翻译这篇文章的目的是想让大家了解一下国外是怎样做设计开发的，运用科学的方法做研究，这种方法可以运用到工艺、清洁程序等开发中，虽然我们对本文也不是理解太透彻，但我们很想把这篇文章抛给大家，供大家参考。另：就是TOC法（总有机碳法）被国外广泛应用到设备清洁和清洁验证中。

 

This article examines using experimental design methods to define different procedures for intermediate bulk container cleaning. The authors have evaluated this new approach, in which a highly soluble, low-dose product and a relatively insoluble high-dose product constituted experimental input variables.

 

本文研究使用实验设计方法来定义中型散装容器（IBC）的不同清洁程序。作者用高溶解性低剂量产品和相对溶解性不好高剂量的产品构成实验的输入变量评估了这种新方法。

 

Defining cleaning procedures is crucial to ensuring the elimination of product residues from non-dedicated process equipment. This process can be expensive and challenging, however, in facilities where 30-40 different oral solid-dose products may be manufactured each year.

 

定义清洁程序对于确保从非专用工艺设备上消除产品残留来说是至关重要的。然而对于设备上每年可能会生产30-40种不同的口服固体制剂产品，这一过程可能是花费昂贵并且具有挑战性的。

 

Generally, best cleaning procedures are defined based on monitoring final drug content, pH, and the conductivity of water samples for each product until they are within acceptable ranges.

 

一般来说，最好的清洁程序的定义是基于监控每个产品淋洗水样的终产品的含量、pH和的电导率，直到它们都在可接受范围内。

 

This article examines a new way of doing this, using experimental design methods to define different procedures for intermediate bulk container (IBC) cleaning.

 

本文研究一种新的方式来做到这一点，使用实验设计方法来定义中型散装容器（IBC）的不同清洁程序。

 

The authors have evaluated this new approach, in which a highly soluble, low-dose product and a relatively insoluble high-dose product constituted experimental input variables.

 

作者们评估了这种新方法，用高溶解性低剂量产品和相对溶解性不好高剂量的产品构成实验的输入变量。

 

Given the number and wide variety of APIs, ingredients, cleaning and processing materials used in pharmaceutical manufacturing, pharmaceutical products could potentially be contaminated with any number of substances. This is why verifying clean liness through cleaning validation is so crucial. Validation identifies potential residues, whether from APIs, ingredients, cleaning agentsand microorganisms, and sets up a process through which potential contaminationis reduced to the lowest acceptable limits (1).

 

考虑到制药生产中使用的API，辅料，清洁和加工材料数量和种类繁多，药品可能会受到许多物质的污染。这就是为什么通过清洁验证来确认清洁是如此关键。验证确定了潜在的残留物，无论是来自于API，辅料，清洗剂还是微生物，并建立了一个过程，通过这种过程潜在的污染物被降至最低可接受的限度。

 

Usually, these limits are defined based on visual, chemical, and microbiological data(2). Chemical limits are expressed as the maximum concentration in the next product (3), amount per surface unit (4), or concentration in the extraction solvent (5). An acceptance limit plus an internal action limit allows pharmaceutical manufacturers to achieve more stringent process control.   

 

通常，这些限度的定义是基于目视，化学，和微生物数据。化学限度表示为在下一产品中的最大浓度，每单位表面积上的数量，或者萃取溶剂中的浓度。可接受限度加上内部行动限度使得制药企业能够实现更严格的工艺控制。

 

Once potential residues have been defined, detection methods must be established.Usually, specific methods such as high-performance liquid chromatography (HPLC)(6) and ultraviolet spectroscopy (UV) (7) are used, but non-specific methods such as total organic carbon (TOC) (8, 9) may also be applied, and pH and conductivity determinations should also be used to evaluate the performance ofthe cleaning procedure.

 

一旦定义了潜在残留物，必须要建立检验方法。通常情况下，会使用专属性方法，如HPLC和UV，但是非专属性方法如总有机碳（TOC）也可能被使用，同时pH、电导率检测也应该被用来评估清洁程序的性能。

 

Disadvantages of specific methods include the need to develop assays for each new API and validate those methods, a process that can be long and expensive (10, 11). However, TOCcan potentially be applied to any product, is sensitive enough to detect quantities down to μg/L or parts per billion (ppb), and typically involves less time-consuming sample preparation than other methods (12).

 

专属性方法的缺点包括需要为每一种新的API开发检测方法并进行方法学验证，这一过程可能是漫长而昂贵的。然而，总有机碳法（TOC）可被应用到任何产品，灵敏度足以使检测限达到μg/L或ppb，相比其他方法会较少涉及耗时样品的制备。

 

Although specific methods are usually preferred, non-specific techniques can be used if there is a scientific justification for doing so in continuous process monitoring, whereas specific methods could used only for initial validation.

 

尽管专属性方法通常是首选的，如果在连续工艺监控中有特定的科学依据也是可以使用非专属性技术的，专属性方法只能用于初始验证。

 

Sampling methods are then selected by assaying rinse water or by surface swabbing (13).Rinse water sampling is used when dealing with very large pieces of equipment or piping, or in situations where there is limited access to equipment surfaces(14). These samples should be correlated with direct swab sampling to be sure that residues are being detected properly and do not remain undissolved oninaccessible equipment surfaces.

 

取样方法选择检测淋洗水法或表面擦拭法。在处理非常大的设备、管道、或者在设备表面接触受限的情况下采用淋洗法取样。涉及采用直接擦拭取样的样品情况，应确保残留物能够被合理检测到，并且在接触不到的设备表面不会有未溶解的残留物。

 

实验设计简化操作流程

 

The authors launched a study to apply the principles of experimental design, and recommendations for cleaning validation, to develop a faster way to set andvalidate procedures for cleaning intermediate bulk containers (IBC) for granulated products. They extended this methodology and applied it to set cleaning validation limits for a vertical granulator in a multiproduct oral solid dosage form facility. The experiments were conducted during the performance qualification step of both of these systems.

 

作者开展了一项研究，应用实验设计原理和清洁验证的建议，开发一种更快速的方法为颗粒状产品的IBC容器的清洗来设定和验证程序。他们扩展了这一方法，并将其应用于在多产品口服固体制剂设备中，为立式制粒机设定了清洁验证范围。这些实验是在这两种系统的性能鉴定步骤中进行的。

 

Plackett-Burman experimental design methods were used to identify the most important cleaning process parameters, with the minimum number of experimental runs, early in the experimentation phase. To evaluate interactions between variables, a two-level fractional factorial design was conducted after the Plackett-Burman modeling.

 

Plackett-Burman实验设计（筛选试验设计）方法用于识别最重要的清洁工艺参数，在实验阶段的早期，运行最少数量的实验。为了评估变量之间的相互作用，在Plackett-Burman模型以后进行了二水准部分阶乘实验设计。

 

Although the equation derived from the two-level factorial design explained the cleaning processes as a third-order equation, the initial Plackett-Burman fitting first-order model (which detected only linear effects) was more valuable inpredicting the process parameters in each step of the cleaning process. The process was scaled up for a vertical granulator, and the cleaning design space was verified.

 

尽管从二水准部分阶乘实验设计得到的方程式解释了清洁过程是一个三阶方程，但最初的适用于一阶模型的Plackett-Burman在每一步清洗过程中预测工艺参数上更有价值。这一过程是为了扩大立式制粒机的规模，清洗设计空间也得到了验证。

 

Results suggest that, although factorial design is useful for understanding process behavior and input variables interactions, Plackett-Burman designs allowed definition of linear models to predict process parameters for 13 new cleaning procedures based on product dose. Five of the cleaning procedures (or recipes), predicted from experimental design, were experimentally confirmed using nine different products. Previous analytical detergent characterization helped todefine the acceptance limits equal to the United States Pharmacopeia(USP) purified water pH and conductivity values.  

 

结果表明，虽然阶乘设计对于理解过程行为和输入变量的相互作用非常有用，但Plackett-Burman设计允许线性模型的定义可以根据产品剂量来预测13个新的清洗过程的工艺参数。从实验设计中预测的5个清洗程序(或配方)，通过使用9种不同的产品得到了实验确认。先前的分析清洁剂的特性帮助定义了美国药典(USP)纯化的水pH值和电导率值的可接受限度。

 

The ratio between water volume and equipment surface allowed the procedure to be scaled up and used for granulator cleaning. The design space for the cleaning procedure was also checked in four independent runs for two high-dose products. Results show that the experimentally-derived models provided a high level of assurance that the cleaning procedures met specifications.

 

用用水量和设备表面的比例使得该程序规模得到扩大并用于制粒机的清洁。清洁程序的设计空间也在对两种高剂量产品的四次独立运行中被检测。结果表明，实验得出的模型对于清洁程序符合规定提供了高水平的保证。

 

材料和方法部分

 

Cleaning stations. Automatic cleaning was conducted in a clean-out-of-place (COP) AISI 304 cabin (WB Model, Cosmec,Italy) for IBC and in a clean-in-place (CIP) system for a 600-L Glatt vertical granulator. A sequential process was used, combining an initial rinse phase to remove larger amounts of adhered product, and a second phase in which detergent was applied. In addition, there were two final rinse steps. Samples of the final rinse water were taken to determine external TOC, pH, and conductivity.The systems were designed to combine, in a synergistic mode, the spectrum of critical cleaning parameters (e.g., time, water, pressure, chemical action and temperature), and were automatically controlled to achieve a robust process(15).

 

清洁站IBC容器自动清洁采取离线清洁(COP)的方式在AISI304舱室中进行(WB型，化妆品，意大利)进行和600-L的格拉特立式制粒机系统采取在线清洁(CIP)的方式进行。使用了一个连续的过程，由初始阶段淋洗液来移除大量附着的产品，第二阶段在其中加入清洁剂。此外，还有两个最后的淋洗步骤。收集最终淋洗水，检测离线的总有机碳（TOC），pH和电导率。该系统的设计目的是在一个协同的模式下，将关键清洗参数(如：时间、水、压力、化学反应和温度)的条件合并，并被自动控制以达到一个稳健的过程。

 

分析方法

 

TOC was automatically conducted in a GE SIEVERS 900 TOC analyzer, while pH and conductivity were measured in a Mettler-Toledo Seven Multi instrument. Each measurement was repeated at least once. Purified water samples were taken during the last 10 seconds of the final rinse step in ERA ultra-low TOC contentcertified 40-mL TOC flasks.

 

总有机碳（TOC）是在GESIEVERS 900系列总有机碳分析仪中自动进行检测的，而pH和电导率则是用梅特勒-托利多的综合测试仪来测量的。每一次检测至少重复一次。纯化的水样在最后10秒的最后淋洗步骤中被收集到ERA超低TOC含量认证的40ml总有机碳（TOC）烧瓶中。

 

实验设计

 

For experimental design definition, version 6.0.1 of Stat-Ease, Inc.’s Design-Expert software was used. A Plackett-Burman design for six factors(i.e., initial rinse volume, detergent volume, detergent concentration, final rinse volume, purified water rinse volume, and product dose) was conducted as 18 runs in a single block, including two central points.

 

在实验设计的定义中，使用了Stat-Ease, Inc.公司的 Design-Expert软件的6.0.1版本。Plackett-Burman设计的 6 个因素(即：初始的淋洗体积、清洁剂体积、清洁剂浓度、最终淋洗量、纯化水淋洗量和产品剂量)在一个单一的模块（包括两个中心点）中进行了 18 次运行。

 

Another two-level fractional factorial design (for the same six factors) was conducted to estimate the effects of all interactions in 18 runs, including two central points. Experiment was conducted as a single block.

 

另外一个二水准部分阶乘实验设计（相同的6个因素）被用来估计18次运行中所有相互作用的影响，包括两个中心点。实验是作为一个单一的模块进行的。

 

Both designs were conducted with two different products, used as categorical variables: Product 1 (20-mg dose, highly soluble in water) and Product 2(400-mg dose with low water solubility).

 

两种设计都是在两种不同的产品中进行的，分别是：产品1(20mg剂量，高水溶性)和产品2(400mg剂量，低水溶性)。

 

A neutral detergent (Steris’ CIP 300) was used in the experimental phase and during evaluation of the predicted procedures. Steris’ CIP 200 acid detergent and its CIP 100 basic detergent were also evaluated during process performance qualification.

 

在实验阶段和预测过程的评估中使用了一种中性清洁剂(Steris公司的CIP300)。在工艺性能评定过程中，还对Steris公司的CIP200酸清洁剂和CIP 100碱清洁剂进行了评价。

 

结果与讨论部分

 

Cleaning validation constitutes the documented evidence that a cleaning procedure provides equipment ready for manufacturing process, mainly in amultiproduct facility. Activities related to validation studies could be classified in three phases (16). Phase one, commonly called pre-validation, involves research, development, and equipment qualification. Phase two is designed to verify that all the critical parameter limits that have been established are valid, and that the process generates products with sufficient levels of critical quality attributes, even in worst-case situations.

 

清洁验证是一种文件证据，证明一种清洗程序提供的设备可以用于生产过程，主要是多产品设备。与验证研究相关的活动可以分为三个阶段。第一阶段，通常叫做预验证，涉及调研，开发和设备确认。第二阶段的设计是为了验证所有已经建立的关键工艺参数限制都是有效的，并且这个工艺能够生产出具有足够水平的具备关键质量属性的产品，即使在最坏的情况也是如此。

 

Phase three, the validation maintenance state, involves the frequent review of all documents related to the process performance, to ensure that no deviations, failures, or changes to the production process occurs. A careful design of systems and process controls assures process robustness and quality products.

 

第三阶段，验证维护状态，涉及到与工艺性能相关的所有文档的周期性审核，以确保不会出现任何偏差、故障或对生产工艺的更改。系统和工艺控制的仔细设计确保了工艺的耐用性和高质量的产品。

 

These phases may be applied to cleaning processes, considering that their developmentand validation are based on defining cleaning procedures and controlling analytical assays, acceptable residue limits, critical sampling points, and methods. All of these elements must be established during phase one. In this article, the authors will summarize the process used to establish cleaning procedures for two critical equipment involved in oral solid dose manufacturing in early pre-validation phase.

 

这些阶段可以应用于清洁工艺，考虑到他们的开发和验证是基于定义清洁程序和控制分析含量、可接受残留限度、关键取样点和方法。所有这些元素必须在第一阶段建立。本文中，作者们将总结在早期预验证阶段中，为口服固体制剂生产中所涉及的两个关键设备建立清洁程序的过程。

 

确定合理的残留限度

 

In most cases, the first cleaning agent to evaluate in cleaning procedure development is a neutral pH detergent. If it does not provide consistent product removal performance, other acid or basical ternatives are assayed (17). Determining the relationship between detergent dilution in purified water and pH/conductivity values is a prerequisite for using these assays as indicators of cleaning agent removal in cleaning validation studies (18). That is why, in this research, the authors tested three different detergents to evaluate its influence in equipment cleaning procedure performance.  

 

大多数情况下，在清洁程序开发中评估首选的清洁剂是中性pH的清洁剂。如果它不能够很好的去除产品，其他酸或碱清洁剂将被试验。确定在纯化水中稀释的清洁剂与PH/电导率值之间的关系是在清洁验证研究中使用这些测定作为清洁剂去除的指示剂的先决条件。这就是为什么，在这项研究中，作者们测试三种不同的清洁剂来评估他们在设备清洁程序性能上的影响。

 

For the CIP 300 detergent, pH values of different concentrations indicated that pH assays could only detect concentrations exceeding 1000 ppm. For lower levels, pH values corresponded to those of purified water (5.0-7.0). Conductivity values regarding detergent concentration showed that this type of assay could detect concentrations above 10 ppm (1.5 μS/cm-2.7 μS/cm), confirming conductivity as a better assay for determining detergent residues in final rinse water samples than pH measures as previously reported (19).

 

对于CIP300清洁剂，不同浓度的pH值显示pH测试仅能够检测超过1000ppm的浓度。对于更低的水平，pH值对应的是纯化水的pH（5.0-7.0）。电导率值与清洁剂的浓度有关显示，这种类型的分析能够检测的浓度高于10ppm(1.5μS/cm-2.7 μS/cm)，确认电导率为比pH测试更好的检测清洁剂在最终淋洗水样品中残留的分析在之前报道过。

 

In the case of the CIP 200 acid detergent, 1 ppm corresponded to the lower limit of water pH (5.20-5.76) and conductivity was in the range of 3.0-3.73 μS/cm. For the CIP100 basic detergent, the 1 ppm pH range slightly exceeded the upper limit water pH (6.92-7.31) and conductivity was between 2.29 and 3.98 μS/cm.

 

在CIP200的酸清洁剂情况下，1ppm相当于水pH的下限（5.20-5.76），电导率在3.0-3.73μS/cm的范围内。对于CIP100的碱清洁剂，1ppm的pH范围稍微超过水pH的上限(6.92-7.31)，电导率在2.29- 3.98 μS/cm之间。

 

Taking these results into consideration, USP purified water specification based on pH, conductivity, and TOC were adopted as acceptance criteria to evaluate cleaning process development results. Although these limits below 1 ppm for the three detergents tested is lower than the universally recognized limits of 10 ppm(20), the authors initially considered the potential cumulative effect that residues over surfaces in multiple equipment processes could have on the final product (21). The authors opted for an over dimensioned process to mitigate patient risk, but other factors could justify establishing higher limits. 

 

将这些结果考虑在内，采用了基于pH，电导率和总有机碳（TOC）的USP纯化水的规格被作为可接受限度来评估清洁工艺开发的结果。尽管这三种清洁剂的检测限度低于1ppm，低于公认的限度10ppm，作者们最初认为，在多产品设备过程中，表面残留物可能对终产品有潜在的累积效应。作者们选择了一个超维度的过程来减少病人的风险，但是其他因素能够证明建立更高限度是合理的。

 

Plackett-Burman 设计结果

 

In Plackett-Burman design, the main effects of selected experimental design variables have a complicated confounding relationship with two-factor interactions (22). Therefore, these designs should be used only to study main effects of process parameters when it can be assumed that two-way interactions between them are negligible.

 

在Plackett-Burman设计中，选择实验设计变量的主要影响是与两因素相互之间有一个复杂的混淆关系。因此，当可以假定双方之间的相互作用是可以忽略的时候，这些设计应该只用于研究工艺参数的主要影响。

 

In evaluating the results obtained from Plackett-Burman experiments, a linear model was developed between TOC values and operational cleaning parameters. The Analysis of Variance test showed that the effect dose has a probability value below 0.05, indicating its statistical significance at a confidence level of 95.0% (Table I).

 

在对Plackett-Burman实验结果的评价中，在总有机碳（TOC）值和操作清洗参数之间建立了一个线性模型。方差测试的分析表明，影响剂量的概率值低于0.05，表明其统计意义在95.0%的置信水平上(表I)。

 

 

Table I: Variance analysis fortotal organic carbon (TOC) from sample analysis of the Plackett-Burman experimental design. (DF = degrees of freedom; Prob = statistical probability associated with the given F value.)

 

表1：通过对Plackett-Burman实验设计的样品分析得到TOC的方差分析。（DF=自由度；Prob=与给定F值相关的统计概率。）

 

R-squared statistics indicated that the adjusted model explained only 38.102% of the TOC variability. The adjusted R-squared was 14.8902%, suggesting that the linear equation obtained did not completely explain the system’s behavior, so it is likely that higher order interactions take place between the operational variables.

 

R²统计表明调整后的模型只解释了38.102%的总有机碳变异性。调整均方是14.8902%，表明线性方程并没有完全解释系统的行为，因此很可能在操作变量之间进行更高阶的交互。

 

The Durbin-Watson (DW) statistic was more than 5.0%, proving that there was no serial correlation in residuals. The Plackett-Burman final equation (Equation1), in terms of decoded factors, was:

 

杜宾-沃森统计数据超过了5.0%，证明了残差没有连续的相关性。Plackett-Burman的最终方程（方程式1），在解码因子方面是：

 

[Eq. 1] TOC—525.397—0.36299• Initial Rinse — 6.15948 •Detergent Volume + 1.52647 • Detergent Concentration — 1.36716 •Final Rinse — 1.38799 • Purified +0.5559804 • Dose

 

[Eq.1] TOC—525.397—0.36299• 初始淋洗体积 —6.15948 •清洁剂体积 +1.52647 • 清洁剂浓度— 1.36716 •最终淋洗体积— 1.38799 • 纯化的淋洗体积 +0.5559804 • 剂量

 

As expected, this equation indicated that initial rinse, detergent, final rinse,and purified rinse volumes have a negative correlation with TOC values, and increasing dose and detergent concentration also increased the expected TOC results.

 

正如预期的那样，这个方程式表明，初始淋洗，清洁剂，最终淋洗和纯化淋洗体积与总有机碳值有负相关，增加剂量和清洁剂浓度也增加了预期的总有机碳（TOC）的结果。

 

Fractional factorial design results. The low Plackett-Burman adjusted R-squared (14.8902%), indicated that linear equation obtained did not completely explain the system’s behavior. To explain the possible interactions between these variables on TOC variability, a fractional factorial design was completed using the results of the common experiments ofthe Plackett-Burman design. Analysis of variance results are shown in Table II.

 

部分阶乘实验设计的结果。较低的Plackett-Burman调整了R²（14.8902%），表明线性方程并不能完全解释系统的行为。为了解释这些变量在总有机碳（TOC）变异性之间可能的相互作用，利用Plackett-Burman设计的共同实验的结果，完成了一个部分阶乘实验设计。对方差结果的分析如表II所示。

 

 

Table II: Variance analysis for total organic carbon (TOC) from sample analysis of two-level factorial experimental design. (DF = degrees of freedom; Prob = statistical probability associated with the given F value.)

 

表II：从二水准部分阶乘实验设计的样本分析中得到对总有机碳（TOC）的方差分析。(DF=自由度；Prob=与给定F值相关的统计概率。)

 

Values of probability less than 0.0500 indicated significant model terms. In this case, D (Industrial water final rinse), AD (interaction of initial industrial water rinse and Industrial water final rinse), AE (interaction of initial industrial water rinse and purified water final rinse) and ABD (interaction of initial industrial water rinse, detergent volume, and Industrial water final rinse) were significant in model terms. Detergent concentration in the range analyzed was not statistically significant.

 

概率值小于0.0500表示模型是显著的。在这种情况下，D（饮用水的最终淋洗体积），AD（初始饮用水的淋洗体积和饮用水的最终淋洗体积之间的相互关系），AE（初始饮用水的淋洗体积与纯化水的最终淋洗体积之间的相互关系）和ABD（初始饮用水淋洗体积，清洁剂体积和饮用水最终淋洗体积）在模型上是显著的。在分析范围内的清洁剂的浓度是具有统计学意义的。

 

Adequate precision measures the signal-to-noise ratio.  A ratio greater than fouris desirable. The obtained ratio of 10.918 indicated an adequate signal. Residuals were also checked for normality.

 

足够的精度测量信噪比。大于4的比率是可取的。所获得的10.918的比率表明了一个充分的信号。对残留物也进行了常规检查。

 

The final equation (Equation 2) in terms of decoded factors was as follows:

 

[Eq. 2] TOC—268.69—55.06● Final Rinse + 97.81 ● Initial Rinse ● Final Rinse + 67.31 ● Initial Rinse ● Purified Rinse—54.09 ●  Initial Rinse  ● Final Rinse  ●  Detergen t Volume

 

最终方程式（方程式2）的解码因子如下：

 

[Eq.2] TOC—268.69—55.06●最终淋洗体积+ 97.81●初始淋洗体积●最终淋洗体积+ 67.31●初始淋洗体积● 纯化的淋洗体积—54.09● 初始淋洗体积●最终淋洗体积 ●清洁剂体积

 

This model explains as much of 70.56% of the process variability, and is more exactin describing the influence of process parameters than the linear model obtained from the Plackett-Burman design.

 

这个模型解释了过程变异性的70.56%，比从Plackett-Burman设计中获得的线性模型更准确的描述了工艺参数的影响。

 

IBC容器清洁流程开发

 

The Plackett-Burman-derived equation was evaluated for different products doses using Microsoft Excel’s Solver program, in which only process variables were changed. This procedure allowed recipes to be defined for each group of products based on product dose. Because detergent concentration was determined not to be a significant parameter from both experimental designs, it was restricted as a constant to the minimal value assayed. All restrictions were constricted to the range of cleaning process parameters included in the experimental design.  

 

使用Excel的求解程序，Plackett-Burman衍生方程式在不同产品剂量中被评价，在此过程中，只有过程变量发生了改变。这个过程允许根据产品的计量为每组产品定义配方。因为清洁剂浓度的检测不作为实验设计的一个重要参数，他被限制为一个常数，以达到最小值。所有的限制都被限定在实验设计中包含清洁工艺参数的范围之内。

 

The resulting procedures were evaluated in at least two containers for seven other products. Results of this testing demonstrated that predicting cleaning process parameters, using the statistically obtained model, was adequate for meeting the acceptance criteria for different doses and for solubility products of batches ranging from 90 to 180 Kg (Table III).  

 

最终的程序在7个其他产品的至少2个容器中被评估。该实验的结果表明，使用统计学获得的模型在对清洁工艺参数的预测中，足以满足不同剂量、产品批量在90-180kg的可溶解产品的可接受标准。（表III）

 

 

Table III: Results of applying container-cleaning procedures, using recipes developed with Plackett-Burman methods.

 

表III：应用容器清洗程序的结果，用Plackett-Burman方法开发的方法。

 

When used correctly in the right circumstances, visual inspection is a powerful detection method (23, 24). Using the approach outlined in this article, visual inspection of equipment surfaces revealed no residues, suggesting that the method is effective.

 

在正确的情况下正确使用时，目视检查是一种强大的检测方法。使用本文所述的方法，对设备表面的目视检查要做到没有发现残留物，才表明该方法是有效的。

 

Cleaning procedures developed for product dose groups allowed for reducing process time because the worst-case cycle approach requires 11 minutes. For lower doses, the process times were as follows:

 

Product 3-10 mg: 6 minutes

 

Product 1-20 mg: 7 minutes

 

Product 5-200 mg: 8 minutes

 

Product 2-400 mg: 9 minutes.

 

为产品剂量组开发清洁程序允许减少时间过程，因为最坏情况的循环方法需要11分钟。对于较低的剂量，过程时间如下：

 

产品剂量3-10mg：6分钟

 

产品剂量1-20mg：7分钟

 

产品剂量5-200mg：8分钟

 

产品剂量2-400mg：9分钟

 

The Plackett-Burman model was more practical because it allowed the authors to define operational parameters for 13 groups of products based only on the results obtained with two products of different dose and solubility. This method could be easily applied in situations where new automatic systems are installed in a facility to establish cleaning process conditions faster and at lower cost than developing cleaning process conditions for each product separately.

 

Plackett-Burman模型更实用，因为它允许作者们根据不同剂量和溶解度的两种产品得到的结果，为13组产品定义操作参数。这种方法可以很容易的应用于在设备安装的新自动化系统，以更快的建立清洁工艺条件，并且比单独为每一个产品开发清洁工艺条件的成本更低。

 

Other critical elements that should be validated are the maximum delay before cleaning (i.e., the maximum amount of time that the equipment should be dirty before it is cleaned (25) and the maximum time that it could remain clean after the applied procedure (26). These values could be included as input and output variables, respectively, in the experimental design.  

 

其他需要验证的关键因素是清洗前的最大延迟（即：设备在清洗前保持未清洁的最大时间和应用程序后保持清洁的最大时间。在实验设计中，这些数值应该分别被包括在输入和输出变量中）。

 

制粒机清洁流程开发

 

In order to speed development of a cleaning procedure for a vertical granulator, a relationship between the granulator and the container product contact surface area and cleaning volumes was calculated. This approach facilitated the process of develop and evaluating cleaning recipes previous to validation.

 

为了加快立式制粒机的清洁程序的开发，计算了制粒机和容器的产品接触表面积和清洁体积之间的关系。这种方法促进了开发和评估在进行验证之前的清洁配方的过程。

 

For scale-up, a ratio was obtained between the total volume of water required to clean 500-mg dose containers and the container area. This value was related to the correspondent 600-L granulator product contact area, and the relationship used to determine the volume required for cleaning.

 

为了扩大规模放大产品，在清洗500mg剂量的容器所需的总水量和容器面积之间得到一个比率。这个值与600L的制粒机产品接触面积有关，这种关系用于确定清洁所需的体积。

 

The cleaning procedure was adjusted sequentially, in order to reach the total calculated volume. This volume was then used as a central point, and variation around it was determined, using a fractional factorial design for three variables with no central point.

 

清洗程序按照顺序进行调整，为了达到总计算体积。这个体积随后被用作中心点，并确定了围绕它的变量，使用了一个没有中心点的3个变量的部分阶乘设计。（部分析因设计）

 

Two categorical variables were used (two different 500-mg products and two different detergents). Robustness of the design space for the cleaning procedure was determined in eight runs with two different 500 mg product doseand CIP 200 and CIP 100 detergents.

 

使用两种分类变量（两种不同的500mg产品和两种不同的清洁剂）。清洗程序的设计空间的耐受性是在用两种不同的500mg产品剂量和CIP200，CIP100清洁剂的8次运行中确定的。

 

Results indicated (see Table IV) that in all cases, actual residue limits were below the levels calculated, and were in the parts-per-million of TOC for Product 7 and Product 8.

 

结果表明（见表IV）在所有情况下，实际残留限度低于计算的水平，并且在产品7和8的总有机碳的每百万分中（ppm）。

 

 

Table IV: Results of a design space cleaning procedure developed for a 600-L Glatt vertical granulator, using the ratio of water volume to equipment surface. CIP is clean-in-place. Visual inspection confirmed that the procedure development was robust. No residues could be detected on any of the equipment surfaces.

 

表IV：清洗程序空间设计的结果，该程序是为一种600L的格拉特立式制粒机开发的，用了水体积和与设备表面积的比率。目视检查证明了该程序的开发是耐用的。任何设备表面都不能检测到残留物。

 

结论部分

 

Evaluations suggest that the mathematical model obtained from a Plackett-Burman design can predict, with a high degree of accuracy, the optimal conditions for the processof IBC cleaning. Results using the model were checked in nine products in atleast two independent replicates. In all cases, the results were obtained within the acceptance limits of product and detergent removal.

 

评估表明，从Plackett-Burman（筛选试验设计）得到的数学模型能够准确地预测IBC清洗过程的最佳条件。使用该设计模型的结果在9个产品至少两个独立的重复中被检查。在所有情况下，这些结果都是在产品和清洁剂去除的可接受范围内得到的。

 

Application of different cleaning process parameters (in terms of rinse and detergent volumes) for each product allowed water consumption to be reduced by 320 L per container, resulting in better use of installed capacities for clean-out-of-place cleaning of IBCs, because each type of product had a defined process time. If the common approach of determining the “worst-case” procedureis used for all containers, time and water wastes are significantly increased.

 

每一种产品的不同清洁工艺参数（淋洗和清洁剂体积）的应用允许每个容器的耗水量减少至320L，源于更好地利用了IBC的离线清洗组装能力，因为每一类型的产品都有一个确定的工艺时间。如果确定最坏情况程序的常用方法被用于所有的容器，时间和水的浪费会显著增加。

 

When the process was scaled up to clean a vertical granulator, results of the predicted design space were higher than those obtained for IBC, but all results were below 10 ppm, demonstrating that experimental design can be a powerful tool for developing more robust cleaning procedures.

 

当这个过程被扩大规模来清洗立式制粒机时，可预测性的设计空间结果会比IBC要高，但是所有的结果都低于10ppm，表明实验设计可以成为一个强有力的工具来开发更耐用的清洁程序。