Variable and Attribute Control Charts in Trend Analysis of ... · Statistical process control (SPC) tools are an effective collection of techniques and methodologies that are used

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Submitted to Experimental Medicine, page 31-44 www.itspoa.com/journal/em

Variable and Attribute Control Charts in

Trend Analysis of Active Pharmaceutical

Components: Process Efficiency Monitoring

and Comparative Study

Mostafa Essam Eissa1*

1Microbiology and Immunology Department, Faculty of Pharmacy, Cairo University, Cairo, Egypt

Email addresses [email protected] (Mostafa E. Eissa)

*Correspondence: [email protected]

Received: 28 May 2018; Accepted: 15 June 2018; Published: 30 June 2018

Abstract: Assessment of pharmaceutical product quality is important prerequisite to justify safe

and effective release of the medicinal dosage form to the drug market. However,

without rigorous implementation of good manufacturing practice (GMP), routine

quality control testing may be not adequate to conclude compliance with reproducible

procedures. Accordingly, the current study aimed to investigate manufacturing quality

of pharmaceutical product batches through monitoring assay results and trends

retrospectively for three components of the active ingredients using two types of

control charts and to compare the value of each in-process monitoring. This product

was manufactured in a pharmaceutical firm and subjected to the assay (expressed as

relative potency to the claimed labeled dose per tablet) in quality control laboratory.

The active components are Paracetamol (Acetaminophen) (Pa), Chlorpheniramine

Maleate (CM) and Pseudoephedrine Hydrochloride (PH). General performance and

trend of the studied batches were compared using Individual-Moving Range and

Laney U΄ chart which were constructed using statistics software. Box-and-Whisker

diagram that was constructed for the assay of the three active constituents showed that

CM relative potency was significantly higher than Pa and PH using ANOVA (p<0.05).

Capability analysis showed that Pa and PH assays have met the requirement of

analysis. In contrast to CM potency which demonstrated a failure to be maintained

within the specification window level as strong shift outside the upper border (right

drift) could be observed. Both types of control charts variable (Individual-Moving

Range) and attribute (Laney U΄) showed same control limits. But Individual-Moving

Range was more sensitive in detection of out-of-control states.

Keywords: Individual-Moving Range, Laney U΄, Capability Analysis, Upper Control Limit,

Lower Control Limit, GMP

1. Introduction

mailto:[email protected]

mailto:[email protected]

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With advancement in the pharmaceutical industry, new techniques and technologies

are available to improve medicinal dosage forms quality, safety and rigorous

monitoring. However, this breakthrough is accompanied by the challenge of the ever-

increasing number of health-deficient population of patients at stake [1].

Apparently, that drug recall from the market has become an increasing trouble that

faces regulatory authorities with pharmaceutical industrials' engines, as could be seen

from Figure 1 which was derived from Gaffiney (2014) [2]. Surging in the number of

recalls of the medicinal products has just occurred in the past few years as was

discussed by Cossman (2017) [3].

Figure 1. Recall rate per year as a total, critical, major and minor [2].

Recently, Food and Drug Administration (FDA) has segregated the recalls into

what is called "three-tier system"[4]. Pharmaceutical products that violate regulations

may: be life-threatening because they may cause severe adverse side effect which may

lead to death (class I), cause reversible or temporarily adverse medical effects with

remote possibility to cause serious health effects (class II) and finally, not impose any

adverse health problems(class III) [5].

Statistical process control (SPC) tools are an effective collection of techniques and

methodologies that are used to solve problems by enhancement of process quality,

stability and capability through minimization of the variability. Shewhart control

charts are considered pivotal tool in SPC that are used to monitor the state of control

of the inspected characteristics [6].

Shewhart (process-behavior) charts offer several benefits in the pharmaceutical

industry. These advantages include improvement of the manufacturing process

economically if used correctly. Also, control charts provide a measure of the process

capability to meet the specifications and trending of data with an additional advantage

of the presence of upper control limit (UCL), lower control limit (LCL) and the

process average lines. Moreover, control charts are a graphical presentation that

facilitates visualization of the points where the process is out-of-control [7].

Several researchers have applied control charts in the monitoring of different

inspection properties (example: assay, hardness, content uniformity and disintegration)

in medicinal dosage forms such as tablets and demonstrated their value in the

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pharmaceutical manufacturing industry to monitor the inspected properties and

determine the process performance accordingly [8-10].

Individual-moving range (I-MR) is a type of variable control chart that is actually

composed of two charts I and MR. I chart allows to track the process level for each

reading. While MR chart displays the variation between successive readings [11]. On

the other hand, Laney U΄ chart is a type of attribute control charts that correct for

over-dispersion or under-dispersion of data that may otherwise interfere with correct

results interpretation if conventional U chart was used [12]. Accordingly, one can

access the overall performance of the process easily visually early enough before any

true excursion beyond acceptance limits could occur.

In the light of the preceded challenges, the current study aimed to investigate and

compare process monitoring using both attribute and variable control charts

simultaneously using Laney U΄ and Individual-Moving Range (I-MR) charts,

respectively. The present work would elucidate also the manufacturing process

efficiency through monitoring the state of control of the inspected product properties.

This work is part of a broad collaborative study to monitor the level of compliance of

the pharmaceutical firm to good manufacturing practice (GMP) through SPC

monitoring tools.

2. Materials and Methods

A small pharmaceutical plant based in the industrial zone was built in South Delta,

Egypt. The firm consists of small class D production area and has launched the

manufacturing of new oral film-coated tablet (FCT) product [13]. The product is used

for the treatment of common cold signs and the active pharmaceutical ingredient (API)

is based on triple complementary components viz. Paracetamol (Acetaminophen) (Pa),

Chlorpheniramine Maleate (CM) and Pseudoephedrine Hydrochloride (PH). Other

inactive ingredients include Povidone (binder), Sodium Croscarmellose

(disintegrating agent), Microcrystalline Cellulose (filler), Colloidal Silicone Dioxide

(glidant) and Magnesium Stearate (lubricant). The coating material is composed of

coating polymer, plasticizer, opacifying agent and coloring material.

A project was established by monitoring the manufacturing process efficiency using

SPC on results collected by quality assurance team about inspection properties of the

medicinal product over the year 2016. This covered 195 batches of the product and

the property being inspected was the relative potency assay of the three APIs with

acceptance criterion of 90 - 110 % [14]. The assay results of APIs are the pooled

outcome of the whole manufacturing process [15]. A total number of 150 tablets were

sampled by a trained QC sampler for each batch and submitted to the laboratory for

analysis. The pooled bulk sample was collected after coating of the core of the tablets

in the coating machine. Each assay point (batch) is expressed as relative potency ratio,

where obtained result of the analysis is divided by the labeled theoretical value. After

that, every point is added into a cell in a column of the statistical software program

sequentially to construct the control chart and/or other statistical calculations.

Distribution fitting was assessed initially in the current study to determine the

appropriateness of data distribution to specific control chart. Determining correct

distribution is crucial for the validity of the statistical analysis and SPC performed and

any interpretation or conclusion derived from the analysis [16]. Out-of-control alarms

in control chart is interpreted as the following: Alarm "1" = One point more than 3 x

standard deviation from mean line, Alarm "2" = Nine successive points on the same

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direction of the mean line, Alarm "3" = Six successive points with general trend of all

being increasing or decreasing, Alarm "4" = 14 successive points with up and down

zigzag pattern, Alarm "5" = Two out of three points that are more than 2 x standard

deviation in one direction from the mean line, Alarm "6" = Four out of five points that

are more than standard deviation in one direction from the mean line, Alarm "7" = 15

successive points that are confined in the range of one standard deviation on both

sides of the mean line and Alarm "8" = Eight successive points that fall outside one

standard deviation on both sides of the mean line. Attribute control charts can only

detect the first four types of assignable cause alarms. Derivation of control limits (CLs)

is shown in Table 1 according to Minitab® 17.1.0 manual.

Table 1.Parameters derivation for I-MR and Laney U΄ control charts using statistical software

(Minitab®

17.1.0).

Control

Limits

(CLs)

I-MR Laney U΄

Individuals chart Moving range chart

Mean Line Σxi/Σni MR(bar).d2(w) Σxi/Σni

Upper K d2 (w) + k d3 (w) ̅ + K (ui)

Lower K d2 - k d3 [If LCL <

0, LCL = 0] ̅- K (ui) [or zero

whichever is greater]

d2(w) = Unbiased constant = Standard deviation ̅ = Center line

xi = number of items in subgroup i (Laney U΄) or individual observations (I-MR)

ni = subgroup size for subgroup i (Laney U΄) or number of individual observations (I-MR)

ui = proportion of items for subgroup i = Mean line

K = the parameter that is specified for Test 1 of the tests for special causes, 1 point > K standard deviations from center line (in the current study = 3).

w = The number of observations that are used in the moving range MR(bar) = The estimate of the average moving range for the method that you use to estimate

the standard deviation

d2 = A constant used to estimate the standard deviation

d3 = A constant used to calculate control limits for ranges

Data collected for the assay were subjected to statistical analysis using One Way-

ANOVA at p<0.05 and distribution fitting by GraphPad Prism 6 for Windows and

XLSTAT Version 2014.5.03, respectively. Capability plot and histogram, in addition,

the control charts were generated by Minitab® 17.1.0. The present study would focus

on the consistency of the product manufacturing period of the 195 lots in addition to

the comparison between the application of both types of control charts I-MR and

Laney U΄ modification as an example of variable and attribute charts, respectively.

Application of these programs has been discussed previously in other works [17-19].

3. Results and Discussion

Distribution fitting study can be examined from Table 2 and it showed that the

results pattern of the assay of the three APIs demonstrates a variable degree of

normality. None of them passed Poisson or Binomial distribution tests. Accordingly,

conventional attribute charts may deem not suitable for results interpretation. In

addition, the software could identify the closest distribution that fit each result from

series of different distributions as the best-fit test.

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Table 2.Distribution fitting test conducted on the three APIs of coated tablet using statistical

software program.

Distribution Fitting Pa CM PH

Gaussian Fitting Yes Yes Yes

Pass at P = 0.3747 0.1571 0.5148

Binomial Fitting no no no

P = <0.0001 <0.0001 <0.0001

Poisson Fitting no no no

P = <0.0001 <0.0001 <0.0001

Maximum Likelihood Distribution Fitting Logistic Beta 4 Weibull 3

P = 0.7974 0.5391 0.7822

Box-and-Whisker diagram that was constructed for the assay of the three active

constituents showed that CM relative potency was significantly higher than Pa and PH

using One Way-ANOVA at p<0.05 as shown in Figure 2. Interestingly, points

denoted by asterisks "*" in Pa and CM are aberrant batches that show unusual results

of the assay of both components and this requires further investigation to elucidate the

cause.

Figure 2.Box-and-Whisker Plot for the assay of three active pharmaceutical ingredients of film-

coated tablet.

On the other hand, capability analysis showed that both Pa and PH assays have met

the requirement of analysis in both long run and short-term capability study. This

contrasts with CM potency which demonstrated a failure to be maintained within the

specification window level as strong shift outside the upper border (right drift) was

evident. While Figure 3A and C histograms were nearly centered and approximate,

the bell-shaped of the normal distribution, Figure 3B was partially truncated from the

right side i.e. at the border of the upper specification limit (USL).

Figures 4-6 show the control charts for the three active components with out-of-

control batches are red marked. Two types of control charts were constructed for each

assay trend namely variable control chart (Individual-Moving Range = I-MR) and

attribute control chart (Laney U΄). Both types of charts were on the same line in terms

of CL, upper control limit (UCL), lower control limit (LCL) and some alarm points.

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However, I-MR charts demonstrated additional alarm points not present in the

counterpart Laney U΄. Moreover, I-MR chart could assess process variation through

the MR chart apart from I that trends the process center.

Interestingly, out-of-control batches (marked by numbered red dots) showed

abnormal assay values in greater magnitude with Pa followed by PH then CM but

none exceeded the specification limits. This finding is currently subjected to an

extensive investigation to determine the most probable root causes. Such factors that

were not related to normal process fluctuations are critical to being identified and

corrected as they may intercept with other similar products manufacturing in the

facility.

Figure 3.Capability Histogram and plot for Paracetamol (A), Chlorpheniramine Maleate (B) and

Pseudoephedrine Hydrochloride (C).

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Figure 4. I-MR and Laney U΄ chart of Paracetamol for 195 batches of film-coated tablet.

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Figure 5.I-MR and Laney U΄ chart of Chlorpheniramine Maleate for 195 batches of film-coated

tablet.

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Figure 6. I-MR and Laney U΄ chart of Pseudoephedrine Hydrochloridefor 195 batches of film-

coated tablet.

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Preliminary data analysis before the use of suitable Shewhart chart is mandatory to

obtain valuable outcomes and conclusions. The application of conventional attribute

control charts, either P or U may suffer from over- or under-dispersion in data, in

addition, they may not follow Poisson or Binomial distributions too which is part of

important assumption in control chart construction [20-22]. In such situation, Laney

corrected control charts can bypass these barriers [23]. On the other hand, it was

recommended by other experts to apply X-mR (I-MR) charts for discrete data that

failed to follow binomial or Poisson distribution [24]. This type of variable control

charts requires that the results show a certain degree of normality [25]. Accordingly

and based on the distribution fitting results of Table 1, the application of classical

attribute charts may be deemed not appropriate with the possibility of the emergence

of false alarms which leads to a wrong interpretation of data. Thus, Laney U΄ chart

may be appropriate approach to solve this challenge. However, data distribution show

some degree of normality that may not obviate the use of I-MR charts as

recommended by some experts [26].

Visualization of data distribution and pattern could be achieved using Box Plot

diagram and any abnormally extreme points might be observed easily as could be seen

in Figure 2 [27]. In the same line, the shape of histogram provides help in the

inspection of the process behavior and detection of unusual pattern such as external

force excreted on the operation and not pertained to the process [28, 29].

Complementarily, capability plot facilitates the visualization of the ability of the

process to meet the requirements over short-term (within) and long-term (overall) [30].

Moreover, process drift or shift from the center could be easily detected [31].

The ability of the SPC software package to construct control charts either

conventional attribute or Laney modified in addition to variable control charts

facilitated the comparison and assessment of the advantages of both types [32, 33].

Minitab can detect four additional types of out-of-control states in variable control

charts over that in attribute ones including those having the ability to provide early

warning for early process shift. Interestingly, they can measure also process variation

in another chart [34-36]. Conclusively, Laney type of attribute charts is easily

implemented, time-saving and avoids assumptions needs required for conventional

control charts. They could be reserved when normality assumption is no longer works

at all for application of I-MR charts which may show an advantage over attribute

charts

Shewhart charts provide an indispensable tool to discriminate between batches that

are within normal manufacturing process variability and those ones with outlier values.

Accordingly, an immediate action(s) could be taken (when the out-of-control point(s)

emerged) before any true excursion(s) may occur if they are constructed

chronologically with manufacturing progress. Extension of SPC to other medicinal

products would help to spot the major sources of defects in the system to correct them

long before any out-of-specification (OOS) events may evolve. SPC tools could detect

the non-consistency in the production batches which requires further investigation to

determine the causative source which may be either laboratory related or production

issue. Specifically, CM potency provided a serious case that requires attention and

immediate correction, probably due to the weight problems that are significantly in

excess compared to the other two components.

4. Conclusions

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When the manufacturing steps and operations are followed literally, the trended

process should show stability and controlled variation within normal range over time.

In another word, a strict implementation of GMP during operation will ensure

delivering a product with the same expected properties every time. Control charts

possess the ability to detect and isolate variability in a certain process due to an

assignable cause from that due to a common cause (normal process fluctuations). In

such instances, the aberrant result could be investigated to correct the root cause and

improve the process stability and efficiency. Thus ensure compliance with GMP.

Laney U΄ and I-MR charts have the same control limits, but the later is more sensitive

in detecting out-of-control states - including early warning of process shift. On the

other hand, Laney U΄ chart is easier to interpret and does not require special

prerequisites before implementation.

Conflicts of Interest

The author declares that there is no conflict of interest regarding the publication of

this article.

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Variable and Attribute Control Charts in Trend Analysis of ... · Statistical process control (SPC) tools are an effective collection of techniques and methodologies that are used