Vevo Therapeutics Launches With $12 Million Seed Funding

<p><strong><span class&equals;"legendSpanClass"><span class&equals;"xn-location">SAN FRANCISCO<&sol;span><&sol;span><&sol;strong> &&num;8212&semi; Vevo Therapeutics&comma; a biotechnology company using its Mosaic <i>in vivo<&sol;i> drug discovery platform and next generation AI models to uncover better drugs for more patients&comma; has launched with an oversubscribed and upsized <span class&equals;"xn-money">&dollar;12 million<&sol;span> seed financing round&period; Wing Venture Capital and General Catalyst co-led the round with participation from Mubadala Capital&comma; AIX Ventures&comma; and Camford Capital&period;<&sol;p>&NewLine;<p>The company&&num;8217&semi;s Mosaic platform is the first to make <i>in vivo <&sol;i>data generation scalable&comma; with single-cell precision&comma; while capturing patient diversity in drug response&period; In a single <i>in vivo <&sol;i>experiment&comma; Mosaic can measure how a drug impacts cells from tens to hundreds of patients&comma; generating millions of datapoints on drug-induced changes in gene expression&period; With the seed funding&comma; Vevo will perform thousands of Mosaic experiments to create what is deemed impossible today&colon; an <i>in vivo <&sol;i>atlas of how chemistry perturbs biology&period; Vevo&&num;8217&semi;s AI models will be trained on this atlas to uncover novel targets and drugs undetectable by other technologies&period;<&sol;p>&NewLine;<p>Vevo&&num;8217&semi;s platform builds on technology developed by two of its co-founders&comma; Hani Goodarzi&comma; Ph&period;D&period;&comma; Associate Professor at the <span class&equals;"xn-org">University of California&comma; San Francisco<&sol;span> &lpar;UCSF&rpar;&comma; and <span class&equals;"xn-person">Johnny Yu<&sol;span>&comma; Ph&period;D&period;&comma; Chief Scientific Officer of Vevo Therapeutics&period; The company holds an exclusive license to the technology from UCSF&&num;8217&semi;s Innovation Ventures office&comma; which leads licensing and business development on behalf of the university&period;<&sol;p>&NewLine;<p>&&num;8220&semi;We founded Vevo to address the key challenge in drug discovery – that drugs discovered in <i>in vitro<&sol;i> models are failing patients&comma;&&num;8221&semi; said Nima Alidoust&comma; Ph&period;D&period;&comma; Chief Executive Officer and Co-founder of Vevo&period; &&num;8220&semi;Drug discovery is only as powerful as the data that fuels it&comma; and today that data is generated out of context from how disease occurs in living organisms while also failing to account for the diverse mosaic of genetic backgrounds across patients – each with the potential to react differently to any one drug&period; By starting and guiding drug discovery with high-resolution <i>in vivo<&sol;i> data&comma; we are flipping the script on traditional discovery methods&period;&&num;8221&semi;<&sol;p>&NewLine;<p>Despite being the gold standard of disease modeling&comma; <i>in vivo<&sol;i> models are not scalable or precise enough for early-stage discovery&period; Limited to <i>in vitro<&sol;i>-based assays&comma; early discovery efforts often overlook valuable targets that would only be detectable <i>in vivo<&sol;i>&period; Even when novel targets and drugs are found <i>in vitro<&sol;i>&comma; many will be irrelevant when tested <i>in vivo <&sol;i>or in humans&period;<&sol;p>&NewLine;<p>&&num;8220&semi;Most first-generation small molecule drugs will work with limited efficacy in a small number of patients&comma; with improvements made slowly over the course of second and third generation advances&comma;&&num;8221&semi; said <span class&equals;"xn-person">Kevan Shokat<&sol;span>&comma; Ph&period;D&period;&comma; Co-founder of Vevo Therapeutics and Professor at UCSF&period; &&num;8220&semi;Our ability to test drugs across many patients and generate single-cell data using <i>in vivo<&sol;i> models at the start of drug discovery will finally allow us to bypass generations of incremental improvements to get better medicines to patients faster&period;&&num;8221&semi;<&sol;p>&NewLine;<p>&&num;8220&semi;The advent of AI in drug discovery has done little to improve the failure rate of drugs in clinical trials&comma; because many are using AI to paper over the cracks in a flawed system&comma;&&num;8221&semi; said <span class&equals;"xn-person">Gaurav Garg<&sol;span>&comma; Partner at Wing Venture Capital and a member of Vevo&&num;8217&semi;s Board of Directors&period; &&num;8220&semi;Vevo has made it possible to train AI on the best-known data from the start&comma; creating an entirely new way of doing drug discovery&period;&&num;8221&semi;<&sol;p>&NewLine;