Speaker Profile

Ph.D., Co-Founder/COO and Chief Scientific Officer, AMPEL BioSolutions

Dr. Amrie Grammer is a Translational and Computational Immunologist specializing in Autoimmune and Inflammatory Diseases, both human patients and pre-clinical mouse models. Amrie co-founded AMPEL in 2013 to bring precision medicine to patients, especially those with lupus and gout. With bioinformatic tools her team created, she designed and implemented a pipeline to predict “flares” and the “right drug for the right patient at the right time” using predictive analytics, machine learning and AI. Top drugs Amrie identified for repositioning have had positive clinical trials. Her early scientific training was in Chemistry (BS ’89), Pharmacology (MS’91) and Immunology (PhD’96). After her post-doc, Amrie was recruited to the NIH to establish the B Cell Biology Group in the Autoimmunity Branch of NIAMS during the human genome sequencing project. She received multiple NIH awards for her work comparing signaling pathways and genes expressed in patients vs healthy individuals, including the prestigious Director’s Award.

AI & Data Sciences Showcase:
AMPEL BioSolutions

AMPEL BioSolutions is a technology company who has developed a clinical genomic test in the Immunology space to predict flares and the correct drug for a patient based on machine learning of gene expression (target markets: Pharma, Patient/Physician and Payor).

Machine Learning Predicts Lupus Disease Activity "Flares"
Currently used to select patients most likely to be responders in clinical trials, thereby improving outcomes. Future use will be a decision support biomarker test for physicians to monitor disease activity and most appropriate treatments based on a patient’s genes.

 Session Abstract – PMWC 2020 Silicon Valley

The PMWC 2020 AI Company Showcase will provide a 15-minute time slot for selected AI companies to present their latest technologies to an audience of leading investors, potential clients, and partners. We will hear from companies building technologies that expedite the pre-clinical and clinical drug discovery and development process, accelerate patient diagnosis and treatment, or develop scalable systems framework to make AI and deep/machine learning a reality.