bioequivalence of generic medication.pdf
TRANSCRIPT
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1PCOL3021Drug Therapy, Department of Pharmacology, School of Medical Sciences,
University of Sydney, Sydney NSW 2006, Australia
Bioequivalence of generic medications
A spreadsheet elaboration for assessment of bioequivalence
Joao Bosco Ferreira da Conceicao
1
Background and purpose: The increasing use of generic medications, occasioned by their
reduced price and current interest in reduce healthcare costs, has raised concernments about
their safety. Bioequivalence (BE) studies has gained more attention as a tool to provide
information regarded to safety and efficacy of generics. In this study, we aimed to develop a
spreadsheet to easy evaluation of BE of generic medications.
Experimental Approach: Pharmacokinetic (PK) data of three different drugs (digoxin,
fluoxetine and amlodipine) were obtained from the literature, and used to simulate a BE assay
in order to investigate potential generic candidates for these formulations, by analyzing both
generic and reference data. Using Microsoft Excel, the data set was subjected to
logarithmic transformation and ANOVA. BE was assessed using two one-sided t-test
approach and 90% confidence interval procedure.
Key results: Applying the spreadsheet to the elaborated data, it was observed that two
(digoxin and fluoxetine) of the three drugs were bioequivalent to their innovator product
and one of the formulations (amlodipine) was rejected, considered bioinequivalent.
Conclusions: The spreadsheet showed functionality to analyse BE of generic medications
using consolidated statistical approaches applied to perform these studies.
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Introduction
Generic medications are defined as pharmaceutical products, which contain the same
active ingredient of an innovator product, allowed to be manufactured by any company due to
expiration of patent or other exclusive rights (WHO, 2014). They also tend to present the
same characteristics of safety, quality, efficacy, dosage form, route of administration and
therapy indication. Differences regarding to excipients, packaging, shape and other minor
aspects are permitted (FDA, 2012).
The low cost of generic drugs associated to the necessity of a healthcare cost reduction
has caused an increase in the use of this type of medication. Concomitantly, evidences of
marked differences in the therapeutic response of medications that present the same amount
of a drug have been reported (Qayyum, 2012). These differences may be related to
pharmacokinetic aspects, such as dissimilar drug plasma levels caused by impaired
absorption (Ramos-Gabatin, 1982). Due to these facts, BE of generic medications and
innovator products has gained more attention.
BE refers to equivalent bioavailability presented by two medicinal products containing
the same active substance after administration in the same molar dose. In order to make
possible an in vivo performance comparison, acceptable predefined limits are designed to
estimate similarities in terms of safety and efficacy (EMA, 2010).
Parameters analysed to determine BE of medicinal products (considering a single dose
administration) are the area under the concentration time curve (AUC) and the peak plasma
concentration (Cmax). Analysis of the time to reach Cmax (Tmax) is not required to determine
BE (EMA, 2010; TGA, 2012). The assessment of BE is performed on basis of 90%
confidence intervals for the ratio of the test (generic) and reference (innovator) products.
These pharmacokinetic parameters (PK) should be subjected to logarithmic transformation
and analysis of variance (ANOVA) prior to confidence interval determination (Abdallah,
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1995; EMA, 2010). In this project, we aim to generate a spreadsheet using Microsoft Excel
for the assessment of BE of generic and innovator medications applying traditional statistic
approaches.
Method
Pharmacokinetic data
AUC and Cmax profiles for both reference and test medication were generate in a
spreadsheet file using Microsoft Excel 2013 software. Digoxin, fluoxetine and amlodipine
were used as point of reference to search PK data available in the literature (Miyazawa, 2002;
Moraes, 1999; Bainbridge, 1993). The set of data for these parameters, based on the
literature, was used to simulate a randomized, two-period, two-sequence, single dose
crossover design.
Statistical approach
The PK data created to represent the generic and reference drug was transformed into
natural logarithms (ln), followed by ANOVA analysis to detect intra-subject and treatment
variation. Following, two one-sided t-test procedure was applied to verify the null hypothesis
(H0) of bioinequivalence at 95% level of significance ( = 0.05). First one-sided test was
applied on the hypotheses {H01: TR 1; H11: TR> 1} andthe second one-sided test
was applied on the hypotheses {H02: TR1; H12: TR< 2}. The Tand Rvalues
represent the mean of the PK parameter analysed for test and reference medication,
respectively, while 1 and 2 are the allowable range for BE. BE between generic and
reference medication was assumed when both null hypotheses (H01 and H02) were rejected.
90% confidence intervals for the ratio of means of the two treatment (generic and reference
medication) was also determined.
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Results
Functionality of the spreadsheet constructed to assess BE of generic medications was
tested simulating its application to analyse three generic medication containing digoxin,
fluoxetine or amlodipine. The PK data representing the innovator product for these three
drugs was elaborated based on literature values. This study simulates a BE trial carried out on
sample of 12 individuals subjected to a randomized, two-period, two-sequence, single dose
crossover study design.
Mean of PK values for the three drugs and their respective generic candidates used in
this simulation are shown in Table 01. Table 02 shows the ANOVA results for the PK
parameters after natural logarithmic transformation for both reference and test medications.
ANOVA results show no significant impact of carryover effect on the BE study.
Confidence interval forRT
were built on basis of the equation 01, where is the
consumer risk (risk to wrongly conclude to BE), dfis the degree of freedom of the variance,
2 is the variance (MS error from ANOVA). BE was concluded when the interval for
RT was totally included in the equivalence interval [ln0.8; ln1.25].
Table 01.Pharmacokinetic data summary of reference and generic medicationsPK parameter Digoxin Fluoxetine Amlodipine
AUC (ng/mL h)Reference
46.3(35.457.6)
536.3(353.9724.1)
222.3(99.0312.0)
AUC (ng/mL h)
Generic
52.9
(33.871.1)
491.4
(311.5660.4)
241.2
(112.0347.0)
Cmax(ng/mL)
Reference
7.1
(5.88.9)
11.8
(10.313.1)
10.4
(8.512.4)Cmax(ng/mL)
Generic
7.8
(6.19.3)
13.1
(11.114.7)
10.0
(7.411.9)
RT
dfRT
RT
dfRTnn
Atnn
At 11
;11
2121
(1)
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From the application of the created BE spreadsheet, it was observed that amlodipine
generic formulation is not bioequivalent to the reference medication. The 90% confidence
interval for the amlodipine AUC values (0.88; 1.35) was not totally included within the
allowed BE interval (0.8; 1.25) (Figure 1C). Considering the digoxin and fluoxetine PK value
analyses, the spreadsheet showed that the PK parameters of the generic medications of these
drugs are bioequivalent to the reference formulation. Both AUC and C max values of the 90%
confidence interval forRT
digoxin(1.02; 1.23) and RT fluoxetine(0.83; 1.00) were totally
included in the BE interval (Figure 1A and 1B).
Table 02. ANOVA for AUC and Cmaxafter logarithmic transformation
DrugPK
parameter
Source of
VariationSS df MS F P-value F-cri t
Digoxin
AUC
Subject 0.573738 11 0.052158 1.367985 0.306097 2.81793
Treatment 0.082307 1 0.082307 2.15873 0.169773 4.844336
Error 0.419404 11 0.038128
Cmax
Subject 0.316638 11 0.028785 2.590236 0.064784 2.81793
Treatment 0.056692 1 0.056692 5.101417 0.0452 4.844336
Error 0.122243 11 0.011113
Fluoxetine
AUC
Subject 1.028219 11 0.093474 2.860127 0.047708 2.81793
Treatment 0.045503 1 0.045503 1.392307 0.262905 4.844336
Error 0.359501 11 0.032682
Cmax
Subject 0.135718 11 0.012338 2.083291 0.119568 2.81793
Treatment 0.060507 1 0.060507 10.21678 0.008509 4.844336
Error 0.065146 11 0.005922
Amlodipine
AUC
Subject 1.315859 11 0.119624 0.656146 0.751951 2.81793
Treatment 0.046447 1 0.046447 0.254767 0.623697 4.844336
Error 2.005434 11 0.182312
Cmax
Subject 0.281571 11 0.025597 2.716222 0.056065 2.81793
Treatment 0.010027 1 0.010027 1.064025 0.324446 4.844336
Error 0.103663 11 0.009424
SS: Sum of squares; df: degrees of freedom (n1); MS: Mean sum of square. P-value: significance ofthe variability
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Figure 1. 90% confidence interval (CI) ofRT
/ for three generic medications.
Pharmacokinetic factors (AUC and Cmax) of generic and innovator medications containing
digoxin (A), fluoxetine (B) or amlodipine (C) were subjected to ANOVA and two one-sided
t-test in order to generate a 90% CI for these parameters. Then, BE was assessed by analysis
of the 90% CI for each drug formulation against a predefined 90% CI (0.8; 1.25) for BE.
0.73 0.80 0.88 0.95 1.03 1.10 1.18 1.25 1.33
2
90% confidence interval for BE1
90%CI T/R (AUC) digoxin
90%CI T/R (Cmax) digoxin
A
0.73 0.80 0.88 0.95 1.03 1.10 1.18 1.25 1.33
2
90% confidence interval for BE
1 90% CI T /R (AUC) fluoxetine
90% CI T/R (Cmax) fluoxetine
B
0.73 0.80 0.88 0.95 1.03 1.10 1.18 1.25 1.33
290% confidence interval for BE
1
90% CI T/R (AUC) amlodipine
90% CI T/R (Cmax) amlodipine
C
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Discussion
The two one-sided t-test procedure for BE determination was proposed in 1987 by
Donald J. Schuirmann. It consists of decomposing the hypotheses in two sets of one-sided
hypotheses, followed by application of two separate t-tests. This procedure generates an
interval hypothesis, which represents the equivalence as an alternative hypothesis and the
inequivalence as null hypothesis. BE is concluded when the P-value obtained in both one-
sided tests reject the null hypothesis of bioinequivalence (p
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In the recent years, a significant increasing in the number of generic alternatives for
important prescriptions has been observed. Drugs with a narrow therapeutic index (NTI),
such as digoxin, and certain critical care drugs have raised considerable concerns about
generic equivalent substitution (Reiffel, 2001; Kumet, 2005). Reasons are related to possible
changes in pharmacodynamic response to these drugs due to a small modification in the
absorption process.
Bioinequivalences between a generic and innovator formulation might be related to
the presence of different excipients in a particular product, which can affect the absorption of
the active substance to the blood circulation. The PK profile of formulations containing the
same therapeutic substance might also differ due to distinguished form of presentation of the
drug. Modifying a substance from its free base or acid form to a salt form can cause
significant impact on the chemical and biological properties of this substance without
changing its structure. This alteration may be sufficient to affect the absorption process of a
substance in the body (Borgherini, 2003). These factors may be related to the
bioinequivalence presented by amlodipine formulation (Figure 1C).
Limitations in the use of this Excel-based spreadsheet to evaluate BE of generic
medications were found during the ANOVA performance. The values in the ANOVA tables
are not automatically updated when new PK values are introduced in the spreadsheet. It
requires that previous ANOVA results are removed before start a new BE assessment or if
alteration in the data set is necessary. In this case, new ANOVA must also be carried out.
Conclusion
In conclusion, the spreadsheet created in this work was functional to assess the BE of
three theoretical generic product candidates. The statistical approaches recommended to
perform BE studies were well fitted in this Excel-based BE spreadsheet. This functionality
can be expanded, which can make it a useful tool to support trials of BE studies.
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