28 July 2019
The biomedical field is always evolving as discoveries and new technologies reshape how scientists once thought of a disease or its cure. This past year has seen major advancements in biomedicine with innovative methods that tackled diseases such as Alzheimer’s, and ventured into the exciting areas of artificial intelligence and digital imaging. We look back on seven of the most significant discoveries of 2018. Along with researchers at KAIMRC, scientists around the world have been focused on diseases that affect millions around the world, including cancer, Alzheimer and cardiac diseases.
A promising drug for Alzheimer’s
In a breakthrough for the study of Alzheimer’s disease, a group of scientists from the University of Cambridge, and Lund University, in Sweden have developed a systematic method to target the toxic particles believed to cause the deterioration of healthy brain cells.
The study1 is the first method developed to target the pathogens behind Alzheimer’s, a form of dementia that affects between 60 to 70 percent of the 50 million dementia patients around the world. Drugs used to treat Alzheimer’s dealt only with the symptoms of the disease, but no treatment has yet been able to halt the onset or progression of the disease. The team behind the study hopes that their treatment can go into clinical trials at the end of 2020.
“The Alzheimer’s field is making tremendous advances,” says Michele Vendruscolo, from the University of Cambridge and one of the lead researchers on the study. Until recently, scientists were not in agreement over the cause of the disease, but recent findings identified the pathogens behind Alzheimer’s as small clumps of proteins called oligomers, allowing for potential target treatment.
“The emergence of highly quantitative methods in drug discovery and diagnostics is very exciting,” says Vendruscolo. “It will likely transform the way in which we will deal with Alzheimer's disease within a decade.”
Immunotherapy takes the spotlight
In October, 2018, James Allison of the University of Texas MD Anderson Cancer Center in Houston, and Tasuku Honjo of Kyoto University in Japan won the Nobel Prize in Physiology or Medicine for their discoveries in cancer immunotherapy.
Immunotherapy, or the artificial stimulation of the immune system to target cancer cells, has recently been a focus of cancer treatment.
“The past year was all about immunotherapy and how it works [combined with chemotherapy] against so many different types of cancer,” says Roy Herbst, director of the Yale Center for Immuno-Oncology.
The Nobel laureates showed how proteins on immune cells can be used to manipulate the immune system to attack cancer cells, a technique upon which various therapies were based, extending the life of cancer patients and curing others.
More recently, a group of researchers from Queen Mary University of London and St Bartholomew's Hospital demonstrated how a combination of immunotherapy and chemotherapy can treat an aggressive form of breast cancer. The study2 showed that the treatment reduces the spread of the cancer by 40 percent, improving survival by 10 months.
Immunotherapy has been proven to work on 20 percent of patients, according to Herbst. He adds that it is now time for collaboration between researchers and clinical personnel to determine why it works on some patients and not others, and how to make it beneficial for a larger group of people.
Revolutionary blood test for cancer screening
Early detection is often key to successful cancer treatment. Researchers at the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins may have developed a way to screen for eight different types of cancers, as well as identify the location of the tumour.
CancerSEEK3 is a simple, non-invasive blood test that examines the levels of proteins associated with the eight types of cancer and gene mutations that may emerge in the DNA circulating in the blood. It tests for the most common types of cancer, including lung, ovarian and breast.
According to the study, the blood test detected cancer with a sensitivity of 69 to 98 percent and 99 percent specificity. The researchers estimated that would cost around 0 per patient.
AI that can predict disease
The use of artificial intelligence as a tool in biomedicine could have a significant impact on the field.
In 2018, Google announced an AI algorithm that can predict a person’s risk of heart disease by scanning their eyes. The research4, conducted by scientists at the Verily, formerly known as Google Life Sciences, and published in Nature Biomedical Engineering, details that through the scan, the machine would be able to identify a person’s age, blood pressure, and whether or not they smoke, without the need for a blood test.
Another study5, by a team of researchers at the Department of Radiology & Biomedical Imaging at the University of California in San Francisco, has developed an AI system that predicts the risk of Alzheimer’s disease. The machine was trained to find irregularities in the brain’s metabolism, which could be indicative of Alzheimer’s.
“[AI] is already reshaping medicine,” says Anant Madabhushi, professor of biomedical engineering at Case Western Reserve University in Cleveland and director of the university’s Center for Computational Imaging and Personalized Diagnostics, pointing to emerging technologies that are awaiting regulatory approval.
Another technology trend reshaping the biomedical field is 3D imaging. In November, 2018, a device that took more than a decade to develop was finally released as the world’s first, full-body medical scanner. The EXPLORER captures a snapshot the full human body and produce a 3D image of it with just one scan in 20 to 30 seconds.
The device was developed by a group of scientists from UC Davis and engineers from Shanghai-based United Imaging Healthcare, and is said to produce more detailed images than the regular x-ray, with less radiation.
Madabhushi says that some radiology departments fear the new technology could make their role redundant, while others have reservations about trusting a machine. But he says, “but AI renders diagnosis faster and actually empowers us.”
Creating organs, digitally
Printing whole organs for transplantation may soon become a reality. Research into 3D bioprinting of organs such as the heart, kidney and liver has moved quickly in the past decade. In October 2018, a study6 at the University of Colorado Boulder developed a new printing method t to allow for the mimicking of blood vessels and arteries by controlling the printed object’s firmness, This could lead to more personalized treatments for hypertension and vascular disease patients. Another group of researchers at biotechnology company, BioLife4D is working on a process that could create a beating heart using a person’s own cells.
An evolution of the genes
Early trial phases of gene editing for use against genetic diseases are underway. In September, 2018, researchers from Sangamo Therapeutics in Richmond, California published a study on gene editing for Hunter Syndrome and first trial results of the 16-week clinical study were promising.
- Chia, S., Habchi, J., Michaels, T.C.T., Cohen, A.I.A, Linse, S. et al. SAR by kinetics for drug discovery in protein misfolding diseases. PNAS 115,10245-10250 (2019).
- Schmid, P., Adams, S., Rugo, H.S., Schneeweiss, A., Barrios, C.H. et al. Atezolizumab and Nab-Paclitaxel in advanced triple-negative breast cancer. The New England Journal of Medicine 379, 2108-2121 (2018).
- Cohen, J.D., Li, L., Wang, Y., Thoburn, C., Asfari, B. et al. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science 359, 926-930 (2018) .
- Poplin, R. Varadarajan, A.V., Blumer, K., Liu, Y., McConnell, M.V. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature Biomedical Engineering 2, 158-164 (2018).
- Ding, Y., Sohn, J.H., Kawczynski, M.G., Trivedi, H., Harnish, R. et al. A deep learning model to predict a diagnosis of Alzheimer Disease by Using 18F-FDG PET of the brain. Radiology 290, 456-464 (2018).
- Yin, H., Ding, Y., Zhai, Y., Tan, W., Yin, X. Orthogonal programming of heterogeneous micro-mechano-environments and geometries in three-dimensional bio-stereolithography. Nature Communications9 (2018).