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NIGMS R01 (2019)

NCATS R21

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LINCS Meeting Travel Fellowship

Intel Science Talent Search (Intel STS) Research Teacher

Presidential Trainee Award ASCPT

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Department of Pharmacology and Toxicology 

Computational Discovery of Niclosamide Ethanolamine, a Repurposed Drug Candidate That Reduces Growth of Hepatocellular Carcinoma Cells In Vitro and in Mice by Inhibiting Cell Division Cycle 37

Reversal of cancer gene expression correlates with drug efficacy and reveals therapeutic targets

In silico and in vitro drug screening identifies new therapeutic approaches for Ewing sarcoma

The long-term interest of the Chen lab is to harness big genomic data and artificial intelligence to discover new or better therapeutic candidates for cancers through collaborating with bench scientists and clinicians. The past few years have witnessed the generation of voluminous omics data across multiple modalities —from bulk tissues to single cells, from patients to preclinical models, from disease samples to drug-treatment samples. The Chen lab develops advanced AI methods to find molecular patterns for diseases and drugs and then match a disease to the best drug based on those patterns. Using this approach, they have successfully identified drug candidates for three cancers: Ewing’s sarcoma (Oncotarget, 2016), liver cancer (Gastroenterology, 2017) and basal cell carcinoma (JCI Insight, 2017). They recently discovered that deworming pills might be used to treat liver cancer. Now they are using a similar strategy to discover new therapeutics for rare diseases including DIPG, a pediatric cancer with a five-year survival rate of less than 1%.


Dr. Chen was recruited to MSU through the Global Impact Initiative. Prior to this position, Dr. Chen was an assistant professor in the Institute for Computational Health Sciences at University of California, San Francisco. Dr. Chen is also the founding member of DahShu, a non-profit organization to promote research and education in data sciences. Dr. Chen trained as a chemist in college, worked as a software engineer before graduate school, trained as a chem/bioinformatician in graduate school, worked as a computational scientist at Novartis, Pfizer and Merck. He received his PhD in informatics at Indiana University, Bloomington and pursued postdoctoral training in Dr. Atul Butte’s lab at Stanford University. His work has been featured in STAT, GEN, GenomeWeb and KCBS. As a PI, he has received >$4.5 million research funding. He has also contributed to several big grants (e.g., P01 and U24) as a co-investigator.

Cancer Therapy

Drug Repositioning

Translational Bioinformatics

Big Data

Cancer Therapeutic Discovery

Cheminformatics

Precision Medicine

Michigan State University Faculty Profile

Michigan State University Profile

Bin Chen Lab Website

Google Scholar Profile

Researchers team up to find new treatments for 'orphan diseases'

MSU and Spectrum Health researchers team up to find new treatments for 'orphan diseases'

Spectrum Health, MSU researchers aim to find new or better treatments for "orphan diseases"

Big data helps identify better way to research breast cancer's spread

BD2K K01 RFA-ES-16-002

Henlius

new-process-to-identify-existing-drugs-for-potential-covid19-treatments

Assistant Professor of Pediatrics and Human Development

 In late January, as the world watched the growing COVID-19 epidemic with increasing unease, a Michigan State University laboratory, which specializes in the use of artificial intelligence and big data to discover therapeutics for cancers, switched gears to face the coming challenge.

The Chen Lab, led by Bin Chen, assistant professor in the Department of Pediatrics and Human Development, and the Department of Pharmacology and Toxicology, put its expertise to work. They developed a computational process for identifying existing drugs that may be repurposed to fight the SARS-CoV-2 virus without needing access to the virus itself.

 When a virus infects a human cell, it hijacks the reproductive capabilities to replicate and survive. In doing so, the virus interferes in the activity of the host cell’s genes. Each virus leaves a unique imprint on the cell at a certain point of infection — known scientifically as a gene expression signature — that is detectable by modern laboratory technologies. “We wanted to find a drug that could block the gene expression change in the host cells, hoping to mitigate disease progression and alleviate symptoms,” Chen said. Meanwhile, scientists worldwide knew almost nothing about the new virus and access to live virus samples was limited at best, he said. Based on a number of publicly-available datasets, Chen and his team surmised that other members of the coronavirus family — SARS and MERS — could approximate the gene expression signature of the new virus. Using the lab’s existing library of FDA-approved or clinically-investigated drugs and an established drug prediction pipeline, the team examined thousands of potential drug candidates through a complex methodology of scoring, rating and ranking potential candidates against known gene expression signatures.

“Fortunately, we found a number of drugs that could be effective,” Chen said. “But we needed to do more. We needed biological validation.” In collaboration with researchers at the University of Texas Medical Branch, Chen tested the top-rated drug candidates on kidney cells derived from an African green monkey, a common cell line used in toxicology and virology research. The cells were first treated with the drug and later infected by the new SARS-CoV-2 virus.

“We sent 10 drugs to them and it turns out that four drugs were able to prevent the virus-induced effects,” Chen said. “Unfortunately, the drugs, which are used to treat cancer, are also rather toxic. But the concept worked. Our process worked. We can now find more potential drugs to reverse the impacts of the virus and keep those less toxic drugs for further investigation.”  “I’m very appreciative of how open the scientific community has been,” Chen said. “We knew very little at the beginning, but scientists have been making their work available to the community so that we can act.”

Researchers in South Korea tested 35 FDA-approved drugs for antiviral efficacy against actual SARS-CoV-2 samples. Fourteen positive drugs overlapped with Chen Lab’s screening library and were also ranked highly by the methodology. This data also externally validated the predictive ability of Chen’s discovery process.

“We are striving to use the best science possible to help patients,” Chen said.
He intends to make their work available immediately. “We need to release this data to the public. Other laboratories across the world may be able to learn from our work,” Chen said. “They can select new compounds to investigate. There are so many drugs to screen. We alone cannot test them all.”

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New process to identify existing drugs for potential COVID-19 treatments

Currently, only 20% of cancer patients respond to immunotherapy checkpoints. Dr. Chen's technology aims to change that by using Big Data and artificial intelligence to collect information from publicly available sources, to better diagnose and treat cancer with immunotherapy.

Corporate Research Partnership - Bin Chen

Bin Chen, Assistant Professor in the MSU Department of Pediatrics and Human Development, presents during a Department of Pharmacology and Toxicology Drug Discovery Seminar.

The long-term goal of Chen's lab is to develop computational methods and tools to discover new or better therapeutic candidates for cancers through collaborating with bench scientists and clinicians. Rapidly decreasing costs of molecular measurement technologies not only enable profiling of disease sample molecular features (e.g., transcriptome, proteome, metabolome) at different levels (e.g., tissues, single cells), but also enable measuring of cellular signatures of individual drugs in clinically relevant models. His lab is interested in leveraging these data and artificial intelligence to connect different components (patients, tissues, in vitro models and in vivo models) in translational research. We currently focus on liver cancer, breast cancer, and pediatric cancers.

Leveraging Big Data and Artificial Intelligence in Therapeutic Discovery

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