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 This stage continues until the product is withdrawn from the market.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
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Ready to explore the opportunities and prepare for the challenges of AI-powered Life Sciences language services for your drug’s post-market stage? We offer life sciences content translation and solutions for every touchpoint in your drug development journey. Let’s get in touch.
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为生命科学公司提供的人工智能服务重塑了这个行业。行业领导者正在重新考虑自20世纪90年代以来内容的创建、处理和翻译方式(将ICH协调引入药物开发)。生命科学翻译服务公司正在开发新的最佳实践,以推动生成式人工智能在受监管文档中的大规模效益,并降低风险。虽然生命科学行业必须谨慎地接受人工智能,但Lionbridge建议药品赞助商认真探索为生命科学公司提供人工智能服务的机会。
本博客系列将从内容和语言的角度探讨药物生命周期阶段,特别是生命科学翻译和生命科学本地化。我们将为安全有效地应用法学硕士提供指导,甚至适用于受监管的产品翻译。您将发现应用LLM的挑战和AI在生命科学中的机遇。
在我们之前的博客中,我们讨论了药物生命周期的上市前和上市阶段。本博客将重点关注新药上市后阶段。
面向生命科学的AI翻译和本地化服务:上市后药物阶段
一旦药品上市,即进入上市后阶段。这个阶段一直持续到产品退出市场。
上市后活动包括:
上市许可的更新、变更和延期
作为初始上市产品授权的一部分,监管机构要求的上市后临床研究承诺
上市后监督研究,以进行持续的安全性监督、评估疗效和优化产品使用
支持标签扩展或特殊人群的上市后临床研究
汇总定期安全性更新报告,包含对产品风险-受益的全面、简明和批判性分析
个体病例安全性报告,以收集疑似不良反应
产品营销和销售培训活动
社交媒体活动
临床和上市许可语言资产的AI优势
通常对新药进行多项上市后临床研究,以获得更多常规治疗经验。在上市前阶段根据临床开发计划进行的临床研究不同。它们满足特定的要求和测试假设,以生成药物疗效和安全性的统计证据。这将使监管审查人员能够确定产品的风险-收益平衡。上市后阶段的临床研究可能满足多种其他目的。例如,他们可以学习:
长期安全性
药物相互作用
儿科人群
流行病学
无论这些研究的目的是什么,上市前临床试验期间开发的语言库都可以用于上市后研究并带来收益。语言资产,如翻译记忆、词汇表、术语和风格方面,可以分层到提示工程中,以改善人工智能驱动的语言结果。如果在药物开发的早期建立语言策略,则非方案特定的传统背景内容可以帮助提高效率和节省成本。
此外,由于上市许可是动态的,因此上市药物的新内容创建和文件记录在上市后阶段继续发展。制造商必须更新支持授权的档案,并确保产品符合科学进步和新的法规要求。因此,上市产品在初始授权后通常会有多种变体。许多新的活性原料药也将延长其上市许可,这需要新的上市许可。
活性原料药的内容物生命周期可能跨越多个商业产品和多个上市许可程序。由于药品赞助商很少有涵盖药品整个生命周期及其上市后变化的语言策略,因此他们错过了显著的成本节约和语言优化。此外,在小型或独立的翻译项目中应用AI不会带来大型语言模型所承诺的效率。
药物生命周期中大型语言模型的挑战和风险
在这个药物生命周期博客系列中,我们已经解决了上市前,发布和上市后阶段。我们认为,如果语言资产在各个阶段都能使用,并应用大型语言模型,人工智能可以显著节省成本,并为受监管的翻译提供语言支持。然而,人工智能驱动的监管翻译并非没有风险。大型语言模型可能会产生幻觉(虚构的内容与输入数据不一致)。大型语言模型的这些和其他挑战在我们的电子书《生命科学中的人工智能和语言策略》中有详细介绍。
上市后阶段生命科学翻译的AI机会
利用上市前临床试验期间积累的人工智能和语言资产,实现上市后临床研究的成本节约和语言一致性
首次批准和授权后,上市许可更新、变更和延期的成本节约和语言一致性
从上市前到上市后安全活动的成本节约和语言一致性
从发布到上市后活动,消息传递和产品声明沟通的成本节约和语言一致性
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