Navigating the intersection of technology and human expertise in life sciences


In this age of rapidly evolving technologies that fundamentally shift the way businesses operate, such as large language models (LLMs) and natural language processing (NLP), organizations are quickly realizing that success extends beyond innovative technology solutions alone. While these technologies offer immense potential in terms of innovation, data insights and operational efficiency gains, constraints exist in areas such as meeting regulatory requirements, managing data availability/ date volume/ data congruence and, in some cases, ensuring commercial viability. These limitations must be addressed for successful adoption across the healthcare industry and for implementation of such tools into broad based quality management systems and product solutions.

True success hinges on the effective implementation and strategic utilization of technology, guided by human expertise and supported by well-designe…

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From gatekeeper to strategist: The evolution of the CISO role in drug development

[Gorodenkoff/Adobe Stock]

There’s an old joke about chief information security officers (CISOs) being gatekeepers of new technologies and initiatives – the infamous “Department of No.” Imagine a bouncer who, strangely, doesn’t let anyone in, saying the club is already too full, even when it’s clearly empty.

But that image is outdated — especially in risk-focused industries like financial services where CISOs are integral to digital transformation projects and the broader risk management considerations. 

From CIS-‘no’ to risk maestro

“Drug development is a risk-focused industry as well,” said Daniel Ayala, chief security and trust officer for Dotmatics. “There is a huge amount of risk.” Consequently, CISOs working in pharma contexts are increasingly expanding their roles from technical experts to risk-aware business leaders who happen to have deep technical expert…

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

[Made/Adobe Stock]

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…

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