The collaboration sets a precedent in longevity drug discovery and development.(metamorworks | iStock)
Anti-aging drugs aim to maintain or achieve health and biological efficiency irrespective of how old we are. But creating anti-aging drugs that work in humans has been tricky and elusive.
Japan-based Taisho Pharmaceutical Co., Ltd., and Hong Kong-based Insilico Medicine recently entered into a research collaboration to identify novel therapeutics against aging. “It is believed that aging is a universal phenomenon that we cannot stop. However, emerging scientific evidence has shown that one may be able to reverse some of the age-associated processes,” said Jimmy Yen-Chu Lin, Ph.D., CEO of Insilico Medicine Taiwan, a fully-owned subsidiary of Insilico Medicine, in a press release.
Drug discovery and development are among the most important translational science activities that contribute to human health and wellbeing. However, the development of a new drug is a very complex, expensive, and long process that typically costs 2.6 billion USD and takes 12 years on average. How to decrease the costs and speed up new drug discovery has become a challenging and urgent question in the industry. Artificial intelligence (AI) combined with new experimental technologies is expected to make the hunt for new pharmaceuticals quicker, cheaper, and more effective.
This partnership brings together Insilico’s state-of-art AI technologies in drug discovery with Taisho’s expertise in drug development, which is aimed to extend human healthspan and longevity. “Through this collaboration, we will adopt our AI-powered drug discovery suites together with Taisho’s validation platform to explore the new space of anti-aging solutions,” said Dr. Lin. “We’re delighted to collaborate with Taisho pharmaceutical, a well-recognized leader in the pharmaceutical industry and healthcare sector,” said Dr. Lin.
Insilico Medicine will be responsible for early target discovery in this collaboration. To do so, it will utilize PandaOmics and Chemistry42, the target discovery and generative chemistry parts of its Pharma.AI platform, respectively. Its PandaOmics Discovery Platform for multi-omics target discovery and deep biology analysis engine will identify novel targets for anti-aging drugs. The Chemistry42 platform can identify novel lead-like molecules through an automated, machine learning drug design and scalable engineering platform.
“The drug discovery process consists of many phrases and often takes decades. In preclinical phases, the failure rates are over 99%. Our AI can be used in all phases and, in some cases, leads to superhuman results. Our AI is exceptionally good at finding the molecular targets in specific diseases and inventing new chemistry,” said Alex Zhavoronkov, CEO of Insilico Medicine. Optimistically, the traditional method usually takes at least 2 years to design, synthesize, and validate a novel drug candidate, but AI only needs 46 days, which is 15 times faster than the best pharma companies.
Once anti-aging drug candidates are identified, Tokyo-based Taisho will then step in to test the computer-generated compounds. With over a century of history, Taisho Pharmaceutical Holdings has the largest share of Japan’s over-the-counter pharmaceutical market.
“It is our great honor to be collaborating with the scientists of Taisho Pharmaceutical, one of the top 100 pharmaceutical companies in the world operating since 1912,” said Alex Zhavoronkov, Ph.D., founder and CEO of Insilico Medicine, in a press release. “The high level of the scientists we are interfacing, and our previous successes in the application of the Pharma.AI platform for discovery of novel targets and molecules in fibrosis, and previous experience in senolytic drug discovery gives us confidence that this collaboration will be successful.”
This approach to drug discovery is becoming more common as most sizable biopharma players have similar collaborations or internal programs. Sanofi has signed a deal to use UK start-up Exscientia’s AI platform to hunt for metabolic-disease therapies, and Roche subsidiary Genentech is using an AI system from GNS Healthcare in Cambridge, Massachusetts, to help drive the multinational company’s search for cancer treatments. If the proponents of these techniques are right, AI and machine learning will usher in an era of quicker, cheaper, and more effective drug discovery.