AMPEL BioSolutions Latest Innovation in Precision Medicine Predicts Drug Options Using RNA Analytics and Machine Learning

A breakthrough in precision and customized medicine, announced by AMPEL BioSolutions, may update how physicians treat patients with various illnesses, including cancer, infectious disorders, and autoimmune. The first-of-its-kind platform technology, unveiled at the Precision Medicine World Conference in Silicon Valley, California, uses RNA analytics and machine learning to characterize a person’s gene expression and offer clinical decision support to doctors who can choose the best course of action for their patients. Over the next five years, a portfolio of 10+ clinical tests will be launched using the technology, which has just been an idea over the past few years, to give decision assistance for diseases that impact more than 50 million Americans.

AMPEL BioSolutions, a precision medicine company is commercializing a pipeline of gene expression assays for blood or tissue samples that are CLIA-certified, and that help clinical decision-making by diagnosing disease states, locating molecular pathways, and forecasting treatment possibilities. The technology used by AMPEL is a cloud-based platform that houses unique RNA analysis tools and machine learning algorithms that are the subject of 80+ peer-reviewed papers in high-impact journals and 25+ filed/pending patents. Over 95% of all known genes are protected by AMPEL’s technology, and machine learning predictions are supported by the proprietary curated library of >15,000 individual gene expression profiles with extensive clinical information. Systemic Lupus Erythematosus, Psoriasis, Scleroderma, Atopic Dermatitis, Lupus Nephritis, Fibromyalgia, Cardiovascular, Sjogren’s Syndrome, ASD, Wellness, Lung Cancer, and SARS-Cov2 are among the diseases covered by AMPEL’s portfolio of precision medicine tests. Early in 2022, AMPEL BioSolutions was chosen to join the Coalition for 21st Century Precision Medicine.

Pharmaceutical firms must enroll patients with the highest chance of responding to the evaluated treatment while testing medications in clinical trials. Enrolling the “wrong” people can lead to trial failure, which frequently causes the FDA to halt the development of a medicine that would help a certain patient subgroup. Pharmaceutical firms may proactively identify the patients most likely to react to certain drugs thanks to AMPEL’s technology, which allows for enhancing clinical trial outcomes and patient quality of life. Dr. Peter Lipsky at the Precision Medicine World Conference highlighted AMPEL’s Pharma work during a panel discussion on the application of machine learning to clinical trial patient selection and outcome prediction, and Dr. Amrie Grammer at a Google-Reuters webinar on machine learning strategies for trial patient selection at the right time.

By enabling doctors to more precisely pinpoint the underlying cause of patient disease symptoms and choose the best course of treatment, AMPEL’s ground-breaking machine learning approach can significantly impact healthcare. The method used by AMPEL is sensitive enough to identify early illness indicators and classify individuals according to how severe their conditions are. It is now prepared to be developed as a clinical decision support biomarker test. More than 15 pharmaceutical firms directly benefit from using AMPEL’s technology in developing new drugs and clinical trials.

Machine learning is an analytical method for teaching computers to evaluate data and anticipate outcomes. To determine whether a person with lupus, psoriasis, atopic dermatitis, or scleroderma is experiencing a flare in disease activity, AMPEL has used this approach in a novel way to train a computer to analyze data obtained from assessing a type of “Big Data,” namely that obtained by evaluating gene expression information. Gene expression analysis can light the whole range of genetic aberrations by looking at the quantity and pattern of the genes expressed at any particular time.

Unexpected flare-ups of several chronic conditions significantly impact patients’ quality of life. Additionally, while regular illness therapies have been created based on a patient population, some people may react differently or not to existing treatments. To solve this issue, the scientists and physicians at AMPEL have been developing concepts to tailor medicines for a patient in contrast to a patient population for the past nine years. The viability of AMPEL’s idea, which is currently in the commercialization stage, has been documented in peer-reviewed papers.

The test can be utilized for various autoimmune or inflammatory illnesses, while AMPEL’s primary emphasis is lupus. Blood and tissue biopsy tests from AMPEL are prognostic and staging biomarkers that will assist their doctor’s choice of the best medications for the patient at that particular time.

Patients with inflammatory and autoimmune disorders frequently experience spontaneous disease activity that interferes with normal everyday activities, including work and family life. The capacity to forecast deteriorating illness and systemic involvement with routine testing has significant implications for health care and health economics since unexpected symptoms frequently lead to referrals to the emergency room. In the following years, AMPEL plans to release its first two products, the DermaGENE® skin biopsy test for psoriasis, atopic dermatitis, scleroderma, and the LuGENE® blood test for lupus. The CovGENE® blood test from AMPEL, which forecasts the severity of a COVID patient’s illness course and may be relevant to “long COVID,” is also prepared for licensing or co-development with a business that already provides COVID diagnostic testing.

Conclusion

AMPEL’s Genomic Platform technology with machine learning is a significant step toward implementing routine testing to monitor disease activity and provide decision support for treatment based on a patient’s gene expression when combined with its pipeline of tools to analyze vast and complex clinical datasets (“Big Data”). Using the data gathered by the lab test and analyzed by machine learning to diagnose, characterize the specific molecular abnormalities, and treat diseases before damage begins, will revolutionize the way doctors treat patients and spare patients from the suffering and inconvenience of illnesses that would otherwise significantly affect their lives.

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