Uniformidadcontenido Troubleshooting Pharm Tech

he need for routine blend uniformity testing, followingprocess validation, for ensuring content uniformity offinal product has been a topic of extensive debate overthe past several years. Public health concerns with respect

to suboptimal blend and content uniformity is expressed in sev-eral regulatory policy documents. Recently, FDA issued an ab-breviated new drug application (ANDA) draft guidance docu-ment that further elaborated on the issue of blend uniformity.Eliminating inadequate potency and content uniformity prob-lems for marketed product is the public health objective of reg-ulatory policies. For the past several years, a lack of adequate po-tency and/or content uniformity has been the number-oneproduct quality reason for the recall of marketed solid dosageforms. From a regulatory perspective, recalls as a result of po-tency or content uniformity raise doubt in the adequacy of man-ufacturing controls and product release testing methods. Thelack of blend homogeneity is one of several possible reasons thatmay contribute to unacceptable potency or content uniformityin marketed products. A clear understanding of the root causesof these problems is desired.

The uniformity of a solid dosage form, as opposed to that ofa liquid or cream, is of particular concern. Unfortunately, thescience of powder blending and powder handling often is notgenerally well understood by those who formulate products orselect processing equipment. Therefore, some processes that areprone to problems with blending, segregation, and flow havebeen developed and introduced into production, ultimately re-sulting in varying degrees of content uniformity issues. Thisproblem is further complicated by the difficulties encounteredin physically sampling a stationary powder bed using samplethieves, which have been demonstrated to be very prone to sam-pling error. The result of this error is samples that do not rep-resent the state of the blend from where they were collected,which may lead to erroneous conclusions about the true uni-formity of the powder blend.

The Product Quality Research Institute (PQRI) — a con-sortium of industry, academia, and regulatory scientists — hasbeen formed with the specific goal of addressing gaps betweenscientific knowledge and regulatory policy. The Blend Unifor-mity Working Group (BUWG) was formed to examine issuesinvolving blending principles, sampling, and analysis of pow-der blends. To address these issues, a number of initiatives have

68 Pharmaceutical Technology MARCH 2001 www.pharmaporta l .com

A Solid Dosage and Blend Content UniformityTroubleshooting DiagramJames K. Prescott* and Thomas P. Garcia

James K. Prescott is a senior projectengineer at Jenike & Johanson, Inc.(Westford, MA), tel. 978.392.0300, [email protected]. Thomas P.Garcia is a manager in the Solid DosageForm Technology Transfer Group at Pfizer,Inc., Global Research and Development(Groton, CT).

*To whom all correspondence should be addressed.

T

For both therapeutic and safety reasons,pharmaceutical dosage forms mustaccurately deliver the proper dose to thepatient each time the product is consumed.Content uniformity of the finished productcannot be achieved without the prepara-tion of a uniform blend that does notsegregate between the blending andcompression–filling operations. This articleexamines a number of variables that maycontribute to content uniformity problemsfor both powder blends and final dosageforms. It is meant to serve as a trouble-shooting guide to assist pharmaceuticalscientists in the identification andresolution of root causes of contentuniformity problems.

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been identified, in-cluding● enhanced in-process producttesting to indirectlyassess blend uni-formity and avoidbias introduced bypowder-samplingdevices ● the use of on-line techniquessuch as NIR tomonitor blend uni-formity ● the evaluationand identificationof meaningful ac-ceptance criteria

● identifying key formulation and process variables that impactthe quality of blending.However, addressing sampling error and modifying ac-

ceptance criteria alone cannot improve upon deficientprocesses that are not providing adequate uniformity. Onecannot expect to manufacture a product of acceptable qual-ity without first preparing an adequately mixed blend thatmaintains its degree of homogeneity during subsequent trans-fer and compression–filling operations.

The purpose of this article is to provide formulation andprocess development scientists an aid that will assist them introubleshooting content uniformity problems. A number ofpotential causes for uniformity problems that may be observedare discussed. Areas for further investigation and possible cor-rective actions are also presented.

As with any tool, misuse can result in erroneous conclusions.This article is not intended to serve as the basis for regulatorypolicy or to serve as acceptance criteria for manufacturing. In-stead, this article should be used as a means of qualitative as-sessment of processes where uniformity could be improved.

Product and blend data definitions discussed in thetroubleshooting diagramThe troubleshooting diagram is intended to link poor blendand/or product uniformity data to possible root causes of theproblem. The diagram and supporting information in this ar-ticle may be applied in principle to any powder-derived dosageform such as tablets, capsules, powder-filled bottles or vials, andsachets. The relative standard deviation (RSD) is calculated asfollows:

[1]

The situations that are described are general behaviors onlyand are not correlated necessarily to any particular specifica-tions such as the USP content uniformity test. For example,the term high RSD relates only to an RSD that one wishes toimprove. Therefore, a failing or out-of-specification resultdoes not need to be obtained to use this diagram. Many of theproblematic situations presented can be improved once theroot cause of the behavior is understood.

The plots in Figures 1–6 describe the six basic trends com-monly observed for product uniformity and blend samples. Thedata collected to prepare the plots were obtained by stratifiednested sampling. Stratified sampling is the process of collectingblend samples deliberately from specific (planned) locationswithin a blender or by collecting product samples during theentire compression–filling process. Nested sampling is the si-multaneous collection of multiple samples within a location andis required to provide the data necessary to demonstrate the vari-ability inherent in a single location. For the purposes of this ar-ticle, the term sampling location refers to a physical location inthe blender (i.e., for blend data) or a sampling time during thecourse of the compression–filling operation. The described trendsare based on tendencies of the mean, the between-location vari-ance, and the within-location variance. Note that blend databased on samples taken outside the blender (e.g., bin or drum),though informative, are less applicable to the diagram becausesegregation may have been induced during discharge of theblender.

1. Satisfactory. Satisfactory data demonstrate that the processproduces a product of acceptable content uniformity, which isreproducible for all batches (see Figure 1). For instances in whichthe data are for the product, the product should pass Bergum’scriteria; for the cases of blend data, the blend sample should com-ply with the standard deviation prediction interval.

2. High within-location variability. The variability of individualassay values obtained within each sampling location is wide (seeFigure 2). When the data are subjected to component varianceanalysis, the within-location error term is larger than the between-location term. No clear trend of data is observed withina batch, and a consistent pattern between multiple batches isseldom observed. Potential causes of this problem include poormicroblending, insufficient particle distribution, sampling thieferror (i.e., for blend data only), or poor powder flow resultingin variable fill weights (i.e., for product data only).

3. High between-location variability. When data contain high

RSD5samplestandarddeviation

samplemean3100%

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Figure 1: Example of satisfactory productdata; satisfactory blend data are similar.

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Figure 3: (a) Example of high between-location variation (wandering) product data;(b) example of high between-locationvariation blend data.

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Figure 2: Example of high within-locationvariation product data; high within-locationvariation blend data are similar.

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Figure 4: Example of stray value in productdata; stray value in blend data is similar.

70 Pharmaceutical Technology MARCH 2001 www.pharmaporta l .com

between-location variability, althoughthe difference in the mean values forsamples taken from the various locationsis large, little variability is observed in the values of individualsamples within a sample location (see Figure 3). Componentvariance analysis demonstrates that the contribution of the between-location error to the overall variability observed ismuch greater than that attributed to the within-location term.A distinct pattern may or may not be apparent, both within asingle batch and between multiple batches of product. Theterm wandering is used to describe high between-location vari-ability for the product because the mean of the samples seemsto wander over time. Potential causes of high between-loca-tion variability include poor macroblending (i.e., quality of theblend on a large scale), segregation, and poor weight controlmechanisms (for product data only).

4. Stray value. Single or multiple stray values may be observedwell beyond typical variability (see Figure 4). The problem maynot be observed in each batch because the probability of find-ing such samples may be low. Potential causes of stray valuesare agglomeration of the active, an analytical or sample-han-dling error, or a dead spot in the blender. The magnitude anddirection that the value(s) deviate from the mean can assist inidentifying the problem (e.g., greater than 150–200% label claimmay suggest that the sample is super potent as a result of ag-glomeration of the active).

5. Trending and hot spots. Trending occurs when one observesin the data a distinct direction in the assay values (see Figure5a). The trend may be observed as one progresses from thetop to bottom locations when sampling a blend or may be seenas the compression–filling operation progresses over time.Trending commonly is associated with product made fromthe end of a bin, drum, or batch and generally is repeatablefrom batch to batch. Although the location’s mean often issubstantially different from that for the remainder of the over-all batch, within-location error typically is low. Potential causesof trending are segregation by particle size, which results inassay or powder density variations, or static charge bias (forblend data).

Hot spots generally are the result of incomplete blending ina specific region (i.e., a dead spot) of a blender (see Figure 5b).Individual and mean assay values for samples taken from the

hot spot are significantly greater than are those for the remainderof the batch. Unlike trends, hot spots do not necessarily occurat the top or bottom of a blender or at the beginning or end ofthe compression–filling operation. The trouble spot generallyoccurs in the same location for each batch. Potential causes ofhot spots are dead spots in the blender or, though less likely, bi-ased sampling locations.

6. Assay shift. An assay shift occurs when the mean assay val-ues are no longer centered on 100% label claim (see Figure 6).Both between-location and within-location errors are typicallylow, and the abnormality may be repeatable between batches.Potential causes for assay shifts are the loss of one component(through adsorption or extraction during processing), analyt-ical error, factoring or dispensing error, sampling bias (for blenddata), or improper fill weight (for product data).

Potential root causes of blend or product content uniformity problemsAlthough factors not discussed in this article could contributeto blend and product content uniformity problems, seven com-mon root causes are as follows:

1. Non-optimum blending.Non-optimum blending results whenthe blender does not provide the best blend that is theoreticallypossible (i.e., a randomized blend of particles). Failure to achieveadequacy of mix could be the result of poor formulation de-velopment, inadequate blender operation (e.g., fill level, load-ing, number of revolutions), or poor selection of blending equip-ment. Figure 7 demonstrates the trend that should be observedfor acceptable blending processes. The RSD value decreases ini-tially. With further blending, the RSD levels off at the point atwhich the best possible uniformity for this system is achievedand remains stable regardless of further blending.

Figure 8 illustrates a situation whereby a powder blend seg-regates with further blending. The blend operation will yield anoptimal product when the blender is operated in the time rangethat the RSD is at its lowest values. Poor macroblending is ob-served if the lowest RSD value achieved is unacceptably high (seeFigure 9). This may be caused by a persistent dead spot (perhapscaused by using an improper volume of material in the blender),

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Figure 5: (a) Example of trending product; (b)example of hot spot in blend data.

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A B C D G H I J K LSample location (spacial)

Figure 6: Example of blend data with an assayshift; assay shift with product data is similar.

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Figure 7: Example of satisfactory temporalblend data.

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Too short

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Figure 8: Example of poor blend times(incomplete blending left, deblending right).

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Figure 9: Example of poor macroblending(RSD remains high).


by errors in loading the blending container, or byusing an improper blend speed. If blend-samplingbias is present, a similar plot could result. Figure10 shows an example of poor microblending. Inthis instance, the blender is incapable of blendingthe materials on a microscopic scale, which couldcorrespond to the product’s unit dose. Potentialcauses of poor microblending include segregation,agglomeration, or large differences in particle size, shape, or den-sity. Note that with a sufficiently high number of blend samples,poor microblending may manifest itself as a steady, high RSDidentical to the example of poor macroblending.

In many cases, especially after process development, the blendRSD is not measured across the blend cycle and instead is mea-sured only at the endpoint. If during routine monitoring thefinal RSD increases significantly over previous runs, one shouldinvestigate the root cause of this observation. This problemcould be the result of many factors, including a poor choice ofblenders, poor microblending, overfill or improper filling ofthe blender, sampling problems, or formulation problems. Inthis case, when the final RSD varies widely from batch to batch,blend sampling such as complete blend cycle information alongwith nested sampling and enhanced product sampling may beuseful in further diagnosing the problem.

2.Thief sampling error.Sampling error results when the samplingdevice does not obtain samples that are representative of the blend.This can be caused by a number of factors such as the design andoperation of the thief, the sampling technique, static charge, andthe physical properties of the formulation being sampled. Sam-pling bias, a form of sampling error, results when there is a re-peatable shift in the mean of the samples because of preferentialflow of one or more components into the sampling cavity.

Note that thief sampling error can result in false negatives(i.e., the blend is poor but thief data say otherwise) in additionto the more common concern of false positives. A thief proneto false negatives is sometimes called a counterfeiter.

3.Segregation after discharge.Segregation occurs when the blenddemixes as a result of powder transfer from the blender to thecompression–filling equipment. The blend also can demix inhoppers during the course of the compression–filling operation.Three segregation mechanisms common with typical pharma-ceutical powders are sifting, fluidization, and dusting.

Sifting segregation is a process by which smaller particles movethrough a matrix of larger ones. During the filling of a bin ordrum, a concentration of fine particles develops under the fillpoint while the larger particles roll or slide to the periphery ofthe pile. This often results in fines discharging first, followed bycoarse particles at the end of a container.

Fluidization segregation results in a top-to-bottom segregationpattern with fines at the top of a bin or drum. This can be theresult of air counterflow, such as discharging from one closedcontainer to another, or it can be caused by high dischargerates.

Dusting, or entrainment in air, results in fines accumulating atthe perimeter of a bin or drum. These fines often discharge atthe end of a container unless design precautions are taken.

4. Weight control. Wide variance in product fill weights can

create poor dose uniformity ofthe finished product, even if theconcentration of the blend re-mains uniform across the batch.Poor weight control could resultif the formulation possessespoor flow properties or ifequipment weight controls mal-

function during the compression–filling operation.5.Wrong mass or loss of component. The wrong quantity of drug

substance may be added to the batch as the result of dispens-ing errors, improper factoring of the drug substance (for thosecases in which the amount of drug added to the product is ad-justed to account for inherent drug substance potency varia-tions), or low assay values of the input drug substance. Loss ofdrug or excipient also can occur during processing — for ex-ample, by adsorption of a component onto an equipment sur-face, by becoming trapped in filter socks, or by being physicallyremoved through powder extraction devices.

6. Analytical error. Analytical error leads to results that are notrepresentative of the sample collected for analysis. These errorscould be the result of poor sample splitting (particularly withpowder blends), dilution errors, improperly prepared standards,weighing errors, and container tare errors or vial–cap mix-ups(i.e., for blend samples).

7. Insufficient particle distribution. If the particle distributionis not considered during formulation development, the ran-dom mixture of particles can be incapable of meeting unifor-mity requirements. This could be the result of the particle-sizedistribution of the drug, improper sizing of granulations, orthe agglomeration of a component at any point in the process.For the particles in the system, this random variation is irre-ducible and not a function of blending or segregation.

The appendix discusses how a shift in assay and/or increasedvariability is related to root causes.

Using the solid dosage and blend uniformity troubleshooting diagramStep 1: Identify the product trend for your product. Plot the prod-uct content uniformity data as a function of sample location(i.e., time of production). Identify the product trend behaviorfrom Figures 1–6 that is most similar to your data.

Step 2: Identify the blend sample result of your product. Plot theblend content uniformity data as a function of sample location(i.e., position in the blender). Again using Figures 1–6, identifythe blend sample behavior that is most similar to your data.

Step 3: Identify a reference number. The combination of theproduct trends and blend sample results, in addition to thosesituations in which data are not available for either the prod-uct or the blend, yields 48 cases. Because the diagrams coverseveral pages, each of these cases is identified by a referencenumber for convenience.

The reference numbers consist of a composite of the prod-uct trend and blend sample result numbers. The reference num-ber is derived in the following format: X.Y, where X is the prod-uct trend number, and Y is the blend sample result numberseparated by a period. For example, the case of trending with

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Figure 10: Example of poor microblending(RSD wanders).

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tablets (5) along with satisfactory blend results (1) yields a ref-erence number of 5.1.

Once a reference number is identified, stay within that ref-erence number’s row across the next pages to identify root causes(see Step 4). Each combination of product and blend trends(i.e., the reference number) also is given a relative probabilityof occurring (e.g., high, medium, or low) as a result of a singleroot cause. If a low-probability case is encountered, multipleroot causes of the problem probably are occurring.

Step 4: Identify potential root causes.This article identifies sevencommon root causes of blend sample and content uniformityproblems. Each reference number has a relative probability ofbeing caused by any of these seven root causes. For each refer-ence number, a root cause is assigned a qualitative probabilitybased on theoretical grounds and the practical experiences ofthe authors. These probabilities are presented in matrix formon the diagram as follows:

4 This is a common and highly likely root cause for the prob-lem. Start your investigation here.

3 This is a likely root cause of the problem, but seek sup-porting data to confirm it.

2 There is a good chance that this root cause is contribut-ing to the problem, but be aware of other possible causes.

1 This is not very likely to be linked to the problem, andother more likely root causes should be ruled out first. Beaware that multiple root causes may be present.

0 It is highly unlikely that this is a contributing factor to theproblem. Seek other reasons and be aware that multiple root causes may be causing the problem.

Select the highest probable root cause and drop down verti-cally within that column for the next steps. Note that in someinstances several root causes have equally high probabilities ofoccurring. These cases warrant further investigation for mul-tiple root causes.

As described earlier, a thief sampling error occasionally canresult in false negatives. Reference numbers 2.1, 3.1, 4.1, and 5.1all have acceptable blend data but leave open the possibility thatthe blend was poor even though the blend sample data looked(falsely) acceptable. For this reason, the root causes of non-optimum blending and thief sampling error are boxed together.

Using the troubleshooting diagram is much easier if all of itspages are placed next to each other. This allows the user to readeasily across the rows of the diagram.

As an example of using the chart, reference number 5.1(trending with product and satisfactory blend results) has ahigh probability of a single root cause. Although non-optimumblending and thief sampling error can be a possible root cause(together with a probability of 2), segregation is a much morelikely candidate causing the problem with a probability of 4.

One should consider a number of additional points such asthe entire history of the product and process when interpret-ing the recommendations from the troubleshooting diagram.Questions one should ask include● Is this a new product or an existing one with a significant body

of data? ● Has this problem been seen with this product or one similar

to it?

● What is unique or different about this product or process? ● Have the materials, processes, operators, equipment, or envi-

ronmental control changed recently? ● How do the physical characteristics of the materials used for

this batch compare with what was intended? ● Is the problem repeatable among multiple batches, or is this

an isolated incidence? ● Did the operators observe any anomalies during the manu-

facture of the batch? ● Were any equipment malfunctions encountered during batch

manufacture? ● How do the mean and RSD values for the blend and product

compare? ● How do the measured RSDs compare with the theoretical

RSD of a randomized blend of particles? Addressing each of these questions will further help the sci-

entist identify the cause(s) of the problem and its successfulresolution.

Steps 5 and 6: Further investigations and possible solutions. Oncea likely root cause has been identified, stay within that columnand drop down to Steps 5 and 6. Step 5 presents some startingpoints to initially consider in identifying and confirming thesuggested root cause. If further investigation reveals data thatdo not support the root cause selected, go back to Step 4 andidentify other likely root causes. Once the root cause has beenconfirmed, Step 6 provides suggestions for corrective actions.For example, in the case discussed above, segregation was iden-tified as the most likely root cause.

Further information about each root cause can be foundwithin the references that are listed at the end of this article.

Handling multiple product or blend problemsEach of the problems presented in this article has been reducedto the simplest behavior. However, real-world situations seldomfollow such neat guidelines. If multiple behaviors such as prod-uct trending with a product assay shift are observed, then thisproblem should be split into two cases and be considered sep-arately. In this example, if the blend data were satisfactory, thenreference numbers 5.1 and 6.1 must be evaluated. In cases wheremultiple problems are present, multiple root causes also areprobably responsible. However, one should give special con-sideration to those root causes that are common to both cases.

ConclusionMany variables can affect a process’s ability to produce a blendand product of acceptable content uniformity. This article dis-cusses a number of potential causes leading to content unifor-mity problems. However, additional factors also could be con-tributing to the quality of the blend and product. It should beemphasized that without proper formulation and process de-velopment, as well as the selection of appropriate blending andtransfer equipment, the chances of obtaining a blend and prod-uct of acceptable uniformity are reduced significantly. The au-thors hope that this article will provide pharmaceutical scien-tists with a useful reference tool that will allow them to workthrough future problems that they may encounter during themanufacture of solid dosage forms.


One can describe the mean and standard deviation of samples from a given batch as follows:

Appendix

Population Contributing Components*Initial Blend Thief Segregation Weight Loss Analytical Random Sampling

1. mb 5 Xinitial 1 Dxthief 1 Dxlossb 1 Dxab 1 Dxab

2. mp 5 Xinitial 1 Dxwp 1 Dxlossp 1 Dxap 1 Dxsp

3. sb2 5 {1 s2

b 1 s2thief} 1 s2

ab 1 s2r 1 s2

sb

4. sp2 5 {1 s2

b 1 s2seg} 1 s2

wp 1 s2ap 1 s2

r 1 s2sp

*With the exception of the sampling term (discussed below), each contributing component above is one of the root causes of the troubleshooting diagram. The initial and loss terms together make up the “wrong mass of component” root cause.

Equation 1

mb is the population mean assay of the blend (normalized to the weight of thesample).

Xinitial is the initial content of the blend based on the actual mass of eachcomponent used, which could be different from the theoretical because ofdispensing errors.

Dxthief is a change in mean sample content caused by preferential fill of one ormore components into the thief cavity (this term is called bias).

Dxlossb is the mean loss of content up to the point of collecting samples from theblender (a negative term for loss of active, or positive for a loss of excipient).

Dxab is the mean shift in assay caused by analytical and other lab errors foranalysis of the blend samples (e.g., calibration).

Dxsb is the shift in assay caused by the measured value being based on a finitenumber of blend samples. As the number of samples increases, this term isreduced.The magnitude of this term also is larger for a larger populationvariance.The measured assay value is in fact only an estimate of the populationmean assay.The actual mean assay of the entire batch of blended powder(population) is equal to the measured value less Dxsb.This term is notdeterministic, but rather is a statistical or probabilistic term.

Equation 2mp is the populations mean assay of the product.

Xinitial is the initial content of the blend.

Dxwp is a change in mean assay caused by weight changes of the product (i.e., ashift in mean weight).

Dxlossp is the mean loss of content up to the point of creating the dose.

Dxap is the mean shift in assay caused by analytical and other lab errors for theanalysis of the product.

Dxsp is the shift in assay caused by the measured value being based on a finitenumber of product samples (again, this is a probabilistic term).

Note Dxap 5 Dxab if the analytical methods are the same for both product andblend.

Equation 3sb

2 is the population variance (standard deviation squared) of the blend.s2

b is the variance of the powder blend caused by non-optimum blending.s2

thief is the variance caused by thief sampling error.s2

ab is the variance caused by analytical and other lab errors for the analysis ofthe blend samples.s2

r is the variance caused by random distribution of individual particles.s2

sb is the shift in variance caused by the measured value being based on a finite

number of blend samples. As the number of samples increase, and as thepopulation variance decreases, this term decreases.

Note the terms {s2b 1 s2

thief} are bracketed. It is often assumed that thiefsampling error is always additive to the variation of the blend.This is not alwaysthe case. In some instances, thieves can disturb the powder bed in a way tosmear the sample.This in effect improves the uniformity of the blend where thesample is collected, resulting in samples that are more uniform than is the initialstate of the blend. Because s2

thief cannot be a negative term, the terms {s2b 1

s2thief} must be considered together.This is why the thief error and non-optimum

blending are tied together for reference numbers 2.1, 3.1, 4.1, and 5.1.In a variance components analysis, it is often assumed that the between-

location error term is equal to s2b, the within-location error term is equal to s2

thief,and the terms s2

ab, s2r and s2

sb are neglected.

Equation 4sp

2 is the population variance of the product.

s2b is the variance of the powder blend caused by non-optimum blending.

s2seg is the increase in variance caused by segregation upon discharge and

handling.

s2wp is the variance to weight variability of the product.

s2ap is the variance to analytical and other lab errors for the analysis of the

product.

s2r is the variance caused by random distribution of individual particles.

s2sp is the shift in variance caused by the measured value being based on a finite

number of product samples.

Note s2ap 5 s2

ab if the analytical methods are the same for both product andblend.

Note the terms {s2b 1 s2

seg} are bracketed. In actuality, these terms shouldform a single irreducible term describing the variance of the powder blendcaused by the combination of blending and segregation; this mathematicallycannot be separated into two components. However, showing this as two termsis illustrative. If the blend segregates and powder uniformity gets worse, thenthe term s2

seg is positive. If additional handling improves the uniformity of theblend, the powder becomes more uniform than the initial state of the blend. Inthis instance, the term s2

seg would be negative. Because this cannot be a negativeterm, the terms {s2

b + s2seg} must be considered together.

Finally, note that s2r is formulation specific, and highly depends upon the

particle-size distribution of the active.This term will not change between theblend samples and the product, unless the powder’s particle size changes upontransfer from the blender to the creation of the dose. Of all the terms used here,this is the only term that can be calculated, a priori, on first principles.

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Steps 1 and 2: Describe the product (dose) and blend data

Table I: Solid dosage and blend content uniformity troubleshooting diagram

Some additional considerations:● Is this a new product or an existing one with a significant body of data?● Has this problem been seen with this product or one similar to it?● What is unique or different about this product or process?● Have materials, processes, operators, equipment, or environmental control changed recently?● How do the physical characteristics of materials used for this batch compare to what was intended?● Is the problem repeatable among multiple batches or was this an isolated incidence?● Did the operators observe any anomalies during the manufacture of the batch?● Were any equipment malfunctions encountered?● Compare the mean of product to the mean of blend.● Compare the RSD of product to the RSD of blend.

Poster availableA poster-size version of the troubleshootingdiagram is available by contacting Jenike &Johanson, Inc. at [email protected].

Step 3Referencenumber1. First, describe the PRODUCT 2. Next, describe the BLEND SAMPLES

(see “Product and Blend Data Definitions” and Figs. 1–6) (see “Product and Blend Data Definitions” and Figs. 1–6)

(CONTINUED ON NEXT PAGE)

Keys to probabilities of possible root causes

4 Highly likely root cause. Start here first.3 Likely, seek supporting data.2 Good chance, but keep your eyes open for other possibilities.1 Not likely, rule out other reasons first; multiple root causes may be present.0 Very unlikely, seek other reasons; multiple root causes may be present.


Table I: Solid dosage and blend content uniformity troubleshooting diagram (continued)

Step 3Referencenumber

Step 4: Correlate the data with possible root causes; continue with Steps 5 and 6 belowProbability is given on a scale of 0–4 (see Key, pg. 80)

CONTINUED ON NEXT PAGE)

➡ ➡ ➡➡ ➡ ➡ ➡ ➡ ➡

➡ ➡ ➡➡ ➡ ➡ ➡ ➡ ➡

Step 5: With possible root causes identified, continue with further investigation

Step 6: With additional data to support root cause, consider possible solutions


Table I: Solid dosage and blend content uniformity troubleshooting diagram (continued)

Step 4: Correlate the data with possible root causes; continue with Steps 5 and 6 belowProbability is given on a scale of 0–4 (see Key, pg. 80)

➡ ➡ ➡ ➡ ➡ ➡ ➡ ➡ ➡ ➡ ➡ ➡

➡ ➡ ➡➡ ➡ ➡ ➡ ➡ ➡ ➡ ➡ ➡

Step 6: With additional data to support root cause, consider possible solutions

Step 5: With possible root causes identified, continue with further investigation

www.pharmaporta l .com78 Pharmaceutical Technology MARCH 2001

AcknowledgmentsThis article is part of the efforts of the PQRI BUWG to addressthe gaps between scientific theory and regulatory compliance.The authors would like to acknowledge those scientists, engi-neers, and operators who have worked with them over the yearsto identify and solve various content uniformity problems. Theauthors would also like to acknowledge input received from theProduct Quality Research Institute Blend Uniformity WorkingGroup.

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