QuantHealth taking a data-driven predictive approach to simulate clinical trials

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In drug discovery, the process of shooting for regulatory approval can feel less like a sprint and more like a marathon, but with no guarantee of crossing the finish line. Despite the hefty investment of time, effort and resources, the success rate for bringing new drugs to market hasn’t improved in recent decades. The American Council on Science and Health estimates an average success rate from 2000 to 2015 of only 13.8%, with costs reaching hundreds of millions per drug. The situation appears more grim with the average cost of developing a drug surpassing $2 billion, according to Deloitte. 

“Despite increased understanding of diseases and technologies for drug discovery, developing new medicines remains an unpredictable endeavor filled with many failures,” said David Dornstreich, chief commercial officer and general manager U.S. at QuantHealth.

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CEO: Insilico on how AI can ‘imagine the perfect molecules’ for drug targets

Insilico’s AI-driven drug discovery process, showcased in their AI-powered robotics lab [Image courtesy of Insilico Medicine]

Insilico Medicine, an AI-based biotech startup, announced details of their first AI-designed drug candidate to enter human clinical trials. INS018_055 is an experimental treatment for idiopathic pulmonary fibrosis, a rare lung disease. Through their Insilico AI-driven drug discovery platform, they discovered and designed INS018_055 in just 30 months, significantly faster than the industry average. Phase 1 trials of INS018_055 have been completed in New Zealand and China, showing a favorable safety profile. Insilico plans to launch Phase 2 trials in 2023 to further evaluate the drug’s efficacy and tolerability. If successful, INS018_055 could provide a new treatment option for patients with idiopathic pulmonary fibrosis. The trial results also demonstrate the potential of AI to accele…
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Quantum computing promises new frontier in drug discovery and bioinformatics

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Quantum computing — described by pop astrophysicist Neil deGrasse Tyson as “computing with atoms” — is an emerging technology with a potential for immense computational speed and power. For some problems, quantum computers can be exponentially faster than classical computers, while for others the speedup may be more measured. The promise for drug discovery could be significant.

But the promises of quantum computing extend beyond mere increases in data processing horsepower. In an era dominated by generative AI models, which rely heavily on massive volumes of data for predictions, a different perspective emerges. Kristin M. Gilkes, EY Global Innovation Quantum Leader, underscores this shift in perspective. “I don’t believe we’ll need as much data with quantum computing,” she said. Gilkes sees the focus is shifting towards becoming data-centric, prioritizing the right set…

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