C13 Investigations for optimising HIV treatment


Treatment of HIV infection has been revolutionalised by the regular use of highly active antiretroviral therapy (HAART). This is supported by the measurement of CD4 count and viral loads, which are now the standard monitoring tools in clinical services. These routine investigations serve to track progress and inform responses. To further optimise current therapy, supplemental forms of monitoring have been introduced, some of which may eventually become standard tools. Resistance testing, therapeutic drug monitoring (TDM) and tropism assay are investigations that have evolved from experimental tools to clinical measures. Resistance testing aims at to predict treatment response while TDM aims to guide treatment through an understanding of the pharmacokinetics of antiretroviral agents. HIV tropism assay is an add-on investigation tool for treatment guidance.

This chapter is devoted to a description of these supplemental investigations. An algorithm at the end of the chapter gives an overview of the potential roles for their application in clinical practice.[Algorithm 13]

Forms of resistance testing

There are two main forms of HIV resistance: Primary or transmitted resistance refers to that arising from the infection by a virus that is already resistant to selected antiretrovirals. The prevalence of primary resistance varies from one population to another. The other form is secondary or acquired resistance, a condition that evolves following exposure to antiretroviral drugs.

Acquired resistance has gradually become an important clinical condition. In the past decades, HAART has generally lowered morbidity and mortality of HIV infection.[Chapter C10] A proportion of patients on HAART does not achieve optimal viral suppression or may experience viral rebound within a short period. HAART induced mutations in HIV-1 reverse transcriptase (RT), protease (PR), integrase (IN) and envelope (ENV) genomic regions allows virus to escape from drug suppression, a phenomenon that is becoming well characterised. The determination of primary resistance is driven by public health needs through the establishment of surveillance mechanisms. From a regional drug resistance monitoring study held between 2007 and 2009, the average prevalence of HIV primary resistance was 13.8% among treatment naive patients in 3 locations of Asia (Hong Kong, Malaysia and Thailand).[1] Nucleoside reverse transcriptase inhibitor (NRTI) and non-nucleoside reverse transcriptase inhibitor (NNRTI) resistance was found in 8.4% and 6.5% treatment naive patients respectively. The prevalence of Protease Inhibitor (PI) primary resistance in Asia remains at <1%, which is much lower compared to similar studies from the sub-Saharan Africa and Western countries.

Resistance is one important cause of antiretroviral treatment failure.[Chapter C11] The nature, extent and potential impacts of resistance-associated failure cannot be inferred from conventional routine laboratory tests (CD4 and viral load). The World Health Organization has recommended the application of HIV resistance testing which could improve treatment response to HAART. This has led international authorities to work towards the incorporation of resistance testing into patient management in some settings.

Types of resistance assays

Antiretroviral resistance can be determined using either genotypic or phenotypic method. The genotyping resistance assay identifies nucleotide mutations or changes that are known to confer decreased susceptibility to antiretroviral drugs. The phenotype is a trait or behaviour resulting from the expression of a specific genotype. Phenotyping resistance assay measures the ability of virus to replicate and infect in vitro cell lines in the presence of antiretroviral agents. Results of phenotyping resistance assays are typically reported as the inhibitory concentration (IC50), which is the concentration of the tested drug that can suppress in vitro HIV-1 replication by 50%. These assays involve highly complex analytical protocols and safety procedures undertaken in sophisticated laboratory environment. Todate, only a small number of specialised clinical reference laboratories around the world have the facilities and technical expertise to deliver reliable phenotypic drug susceptibility results in the clinically relevant time frame. In Hong Kong, phenotyping resistance assay is still on a developing stage in research setting and is not available as regular service for clinical HIV management.

HIV-1 genotyping resistance test (GRT) involves the determination of nucleotide sequences of the RT, PR, IN and ENV regions of the viral genome. Sequence changes that are associated with antiretroviral resistance are catalogued. Point mutations associated with drug resistance may affect the fitness or replication capacity of the virus. These resistance mutations are mainly classified into major or minor forms. Major resistance mutations impact drug susceptibility significantly and allow the virus to survive and replicate under antiretroviral treatment. Minor resistance mutations are less clinically relevant. They usually appear after major mutations to compensate for changes that have been generated. Genotypic analysis of plasma HIV RNA involves several steps: extraction and purification of total RNA, reverse transcription, amplification of HIV DNA sequences encompassing the RT, PR, IN and ENV regions, and sequencing of amplified products for detection of mutations associated with antiretroviral resistance. In Hong Kong, GRT was introduced in 2001. Although both commercial and laboratory homebrew genotypic assays are available, due to public health resource constraint access remains limited.[2]

Conventionally HIV-1 GRT is performed by Sanger sequencing methods. However, it is well-known that Sanger sequencing can only detect variants with prevalence >20%. In recent years, the efficacy of using next generation sequencing (NGS) technologies for HIV-1 GRT in clinical practices have been extensively evaluated. NGS, also known as Massively Parallel Sequencing, can process millions of DNA fragments sequences in massively parallel fashions. Commonly-used NGS technologies such as Illumina sequencing platforms can produce sequencing reads of average lengths of 75-250 base-pairs with more than 85% of the bases have Phred Quality Score (Q-score) over 30 (1 error in 1,000 bases). With maximum output ranging from one Gigabase-pair (Gbp) to 1000Gbp in a single run, NGS platforms allows sequencing of a targeted genome region for thousands of times, creating ultra-deep sequencing data for the analysis of minor variant mutations. Minority HIV-1 drug resistance mutations were previously described in treatment-naive populations, and it has been shown to increase the risk of treatment failure even at low minority variant frequencies. The extraordinary sensitivity of NGS platforms allows the detection of minor variants down to as much as 5%, which provides accurate prognosis to guide clinicians in the choice of appropriate first-line regimens with the highest likelihood of desirable virological response.

Clinical utility of resistance testing

Compared to phenotyping assays, GRT is more commonly used in support of clinical HIV management. Clinical guidelines have been established by various authorities, including the International AIDS Society-USA Panel, Euroguidelines Group for HIV Resistance and DHHS Panel on Antitretroviral Guidelines for Adults and Adolescents. A summary of the general indications for GRT is at Box 13.1. Recent study has shown that the use of GRT to determine the next regimen following treatment failure is cost-effective. In addition, the use of resistance testing to guide the initial treatment regimen appears to be cost-effective if the prevalence of primary genotypic resistance in the population is at least 4%.[3]

Box 13.1. Indication of genotypic resistance testing

Indication* Rationale Remarks
Primary and recent infection To support decision on regimen that may vary with the pattern of transmitted resistance Recent infection refers to those acquired within the last one or two years.
Treatment failure in established infection To inform choices on the change of regimens. Testing before switching and while on the failed therapy.
Post exposure prophylaxis To test the source person for guiding selection of regimen for the injured. Delay should be minimised, and that source person’s consent must be obtained.
HIV + pregnancy To optimise regimen for preventing mother-to-child infection, treatment of mother and the baby Possible only when there is detectable plasma virus RNA
*viral load of >1000

Currently, 6 general classes of antiretroviral drugs are used in clinical care, NRTIs, NNRTIs, PIs, integrase inhibitors (INSTIs), fusion inhibitor (FI) and entry inhibitor (EI). Viral resistance can occur with each of these classes of drug, particularly when viral replication is not maximally suppressed while on therapy. This may arise from non-adherence to HAART, and suboptimal treatment like monotherapy or two drug-combinations, which are no longer used in clinical practice.

Interpretation and reporting of genotypic resistance testing

Genotypic assays involve two independent processes, identification of resistance mutations and interpretation of how these mutations alter HIV-1 drug susceptibility. An error in either process will lead to an inaccurate genotyping results. Currently two types of interpretation systems have been designed by various authorities. Firstly, the ‘rules-based’ systems are deduced by experts, from clinical experiences and literature. In addition to listing the mutations identified in the RT and PR genes, an interpretative report is provided that lists each drug and provides a designation of either “susceptible,” “low-level resistance,” “intermediate resistance,” or “high-level resistance”. “Susceptible” is reported if no known mutations are detected or if reduced susceptibility to a specific drug has not been associated with the mutation. “Intermediate resistance” is reported when the mutation detected have been associated with diminished virological responses in some but not all patients. “High-level resistance” refers to mutations that have been associated with a maximum reduction in susceptibility to the drug. This rules-based system provides clinicians with results in a user-friendly format that can be easily understood without the need for an extensive knowledge of the genetics of HIV-1 resistance. The Stanford database https://hivdb.stanford.edu/ is an example. On the other hand, there’s the “database-driven” system with algorithms derived from bioinformatics approaches. The vircoTYPE™ is a commercially available interpretation system that predicts phenotypes from genotypes by comparing the query sequence with those available in the database and through averaging the resistance of the matching sequences.

A list of mutations is maintained by the International AIDS Society-USA Drug Resistance Mutations Group and the Stanford University HIV Drug Resistance Database, which are brought updated from time to time.[4] A list of common mutations is at Box 13.2. The main features of the resistance patterns of the main classes of antiretrovirals are:

NRTI – TAMs (thymidine analogue mutations) include a number of mutations that have developed under treatment with various NRTIs. Reduction of virological responses is proportional to the number of TAMs present. Cross resistance is common in this drug class. The lamivudine/emtricitabine specific resistance M184V/I is, on the other hand, a single mutation that renders complete resistance to lamivudine and emtricitabine. Reduction of virus replication may occur with some mutations like M184V/I and K65R, while other minor mutations can compensate the loss. Some mutations like M184V/I and K65R have the ability to revert susceptibility of other antiretroviral agents (e.g. zidovudine).

NNRTI – The common characteristics of most NNRTIs is their low genetic barrier, causing broad class resistance after the selection of a single mutation. K103N and Y181C/I are the most commonly selected mutations which, if present, could lead to cross resistance to other NNRTIs. Recently launched etravirine and rilpivirine appear to have improvement on this genetic barrier problem.

PI – Major mutations are relatively inhibitor specific and may alter the drug susceptibility. Major resistance mutations associated with the use of boosted PI (with ritonavir) is generally less common than unboosted PI. Clinically significant resistance occurs when there’s accumulation of multiple mutations. Minor mutations alone may have no significant effect on viral susceptibility but may improve viral fitness, allowing a virus with major mutations to improve its replicative capacity. Polymorphisms at some genetic positions may in fact compensate the reduced protease activity. E35 insertion and M36I, for example, are natural polymorphisms that are found in subtype B and CRF01_AE viruses in Hong Kong respectively.[5]

INSTI – The INSTI inhibits DNA strand transfer by HIV integrase. Resistance mutations typically develop in the integrase gene encoding the catalytic domain. The major resistance mutations (Q148H/K/R and N155H) can trigger conformational changes within the catalytic pocket that result in an alteration of the binding energy of INIs. On the other hand, Y143R mutation is thought to interact directly with the INI by pi-stacking between the tyrosine phenol ring and the 1,3,4 oxadiazole group.

Among the three commonly used INSTIs, cross-resistance is commonly observed across first-generation compounds raltegravir (RAL) and elvitegravir (EVG). Dolutegravir (DTG), on the other hand, is a second-generation INSTI which has a much higher genetic barrier and exhibit very little cross-resistance to first-generation INSTIs with improved antiviral potency. This is attributed by the structural difference in DTG from other INIs. The halobenzolic ring of DTG allows deeper penetration to the catalytic domain of the integrase, and the lack of oxadiazol ring reduces the drug’s dependency to Y143 pi-stacking. DTG can undergo position and conformation readjustment in response to the development of resistance mutation at integrase active site. Missense mutation in Q148H/K/R can moderately increase DTG resistance only in the presence of E138K and G140S secondary substitution with subsequent T97A substitution. With the high genetic barrier, the occurrence of high-level resistance to DTG is virtually impossible, and there has been no history of DTG resistant clinical cases reported so far.[6]

Resistance pattern of 2 less commonly used classes of antiretrovirals are characterised as follows:

FI – As FI is restricted for use by injection, non-adherence is common, and resistance mutations in viral envelope gp41 region can be selected in short period of time. Cumulative mutations can cause total failure of this drug class.

EI – The only FDA-approved EI targets the surface protein, C-C motif chemokine receptor 5 (CCR5) of host white blood cells. A few reports have suggested mutation development in the HIV-1 gp120 V3 loop, the major determinant of viral tropism. However, no consensus on specific signature mutation for CCR5 antagonist resistance is identified. Some other EI resistant viruses selected in vitro have shown mutations in gp41 without mutations in V3; the clinical significance of such mutations is not yet known.

Box 13.2. Important resistance mutations for different antiretroviral drugs from Stanford database, updated in October 2018 [13][14]

Class Antiretroviral Major mutations Significance
NRTI Zidovudine (AZT) TAMs (M41L, D67N, K70R, L210W, T215Y/F, K219Q/E), Q151M, T69 insertion Clinically significant when multiple mutations (say, 3 or more) are present
Stavudine (d4T)
Lamivudine (3TC) M184V/I, K65R, T69 insertion, Q151M High resistance associated with M184V/I but may increase activity for other NRTIs
Emtricitabine (FTC)
Abacavir (ABC) K65R, K70E, L74V/I, Y115F, T69 insertion, Q151M
Didanosine (ddI) K64R, K70E, L74V/I, T69 insertion, Q151M
Tenofovir (TDF) K65R, T69 insertion, Y115F, Q151M
NNRTI Nevirapine (NVP) L100I, K101E/P, K103N/S, V106A/M, Y181C/I/V, Y188C/H/L, G190A/E/S, M230L High resistance resulting from single mutation
Efavirenz (EFV) L100I, K101P, K103N/S, V106M, Y188L/C, G190E/S, M230L
Etravirine (ETR) L100I, K101P, Y181I/V/C, G190E, M230L
Rilpivirine (RPV) L100I, K101P/E, E138K/A/G/Q, Y181I/V/C, Y188L, G190E, M230L
PI Nelfinavir (NFV) D30N, M46I/L, G48M/V, I54A/L/M/T/V, V82A/F/S/T, I84V, N88D/S, L90M Normally multiple mutations are required for resistance to be clinically significant; nearly all PIs are boosted with ritonavir to increase potency, and there’s higher genetic barrier to develop resistance.
Indinavir w/ Ritonavir (IDV/r) I32V, M46I/L, I54A/T/V, L76V, V82A/F/S/T, I84V, L90M
Saquinavir w/ Ritonavir (SQV/r) G48M/V, I84V, I54A/T/V, L90M
Lopinavir w/ Ritonavir (LPV/r) I47A, I50V, I54A/L/M/T/V, L76V, V82A/F/S/T, I84V
Fosamprenavir w/ Ritonavir (FOS-APV/r) V32I, L33F, M46I/L, I47A/V, I50V, I54L/M, L76V, V82A, I84V
Atazanavir w/ Ritonavir (ATV/r) G48M/V, I50L, I84V, N88S, L90M
Tipranavir w/ Ritonavir (TPV/r) I47A/V, I54A/T/V, V82L/T, I84V
FI Enfuvirtide (T-20) G36D/E/V, V38A/E, Q40H, N42T, N43D
INSTI Raltegravir (RAL) T66K, E92Q, G118R, E138K/A/T, G140S/A/C, Y143R/C/H, Q148H/R/K, N155H
Elvitegravir (EVG) T66A/I/K, E92Q, G118R, E138K/A/T, G140S/A/C, S147G, Q148H/R/K, N155H, R263K
Dolutegravir (DTG) Uncommon High level genetic barrier to develop resistance to DTG.
EI Maraviroc (MVC) Uncommon

Tropism assay

The HIV chemokine co-receptors, CCR5 and CXCR4, are essential for viral entry in host cells. CCR5 antagonist is, a new class of antiretroviral drug which can block co-receptor binding. CCR5 antagonist has, however, inhibitory effects only to R5-tropic viruses but not to the X4 variants. The US FDA has approved the use of Maraviroc (MVC), a CCR5 antagonist, for use in treatment-experienced patients when R5-tropic virus is solely detected. This has led to the need for determining viral tropism before MVC treatment initiation.

The phenotyping tropism assay is currently the gold standard for HIV tropism determination. Enhanced Sensitivity Trofile® Assay (ESTA) (Monogram Biosciences, USA) is a standard test the service of which is only available in the United States.[7] Due to its limited accessibility, other in-house phenotyping assays as well as genotyping assays have been developed for co-receptor status prediction. Although phenotyping assays provide better sensitivity and specificity, they require intensive laboratory set-up and longer turn-around time, which is not ideal for clinical applications. The development of genotyping assays is becoming the global trend as regards tropism prediction testing.

HLA-B*5701 screening

Abacavir (ABC) is a NRTI which is used in combination of other antiretorvirals for HIV-1 treatment. Despite its high efficiency, hypersensitivity towards abacavir is a potential side-effect which occurs within the first six weeks of its administration. ABC hypersensitivity is strongly associated with the presence of major histocompatibility complex class I allele HLA-B*5701. Prevalence of HLA-B*5701 allele could greatly vary in different ethnic population, ranging from 6.49% in Caucasian populations to 0.39% in sub-Saharan African populations. In a recent study conducted on 1264 HIV-1 infected patients from Hong Kong, HLA-B*5701 was identified in 3% Caucasians, 1% non-Chinese Asian and 0.5% Han-Chinese populations.[8]

HLA-B*5701 screening test can help to predict patients with high risk of developing ABC hypersensitivity. However, given the low prevalence of HLA-B*5701 positive cases among Han-Chinese population in Hong Kong, routine HLA-B*5701 pre-treatment screening is not warranted. However, HLA-B*5701 screening could still be considered for patients of other ethnicity. Samples could be first screened for HLA-B*5701 using a validated protocol with PCR-sequence-specific primers. In cases of positive HLA-B*5701 preliminary screening result, the exact identities of HLA-B*5701 can be confirmed by direct DNA-sequencing.[8]

Therapeutic drug monitoring

Antiretroviral compounds are prescribed in standard doses for adults, regardless of patient characteristics such as body weight and gender. From time to time, adjustments are recommended for anticipated drug-drug interactions (DDI), such as between PI and rifamycins. It is now known that considerable inter- and intra-individual antiretroviral levels occur, even without concurrent interacting drugs. To improve clinical outcome, therapeutic drug monitoring (TDM) has been developed as a strategy for guiding HIV treatment.

Methodologically TDM is commonly performed on plasma by high performance liquid chromatography coupled with spectrofluorometric detection. As with some antibiotics, Cmin and Cmax are used to monitor for treatment efficacy and drug toxicity. However, for some drugs, the Area Under Curve (AUC) may be more predictive. With resistant virus, the relationship between the plasma level and IC50 (the inhibitory quotient, IQ) is used to predict efficacy of non-standard regimens or dosage of conventional regimens. The effectiveness of boosted-PI and NNRTI-based regimens have been attributed to a favourable IQ, among other factors. Some studies have in fact successfully correlated drug exposure with short-term treatment success in patients with resistant virus. With the increasing research on TDM, its application has also been extensively reviewed in the past few years.[9] Currently, of the main classes of antiretrovirals prescribed, PI, NNRTI and INSTI can be assayed for their plasma levels. NRTI, though an important backbone of standard HAART, is activated intracellularly. Its plasma concentration may not be meaningful pharmacologically and very little data are available with intracellular monitoring of NRTI levels as a clinical tool. For patients with susceptible virus, minimum target trough level for some antiretrovirals have been established as a result of better understanding of dose-response relationship. The association between plasma level and drug toxicity is not as strong, as there is often the influence of, for example, host genetics.[10] On the other hand, the minimum target trough level in treatment-experienced patients with virologic failure is even harder to establish.

Today, TDM is often used as a supplemental or research tool; its routine use for guiding antiretroviral therapy is not advisable. It suffers from the following limitations:

  1. It is not generally available or accessible, and there is limited expertise in interpreting results.
  2. A full pharmacokinetic profile is technically cumbersome and time-consuming to perform, while consensus is not yet achieved on a protocol of simplified sampling method for all drugs.
  3. A lag time of at least 2 weeks is required for steady state concentrations to be achieved; by then, it may be too late to reverse some clinical decisions.
  4. The body of knowledge regarding the relationship between treatment outcome and various pharmacokinetic parameters is still incomplete.
  5. Current antiretroviral drugs are only available in limited denominations, precluding fine adjustment of dosage.
  6. There are limited supportive data on the long term clinical benefit of adopting TDM, especially in treatment-experienced patients.

Despite these concerns, an increasing number of HIV services have adopted testing, in response to the needs for optimising antietroviral therapy. Currently, TDM may be considered in the following situations:

  1. Suspected non-adherence. [Chapter C12]
  2. Concerns of DDI, notably in HIV/TB co-infection (often as a result of CYP induction/inhibition), and drug-food interactions.
  3. Unpredictable pharmacokinetics – such as in pregnant women, adolescents or patients with liver failure, when dose adjustment may be advantageous.
  4. Management of antiretroviral toxicities.
  5. Investigations of virologic failures in adherent patients, those who have been heavily pretreated, or in case of reduced susceptibility.

Replicative capacity

Replicative capacity (RC) is the laboratory measurement of viral fitness, a term that describes the adaptability of HIV in a given environment, typically in competition with other strains. Viral fitness may differ according to subtypes, accounting for the differential spread in different parts of the world. It may also differ according to eras, leading some to suggest that HIV has attenuated over time.

At the clinical level, viral fitness is important as it may decrease as mutations develop in response to the selection pressure exerted by antiretrovirals. As the selection pressure continues, secondary mutations usually develop to restore viral fitness and presumably with renewed pathogenicity. However, until then, discordance between a detectable viral load and a stable CD4 count may occur. In most cases, it is impossible to predict how long this discordance will persist before restored viral fitness will lead to a fall in CD4 count. Yet in patients with a borderline CD4 count and limited treatment options, the ability to predict and hence switch therapy to maintain this viral load-CD4 discord is theoretically useful.

There is some evidence that baseline RC predicts subsequent disease progression.[11] In treatment-experienced patients on salvage therapy, RC also correlates with CD4 count and viral load. However, in a setting with access to resistance testing and viral load, it is unclear how RC will affect the clinical outcome. It is also premature to introduce RC into clinical practice, there being no standard assay of viral fitness. Although the principle remains one of comparing the fitness against that of wild type, different methodologies have been used. In one, plasma-derived RT and PR sequences are inserted into a retrovirus vector containing a luciferase indicator gene. RC is then determined by measuring luciferase activity in infected cells after a single round of replication, as % of wild type strain. In another, the virus is simply co-cultured with wild type virus.

Currently, the use of RC in Hong Kong is limited to research settings and is not recommended for routine clinical use.

Emerging issue

HIV-1 non-B viruses, especially CRF01_AE have become the predominant HIV-1 subtype circulating in Hong Kong and in some other Asian countries. However, most of the published data on the treatment responses to antiretrovirals refer to B subtypes. In addition, many of the virologic diagnostic tests, for example, phenotypic and genotypic HIV-1 drug resistance assays, have been derived largely from studies on subtype B viruses. This has arisen from the predominance of drug therapy in those areas of the world in which subtype B viruses predominate. The applicability and validity of the available knowledgebase to non-B subtypes is still under debate.[12] The same challenge also exists when newer tests like TDM, tropism and replicative capacity gradually become introduced. There is a need for new algorithm and interpretation systems for supporting the optimization of clinical management of non-B HIV subtype infected populations, including Hong Kong and many countries in the developing world.

Finally, with the increased access to HAART in countries around the world, the ethnic background of HIV patients has diversified. Host genetic factors would also need to be considered in tailoring treatment regimens for patients at community, or even individual levels. The metabolism of efavirenz (EFV), an NNRTI through the CYP2B6 pathway is a case in point. Orientals and African populations have a higher prevalence of TT genotype at G516Tof CYP2B6, which is associated with decreased metabolism of the drug and a higher chance of toxicity.[10] Likewise the metabolism of other antiretrovirals, as well as their toxicity profiles, especially PI, could vary with genetic mutations of the implicating. Genotype-driven dose adjustment of antiretrovirals would potentially be another strategy to be pursued in further optimizing HIV treatment in future.

Algorithm 13. Layout of current protocol for implementing antiretroviral therapy (clear boxes and flow chart), and the potential roles of new investigations (darkened boxes) for optimising treatment

Algorithm 13. Layout of current protocol for implementing antiretroviral therapy (clear boxes and flow chart), and the potential roles of new investigations (darkened boxes) for optimizing treatment


  1. Sungkanuparph S, Oyomopito R, Sirivichayakul S, Sirisanthana T, Li PC, Kantipong P, Lee CK, Kamarulzaman A, Messerschmidt L, Law MG, Phanuphak P; TREAT Asia Studies to Evaluate Resistance-Monitoring Study (TASER-M ). HIV-1 drug resistance mutations among antiretroviral-naive HIV-1-infected patients in Asia: results from the TREAT Asia Studies to Evaluate Resistance-Monitoring Study. Clin Infect Dis 2011;52(8):1053-7. link
  2. Chen JH, Wong KH, Chan K, Lam HY, Lee SS, Li P, Lee MP, Tsang DN, Zheng BJ, Yuen KY, Yam WC. Evaluation of an in-house genotyping resistance test for HIV-1 drug resistance interpretation and genotyping. J Clin Virol 2007;39(2):125-31. link
  3. Weinstein MC, Goldie SJ, Losina E, Cohen CJ, Baxter JD, Zhang H, Kimmel AD, Freedberg KA. Use of genotypic resistance testing to guide HIV therapy: clinical impact and cost-effectiveness. Ann Intern Med 2001;134(6):440-50. link
  4. Wensing AM, Calvez V, Günthard HF, Johnson VA, Paredes R, Pillay D, Shafer RW, Richman DD. 2017 Update of the drug resistance mutations in HIV-1. Top Antivir Med 2017;24(4):132-133. link
  5. Yam WC, Chen JH, Wong KH, Chan K, Cheng VC, Lam HY, Lee SS, Zheng BJ, Yuen KY. Clinical utility of genotyping resistance test on determining the mutation patterns in HIV-1 CRF01_AE and subtype B patients receiving antiretroviral therapy in Hong Kong. J Clin Virol 2006;35(4):454-7. link
  6. Llibre JM, Pulido F, Garcia F, Garcia Deltoro M, Blanco JL, Delgado R. Genetic barrier to resistance for dolutegravir. AIDS Rev 2015;17(1):56-64. link
  7. Vandekerckhove LP, Wensing AM, Kaiser R, Brun-Vˆmzinet F, Clotet B, De Luca A, Dressler S, Garcia F, Geretti AM, Klimkait T, Korn K, Masquelier B, Perno CF, Schapiro JM, Soriano V, Sonnerborg A, Vandamme AM, Verhofstede C, Walter H, Zazzi M, Boucher CA; European Consensus Group on clinical management of tropism testing. European guidelines on the clinical management of HIV-1 tropism testing. Lancet Infect Dis 2011;11(5):394-407. link
  8. To SWC, Chen JHK, Wong KH, Chan KCW, Tsang OTY, Yam WC. HLA-B*5701 genetic screening among HIV-1 infected patients in Hong Kong: is this a practical approach in Han-Chinese? Int J STD AIDS 2013;24(1):50-2. link
  9. Punyawudho B, Singkham N, Thammajaruk N, Dalodom T, Kerr SJ, Burger DM, Ruxrungtham K. Therapeutic drug monitoring of antiretroviral drugs in HIV-infected patients. Expert Rev Clin Pharmacol 2016;9(12):1583-1595. link
  10. To KW, Liu ST, Cheung SW, Chan DP, Chan RC, Lee SS. Pharmacokinetics of plasma efavirenz and CYP2B6 polymorphism in southern Chinese. Ther Drug Monit 2009;31(4):527-30. link
  11. Sarmati L, Nicastri E, Montano M, Dori L, Buonomini AR, d’Ettorre G, Gatti F, Parisi SG, Vullo V, Andreoni M. Decrease of replicative capacity of HIV isolates after genotypic guided change of therapy. J Med Virol 2004;72(4):511-6. link
  12. Franzetti M, Violin M, Casazza G, Meini G, Callegaro A, Corsi P, Maggiolo F, Pignataro AR, Paolucci S, Gianotti N, Francisci D, Rossotti R, Filice G, Carli T, Zazzi M, Balotta C. Human immunodeficiency virus-1 B and non-B subtypes with the same drug resistance pattern respond similarly to antiretroviral therapy. Clin Microbiol Infect 2012;18(3):E66-70. link
  13. Shafer RW. Rationale and Uses of a Public HIV Drug-Resistance Database. J Infect Dis 2006;194 Suppl 1:S51-8 link
  14. Stanford University. HIV Drug Resistance Database. Available from: link