Using RNA Analytics, Bioinformatics, and Machine Learning/AI
AMPEL Genomic Platform (AGP®) and
clinical genomic blood tests for patients:
Evaluate disease status
Identify molecular pathways
Predict drug options
Precise Patient Solutions
AMPEL Genomic Platform (AGP®), Bioinformatic Tools and Machine Learning Algorithms (AI/ML)
Step 1:
Create A Genomic Fingerprint
Comprehensive RNA analytics of gene expression from blood or biopsy to determine disease activity, molecular pathways and drug options.
Step 2:
Analyze The Fingerprint With ML/AI
Covers >95% of all known genes and is disease agnostic.
Step 3:
Provide Decision Making Support
Status of a patient’s disease and most appropriate treatment options at the time of the test are provided in a report.
Step 4:
Market ready and Reproducible
First-in-class CLIA-certified gene-based Lab Developed Test (LDT).
Key Stats
Lead product LuGENE® expected Q4 2022. DermaGENE® expected Q4 2023.
Products for other indications available for partnering.
95%
coverage of all known genes and is disease agnostic
15,000+
autoimmune and normal patient gene expression profiles with rich clinical information – world’s largest for Lupus
22+
technology patents filed & pending with the freedom to operate
15+
pharma customers validate the technology
5+
additional autoimmunity products in development
About the Founders
AMPEL’s co-founders, Amrie Grammer and Peter Lipsky, are internationally recognized scientists and successful entrepreneurs, having founded and managed AMPEL to profitability consistently for 5+ years. Their success has been a combination of world-class science with business acumen. Dr. Grammer was elected to the class of 2021 of SIBF (Society of International Business Fellows), an organization of 1400 in 45 countries world-wide and was awarded a Virginia SBIG grant from AMPEL’s first investment round. She is a second term board member of Virginia Bio. View Executive Team.
Peer-Reviewed Journal Articles
Published in Internationally Recognized Peer-Reviewed Journals
The pathogenesis of systemic lupus erythematosus: Harnessing big data to understand the molecular basis of lupus
Altered expression of genes controlling metabolism characterizes the tissue response to immune injury in lupus
Transcriptomics data: pointing the way to subclassification and personalized medicine in systemic lupus erythematosus
Analysis of Trans-Ancestral SLE Risk Loci Identifies Unique Biologic Networks and Drug Targets in African and European Ancestries
Deconvoluting the heterogeneity of SLE: The contribution of ancestry
