T cells expand and bind to their tumor target with no serious side effects related to the investigational approach. Penn is conducting the ongoing study in cooperation with the Parker Institute for Cancer Immunotherapy and Tmunity Therapeutics.
“This trial is primarily concerned with three questions: Can we edit T cells in this specific way? Are the resulting T cells functional? And are these cells safe to infuse into a patient? This early data suggests that the answer to all three questions may be yes,” says the study’s principal investigator Edward A. Stadtmauer, section chief of Hematologic Malignancies at Penn. Stadtmauer will present the findings next month at the 61st American Society of Hematology Annual Meeting and Exposition.
Because of the opioid epidemic sweeping the nation, Moore notes that there’s a rapid search going on to develop non-addictive painkiller options. However, he also sees a gap in adequate models to test those new drugs before human clinical trials are allowed to take place. Here is where he hopes to step in and bring some innovation to the field, by integrating living human cells into a computer chip for modeling pain mechanisms. Through his research, Moore wants to better understand not only how some drugs can induce pain, but also how patients can grow tolerant to some drugs over time. If successful, Moore’s work will lead to a more rapid and less expensive screening option for experimental drug advancements.
New machine learning-assisted microscope yields improved diagnostics
Researchers at Duke University recently developed a microscope that uses machine learning to adapt its lighting angles, colors, and patterns for diagnostic tests as needed. Most microscopes have lighting tailored to human vision, with an equal distribution of light that’s optimized for human eyes. But by prioritizing the computer’s vision in this new microscope, researchers enable it to see aspects of samples that humans simply can’t, allowing for a more accurate and efficient diagnostic approach.
Led by Roarke W. Horstmeyer, Ph.D., the computer-assisted microscope will diffuse light through a bowl-shaped source, allowing for a much wider range of illumination angles than traditional microscopes. With the help of convolutional neural networks — a special kind of machine learning algorithm — Horstmeyer and his team were able to tailor the microscope to accurately diagnose malaria in red blood cell samples. Where human physicians typically perform similar diagnostics with a rate of 75 percent accuracy, this new microscope can do the same work with 90 percent accuracy, making the diagnostic process for many diseases much more efficient.
Case Western Reserve University researchers create first-ever holographic map of brain
A Case Western Reserve University team of researchers recently spearheaded a project in creating an interactive holographic mapping system of the human brain. The design, which is believed to be the first of its kind, involves the use of the Microsoft HoloLens mixed reality platform. Lead researcher Cameron McIntyre, Ph.D., sees this mapping system as a better way of creating holographic navigational routes for deep brain stimulation. Recent beta tests with the map by clinicians give McIntyre hope that the holographic representation will help them better understand some of the uncertainties behind targeted brain surgeries.
More than merely providing a useful tool, McIntyre’s project also brings together decades’ worth of neurological data that has not yet been seriously studied together in one system. The three-dimensional atlas, called “HoloDBS” by his lab, provides a way of finally seeing the way all of existing neuro-anatomical data relates to each other, allowing clinicians who use the tool to better understand the brain on both an analytical and visual basis.
Implantable cancer traps reduce biopsy incidence and improve diagnostic
Biopsies are one of the most common procedures used for cancer diagnostics, involving a painful and invasive surgery. Researchers at the University of Michigan are trying to change that. Lonnie Shea, Ph.D., a professor of biomedical engineering at the university, worked with his lab to develop implants with the ability to attract any cancer cells within the body. The implant can be inserted through a scaffold placed under the patient’s skin, making it a more ideal option than biopsy for inaccessible organs like lungs.
The lab’s latest work on the project, published in Cancer Research, details its ability to capture metastatic breast cancer cells in vivo. Instead of needing to take biopsies from areas deeper within the body, the implant allows for a much simpler surgical procedure, as biopsies can be taken from the implant itself. Beyond its initial diagnostic advantages, the implant also has the ability to attract immune cells with tumor cells. By studying both types of cells, the implant can give information about the current state of cancer in a patient’s body and about how it might progress. Finally, by attracting tumor and immune cells, the implant has the ability to draw them away from the area of concern, acting in some ways as a treatment for cancer itself.
People and Places
The Philadelphia Inquirer recently published an article detailing the research of Penn’s Presidential Assistant Professor in Psychiatry, Microbiology, and Bioengineering, Cesar de la Fuente, Ph.D. In response to a growing level of worldwide deaths due to antibiotic-resistant bacteria, de la Fuente and his lab use synthetic biology, computation, and artificial intelligence to test hundreds of millions of variations in bacteria-killing proteins in the same experiment. Through his research, de la Fuente opens the door to new ways of finding and testing future antibiotics that might be the only viable options in a world with an increasing level of drug-resistant bacteria
Emily Eastburn, a Ph.D. candidate in Bioengineering at Penn and a member of the Boerckel lab of the McKay Orthopaedic Research Laboratory, recently won the Ashton fellowship. The Ashton fellowship is an award for postdoctoral students in any field of engineering that are under the age of 25, third-generation American citizens, and residents of either Pennsylvania or New Jersey. A new member of the Boerckel lab, having joined earlier this fall, Eastburn will have the opportunity to conduct research throughout her Ph.D. program in the developmental mechanobiology and regeneration that the Boerckel lab focuses on.