The GxP AI Playbook: Critical Concepts Explained

 

GxP AI 手册：关键概念解释

 

For artificial intelligence to truly support GxP operations, understanding key concepts is essential. However, the rapid evolution of AI has led to varying interpretations of key terms, making it crucial to establish a common language.

 

要使人工智能真正支持 GxP 操作，了解关键概念至关重要。然而，AI 的快速发展导致了对关键术语的不同解释，因此建立通用语言至关重要。

 

To address this, the Parenteral Drug Association (PDA) is developing an AI Glossary to standardize terminology across drug manufacturing. Once finalized, this resource will provide a shared framework for understanding AI in GxP environments. In the meantime, industry professionals can build their knowledge by familiarizing themselves with essential AI concepts.

 

为了解决这个问题， PDA正在开发一个 AI 词汇表，以将整个药物制造的术语标准化。一旦最终确定，该资源将提供一个共享框架，用于了解 GxP 环境中的 AI。同时，行业专业人士可以通过熟悉基本的 AI 概念来构建自己的知识。

 

Below, we break down some of the most critical terms shaping AI’s role in pharmaceutical manufacturing:

 

下面，我们来分解形成 AI 在制药行业应用的一些最关键的术语：

 

Explainability

 

可解释性

 

What It Is: The ability of an AI     system to provide clear, human-understandable reasons for its decisions     and predictions.

 

什么是AI的可解释性：AI 系统为其决策和预测提供清晰、人类可理解的理由的能力。

 

Why It Matters: Regulatory agencies     require transparency to ensure decisions made by AI systems are traceable     and justifiable, particularly in critical processes like batch release or     deviation management.

 

为什么重要：监管机构需要透明度，以确保 AI 系统做出的决策是可追溯和合理的，尤其是在批放行或偏差管理等关键流程中。

 

Example: A model predicting     equipment failure explains that temperature trends and vibration patterns     are the leading indicators.

 

示例：一个用以预测设备失败的模型     解释温度趋势和振动模式是主要指标。

 

Data Integrity

 

数据完整性

 

What It Is: Ensuring the accuracy,     completeness, and consistency of data throughout its lifecycle.

 

什么是AI的数据完整性：确保数据在整个生命周期中的准确性、完整性和一致性。

 

Why It Matters: GxP regulations     demand that manufacturing decisions rely on trustworthy data. Any     compromise can lead to compliance violations or product recalls.

 

为什么重要：GxP 法规要求生产决策依赖于可信的数据。任何妥协方案都可能导致违规或产品召回。

 

Example: AI systems should operate     on ALCOA+ principles (Attributable, Legible, Contemporaneous, Original,     Accurate, and more).

 

示例：AI 系统应按照 ALCOA+ 原则（可追溯、清晰、同步、原始、准确等）运行。

 

Validation

 

验证

 

What It Is: The documented process     of proving that an AI system consistently produces reliable, intended     outcomes under GxP conditions. In pharma, validation focuses on compliance     with strict regulatory requirements, differing from validation in the AI     field, which emphasizes algorithm performance.

 

什么是AI验证：证明 AI 系统在 GxP 条件下始终产生可靠、预期结果的书面过程。在制药领域，验证侧重于符合严格的监管要求，这与 AI 领域的验证不同，后者强调算法性能。

 

Why It Matters: AI solutions must     meet stringent Pharma regulations, including performance qualification and     risk assessment, to be deemed safe and effective.

 

为什么重要：AI 解决方案必须满足严格的制药法规，包括性能确认和风险评估，才能被视为安全有效。

 

Example: Validating an AI-driven     predictive maintenance model ensures it operates accurately within its     defined parameters as outlined in the problem statement and the User     Requirements Specification (URS).

 

示例：验证AI 驱动的预测性维护模型可确保其在问题陈述和用户需求规范 （URS）中概述的规定参数内准确运行。

 

Governance

 

管理

 

What It Is: The framework for     managing AI systems responsibly, ensuring compliance with industry     regulations, ethical standards, and organizational goals.

 

什么是AI管理：负责任地管理 AI 系统，确保符合行业法规、道德标准和组织目标的框架。

 

Why It Matters: Effective     governance prevents misuse, ensures data privacy, and aligns AI operations     with business and compliance requirements. It also ensures proper model     retirement when objectives are achieved or the system becomes obsolete.

 

为什么重要：有效的管理可防止滥用，确保数据隐私，并使 AI 操作与业务和合规性要求保持一致。它还确保在实现目标或系统淘汰时正确停用模型。

 

Example: Establishing an AI     oversight committee to monitor system performance and compliance risks and     planning model retirement as necessary.

 

示例：建立AI 监督委员会来监控系统性能和合规性风险，并根据需要规划模型停用。

 

Digital Twins

 

数字孪生

 

What It Is: Virtual replicas of     physical manufacturing processes or equipment, integrated with AI to     simulate real-world scenarios and optimize performance.

 

什么是数字孪生：实体制造流程或设备的虚拟副本，与 AI 集成以模拟真实场景并优化性能。

 

Why It Matters: They allow for     predictive analytics and testing changes without impacting actual     production, ensuring efficiency and compliance.

 

为什么重要：它们允许在不影响实际生产的情况下进行预测分析和变更测试，从而确保效率和合规性。

 

Example: Using a digital twin to simulate how a process deviation affects product quality.

 

示例：使用数字孪生来模拟工艺偏差如何影响产品质量。

 

In-Process Deployment

 

工艺部署

 

What It Is: Implementing AI models     within active manufacturing workflows to enhance real-time decision-making     and process control.

 

什么是工艺部署： 在生产工作流程中实施 AI 模型，以增强实时决策和过程控制。

 

Why It Matters: It enables     continuous monitoring and adjustments, reducing deviations and improving     efficiency during production runs.

 

为什么重要：它支持持续监控和调整，减少偏差并提高生产运行期间的效率。

 

Example: AI algorithms dynamically     adjusting mixing speeds to maintain product consistency.

 

示例：AI 算法动态调整混合速度以保持产品一致性。

 

Monitorization

 

监测

 

What It Is: The continuous     monitoring of AI systems to ensure their outputs remain accurate,     reliable, and compliant over time.

 

什么是AI监测： 持续监控 AI 系统，以确保其输出随着时间的推移保持准确、可靠和合规。

 

Why It Matters: As processes and     datasets evolve, regular monitoring ensures AI performance doesn’t drift     or lead to non-compliance.

 

为什么重要：定期监控可确保     AI 性能不会随着流程和数据集的发展而产生偏差或导致不合规。

 

Example: Tracking an AI model’s     prediction accuracy for yield optimization over multiple production     cycles.

 

示例：跟踪AI 模型的预测准确性，以便在多个生产周期内实现优化。

来源：GMP办公室

关键词： AI