SYSTEMATIC REVIEW:
Drug-drug Interactions between Antiretrovirals and medications used to treat TB, Malaria, Hepatitis B&C and opioid dependence
SH Khoo1, S Gibbons1, K Seden2, DJ Back1
1 Department of Pharmacology, University of Liverpool, 70 Pembroke Place, Liverpool L69 3GF, UK
2 NIHR Biomedical Research Centre in Microbial Diseases, Royal Liverpool University Hospital, Prescott St, Liverpool L7 8XP, UK
+44 151 794 5560 +44 151 794 5656 (fax)
Correspondence to [email protected]
Table of Contents:
Page
1 Introduction 3
2 Methods 5
2.1 Search Strategy 2.2 Study Selection 2.3 Study Quality Assessment 2.3.1 Strength of recommendation 2.3.2 Grading of evidence 2.4 Data Synthesis
3 Results 8
3.1 Antimalarials 3.2 TB Drugs 3.3 Hepatitis B & C Treatment 3.4 Opioid dependence
4 Discussion
5 Acknowledgements and Declarations
6 References
7 Appendix A – Antimalarial Drugs
Appendix B – Antituberculous Drugs
Appendix 3 – Hepatitis B and C Treatment
Appendix 4 – Drugs used in Opioid Dependence
1 INTRODUCTION Drug-drug interactions (DDIs) are an important and widely under-recognised source of medication errors, which represent significant risk of harm to patients and opportunity cost for healthcare systems. The co-administration of contraindicated drugs has been found to account for 5.2% of 209 hospital admissions in the USA in patients receiving antiretrovirals (ARVs) [Rastegar et al, 2006]. Although studies are limited, clinically significant DDIs involving ARVs are common, affecting at least 14-41% of patients in the US, the Netherlands and UK [Shah et al, 2007; de Maat et al, 2004; Cottle et al, 2009]. A substantial proportion of these have the potential for an adverse impact on ARV exposure. Conversely, DDIs may result in increased exposure to ARVs or co- administered drugs, precipitating drug toxicity or greater severity and incidence of adverse reactions. Data from developing countries are sparse, though it is likely that clinically significant DDIs are prevalent [Kigen et al, 2008].
DDIs may be pharmacokinetic or pharmacodynamic in nature. ARVs are among the most therapeutically risky drugs for DDIs, due to potent inhibition or induction of liver enzymes such as the cytochrome P450 isoenzymes (CYP450), which metabolise a broad array of other medications. DDIs involving protease inhibitors (PI) and non-nucleoside reverse transcriptase inhibitors (NNRTI) are more likely to be attributable to hepatic metabolic pathways than DDIs involving nucleoside or nucleotide reverse transcriptase inhibitors (NRTI), which in some cases can be due to competition for renal tubular secretion. DDIs are more prevalent in regimens containing PIs than NNRTIs [Miller et al, 2007; Cottle et al, 2009].
Although all patients receiving ARVs are potentially at risk of DDIs, this risk is increased in certain patient groups and clinical scenarios:
1.1 Use of New HIV Drugs Assessment of the potential for DDIs during the clinical phase of drug development, although comprehensively undertaken, is at best incomplete. Screening of a new molecular entity for potential as a substrate, inducer or inhibitor of phase I and II metabolic enzymes and influx/efflux drug transporters is limited by a lack of validated expression systems and standardised protocols, particularly for drug transporters.
It cannot be assumed that drugs from the same class have broadly similar potential for interaction. For example, the integrase inhibitors raltegravir and elvitegravir differ in metabolic pathways and interaction potential with regard to cytochrome P450 enzymes. Further, raltegravir has significant interactions with proton pump inhibitors as a result of physiochemical characteristics which lead to pH-dependent solubility. In addition, there will always be surprises in the form of unanticipated DDIs which emerge after licensing, and may lead to diminished therapeutic effect of ARVs, such as lopinavir and rosuvastatin. This highlights the need for standard protocols for interaction screening of new drugs, as well as clinical vigilance as experience in their use develops.
1.2 Co-infections, particularly in the developing world Throughout the world, HIV overlaps with other epidemics such as tuberculosis (TB), malaria and chronic viral hepatitis. TB is the leading cause of death among people living with HIV in Africa, and globally 456,000 people died of HIV-associated TB in 2007. Difficulties in treating TB in HIV patients may arise due to interactions with rifampicin which is a potent inducer of liver enzymes. Several ARVs contraindicate the use of rifampicin and others may require dose modification. HIV also has a considerable impact on malaria, affecting parasitaemia, disease severity (in areas of unstable transmission) and mortality during pregnancy. Drug interactions are understudied, but important interactions have already been identified between antiretrovirals and quinine, amodiaquine and lumefantrine.
Worldwide, an estimated two billion people have been infected with the hepatitis B virus (HBV), and more than 350 million have chronic (long-term) liver infections. An estimated 170 million persons are chronically infected with Hepatitis C (HCV) with 3 to 4 million persons are newly
infected each year. Therapy for chronic HCV infection is set to dramatically change with the proliferation of new drugs directed against HCV polymerase, protease and other targets, and although data are sparse, interactions between HIV nucleoside reverse transcriptase inhibitors and PIs, and new or existing HCV drugs have been identified. The inflexibility of dosing when using fixed-dose combinations of ARVs makes many DDIs harder to manage. ARV coverage in middle and low income countries has increased 45% between 2006 and December 2007 [UNAIDS, 2008], and with increasing coverage, it is likely that access to other medication will also improve, for example various integrated programmes for ‘neglected’ tropical diseases, which aim to combine mass drug administration for conditions such as helminth infection. This inevitably increases the scope for DDIs.
1.3 Polypharmacy in an ageing population There are an increasing number of patients over 50 years living with HIV [Nguyen et al, 2008], in whom chronic conditions associated with ageing may co-exist. These include cardiovascular drugs, lipid lowering agents, antihypertensives and analgesics. The use of non-prescribed medications in patients taking ARV in Canada and the UK is widespread [Dhalla et al, 2006, Ladenheim et al, 2008]. These include recreational or illicit drug use.
1.4 Decentralised models of care In many healthcare settings, the provision of antiretroviral therapy is progressively devolving from tertiary care- in developing countries, this means the decentralisation of care to district level. Even with an intensive programme of training and education, practitioners with less expertise in prescribing of ARVs may be less likely to identify DDIs or recognise their adverse consequences.
1.5 Lack of monitoring in resource-poor settings Lack of pharmacovigilance structures, and laboratory monitoring coupled with the high background of febrile and other illness may mask clinically significant DDIs in resource-poor settings. Moreover the syndromic management of illness, the high rates of self-treatment (especially for malaria) and widespread use of traditional medicines (which may contain ingredients such as St John’s Wort and steroids) make a complete list of patient medications difficult to compile.
Minimising Harm From DDIs While DDIs involving HIV drugs are often unavoidable, many can be better managed. Lack of awareness and recognition of clinically significant DDIs is a major obstacle to safe ARV prescribing. This review undertakes a systematic evaluation of potential DDIs between ARVs and drugs used to treat TB, malaria, chronic Hepatitis B&C infections and opioid dependence.
2 METHODS
2.1 Search Strategy The following searches were used on PubMed (1987-July 31st 2009). If the standard searched returned numerous results which were not relevant, then the refined search was used.
Standard Search DrugName AND CoMed AND english[Language] NOT review[Publication Type] NOT child[MeSH Terms]
Refined Search DrugName AND CoMed AND english[Language] NOT review[Publication Type] NOT child[MeSH Terms] AND (drug interactions[MeSH Terms] OR anti-hiv agents/pharmacokinetics[MeSH Terms] OR reverse transcriptase inhibitors/pharmacokinetics[MeSH Terms])
For all antiretrovirals, we searchedthe manufacturer’s Summary of Product Characteristics (Europe) (http://emc.medicines.org.uk/) and Product Information (USA) (from each anti-retroviral manufacturer’s website). Websites were accessed (to August 21st 2009) .
We searched the following conference reports for (peer-reviewed) DDI abstracts: • Conference on Retroviruses and Opportunistic Infections (2004 - February 2009) • International AIDS Society Conference (2005 – July 2009) • World AIDS Conference (2004- July 2008) • Interscience Conference on Antimicrobial Agents and Chemotherapy (2004 - Sept 2008) • International Workshop on Clinical Pharmacology of HIV Therapy (2004 - April 2009) • International Congress on Drug Therapy in HIV Infection (2004 - December 2008) • European AIDS Clinical Society (2005, 2007)
2.2 Study Selection We included all studies that evaluated pharmacokinetic data when antiretrovirals were combined with: TB drugs, antimalarials, hepatitis B treatment, hepatitis C treatment, and drug used to treat opioid dependence. Studies which reported clinical interactions only, or overlapping toxicity were not included. Drugs in development which were not yet licensed were excluded. Studies involving children were excluded.
2.3 Study Quality Assessment In order to develop a system which is robust, easy to apply in a consistent manner and allows the user to assess the applicability of existing data to clinical practice, we will apply the GRADE system of classification to the strength of recommendation (Table 1), and the quality of evidence (Table 2) [Atkins et al, 2004]. The strength of evidence is framed in the following question: Is it safe to administer both drugs? We will utilise our existing ‘traffic lights’ system, which maps onto GRADE equivalents outlined in Table 1.
2.3.1 Strength of Recommendation
Table 1 ‘Traffic lights’ summary of Drug-drug interactions
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2.3.2 Grading of Evidence Grading of quality of evidence will be achieved using a methodology based upon the GRADE system of classification [Atkins et al, 2005,] (Table 2). Four categories are proposed, which reflect a hierarchy of methodological design and execution of a study. Ability to up- or down-grade the assessment of quality is also set out in Table 2 and closely mirrors GRADE.
Table 2 Assessment of Quality of DDI Evidence Dw!59 " / 5 9 Ü.9
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* 1= move up or down one grade (e.g. from high to moderate); 2= move up or down two grades (e.g. from high to low) Formatted: Font: 9 pt, Complex Script Font: 9 pt ** A statistically significant relative risk of >2 (<0.5), based on consistent evidence from two or more observational studies, with no plausible confounders
*** A statistically significant relative risk >5 (<0.2) based on direct evidence with no major threats to validity
Evidence based on population pharmacokinetic modelling was graded according to the quality of the primary data upon which that model was based. Examples of issues impacting study quality or directness, which led to downgrading were: trough, or ‘random’ pharmacokinetic sampling or sparse sampling not supported by a validated population PK model, use of single dose studies (more acceptable for known enzyme inhibitors, less acceptable for known enzyme inducers), healthy volunteer data where existing literature suggests different plasma drug exposure in disease. Studies available as abstracts only, or evidence from the manufacturer’s in-house clinical Deleted: in-house studies of drug interactions which were submitted to the regulatory authorities but not published in peer-reviewed journals were graded as ‘very low’. Evidence based on case reports, descriptive Formatted: Font: (Default) reports, experience of experts or knowledge of mechanisms of drug disposition which predict Arial, 11 pt, Complex Script Font: Arial, 11 pt presence or absence of interaction, were also graded ‘very low’. These included recommendations in Manufacturer’s SPC or Prescribing Information, where published clinical data were lacking. Formatted: Font: (Default) Arial, 11 pt, Complex Script Font: Arial, 11 pt All grading was carried out by 4 assessors (SK, DJB, SG, KS). Where there was discordance, the Formatted: Font: (Default) studies involved were discussed to arrive at an agreed grading. Arial, 11 pt, Complex Script Font: Arial, 11 pt Deleted: ¶ 2.4 Data Synthesis ¶ We utilised a standard data extraction template to systematically assess and summarise the evidence, and extracted relevant data into evidence templates (presented in Appendices)
D 3 RESULTS
3.1 Antimalarials In general there are potential interactions between HIV protease inhibitors and NNRTIs and lumefantrine, quinine and amodiaquine. However, only few drug interaction studies have been performed. These studies have varied in design and quality, utilising both healthy volunteers and HIV-positive subjects. They may not reflect what happens in real life, particularly as the pharmacokinetics of many antimalarials alter with disease. For example, the protein binding and plasma half-life of quinine increases with severity of malaria, lumefantrine absorption is decreased during acute malaria and the pharmacokinetics of mefloquine also alters with disease. Concentrations of quinine and lumefantrine also accumulate with multiple dosing, and single dose studies only yield limited data. In addition, pharmacogenetic effects are not usually explored in small drug interaction studies, for example pyrimethamine is predominantly metabolised by CYP2C19, and the frequency of poor metabolisers differs between Africans (3%), South East Asians (20%) and Caucasians. Specific points to note are:
3.1.1 Quinine – Quinine is extensively metabolised by CYP3A4, and its AUC is increased over fourfold by ritonavir (200mg) in healthy volunteers. However, the impact of the more usual dose of ritonavir (100mg) is uncertain, and this study needs repeating. It seems likely, given the limited data, that HIV positive patients on boosted PIs may be at increased risk of cinchonism. An important issue is whether or not a loading dose of quinine is required in patients with severe malaria who are receiving a boosted protease inhibitor. The AUC of quinine is reduced by approximately a third with nevirapine, although the clinical significance of this is uncertain.
3.1.2 Amodiaquine – Excessive risk of hepatotoxicity has been reported in healthy volunteers who were also given efavirenz. There are no data for nevirapine and other boosted protease inhibitors although such studies should be undertaken very cautiously (using very low doses initially) in healthy volunteers. Prolonged neutropenia has been reported in Ugandan children treated with amodiaquine, who were also receiving antiretrovirals.
3.1.3 Lumefantrine – Lumefantrine is extensively metabolised by cytochrome P450 CYP 3A4. Lumefantrine does not seem to prolong the QT interval, but its pharmacokinetics are complex and variable and a marked food effect is observed. Interactions with PIs and NNRTIs are likely, and the manufacturer’s SPC advises that co-administration of CYP3A4 inhibitors such as PIs are contraindicated. An approximately twofold rise in AUC was reported in healthy volunteers who were given lumefantrine with lopinavir/ritonavir. This interaction may be beneficial if it could be shown to reduce the marked pharmacokinetic variability of lumefantrine, or to abolish the food restrictions required with this antimalarial.
3.1.4 Artemether is metabolised via CYP3A4 to dihydro artemesinin (although both compounds have anti-malarial activity, dihydro artemesinin has greater potency). Inhibition of 3A4 would reduce dihydro artemesinin, but increase artemether and potentially increase the short half life of artemether (1-2 h). The effects of PIs and NNRTIs are unclear.
3.1.5 Mefloquine had variable effect on ritonavir metabolism - no interaction was noted after a single dose but ritonavir plasma AUC was reduced by 31% and Cmax by 36% after multiple dosing. PK of mefloquine was not significantly influenced by RTV.
3.1.6 Since proguanil is a pro-drug and is partially activated (CYP2C19) to cycloguanil there is concern that inhibition of metabolism by ritonavir or ritonavir-containing boosted PI regimens will reduce pharmacological effect. However, synergy with atovaquone is related to proguanil, not cycloguanil. When both drugs are co-administered, CYP2C19 inhibition could potentially enhance this synergistic effect, which may off-set decreased cycloguanil formation.
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3.1.7 Atovaquone decreases zidovudine oral clearance leading to a 35 % ± 23 % increase in plasma zidovudine AUC. The clinical significance of this is unknown, and no dose modification is recommended.
Lopinavir may decrease plasma concentrations of atovaquone, the clinical significance of which is unknown, however, increases in atovaquone doses may be needed. Atovaquone lowers indinavir exposure, reducing Cmin by ~23%. Another healthy volunteer study observed indinavir AUC decrease of 5%, but increase in atovaquone AUC (13%) and Cmax (16%) when both drugs were co-administered. No dosage adjustments are necessary for atovaquone when given with indinavir. The clinical significance of lowered indinavir concentrations is uncertain since these were healthy volunteer studies carried out without ritonavir boosting (which is no longer the preferred means of giving indinavir ). Moreover, clinical studies have shown higher plasma indinavir in Thai patients (who have lower body weight), and given the toxicity of indinavir at higher doses, dosage adjustments are not indicated for indinavir (boosted with ritonavir) when dosed with atovaquone or malarone.
3.1.8 Previous formulations of ddI (buffered tablets) decrease dapsone concentrations, in some cases leading to failure of Pneumocystis prophylaxis. No interaction was observed with newer formulations.
Interactions between co-trimoxazole use and malaria, or antiprotozoal effects of protease inhibitors are not within the scope of this review.
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3.2 TB Drugs Drug-drug interactions should be viewed as only one part of a complex and multi-faceted clinical problem when treating both diseases. Factors such as timing the introduction of antiretroviral therapy, its effect in preventing early mortality, use of alternative rifamycins, overlapping toxicity and immune reconstitution are important issues which are currently being assessed within clinical trials. Suffice to say, despite recent advances in drug development, there remains no real alternative to rifampicin use in developing countries in the foreseeable future.
3.2.1 In general, interactions between rifampicin and protease inhibitors (boosted/unboosted) result in substantially lowered PI exposure, which renders these interactions difficult or impossible to manage in clinical practice. Strategies to overcome this interaction have yielded only limited success. Ritonavir (at full dose) is poorly tolerated. Doubling the dose of lopinavir/ritonavir, or ‘super-boosting’ with higher ritonavir doses increased lopinavir exposure in one PK study, but double dose lopinavir/ritonavir failed to prevent low drug exposure in a significant number of children in South Africa with HIV/TB co-infection [McIlleron et al 2009]. Moreover, healthy volunteers given rifamycin with boosted protease inhibitors (saquinavir, lopinavir, atazanavir) appeared to be at excessive risk of hepatotoxicity, making this pharmacokinetic interaction difficult to study safely.
3.2.2 NNRTIs – Pharmacokinetic studies suggest that rifampicin has a greater impact in lowering drug exposure of nevirapine (AUC ↓ 40-58%) compared with efavirenz (AUC ↓ 26%). One large cohort study reported that when antiretroviral therapy is commenced in patients receiving rifampicin-containing TB treatment, treatment outcomes with standard dose efavirenz are superior to nevirapine, and comparable with patients on efavirenz who were not receiving TB therapy. No difference in efficacy was observed in patients receiving either efavirenz or nevirapine who subsequently required TB therapy. These differences could have resulted from the lead-in phase of dosing of nevirapine undertaken during rifampicin therapy.
3.2.3 Current international treatment guidelines prefer efavirenz to nevirapine in patients requiring rifampicin. However, there is no universal consensus about how to manage the efavirenz-rifampicin interaction. Lopez-Cortes et al [2006] conducted a two period sequential study which supported weight-based dose increment of efavirenz during rifampicin therapy. However, other studies in Africans and South-East Asians have shown that while pharmacokinetic variability of efavirenz is markedly increased in the presence of rifampicin, median exposures are adequate, and outcome is good. Numerous factors may account for these differences, not least sampling strategy (trough versus ‘random’ versus AUC sampling), body weight and pharmacogenetic influences (cytochrome P450 CYP2B6 poor metabolisers are more common in black Africans and South East Asians compared with Caucasians).
3.2.4 NRTIs – Although use of triple NRTI regimens as first line agents has resulted in inferior outcomes, combination treatment with three (zidovudine, lamivudine, abacavir) or four (zidovudine, lamivudine, abacavir plus tenofovir) drugs during TB therapy has yet to be properly assessed.
3.2.5 Newer drugs – Of the newer agents, raltegravir has shown promise as an effective antiretroviral in patients receiving rifampicin, since plasma exposure is only modestly reduced. Furthermore, dose ranging studies have shown that the marked antiviral effect of raltegravir is not blunted even when doses as low as 100mg twelve hourly (a quarter of the adult daily dose) are administered. This lack of a clear pharmacokinetic-pharmacodynamic relationship has caused regulatory authorities to differ in recommendations when raltegravir is co-prescribed with rifampicin. The FDA recommends no dose increment, while the EMEA suggests that a dose increment of raltegravir could be considered. Clinical trial data are awaited. Enfuvirtide is also an option, but the high cost and need for twice daily injections makes this a second line option.
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3.3 Hepatitis B and C treatment
3.3.1 Hepatitis B Hepatitis B infection is widely prevalent in Asia and Africa. Infection with HIV is common. Management of both diseases is complex since in addition to increased risk of liver dysfunction with antiretrovirals, HIV drugs such as lamivudine, emtricitabine and tenofovir have activity against hepatitis B virus. Entecavir (a hepatitis B drug) may also exhibit anti- HIV activity. There is concern that lack of routine hepatitis B testing within national antiretroviral programmes in developing countries, coupled with the use of first line regimens based on stavudine/lamivudine or zidovudine/lamivudine plus an NNRTI will effectively deliver 3TC monotherapy, and may result in widespread drug resistance to hepatitis B in resource-limited settings.
Clinically significant drug interactions mainly involve tenofovir and are listed below.
3.3.1.1 Tenofovir has known interactions with HIV protease inhibitors, increasing the exposure of darunavir and saquinavir modestly. In contrast, plasma exposure to atazanavir is decreased (AUC ↓ 25%) by tenofovir. This interaction may to some degree be offset by the use of boosted atazanavir (at doses of either 300mg or 400mg combined with 100mg of ritonavir).
3.3.1.2 Tenofovir exposure is also modestly increased by certain boosted protease inhibitor combinations such as lopinavir/ritonavir, saquinavir/ritonavir and darunavir/ritonavir.
3.3.1.3 Tenofovir significantly increases didanosine exposure (through inhibition of purine nucleoside phosphorylation) and the combination is contraindicated.
3.3.2 Hepatitis C Recent advances in the development of agents that act specifically to inhibit hepatitis C virus (HCV) look set to fundamentally change the way that patients will be treated. New directly acting anti-HCV agents such as protease and polymerase inhibitors will initially be added to standard care with pegylated interferon alfa and ribavirin. However, future therapy is likely to constitute combinations of agents which act at distinct stages of viral replication and have differing resistance profiles. While directly acting anti-HCV agents will undoubtedly improve treatment outcomes, the introduction of combination therapy may not be without complication in some patient groups. HIV positive patients who are receiving antiretrovirals are relatively highly represented among those with HCV infection, and are at high risk of drug-drug interactions.
3.3.2.1 Concomitant administration of abacavir with PEG-IFN and ribavirin has been associated with an increased risk of non-response to anti-HCV therapy [Bani-Sadr et al, 2007] and an interaction between abacavir and ribavirin has been suggested. As both drugs are guanosine analogues and have some metabolic pathways in common, an inhibitory competition for phosphorylation may be possible between ribavirin and abacavir [Mira et al, 2008].
3.3.2.2 Combinations of zidovudine with ribavirin and PEG-IFN can lead to increased risk of severe haematological toxicity, including anaemia. The use of zidovudine has been identified as an independent factor contributing to haematological adverse events in patients undergoing ribavirin and PEG-IFN treatment; the combination is not recommended [Mira et al, 2007].
3.3.2.3 The use of didanosine alongside ribavirin is associated with increased risk of mitochondrial toxicity, which may be attributed to increased exposure to the active metabolite of didanosine, dideoxyadenosine 5’-triphosphate when didanosine is
coadministered with ribavirin [Bani-Sadr et al, 2005; Montes Ramirez et al, 2002; Videx US Prescribing Information]. Toxicity may be severe and coadministration is not recommended.
3.3.2.4 Mitochondrial toxicity has also been observed with combinations of stavudine and ribavirin. In vitro data has shown that ribavirin can inhibit phosphorylation of zidovudine and stavudine. The clinical significance is not clear, however manufacturers of ribavirin advise close monitoring of HIV RNA with this combination.
3.3.2.5 Although clinical significance is not thought to be high, the use of atazanavir with ribavirin and IFN has been associated with hyperbilirubinaemia [Rodriguez-Novoa et al, 2008].
3.3.2.6 In the case of patients receiving efavirenz alongside PEG-IFN, monitoring of central nervous system effects is important, as incidence of depressive symptoms in patients with HIV/HCV co-infection treated with IFN is reportedly high [Laguno et al, 2004].
Currently, ARV treatment may be adjusted, as far as is practicable, to enable optimal administration of anti-HCV therapy, without compromising ARV efficacy. This will become increasingly complex to manage with the addition of new Hepatitis C agents.
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E 3.4 Opioid Dependence Methadone and buprenorphine are the two most commonly used drugs as replacement therapy for opioid dependency. Methadone is usually prescribed as a racemic mixture, containing equal proportions of R-methadone (the active form) and S-methadone (with less activity, but may be responsible for some toxicity). Buprenorphine undergoes extensive first pass metabolism, and is consequently administered sublingually. It is metabolised principally by hepatic cytochrome P450 CYP3A4 (to norbuprenorphine), and by glucuronidation. As a result there are potential significant interactions with protease inhibitors.
3.4.1 Methadone – Boosted protease inhibitors and NNRTIs increase the clearance of R-methadone by enzyme induction. Clinical symptoms of opioid withdrawal are well documented. If co-administered, consider increasing the dose of methadone.
3.4.2 Buprenorphine – Some studies have reported that exposure of buprenorphine and its metabolites may be increased by concomitant protease inhibitor use, while others have failed to observe this. The clinical significance is uncertain. It seems prudent to commence replacement with a low dose of buprenorphine in a patient receiving boosted protease inhibitors.
Table 6 Opioid Replacement Therapy
Opioid Replacement Therapy & PIs
Protease Inhibitors ATV DRV FPV IDV LPV NFV RTV SQV TPV Buprenorphine Amber Amber Amber Amber Green Amber Amber Amber Amber (3) (4) (4) (4) (3) (3) (3) (4) (4) Methadone Green Amber Amber Amber Amber Amber Amber Amber Amber (2) (4) (2) (2) (2) (2) (2) (2) (4)
Opioid Replacement Therapy & NNRTIs, Others
NNRTIs Others EFV ETV NVP MVC RAL Buprenorphine Amber Amber Amber Green Green (2) (4) (4) (4) (4) Methadone Amber Amber Amber Green Green (2) (3) (2) (4) (4)
Opioid Replacement Therapy & NRTIs
NRTIs ABC ddI FTC 3TC d4T TDF ZDV Buprenorphine Amber Green Green Green Green Green Green (4) (4) (4) (4) (4) (4) (3) Methadone Amber Amber Green Green Amber Green Amber (4) (3) (4) (3) (3) (2) (2)
F 4 DISCUSSION
Drug-drug interactions are one of the commonest causes of medication error in developed countries, and antiretrovirals among the most therapeutically risky drugs for clinically significant drug interactions. Studies in the Netherlands and New York involving 115 and 550 patients suggest a prevalence of 20-25% CSDIs [de Maat at al, 2004; Shah et al, 2007]. A second study in New York involving 153 patients reported a prevalence of 41.2% [Miller et al, 2007]. Two recent studies conducted in Liverpool (159 patients) and Switzerland (771 patients) reported prevalence of 26.3% and 61% respectively [Cottle at al, 2009; Marzolini at al, 2008]. Although definitions differed, four out of five of these studies utilised the Liverpool Drug Interactions website to screen for interactions. There have been no such studies in resource-limited settings where risk is arguably increased as a result of less laboratory monitoring, high rates of background illness (which may result in adverse effects being missed), lack of affordable alternative treatments, use of fixed dose combinations (that offer less flexibility for managing interactions) and lack of pharmacovigilance data. In addition, there is a higher cost of treatment failure in these settings, since options are limited compared with developed countries.
Use of therapeutic drug monitoring is not feasible as a strategy for managing CSDIs in resource- poor settings. Practical steps that can be instituted to reduce the risk of adverse outcomes from CSDIs include integrating national treatment programmes for HIV and other diseases (with protocols that minimise drug interactions), establishing regional networks for pharmacovigilance, and improving the quality of prescribing through training and education of health care workers. Knowledge of common interactions involving antiretrovirals on a country-specific basis will allow targeted training, monitoring and protocol development. Finally, we believe that large antiretroviral programmes should consider undertaking an audit of clinically significant drug interactions as a proxy for the quality of prescribing within that scheme.
H 5 Acknowledgements & Declarations
We thank Holger Schunemann and Paul Garner for advice on applying GRADE criteria to the assessment and classification of drug interactions. Our classification system was developed in Liverpool and it’s use does not imply endorsement from the GRADE Working Group.
The Liverpool HIV Drug interactions Website (www.hiv-drug-interactions.org) receives educational grants from Abbott, Gilead, Merck, Bristol-Myers-Squibb, Pfizer, Tibotec, GlaxoSmithKline and Boehringer Ingelheim. Support has also been received from research grant funding from the UK National Institute for Health Research, and the EU. Editorial content remains entirely independent. SK and DJB have received research grant support, PhD studentships, travel bursaries and consultancy fees from Boehringer, GlaxoSmithKline, Tibotec, Merck, Bristol-Myers-Squibb and Pfizer.
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