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. 2023 Mar 2;16(5):872–885. doi: 10.1111/cts.13497

Females present higher dose‐adjusted drug concentrations of metoprolol and allopurinol/oxypurinol than males

Jessica Hindi 1,2,3, Marc‐Olivier Pilon 1,2,3, Maxime Meloche 1,2,3, Grégoire Leclair 1, Essaïd Oussaïd 2,3, Isabelle St‐Jean 1, Martin Jutras 1, Marie‐Josée Gaulin 2,3, Ian Mongrain 2,3, David Busseuil 2,3, Jean Lucien Rouleau 2,4, Jean‐Claude Tardif 2,3,4, Marie‐Pierre Dubé 2,3,4, Simon de Denus 1,2,3,✉
PMCID: PMC10175982  PMID: 36864560

Abstract

Females present a higher risk of adverse drug reactions. Sex‐related differences in drug concentrations may contribute to these observations but they remain understudied given the underrepresentation of females in clinical trials. The aim of this study was to investigate whether anthropometric and socioeconomic factors and comorbidities could explain sex‐related differences in concentrations and dosing for metoprolol and oxypurinol, the active metabolite of allopurinol. We conducted an analysis of two cross‐sectional studies. Participants were self‐described “White” adults taking metoprolol or allopurinol selected from the Montreal Heart Institute Hospital Cohort. A total of 1007 participants were included in the metoprolol subpopulation and 459 participants in the allopurinol subpopulation; 73% and 86% of the participants from the metoprolol and allopurinol subpopulations were males, respectively. Females presented higher age‐ and dose‐adjusted concentrations of both metoprolol and oxypurinol (both p < 0.03). Accordingly, females presented higher unadjusted and age‐adjusted concentration:dose ratio of both metoprolol and allopurinol/oxypurinol compared to males (all p < 3.0 × 10−4). Sex remained an independent predictor of metoprolol concentrations (p < 0.01), but not of oxypurinol concentrations, after adjusting for other predictors. In addition to sex, age, daily dose, use of moderate to strong CYP2D6 inhibitors, weight, and CYP2D6 genotype‐inferred phenotype were associated with concentrations of metoprolol (all p < 0.01). Daily dose, weight, estimated glomerular filtration rate (eGFR), and employment status were associated with oxypurinol concentrations (all p < 0.01). Females present higher dose‐adjusted concentrations of metoprolol and oxypurinol than males. This suggests the need for sex‐specific dosing requirements for these drugs, although this hypothesis should be validated in prospective studies.

Study Highlights.

  • WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?

Females are underrepresented in drug development trials and there is no actual sex‐tailored drug regimen. It has been shown repeatedly that females have more adverse drug reactions (ADRs) than males for several medications. These differences could be explained by higher drug concentrations in females.

  • WHAT QUESTION DID THIS STUDY ADDRESS?

This study addresses whether females present higher drug/metabolite concentrations for two widely prescribed drugs, metoprolol and allopurinol, independently of covariables.

  • WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE?

The higher dose‐ and age‐adjusted drug/metabolite concentrations we observed in females may contribute to the higher risk of ADRs for these drugs. Our results suggest the need for a more personalized approach to dosing, which could either be based on sex or factors contributing to these observed sex differences.

  • HOW MIGHT THIS CHANGE CLINICAL PHARMACOLOGY OR TRANSLATIONAL SCIENCE?

Considerations should be given by health authorities to increase the sample size requirements of early clinical pharmacokinetic studies so that sponsors can develop more tailored medication regimens for females to improve safety.

INTRODUCTION

Until recently, clinical trials were conducted in patient populations that consisted almost exclusively of males. In 1998, the Food and Drug Administration (FDA) required that early‐phase studies be conducted in both sexes and that New Drug Applications (NDAs) must present safety and efficacy data by sex, yet not all approved drugs since then have included those sex‐specific data. 1 , 2 Despite increased efforts to include females in early‐phase clinical trials, such efforts remain insufficient to generate data representative of real‐life given the small sample size and the exclusion of comorbidities and polypharmacy in those trials. 3 The absence of a potential “sex‐tailored” dosing has thus maintained the “one‐size fits‐all” dosing regimens status quo, which may contribute to the higher risk of adverse drug reactions (ADRs) observed in females. 2 , 4

Multiple physiological and pharmacokinetic (PK) factors have been reported to differ between males and females. 4 , 5 , 6 , 7 , 8 For example, body composition differences may affect drug distribution as females have a higher level of fat, although they generally have a lower body weight. Lipophilic drugs may thus have a greater volume of distribution in females as opposed to hydrophilic drugs. 9 , 10 In addition, activity, or expression of drug‐metabolizing enzymes, such as members of the cytochrome P450 (CYP) family, and the activity/expression of drug transporters may differ between males and females. 11 , 12 , 13 Furthermore, at any age, females present, on average, a lower estimated glomerular filtration rate (eGFR), which represents a primary route of elimination for many drugs. 11 Moreover, males and females may have different socioeconomic profiles. 14 The implication, if any, of these differences on drug PK requires further investigation.

The objective of this study was to compare drug dosing and drug/metabolite concentrations between females and males for two widely used drugs, the β1‐adrenergic receptor antagonist metoprolol and the uric acid‐lowering agent allopurinol, and to identify potential clinical, anthropometric, genetic, and socioeconomic factors that could explain PK differences between males and females. Metoprolol and allopurinol were selected because they had been investigated as part of two cross‐sectional studies conducted at the Montreal Heart Institute (MHI). 15 , 16

METHODS

Study design

We performed a substudy of two cross‐sectional studies derived from the MHI Hospital Cohort. The participants of those studies were reported taking metoprolol 15 or allopurinol 16 . The methods of the MHI Hospital Cohort and of the two cross‐sectional studies have been previously described. 15 , 16 , 17 , 18 Data collected as part of the MHI Hospital Cohort were provided by the participants or collected from their medical records and/or MHI databases and contain information on their medical, genealogical, psychological, biological, pharmacological, and genetic profiles.

Study population

The selection of individuals was previously reported. 15 , 16 Succinctly, the two studies included self‐reported “White” males and females aged ≥18 years. All participants reported metoprolol or allopurinol as part of their daily pharmacotherapy regimen at the time of enrollment in the MHI Hospital Cohort at which moment plasma was collected. Exclusion criteria were limited to solid organ transplantation as previously reported. 15 , 16 Participants self‐identified as male or female at the baseline visit of the MHI Hospital Cohort.

Study endpoints

The primary objective of this study was to compare concentrations and dose‐adjusted concentrations of metoprolol and oxypurinol, the active metabolite of allopurinol, between females and males. In the allopurinol subpopulation, we have focused our analyses on oxypurinol because of its extended half‐life compared to allopurinol (~24 vs. ~1.5 h) and its more potent impact on uric acid‐lowering. 19 , 20 , 21 , 22 Secondary endpoints included allopurinol and metoprolol daily dosing. We also aimed at identifying potential determinants of any existing sex differences in study endpoints.

Measurement of drug and metabolite concentrations

The collection and the quantification of all plasma samples have been previously described. 15 , 16 Blood sampling was performed randomly in regard to drug intake. This approach has been validated in two previous publications. 15 , 16 Quantification of all samples for both studies was performed at the bioanalytical laboratory of the Platform of Biopharmacy at Université de Montréal using high‐pressure liquid chromatography coupled to electrospray ionization tandem mass spectrometry (LC–MS/MS) based on selective multiple reaction monitoring (MRM). Quantification ranges were 1 to 1000 ng/ml and 10 to 50,000 ng/ml for metoprolol and oxypurinol, respectively. Concentrations below the lower limits of quantification (LLOQ) were attributed a value of zero.

Genotyping

The genotyping of selected samples was completed at the Université de Montréal Beaulieu‐Saucier Pharmacogenomics Centre (PGx Center) as previously described. 15 Briefly, the genotyping was performed using a combination of Agena iPLEX Gold Chemistry (Agena Bioscience) and the MassARRAY® Analyzer Compact system. For the metoprolol subpopulation, participants were categorized into poor metabolizer (PM), intermediate metabolizer (IM), normal metabolizer (NM), and ultrarapid metabolizer (UM) phenotypes in respect to the current CYP2D6 classification guidelines. 23

Selection of variables

Variables were preselected among data contained in the Biobank of the MHI Hospital Cohort based on potential differences between males and females with respect to drug PK. Thirty‐four common variables were investigated in both studies, 32 of which were identical. Most of these identical variables can be categorized into three subgroups: anthropometric factors (e.g., weight), socioeconomic factors (e.g., annual income), and comorbidities (e.g., heart failure, Crohn's disease). The two non‐identical variables were specific to each drug, but similar in their potential impact. First, we included significant drug interactions. Thus, for the allopurinol subpopulation, the coadministration of diuretic drugs was investigated as a variable because of its previously shown association with oxypurinol concentrations. 16 , 24 , 25 The coadministration of moderate and strong CYP2D6 inhibitors, which is known to be associated with metoprolol concentrations, 15 , 26 , 27 , 28 was considered as a variable for the metoprolol subpopulation. Second, we investigated the potential contribution of metabolism/eliminations pathways specific to each medication. CYP2D6 genotype‐inferred phenotype was incorporated as a variable in the metoprolol subpopulation given its direct influence on metoprolol concentrations, 15 , 29 , 30 , 31 while eGFR was used for allopurinol/oxypurinol. All variables considered are listed in Table 1.

TABLE 1.

Baseline characteristics.

Characteristic Metoprolol population Allopurinol population
Male (N = 734) (72.89%) Female (N = 273) (27.11%) All (N = 1007) (100.0%) P value Male (N = 394) (85.84%) Female (N = 65) (14.16%) All (N = 459) (100.0%) P value
Age (years) ± SD 66.29 ± 8.68 67.17 ± 8.98 66.53 ± 8.76 0.0877 69.26 ± 8.10 70.38 ± 7.16 69.42 ± 7.98 0.3933
Weight (kg) ± SD 87.63 ± 16.42 75.96 ± 15.75 84.47 ± 17.05 3.4 × 10 −26 91.65 ± 18.31 82.29 ± 16.83 90.32 ± 18.38 0.0003
(N = 734) (N = 273) (N = 1007) (N = 394) (N = 65) (N = 459)
Height (m) ± SD 1.71 ± 0.07 1.58 ± 0.06 1.68 ± 0.09 2.0 × 10 −94 1.71 ± 0.07 1.57 ± 0.06 1.69 ± 0.09 3.8 × 10 −27
(N = 732) (N = 273) (N = 1005) (N = 388) (N = 62) (N = 450)
Waist girth (cm) ± SD 105.50 ± 12.83 96.95 ± 14.02 103.18 ± 13.69 5.9 × 10 −20 109.01 ± 12.79 102.78 ± 15.16 108.13 ± 13.31 0.0089
(N = 732) (N = 273) (N = 1005) (N = 390) (N = 64) (N = 454)
Hip girth (cm) ± SD 105.61 ± 9.58 109.37 ± 12.27 106.62 ± 10.50 0.0001 108.47 ± 9.93 112.88 ± 13.70 109.09 ± 10.63 0.0098
(N = 734) (N = 272) (N = 1006) (N = 392) (N = 64) (N = 456)
BMI (kg/m2) ± SD 29.91 ± 5.06 30.50 ± 6.19 30.07 ± 5.39 0.4258 31.22 ± 5.35 33.25 ± 6.30 31.50 ± 5.53 0.0061
(N = 732) (N = 273) (N = 1005) (N = 388) (N = 62) (N = 450)
Diastolic blood pressure (mmHg) ± SD 75.15 ± 9.56 72.79 ± 9.96 74.51 ± 9.72 0.0002 73.13 ± 10.19 70.45 ± 9.46 72.75 ± 10.12 0.0725
(N = 734) (N = 272) (N = 1006) (N = 394) (N = 65) (N = 459)
Systolic blood pressure (mmHg) ± SD 128.12 ± 18.45 129.90 ± 20.06 128.60 ± 18.91 0.2258 127.61 ± 19.09 131.37 ± 20.82 128.14 ± 19.36 0.2189
(N = 734) (N = 272) (N = 1006) (N = 394) (N = 65) (N = 459)
Heart rate ± SD 63.43 ± 10.85 65.04 ± 10.78 63.86 ± 10.85 0.0297 66.71 ± 11.91 67.91 ± 10.70 66.88 ± 11.74 0.2518
(N = 734) (N = 273) (N = 1007) (N = 394) (N = 65) (N = 459)
Smoking status, n (%)
Past smoker 512 (69.75%) 130 (47.62%) 642 (63.75%) 3.8 × 10 −15 281 (71.32%) 35 (53.85%) 316 (68.85%) 0.0040
Never smoker 154 (20.98%) 128 (46.89%) 282 (28.00%) 99 (25.13%) 23 (35.38%) 122 (26.58%)
Current smoker 68 (9.26%) 15 (5.49%) 83 (8.24%) 14 (3.55%) 7 (10.77%) 21 (4.58%)
Cigarettes per day ± SD 16.49 ± 11.45 16.00 ± 7.76 16.40 ± 10.84 0.8348 20.14 ± 13.85 9.86 ± 7.47 16.71 ± 12.89 0.0670
(N = 68) (N = 15) (N = 83) (N = 14) (N = 7) (N = 21)
Mean daily dose (mg) ± SD Metoprolol Allopurinol
83.69 ± 56.79 85.25 ± 57.41 84.11 ± 56.93 0.6188 197.46 ± 75.30 173.74 ± 86.36 194.09 ± 77.31 0.0061
(N = 733) (N = 272) (N = 1005) (N = 393) (N = 65) (N = 458)
Mean moiety concentration (ng/ml) ± SD Metoprolol Oxypurinol
101.08 ± 131.20 138.44 ± 162.06 111.22 ± 141.15 0.0001 12480.0 ± 8433.01 16334.6 ± 9708.69 13025.9 ± 8718.04 0.0015
(N = 733) (N = 273) (N = 1006) (N = 394) (N = 65) (N = 459)
Quantifiable moiety concentration, n (%) 717 (97.82%) 271 (99.27%) 988 (98.21%) 0.1798 385 (97.72%) 64 (98.46%) 449 (97.82%) 1.0000
Degree of education, n (%) 0.0012 0.0003
<High school 148 (20.16%) 74 (27.21%) 222 (22.07%) 84 (21.32%) 23 (35.38%) 107 (23.31%)
High school 185 (25.20%) 84 (30.88%) 269 (26.74%) 80 (20.30%) 21 (32.31%) 101 (22.00%)
Trades certificate or diploma 90 (12.26%) 34 (12.50%) 124 (12.33%) 47 (11.93%) 12 (18.46%) 59 (12.85%)
College or other non‐university diploma 77 (10.49%) 20 (7.35%) 97 (9.64%) 33 (8.38%) 3 (4.62%) 36 (7.84%)
University (certificate and bachelor) 139 (18.94%) 44 (16.18%) 183 (18.19%) 103 (26.14%) 5 (7.69%) 108 (23.53%)
Graduate degree 78 (10.63%) 9 (3.31%) 87 (8.65%) 39 (9.90%) 1 (1.54%) 40 (8.71%)
No answer 17 (2.32%) 7 (2.57%) 24 (2.39%) 8 (2.03%) 0 (0.00%) 8 (1.74%)
Annual income ($), n (%) 2.4 × 10 −12 0.0001
<15,000 19 (2.59%) 12 (4.41%) 31 (3.08%) 9 (2.28%) 1 (1.54%) 10 (2.18%)
15,000–24,999 26 (3.54%) 37 (13.60%) 63 (6.26%) 19 (4.82%) 12 (18.46%) 31 (6.75%)
25,000–34,999 68 (9.26%) 37 (13.60%) 105 (10.44%) 45 (11.42%) 5 (7.69%) 50 (10.89%)
35,000–54,999 160 (21.80%) 55 (20.22%) 215 (21.37%) 68 (17.26%) 15 (23.08%) 83 (18.08%)
55,000–74,999 118 (16.08%) 28 (10.29%) 146 (14.51%) 61 (15.48%) 9 (13.85%) 70 (15.25%)
75,000–99,999 80 (10.90%) 14 (5.15%) 94 (9.34%) 44 (11.17%) 2 (3.08%) 46 (10.02%)
≥100,000 136 (18.53%) 22 (8.09%) 158 (15.71%) 93 (23.60%) 4 (6.15%) 97 (21.13%)
No answer 127 (17.30%) 67 (24.63%) 194 (19.28%) 55 (13.96%) 17 (26.15%) 72 (15.69%)
Working status, n (%) 1.3 × 10 −9 2.4 × 10 −6
Employed full time 154 (21.04%) 28 (10.45%) 182 (18.20%) 59 (14.97%) 2 (3.08%) 61 (13.29%)
Employed part time 64 (8.74%) 20 (7.46%) 84 (8.40%) 50 (12.69%) 2 (3.08%) 52 (11.33%)
Employed seasonal or subcontractor 0 (0.00%) 1 (0.37%) 1 (0.10%) 0 (0.00%) 1 (1.54%) 1 (0.22%)
Retired 447 (61.07%) 186 (69.40%) 633 (63.30%) 262 (66.50%) 52 (80.00%) 314 (68.41%)
Home 3 (0.41%) 18 (6.72%) 21 (2.10%) 0 (0.00%) 3 (4.62%) 3 (0.65%)
Unemployed 16 (2.19%) 4 (1.49%) 20 (2.00%) 3 (0.76%) 1 (1.54%) 4 (0.87%)
Disability 35 (4.78%) 9 (3.36%) 44 (4.40%) 10 (2.54%) 4 (6.15%) 14 (3.05%)
Social security 4 (0.55%) 0 (0.00%) 4 (0.40%) 3 (0.76%) 0 (0.00%) 3 (0.65%)
No answer or NA 9 (1.23%) 2 (0.75%) 11 (1.10%) 7 (1.78%) 0 (0.00%) 7 (1.53%)
Drug/medication allergies or intolerances (Yes) 207 (28.20%) 137 (50.18%) 344 (34.16%) 6.2 × 10 −11 157 (39.85%) 36 (55.38%) 193 (42.05%) 0.0187
(N = 734) (N = 273) (N = 1007) (N = 394) (N = 65) (N = 459)

Cardiovascular chronic conditions, n (%)

(Yes)
Hypertension 574 (78.20%) 217 (79.49%) 791 (78.55%) 0.6586 339 (86.04%) 58 (89.23%) 397 (86.49%) 0.4857
(N = 734) (N = 273) (N = 1007) (N = 394) (N = 65) (N = 459)
Type 1 diabetes 7 (0.95%) 0 (0.00%) 7 (0.70%) 0.0181 1 (0.25%) 0 (0.00%) 1 (0.22%) 0.2905
Type 2 diabetes 232 (31.61%) 67 (24.54%) 299 (29.69%) 156 (39.59%) 32 (49.23%) 188 (40.96%)
(N = 734) (N = 273) (N = 1007) (N = 394) (N = 65) (N = 459)
Dyslipidemia 648 (88.28%) 205 (75.09%) 853 (84.71%) 2.3 × 10 −7 345 (88.01%) 54 (83.08%) 399 (87.31%) 0.2685
(N = 734) (N = 273) (N = 1007) (N = 392) (N = 65) (N = 457)
MI 359 (48.91%) 71 (26.01%) 430 (42.70%) 6.5 × 10 −11 159 (40.66%) 24 (36.92%) 183 (40.13%) 0.5687
(N = 734) (N = 273) (N = 1007) (N = 391) (N = 65) (N = 456)
PCI 294 (40.05%) 67 (24.54%) 361 (35.85%) 0.0001 130 (32.99%) 14 (21.54%) 144 (31.37%) 0.0651
(N = 734) (N = 273) (N = 1007) (N = 394) (N = 65) (N = 459)
CABG 333 (45.37%) 48 (17.58%) 381 (37.84%) 6.4 × 10 −16 135 (34.26%) 17 (26.15%) 152 (33.12%) 0.1980
(N = 734) (N = 273) (N = 1007) (N = 394) (N = 65) (N = 459)
CVA 75 (10.22%) 29 (10.62%) 104 (10.33%) 0.8512 59 (15.01%) 14 (21.88%) 73 (15.97%) 0.1647
(N = 734) (N = 273) (N = 1007) (N = 393) (N = 64) (N = 457)
Heart failure 128 (17.53%) 38 (13.92%) 166 (16.55%) 0.1704 100 (25.51%) 21 (32.81%) 121 (26.54%) 0.2199
(N = 730) (N = 273) (N = 1003) (N = 392) (N = 64) (N = 456)
Heart transplant 0 (0%) 0 (0%) 0 (0%) NA 0 (0%) 0 (0%) 0 (0%) NA
(N = 734) (N = 273) (N = 1007) (N = 394) (N = 65) (N = 459)
Arrhythmia 370 (50.41%) 143 (52.57%) 513 (50.99%) 0.5418 205 (52.30%) 39 (60.00%) 244 (53.39%) 0.2488
(N = 734) (N = 272) (N = 1006) (N = 392) (N = 65) (N = 457)

Non‐cardiovascular conditions, n (%)

(Yes)
Chronic renal failure 86 (11.72%) 21 (7.69%) 107 (10.63%) 0.0655 113 (28.68%) 24 (36.92%) 137 (29.85%) 0.1785
(N = 734) (N = 273) (N = 1007) (N = 394) (N = 65) (N = 459)
Crohn's disease 1 (0.14%) 1 (0.37%) 2 (0.20%) 0.4689 3 (0.76%) 0 (0.00%) 3 (0.65%) 1.0000
(N = 734) (N = 273) (N = 1007) (N = 394) (N = 65) (N = 459)
Ulcerative colitis 9 (1.23%) 2 (0.73%) 11 (1.09%) 0.7366 9 (2.28%) 0 (0.00%) 9 (1.96%) 0.3712
(N = 734) (N = 273) (N = 1007) (N = 394) (N = 65) (N = 459)
Gastritis and peptic ulcer disease 99 (13.49%) 31 (11.36%) 130 (12.91%) 0.3697 61 (15.48%) 11 (16.92%) 72 (15.69%) 0.7673
(N = 734) (N = 273) (N = 1007) (N = 394) (N = 65) (N = 459)
Celiac disease 3 (0.41%) 2 (0.73%) 5 (0.50%) 0.6167 3 (0.76%) 1 (1.54%) 4 (0.87%) 0.4583
(N = 734) (N = 273) (N = 1007) (N = 394) (N = 65) (N = 459)
Lactose intolerance 14 (1.91%) 9 (3.30%) 23 (2.28%) 0.1896 8 (2.03%) 1 (1.54%) 9 (1.96%) 1.0000
(N = 734) (N = 273) (N = 1007) (N = 394) (N = 65) (N = 459)
Food allergy 42 (5.72%) 22 (8.06%) 64 (6.36%) 0.1767 26 (6.60%) 6 (9.23%) 32 (6.97%) 0.4401
(N = 734) (N = 273) (N = 1007) (N = 394) (N = 65) (N = 459)

Known interacting medications, n (%)

(Yes)

 Moderate to strong CYP2D6 inhibitor Diuretic agents
23 (3.13%) 15 (5.5%) 38 (3.78%) 0.0925 221 (56.09%) 54 (83.08%) 275 (59.91%) 0.0001
(N = 734) (N = 273) (N = 1007) (N = 394) (N = 65) (N = 459)
Drug metabolism/elimination‐related phenotype, n (%) CYP2D6 poor metabolizer (PM) phenotype eGFR a
33 (4.54%) 11 (4.09%) 44 (4.42%) 0.1951 61.55 ± 20.53 49.23 ± 20.99 59.77 ± 21.02 1.7 × 10 −5
(N = 727) (N = 269) (N = 996) (N = 350) (N = 59) (N = 409)
CYP2D6 intermediate metabolizer (IM) phenotype
252 (34.66%) 90 (33.46%) 342 (34.34%)
(N = 727) (N = 269) (N = 996)
CYP2D6 normal metabolizer (NM) phenotype
400 (55.02%) 142 (52.79%) 542 (54.42%)
(N = 727) (N = 269) (N = 996)
CYP2D6 ultra metabolizer (UM) phenotype
42 (5.78%) 26 (9.67%) 68 (6.83%)
(N = 727) (N = 269) (N = 996)
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Note: P values for continuous variables: Kruskal–Wallis test, categorical variables: Chi‐square test or Fisher exact test. Significant p values (<0.05) are highlighted in bold.

Abbreviations: BMI, body mass index; CABG, coronary artery bypass graft surgery; CVA, cardiovascular accident; eGFR, estimated glomerular filtration rate; MI, myocardial infarction; NA, not applicable; PCI, percutaneous coronary intervention; SD, standard deviation.

a

eGFR (according to the Chronic Kidney Disease Epidemiology Collaboration (CKD‐EPI) creatinine equation).

Statistical analysis

We conducted descriptive statistics of demographic and clinical variables for all included participants. Categorical variables were reported as frequency and proportion whereas continuous variables were reported as mean and standard deviation (SD). We performed multiple linear regression analyses to investigate the associations between sex and drug concentrations, as well as concentration:dose ratios in participants of both studies. These were conducted using a model adjusted for age and drug daily dose, where applicable. Outcomes were log‐transformed so as to satisfy the normality assumption.

We then performed a stepwise regression, in which sex was treated as a variable of interest. The variables considered were those from the initial 34 variables that differed between females and males in both cohorts. A p < 0.15 was used for entry criteria and p > 0.05 for exit criteria. The three socioeconomic variables included in these models were categorized as follows: annual income was divided into four subcategories (<$25,000; $25,000–$54,999; $55,000–$74,999; ≥$75,000), working status, in four subcategories (employed full time; part time [including seasonal job]; retired; other [including home, unemployed, disability, and social security]) and degree of education, in three sub‐categories (less than high school; high school and trades certificate or diploma; college, university and graduate).

Furthermore, we conducted a sensitivity analysis by excluding the variable “annual income” due to the high number of missing entries in the Biobank of the MHI Hospital Cohort (n = 194 for metoprolol users and n = 72 for allopurinol users). Statistical comparisons were two‐tailed and a p value of less than 0.05 was considered statistically significant. All analyses were conducted using SAS version 9.4 (SAS Institute).

Ethics statement

The abovementioned studies were conducted according to the Management Policy of the MHI Hospital Cohort and were approved by the institution's Scientific and Ethics Committees. All patients had previously given informed written consent to be included in MHI Hospital Cohort.

RESULTS

Study populations

A total of 1007 participants were included in the metoprolol subpopulation. Patients were mainly males (73%) aged 66.5 ± 8.8 years and were treated with a mean metoprolol daily dose of 84 ± 57 mg, with total daily doses ranging from 6.25 to 400 mg. Both males and females received similar metoprolol doses (Table 1). In the allopurinol subpopulation, 459 participants were included, with a mean age of 69.4 ± 8.0 years. Of those participants, 86% were males. Mean daily dose of allopurinol overall was 194 ± 77 mg, ranging from 42.9 mg (100 mg 3 days per week) to 600 mg. Males received significantly higher doses of allopurinol than females (p = 0.006, Table 1).

Impact of sex on metoprolol and oxypurinol concentrations

Females presented significantly higher concentrations of metoprolol and oxypurinol compared to males (both p < 0.002, Table 1). This effect persisted after adjusting for age and daily dosing of the respective medication (both p < 0.025, Table 2). Consistently, the concentration:dose ratios of metoprolol and oxypurinol were respectively 21% and 60% higher in females for the two medications (both p < 2.0 × 10−4, Figure 1).

TABLE 2.

Association between sex and metoprolol and oxypurinol concentrations in adjusted model.

Effect Metoprolol concentrations Oxypurinol concentrations
Estimate (SE) P value R 2 Estimate (SE) P value R 2
28.38 11.22
Sex (female) 0.350 (0.083) 2.5 × 10 −5 1.55 0.444 (0.196) 0.0243 0.67
Age 0.017 (0.004) 0.0001 1.35 0.042 (0.009) 1.5 × 10 −6 3.19
Daily dose 0.012 (0.001) 4.0 × 10 −68 25.48 0.005 (0.001) 1.9 × 10 −9 7.36
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Note: Crude model adjusted for age and daily dose; intercepts for metoprolol concentrations: 2.102, oxypurinol concentrations: 5.473. Significant p values (<0.05) are highlighted in bold.

Abbreviation: SE, standard error.

FIGURE 1.

FIGURE 1

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Sex‐related differences in concentration:dose ratio for metoprolol (left) and allopurinol/oxypurinol (right). Sex was associated with concentration:dose ratios of metoprolol and allopurinol (both p < 2.0 × 10−4) and to age‐adjusted concentration:dose ratios of metoprolol and allopurinol (both p < 3.0 × 10−4). Central bar of the box: median; lower bar of the box: first quartile; upper bar of the box: third quartile; diamond: mean; bar below the box: minimum (excluding outliers); bar above the box: maximum (excluding outliers).

Multivariable modeling

Based on the stepwise regression model, variables that were differently distributed between males and females in both studies (p < 0.05) were selected from Table 1. These variables consisted of weight, height, waist girth, hip girth, smoking status, degree of education, annual income, working status, and drug/medication allergies or intolerances. Key drug interactions (CYP2D6 moderate to strong inhibitors for metoprolol; diuretics for allopurinol), key drug metabolism/elimination‐related phenotypes (CYP2D6 genotype‐inferred phenotype for metoprolol; eGFR for allopurinol), and age were also included for both drugs.

Multivariable modeling: metoprolol and oxypurinol concentrations

Sex remained an independent predictor of metoprolol concentrations (p < 0.004, Table 3), but not of oxypurinol concentrations, after the inclusion of other predictors. Daily drug dosing, weight, and drug metabolism/elimination‐related phenotype were common predictors in both models. Specifically, age, higher daily dose, and use of moderate to strong CYP2D6 inhibitors were associated with higher concentration of metoprolol, as was female sex (all p < 0.007, Table 3). Variables associated with lower metoprolol concentrations were weight and CYP2D6 genotype‐inferred metabolizing phenotype (all p < 0.008, Table 3). As for allopurinol, increasing dose was a predictor of higher oxypurinol concentrations (p = 5.2 × 10−10, Table 3), while weight and eGFR were associated with lower oxypurinol concentrations, as was the working status subcategorized as “other” (home, unemployed, disability, and social security) compared to retired individuals (all p < 0.004, Table 3).

TABLE 3.

Association between variables of interest and metoprolol and oxypurinol concentrations.

Effect Metoprolol concentrations (N = 701) Effect Oxypurinol concentrations (N = 328)
Estimate (SE) P value Estimate (SE) P value
Sex (female) 0.292 (0.100) 0.0037 Sex (female) 0.237 (0.252) 0.3467
Daily dose 0.013 (0.001) 7.4 × 10 −62 Daily dose 0.007 (0.001) 5.2 × 10 −10
Weight −0.007 (0.003) 0.0076 Weight −0.014 (0.005) 0.0034
CYP2D6 genotype‐inferred phenotype −0.598 (0.060) 6.8 × 10 −22 eGFR −0.015 (0.004) 0.0007
Age 0.013 (0.005) 0.0061 – – –
Use of CYP2D6 inhibitor 0.359 (0.121) 0.0032 – – –
– – – Working status (full time) a −0.394 (0.250) 0.1153
– – – Working status (part time) a −0.232 (0.260) 0.3730
– – – Working status (other) a −1.445 (0.377) 0.0002
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Note: Stepwise model; intercepts for metoprolol concentrations: 3.481, oxypurinol concentrations: 9.997. Significant p values (<0.05) are highlighted in bold.

Abbreviations: eGFR: estimated glomerular filtration rate; SE, standard error.

a

Working status category “Retired” was set as the comparator.

Multivariable modeling: drug dosing

Sex was not associated with metoprolol nor allopurinol dosing after adjusting for other predictors. Yet, CYP2D6 genotype‐inferred phenotype and eGFR, both drug metabolism/elimination‐related markers of either one or the other drug, were associated with metoprolol and allopurinol dosing, respectively (both p < 0.002, Table 4). In both cases, greater elimination (CYP2D6 ultra‐metabolizer phenotype for metoprolol; lesser impaired eGFR for allopurinol) predicted higher drug dosing. Interestingly, waist girth was associated with daily drug dosing in both models (both p < 0.030, Table 4). Patients with greater waist circumference received higher metoprolol and allopurinol dosing. Higher metoprolol dosing was also predicted by allergy/intolerance to medication, and lower metoprolol dosing, by height (all p < 0.030, Table 4). Higher allopurinol dosing was associated with diuretic use (p < 0.006, Table 4).

TABLE 4.

Association between variables of interest and metoprolol and allopurinol daily dose.

Effect Metoprolol daily dose (N = 701) Effect Allopurinol daily dose (N = 328)
Estimate (SE) P value Estimate (SE) P value
Sex (female) −0.103 (0.075) 0.1696 Sex (female) −0.061 (0.074) 0.4129
Waist girth 0.010 (0.002) 2.8 × 10 −7 Waist girth 0.004 (0.002) 0.0212
CYP2D6 genotype‐inferred phenotype 0.112 (0.035) 0.0015 eGFR 0.005 (0.001) 0.0001
Height −1.133 (0.350) 0.0013 – – –
Allergy to medication 0.120 (0.052) 0.0218 – – –
– – – Use of diuretic 0.150 (0.053) 0.0053
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Note: Stepwise model; intercepts for metoprolol daily dose: 4.934, oxypurinol daily dose: 4.359. Significant p values (<0.05) are highlighted in bold.

Abbreviations: eGFR, estimated glomerular filtration rate; SE, standard error.

Sensitivity analyses with the exclusion of annual income

Results from the sensitivity analyses with the exclusion of the variable “annual income” were similar to those in primary analyses, with only a few minor differences. Variables associated with concentrations of metoprolol and oxypurinol were consistent in the sensitivity analyses, apart from the working status, which was significantly associated with metoprolol concentrations, while age was not. Results are shown in Table S1.

All previously associated predictors were still predictors of metoprolol daily dose, apart from the variable “Drug/medication allergies or intolerances”. Male sex was associated with higher allopurinol dosing, as was eGFR, which remained consistent in all analyses (both p < 0.030, Table S2). In addition, hip girth, and not waist girth, was included in the final model predicting allopurinol dosing, with higher dosage of the medication being given to patients with greater hip circumference (p = 0.018, Table S2).

DISCUSSION

Our study demonstrates that females present higher dose‐ and age‐adjusted concentrations of metoprolol and oxypurinol, the active metabolite of allopurinol. The fact that females presented concentration:dose ratios 21% to 60% higher than males suggests that lower dosages may be required in females to produce consistent concentrations between patients of both sexes.

Although this has to be confirmed in future prospective research, the higher dose‐ and age‐adjusted drug/metabolite concentrations we observed in females are likely to contribute to the higher risk of ADRs described for multiple drug classes, including beta‐blockers metabolized by CYP2D6. 32 , 33 Data regarding the risk of ADRs in females treated with allopurinol are more scarce, but lean toward an increased risk in older females. 34 Interestingly, in our study, more than half of females also reported previous drug allergies and intolerances to medication, which was consistently more than what was reported in males.

The association between sex and metoprolol concentrations remained significant despite the introduction of predictors of drug concentrations, such as CYP2D6 genotype‐inferred phenotype. Conversely, the impact of sex on oxypurinol concentrations was attenuated after the inclusion of weight, eGFR, and working status, suggesting a major contribution of these variables to sex differences in allopurinol and that these factors may be useful to guide dosing, irrespective of sex. Indeed, females generally present lower weight and eGFR than males. 3 , 11 , 35 Thus, these two factors may largely explain sex differences on oxypurinol concentrations, in addition to allopurinol dose.

One explication behind the divergences regarding the importance of sex in the multivariate models is that CYP2D6 genotype‐inferred phenotype is not a direct indicator of the CYP2D6 metabolizing capacity which, for a given genotype‐inferred phenotype, may have different metabolizing capacity between individuals. This further underscores the fact that there is no readily available phenotypic marker of hepatic function available to the clinician, either specific to a CYP or the general liver function, as it is the case for eGFR and renal function.

We also found that in all models, either age or working status was a predictor of drug concentrations. Given that retired individuals demonstrated higher concentrations, the association with working status may simply portray a component of a more advanced age and reduced elimination. Unsurprisingly, daily dose was the factor most strongly associated with metoprolol and oxypurinol concentrations. Thus, globally, these results suggest the need for a more personalized approach to dosing to reach comparable drug concentrations between females and males, which could either be based on sex or factors contributing to these sex differences in drug concentrations. Yet, this hypothesis should be validated in prospective studies.

Surprisingly, waist girth (or hip girth) rather than weight was a predictor of drug dosing. Our results show that higher dosing of both drugs was observed in patients with greater waist circumference. One can hypothesize that the prescribing patterns of healthcare providers could be influenced by the physical appearance of patients, possibly as a consequence of implicit bias. Data suggest that healthcare practitioners often estimate anthropometric characteristics, like weight or obesity, by appearance. 36 , 37 Yet, perception of appearance can be inaccurate. According to one study, healthcare practitioners wrongly classified the weight of more than one in three patients. 38 Waist girth, if estimated by appearance, could therefore be an inaccurate indicator of weight and thus dosing requirements. In the metoprolol subpopulation only, height was another anthropometric parameter that predicted lower dosing of metoprolol in taller patients. This observation correlates with the current perception that being tall makes someone look thinner 39 and reinforces the imprecision of anthropometry visual estimates as the only basis for drug dosing. Our results indicate that weight remains a more appropriate predictor of drug concentrations. If necessary, measure of weight, and not estimate, should guide the prescriber to dose and titrate properly. Estimations of other anthropometric traits alone should not determine the dosage of a drug.

A strength of our study is that it was conducted in a large number of participants which represent a polymedicated population with multiple comorbidities. Most studies evaluating PK differences between males and females tend to rely on smaller cohorts of younger and healthy individuals without representing “real‐life” populations. 3 In addition, although we studied two different drugs, drug daily dose, weight, and drug metabolism/elimination‐related phenotype remained important predictors of drug concentrations for both subpopulations. This suggests that our findings may be applicable to other drugs. Finally, we chose an important selection of variables to include in our analyses, many not typically included in PK investigations, thus showing the importance of large‐scale observational studies. Consistent with multiple other investigations, we observed that males in both studies had higher annual income, were more often currently employed, and had a higher degree of education, and yet these were not consistently found to be independent predictors of drug concentrations.

One limitation is the uneven representation of males and females in the allopurinol subpopulation (86% and 14%, respectively). This is because gout is much more prevalent in males than in females. 40 , 41 Yet, our study presents a higher proportion of females than most allopurinol studies. 42 , 43 , 44 Cardiovascular diseases are more commonly diagnosed in males, 45 hence the higher prevalence of males in the metoprolol subpopulation. We included some social factors which may be important in delineating gender. However, we did not directly assess gender identity or gender roles. As such, the present project is focused on characterizing the differences in the biological factors underlying drug response between females and males. The random timing of blood sampling in regard to the last dose taken may also have limited our approach by increasing the heterogeneity of our results. However, we have shown in previous studies that this limitation does not prevent the observation of consistent associations. 15 , 16 The cross‐sectional nature of both studies did not allow for the evaluation of the change of drug dosing and concentrations over time. However, the dose taken at time of blood collection was properly noted and changes in treatment would not affect the reported drug concentrations. Liver dysfunction could not be evaluated since transaminases or other laboratory values were not collected as part of these studies. Lastly, only self‐described “White” participants were included in both cross‐sectional studies to minimize the risk of confounding due to population structure in the genetic associations. The lack of racial diversity limits the generalizability of our results to other populations.

In conclusion, females present higher metoprolol and oxypurinol concentrations than males. These differences may account for existing data regarding the higher risk of ADRs in females compared to males for these drugs and suggests the need for sex‐specific dosing requirements. Prospective studies will be needed to confirm this hypothesis. In order to produce such data, considerations should be given by the FDA and other health authorities to increase the sample size requirements of early clinical PK studies so that sponsors can develop more sex‐specific studies which may in turn lead to a more tailored medication regimen for females to improve safety, specifically if similar findings are reported for other drugs.

AUTHOR CONTRIBUTIONS

J.H., M.‐O.P., E.O., and S.dD. wrote the manuscript. J.H., M.‐O.P., M.M., E.O., G.L., M.J., I.M., J.‐L.R., J.‐C.T., M.‐P.D., and S.dD. designed the research. J.H., M.‐O.P., M.M., E.O., G.L., I.S.‐J., M.J., M.‐J.G., I.M., D.B., J.‐C.T., M.‐P.D., and S.dD. performed the research. J.H., M.‐O.P., M.M., E.O., I.M., M.‐P.D., and S.dD. analyzed the data. G.L., I.S.‐J., M.J., D.B. and M.‐P.D. contributed new reagents/analytical tools.

FUNDING INFORMATION

This work was supported by the Canadian Institutes of Health Research (funding reference number: 154862), the Montreal Heart Institute Foundation, and the Université de Montréal Beaulieu‐Saucier Chair in Pharmacogenomics.

CONFLICT OF INTEREST STATEMENT

S.dD. reports grants outside the submitted work from Pfizer, AstraZeneca, and RMS/Dalcor. A patent pertaining to pharmacogenomics‐guided CETP inhibition was granted and J.‐C.T. and M.‐P.D. are mentioned as authors. M.‐P.D. has a minor equity interest in DalCor. M.‐P.D. has research support (access to samples and data) from AstraZeneca, Pfizer, Servier, Sanofi, and GlaxoSmithKline. J.‐C.T. has received research grants from Amarin, AstraZeneca, Ceapro, DalCor Pharmaceuticals, Esperion, Ionis, Novartis, Pfizer, RegenXBio, and Sanofi; honoraria from AstraZeneca, DalCor Pharmaceuticals, HLS Pharmaceuticals, Pendopharm, and Pfizer; and minor equity interest in DalCor Pharmaceuticals. All other authors declared no competing interests for this work.

Supporting information

Table S1.

Click here for additional data file. (17.9KB, docx)

Table S2.

Click here for additional data file. (17.2KB, docx)

ACKNOWLEDGMENTS

No medical writers or proofreaders were used in the preparation of this article.

Hindi J, Pilon M‐O, Meloche M, et al. Females present higher dose‐adjusted drug concentrations of metoprolol and allopurinol/oxypurinol than males. Clin Transl Sci. 2023;16:872‐885. doi: 10.1111/cts.13497

Jessica Hindi and Marc‐Olivier Pilon contributed equally to this work.

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Associated Data

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Supplementary Materials

Table S1.

Click here for additional data file. (17.9KB, docx)

Table S2.

Click here for additional data file. (17.2KB, docx)

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