AI services for life sciences companies have reshaped the industry. Industry leaders are reconsidering how content has been created, processed, and translated since the 1990s (with ICH harmonization introduced to drug development). Life science translation services companies are developing new best practices to drive large-scale benefits of generative AI for regulated documentation and mitigate risks. While the Life Sciences industry must cautiously embrace AI, Lionbridge recommends drug sponsors seriously explore the opportunities for AI services for life sciences companies.     This blog series will explore the drug life cycle stages from a content and language-focused perspective, especially for life sciences translation and life sciences localization. We’ll provide guidance for safely and effectively applying LLMs, even to regulated product translations. You’ll uncover the challenges of applying LLMs and the opportunities for AI in life sciences. In our previous blogs, we addressed the pre-market and launch stages of the drug life cycle. This blog will focus on the post-market stage of new drugs. AI Services for Life Sciences Translation and Localization: Post-Market Drug Stage  Once a medicinal drug product has been launched on the market, it enters the post-market stage. This stage continues until the product is withdrawn from the market. Post-market activities include: Renewals, variations, and extensions of marketing authorizations Post-market clinical study commitments required by regulatory authorities as part of the initial marketing product authorization Post-market surveillance studies to perform ongoing safety surveillance, assess efficacy, and optimize product usage Post-market clinical studies in support of labeling extensions or special populations Aggregate periodic safety update reports containing a comprehensive, concise, and critical analyses of risk-benefit of a product Individual case safety reporting to capture suspected adverse reactions Product marketing and sales training activities Social media campaigns AI Benefits with Clinical and Marketing Authorization Language Assets Several post-market clinical studies are often executed for a new medicinal drug to obtain more experience with the therapy in routine practice. Clinical studies conducted under the clinical development plan in pre-market stage are different. They fulfill specific requirements and test hypotheses to generate statistical evidence for the drug’s efficacy and safety. This will enable regulatory reviewers to determine the product’s risk-benefit balance. Clinical studies in post-market stage may fulfill multiple other purposes. For example, they may study: Long-term safety Drug interactions Pediatric populations Epidemiology Whichever the purpose of these studies, the language repository developed during pre-market clinical trials can be carried over to post-market studies and drive benefits. Language assets, such as Translation Memories, glossaries, terminologies, and stylistic aspects, can be layered into prompt engineering to improve AI-driven language outcomes. Legacy background content, which is not protocol-specific, can help drive both efficiencies and cost-savings if a language strategy is established early in drug development. Additionally, new content creation and documentation on marketed drugs continue evolving in the post-market stage because a marketing authorization is dynamic. The manufacturer must update the dossier supporting an authorization and ensure the product aligns with scientific progress and new regulatory requirements. As a result, a marketed product will often have multiple variations after initial authorization. Many new active drug substances will also have their marketing authorizations extended, which requires a new marketing authorization. The content life cycle of an active drug substance may span multiple commercial products and multiple marketing authorization procedures. Because drug sponsors rarely have a language strategy covering the full life cycle of a drug and its post-market changes, they miss out on significant cost-savings and language optimization. Additionally, applying AI on small or standalone translation projects won’t deliver the efficiencies Large Language Models promise. Challenges and Risks of Large Language Models through the Drug Life Cycle In this drug life cycle blog series, we’ve addressed the pre-market, launch, and post-market stages. We argue that if language assets are carried over across the stages and Large Language Models applied, AI can significantly drive cost-savings and language consistencies for regulated translations. AI-powered regulatory translations, however, are not without risks. Large Language Models may produce hallucinations (made-up content inconsistent with input data). These and other challenges of Large Language Models are addressed in detail in our eBook AI and Language Strategy in Life Sciences, what you need to know. AI Opportunities for Life Sciences Translation during the Post-market Stage Cost-savings and language consistency for post-market clinical studies leveraging AI and language assets accumulated during pre-market clinical trials Cost-savings and language consistency for marketing authorization renewals, variations, and extensions after initial approval and authorization Cost-savings and language consistency from pre-market to post-market safety activities Cost-savings and language consistency in messaging and product claims communication, from launch to post-market activities Get in touch Ready to explore the opportunities and prepare for the challenges of AI-powered Life Sciences language services for your drug’s post-market stage? 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