Category Archives: 9. Mitochondrial Toxicity of NRTIs

HIV Resistance and Viral Tropism Testing

– Patrick Braun and Eva Wolf –

The goal of antiretroviral therapy is to achieve maximum suppression of viral replication. Viral blips while on suppressive ART are relatively common and are mostly owed to random biological and statistical fluctuations. However, patients with repeated episodes of detectable viremia – suggesting ongoing viral replication rather than virus release from latent reservoirs due to immune activation – are at increased risk for the development of drug resistance. The level of viral load while on therapy is the best predictor of subsequent virological failure which is increased at viral load levels between 100 and 300 copies/ml (Nettles 2005, Delaguerre 2009, Garcia-Gasco 2008).

The rapid development of resistant variants is due to the high turnover of HIV – in an untreated HIV-infected patient approximately 10 million new viral particles are produced every day (Perelson 1996) – and the exceptionally high error rate of HIV reverse transcriptase. This leads to a high mutation rate and constant production of new viral strains, even in the absence of treatment. In the presence of antiretroviral drugs, the development of HIV-1 resistance depends on the selection of resistance-associated mutations. If a virus has acquired one or more resistance-associated mutations leading to reduced drug sensitivity, the mutant virus attains a replication advantage in comparison to wild-type virus when exposed to drugs. The development of resistant viral strains is one of the main reasons for virological failure of antiretroviral therapy. However, with the strategic use of the newer drug classes, effective regimens are available even in salvage situations.

The discussion about genotypic resistance in this chapter focuses on the methods of resistance testing, on mutation patterns emerging on ART, and their interpretations and clinical relevance. Most data are derived from patients with subtype B viruses, representing the main subtype in North America, Australia and Europe, but only about 12% of the global HIV-1 epidemic. During recent years, non-B subtype viruses have been investigated, some with different resistance pathways and patterns (Snoeck 2006).

Assays for resistance testing

There are two established assays for measuring resistance or sensitivity of HIV to specific antiretroviral drugs – the genotypic and the phenotypic resistance tests (Wilson 2003). Assays accredited by the FDA are:

  • HIV-1 TruGene™ (Siemens Healthcare Diagnostics)
  • ViroSeq™/ABI Prism®
  • 3100 Genetic Analyzer (Abbott Molecular/Applera Corporation of Applied Biosystems and Celera).

Standard (population-based) genotypic tests can only detect viral mutants when these comprise at least 20% of the total virus population. Ultrasensitive methods (allele-specific real-time PCR, single genome sequencing) with detection limits of <0.1–5% are available only in few laboratories. The clinical relevance of minority populations remains a controversial issue. However, there is evidence especially for minor variants with NNRTI mutations (Li 2011).

Commercial phenotypic resistance tests include:

  • Antivirogram™ (Virco)
  • PhenoSense™ (Monogram Biosciences)
  • PhenoTecT™ (InPheno)
  • Phenoscript™ (Viralliance)

The cost of genotyping ranges from 260 to 400 Euros, depending on the assay and laboratory used. It is approximately twice that much for phenotyping. The drawback of both methods is that a minimum amount of virus is necessary in order to perform the test. Depending on the method and on the laboratory 100-1,000 copies/ml are required for detection of resistance. Tables 1 and 2 show the advantages and disadvantages of phenotypic and genotypic resistance analyses.


Phenotypic resistance tests involve direct quantification of drug sensitivity. Viral replication is measured in cell culture under the selective pressure of increasing concentrations of antiretroviral drugs and is compared to viral replication of wild-type virus.

Drug concentrations are expressed as IC50 values (50% inhibitory concentration), the concentration of drug required to inhibit viral replication in cell cultures by 50%. The sensitivity of the virus is expressed as the IC50 divided by the IC50 of a wild-type reference virus (fold-change value also reported as resistance factor) and compared to the so-called cut-off value. The cut-off value indicates by how much the IC50 of an HIV isolate can be increased in comparison to that of the wild-type and still be classified as sensitive. The determination of the cut-off is crucial for the interpretation of the results.

Table 1. Advantages and disadvantages of phenotypic resistance analysis.
Phenotypic resistance analysis
Advantages Disadvantages
  • Direct measure of drug susceptibility
  • Measure of drug susceptibility feasible irrespective of the presence of unknown resistance mutations
  • Considers the complexity of resistance patterns and the presence of re-sensitizing mutations
  • Only detection of viral mutants comprising ≥20-30% of the total virus population
  • Clinical cut-offs not available for all drugs
  • Expensive (reimbursement by health insurance often not guaranteed)
  • Time-consuming (several weeks)
  • HIV-1 subtyping not possible
  • Interactions between antiviral drugs are not reflected in the test results
  • Test results are not affected by amino acid exchanges, which are only an intermediate step to resistance

Cut-offs: technical, biological and clinical

Three different cut-offs are currently being used.

The technical cut-off is a measure of the methodological variability of the assay.

The biological cut-off involves the inter-individual variability of wild-type virus isolates from ART-naïve HIV-positive patients. If the IC50 is below the biological cut-off, virological success is very likely. However, an IC50 above the biological cut-off does not allow prediction of the virologic response to a drug.

In contrast, the clinical cut-off indicates up to what levels of IC50 virologic effectiveness can still be expected. Complete resistance to a drug (i.e., to protease inhibitors) generally evolves gradually with the acquisition of several amino acid changes.

In general, lower and upper clinical cut-offs are defined. The lower clinical cut-off is the fold-change in IC50 which indicates slightly reduced virological response. A fold-change above the upper clinical cut-off indicates resistance, and a fold-change between the two cut-offs indicates partial resistance. Due to limited clinical experience cut-off data is often lacking for recently approved drugs. In these cases, interpretations are based on biological cut-offs.

In the phenotypic analysis, mutations that do not confer resistance by themselves but provide evidence for transmitted, emerging or reverting resistance have no influence on the measure of resistance.


Background & nomenclature

The HIV genome consists of 2 RNA (ribonucleic acid) strands containing the genetic information of the virus. Within the nucleotide sequence of the HIV genome, a group of three nucleotides, called a codon, code for a particular amino acid in the protein sequence. Resistance mutations are described using a number for each gene, showing the position of the relevant codon, and two letters, the letter preceding the number corresponding to the amino acid specified by the codon at this position in the wild-type virus, while the letter after the number describes the amino acid that is produced from the mutated codon.

A change in the nucleotide sequence of a codon is called a mutation. ‘Silent’ mutations code for the same amino acid. ‘Lethal’ mutations cause a defective protein structure leading to a stop of the viral replication cycle. Only those mutations that code for a different amino acid that leads to a change in the protein structure are clinically relevant. This affects protein function and can contribute to the development of resistance to antiretroviral agents. M184V indicates a mutation in codon 184 of the reverse transcriptase gene leading to a valine for methionine substitution in the reverse transcriptase enzyme and rendering the virus resistant to 3TC and FTC.

Genotypic assays are based on the analysis of mutations associated with resistance. These are determined by the direct sequencing of the amplified HIV genome or by specific hybridization techniques with wild-type or mutant oligonucleotides. For therapeutic decision making, sequencing of the pol region, which encodes for the viral enzymes protease, reverse transcriptase and integrase, and sequencing of the env region, which encodes for the glycoproteins of the viral envelope, gp41 and gp120, are of relevance. Other gene regions, in particular RNase H and gag, are reported to be associated with phenotypic drug resistance. However, sequencing of these regions has only been performed in the context of research and is not part of routine diagnostics.

The interpretation of genotypic resistance patterns is based on the correlation between genotype, phenotype and clinical response. There is data available from in vitro studies, clinical studies, clinical observations and duplicate testing, in which genotypically localized mutations have been investigated for phenotypic resistance.

Table 2. Advantages and disadvantages of genotypic resistance analysis.
Genotypic resistance analysis
Advantages Disadvantages
  • Quickly performed (results within days)
  • Widely used (no specific safety requirements for laboratory)
  • Listing of all changes in the nucleotide sequence
  • Detection of any mutation – with either evidence of resistance, emerging resistance or reverting resistance
  • HIV-1 subtyping possible
  • In general reimbursement by health insurance (i.e., sequencing of the protease and the RT genes)
  • Indirect measurement of resistance
  • Only detection of viral mutants comprising ≥20-30% of the total virus population
  • Complex resistance patterns are often difficult to interpret
  • Unknown mutations are not considered for interpretation
  • Interpretation systems must be updated regularl

Rules-based interpretation systems

For the phenotypic interpretation of genotypic mutation patterns rules-based interpretation systems are commonly available. Expert panels such as HIV-GRADE have developed algorithms based on the literature and clinical outcomes. Table 3 shows an overview of widely used interpretation systems.

Several commercial providers of resistance assays have integrated interpretation guidelines into their systems (e.g., virco®Type HIV-1 from Virco or GuideLines© (TruGene™) from Siemens Healthcare Diagnostics).

Data-based interpretation systems and virtual phenotype

Contrary to the knowledge-based interpretation algorithms developed by experts, data-based interpretation systems like geno2pheno or vircoType™ are mathematical approaches to predicting (“virtual”) phenotype from genotypic information. The virtual phenotype is characterized by the fact that phenotypic information is derived from genotype without performing a phenotypic resistance test in the laboratory. Phenotypic estimates derive from large databases of paired genotypic and phenotypic information.

The interpretation system geno2pheno, available free of charge, uses machine learning techniques such as decisiontrees and support vector machines (Beerenwinkel 2003).

The vircoType™ interpretation is based on a multiple linear regression model, which is applied to a database consisting of more than 61,000 (as of August 2010) matched genotype/phenotype pairs: For every drug the fold-change in IC50 is a function of all mutations of the patient’s virus that contribute to specific drug resistance. To account for synergistic and antagonistic effects between mutations, specific pairs of mutations are included in the model. According to their relevance, drug-specific weight factors are attributed to individual mutations or pairs of mutations. Weight factors are positive for mutations or pairs that contribute to resistance, negative for mutations or pairs with a resensitizing effect.

Table 3. Genotypic resistance interpretation systems: an overview.
Interpretation system Interpretation Available free of charge Internet address:http://www.
HIV-GRADE (07/2010), Germany Rules-based Yes
Rega V8.0.2 (HIV-1&2) (06/2009), Belgium Rules-based Yes
HIVdb Version 6.0.9 (08/2010), USA Rules-based Yes (no www)
ANRS (HIV1&2) V19 (07/2010), France Rules-based Yes
EuResistEuResist Network GEIE Data-based Yes
MGRM GeneSure®  MG(Monogram Bioscience) Rules- and data-based No
geno2phenoGermany Data-based(virtual phenotype) Yes
virco®Type HIV-1 (Virco) Data-based(virtual phenotype) No

Methods of tropism testing

To enter the target cell, HIV binds to the CD4-receptor and so called chemokine co-receptors, of which the most important are CCR5 and CXCR4. Dependent on the use of co-receptors (“tropism”) the virus is classified as CCR5- (“R5”-) tropic or CXCR4- (“X4”-) tropic. Viral strains using both co-receptors are called dual-tropic. Since tropism tests cannot distinguish between dual-tropic viral isolates and a mixture of R5- and X4-tropic viral isolates, the term dual/mixed (D/M) tropic is used.

Analogous to resistance testing, tropism testing can be performed genotypically or phenotypically. Due to its use in clinical trials, Trofile™ is the best-known phenotypic tropism test. The original standard test had a sensitivity limit of 5 to 10%. With the enhanced sensitivity TrofileTM assay (ESTA) minor virus populations can be detected that comprise less than 1% of the total virus population. Another phenotypic test is Phenoscript® ENV (EuroFins/VIRalliance). An 85% agreement between both assays was reported (Skrabal 2007).

For genotypic tropism analysis, the V3 domain of the gp120 gene – which is crucial for co-receptor binding and encodes for the viral tropism – is sequenced. Web-based bioinformatic tools are used to predict viral tropism from the respective nucleotide sequence. These tools have implemented methods like the charge rule, support vector machines or decision trees (Skrabal 2007, Garrido 2008, Obermeier 2008). Tropism prediction tools for genotypic sequences can be found at the following web addresses:

The interpretation with the coreceptor tool of geno2pheno is widely used and shows good concordance with ESTA (Prosperi 2010). In contrast to phenotypic analysis, genotypic analysis cannot distinguish between X4-tropic and dual-tropic or mixed populations. The result of the geno2pheno co-receptor tool is the so-called false positive rate (FPR), which is the probability of classifying an R5-virus falsely as X4. A false positive rate of 0.1% means that X4-tropism is very likely, whereas a FPR of 90% means that X4-tropism is very unlikely because an X4-prediction would be false with a 90% probability.

The current FPR cut-offs in international guidelines are ≤10-12% for X4-prediction and ≥20% for R5-prediction. Phenotypic testing is recommended for indeterminate results. For tropism testing from proviral DNA, which is used in case of undetectable viral load or low level viremia, the same FPR can be used. There are discussions about further reducing the cut-offs (Walter 2009, Vandekerckhove 2011).

As for genotypic resistance testing, a distinction is made between standard population sequencing (detecting X4-tropic virus variants if they comprise at least 20% of the total virus population) and ultrasensitive methods (such as ultra-deep sequencing (UDS) with detection limits of a few percent or less).

In a study using Maraviroc+Atazanavir/r in ART-naive patients, ESTA was used for tropism testing. All samples were analyzed using population sequencing and UDS, each with a FPR of 5.75%. Using ESTA, R5-tropic virus was found in 123 samples (69%); D/M-tropic virus was detected in 39 samples (22%). In 16 samples, tropism testing failed. Using population sequencing, R5-tropic virus was found in 82% of samples, X4-tropic virus was found in 15%. In 3% of samples genotyping was not successful. The concordance for R5-tropic virus between population sequencing and UDS was 95%. Of samples classified as R5-tropic by population sequencing, only 3% (3 of 114) harbored X4-tropic virus of more than 2%. For all failing ESTA measurements viral tropism was determined with population sequencing (Portsmouth 2010a).

The advantages of genotypic tropism testing are its wide availability and the rapid results. Analyses that have correlated genotypic and phenotypic tropism results with virologic response showed that the two methods can be considered as equivalent (Braun 2009, Harrigan 2009). As a consequence, genotypic tropism testing has been included in national and international guidelines on the management of HIV-1 tropism testing (Vanderkerckhove 2011).

A key advantage of genotypic tropism testing is its feasibility in samples with undetectable plasma viral load. This may be important in patients suffering from side effects from a By sequencing proviral DNA, good concordance has been shown between TrofileTM results and the genotypic tropism predictions (Obermeier 2008). Genotyping of proviral DNA is of clinical importance in successfully treated patients requiring treatment change due to side effects. Recently, TrofileTM has also become available for the testing of proviral DNA.

In the European guidelines concerning the use of tropism testing both the enhanced sensitivity Trofile assay and V3 loop population sequencing were recommended. However, the choice of the test should be based on the local capacity, logistics, cost and turnaround time. The preferred method of analysis for patients with a viral load between 50 – 1000 copies/ml is the V3 loop population sequencing.

Table 4. Advantages (+) and disadvantages (-) of genotypic and phenotypic tropism testing, (Examples using geno2pheno and Trofile™).
Phenotypic tropism testESTA ™

  • Phenotypic analysis using the complete gp160
  • Result derives from cell culture
Genotypic tropism testgeno2pheno

  • Genotypic analysis based on V3 sequence
  • Prediction of tropism using bioinformatics tools
+ Validated by clinical data+ Differentiation of R5-, X4- and D/M(dual/mixed)-tropic HIV-  Commercial test / expensive

–  Result within about 3-4 weeks

–  Required viral load of ≥500 – 1,000

copies/ml when using RNA

+ Feasible in case of low/undetectable

plasma viral load when using proviral


+   Validated by clinical data+   Result based on the exclusion ofX4-tropic virus+   Feasible in molecular biology


+   Widely available / less expensive

+   Result within about 5 days

–    Required viral load of ≥500 – 1,000

copies/ml when using RNA

+  Genotyping of proviral DNA in case of

low or undetectable viral load

Mechanisms of resistance

NRTIs are prodrugs that only become effective after being intracellularly converted to triphosphates. Nucleotide analogs require only two instead of three phosphorylation steps. Phosphorylated NRTIs compete with naturally occurring dNTPs (deoxynucleotide triphosphates). The incorporation of a phosphorylated NRTI into the proviral DNA blocks elongation of the DNA resulting in interruption of the chain. There are two main biochemical mechanisms that lead to NRTI resistance (De Mendoza 2002):

Sterical inhibition is caused by mutations enabling reverse transcriptase to recognize structural differences between NRTIs and dNTPs. Incorporation of NRTIs is then prevented in favor of dNTPs, e.g. in the presence of mutations M184V, Q151M, L74V, or K65R (Naeger 2001, Clavel 2004).

Phosphorolysis via ATP (adenosine triphosphate) or pyrophosphate leads to the excision of the NRTIs already incorporated into the growing DNA chain. This is the case with M41L, D67N, K70R, L210W, T215Y and K219Q (Meyer 2000). Phosphorolysis leads to cross-resistance between NRTIs, the degree of which may differ between agents (AZT, d4T > abacavir > ddI > 3TC). Contrary to the excision mutations, K65R leads to a decreased excision of all NRTIs when compared to wild-type, resulting in a greater stability once incorporated. For K65R, the combined effect of its opposing mechanisms (decreased incorporation and decreased excision) results in a decreased susceptibility to NRTIs but an increased susceptibility to AZT (White 2005).

NNRTIs also inhibit the viral enzyme reverse transcriptase (RT). NNRTIs are small molecules that bind to the hydrophobic pocket close to the catalytic domain of the RT. Mutations at the NNRTI binding site reduce the affinity of the NNRTIs to the RT and thus lead to a loss of antiviral activity due to NNRTI treatment failure. Whereas a single mutation can confer resistance to first generation NNRTIs, resistance patterns are more complex for second generation NNRTIs (Vingerhoets 2008, Molina 2008).

PIs hinder the cleavage of viral precursor gag-pol-polyprotein by the HIV protease, thereby producing immature, non-infectious viral particles. PI resistance usually develops slowly, as several mutations must first accumulate. This is also referred to as the genetic barrier. For PIs, a distinction is made between major (or primary) and minor (or secondary) mutations.

Table 5. PI-specific resistance mutations.
Major mutations           
D30N, V32I, M46I/L, I47V/A, G48V/M/A/L/S/T/Q, I50V/L, F53L, I54V/A/M/L/T/S, L76V, V82A/C/F/L/M/S/T, I84V/A/C, N88S, L90M
Minor mutations (a selection)
L10F/I/R/V/Y, V11I, L23I, L24I, D30 other than N, V32 other than I,  L33F/I, E35G, K43T, M46V, I47 other than V/A, I50 other than V/L, F53L/Y, Q58E, A71V/T/I/L, G73C/A/T/S, T74P, L76 other than V, N83D, I84other than V/A/C, N88D/G/T, L89V
(HIV Drug Resistance Database, Sequence Analyses Program, version 6.0.9, 2010-08-24;

Major mutations are responsible for phenotypic resistance. They are selected early in the process of resistance to a drug and/or are located within the active site of the target enzyme, the HIV protease. They reduce the ability of the protease inhibitor to bind to the enzyme. Major or primary mutations may also lead to reduced activity of the protease.

Minor mutations (often referred to as secondary mutations) are located outside the active site and usually occur after major mutations. Minor mutations are commonly found at polymorphic sites of non-B subtypes. Minor mutations compensate for the reduction in viral fitness caused by major mutations (Nijhuis 1999, Johnson 2007b). Mutations at positions 20, 36, 63, and 77 are polymorphisms which are observed without specific selective drug pressure particularly in non-B subtypes. Their contribution to resistance is minor and depends on the presence of other mutations.

Entry inhibitors prevent HIV from entering target cells. The first step in cell entry occurs when the HIV envelope glycoprotein gp120 binds to the CD4-receptor leading to conformational changes in gp120 and enabling the binding of the V3 loop of gp120 to the chemokine co-receptors, CCR5 or CXCR4, of the target cell. Interactions between the two heptad repeat regions HR1 and HR2 within the transmembrane glycoprotein subunit gp41 lead to a conformational change in gp41 and enable fusion of the viral and cellular membranes.

CCR5 antagonists bind to the CCR5 co-receptor and thereby impede interaction with the viral surface protein gp120 necessary for entry into the target cell.

The fusion inhibitor T-20, a synthetic peptide consisting of 36 amino acids, mimics the C-terminal HR2 domain of gp41 and competitively binds to HR1. Thus, interactions between HR1 and HR2 are blocked and the conformational change of gp41 that is necessary for fusion of virions to host cells is inhibited. A single amino acid substitution in HR1 can reduce the efficacy of T-20.


Integrase inhibitors prevent insertion of HIV DNA into the human DNA genome. The primary role of viral integrase is to catalyze the insertion of the viral cDNA into the genome of infected cells. Integrase inhibitors like raltegravir or elvitegravir block the strand transfer step. They bind to the catalytic pocket of the integrase and are transported as a component of the DNA/integrase pre-integration complex into the cell nucleus where strand transfer activity of integrase is inhibited. The selection of key mutations in the integrase gene confers resistance to integrase inhibitors. Strand transfer as well as the preceding step of 3’ processing (cleavage of the terminal dinucleotides from both 3’ ends of viral cDNA to which integrase binds) can be affected by these mutations. Different resistance pathways have been observed. The accumulation of additional mutations leads to a further decrease in susceptibility (Fransen 2008, Miller 2008).

Transmission of resistant HIV strains

The prevalence of mutations already present in treatment-naïve patients differs among demographic regions. Prevalence – of more than 20% – has been observed in large US cities with significant populations of homosexual men and a long history of access to antiretroviral treatment. Data on the incidence and prevalence of primary drug resistance which were published before 2007 should be interpreted with caution, since a consensus definition of transmitted genotypic drug resistance had not been established at that time. In 2007, an international research group agreed upon criteria defining mutations indicative for transmitted drug resistance. The corresponding list of mutations was again updated in 2009 (Bennett 2009). This standardization allows for comparisons of epidemiological data across geographic regions and periods of time.

In the German seroconverter study of the Robert Koch Institute the prevalence of resistance mutations was 12.4% between 1996 and 2007. Although the proportion of isolates with primary resistance remained stable during the observation period, the proportion of NRTI resistant virus populations decreased (to 7.5%), while there was a trend to more NNRTI resistance (3.5%) (Bartmeyer 2010). In chronically-infected patients of the RESINA study, the proportion with primary resistance was 14% between 2001 and 2007 (Oette 2008).

The European CATCH study, a substudy of SPREAD (Strategy to Control Spread of HIV Drug Resistance), determined a proportion of 10.4% with primary resistance out of 2,208 new HIV diagnoses between 1996 and 2002 (Wensing 2005). Whereas NRTI resistance decreased over time, NNRTI resistance increased. PI resistance remained relatively constant. Primary resistance was observed especially in subtype B infections (70% of all new diagnoses). However, primary resistance increased also in non-B subtypes.

European-wide data from the years 2006-2007 derive from SPREAD (Strategy to Control Spread of HIV Drug Resistance), a program established to monitor primary resistance in newly infected patients and ART-naïve patients. 9.7% of 1630 newly diagnosed HIV patients were infected with virus harboring at least one resistance mutation. The proportion of isolates with NRTI, NNRTI and PI resistance was 5.7%, 3.9% and 1.7%, respectively. Two-class resistance was present in less than one percent (Frentz 2011).

Ultrasensitive methods such as allele-specific real-time PCR (AS-PCR) or ultra-deep sequencing detect resistance mutations more often than conventional sequencing methods. In a Swiss study, M184V and/or K103N quasi-species were detected as minor variants in 18% (13/74) of patients with primary HIV infection and documented wild-type virus (Metzner 2007a). In a study from Atlanta focusing on L90M, M41L, K70R, K103N, Y181C, M184V, T215F and T215Y, resistance mutations were detected in 33/205 acutely or chronically infected patients (16%) (Johnson 2007a). In a British study investigating 165 anonymized samples from the years 2003-2006, drug resistance was detected in 13% of samples when using the standard assay compared to 19% when using an assay more sensitive for K103N, Y181C or M184V. In particular, the proportion of M184V isolates increased from 0.6% to 8%. The prevalence of drug resistance was almost the same for treatment-naive patients with either primary or chronic HIV infection (19% and 20%) confirming data showing that  primary resistance can persist for a long time (Buckton 2010, Pao 2004). In a Spanish study a (partial) reversion of transmitted drug resistance was observed in only 3 of 10 seroconverters after a median time of 41 months (De Mendoza 2005b). Contrary to K103N or M184V, K65R is a rare primary mutation. K65R was observed in only 4/194 patients (2%) as a minority variant at initiation of treatment (Metzner 2007b).

Transmitted resistance mutations can limit further treatment options and reduce treatment response rates (Little 2002, Wittkop 2010). This was also confirmed by a meta-analysis for minor NNRTI resistant virus variants (10 studies, 985 patients) (Li 2011). However, with special regard to existing resistance, treatment success is often possible (Oette 2006, Reuter 2008).

In early 2005, one patient from New York caused a sensation: he was infected with a multidrug resistant virus with a replication capacity comparable to that of wild-type virus. As a consequence, his remaining treatment options were very limited. Even though the transmission of multidrug resistant virus and rapid clinical progression are rare events, this case report demonstrates the possible clinical consequences of primary drug resistance (Markowitz 2005). In 2010 the transmission of a virus resistant to integrase inhibitors was reported for the first time. The virus also harboured NRTI, NNRTI and PI resistance mutations. Therefore the author recommended sequencing the integrase gene in case of transmitted multidrug resistance (Young 2010).

Table 6. Prevalence of resistance prior to initiation of therapy (a selection).
Author Region Period Patient population N Primary resistance
Bartmeyer 2010 Germany 1996-2007 Seroconverters 1298 12.4%
De Mendoza 2005 Spain 1997-2004 Seroconverters 198 12.1%
Recordon 2007 France 1996-2005 Seroconverters 194 15.7%*
Little 2002 USA 1995-2000 Seroconverters 377 22.7%
Chaix 2007 France 2005-2006 Seroconverters + chronically infected 289 10.4%
Frentz 2011 Europe 2006-2007 Newly diagnosed 1630 9.7%
Truong 2006 San Francisco 2004 Newly diagnosed 129 13.2%
Jayaraman 2006 Canada 1999-2003 Newly diagnosed 768 10.2%
Nkengafac 2007 Cameroon 2005-2006 Newly diagnosed 180 7.8%
Oette 2008 Germany 2001-2004 Chronically infected 1373 14%
Cane 2005 Great Britain 1996-2005 Chronically infected 2357 14.2%
 *(18.2% subtype B, 8.3% non-B)

Clinical studies

The clinical importance of resistance testing before making changes to therapy has been demonstrated in several prospective, controlled studies using genotypic tests such as VIRADAPT, CPCRA 046 or Havana (Durant 1999, Baxter 2000, Tural 2002) as well as in studies using phenotypic tests like VIRA 3001 (Cohen 2002). Patients whose physicians had access to information about existing mutations before the therapy was changed usually had more significant decreases in their viral loads than patients in whom ART was changed without knowledge of the resistance profile.

With regard to the increased number of NRTIs, NNRTIs or PIs with different resistance profiles, the clinical relevance of resistance testing might be even greater today. For ethical reasons no longer justifiable are studies that prospectively examine the benefits of resistance analysis, i.e. for regimens including new drug classes such as integrase inhibitors or CCR5 antagonists.

A resistance test before ART initiation is part of routine diagnosis in regions where the transmission of resistant HIV viruses is observed. The impact of transmitted HIV resistance on the initial success of ART was investigated in a retrospective analysis of the Eurocoord chain project. Of 10,458 patients initiated on ART in 1998, blood samples from the period before therapy were retrospectively examined for resistance. The initial regimens’ activities were essential for durable therapeutic success. Patients who were treated with only partially active regimens had a 2.6-fold higher risk of treatment failure (Wittkop 2010).

Resistance testing at time of treatment initiation and at time of virological failure is an integral part of national and international guidelines for the management and treatment of HIV infection.

Interpretation of genotypic resistance profiles

The algorithms cited in the following chapter are only indicative. Treatment decisions should not be made based on these data alone. We recommend the use of a resistance interpretation system listed in Table 3.


For several NRTIs such as 3TC and for NNRTIs a high degree of resistance develops following just a single mutation. For this reason, such drugs should only be used as part of highly effective regimens. However, the 3TC-specific mutation, M184V, also reduces viral replication capacity (often referred to as reduced viral fitness) by 40-60% (Miller 2003, Deval 2004). After 52 weeks with 3TC monotherapy, the viral load remained 0.5 log below the initial levels despite early development of the M184V mutation (Eron 1995). When compared to treatment interruptions, continuous monotherapy with 3TC delays virological and immunological deterioration (Castagna 2006). FTC has nearly the same genotypic and phenotypic resistance pattern as 3TC (Borroto-Esoda 2007).

M184I is often detected before M184V, but is then quickly replaced by M184V (Schuurmann 1995). Depending on the co-medication, M184V is more common on 3TC than on FTC, especially in combination with tenofovir (Svicher 2010). However, in the HEAT study which compared Tenofovir+FTC+lopinavir/r with Abacavir/3TC/lopinavir/r, M184V was more common on FTC (Smith 2008).

T69I is a rare mutation, which is observed in 0.5% of treated patients and 0.2% of ART-naive patients. This mutation causes high-level resistance to 3TC, FTC, and possibly also against tenofovir (Svicher 2010).

Thymidine analog mutations, known as TAMs, include the mutations M41L, D67N, K70R, L210W, T215Y and K219Q, which were first observed on treatment with AZT (Larder 1989), but were also selected on d4T (Loveday 1999).

There are two mutation pathways: the so-called TAM-1 path with 41L, 210W and 215Y, and the TAM-2 path with D67N, K70R, T215F, and K219Q/E (Flandre 2004). Depending on the individual TAMs and their combination, AZT resistance factors and the degree of resistance vary largely. The variation of the corresponding d4T resistance factor is much smaller. However, for full resistance the resistance factor is much lower for d4T than for AZT. This demonstrates that resistance factors of different drugs can not be compared. On AZT- and d4T-based regimens the TAM-1 pathway is more commonly observed (Cozzi-Lepri 2009).

The term NAMs (nucleoside analog mutations) is also used, as these mutations are associated with cross-resistance to all other nucleoside analogs, with the exception of 3TC and FTC (Harrigan 2000). In particular, the combination of certain TAMs can largely affect the effectiveness of abacavir, ddI and tenofovir (Table 7). The presence of L210W reduces the virological response to tenofovir (Antinou 2003). As with abacavir and ddI, TAMs do not arise on tenofovir, but can be re-selected.

The V75T mutation, which is associated with an approximately 5-fold increase in resistance to d4T and ddI, is only rarely observed (Lacey 1994).

Under failing therapy with abacavir or ddI the mutation L74V/I usually occurs. More rarely, the mutation K65R can occur. Y115F is a specific abacavir-associated resistance mutation.

Tenofovir primarily selects for the K65R mutation and leads to an (intermediate) resistance to tenofovir, abacavir, ddI, 3TC, FTC, and possibly d4T (Shafer 2002, Garcia-Lerma 2003). Although K65R may emerge on abacavir, K65R was rarely seen before the introduction of tenofovir. The reason is that with combination therapies containing AZT, the incidence of the K65R mutation is lower. Prior to tenofovir, abacavir was mainly used as part of the combination AZT+3TC+abacavir (Trizivir®).

K65R seldom emerges in the presence of TAMs. Since K65R and TAMs represent two antagonistic resistance pathways (see Mechanisms of resistance), K65R is only rarely observed on the same genome together with TAMs, and almost never together with L74V (Wirden 2005). Corresponding to observations made in large clinical trials using tenofovir within divergent (PI- or NNRTI-containing) treatment regimens, the incidence of K65R stabilized at ≤5%. However, virological failure of other triple-nuke combinations such as tenofovir+3TC+abacavir or tenofovir+3TC+ddI was often associated with the development of the K65R (Gallant 2003, Landman 2003, Jemsek 2004). The main reason for the high failure rate seems to be the low genetic barrier of these regimens: the emergence of the K65R induces a loss of sensitivity to all three drugs.

K65R increases sensitivity to AZT and induces a resensitization to AZT in the presence of (few) TAMs (White 2005, Underwood 2005). Vice versa, TAMs reduce the K65R-associated resistance to tenofovir, abacavir, and ddI (Parikh 2007).

As with M184V, the mutation K65R leads to a reduction in the viral replication capacity (RC), which is not the case with TAMs or the L74V/I. The median RCs for viruses with M184V/I (n=792), K65R (n=72) or L74V/I (n=15) alone were 68% (p<0.0001), 72% (p<0.0001) and 88% (p=0.16), respectively (McColl 2005). If both mutations K65R and M184V were present, an RC of only 29% was observed (Miller 2003, Deval 2004).

Less frequently than K65R, the mutation K70E was observed on failing therapy with tenofovir, particularly in NRTI-based regimens with abacavir and 3TC (Delaugerre 2008). M70E and K65R may be observed simultaneously, but it is unlikely that these mutations emerge on the same genome (Lloyd 2005). There is one case report of the development of K70E and M184V during therapy with tenofovir and FTC, which were then replaced by K70G and M184V. Both mutations were located on the same genome and conferred phenotypic resistance to 3TC, FTC, abacavir, ddI, and tenofovir, but not to AZT or d4T (Bradshaw 2007).

The 3TC-associated mutation, M184V, as well as the L74V mutation and the NNRTI-specific mutations, L100I and Y181C, may have an antagonistic effect on the further development of resistance (Vandamme 1999).

M184V induces resensitization to AZT, resulting in a 50-60% reduction of IC50. Resensitization to d4T results in a 30% reduction of IC50. However, resensitization is of clinical relevance only if there are no more than three other AZT- or d4T-associated mutations present (Shafer 1995, Underwood 2005). In one genotypic and phenotypic resistance study consisting of 9,000 samples, a combination of M41L, L210W and T215Y decreased the susceptibility to AZT by more than 10-fold in 79% of cases. If the M184V mutation was also present, only 52% had a more than 10-fold decreased susceptibility to AZT (Larder 1999). The M184V mutation also increases sensitivity to tenofovir (Miller 2001, Miller 2004a). In contrast, the presence of M184V plus multiple NAMs or mutations at positions 65, 74 or 115 increases resistance to abacavir (Harrigan 2000, Shafer 2003).

So-called multidrug resistance (MDR) to all nucleoside analogs – except 3TC and probably FTC – is established if one of the following combinations occurs: T69SSX, i.e., the T69S mutation plus an insertion of 2 amino acids (SS, SG or SA) between positions 69 and 70, plus an AZT-associated mutation or Q151M, plus another MDR mutation (V75I, F77L or F116Y) (Masquelier 2001).

The MDR mutation Q151M is relatively uncommon, with a prevalence of less than 5%. Q151M alone leads to intermediate resistance to AZT, d4T, ddI and abacavir and involves only a minor loss of tenofovir activity. Q151M combined with mutations at positions 75, 77, and 116 confers high-grade resistance to AZT, ddI, d4T and abacavir and intermediate resistance to tenofovir (Shafer 2003). Instead, the T69SSX insertion induces an approximately 20-fold increase in the resistance to tenofovir (Miller 2001, Miller 2004a).

The insertion T69SSX together with the mutation M184V, as well as the mutation Q151M together with M184V, leads to a 70% reduction in viral replication capacity (Miller 2003, Deval 2004).

The L74V mutation emerges after exposure to ddI or abacavir and leads to a 2-5 fold increase in the resistance to ddI (Winters 1997). L74V/I with or without M184V leads to a reduction in IC50 of about 70%; phenotypic susceptibility increases by a factor of 3 (Underwood 2005).

In large patient cohorts, quantitative measurements of sensitivity have shown that up to 29% of NRTI-experienced patients have a hypersusceptibility to NNRTIs (i.e., a reduction in the inhibitory concentration by a factor of 0.3-0.6). A reduction in AZT or 3TC sensitivity correlates inversely with an increased NNRTI susceptibility (Shulman 2000). The reverse transcriptase mutations T215Y, H208Y and V118I seem predictive for EFV hypersusceptibility. A database analysis of pair-wise genotypes and phenotypes showed NNRTI hypersusceptibility for TAMs and for non-thymidine analog-associated NAMs. Hypersusceptibility for efavirenz was detected for 1-2 TAMs, multiple TAMs plus M184V and for non-thymidine analog-associated NAMs like K65R, T69X, M184V and in particular for K65R+M184V (Whitcomb 2000, Shulman 2004, Coakley 2005a). However, these results have not influenced treatment strategies so far.


First generation NNRTIs (efavirenz, nevirapine)

NNRTI resistance mutations may occur individually or in combination. A single mutation can confer high-level resistance to one or more NNRTIs.

The relatively frequent K103N mutation leads to a 20- to 50-fold increase in resistance to efavirenz and nevirapine (Petropolus 2000). V106M is more frequent in subtype C viruses and leads to a 30-fold increase in nevirapine resistance. V106M is associated with high-level resistance not only to nevirapine but also to efavirenz (Grossman 2004). Y181C/I causes a 30-fold increase in nevirapine resistance, and response to efavirenz is only temporary. G190A is associated with a high degree of nevirapine resistance and an intermediate resistance to efavirenz. G190S and Y188C/L/H are mutations that result in a high degree of resistance against both drugs (Shafer 2003, De Mendoza 2002).

A98G/S alone (more common in subtype C) or V108I alone are usually not clinically relevant. Mutations like K101E or L101P can confer intermediate resistance to first generation NNRTIs. V106A confers a more than 30-fold resistance to nevirapine. In contrast to subtype B virus, V106M more frequently emerges in subtype C virus; V106M confers resistance against both drugs (Grossman 2004).

Continued use of first generation NNRTIs is not recommended in the presence of respective mutations, because further mutations may be selected, which may influence the effectiveness of second generation NNRTIs.

Second generation NNRTIs

Etravirine is effective against variants with single NNRTI mutations like K103N, Y188L and/or G190A (Andries 2004). Compared to earlier NNRTIs, etravirine has a higher genetic barrier, probably due to flexible binding to the reverse transcriptase site. High-level resistance is usually seen with more than two mutations (Mills 2007, Katlama 2007, Vingerhoets 2007). In a selection experiment, the dominant viral population harbored, after several in vitro passages, the mutations V179F (a new variant at this position) and Y181C. Other mutations that have been selected in vitro are L100I, E138K, Y188H, G190E, M230L, and V179I (Brilliant 2004, Vingerhoets 2005). The frequently occurring mutation K103N does not affect the effectiveness of etravirine (Vingerhoets 2006).

In the DUET studies, the following resistance mutations were identified: V90I, A98G, L100I, K101E/H/P, V106I, E138A, V179D/F/T, Y181C/I/V, G190A/S and M230L. Based on these mutations, an etravirine resistance score weighting the individual NNRTI mutations was developed. The main criteria were the impact of the baseline mutations on virological response at 24 weeks and the correlation between respective mutations and the fold-change in IC50. A weighting factor of 3 was attributed to Y181I/V (with fold-changes of 13 and 17 in site-directed mutants), followed by a weighting factor of 2.5 for L100I, K101P, Y181C, and M230L. The mutations E138A, V106I, G190S, and V179F received a weighting factor of 1.5 and the other mutations were weighted with 1. Total scores of 0-2, 2.5-3.5 and ≥4 corresponded to 74%, 52% and 38% virological response rates in the DUET studies (Vingerhoets 2008).

In a panel of 4248 NNRTI-resistant clinical HIV-1 isolates, the mutations with the highest weight, Y181I and Y181V, had a low prevalence of 1.5% and 0.9%, respectively. The mutation Y181C, which is selected more frequently in patients taking nevirapine than with efavirenz, had a prevalence of 32% (Vingerhoets 2008).

Monogram has developed a weighted score including 37 mutations. Mutations with the highest level of resistance, i.e. L100I, K101P and Y181C/I/V, received a score of 4. E138A/G, V179E, G190Q, M230L and K238N received a score of 3; 101E, V106A / I, E138K, V179L, Y188L and G190S received a score of 2. V90I, A98G, K101H, K103R, V106M, E138Q, V179D/F/I/M/T, Y181F, V189I, G190A/E/T, H221Y, P225H, and K238T contributed with a score of 1. A loss of efficacy is likely with a total score of 4 or higher (Haddad 2010).

The effectiveness of rilpivirine does not seem to be impaired by single NNRTI resistance mutations such as K103N, V106A, G190S/A; In vitro no resistant variants were selected in the presence of 40 nM rilpivirine over a period of 30 days. When using 10 nM, up to eight mutations were selected within 8 days, including L100L/I, V106V/I, Y181Y/C and M230M/I; the respective increased IC50 was 4.

In a clinical study involving treatment-naïve patients without any (known) NNRTI mutations at baseline, eight emerging mutations were observed during treatment with rilpivirine: L100I, K101E, K103N, E108I, E138K/R, Y181C and M230L (Molina 2008).

In two phase III studies in which rilpivirine was tested against efavirenz, virological failure was more frequent on rilpivirine (10.5% versus 5.7%). Furthermore, the development of resistance mutations was more common in patients failing on rilpivirine (63% versus 54%). The most common mutations were E138K (45%), K101E (13%), H221Y (10%), V189I (8%), Y181C (8%) and V90I (8%). In 46%, 31% and 23% of resistant isolates respectively, 1, 2 or 3 NNRTI mutations were detected. Cross-resistance to etravirine was commonly observed among patients failing rilpivirine (>90%), cross-resistance to efavirenz was rather unlikely.

Besides NNRTI mutations, NRTI mutations were also more frequent among treatment failures on rilpivirine (68% versus 32%) – with primarily M184I on rilpivirine and M184V on efavirenz (Eron 2010).

Protease Inhibitors

The spectrum of PI mutations is very large. Although there is a moderate to high degree of cross-resistance between PIs, the primary mutations are relatively specific for the individual drugs. If treatment is changed early on to another PI combination, i.e., before the accumulation of multiple mutations, the subsequent regimen may still be successful. Most data on primary mutations that are selected for early on in the presence of a PI are derived from studies using unboosted PIs. In first-line therapy with boosted lopinavir, fosamprenavir, saquinavir, atazanavir or darunavir, the emergence of major PI mutations is rare (Eron 2006, Walmsley 2007, Clumeck 2007, Gathe 2008, Lataillade 2008, Molina 2008). The development of primary PI resistance in patients failing boosted PI therapy has been observed in few cases (Lanier 2003, Conradie 2004, Friend 2004, Coakley 2005b, Lataillade 2008).

First-generation PIs

Nelfinavir has a specific resistance profile, with the D30N primary mutation and further secondary mutations, resulting in a relatively low degree of cross-resistance to other PIs (Larder 1999). Virological failure with nelfinavir can also be associated with the emergence of L90M (Craig 1999). In subtype B viruses, treatment with nelfinavir generally leads to the emergence of D30N or M46I plus N88S. In subtype C, G and AE viruses, however, the mutations L90M and I84V occur more frequently. A reason for these different resistance pathways is the prevalence of natural polymorphisms: whereas M36I is present in only 30% of subtype B viruses, it is present in 70–100% of non-B subtypes (Gonzales 2004, Grossman 2004b, Sugiura 2002, Snoeck 2006).

Unboosted saquinavir primarily selects for G48V which leads to a 10-fold decrease in the susceptibility to saquinavir. G48V in combination with L90M reduces susceptibility to an even higher degree (Jakobson 1995). In general, several mutations including I84V/A are required to affect efficacy of ritonavir-boosted saquinavir (Valer 2002). One study re-evaluated the genotypic interpretation of saquinavir resistance in a retrospective analysis of 138 PI-experienced patients. Here, the presence of 3 to 4 mutations out of L10F/I/M/R/V, I15A/V, K20I/M/R/T, L24I, I62V, G73ST, 82A/F/S/T, I84V, and L90M was identified as being most strongly associated with reduced virological response (Marcelin 2007a). In contrast, L76V can lead to a clinically relevant re-sensitization for saquinavir (Braun 2007).

Fosamprenavir/r: In patients with virological failure while on amprenavir, the following mutations have been selected: I54L/M, I50V or V32I plus I47V, often together with the mutation M46I (Maguire 2002). The Zephir study evaluated virological response to treatment with fosamprenavir/r in 121 patients. With less than three mutations of L10I/F/R/V, L33F, M36I, M46I/L, I54L/M/T/V, I62V, L63P, A71I/L/V/T, G73A/C/F/T, V82A/F/S/T, I84V and L90M, viral load was reduced by 2.4 logs 12 weeks after treatment initiation compared to only -0.1 log with 4 or more mutations. At least 80% of patients with a maximum of 3 mutations reached a viral load below 400 copies/ml, compared to 35-45% of patients with 4-7 mutations and only 10% of patients with at least 8 mutations (Pellegrin 2005). In a retrospective study in 73 patients receiving fosamprenavir/r, the mutations L10F/I/V, L33F, M36I, I54L/M/V/A/T/S, I62V, V82A/F/C/G, I84V and L90M were associated with reduced virological response. In a univariate analysis the most striking mutations were I54L/M/V/A/T/S, V82A/F/C/G, and L90M: in the case of two mutations, virological response was reduced, while three mutations conferred resistance. N88S/D was associated with an increased response (Masquelier 2006). L76V may arise on fosamprenavir or on lopinavir (Muller 2004).

As with other boosted protease inhibitors major PI mutations occur very rarely on lopinavir-based first-line therapy. Few case reports of primary lopinavir resistance have been published. In one patient, virological failure was associated with the occurrence of the V82A followed by the mutations V32I, M46M/I and I47A (Friend 2004).

Lopinavir/r: The response in patients who had been exposed to first generation PIs correlated with the number of any of the following mutations: L10F/I/R/V, K20M/R, L24I, M46I/L, F53L, I54L/T/V, L63P, A71I/L/T/V, V82A/F/T, I84V, and L90M. Five mutations or less resulted in an increase in the IC50 by a median factor of 2.7; with 6-7 mutations this factor was 13.5, and with at least 8 mutations it was 44 (Kempf 2001).

A different algorithm to predict lopinavir resistance also includes mutations at novel amino acid positions. Viruses with any 7 mutations out of L10F/I, K20I/M, M46I, L, I50V, I54A/M/S/T/V, L63T, V82A/F/S as well as G16E, V32I, L33F, E34Q, K43T, I47V, G48M/V, Q58E, G73T, T74S, and L89I/M display approximately a 10-fold increase in the IC50. Mutations at positions 50, 54 and 82 particularly affect the phenotypic resistance (Parkin 2003, Jimenez 2005).

In vivo selection of lopinavir resistance was described in 54 PI-experienced patients failing treatment with lopinavir. Mutations at positions 82, 54 and 46 frequently emerged. Mutations such as L33F, I50V or V32I together with I47V/I were selected less frequently. New mutations at positions 84, 90 and 71 were not observed (Mo 2005).

The mutation I47A, which has rarely been observed since the availability of lopinavir, was identified to be associated with lopinavir resistance. I47A reduces the binding affinity to lopinavir and results in an 86- to >110-fold loss of sensitivity. In contrast, I47A leads to saquinavir hypersusceptibility due to an enhanced binding affinity to saquinavir (Kagan 2005).

On failing lopinavir monotherapy, the occurrence of the mutation L76V was reported in subtype CRF02 (Delaugerre 2007). The mutation L76V, selected for by lopinavir and rarely by amprenavir, is associated with resistance to lopinavir, amprenavir und darunavir, but can lead to resensitization to atazanavir, saquinavir and tipranavir – even in the presence of 5-10 PI mutations which normally confer broad PI cross-resistance (Müller 2004, De Meyer 2006b, Braun 2007).

Atazanavir is an aza-peptidomimetic PI. The resistance profile differs in part from that of other PIs. In patients in whom first-line treatment with atazanavir failed, the mutation I50L – often combined with A71V, K45R, and/or G73S – was primarily observed. On the one hand, I50L leads to a loss of sensitivity to atazanavir; on the other hand, I50L leads to an increased susceptibility to other PIs. Mutants harboring I50L plus A71V showed a 2- to 9-fold increased binding affinity to the HIV protease. Even in the presence of other major and minor PI-mutations I50L can increase susceptibility to other PIs (Colonno 2002, Weinheimer 2005, Yanchunas 2005). In PI-experienced patients, the I50L mutation was selected for in only one third of patients failing atazanavir (Colonno 2004).

The accumulation of PI mutations such as L10I/V/F, K20R/M/I, L24I, L33I/F/V, M36I/L/V, M46I/L, M48V, I54V/L, L63P, A71V/T/I, G73C/S/T/A, V82A/F/S/T, L90M, and in particular, I84V, leads to a loss of sensitivity to atazanavir. In the expanded access program using unboosted atazanavir the number of the respective PI mutations correlated with the change in viral load. For unboosted atazanavir, the threshold for resistance is generally met if 3-4 PI mutations are present; for ritonavir-boosted atazanavir, the genetic barrier is higher (Colonno 2004, Gianotti 2005).

The Reyaphar Score, developed by Pellegrin and colleagues for predicting response to ritonavir-boosted atazanavir, includes mutations at 12 positions (L10I/F/R/V, K20I/M/R, L241, M461/L, 154L/M/T/V, Q58E, L63P, A71I/L/V/T, G73A/C/F/T, V771, V82A/F/S/T, 184V and L90M). With less than 5 Reyaphar mutations, the average viral load reduction at 12 weeks was 1.4 logs, compared to only 0.5 log with more than 5 mutations (Pellegrin 2006).

Second-generation PIs

Tipranavir, the first non-peptidic protease inhibitor, shows good efficacy against viruses with multiple PI mutations. Even with reduced susceptibility to darunavir about half of the 586 isolates proved susceptible to tipranavir (De Meyer 2006a). In vitro, L33F and I84V are the first mutations selected by tipranavir, but the loss in sensitivity is only two-fold. Selection experiments ended up with viral isolates harbouring 10 mutations (L10F, I13V, V32I, L33F, M36I, K45I, I54V, A71V, V82L, I84V) resulting in an 87-fold reduced sensitivity (Doyon 2005).

Due to these and other experiments some mutations were regarded as key mutations, the so-called PRAMs (protease inhibitor-associated resistance mutations) which include the following mutations: L33I/V/F, V82A/F/L/T, I84V and L90M. Resistance analyses showed that reduced sensitivity should be expected with at least three PRAMs. However, a sufficient short term viral load reduction of 1.2 logs was seen after two weeks on treatment with boosted tipranavir plus an optimized backbone in patients with at least three PRAMs, compared to only 0.2–0.4 log with amprenavir, saquinavir or lopinavir plus optimized backbone (Cooper 2003, Johnson 2008, Mayers 2004).

In the re-analysis of the Phase II and III trials, some PRAMs have been confirmed, but new resistance mutations were also identified (Kohlbrenner 2004). Resistance mutations in clinical isolates of tipranavir-experienced patients included L10F, I13V, K20M/R/V, L33F, E35G, M36I, K43T, M46L, I47V, I54A/M/V, Q58E, H69K, T74P, V82L/T, N83D, and I84V (Croom 2005).

Hence the “unweighted” tipranavir mutation score was developed, involving 21 protease mutations at 16 positions (I10V, I13V, K20M/R/V, L33F, E35G, M36I, N43T, M46L, I47V, I54A/M/V, Q58E, H69K , T74P, V82L / T, N83D and I84V) (Baxter 2006).

This score was followed by a “weighted” tipranavir score based on clinical data of the RESIST trials (Scherer 2007). The respective model includes mutations of the unweighted score plus five mutations which were related to an increased tipranavir susceptibility. Weight factors were assigned to the mutations according to their contribution to resistance. The weights of the mutations add up to the weighted tipranavir score. The major mutations I47V, I54A/M/V, Q58E, T74P, V82L/T, and N83D contribute significantly to resistance and have a greater weight than minor mutations like I10V, M36I, N43T, and M46L. L24I, I50L/V, I54L and L76V are mutations conferring an increase in sensitivity and carry a negative weight. The mutations L33F as well as I13V and H69K, the most commonly observed in non-B subtypes were removed from this score. Other national resistance algorithms differ particularly in the weighting of mutations (Table 9).

Darunavir has also shown good activity against a wide spectrum of PI-resistant viruses. In vitro, resistance to darunavir develops more slowly than seen with nelfinavir, amprenavir or lopinavir.

After several passages in vitro, further mutations were selected in addition to R41T and K70E, leading to a reduced replication fitness. A mutant virus with a more than 10-fold loss in darunavir susceptibility showed a corresponding loss in saquinavir susceptibility, but not for the other PIs (atazanavir was not tested). This means that primary darunavir failure is not necessarily associated with complete cross-resistance to first generation PIs (De Meyer 2003+2005).

Eleven mutations at 10 positions were associated with a diminished response to boosted darunavir: V11I, V32I, L33F, I47V, I50V/L, I54L/M, T74P, L76V, I84V and L89V. With three or more of these mutations, the response rate was reduced.

Individual mutations appear to influence susceptibility to darunavir in varying degrees. I50V has the highest impact, followed by I54M, L76V and I84V; V32I, and L33F, and I47V have less influence. The weakest impact was associated with V11I, I54L, G73S and L89V. This weighting needs to be validated.

New mutations that have occurred on treatment failure with darunavir are V32I, L33F, I47V, I54L, and L89V. The median increase in darunavir IC50 was 8.14. Approximately 50% of these isolates were sensitive to tipranavir. The median change in the tipranavir IC50 was 0.82. Conversely, over 50% of isolates with reduced tipranavir susceptibility were still sensitive to darunavir (De Meyer 2006a, De Meyer 2006b, Prezista US Product Information 2006, Johnson 2008). Based on an analysis of the POWER and DUET data, the mutation V82A is positively associated with response to DRV (De Meyer 2009).

A database analysis of 50,000 paired geno- and phenotypes showed that for darunavir-resistant samples (n=2141) between 2006 and 2009 the median for darunavir resistance factor increased from 38 to 50, whereas the tipranavir resistance factor decreased from 7.6 to 4.3. During this period, an increase of darunavir-associated mutations was observed, probably due to the increased use of the substance: I50V rose from 11 to 15%, I54L from 17 to 33% and L76V from 5 to 9%, respectively. The three mutations E35N, I47A and V82L were associated with resistance to both drugs. L10F, V82F and G48M were associated with darunavir resistance; I54S, I84V and I84C were associated with tipranavir resistance. Due to these at least partially different mutation patterns the sequencing of both drugs may be feasible in specific situations (Stawiski 2010).

Fusion inhibitors

The gp41 genome consisting of 351 codons has positions of high variability and well-conserved regions. Polymorphic sites are observed in all regions of gp41. The heptad repeat 2 (HR2) region has the highest variability. Primary resistance to T-20, the only fusion inhibitor thus far approved, is a rare phenomenon (Wiese 2005).

A loss of efficacy is generally accompanied by the appearance of mutations at the T-20 binding site which is the heptad repeat 1 (HR1) region of gp41. Especially affected are the HR1-positions 36 to 45, such as G36D/E/S, 38A/M/E, Q40H/K/P/R/T, N42T/D/S, N43D/K, or L45M/L.

The fold change in IC50, which ranges from £10 to several hundred, depends on the position of the mutation and the substitution of the amino acid. The decrease in susceptibility is greater for double mutations than for a single mutation. For double mutations like G36S+L44M, N42T+N43K, N42T+N43S or Q40H+L45M, a fold-change of >250 has been observed. Additional mutations in HR2 also contribute to T-20 resistance (Sista 2004, Mink 2005). In clinical isolates harbouring G36D as a single mutation, a 4- to 450-fold decrease in susceptibility was found. In the isolate showing a 450-fold decrease in susceptibility a heterozygote change at position 126 in HR2 was observed (N/K). Other mutations in the gp41 gene were found at positions 72, 90 and 113 (Sista 2004, Monachetti 2004, Loutfy 2004).

In one small study, 6 of 17 patients with virological failure additionally developed the mutation S138A in the HR2 region of gp41 – mostly combined with a mutation at position 43 in the HR1 region and a range of HR2 sequence changes at polymorphic sites (Xu 2004).

The replication capacity (RC) in the presence of HR1 mutations is markedly reduced when compared to wild-type virus with a relative order of RC wild-type > N42T > V38A > N42T, N43K » N42T, N43S > V38A, N42D » V38A, N42T. Viral fitness and T-20 susceptibility are inversely correlated (r=0.99, p<0.001) (Lu 2004).

CCR5 Antagonists

CCR5 antagonists are to be used in patients with exclusively R5-tropic virus. In the presence of X4-or dual-tropic virus, their use is not recommended. In about 80% of treatment-naïve patients and 50-60% of treatment-experienced patients R5-tropic virus is detected. The detection of solely X4-tropic virus is unlikely but possible (Demarest 2004, Brumme 2005, Moyle 2005, Wilkinson 2006, Hunt 2006, Coakley 2006, Melby 2006). The probability of X4-tropic virus populations is higher with reduced absolute and relative CD4 cell counts, both in treatment-naive and treatment-experienced patients (Brumme 2005, Hunt 2006). For treatment-naïve patients with a CD4 cell count of less than 200/μl, in only 62% of cases an R5-tropic virus population was detected (Simon 2010).

There are two ways to build up resistance to CCR5 antagonists: a receptor switch from R5- to X4-tropic or dual-tropic viruses or the emergence of mutations that enable the virus to use the CCR5 molecules for entry into in the cell in the presence of CCR5 antagonists.

In approximately one third of patients on a failing regimen with maraviroc, a shift from R5- to X4-tropic virus was reported. In individual cases, a receptor-shift was observed in the control arm as well. Retrospective studies using more sensitive methods have shown that some patients harbored minor X4 variants already at baseline (Heera 2008, Greaves 2006, Mori 2007, Lewis 2007).

On failing treatment with maraviroc or vicriviroc (no longer being investigated) different mutations in the V3 loop of the HIV-1 envelope protein gp120 are detected. Respective resistance patterns were not uniform and included mutations outside the V3 loop. The frequency and clinical relevance of these env mutations is still part of clinical research and resistance analysis is not yet routine. Some of the detected mutations were not associated with an increase in IC50. Instead, phenotypic resistance was characterized by dose-response curves that display a reduction in the maximal inhibition (Mori 2008, McNicholas 2009). Reduced maximal inhibition in phenotypic susceptibility assays indicates that viral strains resistant to the CCR5 antagonist maraviroc utilize inhibitor-bound receptors for entry (Landovitz 2006, Westby 2007, Johnson 2008). Cross-resistance between maraviroc and vicriviroc has been described after several in vitro passages, but cross-resistance to other CCR5 antagonists or complete class resistance, such as TAK-652, remains to be determined. R5-tropic virus with resistance to maraviroc may be suppressed by using monoclonal antibodies, such as PRO 140. In contrast to maraviroc or vicriviroc, PRO 140 binds extracellularly to the CCR5 co-receptor. Therefore, cross-resistance between PRO 140 and maraviroc or vicriviroc is unlikely (Jacobson 2009).

Integrase inhibitors

Sequence analysis of viruses from treatment-naïve patients showed that the integrase gene is very polymorphic, but most of the relevant positions for resistance, such as 148 and 155, are conserved (Hackett 2008). Resistance analysis is currently indicated only in the case of virologically failing therapy with an integrase inhibitor therapy.

The key mutation N155H was observed in two patients on primary therapy with raltegravir, tenofovir and 3TC, in one of the cases along with other integrase resistance mutations. In other patients only 3TC mutations were detected. No further raltegravir mutations were observed after week 48 (Markowitz 2007, Rockstroh 2011). In pre-treated patients three raltegravir resistance pathways have been observed. Key mutations are N155H, Q148K/R/H and less frequently Y143R/C. Mutations observed along with N155H were L74M, E92Q, T97A, V151I, G163R, G163K, or S230R. In the presence of Q148K/R/H the following mutations may occur: L74M, T97A, E138A, E138K, G140A, G140S and G163R, whereas mutations at positions 140 prevail.

The mutations N155H and Q148K/R/H do not occur on the same viral genome; this also applies for E92Q and mutations at position 148. Virus variants harboring N155H and secondary mutations are often replaced by variants with higher replicative fitness harboring Q148H + G140S (Fransen 2008, Miller 2008). In order to preserve the efficacy of second generation integrase inhibitors, raltegravir should be discontinued after the first key mutation has occurred.

The key mutation Y143H/R/C involves the mutations E92Q, T97A, V151I, G163R or S230R (Cooper 2007, Fransen 2008, Steigbigel 2008, Hazuda 2007).

The accumulation of additional mutations after the emergence of key mutations causes an increase in resistance and, depending on the pattern of mutations, also an increase in viral fitness. This is particularly true for the mutation Q148H (Goethals 2008, Hatano 2008). It is important to ensure that raltegravir is not used as functional monotherapy in patients with existing resistance mutations. The genetic barrier of raltegravir is therefore not as high as that of a boosted PIs, which, unlike raltegravir, can also be used as monotherapy in special cases (Gatell 2009).

Mutations occurring on failing therapy with elvitegravir were E92Q, E138K, Q148R/H/K and N155H. E92Q is often associated with the compensatory mutation L68V (Goodman 2008). A high level of cross-resistance between raltegravir and elvitegravir is caused by Q148H/R+G140S (McColl, 2007, DeJesus 2007). Response to raltegravir after failing elvitegravir is unlikely (Goodman 2008, Waters 2009). This has been confirmed by case reports (DeJesus 2007).

The integrase inhibitor dolutegravir (DTG, formerly S/GSK1349572), which is currently being investigated in phase II/III studies, shows promising results. Compared to raltegravir and elvitegravir this integrase inhibitor probably has a higher genetic barrier. Depending on the resistance pattern, little or no cross-resistance has been observed in vitro (Lalezari, 2009, Sato 2009).

In the Viking study, 27 patients with raltegravir-specific resistance mutations and a viral load >1,000 copies/ml were treated with dolutegravir 50 mg QD. At day 11, 21/27 patients had a viral load of <400 copies/ml or a viral load reduction of at least 0.7 log. Resistance mutations at positions 143 and 155 had no impact on the effectiveness, in contrast to mutations at position 148 in combination with two other secondary mutations. With a higher dose of dolutegravir (50 mg twice daily), the resistance can be overcome at least temporarily (Eron 2010 +2011).


Resistance and tropism tests belong to the standard diagnostic tools in the management of HIV infection. Primary resistant viral variants can be observed in about 10% of treatment-naïve patients in regions that have access to antiretroviral drugs. Resistance testing prior to initiating ART results in significantly better response rates. The emergence of viral mutants is one of the main causes of virological treatment failure. With the aid of HIV resistance tests, antiretroviral treatment strategies can be improved. Pharmacoeconomic studies have shown that genotypic resistance tests are cost-effective both in treatment-experienced and in ART-naïve patients (Weinstein 2001, Corzillius 2004, Sax 2005). HIV treatment guidelines recommend the use of resistance testing. New classes such as integrase inhibitors and CCR5 antagonists should be included in the evaluation of resistance. However, genotyping including sequence analyses of the integrase or envelope genomes is only partially covered by public health insurance in several countries.

Both, genotypic and phenotypic resistance tests as well as genotypic and phenotypic tropism tests show good intra- and inter-assay reliability. The interpretation of genotypic resistance profiles has become very complex and requires constant updating of respective guidelines. The determination of the thresholds associated with clinically relevant phenotypic drug resistance is crucial for the effective use of (virtual) phenotypic testing.

As for of resistance testing, genotyping has become the preferred method of tropism testing in clinical practice. With the co-receptor tool of geno2pheno viral tropism can be predicted.

Even while treatment failure requires the consideration of all causal factors, such as patient adherence to the regimen, metabolism of drugs and drug levels, resistance testing and measurement of viral tropism are of great importance in antiretroviral therapy. Finally, it needs to be emphasized that even with the benefit of well-interpreted resistance tests only experienced HIV practitioners should start, stop or change antiretroviral therapy keeping in mind the clinical and the psychosocial situation of the patient.

Resistance Tables

All tables are based on different rules-based interpretation systems, such as HIV-GRADE (, the ANRS-AC 11 Resistance Group ( and the Drug Resistance Mutations Group of the International AIDS Society-USA (Johnson 2010) as well as the references mentioned in the text.

These tables are not exhaustive and should not replace the communication between the practitioner and the laboratory experts in resistance interpretation.

Table 7. Mutations on the reverse transcriptase gene leading to NRTI resistance.
RTI Resistance mutations
Zidovudine (AZT) T215 Y/F (esp. with other TAMs)≥3 of the following: M41L, D67N, K70R, L210W, K219Q/EQ151M (esp. with A62V/F77L/F116Y) or T69SSX (insertion)*(Potential resensitizing effect associated with K65R, L74V, Y181C and M184V)
Stavudine (d4T) V75M/S/A/TT215Y/F (usually in combination with other TAMs)≥3 TAMs*Q151M (esp. with A62V/F77L/F116Y) or K65R or T69SSX (insertion)*

(Potential resensitizing effect associated with L74V, Y181C and M184V)

Abacavir M184V + 3 of the following: M41L, D67N, L74I, L210W, T215Y/F, 219Q/E≥5 of the following: M41L, D67N, L74I, L210W, T215Y/F, 219Q/EK65R or Y115F or L74VQ151M (esp. with A62V, F77L, F116Y) or T69SSX (insertion)*
Lamivudine (3TC) M184V/I or T69SSX (insertion)* or K65R (resistance possible)
Emtricitabine (FTC) M184V/I or T69SSX (insertion)* or K65R (resistance possible)
Didanosine (ddI) L74V, esp. with T69D/N or TAMsQ151M (esp. with A62V/F77L/F116Y) or T69SSX (insertion)*K65RT215Y/F and ≥2 of the following: M41L, D67N, K70R, L210W, K219Q/E
Tenofovir DF T69SSX (insertion)*≥3 TAMs with M41L or L210W (only partial resistance)≥3 – 5 of the following: M41L, E44D, D67N, T69D/N/S,  L210W, T215Y/F, K219Q/EK65R or K70E/G

(Potential resensitizing effect associated with L74V and M184V)

TAMs = thymidine analog mutations
* T69SSX in combination with T215Y/F and other TAMs leads to a high degree of resistance to all NRTIs and tenofovir
Table 8. Mutations on the reverse transcriptase-gene leading to NNRTI resistance (mutations associated with a high degree of resistance in bold).


Resistance mutations


L100l or K101E or K103N/H/S/T or V106M

V108I (with other NNRTI mutations)

Y181C(I) or Y188L/C/(H) or G190S/A (C/E/Q/T/V)

P225H (with other NNRTI mutations)


A98G (esp. for HIV-1 subtype C) or L100l

K101E/P/Q or K103N/H/S/T or V106A/M or V108I

Y181C/I/V or Y188C/L/H or G190A/S (C/E/Q/T/V)


≥2*-3 mutations of (V90I, A98G, L100I, K101E/H/P, V106I, E138A/G/K/Q, V179D/F/T, Y181C/I/V, G190A/S, F227C, M230L)

*in combination with a bold mutation

Table 9. Mutations on the protease gene leading to PI resistance.


Relevant resistance mutations and patterns

Further mutations associated with resistance


I84V/A, 48V/M

≥3 of the following: L10F/I/M/R/V, K20I/M/ R/T, L24I, I62V, G73CST, 82A/F/S/T and L90M or

≥4 of the following: L10I/R/V, I54V/L, A71V/T, V77I, V82A/ F/S/T and L90M

Possible L76V-associated resensitizing effect

≥2 PRAMs*






V82A/F/S/T and at least 2 of the following: L10I, M36I, M46l/L, I54V/L/M/T, A71V/T, V77I

≥2 PRAMs*



L76V together with other PI mutations

V32I plus I47V

≥6 of the following: L10F/I/V, K20M/R, E35D, R41K, I54V/L/M, L63P, V82A/F/T/S, I84V or

≥3 of the following: L10I/F/R/ V, L33F, M36I, M46I/L, I54L/ M/T/V, I62V, L63P, A71I/L/V/T, G73A/C/F/T, V82A/F /S/T, I84V and L90M) or

≥3 of the following: L10F/I/V, L33F, M46I/L, I47V, I54L/M/V/A/T/S, A71V, G73S/A/C/T, V82A/F/C/G and L90M

≥2 PRAMs*



≥3 of the following: M46I, I47A/V, L50V, I54A/M/V, L76V, V82FATS, I84V

5-7 of the following: L10F/I/R/V, K20M/R, L24l, V32I, L33F, M46l/L, I47V/A, I50V, F53L, l54L/T/V, L63P, A71l/L/V/T, G73S, V82A/F/T, l84V, L90M

≥2 PRAMs*



Atazanavir/r (300/100 mg QD)

I50L – frequently in combination with A71V –

≥4 of the following: L10I/F, K20R/M/I, L24I, V32I, L33I/F/V, M46I, M48V, I54V/M/A, A71V, G73C/S/T/A, V82A/F/S/T, I84V, N88S and L90M

(Possible L76V-associated resensitizing effect)


≥2 PRAMs*


≥7 mutations/points of the following: K20M/R/V, L33F, E35G, N43T, M46L, I47V, I54A/M/V, Q58E, H69K, T74P, V82L/T, N83D and I84V; V82L/T and I84V with twofold points score

Score >10 of the following: I10V (+1), L24I (-2), M36I (+2), N43T (+2), M46L (+1), I47V (+6), I50L/V (-4) I54A/M/V (+3), I54L (-7) Q58E (+5), T74P (+6), L76V (-2), V82L/T (+5), N83D (+4), I84V (+ 2)

Possible L76V-associated resensitizing effect

Further resistance-ass. mutations: I54S, I84C

6 mutations/points of the following: K20M/R/V, L33F, E35G, N43T, M46L, I47V, I54A/M/V, Q58E, H69K, T74P, V82L/T, N83D and I84V; V82L/T and I84V with twofold points score

Score 3-10 from I10V (+1), L24I (-2), M36I (+2), N43T (+2), M46L (+1), I47V (+6), I50L/V (-4) I54A/M/V (+3), I54L (-7) Q58E (+5), T74P (+6), L76V (-2),.V82L/T (+5), N83D (+4), I84V (+ 2)


≥4 of the following: V11I, V32I, L33F, I47V, I50V, I54L/M, T74P, L76V, I84V, L89V

(with V32I, I50V, I54M, L76V and I84V having a higher impact)

Further resistance-ass. mutations:

L10F, E35N, I47A, V82L, G48M, V82F

≥3 of the following: V11I, V32I, L33F, I47V, I50V, I54L/M, T74P, L76V, I84V, L89V (with I50V, I54M, L76V and I84V having a higher impact)

*PRAMs (protease inhibitor –resistance-associated mutations) include the following mutations: L33I/F/V, V82A/F/S/T, I84V and L90M. They lead to high PI cross-resistance.

Table 10. Mutations leading to entry inhibitor resistance.

Fusion inhibitor

Resistance mutations


G36A/D/E/S/V or I37V oder 38A/M/E/K/V or Q39R

Q40H/K/P/R/T or N42T/D/S or N42T+(N43S/N43K)

N43D/KH/S or L44M or L44M+ G36S or L45M/L/Q

CCR5 antagonists



Individual mutations described as resistance-associated; no consistent pattern

The reduction in susceptibility is generally higher for double than for single mutations.

Table 11. Mutations on the integrase gene leading to raltegravir resistance (and probably cross resistance to elvitegravir)

Integrase inhibitors

Resistance mutations (Resistance pathways and key mutations)

Other mutation- and resistance-profiles conferring resistance





The appearance of additional mutations produces an increase in the level of resistance.


T66I and E92Q



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Wilkin T, Su Z, Kuritzkes D, et al. Co-receptor tropism in patients screening for ACTG 5211, a phase 2 study of vicriviroc, a CCR5 inhibitor. Abstract 655, 13th CROI 2006, Denver, Colorado, USA.

Wilson JW. Update on antiretroviral drug resistance testing: Combining laboratory technology with patient care. AIDS Read 2003; 13:25-38.

Wirden M, Malet I, Derache A, et al. Clonal analyses of HIV quasispecies in patients harbouring plasma genotype with K65R mutation associated with thymidine analogue mutations or L74V substitution. AIDS 2005; 19:630-2.

Wittkop L, (on behalf of the EuroCoord-CHAIN Joint Project Team) Impact of transmitted drug resistence (TDR)on virological and immunological response to initial combination antiretroviral therapy (cART) – EuroCoord – CHAIN joint project (2010). Abstract THLBB108, 18th IAC 2010, Vienna.

Xu L, Pozniak A, Wildfire A, et al. Emergence and evolution of enfuvirtide resistance following long-term therapy involves heptad repeat 2 mutations within gp41. Antimicrob Agents Chemother 2005; 49:1113-9.

Young B, Fransen S, Greenberg K et al.Transmission of integrase strand-transfer inhibitor multi-drug resistant HIV: case report and natural history of response to raltegravir-containing antiretroviral therapy. Abstract TUPE0163, 18th IAC 2010, Vienna.

Table 9. Mutations on the protease gene leading to PI resistance.


Relevant resistance mutations and patterns

Further mutations associated with resistance


I84V/A, 48V/M

≥3 of the following: L10F/I/M/R/V, K20I/M/ R/T, L24I, I62V, G73CST, 82A/F/S/T and L90M or

≥4 of the following: L10I/R/V, I54V/L, A71V/T, V77I, V82A/ F/S/T and L90M

Possible L76V-associated resensitizing effect

≥2 PRAMs*






V82A/F/S/T and at least 2 of the following: L10I, M36I, M46l/L, I54V/L/M/T, A71V/T, V77I

≥2 PRAMs*



L76V together with other PI mutations

V32I plus I47V

≥6 of the following: L10F/I/V, K20M/R, E35D, R41K, I54V/L/M, L63P, V82A/F/T/S, I84V or

≥3 of the following: L10I/F/R/ V, L33F, M36I, M46I/L, I54L/ M/T/V, I62V, L63P, A71I/L/V/T, G73A/C/F/T, V82A/F /S/T, I84V and L90M) or

≥3 of the following: L10F/I/V, L33F, M46I/L, I47V, I54L/M/V/A/T/S, A71V, G73S/A/C/T, V82A/F/C/G and L90M

≥2 PRAMs*



≥3 of the following: M46I, I47A/V, L50V, I54A/M/V, L76V, V82FATS, I84V

5-7 of the following: L10F/I/R/V, K20M/R, L24l, V32I, L33F, M46l/L, I47V/A, I50V, F53L, l54L/T/V, L63P, A71l/L/V/T, G73S, V82A/F/T, l84V, L90M

≥2 PRAMs*



Atazanavir/r (300/100 mg QD)

I50L – frequently in combination with A71V –

≥4 of the following: L10I/F, K20R/M/I, L24I, V32I, L33I/F/V, M46I, M48V, I54V/M/A, A71V, G73C/S/T/A, V82A/F/S/T, I84V, N88S and L90M

(Possible L76V-associated resensitizing effect)


≥2 PRAMs*


≥7 mutations/points of the following: K20M/R/V, L33F, E35G, N43T, M46L, I47V, I54A/M/V, Q58E, H69K, T74P, V82L/T, N83D and I84V; V82L/T and I84V with twofold points score

Score >10 of the following: I10V (+1), L24I (-2), M36I (+2), N43T (+2), M46L (+1), I47V (+6), I50L/V (-4) I54A/M/V (+3), I54L (-7) Q58E (+5), T74P (+6), L76V (-2), V82L/T (+5), N83D (+4), I84V (+ 2)

Possible L76V-associated resensitizing effect

Further resistance-ass. mutations: I54S, I84C

6 mutations/points of the following: K20M/R/V, L33F, E35G, N43T, M46L, I47V, I54A/M/V, Q58E, H69K, T74P, V82L/T, N83D and I84V; V82L/T and I84V with twofold points score

Score 3-10 from I10V (+1), L24I (-2), M36I (+2), N43T (+2), M46L (+1), I47V (+6), I50L/V (-4) I54A/M/V (+3), I54L (-7) Q58E (+5), T74P (+6), L76V (-2),.V82L/T (+5), N83D (+4), I84V (+ 2)


≥4 of the following: V11I, V32I, L33F, I47V, I50V, I54L/M, T74P, L76V, I84V, L89V

(with V32I, I50V, I54M, L76V and I84V having a higher impact)

Further resistance-ass. mutations:

L10F, E35N, I47A, V82L, G48M, V82F

≥3 of the following: V11I, V32I, L33F, I47V, I50V, I54L/M, T74P, L76V, I84V, L89V (with I50V, I54M, L76V and I84V having a higher impact)

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Filed under 10. HIV Resistance and Viral Tropism Testing, Part 2 - Antiretroviral Therapy

Mitochondrial Toxicity of NRTIs

– Nils Venhoff and Ulrich A. Walker –


Two years after the introduction of PIs into the armamentarium of ART, reports of HIV-infected individuals experiencing clinically relevant changes in body metabolism began to surface. These metabolic symptoms were initially summarized under the term “lipodystrophy” (Carr 1998). Nowadays this lipodystrophy syndrome is understood as the result of overlapping yet distinct effects of the different drug components in ART. The main pathogenetic mechanism through which nucleoside analogs are thought to contribute to metabolic changes and organ toxicities is mitochondrial toxicity (Brinkman 1999).

Pathogenesis of mitochondrial toxicity

NRTIs are prodrugs (Kakuda 2000) – they require activation in the cell through phosphorylation before they are able to inhibit their target, HIV reverse transcriptase. In addition to impairing the HIV replication machinery, the NRTI-triphosphates also inhibit a human polymerase called gamma-polymerase, which is responsible for the replication of mitochondrial DNA (mtDNA), a small circular molecule normally present in multiple copies in each mitochondrion and in hundreds of copies in most human cells. Thus, the inhibition of gamma-polymerase by NRTIs leads to a decline (depletion) in mtDNA content (Lewis 2003). The biological task of mtDNA is to encode for enzyme subunits of the respiratory chain, which is located in the inner mitochondrial membrane. Therefore, by causing mtDNA depletion, NRTIs also lead to a defect in the respiratory chain function.

An intact respiratory chain is the prerequisite for numerous metabolic pathways. The main task of the respiratory chain is to oxidatively synthesize ATP, our chemical currency of energy. In addition, the respiratory chain consumes NADH and FADH as end products of fatty acid oxidation. This explains the micro- or macrovesicular accumulation of intracellular triglycerides, which often accompanies mitochondrial toxicity. Last but not least, a normal respiratory function is also essential for the synthesis of DNA, because the de novo synthesis of pyrimidine nucleosides depends on an enzyme located in the inner mitochondrial membrane. This enzyme is called dihydroorotate dehydrogenase (DHODH) (Löffler 1997). The clinical implications of this are detailed below.

The onset of mitochondrial toxicity follows several principles (Walker 2002a):

1. Mitochondrial toxicity is concentration-dependent. High NRTI concentrations cause a more pronounced mtDNA depletion compared to low concentrations. The clinical dosing of some nucleoside analogs is close to the limit of tolerance with respect to mitochondrial toxicity.

2. Mitochondrial toxicity is time dependent and develops with prolonged NRTI exposure. Changes in mitochondrial metabolism are observed only if the amount of mtDNA depletion exceeds a certain threshold. As a consequence, long-term NRTI exposure may lead to mitochondrial effects despite relatively low NRTI concentrations and the onset of toxicity is typically not observed in the first few months of ART.

3. There are significant differences in the relative potencies of nucleoside and nucleotide analogs in their ability to interact with gamma-polymerase. The hierarchy of gamma-polymerase inhibition for the active NRTI metabolites has been determined as follows: zalcitabine (ddC, HIVID®) > didanosine (ddI, Videx®) > stavudine (d4T, Zerit®) > lamivudine (3TC, Epivir®) ≥ abacavir (ABC, Ziagen®) ≥ tenofovir (TDF, Viread®) ≥ emtricitabine (FTC, Emtriva®).

4. Zidovudine (AZT, Retrovir®) may be peculiar because its active triphosphate is only a weak inhibitor of gamma-polymerase. However, another mechanism can explain how zidovudine might cause mtDNA depletion independent from gamma-polymerase inhibition. AZT is an inhibitor of mitochondrial thymidine kinase type 2 (TK2), and, as such, interferes with the synthesis of natural pyrimidine nucleotides, thus potentially impairing the formation of mtDNA (McKee 2004). Indeed, inborn defects of TK2 are known to cause mtDNA depletion in human muscle tissue (Saada 2001). It has also been demonstrated that AZT can be non-enzymatically converted into d4T within the body, at least within some cells (Becher 2003, Bonora 2004).

5. Mitochondrial toxicity is tissue-specific. Tissue specificity is explained by the fact that the uptake of the NRTI prodrugs into cells and their mitochondria as well as activation by phosphorylation may be different in different cell types.

6. There may be additive or synergistic mitochondrial toxicities if two or more NRTIs are used in combination.

7. Data suggest that mitochondrial transcription may also be impaired without mtDNA alterations (Galluzzi 2005, Mallon 2005). However, the mechanism and clinical significance of this are not yet understood.

Clinical manifestations

MtDNA depletion may manifest clinically in one or several target tissues (Figure 1). In the liver mitochondrial toxicity is associated with increased lipid deposits, resulting in micro or macrovesicular steatosis. Steatosis may be accompanied by elevated liver transaminases. Such steatohepatitis may progress to liver failure and lactic acidosis, a potentially fatal, but fortunately rare complication.

Although steatohepatitis and lactic acidosis were already described in the early 90s in patients receiving didanosine monotherapy (Lambert 1990), mitochondrial liver toxicity is now observed with treatment with all NRTIs that have a relatively strong potential to inhibit gamma-polymerase, especially with the so called “d-drugs” ddI, d4T (and formerly, ddC). Liver complications have also been described with AZT. It has been demonstrated in the hepatic tissue of HIV patients that each of the d-drugs leads to a time dependent mtDNA depletion. On electron microscopy, morphologically abnormal mitochondria were observed.

Figure 1: Organ manifestations of mitochondrial toxicity.

A typical complication of mitochondrial toxicity is an elevation in serum lactate. Such hyperlactatemia was more frequently described with prolonged d4T treatment (Carr 2000, Saint-Marc 1999), especially when combined with ddI. The toxicity of ddI is also increased through interactions with ribavirin and hydroxyurea. The significance of asymptomatic hyperlactatemia is unclear. When elevated lactate levels are associated with symptoms, these are often non-specific such as nausea, right upper quadrant abdominal tenderness or myalgias. In the majority of cases, levels of bicarbonate and the anion gap (Na+ –  (HCO3 + Cl)) are normal, although liver transaminases are mildly increased in the majority of cases (Lonergan 2000). Therefore, the diagnosis relies on the logistically more cumbersome direct determination of serum lactate. In order to avoid artifacts, venous blood must be drawn without the use of a tourniquet from resting patients. The blood needs to be collected in fluoride tubes and transported to the laboratory on ice for immediate analysis. Non-mitochondrial causes must also be considered in the differential diagnosis of lactic acidosis (Table 1) and underlying organ toxicities should be looked for. The incidence of lactic acidosis is low; it was estimated as 1 per 1000 patient years in NRTI exposed patients (Imhof 2005). In South Africa, much higher incidence rates (10.6 cases per 1,000 patient years) have been reported (Bolhaar 2007).

Mitochondrial myopathy in antiretrovirally treated HIV+ patients was first described with high dose AZT (Arnaudo 1991). Skeletal muscle weakness may manifest under dynamic or static exercise. The serum CK is often normal or only minimally elevated. Muscle histology helps to distinguish this form of NRTI toxicity from HIV myopathy, which may also occur simultaneously. On histochemical examination, the muscle fibers of the former are frequently negative for cytochrome c-oxidase and carry ultrastructurally abnormal mitochondria, whereas those of the latter are typically infiltrated by CD8-positive T lymphocytes. Exercise testing may detect a low lactate threshold and a reduced lactate clearance, but in clinical practice these changes are difficult to distinguish from lack of aerobic exercise (detraining).

In a murine model of cardiomyopathy, nine weeks of oral AZT and ddC induced a mitochondrial lesions with mtDNA depletion, diminished respiratory chain function and ultrastructural abnormalities of mitochondria (Balcarek 2010).

Table 1: Causes of hyperlactatemia/ lactic acidosis.
Type A lactic acidosis Type B lactic acidosis
(Tissue hypoxia)ShockCarbon monoxide poisoningHeart failure (Other mechanisms)Thiamine deficiencyAlkalosis (pH >7.6)Epilepsy

Adrenalin (iatrogenic, endogenous)

Liver failure

Neoplasm (lymphoma, solid tumors)

Intoxication (nitroprusside, methanol, methylene glycol, salicylates)


Rare enzyme deficiencies

mtDNA mutations

mtDNA depletion

Prolonged treatment with d-drugs can also lead to a predominantly symmetrical, sensory and distal polyneuropathy of the lower extremities (Moyle 1998, Simpson 1995). An elevated serum lactate level can help distinguish this axonal neuropathy from its HIV-associated phenocopy, although in most cases the lactate level is normal. The differential diagnosis may also take into account the fact that the mitochondrial polyneuropathy mostly occurs weeks or months after initiation of the d-drugs. In contrast, the HIV-associated polyneuropathy generally does not worsen and may indeed improve with prolonged antiretroviral treatment. In mice AZT and ddC induce a mitochondrial neurotoxicity with ddC predominantly affecting the peripheral and AZT mainly the central nervous system (Venhoff 2010). This is consistent with findings in patients (Tardieu 2005, Moyle 1998). Although the toxic effects of AZT on the central nervous system have not been studied well in humans, they are plausible because AZT penetrates well into the CNS.

In its more narrow sense, the term lipodystrophy denotes a change in the distribution of body fat. Some individuals affected with lipodystrophy may experience abnormal fat accumulation in some body areas (most commonly in the abdomen or in the dorsocervical region), whereas others may develop fat wasting (Bichat’s fat pad in the cheeks, temporal fat, or subcutaneous fat of the extremities). Both fat accumulation and fat loss may occur simultaneously in the same individuals. Fat wasting (also called lipoatrophy) is partially reversible and generally observed not earlier than one year after the initiation of antiretroviral therapy. In the affected subcutaneous tissue, ultrastructural abnormalities of mitochondria and reduced mtDNA levels have been identified, in particular in subjects treated with d4T (Walker 2002b). In vitro and in vivo analyses of fat cells have also demonstrated diminished intracellular lipids, reduced expression of adipogenic transcription factors (PPAR-gamma and SREBP-1), and increased apoptotic indices. NRTI treatment may also impair some endocrine functions of adipocytes. For example, NRTIs may impair the secretion of adiponectin and through this mechanism may promote insulin resistance. D4T has been identified as a particular risk factor, but other NRTIs such as zidovudine may also contribute. When d4T is replaced by another NRTI, mtDNA levels and apoptotic indices improve (McComsey 2005), along with an objectively measurable, albeit small increase of subcutaneous adipose tissue (McComsey 2004). In contrast, switching away from protease inhibitors did not ameliorate lipoatrophy or adipocyte apoptosis. Taken together, the available data indicate a predominant effect of mitochondrial toxicity in the pathogenesis of lipoatrophy.

Some studies have suggested an effect of NRTIs on the mtDNA levels in blood (Coté 2003, Miro 2003). The functional consequence of such mitochondrial toxicity on lymphocytes is still unknown. In this context, it is important to note that a delayed loss of CD4 and CD8 lymphocytes was observed when ddI plasma levels were increased by co-medication with TDF or when ddI was given to subjects with low body weight (Negredo 2004). Recent in vitro investigations with exposure of mitotically stimulated T lymphocytes to only slightly supratherapeutic concentrations of ddI also detected a substantial mtDNA depletion with a subsequent late onset decline of lymphocyte proliferation and increased apoptosis (Setzer 2005a, Setzer 2005b). Thus, mitochondrial toxicity is the most likely explanation for the late onset decline of lymphocytes observed with ddI. The data suggest that the mitochondrial toxicity of NRTIs on lymphocytes has immunosuppressive properties.

Asymptomatic elevations in serum lipase are not uncommon under ART, but of no value in predicting the onset of pancreatitis (Maxson 1992). The overall frequency of pancreatitis has been calculated as 0.8 cases/100 years of NRTI-containing ART. Clinical pancreatitis is associated with the use of ddI in particular. DdI re-exposure may trigger a relapse and should be avoided. A mitochondrial mechanism has been cited to explain the onset of pancreatitis, but this assumption remains unproven.

Furthermore an elevation of serum urate was observed on therapy with dideoxynucleosides (ddI and d4T). Impaired ATP production as a result of mitochondrial toxicity may increase urate production in the purine nucleotide cycle (Walker 2006b).

After long and controversial discussions, several studies now provide evidence that TDF does cause mitochondrial damage to the kidney. TDF is a nucleotide analog reverse transcriptase inhibitor (Viread®) which has been associated with cases of renal dysfunction, Fanconi’s syndrome and cases of osteomalacia in animals (Tenofovir review team 2001) and patients (Gupta 2008, Wanner 2009). Patients treated with TDF often present with elevated serum alkaline phosphatase and hypophosphatemia due to a diminished renal phosphate resorption (Kinai 2005). Also osteomalacia was observed in patients treated with TDF, especially when combined with lopinavir/r (Parsonage 2005, Wanner 2009). TDF is taken up into the proximal renal tubules by human renal organic anion transporters (hOATs) 1 and 3. Despite the fact that TDF only has a low potency to impair the polymerase gamma, the hOATs may generate high intratubular tenofovir concentrations that then interfere with the replication of mtDNA (Cote 2006). Decreased mtDNA levels have been found in renal biopsies from patients exposed to TDF+ddI, an NRTI combination that is no longer recommended (Cote 2006). In rats, TDF induced an organ-specific nephrotoxicity with mtDNA depletion and tubular dysfunction of mtDNA-encoded respiratory chain subunits (Lebrecht 2009). It is therefore not recommended to use TDF in patients with established renal dysfunction.

The use of AZT to avoid vertical HIV transmission diminishes mtDNA levels in the placenta, as well as in the peripheral cord blood of perinatally exposed newborns (Divi 2004, Shiramizu 2003, Gingelmaier 2009). AZT was found to be incorporated into mtDNA in pregnant monkeys treated with AZT plus 3TC prior to delivery and mtDNA depletion was found in skeletal muscle, heart and brain (Gerschenson 2004) in which perinatally acquired lesions were shown to persist for months after cessation of NRTI exposure in some models.

Mitochondrial symptoms and abnormal cerebral imaging were found at increased frequency in infants perinatally exposed to NRTIs (Blanche 1999, Tardieu 2005). Hyperlactatemia is not infrequently observed and may persist for several months after delivery (Noguera 2003). Other clinical trials in contrast did not detect an increased perinatal risk in association with perinatal AZT prophylaxis although key parameters of mitochondrial dysfunction were not assessed. Long-term follow-up data are urgently needed (Venhoff 2006). The available information however does not justify deviating from the currently recommended strategy to use AZT to prevent vertical HIV transmission as part of a combination therapy for the mother.


There is currently no method to reliably predict the mitochondrial risk of an individual patient. Routine screening of asymptomatic NRTI-treated subjects with lactate levels is not warranted, since elevated lactate levels in asymptomatic subjects are not predictive of clinical mitochondrial toxicity (McComsey 2004). Quantification of mtDNA levels in PBMCs is not reliable. The [13C]methionine breath test can be used to analyze the oxidative capacity of liver mitochondria, but results may be confounded by several factors other than HIV or ART (Sternfeld 2009). Quantifying mtDNA within affected tissues is likely to be more sensitive; however this form of monitoring is invasive and not evaluated with regard to clinical endpoints.

Once symptoms are established, histological examination of a biopsy may contribute to the correct diagnosis. The following findings in tissue biopsies point towards a mitochondrial etiology: ultrastructural abnormalities of mitochondria, diminished histochemical activities of cytochrome c oxidase, the detection of intracellular and more specifically microvesicular steatosis, and the so-called ragged-red fibers.

Treatment & prophylaxis of mitochondrial toxicity

Drug interactions

Drug interactions may precipitate mitochondrial symptoms and must be taken into account. The mitochondrial toxicity of ddI for example is augmented through drug interactions with ribavirin, hydroxyurea and allopurinol (Ray 2004). When ddI is combined with TDF, the dose of ddI must be reduced to 250 mg once daily. The thymidine analog brivudine is a herpes virostatic that may sensitize for NRTI-related mitochondrial toxicity because one of its metabolites is an inhibitor of DHODH (see below). Brivudine should therefore not be combined with antiretroviral pyrimidine analogues.

Mitochondrial toxins

An impairment of mitochondrial metabolism may also result from ibuprofen, valproic acid and acetyl salicylic acid as these agents impair the mitochondrial utilization of fatty acids. Numerous cases have been described in which a life-threatening lactic acidosis was triggered by valproic acid both in HIV-infected patients and in patients with inherited mutations of mtDNA. Acetyl salicylic acid may damage mitochondria and such damage to liver organelles may result in Reye’s syndrome. Amiodarone and tamoxifen also inhibit the mitochondrial synthesis of ATP. Acetaminophen and other drugs impair the antioxidative defense (glutathione) of mitochondria, allowing for their free radical-mediated damage. Aminoglycoside antibiotics and chloramphenicol not only inhibit the protein synthesis of bacteria, but under certain circumstances may also impair the peptide transcription of mitochondria as our bacteria-like endosymbionts. Adefovir and cidofovir are also inhibitors of gamma-polymerase. Alcohol is a mitochondrial toxin and should be avoided. Lastly, TDF nephrotoxicity may be precipitated by coadministation with lopinavir/r (Wanner 2009). Lopinavir/r increases tenofovir serum levels and also may inhibit MDR4, with both mechanisms then contributing to intratubular tenofovir accumulation.

The most important clinical intervention is probably the discontinuation of the NRTI responsible for mitochondrial toxicity. Several studies have demonstrated that switching from stavudine (Zerit®) to a less toxic alternative led to an objective and progressive improvement in lipoatrophy (Martin 2004, McComsey 2004, Moyle 2004). In contrast, a switch from protease inhibitors to NRTIs was not associated with an improvement of lipoatrophy. These findings stress the importance of mitochondrial toxicity in the pathogenesis of fat abnormalities.


When toxic NRTIs can not be avoided, the supplementation of uridine is currently a very promising strategy to treat mitochondrial toxicity. As outlined above, any respiratory chain impairment also results in the inhibition of DHODH, an essential enzyme for the synthesis of uridine and its derived pyrimidines (Figure 2). This decrease in intracellular pyrimidine pools leads to a relative excess of the exogenous pyrimidine nucleoside analogs, with which they compete at gamma-polymerase. A vicious circle is closed and contributes to mtDNA depletion. By supplementing uridine either prophylactically or therapeutically, this depletion may be interrupted, resulting in increased mtDNA levels (Setzer 2008). Indeed, uridine abolishes all the effects of mtDNA depletion in hepatocytes and normalizes lactate production, cell proliferation, the rate of cell death and intracellular steatosis (Walker 2003). In d4T-exposed adipocytes uridine was able to normalize mitochondrial function and lipid metabolism (Walker 2006a). New data indicate that uridine is able to also prevent ddC-induced hepatotoxicity (Lebrecht 2007), and AZT-induced myopathy (Lebrecht 2008), cardiomyopathy and neuropathy (Venhoff 2010) in mice.

The oral substitution of uridine as a pyrimidine precursor is well tolerated by humans, even at high doses (Kelsen 1997, van Groeningen 1986). Mitocnol, a food supplement, was shown to have a more than 8-fold uridine bioavailability over conventional uridine (Venhoff 2005).

Figure 2: Suggested mechanism of mitocnol (NucleomaxXTM) in the prevention and treatment of mitochondrial toxicity.

A randomized placebo-controlled double-blind trial found that mitocnol improved subcutaneous fat in lipoatrophic subjects under continued therapy with d4T or AZT (Sutinen 2007). In a second study mitocnol was found to be efficacious with regard to patient- and physician-assessed lipoatrophy scores (McComsey 2008). ACTG 5229, a prospective, 1:1 randomized, placebo-controlled multicenter trial evaluated the effectiveness of uridine in improving limb fat in HIV-infected individuals with lipoatrophy on a tNRTI-containing regimen (stratified by AZT or d4T). Primary endpoint was change in limb fat from baseline to week 48. Despite a small improvement in limb fat after 24 weeks compared to placebo, the effect was not sustained through 48 weeks of uridine (McComsey 2010). These results dashed the hopes in uridine as a treatment option of tNRTI-associated lipoatrophy.

Mitochondrial steatohepatitis is antagonized by mitocnol in animal models, as well as in HIV-positive patients (Walker 2004, Banasch 2006, Lebrecht 2007). In AZT-induced myopathy in mice mitocnol attenuated mtDNA depletion and muscle atrophy (Lebrecht 2008). Furthermore, mitochondrial cardiomyopathy and neuropathy was successfully prevented by mitocnol in an animal model (Balcarek 2010, Venhoff 2010).

Mitocnol is well tolerated and adverse events have not been observed so far. In one study, a clinically insignificant HDL decline was noted, while in another HDL cholesterol was unchanged (McComsey 2008). There are no known negative interactions of uridine with antiretroviral treatment (Koch 2003, McComsey 2005, Sommadossi 1988, Sutinen 2007). In Europe and North America, mitocnol is available as a dietary supplement called NucleomaxX® and can be acquired in pharmacies and the internet (


With symptomatic hyperlactatemia and with lactic acidosis, NRTIs should be immediately discontinued (Brinkman 2000).

With respect to mtDNA depletion vitamin supplements were not found to be effective either in vitro or in clinical studies (Venhoff 2002, Walker 1995). In animals and humans mitocnol improves hyperlactatemia (Lebrecht 2007, Sutinen 2007). NRTI re-exposure may be possible after normalization of lactate (Lonergan 2003). The supportive treatment of hyperlactatemia and lactic acidosis is summarized in Table 2.

Table 2: Supportive treatment of lactate elevation in HIV-infected patients (non-pregnant adults).
Lactate 2-5 mmol/L + symptoms Lactate > 5 mmol/L or lactic acidosis
Discontinue mitochondrial toxinsConsider vitamins and

NucleomaxX® (36g TID on 3 consecutive days/month)

Discontinue NRTIs and all mitochondrial toxinsIntensive care

Maintain hemoglobin >100 g/L

Avoid vasoconstrictive agents


Correct hypoglycemia

Bicarbonate controversial – 50-100 mmol if pH <7.1

Coenzyme Q10  (100 mg TID)

Vitamin C (1 g TID)

Thiamine (Vit. B1, 100 mg TID)

Riboflavin (Vit. B2, 100 mg QD)

Pyridoxine (Vit. B6, 60 mg QD)

L-acetyl carnitine (1 g TID)

NucleomaxX (36 g TID until lactate <5 mmol/L)


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