Bioequivalence of Generic Medication.pdf

8/10/2019 Bioequivalence of Generic Medication.pdf

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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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Bioequivalence of Generic Medication.pdf