Executive summary 执行摘要
In today’s pharmaceutical environment, cleaning validation has evolved from a simple GMP obligation into a cornerstone of quality risk management.
在当今的制药行业环境中，清洁验证已从一项简单的药品生产质量管理规范要求，发展成为质量风险管理的核心支柱。
Multi-product facilities, complex APIs, and potent compounds demand more than just meeting limits; they require a scientifically justified, integrated approach that demonstrates control, protects patients, and withstands inspection.
多产品生产设施、复杂的活性药物成分以及高活性化合物，要求企业不仅要达到合规限值，更需建立一套科学合理的整合式方法，以此证明工艺可控、保护患者安全并能通过监管检查。
This white paper outlines how to build and maintain a risk-based cleaning validation strategy aligned with EU GMP, EMA, and FDA expectations.
本白皮书阐述了如何构建并维护符合欧盟药品生产质量管理规范、欧洲药品管理局及美国食品药品监督管理局要求的风险导向型清洁验证策略。
It connects regulatory principles with technical practice, from establishing science-based limits to verifying cleanliness with reliable analytical methods.
本文将监管原则与技术实践相结合，内容涵盖从制定基于科学的限值，到采用可靠的分析方法验证清洁效果的全流程。
Introduction 引言
In recent years, regulators have intensified their expectations around contamination control, lifecycle management, and the use of health-based exposure limits in shared manufacturing facilities.
近年来，监管机构对于共用生产设施中的污染控制、全生命周期管理，以及基于健康的暴露限值应用提出了更高要求。
At the same time, pharmaceutical processes have become more complex, increasing the need for robust, science-driven cleaning validation strategies.
与此同时，制药生产工艺的复杂度不断提升，使得企业对完善、以科学为导向的清洁验证策略的需求愈发迫切。
This whitepaper provides a practical, integrated guide to help manufacturers align with modern regulatory standards while maintaining efficiency.
本白皮书为制药企业提供了一套实用的整合式指导方案，助力企业在保持生产效率的同时，符合最新的监管标准。
It is written for QA, validation, engineering, and operations professionals seeking clarity, structure, and inspection-ready approaches.
本文的阅读对象为质量保证、验证、工程及生产运营领域的专业人员，旨在为其提供清晰、系统且可直接应对监管检查的实施方法。
CHAPTER 2 Regulatory Framework and Current Trends in Cleaning Validation
第二章 清洁验证的监管框架与当前趋势
Cleaning validation is a regulatory and scientific discipline that ensures equipment used in pharmaceutical manufacturing is consistently cleaned to prevent cross-contamination.
清洁验证是一门融合监管要求与科学方法的学科，其目的是确保制药生产所用设备的清洁效果具有一致性，从而防止交叉污染。
Authorities such as EMA, FDA, and PIC/S all emphasize that cleaning validation must be science-based, documented, and risk-driven.
欧洲药品管理局、美国食品药品监督管理局、国际药品检查合作组织等监管机构均强调，清洁验证必须以科学为基础、做好文件记录并以风险为导向。
Under EU GMP Annex 15 and EMA guidance, manufacturers are expected to demonstrate:
根据欧盟药品生产质量管理规范第 15 附录及欧洲药品管理局的指导原则，制药企业需证明：
Control of residues from APIs, excipients, cleaning agents, and microbial contaminants.
对活性药物成分、辅料、清洁剂及微生物污染物残留的有效控制。
Justified and reproducible cleaning procedures.
清洁规程的合理性与可重复性。
Data-based acceptance criteria linked to health-based exposure limits (HBELs).
基于数据、并与基于健康的暴露限值相关联的验收标准。
Ongoing verification through lifecycle management and change control.
通过全生命周期管理与变更控制开展持续的验证工作。
One of the most important evolutions is the shift from traditional fixed limits toward PDE-based (Permitted Daily Exposure) or HBEL-based criteria.
清洁验证领域最重要的发展趋势之一，是从传统的固定限值转向以允许日暴露量或基于健康的暴露限值为基础的判定标准。
These scientifically derived limits, established by toxicology experts, ensure that acceptance values are proportionate to actual health risk.
这些由毒理学专家制定的科学限值，确保了验收标准与实际的健康风险相匹配。
This approach not only improves safety and compliance but also supports resource optimization, focusing validation effort where it truly matters.
该方法不仅提升了产品安全性与合规性，还助力企业优化资源配置，将验证工作的重心放在真正关键的环节。
Together, these trends show a clear regulatory direction: cleaning validation is moving toward scientific justification, lifecycle control, and operational efficiency.
综合来看，这些趋势明确了监管方向：清洁验证正朝着科学论证、全生命周期控制与运营效率优化的方向发展。
CHAPTER 3 From Risk Assessment to Methodology Design: A Multifactorial and Integrated Process
第三章 从风险评估到方法设计 —— 一个多因素整合的流程
Cleaning validation is a multifactorial process that demands coordination between toxicology, process engineering, analytical science, and quality assurance.
清洁验证是一个多因素的复杂流程，需要毒理学、工艺工程、分析科学与质量保证等多个领域的协同配合。
No single element defines its success; instead, it’s the integration of risk assessment, analytical verification, and process control that delivers robust, compliant outcomes.
清洁验证的成功并非由单一因素决定，而是需要整合风险评估、分析验证与工艺控制，才能实现完善、合规的验证结果。
This integration applies equally to the way limits are established, how products and equipment are grouped, and how cleanliness is verified analytically - combining specific assays, non-specific techniques and, where relevant, microbiological testing into one cohesive framework.
这种整合性同样适用于限值制定、产品与设备分组，以及清洁效果的分析验证环节 —— 将特异性检测、非特异性检测技术，以及相关的微生物检测整合为一个统一的框架。
The following three components represent the core pillars of an effective cleaning validation strategy.
以下三大核心要素，构成了有效清洁验证策略的基石。
3.1 Foundational Strategy: Building a Robust Validation Concept
3.1 基础策略：构建完善的验证理念
The starting point is a deep understanding of what needs to be cleaned and controlled.
清洁验证的出发点，是深入了解需要清洁和控制的对象。
Every product introduces potential residues - APIs, cleaning agents, degradants, and microbial risks - that must be evaluated.
每种产品都会产生潜在的残留，包括活性药物成分、清洁剂、降解产物以及微生物污染风险，这些均需进行评估。
A risk-based validation begins by:
风险导向型的验证工作从以下环节展开：
Identifying potential residues and classifying them by toxicity, solubility, potency, and cleanability.
识别潜在残留，并根据其毒性、溶解性、活性及可清洁性进行分类。
Defining worst-case scenarios to challenge the cleaning process effectively.
设定最不利场景，对清洁工艺进行严格验证。
Establishing grouping strategies for similar products and equipment, reducing redundant testing while maintaining control.
为同类产品和设备制定分组策略，在保证管控效果的同时减少重复性检测。
These principles, show how smart grouping and documented rationale minimize unnecessary effort without compromising compliance.
这些原则说明了科学的分组方式与书面化的论证依据，能在不影响合规性的前提下，最大限度减少不必要的工作。
Proper documentation - protocols, reports, and change control - ensures inspection readiness and provides clear traceability from rationale to result.
完善的文件记录（包括验证方案、报告及变更控制文件）能确保企业随时应对监管检查，并实现从验证依据到结果的清晰追溯。
Acceptance criteria should therefore reflect all relevant dimensions of cleanliness - visual, chemical, and, when applicable, microbiological - ensuring that residues from products, cleaning agents, and potential microbial contaminants are adequately controlled.
因此，验收标准应涵盖清洁效果的所有相关维度 —— 外观、化学指标，以及适用情况下的微生物指标，确保产品残留、清洁剂残留及潜在的微生物污染物均得到有效控制。
3.2 Analytical verification: integrating specific, non-specific, and microbiological methods for a complete cleaning strategy
3.2 分析验证：整合特异性、非特异性与微生物检测方法，构建完整的清洁策略
Analytical verification is a central pillar of cleaning validation, and it must reflect the inherently multifactorial nature of the process.
分析验证是清洁验证的核心支柱，其方法设计必须契合清洁流程本身的多因素特性。
Cleaning residues can include APIs, excipients, cleaning agents, inorganic components, and - depending on equipment type and hold conditions - microbiological contaminants and endotoxins.
清洁残留可能包含活性药物成分、辅料、清洁剂、无机成分，并且根据设备类型和存放条件的不同，还可能存在微生物污染物和内毒素。
No single method can address all these risks.
单一的检测方法无法应对所有上述风险。
A complete analytical strategy therefore integrates specific assays, non-specific techniques, microbiological testing, and qualified sampling methods, ensuring that results accurately represent the true cleanliness state of equipment surfaces.
因此，一套完整的分析策略需要整合特异性检测、非特异性检测技术、微生物检测，以及经过验证的取样方法，确保检测结果能准确反映设备表面的实际清洁状况。
Specific analytical methods such as HPLC or GC are used when a defined compound (e.g., an API or a toxic impurity) must be quantified at very low levels.
当需要对特定化合物（如活性药物成分、有毒杂质）进行低浓度定量检测时，需采用高效液相色谱、气相色谱等特异性分析方法。
These methods require full validation per ICH Q2 to ensure adequate sensitivity, selectivity, and robustness at the low concentrations relevant for cleaning verification.
此类方法需按照人用药注册技术要求国际协调会 Q2 指导原则完成全面验证，确保其在清洁验证相关的低浓度范围内，具备足够的灵敏度、专属性和耐用性。
They are essential when toxicological risk (e.g., PDE-based limits) drives very stringent acceptance criteria.
当毒理学风险（如基于允许日暴露量的限值）要求制定严格的验收标准时，特异性分析方法不可或缺。
Complementing them, non-specific methods - mainly TOC, conductivity, and pH - provide broader detection capability.
总有机碳检测、电导率检测、pH 值检测等非特异性方法作为补充，具备更广泛的检测能力。
TOC measures total organic residues, capturing APIs, excipients, and organic cleaning agents.
总有机碳检测可测定总有机残留，涵盖活性药物成分、辅料及有机清洁剂。
Conductivity reflects ionic or inorganic residues, including certain detergents or buffers.
电导率检测可反映离子或无机残留，包括部分洗涤剂和缓冲液。
pH can help identify residual acidity or alkalinity, especially important when strong cleaning agents are used.
pH 值检测可辅助识别残留的酸碱性，在使用强腐蚀性清洁剂时，该检测尤为重要。
These methods are efficient and sensitive, but they require well-controlled baselines, validated sampling containers, appropriate rinsing media, and clear understanding of how cleaning agents influence background signals.
这些非特异性方法高效且灵敏，但要求实验基线得到严格控制、取样容器经过验证、冲洗介质选择恰当，同时需明确了解清洁剂对背景信号的影响。
Sampling is equally critical.
取样环节同样至关重要。
Swab sampling allows targeted evaluation of worst-case areas (crevices, gaskets, dead legs, spray-shadow zones) and is largely independent of residue solubility, though highly technique-dependent.
棉签取样可针对性地对最不利区域（缝隙、密封垫、死角、喷淋盲区）进行检测，该方法基本不受残留溶解性的影响，但对操作技术要求极高。
It requires validated swab materials, demonstrated recovery efficiency, and operator training.
棉签取样需使用经过验证的取样棉签、验证取样回收率，并对操作人员进行专业培训。
Rinse sampling helps assess large internal systems such as tanks and pipework and is closely linked to solubility and flow dynamics.
淋洗取样适用于评估储罐、管道等大型内部系统，该方法与残留的溶解性和流体动力学密切相关。
It is also an essential tool to verify the final rinse, ensuring that cleaning agents and product residues have been effectively removed before the equipment enters its hold phase.
同时，淋洗取样也是验证最终淋洗效果的关键方法，确保设备在进入存放阶段前，清洁剂和产品残留已被有效去除。
Microbiological verification is required wherever micro-cleanliness is relevant - typically in aqueous systems, non-sterile manufacturing lines with microbial risk, or equipment with extended clean hold times.
在对微生物清洁度有要求的场景中，均需开展微生物验证，典型场景包括水性生产系统、存在微生物污染风险的非无菌生产线，以及清洁后存放时间较长的设备。
This may include bioburden testing of final rinse samples, surface microbiological sampling, or endotoxin testing when warranted.
微生物验证工作可包括最终淋洗样品的生物负载检测、设备表面微生物取样，以及必要时的内毒素检测。
Acceptance limits should be aligned with product risk, equipment use, and pharmacopoeial expectations.
微生物验收限值需与产品风险、设备用途及药典要求保持一致。
Together, these tools form a cohesive analytical framework.
上述各类检测方法共同构成了一个统一的分析框架。
Acceptance criteria must integrate visual, chemical, and microbiological dimensions, ensuring that verification captures all relevant residues - not only organic carbon but also cleaning agents, inorganic components, and potential microbial contaminants.
验收标准必须整合外观、化学和微生物三大维度，确保验证工作能覆盖所有相关残留，不仅包括有机碳，还涵盖清洁剂、无机成分及潜在的微生物污染物。
Such an integrated approach provides a realistic and defendable assessment of equipment cleanliness within a modern, risk-based cleaning validation program.
在现代的风险导向型清洁验证体系中，这种整合式方法能对设备清洁状况做出真实、且可向监管机构论证的评估。
3.3 Defining Limits: PDE and Health-Based Exposure Values
3.3 限值制定：允许日暴露量与基于健康的暴露限值
The evolution toward PDE- or HBEL-based cleaning limits has been one of the most significant regulatory shifts of the past decade.
过去十年，清洁限值向基于允许日暴露量或基于健康的暴露限值转变，是制药监管领域最重大的变革之一。
Traditional approaches relied on arbitrary limits (e.g., 10 ppm or 1/1000th of dose), which offered limited scientific justification.
传统的清洁限值制定方法采用人为设定的固定值（如 10 百万分比浓度或千分之一剂量），缺乏足够的科学论证依据。
In contrast, PDE-based limits link cleaning validation directly to toxicological and pharmacological data.
与之相反，基于允许日暴露量的限值将清洁验证与毒理学、药理学数据直接关联。
The EMA guideline on setting health-based exposure limits describes how PDEs should be derived through systematic hazard identification, selection of the critical effect, determination of the point of departure (PoD), and application of appropriate uncertainty factors.
欧洲药品管理局关于制定基于健康的暴露限值的指导原则，明确了允许日暴露量的推导流程：通过系统的危害识别、关键效应筛选、基准点确定，以及适用不确定性系数的选取，最终得出允许日暴露量数值。
Once defined, PDE values are translated into Maximum Allowable Carryover (MACO) limits and further into surface or rinse criteria.
允许日暴露量数值确定后，会进一步转换为最大允许残留量限值，并最终细化为设备表面或淋洗样品的验收标准。
These data-driven limits form the foundation for rational decisions on cleaning strategy, validation frequency, and in some cases, the need for dedicated equipment.
这些基于数据的限值，为企业合理制定清洁策略、确定验证频率，以及判断是否需要专用生产设备奠定了基础。
Establishing and managing cleaning limits based on scientifically derived PDE values requires a balanced approach that connects toxicological science with practical implementation.
基于科学推导的允许日暴露量制定和管理清洁限值，需要采用平衡的方法，将毒理学理论与实际生产应用相结合。
To make these limits meaningful, consistent, and defendable across the manufacturing network, three elements are essential:
为使这些限值在企业的全球生产网络中具备实际指导意义、保持一致性且可向监管机构论证，需把握三大核心要素：
Focus cleaning effort proportionally to real risk.
根据实际风险程度，合理分配清洁工作资源。
Justify grouping and worst-case logic.
为产品 / 设备分组及最不利场景设定提供充分的论证依据。
Harmonize validation programs across global sites.
统一全球各生产基地的验证体系。
By grounding cleaning limits in health-based science, manufacturers not only enhance safety but also gain flexibility and defendability during regulatory inspections.
制药企业将清洁限值建立在基于健康的科学依据之上，不仅能提升产品安全性，还能在监管检查中拥有更大的灵活性，且相关验证结果更易得到认可。
CHAPTER 4 Key takeaways 第四章 核心要点
1 Cleaning validation is inherently multifactorial, requiring coordination between risk assessment, analytical verification, and toxicological justification.
清洁验证本质上是多因素的复杂工作，需要风险评估、分析验证与毒理学论证的协同配合。
2 Regulators expect a science- and risk-based approach built around HBELs, PDE values, and lifecycle control.
监管机构要求企业建立以科学和风险为导向的清洁验证体系，核心围绕基于健康的暴露限值、允许日暴露量及全生命周期管理展开。
3 Cleaning verification requires a balanced analytical toolbox: specific methods for critical residues, non-specific techniques such as TOC and conductivity, and microbiological testing where applicable - all supported by validated sampling methods. |
清洁效果验证需要一套均衡的分析方法体系：针对关键残留采用特异性检测方法，搭配总有机碳、电导率等非特异性检测技术，适用时开展微生物检测，且所有检测均需以经过验证的取样方法为支撑。
4 Grouping and worst-case logic enable smarter, leaner validation efforts without compromising robustness.
科学的产品 / 设备分组策略和最不利场景设计，能在保证验证体系完善性的前提下，让验证工作更高效、更精简。
5 Integration is the differentiator: linking toxicology, data, and process design ensures true control and inspection readiness.
5 整合性是清洁验证工作的关键：将毒理学、数据与工艺设计相结合，才能真正实现生产过程的有效管控，并确保企业随时应对监管检查。
CHAPTER 5 Conclusion 第五章 结论
Cleaning validation is both a scientific discipline and an operational challenge.
清洁验证既是一门科学学科，也是一项生产运营层面的挑战。
Companies that succeed treat it as an integrated process connecting toxicology, analytics, and engineering within a single, risk-based framework.
成功的制药企业将清洁验证视为一个整合式流程，在统一的风险导向框架下，融合毒理学、分析科学与工程技术。
