Skip to content

Advertisement

Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Generic-reference and generic-generic bioequivalence of forty-two, randomly-selected, on-market generic products of fourteen immediate-release oral drugs

BMC Pharmacology and ToxicologyBMC series – open, inclusive and trusted201718:78

https://doi.org/10.1186/s40360-017-0182-1

Received: 21 June 2017

Accepted: 22 November 2017

Published: 8 December 2017

Abstract

Background

The extents of generic-reference and generic-generic average bioequivalence and intra-subject variation of on-market drug products have not been prospectively studied on a large scale.

Methods

We assessed bioequivalence of 42 generic products of 14 immediate-release oral drugs with the highest number of generic products on the Saudi market. We conducted 14 four-sequence, randomized, crossover studies on the reference and three randomly-selected generic products of amlodipine, amoxicillin, atenolol, cephalexin, ciprofloxacin, clarithromycin, diclofenac, ibuprofen, fluconazole, metformin, metronidazole, paracetamol, omeprazole, and ranitidine. Geometric mean ratios of maximum concentration (Cmax) and area-under-the-concentration-time-curve, to last measured concentration (AUCT), extrapolated to infinity (AUCI), or truncated to Cmax time of reference product (AUCReftmax) were calculated using non-compartmental method and their 90% confidence intervals (CI) were compared to the 80.00%–125.00% bioequivalence range. Percentages of individual ratios falling outside the ±25% range were also determined.

Results

Mean (SD) age and body-mass-index of 700 healthy volunteers (28–80/study) were 32.2 (6.2) years and 24.4 (3.2) kg/m2, respectively. In 42 generic-reference comparisons, 100% of AUCT and AUCI CIs showed bioequivalence, 9.5% of Cmax CIs barely failed to show bioequivalence, and 66.7% of AUCReftmax CIs failed to show bioequivalence/showed bioinequivalence. Adjusting for 6 comparisons, 2.4% of AUCT and AUCI CIs and 21.4% of Cmax CIs failed to show bioequivalence. In 42 generic-generic comparisons, 2.4% of AUCT, AUCI, and Cmax CIs failed to show bioequivalence, and 66.7% of AUCReftmax CIs failed to show bioequivalence/showed bioinequivalence. Adjusting for 6 comparisons, 2.4% of AUCT and AUCI CIs and 14.3% of Cmax CIs failed to show bioequivalence. Average geometric mean ratio deviation from 100% was ≤3.2 and ≤5.4 percentage points for AUCI and Cmax, respectively, in both generic-reference and generic-generic comparisons. Individual generic/reference and generic/generic ratios, respectively, were within the ±25% range in >75% of individuals in 79% and 71% of the 14 drugs for AUCT and 36% and 29% for Cmax.

Conclusions

On-market generic drug products continue to be reference-bioequivalent and are bioequivalent to each other based on AUCT, AUCI, and Cmax but not AUCReftmax. Average deviation of geometric mean ratios and intra-subject variations are similar between reference-generic and generic-generic comparisons.

Trial registration

ClinicalTrials.gov identifier: NCT01344070 (registered April 3, 2011).

Background

One of the causes of economic inefficiency in healthcare is underuse of generic drug products [1], which is due, in part, to mistrust by healthcare professionals [2] and patients [3] and may be related to information availability [4], educational level [3], and healthcare system maturity [2, 5, 6].

An application for marketing approval of a generic drug product must provide evidence of its bioequivalence (BE) to a reference product that was approved based on clinical trials [79]. Although there are some differences among regulatory agencies worldwide [79], for immediate-release drugs, average bioequivalence (BE) testing is commonly performed in a single-dose, crossover study on healthy volunteers under fasting condition; with measurement of parent drug blood concentration, non-compartmental analysis of logarithmically transformed area-under-the-concentration-time curve (AUC) and maximum concentration (Cmax) data, and computation of the 90% confidence interval (CI) on the test/reference geometric mean ratio, which should generally fall within the 80–125% BE range [10, 11].

Establishing surveillance systems of on-market generic products has been advocated [4] because of sporadic concerns about post-marketing quality [1216]. Although several clinical studies [1721] failed to detect important differences between reference and generic products, direct bioequivalence studies are limited [16, 17, 22].

Under current regulations, BE studies among on-market, reference-bioequivalent, generic products are not required, which raises the theoretical concern that a generic product at one end of the BE range might not be equivalent to another at the other end [2325]. Few studies have addressed the issue; using retrospective analysis of reference-normalized data [2628], simulation [29, 30], or a prospective but restricted approach [31].

One size-fits-all BE approach may not adequately take intra-subject variability and therapeutic windows into account [3234]. Intra-subject variability can be due to intra-drug variability (physiological metabolic variability), intra-product variability (unit to unit or batch to batch), or subject-by-product interaction. Generic intra-product variability and subject-by-product interaction are especially important for narrow therapeutic index (NTI) drugs, for which the 75/75 rule (75% of individual ratios are within ±25%), among other methods of analysis, have been proposed [10, 35]. A simulation study was assuring [25] and few studies specific to antiepileptic medications [17, 28, 31] provided further support of the applicability of current BE standards to NTI drugs and led to revision of the American Epilepsy Society’s guidelines concerning reference-to-generic and generic-to-generic switching [36]. However, there are still concerns that the results may not apply to countries with less stringent control over pharmaceuticals’ quality [37].

In Saudi Arabia, the Saudi FDA requires demonstration of BE (applying the 80.00–125.00% BE limits on Cmax and AUC 90% CIs) before registering generic drug products, registered products are listed in the Saudi National Formulary, generic substitution for none-NTI drugs by pharmacists is permissive with patient’s consent, and generic prescribing is encouraged [38]. Although the Saudi FDA has a policy to reexamine the products for which it receives complaints, it does not systemically assess the BE of on-market generic products. A 2015 study on a random sample of 178 physicians in 2 hospitals in the Riyadh showed that although 52% supported substitution by local generic products, only 22% believed that Saudi FDA-approved, local generic products are therapeutically equivalent to reference products [39].

Given the tremendous cost-saving and potential improvement in healthcare accessibility provided by generic drug products, the serious clinical implications of prescribing products with unacceptable bioavailability or switching between products that are not bioequivalent, the need to alleviate patients and healthcare professionals mistrust, and the paucity of empirical data world-wide, we set the present study as a field test of the current BE standards. Our main aim was to determine the extent of BE between on-market generic and reference products and among reference-bioequivalent generic products. We also examined the percentages of individual, generic/reference and generic/generic, pharmacokinetic parameters ratios that are outside the ±25% range.

Methods

Design

We identified the 15 oral, immediate-release, non-combinational drugs with the highest number of generic products on the Saudi National Formulary. We studied 14 out of the 15 drugs because the reference (R) product of one of them (enalapril) was not available on the Saudi market. On each drug, we conducted four-product, four-sequence, four-period, sequence-randomized, crossover BE study using the R product and 3 randomly-selected generic products (Ga, Gb, and Gc). The four sequences, namely, Ga-Gb-Gc-R, Gb-R-Ga-Gc, Gc-Ga-R-Gb, and R-Gc-Gb-Ga, were designed so that every product appears the same number of time within each period and each sequence, and every product follows every other product the same number of times. Washout periods and blood sampling frames were drug-specific (Table 1) and extended to about 7 and 5 drug plasma half-lives, respectively.
Table 1

Summary of fourteen 4-product, 4-sequence, 4-period, sequence-randomized, crossover bioequivalence studies on 14 immediate-release, non-combinational, oral drugs

Drug

Participants,

no., sex

Age,

mean (SD),

year

BMI,

mean (SD),

kg/m2

Washout period,

day

Sampling frame,

hour

Withdrawals, no. (no. missed periods, reason)

Possible product

failurea, no.

(product, period)

Adverse events (no.)b

Assay

(lower quantification limit)

Amlodipine

10 mg

54 M

2 F

34.0 (7.2)

24.3 (3.0)

14

240

1 (1, venous access)

1(4, personal)

1 (reference, 3rd)

Headache (1)

Drowsiness (1)

LC-MS

(0.20 ng/ml)

Amoxicillin

500

52 M

31.2 (4.5)

24.2 (2.8)

3–7

10

3 (3, personal)

None

Dizziness (1)

HPLC

(0.50 μg/ml)

Atenolol

100 mg

52 M

30.5 (5.0)

23.0 (2.3)

7

36

2 (3, Flu-like symptoms)

2 (4, personal)

None

Flu-like symptoms (2)

Vomiting (1)

HPLC

(0.01 μg/ml

Cephalexin

500 mg

36 M

32.3 (7.3)

24.5 (5.0)

2–7

6

4 (3, personal)

None

Headache (1)

HPLC

(0.50 μg/ml)

Ciprofloxacin

500 mg

44 M

34.6 (6.5)

26.1 (3.7)

7

24

1 (2, personal)

1 (3, skin rash)

1 (4, high BP)

None

Skin rash (1)

HPLC

(0.10 μg/ml)

Clarithromycin

500 mg

48 M

30.8 (5.0)

23.5 (2.6)

7

24

1 (1, venous access)

None

Headache (1)

Stomach upset (1)

LC-MS

(5.0 ng/ml)

Diclofenac

50 mg

72 M

30.9 (5.4)

24.0 (3.0)

2–7

6

2 (1, personal)

1 (1, incompliance)

2 (3, personal)

None

Dizziness (1)

Cough (1)

HPLC

(0.02 μg/ml)

Ibuprofen

400 mg

30 M

2 F

34.6 (9.0)

25.6 (3.3)

7

10

1 (1, personal)

1 (2, personal)

4 (3, personal)

1 (reference, 2nd)

Near fainting (1)

HPLC

(0.25 μg/ml)

Fluconazole

150 mg

28 M

36.9 (8.7)

24.4 (3.0)

14

168

1 (2, skin rash)

2 (4, personal)

None

Skin rash (1)

Headache (1)

HPLC

(0.20 μg/ml)

Metformin

850 mg

52 M

31.9 (5.8)

23.9 (2.6)

7

32

1 (1, personal)

1 (2, personal)

1 (3, personal)

1 (4, personal)

None

Diarrhea (1)

Headache (1)

HPLC

(0.05 μg/ml)

Metronidazole

250 mg

28 M

31.8 (5.6)

24.3 (2.8)

7

48

None

1 (generic b, 1st)

Headache (2)

HPLC

(0.05 μg/ml)

Omeprazole

20 mg

80 M

31.8 (5.0)

24.8 (3.5)

7

12

1 (1, personal)

1 (2, personal)

3 (3, personal)

1 (4, incompliance)

1 (4, high BP)

None

Dizziness (2)

HPLC

(0.01 μg/ml)

Paracetamol

500 mg

44 M

32.3 (6.2)

24.1 (3.6)

2–7

14

1 (2, personal)

3 (3, personal)

1 (4, incompliance)

1 (generic b, 2nd)

None

HPLC

(0.10 μg/ml)

Ranitidine

150 mg

74 M

2 F

31.8 (5.5)

25.2 (3.2)

2–7

14

1 (1, personal)

1 (2, venous access)

1 (3, venous access)

1 (3, incompliance)

1 (3, vomiting)

1 (4, venous access)

None

Vomiting (2)

Diarrhea (2)

Dizziness (1)

HPLC

(0.03 μg/ml)

Eighteen blood samples were obtained during each period of each study

aThe study could not distinguish product failure from failure to take the drug

bAll adverse events were minor and resolved spontaneously. HPLC High performance liquid chromatography, LC-MS Liquid chromatography-mass spectrometry, BP Blood pressure. Flu-like, influenza-like

Participants

We enrolled healthy, non-pregnant adults (age 18–60 years) with a body mass index (BMI) ≤35 kg/m2, who accepted to abstain from taking any medication for ≥2 weeks before, and during the study, and from smoking, alcohol, and xanthene-containing beverages or food for ≥48 h before, and during each of the four study periods. Volunteers were screened by medical history, physical exam, and laboratory tests that included complete blood count, renal profile, and liver profile. Subjects with history of hypersensitivity to the drug to be tested, recent acute illness, or clinically-important laboratory tests’ abnormality were excluded. For menstruating women, the study was conducted 5 to 19 days after last menstrual period and after obtaining a negative urine pregnancy test.

The study was conducted at the King Faisal Specialist Hospital & Research Center (KFSH&RC), Riyadh from May 2011 through April 2015 in accordance with Declaration of Helsinki ethics principles and good clinical practice and after obtaining approval of the KFSH&RC Research Ethics Committee. Each participant gave written informed consent at enrolment and was compensated based on the Wage-Payment model [40] in a prorated manner.

Procedures and interventions

Reference and generic drug products were purchased from retail pharmacies in Riyadh, Saudi Arabia.

After fasting for 10 h, drug products were administered with 240 ml of water at room temperature. Fasting from food and beverages continued for 4 h post-dosing. However, volunteers were allowed 120 ml water every hour, except for 1 h before and 1 h after drug administration. Standardized breakfast and standardized dinner were given 4 and 10 h after drug administration. Meal plans were identical in the four study periods. Volunteers remained ambulatory or seated upright (unless deemed medically necessary) for 4 h after drug administration. Strenuous physical activity was not permitted during study periods.

During each study period, in addition to a baseline blood sample, 17 blood samples were drawn (Additional file 1). Sampling schedules were drug specific and were designed to collect adequate number of samples before and around the expected Cmax and across 5 half-lives of the drug. Blood samples were collected in vacutainer tubes and centrifuged for 10 min at room temperature within 15 min of collection. Plasma samples were harvested in clean polypropylene tubes and placed immediately at –80o C until analysed.

Compliance with study protocol was checked before drug administration in each study period. Volunteers were under continuous observation regarding occurrence of adverse events and compliance with study protocol during the first day of each period. In addition, they were asked about experiencing adverse events at the time of last blood collection of each period and at the beginning of subsequent periods.

Drug concentrations were blindly measured by in-house, locally-validated, reversed-phase high performance liquid chromatography (HPLC) [4152] or liquid chromatography-mass spectrometry (LC-MS) [53, 54]. Lower limits of quantification are listed in Table 1. Intra-assay coefficient of variation (standard deviation/mean * 100) and bias (measured concentration/nominal concentration * 100) were ≤3.1–14.4 and ≤5.0–17.0, respectively. A typical assay run included a series of 10 calibrators and several sets of four quality control samples (1 and 3 times lower quantification limit and 0.5 and 0.8–0.9 upper quantification limit). Samples from the four periods for each volunteer were analyzed in the same run. Samples with drug concentration greater than the upper quantification limit were re-assayed after dilution. Samples with drug concentration below the lower quantification limit were assigned zero concentration. Drug concentrations of missing samples were assigned the average concentration of the two flanking samples in the same period.

Random sampling of generic drug products and randomization

For each of the 14 drugs, all of the Saudi formulary-listed generic products were assigned sequential numbers, the numbers were arranged randomly (by MMH) using an online random number generator [55], and the three generic products corresponding to the first three randomly-arranged numbers were selected and labeled Ga, Gb, and Gc, respectively.

For each of the 14 studies, blocked (block size = 4) randomization sequences were generated (by MMH) using an online program [55]. Randomization sequences were concealed from recruiting study coordinators and from potential participants.

Sample size

Sample size for each study was estimated using an online program [56]; assuming an AUCI and Cmax ratio of generic to reference product of 1.10, a power of 0.9, a left equivalence limit of 0.80, a right equivalence limit of 1.25, and 2 one-sided type I error of 0.05, Bonferroni-adjusted for 6 comparisons (i.e., α = 0.0083). Sample size was rounded and inflated by 3–8 subjects to allow for potential withdrawals/dropouts. Intra-subject coefficient of variation (CV) was estimated from published studies as 50% of reported total CV (Additional file 2).

Outcome measures and analysis

The following pharmacokinetic parameters were determined using standard non-compartmental methods: AUCT (area-under-the-concentration-time curve from time zero to time of last measured concentration) calculated by linear trapezoidal method, AUCI (area-under-the-concentration-time curve from time 0 to infinity) calculated as AUCT plus the ratio of last measured concentration to elimination rate constant, AUCT / AUCI, Cmax (maximum concentration) determined directly from the observed data, Tmax (first time of maximum concentration) determined directly from the observed data, λ (apparent first-order elimination rate constant) calculated by linear least-squares regression analysis from the last 4–8 quantifiable concentrations of a plot of natural log-transformed concentration versus time curve, t½ (terminal elimination half-life) calculated as ln 2/ λ, AUC72 (area-under-the-concentration-time curve truncated to 72 h) calculated by linear trapezoidal method, and AUCReftmax (area-under-the-concentration-time curve to Tmax of reference product, calculated for each subject) calculated by linear trapezoidal method. When λ was not calculable in a given study period, the average of λs in other periods of the same volunteer was used to calculate AUCI for that period. AUCReftmax was not calculated when data for the reference product were missing. Each generic AUCReftmax with zero value was assigned 0.001 in order to perform log-transformation. Pharmacokinetic and statistical analyses included all evaluable data of all volunteers.

Primary outcome measures were Cmax, AUCT, and AUCI. Secondary outcome measures were Tmax, AUCReftmax, and AUC72. The four products of each drug were compared by analysis of variance (ANOVA). The ANOVA model included, product, period, sequence, and subjects nested in sequence. Mean square residual (MSR) was used to test significance of period and product effects. Subjects nested in sequence mean square was used to test significance of sequence effect. For each pharmacokinetic parameter (except Tmax), six pairwise (Ga-R, Gb-R, Gc-R, Ga-Gb, Gb-Gc, and Ga-Gc) 90% CIs on the difference between means of log-transformed values (i.e., geometric mean ratio) were determined using MSR without and with Bonferroni adjustment for 3 or 6 comparisons, and the antilogs of the 90% CI limits were compared to the BE limits of 80.00% and 125.00%. The null hypothesis (lack of bioequivalence) was rejected if the 90% CI was completely within 80.00% to 125.00%. If the null hypothesis was not rejected, the analysis would indicate either failure to show bioequivalence (the 90% CI crosses the BE limits) or bioinequivalence (the 90% CI is completely outside the BE limits). to The following were also calculated: percentage of generic products that are not bioequivalent to their reference product or not bioequivalent to each other based on Cmax, AUCT, AUCI, or AUCReftmax, mean (SD) deviation of AUCT, AUCI, and Cmax generic-reference and generic-generic point estimates from 100% and percentages of the deviations that were <6, <10, or >13 percentage points, percentage of individual Cmax, AUCT, AUCI, AUC72, Tmax, and AUCReftmax generic/reference and generic/generic ratios that are ˂75% or ˃125%, and percentage of drugs that failed to fulfil the 75/75 rule (i.e.,75% of individual ratios are within ±25%) for each of the pharmacokinetic parameters. Pharmacokinetic and statistical analyses were performed (by MMH) on a personal computer using Microsoft Excel (Version 2010) with add-ins (PK Functions for Microsoft Excel, JI Usansky, A Desai, and D Tang-liu, Department of pharmacokinetics and Drug Metabolism, Allergan Irvine, CA, USA) and IBM SPSS Statistics version 21 software, respectively.

Results

The 14 immediate-release, non-combinational, oral drugs with the highest number of generic products on the Saudi National Formulary that were assessed were, in descending order, ciprofloxacin (18 generic products), ranitidine, amoxicillin, paracetamol, atenolol, cephalexin, ibuprofen, diclofenac, metformin, omeprazole, metronidazole, clarithromycin, amlodipine, and fluconazole (7 generic products). Commercial name, manufacturer name, formulation, strength, lot/batch number, manufacture date, and expiry date for the reference and the 3 randomly-selected generic products as well as the number of listed generic products are presented in Additional file 3. About 52% of the 42 generic products were manufactured in Saudi Arabia, 14% in other Gulf States, 31% in Arabic non-Gulf States, and 2% in Portugal.

Seven hundred healthy volunteers participated in 14, four-product, four-sequence, four-period, sequence-randomized, crossover, BE studies. As shown in Table 1, the number of volunteers per study ranged from 28 to 80. The volunteers were 100% males for all but 3 studies which had 3–6% females. Mean (SD) age ranged from 30.5 (5.0) to 36.9 (8.7) years and mean BMI ranged from 23.0 (2.3) to 26.1 (3.7) kg/m2 per study (grand mean age and BMI 32.2 (6.2) years and 24.4 (3.2) kg/m2, respectively). Withdrawal from ≥ one period ranged from 0% to 19% per drug, with a total of 145 missed periods (out of 2800). Withdrawal reasons were mostly personal but also included inadequate venous access, skin rash, vomiting, high blood pressure, and influenza-like symptoms, as well as incompliance (Table 1). Adverse events occurred in 0% (paracetamol) to 7% (fluconazole and metronidazole) of volunteers (Table 1); all were minor and resolved spontaneously.

Baseline drug concentration was not detectable in any period for any of the 14 drugs, indicating adequate wash-out periods. There were 12 missed blood samples (2 for clarithromycin, 5 for fluconazole, and 5 for ranitidine) out of the 47,790 scheduled samples (excluding withdrawals); these samples were assigned the average concentration of the two flanking samples of the same volunteer in the same period. In all samples of one volunteer, there was a plasma peak that interfered with the diclofenac assay; this volunteer was excluded from further analysis. In four volunteers, there was no measurable drug concentration in any sample from one study period only (amlodipine, R, 3rd period; ibuprofen, R, 2nd period; metronidazole, Gb, 1st period; and paracetamol, Gb, 2nd period). The unmeasurable concentrations could be due to product failure as the drugs were administered by one of the investigators and the volunteers denied incompliance when confronted; however, incompliance cannot be ruled out. Mean concentration-time and log-concentration-time curves of the reference and the three generic products of each of the 14 drugs are presented in Additional files 4 and 5, respectively. We were not able to calculated λ in a total of 27 (1%) out of the 2647 pharmacokinetic analyses (clarithromycin: (1) Ga, (3) Gb, and (1) Gc; diclofenac: (4) Ga, (4) Gb, (3) Gc, and (7) R; omeprazole: (1) Gb, (2) Gc, and (1) R). Average of λs in other periods of the same volunteer was used to calculate AUCI for these 27 analyses. No outlier values for any of the pharmacokinetic parameters were identified or removed from analysis. AUCT, AUCI, Cmax, Tmax, λ, t1/2, Cmax/AUCI, AUCT/AUCI, AUCReftmax, and AUC72 of the reference and the three randomly-selected generic products of each drug are summarized in Additional file 6. AUCT/AUCI ranged from 90% (ciprofloxacin) to 98% (clarithromycin), indicating adequate sampling frames.

MSR from ANOVA analysis and calculated intra-subject CV for AUCT, AUCI, and Cmax of each drug are presented in Table 2. Significant product, period, and sequence effects on AUCT, AUCI, and Cmax of the 14 drugs are summarized in Additional file 7. MSR and intra-subject CV for AUCReftmax and AUC72 are presented in Additional files 8 and 9, respectively.
Table 2

Average bioequivalence among 3 randomly-selected generic products and reference product of 14 immediate-release, non-combinational, oral drugs

Drug

AUCT

AUCI

Cmax

Amlodipine

MSR 0.021, CV 14.6%

MSR 0.020, CV 14.2%

MSR 0.027, CV 16.5%

 Generic a vs Reference (54)

98.24% (93.76–102.94)

97.84% (93.48–102.41)

96.735% (91.75–102.00)

 Generic b vs Reference (54)

96.61% (92.20–101.23)

95.83% (91.56–100.30)

94.578% (89.699–99.72)

 Generic c vs Reference (53)

98.95% (94.39–103.72)

98.14% (93.72–102.76)

94.569% (89.645–99.76)

 Generic a vs Generic b (55)

102.34% (97.71–107.19)

102.70% (98.16–107.47)

101.71% (96.51–107.19)

 Generic b vs Generic c (54)

96.90% (92.56–101.63)

97.11% (92.78–101.64)

99.84% (94.69–105.27)

 Generic a vs Generic c (54)

99.25% (94.72–104.00)

99.76% (95.31–104.41)

101.498% (96.26–107.02)

Amoxicillin

MSR 0.012, CV 11.0%

MSR 0.011, CV 10.5%

MSR 0.037, CV 19.4%

 Generic a vs Reference (49)

100.68% (97.01–104.49)

100.48% (96.97–104.12)

98.87% (92.63–105.53)

 Generic b vs Reference (49)

107.45% (103.53–111.52)

106.92% (103.19–110.79)

109.20% (102.30–116.55)

 Generic c vs Reference (49)

104.99% (101.16–108.96)

104.78% (101.12–108.58)

111.32% (104.29–118.82)

 Generic a vs Generic b (49)

93.98% (90.70–97.38)

93.95% (90.67–97.35)

90.54% (84.83–96.65)

 Generic b vs Generic c (49)

101.04% (98.48–105.73)

101.93% (98.36–105.62)

98.10% (91.90–104.71)

 Generic a vs Generic c (49)

95.89% (92.55–99.37)

95.76% (92.41–99.23)

88.82% (83.21–94.80)

Atenolol

MSR 0.037, CV 19.4%

MSR 0.036, CV19.2%

MSR 0.055, CV 23.8%

 Generic a vs Reference (48)

105.84% (99.08–113.05)

105.52% (98.88–112.61)

106.46% (98.24–115.37)

 Generic b vs Reference (48)

103.12% (96.54–110.14)

102.71% (96.25–109.61)

103.10% (95.14–111.73)

 Generic c vs Reference (48)

111.87% (104.73–119.49)

111.41% (104.41–118.90)

106.58% (98.35–115.49)

 Generic a vs Generic b (48)

102.64% (96.09–109.63)

102.74% (96.27–109.64)

103.26% (95.29–111.90)

 Generic b vs Generic c (48)

92.18% (86.30–98.46)

92.19% (86.39–98.38)

96.74% (89.27–104.84)

 Generic a vs Generic c (48)

94.61% (88.58–101.06)

94.71% (88.75–101.08)

99.89% (92.18–108.25)

Cephalexin

MSR 0.008, CV 8.9%

MSR 0.008, CV 8.9%

MSR 0.040, CV 20.3%

 Generic a vs Reference (32)

99.46% (95.77–103.29)

95.50% (92.98–99.16)

107.53% (98.73–117.10)

 Generic b vs Reference (32)

101.43% (97.67–105.34)

101.18% (97.45–105.06)

95.11% (87.33–103.58)

 Generic c vs Reference (32)

98.65% (94.99–102.41)

98.44% (94.81–102.21)

105.52% (96.89–114.92)

 Generic a vs Generic b (32)

94.39% (90.91–98.00)

94.36% (90.88–97.97)

113.06% (103.81–123.13)

 Generic b vs Generic c (32)

102.79% (99.99–106.72)

102.86% (99.07–106.80)

90.13% (82.76–98.16)

 Generic a vs Generic c (32)

97.02% (93.44–100.73)

97.06% (93.48–100.77)

101.90% (93.57–110.98)

Ciprofloxacin

MSR 0.012, CV11.0%

MSR 0.012, CV 11.0%

MSR 0.020, CV14.2%

 Generic a vs Reference (41)

93.40% (89.67–97.29)

92.99% (89.28–96.86)

94.20% (89.37–99.29)

 Generic b vs Reference (41)

98.38% (94.45–102.47)

97.51% (93.62–101.57)

92.92% (88.15–97.94)

 Generic c vs Reference (41)

101.77% (97.71–106.01)

101.37% (97.32–105.59)

103.39% (98.09–108.98)

 Generic a vs Generic b (41)

94.94% (91.15–98.89)

95.36% (91.55–99.33)

101.38% (96.18–106.86)

 Generic b vs Generic c (42)

91.78% (88.11–95.60)

91.74% (88.07–95.55)

91.11% (86.44–96.04)

 Generic a vs Generic c (41)

96.83% (93.01–100.81)

96.39% (92.59–100.35)

90.16% (85.60–94.97)

Clarithromycin

MSR 0.060, CV 24.9%

MSR 0.057, CV 24.2%

MSR 0.100, CV 32.4%

 Generic a vs Reference (48)

96.40% (88.64–104.85)

96.91% (89.30–105.17)

93.85% (84.22–104.60)

 Generic b vs Reference (47)

102.52% (94.18–111.60)

103.61% (95.39–112.54)

96.28% (86.29–107.42)

 Generic c vs Reference (48)

89.22% (82.04–97.04)

89.83% (82.77–97.49)

87.74% (78.73–97.78)

 Generic a vs Generic b (47)

93.97% (86.32–102.29)

93.42% (86.01–101.48)

96.98% (86.92–108.21)

 Generic b vs Generic c (47)

115.23% (105.85–125.43)

115.70% (106.51–125.68)

109.97% (98.56–122.70)

 Generic a vs Generic c (48)

108.05% (99.35–117.51)

107.88% (88.41–117.08)

106.97% (95.98–119.21)

Diclofenac

MSR 0.023, CV 15.3%

MSR 0.022, CV 14.7%

MSR 0.129, CV 37.1%

 Generic a vs Reference (67)

100.03% (95.74–104.52)

100.19% (96.04–104.52)

86.61% (78.08–96.08)

 Generic b vs Reference (68)

99.80% (95.54–104.24)

99.77% (95.68–104.05)

92.48% (83.43–102.51)

 Generic c vs Reference (68)

103.74% (99.32–108.36)

104.01% (99.74–108.47)

86.46% (78.00–95.83)

 Generic a vs Generic b (68)

101.38% (97.06–105.89)

101.50% (97.33–105.85)

95.64% (86.28–106.01)

 Generic b vs Generic c (69)

96.38%(92.30–100.64)

96.06% (92.14–100.14)

107.01% (96.61–118.52)

 Generic a vs Generic c (68)

96.71% (92.59–101.01)

96.56% (92.59–100.70)

100.79% (90.93–111.72)

Ibuprofen

MSR 0.012, CV 10.9%

MSR 0.008, CV 9.1%

MSR 0.026, CV 16.3%

 Generic a vs Reference (27)

106.31%(101.06–111.83)

104.65% (100.30–109.19)

101.67% (94.30–109.62)

 Generic b vs Reference (25)

105.48% (100.05–111.20)

102.94% (98.48–107.59)

113.00% (104.47–122.23)

 Generic c vs Reference (26)

106.30% (100.95–111.94)

105.69% (101.21–110.36)

89.11% (82.52–96.22)

 Generic a vs Generic b (26)

102.27% (97.11–107.69)

103.13% (98.76–107.69)

92.17% (85.36–99.53)

 Generic b vs Generic c (26)

98.38% (93.418–103.60)

96.53% (92.44–100.80)

126.47% (117.11–136.57)

 Generic a vs Generic c (27)

99.87% (94.94–105.06)

98.87% (94.78–103.16)

114.12% (105.85–123.04)

Fluconazole

MSR 0.004, CV 6.3%

MSR 0.004, CV 6.3%

MSR 0.006, CV 7.8%

 Generic a vs Reference (26)

101.33% (98.34–104.42)

102.23% (99.21–105.35)

106.99% (103.13–110.99)

 Generic b vs Reference (25)

101.06% (98.01–104.21)

101.39% (98.33–104.55)

109.79% (105.75–113.99)

 Generic c vs Reference (25)

105.66% (102.47–108.94)

106.07% (102.86–109.36)

109.00% (104.98–113.17)

 Generic a vs Generic b (25)

100.38% (97.35–103.50)

100.39% (97.36–103.52)

97.59% (93.99–101.32)

 Generic b vs Generic c (25)

95.65% (92.77–98.63)

96.21% (93.31–99.21)

100.73% (97.01–104.58)

 Generic a vs Generic c (25)

96.01% (93.12–99.00)

96.59% (93.68–99.60)

98.30% (94.67–102.06)

Metformin

MSR 0.019, CV 13.8%

MSR 0.019, CV 13.8%

MSR 0.027, CV 16.5%

 Generic a vs Reference (48)

93.19% (88.89–97.69)

92.44% (88.17–96.91)

93.05% (87.96–98.44)

 Generic b vs Reference (48)

97.70% (93.19–102.42)

97.31% (92.82–102.01)

98.45% (93.06–104.15)

 Generic c vs Reference (49)

96.06% (91.68–100.66)

95.51% (91.15–100.08)

95.07% (89.92–100.52)

 Generic a vs Generic b (49)

100.80% (97.76–103.94)

95.06% (90.72–99.61)

94.41% (89.298–99.82)

 Generic b vs Generic c (49)

95.60% (92.71–98.57)

102.25% (97.61–107.17)

104.15% (98.50–110.11)

 Generic a vs Generic c (49)

96.36% (93.46–99.36)

97.23% (92.79–101.88)

98.33% (93.00–103.96)

Metronidazole

MSR 0.003, CV 5.5%

MSR 0.003, CV 5.5%

MSR 0.010, CV 10.0%

 Generic a vs Reference (28)

108.73% (106.05–111.48)

108.96% (106.28–111.72)

109.47% (104.60–114.58)

 Generic b vs Reference (27)

99.569% (97.07–102.14)

99.82% (97.31–102.39)

97.60% (93.17–102.24)

 Generic c vs Reference (28)

97.439% (95.04–99.90)

97.46% (95.06–99.92)

100.53% (96.05–105.22)

 Generic a vs Generic b (27)

109.349% (106.60–112.17)

109.32% (106.57–112.14)

111.57% (106.50–116.88)

 Generic b vs Generic c (27)

102.05% (99.49–104.68)

102.29% (99.72–104.93)

96.95% (92.55–101.56)

 Generic a vs Generic c (28)

111.59% (108.84–114.41)

111.81% (109.05–114.63)

108.90% (104.05–113.98)

Omeprazole

MSR 0.035, CV 18.9%

MSR 0.035, CV 18.9%

MSR 0.066, CV 26.1%

 Generic a vs Reference (74)

97.49% (92.62–102.62)

97.44% (92.57–102.56)

90.57% (84.41–97.17)

 Generic b vs Reference (73)

96.21% (91.37–101.30)

97.05% (92.17–102.19)

84.85% (79.04–91.08)

 Generic c vs Reference (74)

98.14% (93.24–103.30)

98.09% (93.19–103.25)

87.82% (81.85–94.23)

 Generic a vs Generic b (73)

101.33% (96.24–106.70)

100.34% (95.30–105.66)

106.59% (99.30–114.4)

 Generic b vs Generic c (74)

97.59% (92.71–102.72)

98.55% (93.63–103.73)

96.35% (89.80–103.37)

 Generic a vs Generic c (74)

99.34% (94.38–104.56)

99.33% (94.37–104.55)

103.13% (96.12–110.64)

Paracetamol

MSR 0.008, CV 8.8%

MSR 0.008, CV 9.1%

MSR 0.031, CV 17.6%

 Generic a vs Reference (40)

91.57% (88.62–94.62)

91.81% (88.76–94.96)

104.99% (98.30–112.14)

 Generic b vs Reference (38)

99.69% (96.35–103.14)

99.66% (96.22–103.22)

103.48% (96.71–110.73%)

 Generic c vs Reference (39)

97.95% (94.71–101.29)

97.86% (94.53–101.30)

101.17% (94.63–108.15)

 Generic a vs Generic b (38)

100.01% (96.71–103.43)

100.26% (96.85–103.79)

101.76% (95.18–108.78)

 Generic b vs Generic c (38)

94.29% (91.18–97.52)

94.29% (91.09–97.62)

102.27% (95.57–109.43)

 Generic a vs Generic c (39)

102.11% (98.74–105.60)

102.46% (98.97–106.07)

104.11%(97.38–111.30)

Ranitidine

MSR 0.021, CV 14.6%

MSR 0.020, CV 14.2%

MSR 0.047, CV 21.9%

 Generic a vs Reference (70)

102.68% (98.57–106.96)

102.43% (98.43–106.59)

105.26% (99.02–111.89)

 Generic b vs Reference (71)

102.54% (98.47–106.79)

102.50% (98.52–106.64)

98.21% (92.43–104.34)

 Generic c vs Reference (72)

101.84% (97.82–106.02)

101.81% (97.89–105.89)

104.51% (98.40–111.00)

 Generic a vs Generic b (70)

100.30% (96.38–104.37)

100.29% (96.37–104.37)

107.89% (101.49–114.69)

 Generic b vs Generic c (71)

100.27% (96.38–104.31)

100.34% (96.45–104.39)

93.64% (88.13–99.49)

 Generic a vs Generic c (70)

100.30% (96.38–104.38)

100.38% (96.45–104.46)

100.66% (94.70–107.01)

AUCT is the area-under-the-concentration-time curve to last measured concentration. AUCI is AUC extrapolated to infinity. Cmax is maximum concentration. Data represent geometric mean ratios and unadjusted 90% confidence intervals. The number of subjects analyzed in each comparison is presented between parentheses in the first column. MSR is mean square residual from analysis of variance (ANOVA). CV is intra-subject coefficient of variation calculated as 100 x (exp(MSR)-1)0.5. Confidence intervals that cross the 80.00%–125.00% bioequivalence limits are bolded

Average bioequivalence of 3 on-market generic products to the reference product of 14 drugs

Table 2 summarizes the results of the 42 predetermined BE analyses comparing three randomly-selected generic products to the corresponding reference product of each of the 14 drugs. The results are also depicted in Fig. 1. None of the AUCT or AUCI 90% CIs failed to show bioequivalence and 9.5% of Cmax 90% CIs only barely failed to show bioequivalence. When analyses were adjusted for 3 comparisons, 2.4% of AUCT 90% CIs, 0% of AUCI 90% CIs, and 11.9% of Cmax 90% CIs failed to show bioequivalence, and none showed bioinequivalence. When analyses were adjusted for 6 comparisons, 2.4% of AUCT 90% CIs (clarithromycin Gc vs. R), 2.4% of AUCI 90% CIs (clarithromycin Gc vs. R), and 21.4% of Cmax 90% CIs (clarithromycin Ga and Gc vs. R; diclofenac Ga, Gb, and Gc vs. R; ibuprofen Gb and Gc vs. R; omeprazole Gb and Gc vs. R) failed to show bioequivalence, and none showed bioinequivalence.
Figure 1
Fig. 1

Average bioequivalence of randomly-selected generic products to the reference product of 14 immediate-release, non-combinational, oral drugs. Each reference product (R) was compared to 3 generic products (Ga, Gb, Gc). Data represent generic/reference geometric mean ratios and unadjusted 90% confidence intervals. The shaded area indicates the area of bioequivalence (80.00%–125.00%). a Evaluation of area-under-the-concentration-time curve to last measured concentration (AUCT). b Evaluation of area-under-the-concentration-time curve extrapolated to infinity (AUCI). c Evaluation of maximum concentration (Cmax)

Mean absolute (SD) deviation of point estimates from 100% in the 42 comparisons was 3.2 (1.8), 3.2 (1.4), and 5.4 (3.3) percentage points for AUCT, AUCI, and Cmax, respectively. Further, the deviation was ˂10 percentage points in 95.2%, 95.2%, and 81.0% of the AUCT, AUCI, and Cmax comparisons, respectively. Furthermore, 0 % of the AUCT and AUCI and 9.5% of the Cmax deviations were >13 percentage points and 78.6%, 81.0%, and 50.0%, respectively, were <6 percentage points.

Figure 2 (a) depicts BE analysis of AUCReftmax between the three generic products and the corresponding reference product of each of the 14 drugs. The data are also summarized in Additional file 8. Twenty two (52.4%) of the 90% CIs failed to show bioequivalence. In addition, 6 (14.3%) showed bioinequivalence. Figure 2 (b) depicts BE analysis of AUC72 between the three generic products and the corresponding reference product of the two drugs with long half-life (amlodipine and fluconazole). BE was demonstrated by all of the six 90% CIs. The data are also summarized in Additional file 9.
Figure 2
Fig. 2

Average bioequivalence of randomly-selected generic products to the reference product of 14 immediate-release, non-combinational, oral drugs. Each reference product (R) was compared to 3 generic products (Ga, Gb, Gc). Data represent generic/reference geometric mean ratios and unadjusted 90% confidence intervals. The shaded area indicates the area of bioequivalence (80.00%–125.00%). a Evaluation of area-under-the-concentration-time curve to time of maximum concentration of reference product, calculated for each subject (AUCReftmax). b Evaluation of area-under-the-concentration-time curve truncated to 72 h (AUC72). Only 2 drugs (amlodipine and fluconazole) in this study have terminal half-life >72 h

Individual pharmacokinetic parameter ratios of 3 on-market generic products to the reference product of 14 drugs

There were 1950 individual generic-reference comparisons. The percentages of individual AUCT, AUCI, and Cmax, ratios that were outside the ±25% range are presented in Fig. 3. On average, 16% of the AUCT ratios (ranging from 2% for cephalexin to 35% for atenolol and clarithromycin), 15% of the AUCI ratios (ranging from 2% for cephalexin to 34% for clarithromycin), and 32% of Cmax ratios (ranging from 8% for metronidazole to 57% for diclofenac), were outside the ±25% range. Further, individual AUCT, AUCI, and Cmax, ratios were within the ±25% range in ˃75% of individuals (i.e., fulfilled the 75/75 rule) for 79%, 79%, and 36% of the 14 drugs, respectively.
Figure 3
Fig. 3

Individual pharmacokinetic ratios of randomly-selected generic products to the reference product of 14 immediate-release, non-combinational, oral drugs. Each reference product (R) was compared to 3 generic products (Ga, Gb, Gc). Data represent percentage of individual generic/reference ratios that are <0.75 (closed bars) or >1.25 (open bars). a Evaluation of area-under-the-concentration-time curve to last measured concentration (AUCT). b Evaluation of area-under-the-concentration-time curve extrapolated to infinity (AUCI). c Evaluation of maximum concentration (Cmax)

Out of 161 and 76 AUC72 individual ratios for amlodipine and fluconazole, 16% and 1%, respectively, were outside the ±25% range (compared to 18% and 3%, respectively, for AUCT).

Figure 4 depicts the percentages of individual generic/reference Tmax and AUCReftmax ratios that were outside the ±25% range. On average, 60% of the Tmax ratios (ranging from 43% for amoxicillin to 72% for ibuprofen) and 58% of the AUCReftmax ratios (ranging from 27% for metformin to 89% for omeprazole) were outside the ±25% range. Individual Tmax and AUCReftmax ratios were within the ±25% range in ˃75% of individuals for none of the 14 drugs, respectively.
Figure 4
Fig. 4

Individual pharmacokinetic ratios of randomly-selected generic products to the reference product of 14 immediate-release, non-combinational, oral drugs. Each reference product (R) was compared to 3 generic products (Ga, Gb, Gc). Data represent percentage of individual generic/reference ratios that are <0.75 (closed bars) or >1.25 (open bars). a Evaluation of time of maximum concentration (Tmax). b Evaluation of area-under-the-concentration-time curve to time of maximum concentration of reference product, calculated for each subject (AUCReftmax)

Average bioequivalence among 3 on-market generic products of 14 drugs

Table 2 also summarizes the results of the 42 predetermined BE analyses among the three randomly-selected generic products of each of the 14 drugs. The results are also depicted in Fig. 5. Only one (2.4%) of each of the AUCT, AUCI, and Cmax 90% CIs failed to show bioequivalence. When analyses were adjusted for 3 comparisons, 2.4% of AUCT and AUCI 90% CIs and 9.5% of Cmax 90% CIs failed to show bioequivalence, and none showed bioinequivalence. When analyses were adjusted for 6 comparisons, 2.4% of AUCT and AUCI (clarithromycin Gb vs. Gc) and 14.3% of Cmax 90% CIs (cephalexin Ga vs. Gb and Gb vs. Gc; clarithromycin Gb vs. Gc and Ga vs. Gc; ibuprofen Gb vs. Gc and Ga vs. Gc) failed to show bioequivalence, and none showed bioinequivalence.
Figure 5
Fig. 5

Average bioequivalence among randomly-selected, reference-bioequivalent generic products of 14 immediate-release, non-combinational, oral drugs. Three generic products (Ga, Gb, Gc) were compared. Data represent generic/generic geometric mean ratios and unadjusted 90% confidence intervals. The shaded area indicates the area of bioequivalence (80.00%–125.00%). a Evaluation of area-under-the-concentration-time curve to last measured concentration (AUCT). b Evaluation of area-under-the-concentration-time curve extrapolated to infinity (AUCI). c Evaluation of maximum concentration (Cmax)

Mean absolute (SD) deviation of point estimates from 100% in the 42 comparisons was 2.5 (2.3), 2.6 (2.2), and 3.3 (3.1) percentage points for AUCT, AUCI, and Cmax, respectively. Further, the deviation was <10 percentage points in 95.2%, 95.2%, and 88.1% of the AUCT, AUCI, and Cmax comparisons, respectively. Furthermore, only 2.4% of the AUCT and AUCI and 7.1% of the Cmax deviations were >13 percentage points and 81.0%, 81.0%, and 59.5%, respectively, were <6 percentage points.

Figure 6 (a) depicts BE analysis of AUCReftmax among the three generic products of each of the 14 drugs. The data are also summarized in Additional file 8. Twenty three (54.8%) of the 90% CIs failed to show bioequivalence. In addition, 5 (11.9%) showed bioinequivalence. Figure 6 (b) depicts BE analysis of AUC72 among the three generic products of the two drugs with long half-life. BE was demonstrated by all of the six 90% CIs. The data are also summarized in Additional file 9.
Figure 6
Fig. 6

Average bioequivalence among randomly-selected, reference-bioequivalent generic products of 14 immediate-release, non-combinational, oral drugs. Three generic products (Ga, Gb, Gc) were compared. Data represent generic/generic geometric mean ratios and unadjusted 90% confidence intervals. The shaded area indicates the area of bioequivalence (80.00%–125.00%). a Evaluation of area-under-the-concentration-time curve to time of maximum concentration of reference product, calculated for each subject (AUCReftmax). b Evaluation of area-under-the-concentration-time curve truncated to 72 h (AUC72). Only 2 drugs (amlodipine and fluconazole) in this study have terminal half-life >72 h

Individual pharmacokinetic parameter ratios among 3 on-market generic products of 14 drugs

There were 1952 individual generic-generic comparisons. The percentages of individual AUCT, AUCI, and Cmax, ratios that were outside the ±25% range are presented in Fig. 7. On average, 17% of the AUCT ratios (ranging from 1% for metronidazole and fluconazole to 40% for clarithromycin), 16% of the AUCI ratios (ranging from 1% for metronidazole and fluconazole to 38% for clarithromycin), and 32% of the Cmax ratios (ranging from 5% for fluconazole to 59% for diclofenac) were outside the ±25% range. Further, individual AUCT, AUCI, and Cmax ratios were within the ±25% range in ˃75% of individuals for 71%, 71%, and 29% of the 14 drugs, respectively,
Figure 7
Fig. 7

Individual pharmacokinetic ratios among randomly-selected, reference-bioequivalent generic products of 14 immediate-release, non-combinational, oral drugs. Three generic products (Ga, Gb, Gc) were compared. Data represent percentage of individual generic/generic ratios that are <0.75 (closed bars) or >1.25 (open bars). a Evaluation of area-under-the-concentration-time curve to last measured concentration (AUCT). b Evaluation of area-under-the-concentration-time curve extrapolated to infinity (AUCI). c Evaluation of maximum concentration (Cmax)

Out of 161 and 76 AUC72 individual ratios for amlodipine and fluconazole, 19% and 1%, respectively, were outside the ±25% range (compared to 25% and 1%, respectively, for AUCT).

Figure 8 depicts the percentages of individual generic/generic Tmax and AUCReftmax ratios that were outside the ±25% range. On average, 58% of the Tmax ratios (ranging from 42% for amlodipine to 73% for fluconazole) and 52% of the AUCReftmax ratios (ranging from 18% for fluconazole to 82% for omeprazole) were outside the ±25% range. Individual Tmax and AUCReftmax ratios were within the ±25% range in >75% of individuals for 0% and 7% of the 14 drugs, respectively.
Figure 8
Fig. 8

Individual pharmacokinetic ratios among randomly-selected, reference-bioequivalent generic products of 14 immediate-release, non-combinational, oral drugs. Three generic products (Ga, Gb, Gc) were compared. Data represent percentage of individual generic/generic ratios that are <0.75 (closed bars) or >1.25 (open bars). a Evaluation of time of maximum concentration (Tmax). b Evaluation of area-under-the-concentration-time curve to time of maximum concentration of reference product, calculated for each subject (AUCReftmax)

Discussion

We assessed the adequacy of the commonly-used BE standards and of their application in a developing country through determining BE extent between on-market generic and reference drug products and among reference-bioequivalent generic drug products. We studied 42 generic products of 14 immediate-release, non-combinational, oral drugs with the highest number of generic products on the Saudi market. We conducted a four-product, four-period, four-sequence, sequence-randomized, crossover BE study with a planned power of 0.9 on a reference and three randomly-selected generic products of each of the 14 drugs. For each drug, we computed six pairwise 90% CIs on geometric mean ratios of AUCT, AUCI, Cmax, AUCReftmax, and AUC72 without and with adjustment for multiple comparisons and determined percentages of individual untransformed ratios that fell outside the ±25%. We found that: 1) On-market generic drug products continue to be reference-bioequivalent. 2) Reference-bioequivalent generic products are bioequivalent to each other. 3) Reference-generic and generic-generic average deviations are small and similar. 4) Reference-generic and generic-generic Cmax intra-subject variations are large but similar. 5) Two thirds of generic-reference and generic-generic AUCReftmax comparisons failed to show average bioequivalence/showed bioinequivalence.

The number of generic products for an off-patent drug is usually related to its market size. Therefore, it is reasonable to assume that the 14 drugs that we studied are among the commonly prescribed drugs in Saudi Arabia. They happened to include drugs for which rapid onset of action is clinically relevant (paracetamol, ibuprofen, diclofenac), drugs that are used in chronically and for which the concept of switchability is relevant (metformin, amlodipine), drugs with long half-life (fluconazole, amlodipine), and highly variable drugs (clarithromycin, diclofenac), but not NTI drugs. Almost all of the generic products were manufactured in Saudi Arabia or in a Middle Eastern state.

Marketed generic products of immediate-release, non-computational, oral drugs continue to be bioequivalent to their corresponding reference products

A generic drug product is commonly approved for continued marketing based on a single pre-marketing study demonstrating BE to its reference product; retesting of BE post-marketing is not routinely required. Our results confirm the validity of such practice. Using the 80.00–125.00% BE range, we found that 100% of the AUCT and AUCI generic-reference 90% CIs showed BE and only 9.5% of the Cmax 90% CIs barely failed to show BE. Even after adjusting for 6 comparisons, only 2.4% of the AUCT and AUCI 90% CIs and 21.4% of the Cmax 90% CIs failed to show BE. Our results are in line with some [17, 22] but not all [15, 16] published studies. Previous studies evaluated generic products on other national markets, examined only one [17] or two [16, 22] generic products of a single drug, or were not performed in vivo [15].

The outcome of a crossover BE study is affected by its sample size and intra-subject variability [57]. We estimated intra-subject CVs from published studies and planned each of the 14 studies to have a power of 0.9. It is of note that for the 4 drugs that failed to show BE in some of the comparisons (clarithromycin, diclofenac, ibuprofen, and omeprazole), current study intra-subject CVs were larger than estimated (Additional file 2). Intra-subject variability can be related to inter-product variability; however, it can be also attributed to the drug substance itself (being readily affected by intra-subject physiological variability), intra-product variability, analytical variability, or unexplained random variability [57]. In fact, in a separate study [58] that compared the reference ibuprofen product used in this study to itself, using the same settings and a larger sample size, the Cmax 90% CI also failed to show BE. This suggests that at least some of the failures to show BE in the current study may not be due to real generic-reference (inter-product) differences.

We found that the mean deviation of the generic/reference ratio from 100% was 3.2%, 3.2%, and 5.4% for AUCT, AUCI, and Cmax, respectively, and that the deviation was <10 percentage points in 95.2%, 95.2%, and 81.0% of the 42 comparisons. Similarly, the US FDA found a mean deviation of 3.47% for AUCT and 4.29% for Cmax in one retrospective study [59] and 3.56% for AUCT and 4.35% for Cmax in another [60], and that in about 98% of the studies, the AUCT difference was <10% [60]. Further, a reanalysis of 141 US FDA-approved antiepileptic generic products found that generic and reference AUCT and Cmax differed by <15% in 99% and 89% of BE studies, respectively [28]. Consistent with these BE findings, several meta-analysis and reviews showed that there is no evidence that cardiovascular [18, 19], antiepileptic [20], or immunosuppressive [21] reference drug products are superior to their generic counterparts in terms of efficacy or side effects.

Reference-bioequivalent generic drug products continue to be underused world-wide, mainly due to mistrust by healthcare professionals [2] and patients [3], in a way that may be dependent on maturity of the country’s healthcare system [2, 5, 6]. The misbelief that generic medicines are counterfeits and the placebo effect of packaging and price differential are important to consider [61]. Further, prescribing a generic product by its brand name rather than its non-proprietary name (generic prescribing) may better convey the impression of individuality and improve patients’ acceptance [62, 63]. Importantly, information availability to healthcare professionals and patients has been identified as a facilitator of generic products uptake [4, 39]. Our results provide strong supporting evidence of the post-marketing quality of generic products and of the adequacy of the current BE standards.

Marketed, reference-bioequivalent, generic products of immediate-release, non-combinational, oral drugs are bioequivalent to each other

Commonly, there are several same-market drug products that are linked by a chain of reference; theoretical concerns have been raised that reference-bioequivalent generic products may not be bioequivalent to each other if their BE point estimates were on the opposite sides within the BE range [23, 24]. Simulation studies predicted that two reference-bioequivalent generic products are likely to be equivalent to each other only under relatively restricted conditions [29, 30]. However, using reference-normalized data to indirectly estimate 90% CIs, analysis of 19 BE studies on 2 anti-epileptic drugs showed generic-generic BE in almost all cases [26] and analysis of 120 BE studies on three immunosuppressants as well as six selected drugs showed BE in 90% of AUCT and 87% of Cmax comparisons with mean absolute deviation from 100% of 4.5% for AUCT and 5.1% for Cmax [27]. Further, a similar analysis of US FDA-approved antiepileptic generic products found that AUCT and Cmax differed by >15% in 17% and 39% of simulated generic-generic switches, respectively [28]. Nevertheless, there is little direct empirical evidence regarding the extent of BE among reference-bioequivalent generic products; two amoxicillin generic products did not show BE [16], whereas two metformin generic products [22] and the two most disparate generic lamotrigine products [31] did.

In our prospective study of 42 direct generic-generic BE comparisons, only one (2.4%) comparison failed to show BE because of Cmax and one because of AUCT and AUCI. After adjusting for 6 comparisons, the percentages were 2.4% and 14.3%, respectively. Further, mean deviation of generic/generic ratio from 100% was only 2.5%, 2.6%, and 3.3% for AUCT, AUCI, and Cmax, respectively, and the deviation was <10 percentage points in 95.2%, 95.2%, and 88.1% of the 42 comparisons. Our results provide strong empirical evidence that it is very unlikely for two reference-bioequivalent generic products not to be bioequivalent to each other. Interestingly, in our study, mean deviation of generic/reference ratios from 100% was in the 6–13 percentage points range in 21.4%, 19%, and 40.5% of the AUCT and, AUCI, and Cmax comparisons, respectively. This suggests that, contrary to the result of previous simulation study [29], even when the bioavailability difference between generic and reference products is in the 6–13 percentage points range, reference-bioequivalent generic products are still likely to be bioequivalent.

Theoretically, the change in drug exposure resulting from generic-generic substitution might be expected to be more pronounced than the change resulting from generic-reference substitution [23, 24]. However, our results indicate that the two changes in exposure are similar. Mean absolute deviation of point estimates in percentage points was 3.2 vs. 2.5 for AUCT, 3.2 vs. 2.6 for AUCI, and 5.4 vs. 3.3 for Cmax in the generic-reference and generic-generic comparisons, respectively. Further, the deviations were <10 percentage points in similar proportions of the two types of comparisons.

Generic-reference and generic-generic intra-subject variability of bioequivalent drug products

Since average BE focuses on mean difference rather than difference between variances or subject-by-product interaction, it is possible that a patient on a reference-bioequivalent but low-quality generic product may be sometimes overdosed and sometimes underdosed and that a patient using two bioequivalent products may have the highest drug exposure with one product and the lowest with another [64]. Such possibilities may be of particular concern when switching patients form one NTI drug product to another [24] and are usually reflected in individual ratios of the pharmacokinetic parameters. Few published studies have addressed BE at the individual level [17, 24, 25]. Despite having 90% CIs within the 80–125% limits, 18% and 38% of individual cyclosporine generic/reference AUC and Cmax ratios, respectively, were <0.80 [24] and 0% of individual lamotrigine generic/reference AUC and Cmax ratios and 3% and 18% of same-product, generic/generic AUC and Cmax ratios, respectively, were outside the ±25% range [17]. A simulation study (assuming 20% inter-subject variability and 10% intra-subject variability) predicted that when mean generic product’s AUC is 80% to 123.5% of reference product’s AUC, 3–4.6% and 9–12% of individual generic/reference and generic/generic AUC ratios, respectively, would fall outside the 0.67–1.5 range [25].

We found that 16% and 17% of individual generic/reference and generic/generic ratios, respectively, were outside the ±25% range in for AUCT, 15% and 16% for AUCT, and 32% and 32% for Cmax. Further, individual generic/reference and generic/generic AUCT, AUCI, and Cmax ratios fulfilled the 75/75 rule for 79% and 71%, 79% and 71%, and 36% and 29% of the 14 drugs, respectively. Based on a relatively large number of drug products, our results document the extent of intra-subject variability that would be expected despite fulfilment of average BE criteria and strongly suggest that the extents of generic-generic switchability and generic-reference switchability are similar.

It is not clear how much of the observed intra-subject variability is due to inter-product rather than intra-product variability. In the simulation study, 11.1% of the reference/reference AUC ratios were predicted to fall outside the 0.8–1.25 range [25]. Further, 3% and 9% of individual lamotrigine reference/reference AUC and Cmax ratios [17] and 23%, 30%, and 30% of individual caffeine AUCT, AUCI, and Cmax ratios [65], respectively, were outside the ±25% range. Furthermore, when the cephalexin, ibuprofen, and paracetamol reference products used in this study were compared to themselves; respectively, 2%, 17%, and 2% of the individual ratios were outside the ±25% range for AUCT (compared to 2%, 8%, and 8% of the generic-reference ratios in the current study), 4%, 3%, and 2% for AUCI, (compared to 2%, 8%, and 9% of the generic-reference ratios in the current study), and 25%, 33%, and 45% for Cmax, (compared to 39%, 22%, and 26% of the generic-reference ratios in the current study) [58]. Together, the data strongly indicate that a major part of the intra-subject variability seen in average BE studies may not be related to comparing two products but rather to factors such as study setting, drug assay, and random variations in subject’s physiologic status (for example, gastric emptying, intestinal transit speed, and luminal pH).

Large variability in AUCReftmax and Tmax despite average bioequivalence

When time of onset of drug effect is important because of therapeutic or toxic issues, it is recommended to perform non-parametric analysis of non-transformed Tmax values and/or evaluate the 90% CI of AUC truncated at reference Tmax median or at reference Tmax, calculated for each subject (AUCReftmax) [7, 8]. Onset of effect may important for only few drugs in the current study, however, we used the data on all the 14 drugs to examine the behaviour of Tmax and AUCReftmax in general.

We found that two thirds of generic-reference and generic-generic AUCReftmax comparisons failed to show BE or showed bioinequivalence. Further, on average, 60% and 58% of generic/reference and 58% and 52% of generic/generic individual Tmax and AUCReftmax ratios, respectively, were outside the ±25% range. Moreover, generic/reference and generic/generic individual Tmax and AUCReftmax ratios fulfilled the 75/75 rule in only 0–7% of the 14 drugs. The results confirm that average BE testing using AUCT, AUCI, and Cmax is insensitive to variability in Tmax and AUCReftmax and suggest that intra-subject variabilities of the two parameters are similar and do not depend on whether a generic product is compared to a reference product or to another generic product.

Some patients’ bad impression of generic products may be theoretically related to their different onset of effect as compared to reference products. However, this is not likely because onset of effect is mostly related to pharmacodynamic rather than pharmacokinetic characteristics. Further, since Tmax values are based on Cmax, which is, in turn, based on a single measurement of drug concentration, Tmax values are also very sensitive to study setting, subject’s physiological status, assay variability, and random error. In fact, when the cephalexin, ibuprofen, and paracetamol reference products used in this study were compared to themselves [58]; respectively, 46%, 63% and 71% of individual ratios were outside the ±25% range for Tmax (compared to 54%, 72% and 69% of the generic-reference ratios in the current study) and 71%, 77% and 67% for AUCReftmax (compared to 75%, 76% and 68% of the generic-reference ratios in the current study). This strongly indicates that most of the observed generic-reference and generic-generic intra-subject variability in Tmax and AUCReftmax is not due to inter-product differences and that the usefulness of Tmax and AUCReftmax in BE evaluation may be very limited.

AUC72 is as informative as AUCT

Two drugs in this study have long plasma half-life (around 49 and 29 h); the half-life for the other 12 drugs was <10 h. We were able to demonstrate average BE in all generic-reference and all generic-generic AUCT and AUC72 comparisons. Further, similar percentages of generic/reference and generic/generic individual AUCT and AUC72 ratios were outside the ±25% range. The results lend further support to using AUC72 instead of AUCT for drugs with long plasma half-life [79].

Limitations

The interpretation of the results of this study may be limited by the following. 1) We only studied non-combinational drug products. However, BE standards for combinational and non-combinational products are the same and it can be assumed that the results apply to combinational products. 2) We only studied solid immediate-release drug products, thus our results may not apply to liquid or modified-release products. 3) Our results may not be generalizable to other solid immediate-release drugs on the Saudi market since the drugs we studied were not randomly selected. Short of more relevant statistics, the number of on-market generic products is a reasonable reflection of the extent of drug utilization. Further, the generic products in our study were randomly selected. Thus it would be expected that the results apply to an important portion of drug products on the Saudi market. 4) Although Saudi Arabia’s BE regulations are very similar to most BE regulations worldwide, our results may not apply to similar drugs on other national markets. 5) Our study was not designed to partition intra-subject variability into its various components. Thus, it is not clear how much of the observed intra-subject variability is related to the generic products themselves (generic product quality variability or subject-by-product variability) and how much to methodological issues. 6) We observed significant (unadjusted) period and sequence effects in 6 and 2 of the 14 studies, respectively. It is likely that the apparent significance is due in large part to multiple comparisons and relatively large sample sizes, since we have also observed significant product effect in 8 of the 14 studies. The presence of period or sequence effect doesn’t influence BE conclusions. Sequence effect and period effect may indicate unequal carryover, which is not likely given the length of the washout periods and the fact that baseline drug concentrations were undetectable in all periods for all 14 drugs. Sequence effect may also indicate that the groups (the 4 sequences) are different, which is also not likely because of randomization. However, it may also be due to product-by-period effect, which cannot be rolled out. Finally, period effect may indicate temporal changes, such as changes in patients’ comfort level, familiarization with study, compliance, venous access, and drug stability. The latter is not likely because analysis of all drugs was performed well within each drug’s pre-established stability period. 7) We have loss of follow up for one or more periods in 13 of the 14 studies, however, this resulted in negligible imbalance among the 4 sequences and negligible loss of power. 8) Finally, in retrospect, few of the 14 studies did not have adequate power to show BE for Cmax, however, this would strengthen the main conclusions of the study.

Conclusions

Based on studying 42 randomly-selected generic products of 14 immediate-release, non-combinational, oral drugs with the highest number of generic products on the Saudi market, we can conclude that: 1) On-market generic products continue to be reference-bioequivalent. 2) Reference-bioequivalent generic products are bioequivalent to each other, despite the presence of some generic-reference deviations that are >6 percentage points. 3) Reference-generic and generic-generic average deviations are small (on average 3–5 percentage points) and similar. 4) Reference-generic and generic-generic Cmax intra-subject variations are large, similar, and can be present despite fulfilment of average BE criteria. However, they may be mostly related to methodological factors. 5) Average BE testing using AUCT, AUCI, and Cmax is insensitive to variability in Tmax and AUCReftmax. However, the intra-subject variabilities of the two parameters are similar, do not depend on whether a generic product is compared to a reference product or to another generic product, and may not be due to inter-product differences; suggesting limited usefulness of Tmax and AUCReftmax in BE evaluation. 6) AUC72 appears as informative as AUCT for drugs with long plasma half-life.

We believe that the study is the most rigorous study of on-market, generic drug products. It provided strong supporting evidence of the post-marketing quality and interchangeability of generic products and of the adequacy of current BE standards. It should allay fears of healthcare professionals and patients about the use of generic products, whether in the form of generic substitution or reference-to-generic or generic-to-generic switching.

Abbreviations

ANOVA: 

Analysis of variance

AUC: 

Area-under-the-concentration-time-curve

AUC72

Area-under-the-concentration-time-curve truncated to 72 h

AUCI

Area-under-the-concentration-time-curve extrapolated to infinity

AUCReftmax

Area-under-the-concentration-time-curve to time of maximum concentration (Tmax) of reference product, calculated for each subject

AUCT

Area-under-the-concentration-time-curve to last measured concentration

BE: 

Bioequivalence

BMI: 

Body mass index

CI: 

Confidence interval

Cmax

Maximum concentration

CV: 

Coefficient of variation (standard deviation/mean)

FDA: 

Food and Drug Administration

Ga: 

Generic product a

Gb: 

Generic product b

Gc: 

Generic product c

HPLC: 

High performance liquid chromatography

KFSH&RC: 

King Faisal Specialist Hospital and Research Center

LC-MS: 

Liquid chromatography-mass spectrometry

MSR: 

Mean square residual

NTI: 

Narrow therapeutic index

R: 

Reference product

SD: 

Standard deviation

t1/2

Terminal elimination half-life

Tmax

Time of maximum concentration

λ: 

Apparent first-order elimination rate constant

Declarations

Acknowledgments

Special thanks to participants of the study for their dedication and contribution to research. The superb assistance by the research staff at the Clinical Studies and Empirical Ethics Department, namely, Faduma A Farah, Faduma S Shire, Ahmed Yusuf, Saleh Al-Dghither, is gratefully acknowledged.

Funding

This study was funded by a research grant to MMH from the King Abdul-Aziz City for Science and Technology (KACST), under National Comprehensive Plan for Science and Technology, Riyadh, Saudi Arabia (10-Bio961–20).

Availability of data and materials

The dataset supporting the conclusions of this article is available upon request from MMH and in the attached Additional Files.

Role of the funder/sponsor

The funder had no role in the design and conduct of the study; in the collection, management, analysis, and interpretation of the data; or in the preparation, review, or approval of the manuscript.

Authors’ contributions

MMH had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: MMH and EAG. Acquisition of data: SP, EAG, NAK. Analysis, or interpretation of data: MMH. Drug concentration measurements: RH, RA, NB, SNA. Statistical analysis: MMH. Manuscript writing: MMH. Critical revision of the manuscript for important intellectual content: all authors. All authors have approved the final version of the manuscript and agreed to be accountable to all aspects of the work.

Ethics approval and consent to participate

The study was conducted according to the ethical guidelines of the Declaration of Helsinki and was approved by the King Faisal Specialist Hospital & Research Center’s Research Ethics Committee (RAC 2101100). All participants provided a written informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Clinical Studies and Empirical Ethics Department, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
(2)
Alfaisal University College of Medicine, Riyadh, Saudi Arabia

References

  1. World Health Organization. Access to new medicines in Europe: technical review of policy initiatives and opportunities for collaboration and research 2015. http://www.euro.who.int/en/health-topics/Health-systems/health-technologies-and-medicines/publications/2015/access-to-new-medicines-in-europe-technical-review-of-policy-initiatives-and-opportunities-for-collaboration-and-research-2015. Accessed June 12, 2017.Google Scholar
  2. Toverud E-L, Hartmann K, Håkonsen H. A systematic review of physicians’ and pharmacists’ perspectives on generic drug use: what are the global challenges? Appl Health Econ Health Policy. 2015;13(Suppl 1):35–45.View ArticlePubMed CentralGoogle Scholar
  3. Dunne SS, Dunne CP. What do people really think of generic medicines? A systematic review and critical appraisal of literature on stakeholder perceptions of generic drugs. BMC Med. 2015;13:173.View ArticlePubMedPubMed CentralGoogle Scholar
  4. Hassali MA, Alrasheedy AA, McLachlan A, Nguyen TA, AL-Tamimi SK, Ibrahim MIM, Aljadheye H. The experiences of implementing generic medicine policy in eight countries: a review and recommendations for a successful promotion of generic medicine use. Saudi Pharm J. 2014;22(6):491–503.View ArticlePubMedGoogle Scholar
  5. O’Leary A, Usher C, Lynch M, Hall M, Hemeryk L, Spillane S, Gallagher P, Barry M. Generic medicines and generic substitution: contrasting perspectives of stakeholders in Ireland. BMC Res Notes. 2015;8:790.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Kumar R, Hassali MA, Saleem F, Alrasheedy AA, Kaur N, Wong ZY, Abdul Kader MASK. Knowledge and perceptions of physicians from private medical centres towards generic medicines: a nationwide survey from Malaysia. J Pharm Policy Pract. 2015;8(1):11.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Davit B, Braddy AC, Conner DP, Yu LX. International guidelines for bioequivalence of systemically available orally administered generic drug products: a survey of similarities and differences. APPS J. 2013;15(4):974–90.Google Scholar
  8. Chen M-L, Shah VP, Crommelin DJ, Shargel L, Bashaw D, Bhatti M, Blume H, Dressman J, Ducharme M, Fackler P, Hyslop T, Lutter L, Morais J, Ormsby E, Thomas S, Tsang YC, Velagapudi R, Yu LX. Harmonization of regulatory approaches for evaluating therapeutic equivalence and interchangeability of multisource drug products: workshop summary report. AAPS J. 2011;13(4):556–64.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Kaushal N, Singh SK, Gulati M, Vaidya Y, Kaushik M. Study of regulatory requirements for the conduct of bioequivalence studies in US, Europe, Canada, India, ASEAN and SADC countries: impact on generic drug substitution. J App Pharm Sci. 2016;6(4):206–22.View ArticleGoogle Scholar
  10. Statistical approaches to establishing bioequivalence. US DHHS, FDA, CDER 2001. https://www.fda.gov/downloads/drugs/guidances/ucm070244.pdf. Accessed June 12, 2017.Google Scholar
  11. Dunne S, Shannon B, Dunne C, Cullen W. A review of the differences and similarities between generic drugs and their originator counterparts, including economic benefits associated with usage of generic medicines, using Ireland as a case study. BMC Pharmacol Toxicol. 2013;14:1.View ArticlePubMedPubMed CentralGoogle Scholar
  12. Hendeles I, Hochhaus G, Kazerounian S. Generic and alternative brand-name pharmaceutical equivalents: select with caution. Am J Hosp Pharm. 1993;50(2):323–9.PubMedGoogle Scholar
  13. US Food and Drug Administration (FDA). Review of therapeutic equivalence generic Bupropion XL 300 mg and Wellbutrin XL 300 mg. Silver Spring: FDA; 2013. https://www.fda.gov/aboutfda/centersoffices/officeofmedicalproductsandtobacco/cder/ucm153270.htm. Accessed June12, 2017Google Scholar
  14. US Food and Drug Administration (FDA). Methylphenidate hydrochloride extended release tablets (generic Concerta) made by Mallinckrodt and Kudco. Silver Spring: FDA; 2014. https://www.fda.gov/Drugs/DrugSafety/ucm422568.htm. Accessed June 12, 2017Google Scholar
  15. Gasser UE, Fischer A, Timmermans JP, Arnet I. Pharmaceutical quality of seven generic Levodopa/Benserazide products compared with original Madopar® / Prolopa®. BMC Pharmacol Toxicol. 2013;14:24.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Del Tacca M, Pasqualetti G, Di Paolo A, Virdis A, Massimetti G, Gori G, Versari D, Taddei S, Blandizzi C. Lack of pharmacokinetic bioequivalence between generic and branded amoxicillin formulations. A post-marketing clinical study on healthy volunteers. Br J Clin Pharmacol. 2009;68(1):34–42.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Ting TY, Jiang W, Lionberger R, Wong J, Jones JW, Kane MA, Krumholz A, Temple R, Polli JE. Generic lamotrigine versus brand-name Lamictal bioequivalence in patients with epilepsy: a field test of the FDA bioequivalence standard. Epilepsia. 2015;56(9):1415–24.View ArticlePubMedGoogle Scholar
  18. Kesselheim AS, Misono AS, Lee JL, Stedman MR, Brookhart MA, Choudhry NK, Shrank WH. Clinical equivalence of generic and brand-name drugs used in cardiovascular disease. A systematic review and meta-analysis. JAMA. 2008;300(21):2514–26.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Manzoli L, Flacco ME, Boccia S, D’Andrea E, Panic N, Marzuillo C, Siliquini R, Ricciardi W, Villari P, Ioannidis JPA. Generic versus brand-name drugs used in cardiovascular diseases. Eur J Epidemiol. 2016;31:351–68.View ArticlePubMedGoogle Scholar
  20. Kesselheim AS, Stedman MR, Bubrick EJ, Gagne JJ, Misono AS, Lee JL, Brookhart MA, Avorn J, Shrank WH. Seizure outcomes following use of generic vs. brand-name antiepileptic drugs: a systematic review and meta-analysis. Drugs. 2010;70(5):605–21.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Singh AK, Narsipur SS. Cyclosporine: a commentary on brand versus generic formulation exchange. J Transp Secur. 2011;2011:480642.Google Scholar
  22. Montoya-Eguía SL, Garza-Ocañas L, Badillo-Castañeda CT, Tamez-de la OE, Zanatta-Calderón T, Gómez-Meza MV, Garza-Ulloa H. Comparative pharmacokinetic study among 3 metformin formulations in healthy Mexican volunteers: a single-dose, randomized, open-label, 3-period crossover study. Curr Therap Res. 2015;77:18–23.View ArticleGoogle Scholar
  23. Anderson S, Hauck WW. The transitivity of bioequivalence testing: potential for drift. Int J Clin Pharm and Therap. 1996;34:369–74.View ArticleGoogle Scholar
  24. Johnston A. Equivalence and interchangeability of narrow therapeutic index drugs in organ transplantation. Eur J Hosp Pharm SciPract. 2013;20(5):302–7.View ArticleGoogle Scholar
  25. Yim D-S. Simulation of the AUC changes after generic substitution in patients. J Korean Med Sci. 2009;24(1):7–12.View ArticlePubMedPubMed CentralGoogle Scholar
  26. Maliepaard M, Banishki N, Gispen-de Wied CC, Teerenstra S, Elferink AJ. Interchangeability of generic anti-epileptic drugs: a quantitative analysis of topiramate and gabapentin. Eur J Clin Pharmacol. 2011;67(10):1007–16.View ArticlePubMedGoogle Scholar
  27. Yu Y, Teerenstra S, Neef C, Burger D, Maliepaard M. Investigation into the interchangeability of generic formulations using immunosuppressants and a broad selection of medicines. Eur J Clin Pharmacol. 2015;71(8):979–90.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Krauss GL, Caffo B, Chang YT, Hendrix CW, Chuang K. Assessing bioequivalence of generic antiepilepsy drugs. Ann Neurol. 2011;70(2):221–8. doi:10.1002/ana.22452.View ArticlePubMedGoogle Scholar
  29. Friesen MH, Walker SE. Are the current bioequivalence standards sufficient for the acceptance of narrow therapeutic index drugs? Utilization of a computer simulated warfarin bioequivalence model. J Pharm Pharmaceut Sci. 1999;2(1):15–22.Google Scholar
  30. Karalis V, Bialer M, Macheras P. Quantitative assessment of the switchability of generic products. Eur J Pharm Sci. 2013;50(3–4):476–83.View ArticlePubMedGoogle Scholar
  31. Privitera MD, Welty TE, Gidal BE, Diaz FJ, Krebill R, Szaflarski JP, Dworetzky BA, Pollard JR, Elder EJ Jr, Jiang W, Jiang X, Berg M. Generic-to-generic lamotrigine switches in people with epilepsy: the randomised controlled EQUIGEN trial. Lancet Neurol. 2016;15(4):365–72. doi:10.1016/S1474-4422(16)00014-4.View ArticlePubMedGoogle Scholar
  32. Atif M, Azeem M, Sarwar MR. Potential problems and recommendations regarding substitution of generic antiepileptic drugs: a systematic review of literature. Spring. 2016;5:182.View ArticleGoogle Scholar
  33. Vercaigni LM, Zhanel GG. Clinical significance of bioequivalence and interchangeability of narrow therapeutic range drugs: focus on warfarins. J Pharm Pharmaceut Sci. 1998;1(3):92–4.Google Scholar
  34. Reiffel JA, Kowey PR. Generic antiarrythmics are not therapeutically equivalent for the treatment of tachyarrhythmias. Am J Cardiology. 2000;85:1151–3.View ArticleGoogle Scholar
  35. Hsuan FC. Some statistical considerations on the FDA draft guidance for individual bioequivalence. Stat Med. 2000;19(20):2879–84.View ArticlePubMedGoogle Scholar
  36. Vossler DG, Anderson GD, Bainbridge J. AES position statement on generic substitution of antiepileptic drugs. Epilepsy Curr. 2016;16(3):209–11. doi:10.5698/1535-7511-16.3.209.View ArticlePubMedPubMed CentralGoogle Scholar
  37. Perucca E. The safety of generic substitution in epilepsy. Lancet Neurol. 2016;15(4):344–5. doi:10.1016/S1474-4422(16)00042-9.View ArticlePubMedGoogle Scholar
  38. Alrasheedy AA, Hassali MA, Aljadhey H, Ibrahim MI, Al-Tamimi SK. Is there a need for a formulary of clinically interchangeable medicines to guide generic substitution in Saudi Arabia? J Young Pharm. 2013;5(2):73–5. doi:10.1016/j.jyp.2013.06.006.View ArticlePubMedPubMed CentralGoogle Scholar
  39. Salhia HO, Ali A, Rezk NL, El Metwallya A. Perception and attitude of physicians toward local generic medicines in Saudi Arabia: a questionnaire-based study. Saudi Pharm J. 2015;23(4):397–404.View ArticlePubMedPubMed CentralGoogle Scholar
  40. Dickert N, Grady C. What’s the price for a research subject? Approaches to payment for research participation. N Engl J Med. 1999;341(3):198–203.View ArticlePubMedGoogle Scholar
  41. Hussein R, Hammami MM. Fully validated diclofenac HPLC assay. ACAIJ. 2009;8(2):124–9.Google Scholar
  42. Al-Dgither S, Yusuf A, Hammami MM. Fluconazole: stability and analysis in human plasma by simple high performance liquid chromatography. FABAD J Pharm Sci. 2011;34:179–86.Google Scholar
  43. Alvi SN, Yusuf A, Al Gaai E, Hammami MM. Rapid determination of ibuprofen concentration in human plasma by high performance liquid chromatography. WJPPS. 2014;3(8):1767–77.Google Scholar
  44. Al-Swayeh R, Alvi SN, Hammami MM. A validated HPLC method for the determination of ranitidine in human plasma: application to bioavailability studies. ACAIJ. 2015;15(8):339–44.Google Scholar
  45. Hussein RF, Hammami MM. Determination of cephalexin level and stability in human plasma by fully validated rapid HPLC analysis. WJPPS. 2014;3(12):20–31.Google Scholar
  46. Binhashim NH, Alvi SN, Hammami MM. A validated reversed phase HPLC assay for the determination of metronidazole in human plasma. WJPPS. 2014;3(12):32–41.Google Scholar
  47. Yusuf A, Alvi SN, Hammami MM. Development and validation of RP-HPLC method for the determination of metformin in human plasma. WJPPS. 2015;4(7):128–38.Google Scholar
  48. Alswayeh R, Alvi SN, Hammami MM. Rapid determination of amoxicillin level in human plasma by high performance liquid chromatography. WJPPS. 2015;4(7):1657–67.Google Scholar
  49. Yusuf A, Alvi SN, Hammami MM. Development and validation of RP HPLC method for the determination of atenolol in human plasma. WJPPS. 2016;5(2):169–79.Google Scholar
  50. Alswayeh R, Hussein RF, Alvi SN, Hammami MM. Rapid determination of ciprofloxacin concentration in human plasma by high performance liquid chromatography. WJPPS. 2016;5(3):1765–74.Google Scholar
  51. Hussein RF, Binhashim NH, Alvi SN, Hammami MM. A validated reversed phase HPLC assay for the determination of omeprazole in human plasma. EJPMR. 2016;3(6):26–30.Google Scholar
  52. Alswayeh R, Alvi SN, Hammami MM. Rapid determination of acetaminophen levels in human plasma by high performance liquid chromatography. A J Pharmtech Res. 2016;6(3):710–9.Google Scholar
  53. Alvi SN, Al Dgither S, Hammami MM. Rapid determination of clarithromycin in human plasma by LCMS/MS assay. Pharm Anal Chem Open Access. 2016;2:1.View ArticleGoogle Scholar
  54. Alvi SN, Hussein RF, Al Dgither S, Hammami MM. Quantitation of amlodipine in human plasma by LCMS/MS assay. Int J Pharm Pharm Sci. 2016;8(8):268–72.Google Scholar
  55. Randomization.com. Dallal GE. http://www.randomization.com/. Accessed June 12, 2017.
  56. FARTSSIE. David Dubins D. http://individual.utoronto.ca/ddubins. Accessed June12, 2017.
  57. Jiang W, Makhlouf F, Schuirmann DJ, Zhang X, Zheng N, Conner D, Yu LX, Lionberger R. A bioequivalence approach for generic narrow therapeutic index drugs: evaluation of the reference-scaled approach and variability comparison criterion. AAPS J. 2015;17(4):891–901.View ArticlePubMedPubMed CentralGoogle Scholar
  58. Hammami MM, Yusuf A, Shire FS, Hussein R, Al-Swayeh R. Does the placebo effect modulate drug bioavailability? Randomized cross-over studies of three drugs. JNCT. 2017;201716:10. doi:10.1186/s12952-017-0075-2.Google Scholar
  59. Henney J. Review of generic bioequivalence studies. JAMA. 1999;282(21):1995.View ArticlePubMedGoogle Scholar
  60. Davit BM, Nwakama PE, Buehler GJ, Conner DP, Haider SH, Patel DT, Yang Y, Yu LX, Woodcock J. Comparing generic and innovator drugs: a review of 12 years of bioequivalence data from the United States Food and Drug Administration. Ann Pharmacother. 2009;43(10):1583–97.View ArticlePubMedGoogle Scholar
  61. Espay AJ, Norris MM, Eliassen JC, Dwivedi A, Smith MS, Banks C, Allendorfer JB, Lang AE, Fleck DE, Linke MJ, Szaflarski JP. Placebo effect of medication cost in Parkinson disease: a randomized double-blind study. Neurology. 2015;84:794–802.View ArticlePubMedPubMed CentralGoogle Scholar
  62. Thakkar KB, Billa G. The concept of generic drugs and patented drugs vs. brand name drugs and non-proprietary (generic) name drugs. Front Pharmacol. 2013;4:113.PubMedPubMed CentralGoogle Scholar
  63. Piguet V, D’Incau S, Besson M, Desmeules J, Cedraschi C. Prescribing generic medication in chronic musculoskeletal pain patients: an issue of representations, trust, and experience in a Swiss cohort. PLoS One. 2015;10(8):e0134661.View ArticlePubMedPubMed CentralGoogle Scholar
  64. Tóthfalusi L, Endrényi L,S-C. Statistical and regulatory considerations in assessments of interchangeability of biological drug products. Eur J Health Econ. 2014;15(Suppl 1):5–11.View ArticlePubMed CentralGoogle Scholar
  65. Hammami MM, Alvi SN. Large intra-subject variability in caffeine pharmacokinetics: randomized cross-over study of single caffeine product. Drug Res. 2017;67:1–8. doi:10.1055/s-0043-110144.View ArticleGoogle Scholar

Copyright

© The Author(s). 2017

Advertisement