AMPEL BioSolutions’ latest innovation in precision medicine predicts drug options using RNA analysis and machine learning

A breakthrough in precision and personalized medicine, announced by AMPEL BioSolutions, could update the way doctors treat patients with various diseases, including cancer, infectious disorders and autoimmune diseases. The first-of-its-kind platform technology, unveiled at the Global Precision Medicine Conference in Silicon Valley, California, uses RNA analysis and machine learning to characterize the gene expression of person and offer clinical decision support to physicians who can choose the best course of action for their patients. Over the next five years, a portfolio of more than 10 clinical trials will be launched using technology, which has just been an idea in recent years, to provide decision support for diseases that affect more than 50 million Americans.

AMPEL BioSolutions, a precision medicine company, markets a pipeline of gene expression tests for CLIA-certified blood or tissue samples, which aid in clinical decision-making by diagnosing disease states, locating molecular pathways and providing treatment options. The technology used by AMPEL is a cloud-based platform that hosts unique RNA analysis tools and machine learning algorithms that are the subject of more than 80 peer-reviewed articles in journals at high impact and more than 25 patents filed/pending. Over 95% of all known genes are protected by AMPEL’s technology, and machine learning predictions are supported by the proprietary curated library of over 15,000 individual gene expression profiles with clinical insights detailed. Systemic lupus erythematosus, psoriasis, scleroderma, atopic dermatitis, lupus nephritis, fibromyalgia, cardiovascular disease, Sjögren’s syndrome, ASD, wellness, lung cancer and SARS-Cov2 are among diseases covered by AMPEL’s portfolio of precision medicine tests. In early 2022, AMPEL BioSolutions was chosen to join the Coalition for 21st Century Precision Medicine.

Pharmaceutical companies need to recruit patients with the best chance of responding to treatment being assessed while testing drugs in clinical trials. Recruiting the “wrong” people can lead to trial failure, which often causes the FDA to halt development of a drug that would help a certain subgroup of patients. Pharmaceutical companies can proactively identify patients most likely to respond to certain drugs using AMPEL’s technology, which helps improve clinical trial outcomes and patient quality of life. Dr. Peter Lipsky at the World Conference on Precision Medicine highlighted AMPEL’s pharmaceutical work during a panel discussion on the application of machine learning to clinical trial patient selection and drug prediction. results, and Dr. Amrie Grammer during a Google-Reuters webinar on machine learning strategies for timely patient selection trials.

By enabling physicians to more accurately identify the underlying cause of patient disease symptoms and choose the best course of treatment, AMPEL’s groundbreaking machine learning approach can have a significant impact on healthcare. The method used by AMPEL is sensitive enough to identify early indicators of disease and classify individuals according to the severity of their condition. It is now ready for development as a clinical decision support biomarker test. More than 15 pharmaceutical companies directly benefit from using AMPEL’s technology in new drug development and clinical trials.

Machine learning is an analytical method for teaching computers to evaluate data and anticipate outcomes. To determine if someone with lupus, psoriasis, atopic dermatitis or scleroderma is experiencing a flare-up of disease activity, AMPEL used this approach in a novel way to train a computer to analyze data obtained from evaluation of a type of “Big Data”. namely that obtained by evaluating gene expression information. Gene expression analysis can illuminate the full range of genetic aberrations by looking at the amount and pattern of genes being expressed at any given time.

Unexpected flare-ups of several chronic diseases have a significant impact on patients’ quality of life. Additionally, although regular disease therapies were created based on a patient population, some people may or may not respond differently to existing treatments. To address this issue, AMPEL scientists and physicians have been developing concepts for tailoring drugs to a patient as opposed to a patient population over the past nine years. The viability of the AMPEL idea, which is currently in the commercialization stage, has been documented in peer-reviewed articles.

The test can be used for various autoimmune or inflammatory diseases, while AMPEL’s main focus is lupus. AMPEL’s blood and tissue biopsy tests are prognostic and staging biomarkers that will help the physician choose the best medications for the patient at that time.

Patients with inflammatory and autoimmune disorders frequently present with spontaneous disease activity that interferes with normal daily activities, including work and family life. The ability to predict disease worsening and systemic involvement in routine testing has important implications for health care and health economics, as unexpected symptoms frequently lead to emergency room referrals. In the coming years, AMPEL plans to launch its first two products, the DermaGENE® skin biopsy test for psoriasis, atopic dermatitis, scleroderma and the LuGENE® blood test for lupus. AMPEL’s CovGENE® blood test, which predicts the severity of a COVID patient’s disease course and may be relevant for “long COVID”, is also prepared for licensing or co-development with a company which already provides COVID diagnostic tests.

Conclusion

AMPEL’s genomics platform technology with machine learning is an important step towards implementing routine testing to monitor disease activity and provide decision support for gene expression-based therapy of a patient when combined with its pipeline of tools to analyze large and complex clinical data sets (“Big Data”). Using data collected by the lab test and analyzed by machine learning to diagnose, characterize specific molecular abnormalities, and treat diseases before damage begins, will revolutionize the way doctors treat patients and save patients the suffering and inconvenience of illnesses that would otherwise drastically affect their lives.

References:

Please Don't Forget To Join Our ML Subreddit

Comments are closed.