Uncertainty in dissolution test of drug release

Chemometrics and Intelligent Laboratory Systems 97 (2009) 82–90

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Chemometrics and Intelligent Laboratory Systems j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / c h e m o l a b

Uncertainty in dissolution test of drug release M. Paakkunainen a,⁎, S. Matero b, J. Ketolainen c, M. Lahtela-Kakkonen b, A. Poso b, S.-P. Reinikainen a a b c

Lappeenranta University of Technology, Department of Chemical Technology, P.O. Box 20, FI-53851 Lappeenranta, Finland University of Kuopio, Department of Pharmaceutical Chemistry, P.O. Box 1627, FI-70211 Kuopio, Finland University of Kuopio, Department of Pharmaceutics, P.O. Box 1627, FI-70211 Kuopio, Finland

a r t i c l e

i n f o

Article history: Received 1 April 2008 Received in revised form 11 December 2008 Accepted 22 December 2008 Available online 7 January 2009 Keywords: Sampling error Sampling theory Uncertainty Drug release Dissolution

a b s t r a c t The uncertainty estimation of measurements in pharmaceutical manufacturing is often neglected in process optimization. For instance, tablet manufacturing consists of several process steps called unit operations where many measurements on the process conditions and quality are carried out. These measurements are assumed to be error-free and the possibility of the cumulative error throughout the process stages that only goes up has been ignored. Good manufacturing practices (GMP) guidelines provide regulations for the pharmaceutical industry on how to establish high quality production. However, they do not provide any instructions on pre-optimizing the process using a proper sampling scheme at each step. The uncertainty tests of measurements, for example in the tableting process, accounts for all significant sources of uncertainty in the production. In this study, the total error of the dissolution test according to the uncertainties described in the Pierre Gy's sampling theory has been studied. The present study shows that the overall uncertainty of the dissolution procedure could be clearly improved with optimization of the sampling chain. © 2009 Elsevier B.V. All rights reserved.

1. Introduction Tablets are the most common dosage form of drug delivery [1]. There are several reasons for this, such as the straightforward manufacturing process and formulation stability compared to other kinds of formulations [2–4]. Tablet manufacturing has to provide predesigned functionality and quality for tablets, since tablets are intended to be safely administered to patients. Thus, every tablet batch has to be tested, and the quality of the tablets is assessed based on the test results. In pharmaceutical research, drug release from a tablet is one of the most important tests to ensure the safety and functionality of tablets. The drug release is measured by applying a dissolution test, where the cumulative amount of the drug that is dissolved into a dissolution medium is measured as a function of time [5]. If dissolution tests for tablet samples taken from the batch show similar drug dissolution, the batch is approved for further processing, such as packaging and selling. In contrast, if the dissolution test shows excessive deviations from reference values, the whole batch is rejected. However, the tablet preparation and especially the heterogeneity of the drug content in the tablets [6] can cause uncertainty in the dissolution profiles [7]. The uncertainty of the measurements during the process that affects also the dissolution test uncertainty has been underestimated in pharmaceutical manufacturing. The dissolution test is an analytical process and it involves several sampling steps [8]. First, the tablets need to be tabletted and mixtures

⁎ Corresponding author. E-mail address: maaret.paakkunainen@lut.fi (M. Paakkunainen). 0169-7439/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.chemolab.2008.12.004

consisting of the active pharmaceutical ingredient, i.e. the drug and excipient or excipients, have to be prepared and tested. The testing involves usage of different equipment, laboratory scales and even different personnel. Several problems may arise during tableting, such as weight and dose variation between tablets. These variations are due to powder materials sticking to punch tips during tableting, and to the manual handling of tableting material. The variations in dosing affect the mechanical properties and strength of the tablets and thus the dissolution. Before running the dissolution tests for the manufactured tablets, the dissolution medium needs to be prepared. Again, different analytical methods with analytical equipment are used. The realistic assumption is that no measurement is error-free, and uncertainty within the measurement results exists. In the process consisting of several process steps with measurements, the errors within measurements from each preparation step are additive, i.e. they affect on the measurement error of subsequent results, and cannot be compensated later. Therefore, it is of importance to estimate the total uncertainty of the process and assess e.g. the effect of the heterogeneity of the samples on results. If the goal is to run an optimum procedure, each process step must be optimized separately and a replicate analysis has to be carried out. However, the replicate analysis requires time and resources, and quite often it has to be omitted. The aim of this study is to optimize the drug dissolution procedure and to improve the reliability of the dissolution profiles using Pierre Gy's sampling theory. In Pierre Gy's sampling theory, all aspects of sampling are analyzed, such as material properties as well as the design of sampling equipment [9]. In order to estimate the overall

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Fig. 1. Experimental procedure of the drug release studies. The procedure includes mixture preparation (mixing the powders), homogeneity tests (checking the homogeneity) and tableting as well as drug dissolution tests.

uncertainty, each uncertainty source should be taken into account and treated separately. By doing this, the contribution of each source to the overall uncertainty is obtained. In this case study, the major error components assumed to affect the global (total) estimation error are: the total analytical error and the error arising from the heterogeneous samples. Also an error component called the Fundamental Sampling Error (FSE), in which sampling is assumed to be ideal, is estimated. The error components are also compared to each other and to the experimental uncertainty determined from dissolution profiles. According to ISO Guidelines [10] in estimating the overall uncertainty, each of the separate contributions to uncertainty is referred to as an uncertainty component. When expressed as a standard deviation, an uncertainty component is known as a standard uncertainty. However, in this study, the authors also use terminology presented by Pierre Gy, and name different analytical error sources as “errors”, e.g. the fundamental sampling error (FSE). For a measurement result, i.e. dissolved concentration of a drug component, the total uncertainty, called the combined standard uncertainty (based on ISO standards) refer in Gy's sampling theory to an estimated standard deviation. The estimated standard deviation equals the positive square root of the total variance obtained by combining all of the uncertainty components. They are, however, evaluated using the law of propagation of uncertainty — as defined in the IUPAC/EURACHEM Guide [11]. In the determination of expanded uncertainty U, several sampling steps and their uncertainty have been discussed and estimated. All uncertainty generating actions were

evaluated, and the size of several of them were approximated based on historical data or a priori information on the determination process. In this paper, the expected major sources of uncertainty are studied closer and discussed further. In this study, directly compressed hydrophobic matrix tablets are used for analysis [12] to discover all possible sources for dissolution test uncertainty. Mixtures of drug compounds and excipients are examined in detail. Directly compressed matrix tablets are the simplest and most economically produced since they are directly compressed from the drug-excipient powder mixture [7,12]. Direct compression, however, skips some commonly utilized unit operations, i.e. the powder mixture granulation, which improves the compaction properties of the mixture, and the uniformity of the components in tablet formulation. Since the drug-excipient powder tends to segregate without granulation, the homogeneity of the tablet needs to be carefully checked [13]. The uncertainty of homogeneity is also tested in this case study. Mixtures of drug compounds and excipients are examined in detail in the present study. Mixing of powders is an important unit operation in the production of a pharmaceutical solid dosage form [13]. The aim of mixing is to obtain as homogeneous mixture as possible, where the individual particles are evenly distributed. However, it has been found that usually the drug compound is not evenly distributed in the tablet, and that the mixture is always heterogeneous within the acceptance limits [6,7]. The physical properties of the powders, i.e. the differences of the densities between the drug compound and excipient, as well as

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the particle shape and size distribution may affect the heterogeneity of the powder mixture. The physical effects of powders and their contribution to the total uncertainty of the process have been taken into account in the present study. 2. Experimental 2.1. Mixtures Thirteen different drug-excipient mixtures were studied utilizing the procedure described in Fig. 1. The mixtures consisted of one drug compound at a time and starch acetate ( ρ = 1.425 g/cm 3 , d95 = 909.06 μm) in a ratio of 20/80 W/W%, respectively. The studied drugs were atenolol, caffeine, chlortiazide, diltiazem, furosemide, ibuprofein, levodopa, nizatidine, paracetamol, perphenazine, propranolol, salicylamide and theophylline. The total amount of powder mixture to prepare was 30 g. The powders were mixed manually in a mortar in a geometric series with the common ratio of two. It means that the amount of added drug at each step was doubled in a way that each sequential term was formed by multiplying the previous one with the common ratio. Each sequence started with an equal amount of components, i.e. 1 g of drug and 1 g of starch acetate, respectively. At the next step, the amount was doubled by adding 2 g of starch acetate into the mixture. The mixture components were sampled applying spatula grab sampling and weighed using common analytical laboratory scales. The reading of the weighing of powder components was recorded with an accuracy of four decimal digits. Prior to the tableting, the homogeneity of the powder mixtures, i.e. the amount of drug in the samples, was tested (Fig. 1). Two separate powder samples of 200 mg were prepared (spatula grab sampling was applied). The samples were dissolved in 500 ml of phosphate buffer (KH2PO4/K2HPO4, pH 6.8) and mixed manually. The suspension was filtered prior to analysis and the UV absorption was measured at the distinctive UV wavelength of the drug compound. The characteristic UV absorbance wavelength was the wavelength with maximum absorbance of the drug. The amount of drug in the suspension was determined from the calibration curve. If the determined drug concentration differed from the target value more than 5%, the mixing was continued.

force is achieved, the upper punch ascended automatically back into the upper position. Within the compression, the lower punch moves upwards in the die. During the tablet ejection, the lower punch ascends until it reaches the top of the die and the prepared tablet is removed [1]. The tablets were prepared with five different pressure profiles equaling to 8 kN (die depth under pressure was 1.4 mm), 12 kN (depth 1.1 mm), 16 kN (depth 0.9 mm), 20 kN (depth 0.8 mm) and 24 kN (depth 0.67 mm), since different porosities of tablets was the goal.

2.2. Tableting

2.3. Dissolution tests

In order to prepare the batch of tablets, 20 g of the powder mixture was taken into a container as described in Fig. 1 and 0.5 wt.% of magnesium stearate was added (spatula grab sampling was utilized). Magnesium stearate was added into the powder mixture just before tableting to ensure a compact tablet. Magnesium stearate is a hydrophobic lubricant that creates a hydrophobic film on the surface of the particles during mixing. Thus, it prevents the tablet from cracking by reducing the friction between the die wall and the tablet. The total mixing time of the magnesium stearate was 2 min. Totally 30 replicate tablets of 350 mg were weighted from the 20 g lot. Five batches were produced, each having different tablet porosities. Each batch of tablets of one porosity consisted of 6 replicate tablets. The mixture of 350 mg was weighed using the same analytical laboratory scales than in the previous phase. Tablets were compressed with the compaction simulator (PCS-1, Puuman Ltd., Kuopio, Finland) presented in Fig. 2. The compaction simulator was equipped with punches of diameter of 12 mm and a die, forcing the drug and starch acetate particles close to each other by powder compression. The compression takes place in a die by the action of lower and upper punches. The tableting consisted of three stages: die filling, tablet formation and tablet ejection. First, the powder was manually put into the die. The upper punch descended automatically until it reached the die and the surface of the powder blend. The powder was compressed between the punches until a tablet was formed. When the maximum

The three tablets from each compaction profile with the most similar porosity characteristics from each batch were chosen for dissolution testing. The dissolution test for each batch of tablets was performed by USP XXVIII rotating basket method, with rotation speed of 75 rpm. The dissolution medium consisted of 900 ml phosphate buffer (pH 6.8, 37 °C). The dissolution testing apparatus used in the studies is shown in Fig. 3A. Samples of 2 ml were taken manually from each dissolution vessel every 5, 10, 15, 20, 30, 40, 50, 60, 120, 240, 360 and 480 min of dissolution. The removed sample volume was replaced with a fresh phosphate buffer and taken into account in the calculations. A diagram of the dissolution instrument based on the rotating-basket for the testing of the tablet dissolution rate is illustrated in Fig. 3B, which shows that the sampling point (A) is given in advance. The sampling point should be halfway from the top of the basket to the surface level of the fluid, no closer than 1 cm to the side of flask. Moreover, it should be also halfway between the basket and the side in the middle of the basket. The samples were collected from the dissolution chamber into tubes and analyzed with a UV spectrometer.

Fig. 2. Compaction simulator (PCS-1, Puuman Ltd., Kuopio, Finland).

2.4. Sampling theory Pierre Gy developed a sampling theory more than 50 years ago. The theory takes all of the aspects of sampling into account, forgetting

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Fig. 3. A) Dissolution testing apparatus (USP I) consisting of 6 dissolution vessels. B) Diagram of a dissolution instrument based on the rotating-basket for the testing of tablet dissolution rate.

neither the material properties nor the design of the sampling equipment [9,14–17]. The theory is consistent with ISO standards and IUPAC recommendations. However, the terminology differs at some stages [10–11]. The uncertainty components of an analytical determination according to Gy's sampling theory are described in Fig. 4, which shows that the global estimation error (GEE) is the sum of the total sampling error (TSE) and the total analytical error (TAE). In the present study, the total analytical error consists of noise from the UV measurements as well as the calibration errors that occur during the drug dissolution tests. The total sampling error is the sum of the point materialization error (PME), the sample weighting error (SWE), the point selection error (PSE), the grouping and segregation error (GSE) and the fundamental sampling error (FSE). The main sources causing the sampling error are the sampling technique and the heterogeneity of the examined powder mixture [9,14–17]. The point materialization error includes an increment delimitation error (IDE), an increment extraction error (IXE) and an increment and sample preparation error (IPE). The preparation error is an error component that can occur at any stage, and it is due to the preparation of the sample. Delimitation and extraction errors refer to the actual removal of the sample from the lot, and how the sample is cut from

Fig. 4. Error components according to Gy's sampling theory.

the lot. In this study, the samples are assumed to be taken correctly from the lot. Since the homogeneity of the mixture is tested, PME is assumed small and can be left out. However, there are several sampling stages in the sampling procedures and PME errors might arise e.g. during the following stages: IDE Sampling from the primary lot. The lot is most probably heterogeneous, so IDE is present if the samples are taken from the top layer of the mixture. IXE If a 2 ml sample is extracted from the dissolution chamber improperly in a way that sampling favors certain particles, e.g. small ones. IPE Preparation of tablets: 350 mg of the mixture is placed manually at the die of the compact simulator to be tabletted. During the procedure, some light particles may be lost as dust. The sample weighting error occurs only if the average of a lot consisting of sub-lots is calculated without taking the sizes of the sublots into account [9,14–17]. In this case study, this uncertainty component would occur only if the uncertainties of the individual tablets, with unequal masses, are combined. Otherwise, like now, the analytical procedure is built up in a way that this uncertainty source can be expected to be minimal. The point selection error consists of two dynamic (process) sampling errors PSE1 and PSE2 (PSE = PSE1 + PSE2). PSE1 is caused by the long range variations in the process, while PSE2 reflects the periodic drifts in the process. The point selection error is the error of the mean of a continuous lot estimated by using discrete samples. In this study, the point selection error has been assumed to be negligible, since the aim is to determine the dissolution profile over a short time period. The grouping and segregation error (GSE) occurs when the sampling increments are not ideal, i.e., consist of a single fragment. This uncertainty component is most likely introduced in all mixing steps. Some uncertainty might arise from the fact that sampling from the mortar is sampling from 3 dimensional lots [9]. GSE may occur also when the dissolution chamber is sampled for the UV analyses. If Pierre Gy's sampling theory is utilized, the dimensionality of the lot is important. The dimensionality depends on how the samples are taken from the lot. The problem in sampling from 3 dimensional lots is that none of the dimensions in the lot are completely included in the sample. Examples of 3 dimensional lots are ore bodies, stockpiles,

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Fig. 5. Estimation of particle shape factor for different types of particles.

grain silos, oceans, seas and lakes. When a lot is 2 dimensional, only 1 dimension of the lot is completely included in the sample. This category includes samples taken from stockpiles of solid fragments. When Pierre Gy's sampling theory is applied, the samples should be taken from 1 dimensional or from 0 dimensional lots, if possible. When the lot is 0 dimensional, the whole lot can be mixed before sampling or taken into the sample. Objects that are linear in space or time, such as rivers, iron castings or a copper wire bar are 1 dimensional. Also objects of flowing streams are considered as 1 dimensional lot [9], but only when two entire dimensions are completely covered in a sample or an increment. In the present study, the lot to be sampled is assumed to be 0 dimensional.

The fundamental sampling error (FSE) is the only error component that cannot be eliminated by correct sampling and sample preparation procedures. It depends on the properties of the material to be sampled, and it is the error component of an ideal sampling. However, the FSE computed in this study does not take the chemical phenomena in the mixing procedure into account [17]. For particulate materials the relative standard deviation of the fundamental sampling error (σr = sFSE) can be estimated from material properties with Eq. (1) 2

3

σ r = C · d0:95 ·




1 1 − Ms ML

 ð1Þ

where C is the sampling constant (material dependent), d0.95 is a particle size square mesh that allows 95% of the material to pass, Ms is the sample mass and, ML is the lot mass. In the present study, the lot mass was 30 g, (6 g of drug compound and 24 g of starch acetate). The sampling constant C is calculated according to Eq. (2) C=c·f ·g·β

ð2Þ

where c is the composition factor, f is the particle shape factor, g is the size distribution factor and, β is the liberation factor. The more detailded theory behind estimations of f, g and β is explained e.g. by Pitard [16, pages 159–166]. The particle shape factor is defined as a coefficient of cubicity. It is the ratio of the volume of a particle passing a certain sieve to the volume of a cube passing the same sieve. The estimations of the particle shape factor for different particles are shown in Fig. 5. SEM (Scanning Electron Microscope, JSM 35 Scanning microscope, Jeol Ltd., Tokyo, Japan) images were utilized to estimate the particle size (d0.95) and the particle shape factor. The particle shape factor of drugs were evaluated according to the visual inspection of the SEM pictures. Salicylamide particles were almost circular (f = 0.5), whereas chlortiazide, diltiazem, levodopa, perphenazine and propranolol particles were less circular (as they are more angular, f = 0.4). Atenolol and nizatidine were platy ( f = 0.2), while caffeine, furosemide, ibuprofein, paracetamol and theophylline looked like thin cylinders ( f = 0.1). The SEM images of caffeine powder and starch acetate are illustrated in Fig. 6 A and 6B. As the figures show, the shape and the size of those two powders are different. The particle size distribution factor g can be estimated from the the particle size distribution as a ratio of 95% and 5% particle sizes: d95/d05. • • • • Fig. 6. Scanning Electron Microscope images. Images are taken with JSM 35 Scanning microscope, Jeol Ltd., Tokyo, Japan. A) Caffeine powder, B) Starch acetate powder (excipient material).

Wide size distribution (d0.95/d0.05 N 4) g 0.25 Medium distribution (d0.95/d0.05 = 4…2) g 0.50 Narrow distribution (d0.95/d0.05 = 1…2) g 0.75 Identical particles (d0.95/d0.05 = 1) g 1.00

All the drug substances studied show wide size distribution, therefore g = 0.25.

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Table 1 Fundamental sampling error (sFSE), theoretical minimum uncertainty (Utheor., min), particle shape factor ( f ), particle sizes (d0.95) and densities (ρ) for the drug compounds studied.

Fig. 7. Estimation of liberation factor.

The liberation factor β equals one if the critical particles, i.e. drug particles, are free in the matrix material. If they are as inclusions, the liberation factor will be estimated from the particle size d0.95 and from the liberation size L, as shown in Fig. 7. In this case study, the liberation factor is assumed to be one. For the calculation of the composition factor c (Eq. (3)), additional material properties are needed [9,14–16]: c=

ð1− aL =α Þ2 · ρc + ð1 − aL = α Þ · ρm aL = α

ð3Þ

Drug compound

ρ, g/cm3

Particle size (d0.95), μm

f, −

s(FSE), %

a Utheor., %

Atenolol Caffeine Chlortiazide Diltiazem Furosemide Ibuprofein Levodopa Nizatidine Paracetamol Perphenazine Propranolol Salicylamide Theophylline

1.190 1.404 1.830 1.245 1.626 1.029 1.520 1.295 1.240 1.312 1.193 1.345 1.434

50.5 88.9 51.1 76.5 80.0 181.8 58.8 29.8 80.9 46.3 33.7 635.3 97.9

0.2 0.1 0.4 0.4 0.1 0.1 0.4 0.2 0.1 0.4 0.4 0.5 0.1

0.030 0.052 0.051 0.079 0.047 0.135 0.058 0.014 0.043 0.038 0.023 2.200 0.061

0.13 0.22 0.22 0.34 0.20 0.60 0.25 0.06 0.19 0.16 0.10 9.45 0.26

a

min,

Minimum theoretical uncertainty: Utheor,min = (U(0.95)) = t(0.95,υ) d s(FSE).

results of the homogeneity tests show. The tests indicate clearly that there are differences between the replicates and the target amount of drug in the tablets. The homogeneity test allows a standard deviation of 5%. For most of the drug compounds studied, the difference is clearly lower, but for some materials this 5% limit is exceeded. If this

where aL is the average concentration of the analyte in the lot, α is the concentration of the determinant in critical particles, ρc is the density of critical particles and, ρm is the density of matrix. In this case study, the average drug concentration in the lot was 20 wt.%, whereas 80 wt.% was starch acetate (ρ = 1.425 g/cm3, d95 = 909.06 μm). The purities of all the drug compounds studied were 98% (= α). The total analytical error (TAE) was estimated from the UV calibration curves of the drug dissolution tests. The calibration of the UV measurements was carried out with two separate calibration sets. The average absorbance values of the calibration samples were calculated and a straight line was fitted between absorbancies and known drug concentrations. The total analytical error (TAE) is the relative standard deviation between “the true (i.e. fitted line)” and “the measured” concentrations of the UV measurements. The values are estimated with Eq. (4): vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uP n u cal abs 2 t ð fit;i Þ i=1

STAE =

ncal − 1

_ abs

· 100k; for i; N ; ncal

ð4Þ

where ncal is the total number of calibrants, absfit is the absorbance _ values estimated from the calibration curve and, abs is the average absorbance of the absorbance values used in fitting the calibration curve. The number of main uncertainty components used was relatively low: the point selection error was neglected, since the aim was to determine the dissolution profile over a short time period and neither the extraction error nor the delimitation error were involved. Extraction and delimitation errors might be present, but they are not included in the estimations, however. This leads the case where the minimum global (total) estimation error can be estimated from the FSE and TAE: sGEE =

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi s2FSE + s2TAE

ð5Þ

In Eq. (5), the result of the homogeneity test is not included. However, the samples are always somehow heterogeneous, as the

Fig. 8. Fundamental sampling error FSE for some drug—starch acetate mixtures as a function of particle size (95% cut-off values). In Fig. 8A, the FSE is estimated for salicylamide, propranolol, furosemide and ibuprofein. In Fig. 8B, the FSE is estimated for diltiazem, chlortiazide, levodopa and perphenazine.

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The fundamental sampling error was utilized as the theoretical minimum standard deviation when the uncertainties of the dissolution profiles were studied. These fundamental sampling errors

representing the situation in which the sampling procedure is optimal are shown in Table 1. The results show that the minimum standard deviations (sFSE) for the examined drug mixtures are small–clearly lower than 1%–except for the salicylamide, where the FSE is more than 2%. Estimated particle shape factors and particle sizes as well as densities of the studied drug compounds are shown in Table 1. The theoretical minimum uncertainties for all examined drug compounds (estimated by Gy's formula) are shown also in Table 1. For example, the uncertainty of ibuprofein tablets would be Utheor., min = U(0.95) = 4.303 · 0.135% = 0.60% when three replicate tablets are analyzed. In order to estimate how FSE depends on particle size, FSE was calculated as a function of particle size (95% cut-off size for each drug component). The results for the salicylamide, furosemide, ibuprofein and propranolol are illustrated in Fig. 8A. These drug particles have different particle sizes, as well as different densities and shape factors (values are in Table 1). The effect of the shape factor f is clearly illustrated in Fig. 8A since salicylamide and propranolol have a higher f and thus a higher FSE compared to furosemide and ibuprofein. The FSEs of chlortiazide, diltiazem, levodopa and perphenazine are illustrated in Fig. 8B. All of these compounds have the same shape factors, as well fairly similar particle sizes (40–80 μm) and densities (Table 1). A nonlinearly increasing trend between FSE and particle size is seen in both (Fig. 8A and B). The uncertainty component arising from the analytical determination was quantified in order to estimate the global estimation error (GEE). This component refers to the total analytical error (TAE). When the UV measurement is expected to be the only source of error, the calibration curves of the measurements can be utilized. The relative standard deviations between “the true” and “the measured” concentrations were determined from the calibration data (Table 2).

Fig. 9. A) Dissolution profile for caffeine. The depth of the die under pressure is 0.9 mm (compaction pressure is 16 kN). B) Experimental relative uncertainty of the mean value of the three samples: Expanded relative uncertainty (at confidence level 95%) and relative standard deviation of the mean value.

Fig. 10. A) Dissolution profile for theophylline. The depth of the die under pressure is 1.1 mm (compaction pressure is 12 kN). B) Experimental relative uncertainty of the mean value of the three samples: Expanded relative uncertainty (at confidence level 95%) and relative standard deviation of the mean value.

Table 2 Fundamental sampling error (sFSE), total analytical error (sTAE), global estimation error (sGEE) and theoretical uncertainty (Utheor) of drug compounds. Drug compound

s(FSE), %

s(TAE), %

s(GEE,1) a, %

s(GEE,2) b, %

Utheor.c, %

Atenolol Caffeine Levodopa Nizatidine Propranolol

0.030 0.074 0.058 0.014 0.023

2.20 0.52 2.10 3.59 1.96

2.2 0.53 2.1 3.6 2.0

5.5 5.0 5.4 6.2 5.4

23.5 21.6 23.3 26.5 23.1

a b c

Total error if the result of homogeneity test is not included. Total error if the 5% standard deviation achieved from homogeneity test is included. Theoretical uncertainty: Utheor = (U(0.95)) = t(0.95,υ) d s(GEE,2).

“maximum” heterogeneity is included, Eq. (5) is re-written into Eq. (6): sGEE =

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi s2FSE + s2TAE + ð5 kÞ2

ð6Þ

The expanded total uncertainty at a 95% confidence level is calculated as Uð0:95Þ = tð0:95; υÞ · sðGEEÞ

ð7Þ

where υ is a degree of freedom. 3. Results

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When the result of the homogeneity test is not included, the minimum global estimation error is calculated based only on the FSE and the TAE, by applying Eq. (5). However, while tableting, the target drug concentrations are allowed to have a 5% standard deviation (which is the result of the homogeneity test). If this highest allowed result of the homogeneity test is included in estimating the global estimation error, Eq. (6) will be used. The global estimation errors are shown in Table 2, which demonstrates that the most dominant error is the error arising from the heterogeneity and the relative standard deviations for the measurements might be higher than 5%. When the FSE, TAE and the 5% standard deviation are included in estimating the global estimation error, the GEE becomes even higher than 20%. For example, for the estimated theoretical uncertainty of caffeine, the global estimation error would be Utheor = U(0.95) = 4.303 · 5.0% = 21.6% when three replicate tablets are analyzed. Drug dissolution is calculated as a function of time based on the UV measurements. The drug dissolution profile for caffeine tablet compacted under a pressure of 16 kN is shown in Fig. 9A. It can be seen that the shape of the profiles for the three replicate samples are similar, and all the profiles are within a 95% confidence limit. However, the profile of one replicate is lower than the other two profiles. For theophylline and paracetamol, all the three replicates give quite similar profiles, as can be seen from Figs. 10A and 11A, respectively. The depth of the die during the tableting for the theophylline was 1.1 mm (12 kN) and for the paracetamol 0.9 mm (16 kN). When estimating the variation between the replicate dissolution profiles, the relative standard uncertainty was determined. The relative uncertainty for caffeine at a confidence level of 95% and the relative standard deviation of the mean value are shown in Fig. 9B. Variations between theophylline replicates are present in Fig. 10B,

Fig. 11. A) Dissolution profile for propranolol. The depth of the die under pressure is 0.9 mm (compaction pressure is 16 kN). B) Experimental relative uncertainty of the mean value of the three samples: Expanded relative uncertainty (at confidence level 95%) and relative standard deviation of the mean value.

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while the results for propranolol are in Fig. 11B. The achieved relative standard deviations seem to be different from the results estimated based on the global estimation error of Gy's theory (Table 2). 4. Discussion Results show (Table 1, Fig. 8A and B) that the FSE is higher for salicylamide because the particles have different diameters and density compared to the matrix and to the other drug particles (diameters are up to ten times larger). As the shape factor (Fig. 8A) or density (Fig. 8B) increases, the FSE also increases. For all the examined drug compounds, the FSE is lower than 5%, even if the 95% particle size is as high as 1.0 mm. If the FSE needs to be estimated more precisely, the size distribution and the particle size should also be estimated more accurately. Thus, the particle shape factor should be evaluated with some other criteria than only visually. There are still a couple of unanswered questions in estimating the FSE. What if the drug particles in the matrix are flocks instead of free particles? If this is the case, the liberation factor is not equal to 1. For further investigation, more information about particles should be obtained, e.g., more SEM images should be analyzed. Figs. 9A–11A show the dissolution profiles of the drug tablets. The analyses are carried out with three replicate tablets. According to the parallel dissolution for caffeine (Fig. 9A), the last replicate tablet has the lowest drug release. There are several reasons for this variation such as the heterogeneity of lot which might have been more than accepted deviation of 5% of drug content. Moreover, the sampling affect on the variation if the most of the drug particles have been situated on the surface of the lot and only the surface particles have been analyzed. Therefore the rest of the lot may not include as many drug particles as expected. However, in most cases the different dissolution profile is achieved with sample number 1 or 2 and thus leads to the question: What is the true tableting order? Is the tablet 1 (=sample 1) in the dissolution procedure tabletted before sample 2? Originally there were 6 replicate tablets for one pressure profile. Is the tablet with a different profile always dissolved in a certain dissolution vessel? And could it be the reason for the differences between replicate dissolution profiles? It is known that the sampling point from the vessel is important in order to avoid the significant wobbles (Fig. 3B) [1]. Since the dissolution profiles of replicate samples are similar (Figs. 9A–11A) and no clear disturbances are seen in the profiles, it indicates that the actual analysis does not cause high standard deviation. As Table 2 shows, the TAE computed with Eq. (4) depends on the material studied. This result can, and should, be confirmed by using parallel samples. Possibly this estimate is too optimistic. As the relative standard deviation depends on the concentration, it can be concluded that the analytical error could be as high as 10% within a certain concentration range if the sample preparation or calibrant preparation is neglected. R2 values for the calibration curves were for most of the cases higher than 0.99. The average of the dissolution profiles was also estimated. The results for caffeine show that at the beginning of the dissolution the relative standard deviation between the three replicate dissolution profiles is about 65%. As the dissolution goes further (time passed more than 300 min.), the relative standard deviation seems to stay constant, around 10%. However, the percentage values are not dissolved drug percentages. Instead, they are standard uncertainties expressed as percentages from dissolved drug percentage values. The expanded relative uncertainty on the 95% confidence level estimated for theophylline is illustrated in Fig. 10B and for propranolol in Fig. 11B, respectively. The relative standard deviation for theophylline decreases from about 10% to 3% in the course of time, whereas for propranolol it remains close to 8% after 40 min of dissolution. The relative standard deviations were found to be largest at the beginning of the dissolution procedure, which is expected since the drug

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dissolution is fast at the beginning, concentrations are low, and there are differences between the dissolving characteristics of drug [7]. The comparison of the variations between the replicate results and the GEE obtained from Gy's theory shows dissimilarity. The relative standard deviation for caffeine after dissolution is around 10% (Fig. 9B), while the global estimation error (GEE,2) is 5.0% (Table 2) . This difference between the two values indicates a missing error component or that the dissolved material may be even more heterogeneous. Fig. 11B illustrates that the standard deviation of propranolol is around 8% whereas the global estimation error is 5.4% (Table 2). The results are an indication of existence of some additional error source, even in the case of propranolol. Some part of the error source is due to the error component mentioned in Gy's theory, but neglected in the present study. These error components arise from sample or increment preparation, increment extraction, increment delimitation, grouping and segregation. The mixing intensity and the total mixing time of the magnesium stearate, that is 2 min in this study, are critical points. If the global estimation error is analyzed in more detail, the influence and the exact behavior of magnesium stearate should be estimated. In this case study, magnesium stearate was added into the sample just before the replicate samples were weighted and tabletted. Thus it can be questioned, what the affect would be if magnesium stearate is added directly into each 350 mg powder sample before tableting? In the present procedure, there is time for stearate and powder to react in the mixture, while the other replicate samples were weighted and tabletted. What if the drug particles form flocks in the matrix, as observed by Pajander et al [18]. These facts should be examined closer. In order to analyze how heterogeneous the lot really is, the whole prepared lot should be sampled. If the lot is as much as 30 g, as in the present study, rotating sampling devices could be used to split the lot into equally sized sub-lots. The sample splitting procedure should always be carried out to ensure that the lot is completely mixed. The UV measurements should be carried out soon after samples are taken from the dissolution vessel. However, in practice the first eight samples (5, 10, 15, 20, 30, 40, 50 and 60 min) are taken and analyzed on time and the rest of the samples (120 to 480 min) are analyzed all at once. This raises a question that is there correlation between the UV measurements and time. How do the results change if each sample is measured immediately after it is taken from the vessel? The effects of these factors must be evaluated and minimized. In order to examine how large the uncertainty components arising from the mixing and tableting procedures are, both steps should be optimized separately. 5. Conclusions The aim of this study was to estimate the global (total) uncertainty of the drug dissolution procedure. The dissolution procedure includes tableting and dissolution steps. In general, the homogeneity of the powder mixture has to be tested before the tablets are prepared. The optimization of the whole dissolution procedure is carried out by utilizing Pierre Gy's sampling theory. Based on their material properties, the mixtures containing different drug components have a minimum error. This error is due to the heterogeneity of the drug components. During the estimations, the only error components that were included were the fundamental sampling error, the total analytical error and the error arising from the heterogeneous samples. The other error components mentioned in Gy's theory are also present, but in this case these errors were assumed to be small and not included in the estimations. The fundamental sampling error is the error component of an ideal sampling, and it can be estimated based on the material properties. For most of the examined drug compounds, the FSE was lower than

1%. Only for salisylamide was the FSE over 2%. This is most probably due to the particle size and shape of the drug particles. The total analytical error was estimated from the calibration curves of the UV measurements, and the values were found to depend on the material studied. TAE for caffeine was 0.52% whereas for nizatidine it was 3.59%. The approximation is too optimistic, if sample preparation or calibrant preparation is neglected. Based on the drug dissolution tests, it can be seen that the shapes of the profiles are similar and the replicated analysis does not include disturbances. This indicates that the actual analysis does not cause high standard deviation. However, there are variations between the replicate analyses. The dissolution profiles show that if the drug release is measured from three replicate tablets the dissolution profile is clearly different for one of the tablets in most of the cases. If 5% standard deviation of heterogeneity is included, the global estimation error is around 5–6%. However, when the relative standard deviations between the drug dissolution profiles are estimated, the standard deviation at the end of the dissolution is 10% for caffeine and 7–8% for propranolol, respectively. These results show that there are other significant error sources in the sampling chain. Those error components are most probably the ones mentioned in Gy's theory: errors arising from sample or increment preparation, increment extraction, increment delimitation, grouping and segregation. According to these results, it is obvious that the optimization of the drug dissolution is important and the reliability of the dissolution profiles can be improved. References [1] V. Lee, J. Yang, Oral drug delivery, Drug Delivery and Targeting for Pharmacists and Pharmaceutical Scientists, vol. 1, Taylor & Francis, London, 2001, pp. 146–184. [2] M.V.S. Varma, A.M. Kaushal, A. Garg, S. Garg, Factors affecting mechanism and kinetics of drug release from matrix-based oral controlled drug delivery systems, Am. J. Drug. Deliv. 2 (2004) 43–57. [3] M. Grassi, G. Grassi, Mathematical modelling and controlled drug delivery: matrix systems. Curr. Drug Deliv. 2 (2005) 97–116. [4] G. Alderborn, C. Nyström (Eds.), Pharmaceutical powder compaction technology, vol. 1, Marcel Dekker Inc., New York, 1996, pp. 420–422. [5] O. Korhonen, S. Matero, A. Poso, J. Ketolainen, Partial least square projections to latent structures analysis (PLS) in evaluating and predicting drug release from starch acetate matrix tablet, J. Pharm. Sci. 94 (2005) 2716–2730. [6] S. Matero, J. Pajander, A.M. Soikkeli, S.P. Reinikainen, M. Lahtela-Kakkonen, O. Korhonen, J. Ketolainen, A. Poso, Predicting the drug concentration in starch acetate matrix tablets from ATR-FTIR spectra using multi-way methods, Anal. Chim. Acta 595 (2007) 190–197. [7] S. Matero, S.P. Reinikainen, M. Lahtela-Kakkonen, O. Korhonen, J. Ketolainen, A. Poso, Estimation of drug release profiles of a heterogeneous set of drugs from a hydrophobic matrix tablet using molecular descriptors, J. Chemometrics 22 (2008) 653–660. [8] M.E. Aulton, Pharmaceutics, The Science of Dosage Form Design, Toronto, 2002. [9] P.M. Gy, Sampling for Analytical Purposes, John Wiley & Sons Ltd., Chichester, 1998. [10] Guide To The Expression Of Uncertainty In Measurement, ISO, Geneva, 1993 ISBN 92-67-10188–9. [11] S.L.R. Ellison, M. Rosslein, A. Williams (Eds.), EURACHEM / CITAC Quide, Quantifying Uncertainty in Analytical Measurement, 2nd Ed., 2000. [12] O. Korhonen, P. Raatikainen, P. Harjunen, J. Nakari, E. Suihko, S. Peltonen, M. Vidgren, P. Paronen, Starch acetates-multifunctional direct compression excipients, Pharm Res 17 (2000) 1138–1143. [13] E.T.S. Skibsted, H.F.M. Boelens, J.A. Westerhuis, D.T. Witte, A.K. Smilde, Simple assessment of homogeneity in pharmaceutical mixing process using a nearinfrared reflectance probe and control charts, J. Pharm. Biomed. Anal. 41 (2006) 26–35. [14] P.M. Gy, Sampling of Heterogeneous and Dynamic Material Systems, Theories of Heterogeneity, Sampling and Homogenizing, Elsevier, Amsterdam, 1992. [15] Special issue: 50 years of Pierre Gy's Theory of Sampling, in: P.M. Gy, K.H. Esbensen, P. Minkkinen (Eds.), Proceedings of the First World Conference on Sampling and Blending (WCSB1), Tutorials on sampling: Theory and Practice, Chemom. Intell. Lab. Syst., vol. 74, 2004, pp. 7–47. [16] F.F. Pitard, Pierre Gy's Sampling Theory and Sampling Practice, vol. I, CRC Press, Boca Raton, 1989. [17] S.L.R. Ellison, M.H. Ramsey (Eds.), EURACHEM / CITAC Quide, Measurement uncertainty arising from sampling: A guide to methods and approaches1st Edition, 2007. [18] J. Pajander, A.M. Soikkeli, O. Korhonen, R.T. Forbes, J. Ketolainen, FTIR imaging of drug release phenomena within a hydrophobic matrix tablet during dissolution, Journal of Pharmaceutical Sciences, J. Pharm. Sci. 97 (2008) 3367–3378.