AI in Pharma Archives - Page 2 of 2 - Medical Design Sourcing

Sanofi puts AI ‘Plai’ app at the center of drug discovery and clinical trial operations

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The French pharmaceutical giant Sanofi has unveiled a new AI-powered app called Plai, developed in partnership with AI platform Aily Labs. This move is part of Sanofi’s plan to become the pioneer in fully integrating AI into all operations, according to CEO Paul Hudson.

Plai, designed to compile and process Sanofi’s internal data from various departments, creates bespoke “what if” scenarios to guide decision-making. Sanofi aims to exploit Plai’s analytical capabilities to enhance strategic planning.

From ChatGPT and Google Maps to Plai

Sanofi’s AI aspirations are longstanding. In 2022, the company launched its inaugural digital accelerator, supporting the adoption of digital, data and AI across operations. That same year, Sanofi acquired Amunix Pharmaceuticals, tapping its AI technology to develop targeted cancer therapies that spare healthy tissue.…

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.

Microsoft details how its technology is advancing drug discovery and biomedical research

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As cloud technology matures, life-sciences companies are finding new avenues for business value in life sciences, extending beyond the cost efficiencies typically associated with cloud migrations, as McKinsey noted. In an email statement, Microsoft outlined how its own cloud computing, AI and research capabilities are driving similar innovation for biopharma companies. This article explores Microsoft Research initiatives related to drug discovery, including technology developments and strategic collaborations.

Quantum Computing’s role in Microsoft Research’s drug discovery and scientific research push

A key facet of Microsoft Research’s impact on drug discovery lies in its quantum computing initiatives. In terms of quantum computing efforts, the company said, “We’d like to first share with you our recent announcement on new advances to Azure Quantum, aimed at accelerating …

Microsoft goes all in on Azure Quantum to accelerate scientific discovery

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In the rapidly evolving landscape of quantum computing, Microsoft is pushing the boundaries with its Azure Quantum platform. As Microsoft CEO Satya Nadella recently announced, “Our goal is to compress the next 250 years of chemistry and material science progress into the next 25,” Nadella said. The executive also noted that, thanks to recent advances in quantum computing, “we’re closer than ever to the promise of a scalable quantum machine capable of solving some of the world’s most intractable problems.”

To accelerate this vision, Microsoft’s Azure Quantum introduces capabilities to expedite scientific discovery. Azure Quantum Elements merges high-performance computing, AI and quantum computing on Microsoft Azure. This combination accelerates quantum chemistry simulations and widens the search space for new materials. Azure Quantum Elements taps AI models traine…

The shift from manual to machine learning in cell and gene therapy drug discovery

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Cell and gene therapy (CGT) manufacturing is rapidly transitioning from scientific curiosity to clinical reality. But the manufacturing complexity of CGTs far outpaces traditional biologics production, presenting a multifaceted challenge that is part scientific, part technological. “The manufacturing process for cell therapies and gene therapies is infinitely more complex than it is for let’s say, monoclonal antibodies or recombinant proteins,” said Betty Woo, vice president and general manager of cell, gene, & advanced therapies at Thermo Fisher Scientific.

Despite the complexity, much of the current cell therapy manufacturing process relies on manual procedures. “Manual intervention introduces a level of human variability,” Woo said. “This not only leads to inconsistencies, but also increases the risk of human error.”

While manufac…

Insilico’s AI-discovered INS018_055 graduates to phase 2

Roughly a year ago, Insilico Medicine announced that it had dosed the first patient in a phase 1 study of INS018_055, an AI-discovered, first-in-class small molecule inhibitor. Now, the company has progressed to the next stage, launching a phase 2 study for the drug candidate.

Insilico, a founding member of NVIDIA Inception, developed its AI drug-discovery platform on NVIDIA GPUs. Inception is a complimentary program that provides startups with technical training and AI platform support.

The discovery of INS018_055 was the result of combining multiple technologies. Insilico’s proprietary Pharma.AI platform incorporates AI models trained on massive amounts of data. Insilico’s target identification platform, PandaOmics, discovered a novel target. Then Insilico’s generative chemistry platform, Chemistry42, designed the molecule’s structure. In addition, these AI systems rely on machine learning techniques like deep generative models, reinforcement lear…

Quantum-inspired power could brighten drug discovery

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In the realm of drug discovery, the Harvard-educated chemist Nick Paras, Ph.D. compares the early stages of identifying new compounds to a game of Zelda, in which a player at times must navigate through dark rooms and solve puzzles to progress. Similarly, medicinal chemists and biochemists too must traverse the vast, often obscure landscape of cellular assays and biochemical screens in pursuit of potential treatments.

“You’re in a dark room, fumbling around for the light switch,” said Paras, a professor at the Institute for Neurodegenerative Diseases at University of California, San Francisco (UCSF). He suggests that the early stages of drug discovery can, in some ways, mirror the experience of navigating the dark, puzzling dungeons of a game like The Legend of Zelda, where players confront a complex, unknown environment. Much like this, researchers often find themselves…

How AI and the cloud can transform R&D workflows and fuel collaboration

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The rise of cloud-based systems and AI-assisted analysis has dramatically transformed the landscape of scientific research and collaboration. One striking example is the mRNA company Moderna, which used the cloud to develop and deliver its first clinical batch of a COVID-19 vaccine candidate for phase 1 trials in a mere 42 days after the initial viral sequencing.

While enabling real-time international collaboration, this new paradigm has also introduced novel challenges. Simon Adar, CEO of Code Ocean, found the struggles of cross-geographical R&D collaboration during his PhD work at Cornell University. While file-sharing systems provided some relief, they fell short when it came to coordinating code, data, software and troubleshooting across different geographies. “It wasn’t enough, because when you have code and data, you also need all the software depe…

Generative AI could boost biopharma R&D productivity by billions, according to McKinsey

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Generative AI stands to add trillions of dollars of value to the world economy, according to a recent McKinsey report. The consultancy noted that about three-quarters of the value of generative AI spans four areas — customer operations, marketing and sales, software engineering and R&D. The latter could see a 12% annual lift in global functional spending as a result of generative AI, representing an approximately $328 billion windfall annually.

An evolution or revolution: UBS and McKinsey’s take on generative AI in biopharma

The investment bank UBS recently reached more muted conclusions, noting that generative AI would represent more of an evolution than a revolution for biopharma.

Conversely, McKinsey bets the technology could deliver productivity gains that account for 10% to 15% of overall R&D costs. This potential largely stems from the applicatio…

Navigating generative AI in drug discovery and data analysis: Seizing the opportunity and avoiding pitfalls

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Along with predictive AI, generative AI is emerging as a promising tool in drug discovery. Thanks in part to the rise of ChatGPT, interest in the technology in drug discovery is on the upswing. In March, a preprint appeared examining the potential to use generative AI to enable de novo antibody design. Also this year, the Japanese conglomerate Mitsui & Co. began working with NVIDIA to launch Tokyo-1, a project aimed at boosting Japan’s pharma industry with generative AI models. The initiative will give Japanese pharma companies and startups access to an NVIDIA DGX AI supercomputer, providing a shot in the arm to the country’s $100 billion pharma sector, which is the third largest globally.

As generative AI gains ground in pharma, businesses considering using the technology to speed up drug discovery should also take its potential drawbacks into considering. To that end, Ali Arsanjan…