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Arpeggio Bio, a preclinical company whose technology provides a mechanistic understanding of how drugs work, today announced that it has closed a $3.2 million seed financing round, which was oversubscribed by over $2 million. Funding will support the ongoing development of a nascent RNA drug screen.

“We’re excited to have the support of our investors to allow us to continue our mission of helping bring new therapies to patients with epigenetically-driven diseases,” said Joey Azofeifa, Ph.D., Founder and CEO, Arpeggio.

Since inception in 2018, Arpeggio has partnered with over twenty biotech and pharmaceutical companies — including four of the world’s top ten — to uncover new insights into their therapeutics. Arpeggio’s early market traction and progress led it to be selected for the prestigious (YC) Summer 2019 batch, which provides emerging startups with funding and mentorship. Following a successful program tenure, Dr. Azofeifa’s YC Demo Day pitch attracted distinguished investors to lead a funding round, including Khosla Ventures, FundersClub, Fifty Years, TechU, and YC.

Arpeggio has built an automated system that collects information about which genes turn on or off for hundreds of time points beginning in the minutes following drug treatment in preclinical models. Using algorithms originally developed for financial forecasting, Arpeggio reconstructs the biological network a drug affects and identifies the genes critical for the success or failure of a drug. This new kind of data allows for the elucidation of novel drug and disease mechanisms, supporting development of safer, more effective therapies by understanding drug effects before they’re given to patients.

The company's platform analyzes its time-series RNA profiles using proprietary machine learning algorithms developed by Dr. Azofeifa. Driven by Arpeggio’s success, Dr. Azofeifa was recently named to the category, recognizing him as one of the country’s top young entrepreneurs.

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Arpeggio's technology combines a proprietary biological assay and machine learning algorithms that, together, enable rapid, high-resolution snapshots of cellular dynamics following drug treatment. These snapshots are then analyzed to reveal the biological networks that determine a drug’s function and guide therapeutic development. To learn more,