AI and Drug Development with Modern Clinical Trial Designs

AI和药物开发与现代临床试验设计

2024-04-15 09:50 lionbridge

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Over the past decade, biomedical technology has been through a massive revolution, leading to innovative breakthrough treatments. At the same time, concerns have grown over the development cycle and escalating costs of drug development. To tackle cost concerns and other modern drug development challenges, new trial designs have emerged. Compared to conventional designs, modern clinical trial designs drive efficiencies. However, they also introduce complexities in the operational and statistical execution of clinical trials. These complexities impact service providers involved in trial execution, including Life sciences translation services. Large Language Models, or Artificial Intelligence are well-suited to address these challenges. AI and life sciences services can: Enhance speed and efficiency in language outcomes Ensure consistency in localized content Maintain style Facilitate strong results communication New Trial Designs for AI and Drug Development Conventional trial designs, characterized by randomized, double-blind comparisons of parallel treatment groups, have long been the gold standard for generating reliable and robust clinical data. Notably, their inherent limitations have been increasingly scrutinized. These limitations include: Long execution phases High costs Need for extensive sample sizes Coupled with advancements in statistical software, classic trial design limitations have spurred development of alternative trial designs. These changes offer greater flexibility and efficiency in clinical trials.   Master or Main protocols, classified as either basket, umbrella, or platform trials, are overarching protocols that contain more sub-studies. These protocols represent a paradigm shift for sponsors, regulators, and patients alike. The protocols enable parallel testing of multiple therapies and/or diseases under the same clinical infrastructure. Implementing a multinational trial protocol is both time and resource-intensive. As a result, a master protocol can deliver significant efficiencies in trial execution when its sub-studies share aspects such as Site selection Patient screening Data management Ethical or monitoring committees Furthermore, potential to share a common control group across sub-studies may increase patient participation. This is due to a heightened likelihood of receiving the active experimental treatment.   Adaptive trials, another modern trial design, allow for modifications during the trial execution based on accumulated data from trial participants. Such changes must be pre-defined. They require interim analyses during the trial to allow for mid-trial adaptations, such as sample size adjustments or discontinuation of certain doses. Adaptive trial designs offer high flexibility and can reduce timeline, costs, and number of patients exposed in a clinical drug development program.   While conventional designs remain the standard, newer trial designs infuse flexibility and efficiency, accelerated enrolment, and reduced research costs. They also present fresh challenges for planning, organization, ethical surveillance, and statistical analysis. Therefore, sponsors are advised to plan language activities already during protocol development.  AI and Drug Development Improve Trial Execution Large Language Models (LLMs) are designed to drive efficiencies, speed, and consistency in language outcomes. They have great potential to support the preparation and efficient execution of new trial designs. Master protocols may necessitate high volumes of submission content during initial clinical trial applications (CTAs) and CTA amendments because multiple sub-studies are submitted under the same protocol. Adaptive trial designs may undergo multiple changes during trial conduct, necessitating new or repeated translations within stringent deadlines.    Like any emerging drug development technology, the application of LLMs should be based on a risk assessment. They should also carefully consider: Content types Intended users Compliance aspects The level of human intervention in translation workflows can be predetermined. A language plan can be established during the trial preparation stage while all essential clinical master documents are being developed. An obvious advantage of protocol-level language planning is that any adaptations, amendments, and new documentation after trial initiation can be expedited.   As a leader in Large Language Models and expert in clinical trial content and requirements, Lionbridge helps trial sponsors build a language strategy that optimizes these new trial designs.  Get in touch Need help with your language strategy? Lionbridge has decades of experience providing clinical trial translation and Life Sciences content solutions. We’re deeply familiar with the challenges of multi-lingual clinical trials, and our team stays updated on the latest regulatory changes for clinical trials. Get in touch to learn more.
在过去的十年中,生物医学技术经历了一场巨大的革命,导致了创新的突破性治疗。与此同时,人们对药物开发周期和药物开发成本不断上升的担忧也在增加。为了解决成本问题和其他现代药物开发挑战,出现了新的试验设计。与传统设计相比,现代临床试验设计提高了效率。然而,它们也在临床试验的操作和统计执行中引入了复杂性。这些复杂性影响了参与审判执行的服务提供商,包括生命科学翻译服务。大型语言模型或人工智能非常适合解决这些挑战。人工智能和生命科学服务可以: 提高语言成果的速度和效率 确保本地化内容的一致性 保持风格 促进强有力的结果沟通 AI和药物开发的新试验设计 传统的试验设计以平行治疗组的随机、双盲比较为特征,长期以来一直是产生可靠和可靠临床数据的金标准。值得注意的是,它们固有的局限性日益受到审查。这些限制包括: 执行阶段长 高成本 需要大量样本 再加上统计软件的进步,经典试验设计的局限性刺激了替代试验设计的发展。这些变化为临床试验提供了更大的灵活性和效率。   主方案或主要方案,分为篮子、伞式或平台试验,是包含更多子研究的总体方案。这些协议代表了申办者、监管机构和患者的范式转变。该协议允许在相同的临床基础设施下对多种疗法和/或疾病进行并行测试。实施多国试验方案是时间和资源密集型的。因此,当其子研究共享以下方面时,主方案可以在试验执行中提供显著的效率, 选址 患者筛选 数据管理 伦理或监测委员会 此外,在子研究中共享共同对照组的可能性可能会增加患者参与。这是由于接受积极的实验性治疗的可能性增加。   适应性试验是另一种现代试验设计,允许在试验执行期间根据试验参与者的累积数据进行修改。这种变化必须预先确定。它们需要在试验期间进行中期分析,以便进行试验中期调整,例如样本量调整或停止某些剂量。适应性试验设计具有高度灵活性,可缩短临床药物开发项目中暴露的时间轴、成本和患者数量。   虽然传统的设计仍然是标准,但较新的试验设计注入了灵活性和效率,加快了入组速度,并降低了研究成本。它们还为规划、组织、道德监督和统计分析提出了新的挑战。因此,建议申办者在方案制定期间就计划语言活动。  人工智能和药物开发改善了试验执行 大型语言模型(LLM)旨在提高语言结果的效率,速度和一致性。它们在支持新试验设计的准备和有效执行方面具有巨大潜力。在初始临床试验申请(CTA)和CTA修订期间,主方案可能需要大量提交内容,因为根据同一方案提交了多个子研究。适应性试验设计在试验进行期间可能会发生多次变化,因此需要在严格的期限内进行新的或重复的翻译。    与任何新兴药物开发技术一样,LLM的应用应基于风险评估。他们还应认真考虑: 内容类型 预期用户 合规方面 翻译工作流程中的人工干预水平可以预先确定。在编写所有重要临床主文件的同时,可在试验准备阶段制定语言计划。方案层面语言规划的一个明显优势是,可以加快试验启动后的任何调整、修订和新文件编制。   作为大型语言模型的领导者和临床试验内容和要求方面的专家,Lionbridge帮助试验申办者制定语言策略,以优化这些新的试验设计。  取得联系 需要语言策略方面的帮助吗?Lionbridge在提供临床试验翻译和生命科学内容解决方案方面拥有数十年的经验。我们非常熟悉多语言临床试验的挑战,我们的团队随时了解临床试验的最新监管变化。请与我们联系以了解更多信息。

以上中文文本为机器翻译,存在不同程度偏差和错误,请理解并参考英文原文阅读。

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