| Name | Title | Contact Details |
|---|---|---|
John Wright |
Chief Technology Officer | Profile |
Triplet Therapeutics is a biotechnology company developing transformational treatments for patients with repeat expansion disorders (REDs) -- a group of more than 50 known genetic diseases including Huntington`s disease, myotonic dystrophy, spinocerebellar ataxias, fragile X syndrome, and familial amyotrophic lateral sclerosis (ALS) -- leveraging insights from patient genetics. Triplet designs and develops potential therapeutics for REDs using its proprietary thRED Engine, which enables the Company to develop a single oligonucleotide targeting the DNA Damage Response (DDR) pathway to potentially treat multiple REDs. Triplet is backed by investments from Atlas Venture, MPM Capital and Pfizer Ventures, along with Invus, Partners Innovation Fund and Alexandria Venture Investments.
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insitro is a data-driven drug discovery and development company that leverages machine learning and high-throughput biology to transform the way medicines are created to help patients. At insitro, we are rethinking the entire drug discovery process, from the perspective of machine learning, human genetics, and high-throughput, quantitative biology. Over the past five decades, we have seen the development of new medicines becoming increasingly more difficult and expensive, leaving many patients with significant unmet need. We`re embarking on a new approach to drug development – one that leverages machine learning and unique in vitro strategies for modeling disease state and designing new therapeutic interventions. We aim to eliminate key bottlenecks in traditional drug discovery, so we can help more people sooner and at a much lower cost to the patient and the healthcare industry. We believe that by harnessing the power of technology to interrogate and measure human biology, we can have a major impact on many diseases. We invest heavily in cutting edge bioengineering technologies to enable the construction of large-scale, high-quality data sets that are designed specifically to drive machine learning methods. Our first application is to use human genetics, functional genomics, and machine learning to build a new generation of in vitro human cell-derived disease models whose response to perturbation is designed to be predictive of human clinical outcomes.