Bin Chen

Assistant Professor of Pediatrics and Human Development Michigan State University

  • Grand Rapids MI

Dr. Chen is the founding member of DahShu, a non-profit organization to promote research and education in data sciences.

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3 min

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

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.” If you are a journalist looking to learn more about Dr. Chen and his ongoing work to treat COVIS-19 then let us help. Dr. Bin Chen is available to speak with media regarding his work simply click on his icon to arrange an interview today.

Bin Chen

Media

Biography

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.

Industry Expertise

Education/Learning

Areas of Expertise

Cancer Therapy
Drug Repositioning
Translational Bioinformatics
AI
Big Data
Cancer Therapeutic Discovery
Cheminformatics
Precision Medicine

Accomplishments

NIGMS R01 (2019)

2019

NCATS R21

2017

BD2K K01 Award

2017

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Education

Stanford University

Postdoc

Bioinformatics

2015

Indiana University

PhD

Informatics

2012

Indiana University

MS

Chemical Informatics

2009

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Affiliations

  • DahShu : Founding Member
  • Department of Pharmacology and Toxicology

News

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

MSU Today  

2019-10-16

Bin Chen, an assistant professor in the College of Human Medicine’s Departments of Pediatrics and Human Development, Pharmacology and Toxicology, is on a mission to help find new or better treatments for an estimated 6,000 diseases considered too rare to attract much research.

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

EurekAlert!  

2019-10-16

Bin Chen, PhD, is on a mission to help find new or better treatments for an estimated 6,000 diseases considered too rare to attract much research.

"Although individually those diseases afflict relatively few people, combined they are suffered by about 25 million Americans, some whose illnesses are life-threatening," said Chen, an assistant professor in the College of Human Medicine's Departments of Pediatrics and Human Development, and Pharmacology and Toxicology.

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

News-Medical.Net  

2019-10-16

Bin Chen, PhD, is on a mission to help find new or better treatments for an estimated 6,000 diseases considered too rare to attract much research.

View More

Show All +

Research Grants

BD2K K01 RFA-ES-16-002

NIH/NIEHS

Integrating transcriptomic, proteomic and pharmacogenomic data to inform individualized therapy in cancers.
Develop biomarkers from cell lines to guide cancer therapy

Henlius

Industry sponsored

Big data approach to identify next generation immuno-oncology targets. This collaborative proposal is aimed to propose novel targets to increase immunotherapy response.

R01

NIH/NIGMS

Repurpose open data to discover new therapeutics for understudied diseases

Journal Articles

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

Gastroenterology

Chen B, Wei W, Ma L, Yang B, Gill RM, Chua MS, Butte AJ, So S

2017

Drug repositioning offers a shorter approval process than new drug development. We therefore searched large public datasets of drug-induced gene expression signatures to identify agents that might be effective against hepatocellular carcinoma (HCC).

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

Nature Communications

Bin Chen, Li Ma, Hyojung Paik, Marina Sirota, Wei Wei, Mei-Sze Chua, Samuel So & Atul J. Butte

2017

The decreasing cost of genomic technologies has enabled the molecular characterization of large-scale clinical disease samples and of molecular changes upon drug treatment in various disease models. Exploring methods to relate diseases to potentially efficacious drugs through various molecular features is critically important in the discovery of new therapeutics. Here we show that the potency of a drug to reverse cancer-associated gene expression changes positively correlates with that drug’s efficacy in preclinical models of breast, liver and colon cancers. Using a systems-based approach, we predict four compounds showing high potency to reverse gene expression in liver cancer and validate that all four compounds are effective in five liver cancer cell lines. The in vivo efficacy of pyrvinium pamoate is further confirmed in a subcutaneous xenograft model. In conclusion, this systems-based approach may be complementary to the traditional target-based approach in connecting diseases to potentially efficacious drugs.

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

Oncotarget

Pessetto ZY, Chen B, Alturkmani H, Hyter S, Flynn CA, Baltezor M, Ma Y, Rosenthal HG, Neville KA, Weir SJ, Butte AJ, Godwin AK

2017

The long-term overall survival of Ewing sarcoma (EWS) patients remains poor; less than 30% of patients with metastatic or recurrent disease survive despite aggressive combinations of chemotherapy, radiation and surgery. To identify new therapeutic options, we employed a multi-pronged approach using in silico predictions of drug activity via an integrated bioinformatics approach in parallel with an in vitro screen of FDA-approved drugs. Twenty-seven drugs and forty-six drugs were identified, respectively, to have anti-proliferative effects for EWS, including several classes of drugs in both screening approaches. Among these drugs, 30 were extensively validated as mono-therapeutic agents and 9 in 14 various combinations in vitro. Two drugs, auranofin, a thioredoxin reductase inhibitor, and ganetespib, an HSP90 inhibitor, were predicted to have anti-cancer activities in silico and were confirmed active across a panel of genetically diverse EWS cells. When given in combination, the survival rate in vivo was superior compared to auranofin or ganetespib alone. Importantly, extensive formulations, dose tolerance, and pharmacokinetics studies demonstrated that auranofin requires alternative delivery routes to achieve therapeutically effective levels of the gold compound. These combined screening approaches provide a rapid means to identify new treatment options for patients with a rare and often-fatal disease.

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