Prevalence of Potential Drug–Drug Interactions in Patients of the Swiss HIV Cohort Study in the Era of HIV Integrase Inhibitors

Abstract

Background

Prevalence of potential drug–drug interactions (PDDIs) between antiretroviral drugs (ARVs) and co-medications was high in 2008 in a Swiss HIV Cohort Study (SHCS) survey. We reassessed the prevalence of PDDIs in the era of human immunodeficiency virus (HIV) integrase inhibitors (INIs), characterized by more favorable interaction profiles.

Methods

The prevalence of PDDIs in treated HIV-positive individuals was assessed for the period 01–12/2018 by linkage of the Liverpool HIV drug interactions and SHCS databases. PDDIs were categorized as harmful (red flagged), of potential clinical relevance (amber flagged), or of weak clinical significance (yellow flagged).

Results

In 9298 included individuals, median age was 51 years (IQR, 43–58), and 72% were males. Individuals received unboosted INIs (40%), boosted ARVs (30%), and nonnucleoside reverse transcriptase inhibitor (NNRTIs) (32%)–based regimens. In the entire cohort, 68% received ≥1 co-medication, 14% had polypharmacy (≥5 co-medications) and 29% had ≥1 PDDI. Among individuals with co-medication, the prevalence of combined amber and yellow PDDIs was 43% (33% amber—mostly with cardiovascular drugs—and 20% yellow-flagged PDDIs) compared to 59% in 2008. Two percent had red-flagged PDDIs (mostly with corticosteroids), the same as in the 2008 survey. Compared with 2008, fewer individuals received boosted ARVs (−24%) and NNRTIs (−13%) but the use of co-medications was higher.

Conclusions

Prevalence of PDDIs was lower with more widespread use of INIs in 2018 than in 2008. Continued use of boosted regimens and increasing needs for co-medications in this aging population impeded lower rates of PDDIs.

HIV, drug–drug interactions, prevalence, Swiss HIV Cohort Study, modern antiretroviral therapy

The life expectancy of people living with human immunodeficiency virus (HIV; PLWH) has significantly improved with potent antiretroviral drugs (ARVs) [1, 2], but at the same time, comorbidities in populations with HIV have increased [3] and are higher than in age-matched individuals without HIV [4, 5]. Chronic immune activation, unhealthy lifestyle, viral coinfections, or ARV toxicity may be related to higher comorbidities in individuals with HIV [6] and treatment of these comorbidities exposes PLWH to higher risk of drug–drug interactions [7]. Antiretroviral drugs are among the medications with the highest potential for drug interactions decreasing or increasing plasma drug levels of co-medication or ARV, which may result in treatment failure or drug toxicity [8].

A previous analysis of the Swiss HIV Cohort (SHCS) from 2008 has shown a high prevalence of potential drug–drug interactions (PDDIs) between ARVs and co-medications. A total of 2% and 59% of ARV-treated SHCS participants with co-medications had contraindicated and potentially clinically significant interactions, respectively, that required dose adjustment or close monitoring at a time when first-line HIV treatments consisted of highly interacting protease inhibitors or nonnucleoside reverse transcriptase inhibitors (NNRTIs) such as efavirenz or nevirapine [9]. In recent years, ARVs such as unboosted integrase inhibitors (INIs) or the NNRTIs rilpivirine and doravirine characterized by improved interaction profiles have been introduced [10] and are among the preferred HIV treatment options in current guidelines [11, 12]. To date, only 1 large Spanish population study has analyzed PDDIs with contemporary ARVs, showing that 4%, 25%, and 14% of PLWH receiving co-medications had contraindicated, potentially clinically significant, and weak clinical relevance interactions, respectively [13].

This work aimed to assess the prevalence of and risk factors for PDDIs between ARVs and co-medications in the SHCS for 2018 and to compare the prevalence with a previous analysis performed in 2008.

METHODS

Swiss HIV Cohort Study

The SHCS is a nationwide prospective cohort enrolling individuals with HIV aged older than 16 years [14] and collecting information on sociodemographic characteristics, clinical course, as well as immunological, virological, clinical chemistry laboratory data, ARV treatment, and co-medications classified by Anatomical Therapeutic Chemical (ATC) code [15].

Data Selection

All SHCS participants treated with ARV and with a visit between January 2018 and December 2018 were included in the analysis. For participants with multiple visits during the study period, only the first visit was considered.

Analysis of Potential Drug–Drug Interactions

The University of Liverpool HIV drug interaction database was used to detect PDDIs between ARVs and co-medications [16]. The database categorizes the severity of interactions using flags: a red flag for contraindicated drug–drug interactions, an amber flag for potential clinically significant interactions manageable by dose adjustment or clinical monitoring, and a green flag for no interaction. In 2017, the Liverpool drug interaction website was revised by implementing a yellow flag for interactions of weak clinical relevance with no need of a priori dosage adjustment or monitoring. Adverse drugs reactions are not systematically collected in SHCS, thus preventing the analysis of actual drug interactions.

Data Management

Medications were extracted from the SHCS and all existing ARV–co-medication pairs were created. The drugs listed in the Liverpool HIV interaction database were coded similarly using the ATC code. The coded SHCS drug pairs were used to extract information about the severity of PDDIs from the Liverpool HIV interaction database via an application programming interface (API) script. The co-medications listed in the Liverpool database enabled to code the severity of PDDIs for 67% of the existing SHCS ARV–co-medication pairs. An expert pharmacologist in the field of drug interactions (C. M.) checked that the automated data extraction was correctly flagged and, in addition, coded the severity of PDDIs for co-medications not listed in the Liverpool HIV drug interaction database. In the end, the severity of PDDIs was coded for 97% of the SHCS ARV–co-medication pairs.

Statistical Analysis

We used absolute numbers, percentages, medians, and interquartile ranges (IQR) to report sociodemographic characteristics and prevalence of PDDIs. Logistic regression was conducted to investigate factors associated with PDDIs, defined as having at least 1 red or amber flag PDDI. The analysis was conducted using SAS version 9.4 (SAS Institute). More details on the methods are provided in the Supplementary Materials.

RESULTS

Study Population and Their Medications

The study population included 9298 PLWH who were followed up in the SHCS during the study period and who were on ARV treatment (Figure 1). The median age of study participants was 51 years (IQR, 43–58 years), and the majority were males (n = 6735, 72%) and virologically suppressed (n = 8431, 91%). Most commonly used ARVs were unboosted INIs (n = 3716, 40%), NNRTIs (n = 2658, 29%), boosted ARVs (n = 2544, 27%), and boosted ARVs + NNRTIs (n = 256, 3%); and the most commonly used backbones were tenofovir alafenamide/emtricitabine (n = 3256, 35%) and abacavir/lamivudine (n = 2874, 31%) (Table 1). Elvitegravir/cobicistat-based regimens (n = 1459, 16%) were classified as boosted ARVs.

Figure 1.

Participant inclusion and prevalence of potential drug–drug interactions. Abbreviations: ARV, antiretroviral drug; PDDI, potential drug–drug interaction; SHCS, Swiss HIV Cohort Study.

Table 1.

Characteristics of the Study Population in 2018 and 2008

	2018			2008
Characteristics	All (N = 9298)	On ARV Only (n = 2974)	On ARV + Co-medications (n = 6324)	All (N = 1497)
Median (IQR) age, years	51 (43–58)	46 (38–53)	53 (46–60)	46 (40–52)
Male sex, n (%)	6735 (72)	2180 (73)	4555 (72)	1003 (67)
White ethnicity, n (%)	7207 (78)	2101 (71)	5106 (81)	1213 (81)
HIV acquisition mode, n (%)
MSM  Heterosexual  IDU  Others or unclear	4330 (47) 3514 (38) 665 (7) 789 (8)	1515 (51) 1165 (39) 103 (4) 191 (6)	2815 (45) 2349 (37) 562 (9) 598 (9)	503 (34) 663 (44) 174 (12) 157 (10)
Current illicit drug use, n (%)	139 (2)	20 (1)	119 (2)	264 (18)
CD4 current, n (%)
<350 cells/μL  350–500 cells/μL  >500 cells/μL	892 (10) 1401 (15) 6898 (74)	227 (8) 463 (16) 2238 (75)	665 (11) 938 (15) 4660 (74)	351 (23) 383 (26) 763 (51)
HIV-1 RNA, <20 copies/mL, n (%)	8431 (91)	2674 (90)	5757 (91)	1272 (85)
ARV drug class, n (%)
Unboosted INIa  NNRTI  Boosted ARVb  Boosted ARV + NNRTI  Others	3716 (40) 2658 (29) 2544 (27) 256 (3) 124 (1)	1076 (36) 946 (32) 858 (29) 46 (2) 48 (2)	2640 (42) 1712 (27) 1686 (27) 210 (3) 76 (1)	4 (0.3) 567 (38) 706 (47) 104 (7) 116 (8)
Backbone, n (%)
TAF + FTC  ABC + 3TC  TDF + FTC  Others	3256 (35) 2874 (31) 2078 (22) 1090 (12)	958 (32) 918 (31) 842 (28) 256 (9)	2298 (36) 1956 (31) 1236 (20) 834 (13)	0 (0) 437 (29) 546 (37) 514 (34)

Abbreviations: ABC, abacavir; ARV, antiretroviral drug; FTC, emtricitabine; HIV, human immunodeficiency virus; IDU, injection drug use; INI, integrase inhibitor; IQR, interquartile range; MSM, men who have sex with men; NNRTI, nonnucleoside reverse transcriptase inhibitor; TAF, tenofovir alafenamide; TDF, tenofovir disoproxil fumarate; 3TC, lamivudine.

^aUnboosted INIs include bictegravir, dolutegravir, and raltegravir.

^bBoosted ARVs include protease inhibitors boosted with ritonavir or cobicistat and elvitegravir boosted with cobicistat.

Overall, 6324 (68%) individuals had 1 or more co-medication (Figure 1). The most commonly prescribed co-medications (n = 18 363) were cardiovascular drugs (27%), central nervous system (CNS) drugs (21%), and gastrointestinal and metabolism drugs (16%), and, with lower frequencies, genitourinary drugs (6%), blood drugs (ie, antiplatelets or anticoagulants) (6%), respiratory drugs (5%), musculoskeletal drugs (5%), and anti-infectives (4%). Individuals receiving co-medications tended to be older, having lower CD4 cell count, and to be treated with unboosted INI-containing regimens (Table 1). Polypharmacy, defined as the concurrent use of 5 or more non-HIV co-medications, was observed in 1336 (14%) individuals and was slightly more common in males (15%) and older patients (6%, 18%, and 33% for the age categories 18–49 years, 50–64 years, and ≥65 years, respectively). Of interest, the use of cardiovascular drugs and proton pump inhibitors was markedly increased in individuals aged 65 years and older compared with the remaining age groups, whereas the use of CNS drugs was only moderately increased in elderly compared with young adults. The use of analgesic drugs or anti-infectives was comparable across age groups (Supplementary Figure 1).

Prevalence of Potential Drug–Drug Interactions

Of 58 287 drug pairs we identified 158 red, 3311 amber, 1679 yellow flag drug–drug interactions and 53 139 green flags without interactions. In the entire cohort, the prevalence of individuals with 1 or more PDDI was 29% (n = 2730). Of individuals receiving 1 or more co-medication (n = 6324), the prevalence of PDDI was 43% and, of those, 153 (2%) individuals had a red, 2093 (33%) an amber, and 1259 (20%) individuals a yellow flag PDDI (Figure 1). Common red and amber flag PDDIs found in the study population are shown in Tables 2 and 3. Most red flag PDDIs were encountered among patients receiving boosted ARVs, whereas only 1 red flag PDDI was observed in those receiving unboosted INIs. The most frequently encountered red flag PDDIs included the coadministration of boosted ARVs and corticosteroids (n = 31, 20%) or quetiapine (n = 31, 20%) followed by the coadministration of atazanavir or rilpivirine with proton pump inhibitors (n = 25, 16%) (Table 2). It should be highlighted that, even though quetiapine is contraindicated with strong CYP3A4 inhibitors, coadministration is possible with potent inhibitors provided that quetiapine dose is reduced to one-sixth of the original dose [16]. This interaction was mostly managed correctly, as reduction in quetiapine dose was performed in 75% of the cases. When considering individuals with 1 or more PDDI (n = 2730), amber flag PDDIs involved mostly the coadministration of boosted ARVs with antidepressants (n = 605, 22%) or statins (n = 570, 21%) (Table 3). Other common amber flag PDDIs included the coadministration of INIs with divalent cations (n = 322, 12%). The simultaneous intake of these drugs should be avoided as they can form a complex at the level of the gastrointestinal tract, thereby reducing the absorption of INIs [17–19]. This interaction can be overcome by separating the intake of these drugs. Available information on the timing of administration indicated that this interaction was correctly managed in 34% of the cases, whereas administration errors were observed in 26% of the cases.

Table 2.

Description of Potential Red Flag Drug–Drug Interactions

ARV Drug	Co-medication	Risks Related to Interaction	n	%
bARV	Fluticasone, mometasone, triamcinolone	Cushing syndrome	31	20
bARV	Quetiapine	QT prolongation (note: quetiapine dose was reduced to 1/6 in 75% of the cases)	31	20
ATV, RPV	Proton pump inhibitors	ARV treatment failure	25	16
bARV	Clopidogrel	Reduced efficacy for clopidogrel	18	11
bARV	Domperidone	QT prolongation	16	10
EFV, NVP	Ketoconazole	Reduced efficacy for ketoconazole	9	6
bARV	Alfuzosin, aliskiren, lecarnidipine	Severe hypotension	8	5
bARV	Simvastatin	Rhabdomyolysis	4	3
bARV	Midazolam, triazolam	Respiratory depression	4	3
EFV	Contraceptive	Contraceptive failure	3	2
bARV	Eplerenone	Hyperkalemia	3	2
DTG, NVP	Phenobarbital, primidone	ARV treatment failure	2	1
bARV	Amiodarone	Cardiac arrhythmias	1	1
bARV	Sirolimus	Sirolimus-related side effects	1	1
SQV	Trazodone	QT prolongation	1	1
bARV	Rivaroxaban	Bleeding	1	1

N = 158.

Abbreviations: ARV, antiretroviral drug; ATV, atazanavir; bARV, boosted antiretroviral drug; DTG, dolutegravir; EFV, efavirenz; NVP, nevirapine; RPV, rilpivirine; SQV, saquinavir.

Table 3.

Prevalence of Selected Amber Flag Drug–Drug Interactions in 2018 Versus 2008

		Prevalence, n (%)
ARV	Co-medication	2018a	2008b[9]
bARV/NNRTI	Antidepressants	605 (22)	139 (23)
bARV/NNRTI	Statins	570 (21)	123 (21)
bARV/NNRTI	Anxiolytics/sedatives	379 (14)	99 (17)
bARV/NNRTI	Erectile agents	285 (10)	17 (3)
bARV/NNRTI	Hormones	206 (8)	40 (7)
bARV/NNRTI	Methadone	174 (6)	115 (19)
bARV	B-Blockers	170 (6)	40 (7)
bARV/NNRTI	Calcium channel inhibitors	145 (5)	34 (6)
bARV/NNRTI	Antipsychotics	114 (4)	37 (6)
bARV/NNRTI	Narcotic analgesics	102 (4)	27 (5)
TDF	Valaciclovir	61 (2)	14 (2)
bARV/NNRTI	Anticonvulsants	52 (2)	19 (3)
INI	Divalent cationsc	322 (12)	NA
INI	Metformin	105 (4)	NA

Abbreviations: ARV, antiretroviral drug; bARV, boosted antiretroviral drug; INI, integrase inhibitor; NA, not applicable; NNRTI, nonnucleoside reverse transcriptase inhibitor; PDDI, potential drug–drug interaction; TDF, tenofovir disoproxil fumarate.

^aThe denominator is the number of individuals with ≥1 PDDI in 2018 (n = 2730).

^bThe denominator is the number of individuals with ≥1 PDDI in 2008 (n = 599).

^cAntacids or supplements containing divalent cations (ie, calcium, magnesium, aluminium, iron, zinc).

Factors Associated With Potential Drug–Drug Interactions

In the multivariable analysis, factors independently associated with an increased risk of red or amber flag PDDIs were treatments with NNRTIs, boosted ARVs, or boosted ARVs and NNRTIs compared with a treatment with unboosted INIs (Table 4). Treatment with the following therapeutic classes was also associated with an increased risk of red or amber flag PDDIs: cardiovascular drugs, blood drugs, gastrointestinal and metabolism drugs, musculoskeletal drugs, CNS drugs, hormones, genitourinary drugs and reproductive hormones, and anti-infectives. Finally, the risk of having PDDIs was also higher with increasing age, for individuals with illicit drug use, or those with a longer history of HIV infection.

Table 4.

Factors Associated With Potential Red or Amber Flag Drug–Drug Interactions in 2018

Factors	Adjusted OR	(95% CI)
Age, per 10 years older	1.10	(1.0–1.2)
Male gender	1.14	(.97–1.3)
White ethnicity	1.00	(.84–1.2)
BMI,a kg/m2
Obese vs normal  Overweight vs normal  Underweight vs normal	0.90 0.98 1.30	(.74–1.1) (.85–1.1) (.95–1.8)
Current illicit drug use	1.98	(1.3–3.1)
Years since HIV infection, per 10 years	1.12	(1.0–1.2)
Prior AIDS defining condition	1.10	(.95–1.3)
CD4 cell count, cells/μL
350–500 vs >500  <350 vs >500	1.00 1.19	(.84–1.2) (.97–1.5)
HIV-1 RNA >20 copies/mL	0.90	(.72–1.1)
ARV drug class
NNRTI vs unboosted INIb  Boosted ARVc vs unboosted INI  Boosted ARV + NNRTI vs unboosted INI	4.55 12.35 19.52	(3.8–5.5) (10.3–14.9) (13.1–29.0)
Backbone
TAF/FTC vs ABC/3TC  TDF/FTC vs ABC/3TC	0.89 1.11	(.74–1.1) (.90–1.4)
Therapeutic classes
Cardiovascular drugs   Blood drugs   Gastrointestinal and metabolism drugs   Musculoskeletal drugs   CNS drugs   Hormones   Respiratory system drugs   Genitourinary drugs and reproductive hormones   Anti-infectives   Herbals   Antineoplastic drugs   Others	1.26 1.39 2.72 1.38 1.77 2.27 0.82 1.87 2.04 0.67 1.14 1.40	(1.1–1.5) (1.2–1.7) (2.4–3.1) (1.2–1.7) (1.6–2.0) (1.7–3.0) (.61–1.1) (1.4–2.4) (1.7–2.5) (.42–1.1) (.58–2.2) (1.2–1.7)

Note: amber and yellow flag PDDIs were merged only to be able compare the prevalence of interactions in 2018 and 2008. However, for the analysis of the factors associated with amber PDDIs, only amber flag PDDIs (as per coding in the 2018 Liverpool HIV drug interactions website) were considered with no yellow flag PDDIs.

Abbreviations: ABC, abacavir; ARV, antiretroviral drug; BMI, body mass index; CI, confidence interval; CNS, central nervous system; FTC, emtricitabine; HIV, human immunodeficiency virus; INI, integrase inhibitor; NNRTI, nonnucleoside reverse transcriptase inhibitor; OR, odds ratio; TAF, tenofovir alafenamide; 3TC, lamivudine.

^aWeight categories are defined as follows: obese, BMI (kg/m²) ≥30; overweight, BMI 25 to <30; normal, 18.5 to 25; underweight <18.5.

^bUnboosted INIs include bictegravir, dolutegravir, and raltegravir.

^cBoosted ARVs include protease inhibitors boosted with ritonavir or cobicistat and elvitegravir boosted with cobicistat.

Comparison of the Study Population in 2018 and 2008

Compared with 2008, the median age of the study population was higher, with fewer individuals receiving boosted ARVs (−24%) and NNRTIs (−13%). Differences were also observed in the distribution of the mode of HIV infection with notably more men who have sex with men but fewer intravenous drug users (IDUs) in 2018 compared with 2008 (Table 1).

Comparison of Co-medication Use in 2018 and 2008

The use of co-medications in 2018 was compared with our previous analysis from 2008 (Figure 2). Both analyses showed a similar pattern of co-medication use, with cardiovascular and CNS being the most prescribed drug classes. However, with the exception of anti-infectives, diuretics, antidiarrheals, and H2 blockers, other drug classes were more commonly prescribed in 2018 compared with 2008.

Figure 2.

Co-medication use in SHCS participants in 2018 compared with 2008. Abbreviations: ACE, angiotensin-converting enzyme; Angio., angiotensin; chan., channel; NSAID, nonsteroidal anti-inflammatory drug; SHCS, Swiss HIV Cohort Study.

Comparison of the Prevalence of Potential Drug–Drug Interactions in 2018 and 2008

The prevalence of red flag PDDIs was 2% both in 2018 and in 2008. However, the prevalence of amber flag PDDIs (taking into account yellow flag PDDIs and counting individuals once if they had 1 amber plus 1 yellow flag PDDI) was reduced from 59% in 2008 to 43% in 2018. Of interest, the prevalence of PDDIs with erectile agents increased in 2018 compared with 2008 (10% vs 3%), whereas interactions with methadone decreased (6% vs 19%) (Table 3).

DISCUSSION

The prevalence of PDDIs in the SHCS in 2018 was 2%, 33%, and 20% for red, amber, and yellow flag PDDIs. A comparable picture was observed in a large Spanish population analysis performed in 2017 with 4%, 26%, and 14% of red, amber, and yellow flag PDDIs when considering ARV-treated individuals on co-medication [13]. Of interest, the prevalence of PDDIs in these 2 cohorts was also similar to a recent analysis conducted in a rural Tanzanian HIV cohort showing a prevalence of 33% and 18% for amber and yellow flag PDDIs, respectively [20]. In the Spanish and SHCS cohorts, amber PDDIs mostly occurred between boosted ARVs and cardiovascular or CNS drugs. The high use of cardiovascular and CNS drugs is explained by the aging of the population with HIV [3] and the fact that mental health problems are common among PLWH [21].

The occurrence of red flag PDDIs in the SHCS did not change in 2018 compared with 2008, with a prevalence of 2%, similar to that reported in other recent UK, US, and Australian cohort studies [22–24]. Red flag PDDIs were mostly observed with boosted ARVs due to their strong inhibitory effect on drug metabolism but were rarely observed with unboosted INIs, which are devoid of inhibitory or inducing properties. The persistence of red flag PDDIs is problematic as contraindicated interactions have been associated with increased healthcare costs in a French HIV cohort [25] and increased risk of hospitalizations in a US HIV cohort [26]. In our study, the most prevalent red flag PDDIs were the association of boosted ARVs with corticosteroids resulting in an increased corticosteroid exposure and related higher risk of developing a Cushing syndrome [27]. Of interest, this red flag PDDI was also shown to be the top 1 contraindicated interaction in 2 large European HIV cohort studies [13, 25]. Potential explanations for the recurrence of this interaction may relate to the fact that corticosteroids are often administered via nonoral routes, thus leading to an underestimation of the magnitude of the interaction. In addition, corticosteroids are used across a large variety of medical specialties and therefore are often prescribed by non-HIV physicians who are not aware of the risk of interactions with ARVs. A short information leaflet was developed following this study and distributed to the participants of the SHCS and their clinicians during cohort visits to increase awareness and prevent this interaction. Another common red flag PDDI was the association of atazanavir or rilpivirine with proton pump inhibitors, leading to a reduced absorption of the ARV and related increased risk of treatment failure [28, 29]. This interaction was also commonly reported in a French cohort [25].

Although the prevalence of red and amber flag PDDIs remains important, our results indicate that interactions can be managed correctly. For instance, quetiapine dose was appropriately reduced when coadministered with boosted ARVs in 75% of the cases and INIs were separated from divalent cations in 34% of the cases, although this number might be underestimated as information on the timing of administration was not always available.

In the multivariable analysis, the use of INIs was associated with a lower risk of having PDDIs; however, older individuals had a higher risk. Older individuals have more age-related comorbidities and are more likely to receive cardiovascular drugs, blood drugs (ie, antiplatelets, anticoagulants), or gastrointestinal and metabolism drugs (ie, proton pump inhibitor, antidiabetics) (Supplementary Figure 1). These drugs are subject to drug interactions, notably with boosted regimens, and are prescribed by non-HIV physicians who may not be aware of this risk. Other individuals at risk of PDDIs are those with mental health diseases and/or substance abuse disorders as well as individuals with a longer HIV infection. Of interest, longer history of HIV infection has been associated with multimorbidity and polypharmacy [30]. Possible explanations for this association include exposure to years with detectable viral load (pre–highly active antiretroviral therapy era) and to first-generation ARVs with higher toxicity profiles, all of which could have contributed to comorbidities and the related polypharmacy. This large cohort study indicates that PDDIs remain an issue in the era of INIs in the context of an aging population with HIV with more co-medications and more complex treatments [31]. Thus, vigilance should be maintained and periodic review of prescriptions is warranted to prevent prescribing errors. Future developments of the SHCS based on this work should aim to implement a real-time flagging of all newly entered co-medications to allow immediate feedback to clinicians and real-time monitoring of drug interactions.

The prevalence of PDDIs in the SHCS has decreased only by 16% in 2018 compared with 2008, despite the fact that first-line treatments have shifted over the years from ARVs with a high to those with a low potential for interactions [10]. This modest reduction is explained by a large proportion of PLWH still treated with boosted ARVs or interacting NNRTIs (ie, efavirenz, etravirine, nevirapine) and by a higher prevalence of co-medications use currently compared with 10 years ago, likely due to the aging of the population with HIV. This statement is indeed supported by the higher use of cardiovascular drugs in 2018 compared with 2008 as illustrated in Figure 2. Conversely, the use of anti-infectives and antidiarrheals was reduced in 2018 compared with 2008. This observation could possibly relate to the current recommendation to initiate ARV treatment regardless of CD4 cell count, which was shown to reduce AIDS-related events in the International Network for Strategic Initiatives in Global HIV Trials (INSIGHT) Strategic Timing of AntiRetroviral Treatment (START) study [32], and among those opportunistic infections. The reduction in antidiarrheals likely relates to the improved tolerability of modern ARVs, unlike the protease inhibitor lopinavir, which is known to cause diarrhea and was commonly used in 2008.

When comparing the type of interactions within the SHCS in 2018 and 2008, the pattern was comparable as most frequent interactions were with cardiovascular drugs or CNS drugs. Of interest, more interactions were observed with erectile dysfunction agents in 2018 compared with 2008. This observation could relate to the aging of the population with HIV and related increase in erectile dysfunction due to comorbidities such as diabetes mellitus, hypertension, and depression [33]. The median age was 51 years (IQR, 43–58 years) in 2018 and 46 years (IQR, 40–52 years) in 2008. Conversely, fewer interactions with methadone were observed in 2018 compared with 2008. This finding could be partly explained by the fact that IDUs represent a smaller percentage of individuals in the SHCS compared with in 2008, as indicated by the distribution of the mode of HIV infection in the participants of the SHCS over the years [14] and consistent with our population data (Table 1).

Some limitations should be acknowledged. It was not feasible to analyze dose adjustments or clinical consequences of PDDIs on a large scale. Thus, we cannot exclude that some interactions were managed appropriately as demonstrated with selected examples. Furthermore, the number of PDDIs could be underestimated as co-medications prescribed by other healthcare specialists may not always be entered in the SHCS database. The SHCS database does not document who prescribed given medications; therefore, provider characteristics leading to inappropriate drug–drug interactions cannot be analyzed. Finally, report of PDDIs relied on the electronic prescribing system in 2018 but on the chart review in 2008.

This study has several strengths. Since 2015, an online drug entry system for the SHCS allows the prospective systematic documentation of all medications, thereby providing a comprehensive analysis of the medication use for the Swiss population with HIV. Nearly all ARV–co-medication pairs in the SHCS (n = 58 287, 97%) were coded for the risk of PDDIs; thus, this analysis provides, to date, the most representative and comprehensive picture of the prevalence of PDDIs in an ARV-treated population with HIV. Finally, this is the first analysis comparing the prevalence of PDDIs at 2 time points in the same population, thus enabling to determine the impact of contemporary ARV use on the reduction in PDDIs.

In conclusion, the prevalence of PDDIs in the SHCS has decreased in the past decade, although a lower extent than anticipated. This is explained by the fact that half of the population were on unboosted regimens characterized by a favorable interaction profile and by a currently higher co-medication use. Co-medications are unavoidable in the context of an aging population and are often prescribed by non-HIV physicians who may not be aware of the drug interaction risk, notably with boosted regimens. PDDIs may be further reduced by the use of unboosted INIs or noninteracting NNRTIs (ie, rilpivirine, doravirine), in particular in PLWH with multimorbidity and polypharmacy, with regular medication reconciliation and review, and with the systematic use of comprehensive drug interaction search tools for drug prescribing in PLWH.

Supplementary Data

Supplementary materials are available at Clinical Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.

Notes

Members of the Swiss HIV Cohort Study. K. Aebi-Popp, A. Anagnostopoulos, M. Battegay, E. Bernasconi, J. Böni, D. L. Braun, H. C. Bucher, A. Calmy, M. Cavassini, A. Ciuffi, G. Dollenmaier, M. Egger, L. Elzi, J. Fehr, J. Fellay, H. Furrer, C. A. Fux, H. F. Günthard (President of the SHCS), D. Haerry (deputy of “Positive Council”), B. Hasse, H. H. Hirsch, M. Hoffmann, I. Hösli, M. Huber, C. R. Kahlert (Chairman of the Mother & Child Substudy), L. Kaiser, O. Keiser, T. Klimkait, R. D. Kouyos, H. Kovari, B. Ledergerber, G. Martinetti, B. Martinez de Tejada, C. Marzolini, K. J. Metzner, N. Müller, D. Nicca, P. Paioni, G. Pantaleo, M. Perreau, A. Rauch (Chairman of the Scientific Board), C. Rudin, A. U. Scherrer (Head of Data Centre), P. Schmid, R. Speck, M. Stöckle (Chairman of the Clinical and Laboratory Committee), P. Tarr, A. Trkola, P. Vernazza, G. Wandeler, R. Weber, S. Yerly.

Financial support. This study was supported within the framework of the Swiss HIV Cohort Study, supported by the Swiss National Science Foundation (grant number 177499), by SHCS project #805, and by the SHCS Research Foundation. The Basel Institute for Clinical Epidemiology and Biostatistics is supported by Stiftung Institut für klinische Epidemiologie. C. M. was supported by a grant from the Swiss National Science Foundation (grant number 166204). The data were gathered by the 5 Swiss university hospitals, 2 cantonal hospitals, 15 affiliated hospitals, and 36 private physicians (listed in www.shcs.ch/180-health-care-providers).

Potential conflicts of interest. H. C. B. has received in the 36 months prior to the submission of this manuscript grants, support for travelling, consultancy fees, and an honorarium from Gilead, BMS, ViiV Healthcare, and Roche that were not related to this project. He serves as the president of the association Contre le HIV et Autres Infections Transmissibles. In this function he has received support for the SHCS from ViiV Healthcare, Gilead, BMS, MSD, and AbbVie. D. L. B. has received honoraria and travel grants from ViiV, Gilead, and Merck. S. K. has received support from ViiV Healthcare, Gilead, Merck, AbbVie, and Janssen for research, and for the Liverpool Drug Interactions prescribing resources. M. C. has received research grants from Gilead and ViiV as well as expert opinion fees from AbbVie, Gilead, ViiV, and Sandoz for his institution. A. H. has received travel grants from ViiV, Gilead, and MSD. A. C. has received financial support for the day hospital to Geneva University Hospital (HIV/AIDS unit) from MSD, AbbVie, Gilead, and ViiV as well as unrestricted educational grants from MSD, ViiV, and Gilead. C. M. has received a research grant from Gilead and speaker honoraria for her institution from MSD. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.