arge language models (LLMs) have been used in drug discovery for some time now, but many in the life sciences industry are just becoming aware of Generative AI: a buzzword that has become increasingly pervasive in society. Frequently and inaccurately associated with OpenAI, the company responsible for creating ChatGPT among numerous tools in the expansive AI domain, this technology garners a wide array of responses: lauded as a transformative innovation at one end of the spectrum and decried as a potential societal threat at the other. This article explores the transformative potential of LLMs underpinning Generative AI, their possible impacts on drug development, the regulatory hurdles they face, and the pressing need for a balanced approach to their utilization. There is a serious need for a judicious blend of innovation, regulatory compliance, and education to reap the maximum benefits of these tools while managing potential risks such as a proliferation of racial bias in healthcare, safety concerns that might arise from the use of AI-enabled decision support, or the undermining of public trust in our healthcare institutions if data is improperly consented for use in training data sets.

ncreasing racial and ethnic diversity in clinical trials will facilitate the meaningful representation of different patient communities. This will contribute to delivering treatments to the market ensuring security and efficacy for wider patient groups. FDA encourages sponsors to design a Race and Ethnicity Diversity Plan at the stage of the clinical trial protocol conceptualization. This is an important step to connect the eligibility criteria considerations in clinical trials to the real prevalence of a disease.

s increased diversity of patient populations who participate in clinical trials becomes more entrenched as a modern business, clinical, and regulatory requirement, new trial technologies and tools continue to emerge to help research and researchers meet these obligations. The combined impact of all these forces (see sidebox) is helping to focus research on more specific patient populations and needs. But this impact has built up enormous complexity for clinical trial data management and analysis.

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n recent years, with the Chinese government’s vigorous support and all-round promotion for innovative drugs, more than 3,000 clinical trials with investigational drugs have been conducted annually. In this context, patient engagement has become increasingly important, and how to keep patients informed of progress in drug development and actively participating in clinical trials has become a hot topic in the pharmaceutical industry.

s the lack of sufficiently efficacious therapeutic products leaves some medical conditions untreated or undertreated (a problem compounded by the emergence of new disease manifestations), innovative therapeutic options are increasingly considered as alternative or additive therapies. The use of digital therapeutics, biological products, gene therapies, medical devices, cosmetics, and other products opens not only better opportunities for a cure but also opportunities for vigilance platforms and processes to monitor the safety of these products and ensure that patient safety is not compromised.