As new diagnostic assays are cleared by regulators, clinical laboratories will play a key role in identifying appropriate patients for new less-invasive Alzheimer’s tests
With multiple companies racing to develop a blood-based test for Alzheimer’s disease (AD), clinical laboratories may soon have new less-invasive diagnostic assays for AD on their menus.
Why a race? Because a less-invasive clinical laboratory test that uses a venous blood draw (as opposed to a spinal tap)—and which has increased sensitivity/specificity—has a potentially large market given the substantial numbers of elderly predicted to develop Alzheimer’s over the next decade. It has the potential to be a high volume, high dollar diagnostic test.
In fact, Mordor Intelligence estimates that the market for Alzheimer’s disease therapeutics will grow from $7.7 billion in 2024 to $10.10 billion by 2029.
Alzheimers.gov, an official website of the US government, says, “Researchers have made significant progress in developing, testing, and validating biomarkers that detect signs of the disease process. For example, in addition to PET scans that detect abnormal beta-amyloid plaques and tau tangles [abnormal forms of tau protein] in the brain, NIH-supported scientists have developed the first commercial blood test for Alzheimer’s. This test and others in development can not only help support diagnosis but also be used to screen volunteers for research studies.”
Additionally, the US Food and Drug Administration (FDA) is clearing new Alzheimer’s drugs for clinical use. The pharma companies behind these drugs need clinical laboratory tests that accurately diagnosis the disease and confirm that it would be appropriate for the patient to receive the new therapeutic drugs, a key element of precision medicine.
“The big promise for blood tests is that they will eventually be accessible, hopefully, cost-effective, and noninvasive,” Rebecca Edelmayer, PhD (above), Vice President, Scientific Engagement, Alzheimer’s Association, told USA Today. “The field is really moving forward with use of these types of tests,” she added. Clinical laboratories may soon have these new assays on their test menus. (Photo copyright: Alzheimer’s Association.)
Companies in the Race to Develop Blood-based Alzheimer’s Tests
Researchers found that C2N’s blood test can detect brain amyloid status with “sensitivity, specificity, positive and negative predictive values that approximate those of amyloid positron emission tomography (PET) imaging,” according to a news release.
“The PrecivityAD2 blood test is intended for use in patients aged 55 and older with signs or symptoms of mild cognitive impairment or dementia who are undergoing evaluation of Alzheimer’s disease or dementia. Only a healthcare provider can order the PrecivityAD2 test,” the news release noted.
“The PrecivityAD2 blood test showed strong clinical validity with excellent agreement with brain amyloidosis by PET,” the researchers wrote.
The PrecivityAD2 test, which is mailed directly by C2N to doctors and researchers, is performed at the company’s CLIA-certified lab, according to USA Today, which added that the cost of $1,450 is generally not covered by insurance plans.
Expanding Test Access with IVD Companies
ALZpath, Inc. has a different approach to the Alzheimer’s disease test market. The Carlsbad, Calif.-based company, set up an agreement with in vitro diagnostics (IVD) company Roche Diagnostics for use of its phosphorylated tau (pTau)217 antibody “to develop and commercialize an Alzheimer’s disease diagnostic blood test that will be offered on the Roche Elecsys platform,” according to a news release.
Roche received FDA breakthrough device designation on the Elecsys pTau217 test earlier this year and will work with pharmaceutical company Eli Lilly to commercialize the test.
Estimates show 75% of dementia cases go undetected—a number which could grow to 140 million by 2050, according to data shared by Roche with Fierce Biotech.
“We plan to leverage our installed base of diagnostic systems, which is the largest in the world, to ensure we are able to create access to this test for those who need it the most,” Matt Sause, CEO, Roche Diagnostics, told Fierce Biotech.
Another IVD company, Beckman Coulter, recently signed an agreement to use ALZpath’s pTau217 antibody test in its DxI 9000 Immunoassay Analyzer. In a news release, Kathleen Orland, SVP and General Manager of the Clinical Chemistry Immunoassay Business Unit at Beckman Coulter, said that the test had “high performance in detecting amyloid pathology” and could “integrate into our advanced DxI 9000 platform to support broad-based testing.”
Clinical Laboratory Participation
The FDA is drafting new guidance titled, “Early Alzheimer’s Disease: Developing Drugs for Treatment” that is “intended to assist sponsors in the clinical development of drugs for the treatment of the stages of sporadic Alzheimer’s disease (AD) that occur before the onset of overt dementia.”
Pharma companies intent on launching new drugs for Alzheimer’s will need medical laboratory tests that accurately diagnosis the disease to confirm the medications would be appropriate for specific patients.
Given development of the aforementioned pTau217 antibody tests, and others featuring different diagnostic technologies, it’s likely clinical laboratories will soon be performing new assays for diagnosing Alzheimer’s disease.
Two studies show the accuracy of perception-based systems in detecting disease biomarkers without needing molecular recognition elements, such as antibodies
Researchers from multiple academic and research institutions have collaborated to develop a non-conventional machine learning-based technology for identifying and measuring biomarkers to detect ovarian cancer without the need for molecular identification elements, such as antibodies.
Traditional clinical laboratory methods for detecting biomarkers of specific diseases require a “molecular recognition molecule,” such as an antibody, to match with each disease’s biomarker. However, according to a Lehigh University news release, for ovarian cancer “there’s not a single biomarker—or analyte—that indicates the presence of cancer.
“When multiple analytes need to be measured in a given sample, which can increase the accuracy of a test, more antibodies are required, which increases the cost of the test and the turnaround time,” the news release noted.
Unveiled in two sequential studies, the new method for detecting ovarian cancer uses machine learning to examine spectral signatures of carbon nanotubes to detect and recognize the disease biomarkers in a very non-conventional fashion.
“Carbon nanotubes have interesting electronic properties,” said Daniel Heller, PhD (above), in the Lehigh University news release. “If you shoot light at them, they emit a different color of light, and that light’s color and intensity can change based on what’s sticking to the nanotube. We were able to harness the complexity of so many potential binding interactions by using a range of nanotubes with various wrappings. And that gave us a range of different sensors that could all detect slightly different things, and it turned out they responded differently to different proteins.” This method differs greatly from traditional clinical laboratory methods for identifying disease biomarkers. (Photo copyright: Memorial Sloan-Kettering Cancer Center.)
Perception-based Nanosensor Array for Detecting Disease
In the Science Advances paper, the researchers described their development of “a perception-based platform based on an optical nanosensor array that leverages machine learning algorithms to detect multiple protein biomarkers in biofluids.
“Perception-based machine learning (ML) platforms, modeled after the complex olfactory system, can isolate individual signals through an array of relatively nonspecific receptors. Each receptor captures certain features, and the overall ensemble response is analyzed by the neural network in our brain, resulting in perception,” the researchers wrote.
“This work demonstrates the potential of perception-based systems for the development of multiplexed sensors of disease biomarkers without the need for specific molecular recognition elements,” the researchers concluded.
In the Nature Biomedical Engineering paper, the researchers described a fined-tuned toolset that could accurately differentiate ovarian cancer biomarkers from biomarkers in individuals who are cancer-free.
“Here we show that a ‘disease fingerprint’—acquired via machine learning from the spectra of near-infrared fluorescence emissions of an array of carbon nanotubes functionalized with quantum defects—detects high-grade serous ovarian carcinoma in serum samples from symptomatic individuals with 87% sensitivity at 98% specificity (compared with 84% sensitivity at 98% specificity for the current best [clinical laboratory] screening test, which uses measurements of cancer antigen 125 and transvaginal ultrasonography,” the researchers wrote.
“We demonstrated that a perception-based nanosensor platform could detect ovarian cancer biomarkers using machine learning,” said Yoona Yang, PhD, a postdoctoral research associate in Lehigh’s Department of Chemical and Biomolecular Engineering and co-first author of the Science Advances article, in the news release.
How Perception-based Machine Learning Platforms Work
According to Yang, perception-based sensing functions like the human brain.
“The system consists of a sensing array that captures a certain feature of the analytes in a specific way, and then the ensemble response from the array is analyzed by the computational perceptive model. It can detect various analytes at once, which makes it much more efficient,” Yang said.
“SWCNTs have unique optical properties and sensitivity that make them valuable as sensor materials. SWCNTS emit near-infrared photoluminescence with distinct narrow emission bands that are exquisitely sensitive to the local environment,” the researchers wrote in Science Advances.
“Carbon nanotubes have interesting electronic properties,” said Daniel Heller, PhD, Head of the Cancer Nanotechnology Laboratory at Memorial Sloan Kettering Cancer Center and Associate Professor in the Department of Pharmacology at Weill Cornell Medicine of Cornell University, in the Lehigh University news release.
“If you shoot light at them, they emit a different color of light, and that light’s color and intensity can change based on what’s sticking to the nanotube. We were able to harness the complexity of so many potential binding interactions by using a range of nanotubes with various wrappings. And that gave us a range of different sensors that could all detect slightly different things, and it turned out they responded differently to different proteins,” he added.
The researchers put their technology to practical test in the second study. The wanted to learn if it could differentiate symptomatic patients with high-grade ovarian cancer from cancer-free individuals.
The research team used 269 serum samples. This time, nanotubes were bound with a specific molecule providing “an extra signal in terms of data and richer data from every nanotube-DNA combination,” said Anand Jagota PhD, Professor, Bioengineering and Chemical and Biomolecular Engineering, Lehigh University, in the news release.
This year, 19,880 women will be diagnosed with ovarian cancer and 12,810 will die from the disease, according to American Cancer Society data. While more research and clinical trials are needed, the above studies are compelling and suggest the possibility that one day clinical laboratories may detect ovarian cancer faster and more accurately than with current methods.
Scientists participating in the modENCORE study have the goal of understanding the causes of hereditary genetic diseases in humans
New discoveries about the interaction of genes and transcription factors in creating different types of RNA will be of interest to pathologists and clinical chemists performing genetic tests and molecular diagnostic assays in their medical laboratories.
The goal of this research is to better understand hereditary genetic disease in humans. The new knowledge is based on studies of the common fruit fly, or Drosophila melanogaster (D. Melanogaster), and to a lesser extent a tiny worm Caenorhabditis elegans (C. elegans). Both have been used as research models to study the human condition.
Research Could Give Pathologists New Diagnostic Tools(more…)
This technology could provide medical labs a quick, cost-effective way to diagnose methicillin-resistant Staphylococcus aureus
Even as in vitro diagnostics manufacturers are bringing rapid molecular tests to market that can identify infectious diseases within hours, a research collaboration involving a major university and a medical laboratory at an air force base has demonstrated the ability to identify antibiotic-resistant strains of Staphylococcus in just minutes.
This innovative research is being done by Auburn University’s College of Veterinary Medicine and clinical laboratory professionals at Keesler Air Force Base. Funding is by the U.S. Air Force. This research was of particular interest to the military because the risk for Staph infection increases when individuals are subjected to unhygienic conditions in close quarters. (more…)
The ATC Chip identifies ovarian cancer cells floating in ascites and may be useful for diagnosing other types of malignancies that involve ascites, like pancreatic cancer
Pathologists will be interested to learn that researchers at Harvard Medical School and Massachusetts General Hospital are developing a “liquid biopsy” technology specifically to enable point-of-care monitoring of the progress of patients undergoing treatment for certain types of cancers.
The goal is to develop a method that community hospitals can use to monitor treatment of ovarian cancer patients without the need for expensive medical laboratory equipment, noted a report published by Biosciencetechnology.com. Researchers estimate that their ‘liquid biopsy’ technology could cost as little as $1 per test when eventually cleared for use in clinical settings. (more…)
