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The GxP AI Playbook: Critical Concepts Explained

 

GxP AI 手冊(cè)：關(guān)鍵概念解釋

 

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，了解關(guān)鍵概念至關(guān)重要。然而，AI 的快速發(fā)展導(dǎo)致了對(duì)關(guān)鍵術(shù)語(yǔ)的不同解釋，因此建立通用語(yǔ)言至關(guān)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.

 

為了解決這個(gè)問題， PDA正在開發(fā)一個(gè) AI 詞匯表，以將整個(gè)藥物制造的術(shù)語(yǔ)標(biāo)準(zhǔn)化。一旦最終確定，該資源將提供一個(gè)共享框架，用于了解 GxP 環(huán)境中的 AI。同時(shí)，行業(yè)專業(yè)人士可以通過熟悉基本的 AI 概念來(lái)構(gòu)建自己的知識(shí)。

 

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

 

下面，我們來(lái)分解形成 AI 在制藥行業(yè)應(yīng)用的一些最關(guān)鍵的術(shù)語(yǔ)：

 

Explainability

 

可解釋性

 

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

 

什么是AI的可解釋性：AI 系統(tǒng)為其決策和預(yù)測(cè)提供清晰、人類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.

 

為什么重要：監(jiān)管機(jī)構(gòu)需要透明度，以確保 AI 系統(tǒng)做出的決策是可追溯和合理的，尤其是在批放行或偏差管理等關(guān)鍵流程中。

 

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

 

示例：一個(gè)用以預(yù)測(cè)設(shè)備失敗的模型     解釋溫度趨勢(shì)和振動(dòng)模式是主要指標(biāo)。

 

Data Integrity

 

數(shù)據(jù)完整性

 

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

 

什么是AI的數(shù)據(jù)完整性：確保數(shù)據(jù)在整個(gè)生命周期中的準(zhǔn)確性、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.

 

為什么重要：GxP 法規(guī)要求生產(chǎn)決策依賴于可信的數(shù)據(jù)。任何妥協(xié)方案都可能導(dǎo)致違規(guī)或產(chǎn)品召回。

 

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

 

示例：AI 系統(tǒng)應(yīng)按照 ALCOA+ 原則（可追溯、清晰、同步、原始、準(zhǔn)確等）運(yùn)行。

 

Validation

 

驗(yàn)證

 

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驗(yàn)證：證明 AI 系統(tǒng)在 GxP 條件下始終產(chǎn)生可靠、預(yù)期結(jié)果的書面過程。在制藥領(lǐng)域，驗(yàn)證側(cè)重于符合嚴(yán)格的監(jiān)管要求，這與 AI 領(lǐng)域的驗(yàn)證不同，后者強(qiáng)調(diào)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.

 

為什么重要：AI 解決方案必須滿足嚴(yán)格的制藥法規(guī)，包括性能確認(rèn)和風(fēng)險(xiǎn)評(píng)估，才能被視為安全有效。

 

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：驗(yàn)證AI 驅(qū)動(dòng)的預(yù)測(cè)性維護(hù)模型可確保其在問題陳述和用戶需求規(guī)范 （URS）中概述的規(guī)定參數(shù)內(nèi)準(zhǔn)確運(yùn)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：負(fù)責(zé)任地管理 AI 系統(tǒng)，確保符合行業(yè)法規(guī)、道德標(biāo)準(zhǔn)和組織目標(biāo)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.

 

為什么重要：有效的管理可防止濫用，確保數(shù)據(jù)隱私，并使 AI 操作與業(yè)務(wù)和合規(guī)性要求保持一致。它還確保在實(shí)現(xiàn)目標(biāo)或系統(tǒng)淘汰時(shí)正確停用模型。

 

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

 

示例：建立AI 監(jiān)督委員會(huì)來(lái)監(jiān)控系統(tǒng)性能和合規(guī)性風(fēng)險(xiǎn)，并根據(jù)需要規(guī)劃模型停用。

 

Digital Twins

 

數(shù)字孿生

 

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

 

什么是數(shù)字孿生：實(shí)體制造流程或設(shè)備的虛擬副本，與 AI 集成以模擬真實(shí)場(chǎng)景并優(yōu)化性能。

 

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

 

為什么重要：它們?cè)试S在不影響實(shí)際生產(chǎn)的情況下進(jìn)行預(yù)測(cè)分析和變更測(cè)試，從而確保效率和合規(guī)性。

 

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

 

示例：使用數(shù)字孿生來(lái)模擬工藝偏差如何影響產(chǎn)品質(zhì)量。

 

In-Process Deployment

 

工藝部署

 

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

 

什么是工藝部署： 在生產(chǎn)工作流程中實(shí)施 AI 模型，以增強(qiáng)實(shí)時(shí)決策和過程控制。

 

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

 

為什么重要：它支持持續(xù)監(jiān)控和調(diào)整，減少偏差并提高生產(chǎn)運(yùn)行期間的效率。

 

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

 

示例：AI 算法動(dòng)態(tài)調(diào)整混合速度以保持產(chǎn)品一致性。

 

Monitorization

 

監(jiān)測(cè)

 

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

 

什么是AI監(jiān)測(cè)： 持續(xù)監(jiān)控 AI 系統(tǒng)，以確保其輸出隨著時(shí)間的推移保持準(zhǔn)確、可靠和合規(guī)。

 

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

 

為什么重要：定期監(jiān)控可確保     AI 性能不會(huì)隨著流程和數(shù)據(jù)集的發(fā)展而產(chǎn)生偏差或?qū)е虏缓弦?guī)。

 

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

 

示例：跟蹤AI 模型的預(yù)測(cè)準(zhǔn)確性，以便在多個(gè)生產(chǎn)周期內(nèi)實(shí)現(xiàn)優(yōu)化。