Tag: machine biology

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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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Harnessing Artificial Intelligence for Real Biological Advances—Meet César de la Fuente

by Eric Horvath

In an era peppered by breathless discussions about artificial intelligence—pro and con—it makes sense to feel uncertain, or at least want to slow down and get a better grasp of where this is all headed. Trusting machines to do things typically reserved for humans is a little fantastical, historically reserved for science fiction rather than science. 

Not so much for César de la Fuente, PhD, the Presidential Assistant Professor in Psychiatry, Microbiology, Chemical and Biomolecular Engineering, and Bioengineering in Penn’s Perelman School of Medicine and School of Engineering and Applied Science. Driven by his transdisciplinary background, de la Fuente leads the Machine Biology Group at Penn: aimed at harnessing machines to drive biological and medical advances. 

A newly minted National Academy of Medicine Emerging Leaders in Health and Medicine (ELHM) Scholar, among earning a host of other awards and honors (over 60), de la Fuente can sound almost diplomatic when describing the intersection of humanity, machines and medicine where he has made his way—ensuring multiple functions work together in harmony. 

“Biology is complexity, right? You need chemistry, you need mathematics, physics and computer science, and principles and concepts from all these different areas, to try to begin to understand the complexity of biology,” he said. “That’s how I became a scientist.”

Read the full story in Penn Medicine News.

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For a New Generation of Antibiotics, Scientists are Bringing Extinct Molecules Back to Life – and Discovering the Hidden Genetics of Immunity Along the Way

by Devorah Fischler

Marrying artificial intelligence with advanced experimental methods, the Machine Biology Group has mined the ancient past for future medical breakthroughs, bringing extinct molecules back to life. (Image credit: Ella Marushchenko)

“We need to think big in antibiotics research,” says Cesar de la Fuente. “Over one million people die every year from drug-resistant infections, and this is predicted to reach 10 million by 2050. There hasn’t been a truly new class of antibiotics in decades, and there are so few of us tackling this issue that we need to be thinking about more than just new drugs. We need new frameworks.”

De la Fuente is Presidential Assistant Professor in the Department of Bioengineering and the Department of Chemical and Biomolecular Engineering at the University of Pennsylvania School of Engineering and Applied Science. He holds additional primary appointments in Psychiatry and Microbiology in the Perelman School of Medicine.

De la Fuente’s lab, the Machine Biology Group, creates these new frameworks using potent partnerships in engineering and the health sciences, drawing on the “power of machines to accelerate discoveries in biology and medicine.”

Marrying artificial intelligence with advanced experimental methods, the group has mined the ancient past for future medical breakthroughs. In a recent study published in Cell Host and Microbe, the team has launched the field of “molecular de-extinction.”

Our genomes – our genetic material – and the genomes of our ancient ancestors, express proteins with natural antimicrobial properties. “Molecular de-extinction” hypothesizes that these molecules could be prime candidates for safe new drugs. Naturally produced and selected through evolution, these molecules offer promising advantages over molecular discovery using AI alone.

In this paper, the team explored the proteomic expressions of two extinct organisms –Neanderthals and Denisovans, archaic precursors to the human species – and found dozens of small protein sequences with antibiotic qualities. Their lab then worked to synthesize these molecules, bringing these long-since-vanished chemistries back to life.

“The computer gives us a sequence of amino acids,” says de la Fuente. “These are the building blocks of a peptide, a small protein. Then we can make these molecules using a method called ‘solid-phase chemical synthesis.’ We translate the recipe of amino acids into an actual molecule and then build it.”

The team next applied these molecules to pathogens in a dish and in mice to test the veracity and efficacy of their computational predictions.

“The ones that worked, worked quite well,” continues de la Fuente. “In two cases, the peptides were comparable – if not better – than the standard of care. The ones that didn’t work helped us learn what needed to be improved in our AI tools. We think this research opens the door to new ways of thinking about antibiotics and drug discovery, and this first step will allow scientists to explore it with increasing creativity and precision.”

Read the full story in Penn Engineering Today.

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Cesar de la Fuente On the “Next Frontier” of Antibiotics

César de la Fuente
César de la Fuente

In a recent CNN feature, César de la Fuente, Presidential Assistant Professor in Bioengineering, Psychiatry, Microbiology, and in Chemical and Biomolecular Engineering commented on a study about a new type of antibiotic that was discovered with artificial intelligence:

“I think AI, as we’ve seen, can be applied successfully in many domains, and I think drug discovery is sort of the next frontier.”

The de la Fuente lab uses machine learning and biology to help prevent, detect, and treat infectious diseases, and is pioneering the research and discovery of new antibiotics.

Read “A new antibiotic, discovered with artificial intelligence, may defeat a dangerous superbug” in CNN Health.

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César de la Fuente Receives 2022 RSEQ Young Investigator Award

César de la Fuente, PhD

César de la Fuente, Presidential Assistant Professor in Psychiatry, Bioengineering, Microbiology, and in Chemical and Biomolecular Engineering has been honored with a 2022 Young Investigator Award by the Royal Spanish Society of Chemistry (RSEQ) for his pioneering research efforts to combine the power of machines and biology to help prevent, detect, and treat infectious diseases.

Read the RSEQ’s announcement here.

This story originally appeared in Penn Medicine News’s Awards & Accolades post for April 2022.

 

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Rapid COVID-19 Diagnostic Test Delivers Results Within 4 Minutes With 90 Percent Accuracy

RAPID, a low-cost COVID-19 diagnostic test, can detect SARS-CoV-2 within four minutes with 90 percent accuracy

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.

Read more about RAPID at Penn Medicine News.

Originally posted on Penn Engineering Today.

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One Step Closer to an At-home, Rapid COVID-19 Test

Created in the lab of César de la Fuente, this miniaturized, portable version of rapid COVID-19 test, which is compatible with smart devices, can detect SARS-CoV-2 within four minutes with nearly 100% accuracy. (Image: Courtesy of César de la Fuente)

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.

César de la Fuente earned his bachelor’s degree in biotechnology, then a doctorate in microbiology and immunology and a postdoc in synthetic biology and computational biology. Combining these fields led him to the innovative work his lab, the Machine Biology Group, does today. (Photo: Eric Sucar)

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.

Continue reading “One Step Closer to an At-home, Rapid COVID-19 Test,” by Michele Berger, at Penn Today.

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César de la Fuente on AIChE’s 35 Under 35 List

César de la Fuente, PhD

César de la Fuente, Presidential Assistant Professor in Psychiatry, Microbiology, and Bioengineering, was named one of the American Institute of Chemical Engineers’ (AIChE) 35 members under 35 for 2020.

“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.