Genotypic predictors of human immunodeficiency virus type 1 drug resistance

The L76V mutation in HIV-1 protease is potentially associated with hypersusceptibility to protease inhibitors Atazanavir and Saquinavir: is there a clinical advantage?

Background

Although being considered as a rarely observed HIV-1 protease mutation in clinical isolates, the L76V-prevalence increased 1998-2008 in some European countries most likely due to the approval of Lopinavir, Amprenavir and Darunavir which can select L76V. Beside an enhancement of resistance, L76V is also discussed to confer hypersusceptibility to the drugs Atazanavir and Saquinavir which might enable new treatment strategies by trying to take advantage of particular mutations.

Results

Based on a cohort of 47 L76V-positive patients, we examined if there might exist a clinical advantage for L76V-positive patients concerning long-term success of PI-containing regimens in patients with limited therapy options.

Genotypic- and phenotypic HIV-resistance tests from 47 mostly multi-resistant, L76V-positive patients throughout Germany were accomplished retrospectively 1999-2009. Five genotype-based drug-susceptibility predictions received from online interpretation-tools for Atazanavir, Saquinavir, Amprenavir and Lopinavir, were compared to phenotype-based predictions that were determined by using a recombinant virus assay along with a Virtual Phenotype™(Virco). The clinical outcome of the L76V-adapted follow-up therapy was determined by monitoring viral load for 96 weeks.

Conclusions

In this analysis, the mostly used interpretation systems overestimated the L76V-mutation concerning Atazanavir- and SQV resistance. In fact, a clear benefit in drug susceptibility for these drugs was observed in phenotype analysis after establishment of L76V. More importantly, long-term therapy success was significantly higher in patients receiving Atazanavir and/or Saquinavir plus one L76V-selecting drug compared to patients without L76V-selecting agents (p = 0.002).

In case of L76V-occurrence ATV and/or SQV may represent encouraging options for patients in deep salvage situations.

Virological failure and HIV type 1 drug resistance profiles among patients followed-up in private sector, Douala, Cameroon

The rate of virological failure was assessed in 819 patients followed up by the private sector of Douala, the economic capital of Cameroon, and treated according to the World Health Organization (WHO) recommendations. In addition, genotypic resistance testing was carried out in the subgroup of 75 selected patients representative of the 254 patients in virological and/or immunological failure receiving a first-line (83%) or second-line (17%) regimen. Overall, 36% of patients treated by antiretroviral drugs (ARV) were in virological failure, as assessed by plasma viral load above 3.7 log(10) copies/ml under treatment for more than 6 months. According to the immunological status, 17% of patients showed a CD4 T cell count under 200 cells/mm(3) and 37% under 350 cells/mm(3), indicating either ongoing immunorestoration or immunological failure under treatment. Twenty percent of patients in virological failure showed wild-type viruses susceptible to all ARV, likely indicating poor adherence. However, 80% of them displayed plasma virus resistant at least to one ARV drug, mostly to the nucleoside reverse transcriptase inhibitors (NRTIs) class (80%), followed by the non-NRTI class (76%) and the protease inhibitor class (19%), thus reflecting the therapeutic usage of ARV drugs in Cameroon as recommended by the WHO. Whereas the second-line regimen proposed by the 2009 WHO recommendations could be effective in more than 75% of patients in virological failure with resistant viruses, the remaining patients showed a resistance genotypic profile highly predictive of resistance to the usual WHO second-line regimen, including in some patients complex genotypic profiles diagnosed only by genotypic resistance tests. In conclusion, our observations highlight the absolute need for improving viral load assessment in resource-limited settings to prevent and/or monitor therapeutic failure.

Proteochemometric modeling of the susceptibility of mutated variants of the HIV-1 virus to reverse transcriptase inhibitors

Background

Reverse transcriptase is a major drug target in highly active antiretroviral therapy (HAART) against HIV, which typically comprises two nucleoside/nucleotide analog reverse transcriptase (RT) inhibitors (NRTIs) in combination with a non-nucleoside RT inhibitor or a protease inhibitor. Unfortunately, HIV is capable of escaping the therapy by mutating into drug-resistant variants. Computational models that correlate HIV drug susceptibilities to the virus genotype and to drug molecular properties might facilitate selection of improved combination treatment regimens.

Methodology/Principal Findings

We applied our earlier developed proteochemometric modeling technology to analyze HIV mutant susceptibility to the eight clinically approved NRTIs. The data set used covered 728 virus variants genotyped for 240 sequence residues of the DNA polymerase domain of the RT; 165 of these residues contained mutations; totally the data-set covered susceptibility data for 4,495 inhibitor-RT combinations. Inhibitors and RT sequences were represented numerically by 3D-structural and physicochemical property descriptors, respectively. The two sets of descriptors and their derived cross-terms were correlated to the susceptibility data by partial least-squares projections to latent structures. The model identified more than ten frequently occurring mutations, each conferring more than two-fold loss of susceptibility for one or several NRTIs. The most deleterious mutations were K65R, Q151M, M184V/I, and T215Y/F, each of them decreasing susceptibility to most of the NRTIs. The predictive ability of the model was estimated by cross-validation and by external predictions for new HIV variants; both procedures showed very high correlation between the predicted and actual susceptibility values (Q² = 0.89 and Q²_ext = 0.86). The model is available at www.hivdrc.org as a free web service for the prediction of the susceptibility to any of the clinically used NRTIs for any HIV-1 mutant variant.

Conclusions/Significance

Our results give directions how to develop approaches for selection of genome-based optimum combination therapy for patients harboring mutated HIV variants.

Computational Analysis of Molecular Interaction Networks Underlying Change of HIV-1 Resistance to Selected Reverse Transcriptase Inhibitors

Motivation

Despite more than two decades of research, HIV resistance to drugs remains a serious obstacle in developing efficient AIDS treatments. Several computational methods have been developed to predict resistance level from the sequence of viral proteins such as reverse transcriptase (RT) or protease. These methods, while powerful and accurate, give very little insight into the molecular interactions that underly acquisition of drug resistance/hypersusceptibility. Here, we attempt at filling this gap by using our Monte Carlo feature selection and interdependency discovery method (MCFS-ID) to elucidate molecular interaction networks that characterize viral strains with altered drug resistance levels.

Results

We analyzed a number of HIV-1 RT sequences annotated with drug resistance level using the MCFS-ID method. This let us expound interdependency networks that characterize change of drug resistance to six selected RT inhibitors: Abacavir, Lamivudine, Stavudine, Zidovudine, Tenofovir and Nevirapine. The networks consider interdependencies at the level of physicochemical properties of mutating amino acids, eg,: polarity. We mapped each network on the 3D structure of RT in attempt to understand the molecular meaning of interacting pairs. The discovered interactions describe several known drug resistance mechanisms and, importantly, some previously unidentified ones. Our approach can be easily applied to a whole range of problems from the domain of protein engineering.

Availability

A portable Java implementation of our MCFS-ID method is freely available for academic users and can be obtained at: http://www.ipipan.eu/staff/m.draminski/software.htm.

Computational mutation scanning and drug resistance mechanisms of HIV-1 protease inhibitors

The drug resistance of various clinically available HIV-1 protease inhibitors has been studied using a new computational protocol, that is, computational mutation scanning (CMS), leading to valuable insights into the resistance mechanisms and structure-resistance correction of the HIV-1 protease inhibitors associated with a variety of active site and nonactive site mutations. By using the CMS method, the calculated mutation-caused shifts of the binding free energies linearly correlate very well with those derived from the corresponding experimental data, suggesting that the CMS protocol may be used as a generalized approach to predict drug resistance associated with amino acid mutations. Because it is essentially important for understanding the structure-resistance correlation and for structure-based drug design to develop an effective computational protocol for drug resistance prediction, the reasonable and computationally efficient CMS protocol for drug resistance prediction should be valuable for future structure-based design and discovery of antiresistance drugs in various therapeutic areas.

Drug susceptibility of human immunodeficiency virus type 1-derived pseudoviruses from treatment-experienced patients to protease inhibitors and reverse transcriptase inhibitors, using a modified single-round assay

BACKGROUND

Genotypic drug resistance assay has been the only method available to provide information related to drug resistance in South Korea since 1999. Phenotypic assay is also a useful method to predict a patient's state related to antiretroviral drug resistance. However, commercial systems and methods for phenotyping have not been introduced into South Korea.

OBJECTIVES

To establish and apply modified phenotypic drug susceptibility assay using treatment-experienced patients' derived HIV-1 in South Korea.

STUDY DESIGN

The genotypic drug resistance and phenotypic drug susceptibility of two different methods, Stanford HIV Drug Resistance Database (Stanford DB) and modified phenotypic drug susceptibility assay were compared especially focused on the HIV-1 protease (PR) and reverse transcriptase (RT) sequences.

RESULTS

There was some discordance in comparing drug susceptibility results (a modified drug susceptibility assay) with the predicted genotypic drug resistance (Stanford DB). Phenotypic drug resistance showed the following order for pseudoviruses from treatment-experienced patients infected with HIV/AIDS: Efavirenz (EFV, 21 to 1,319-fold change), Lamivudine (3TC, 31 to >189-fold change), Indinavir sulfate (IDV, 26 to 63-fold change), Amprenavir (APV, 4 to 35-fold change) and Zidovudine (AZT, 20 to 634-fold change). For patient KRC3221, the AZT-related phenotypic drug resistance was the greatest, with 634-fold change compared with the wild type.

CONCLUSIONS

Application of this modified phenotypic drug susceptibility assay is expected to help in predicting drug resistance as a guideline for clinicians to obtain a combined interpretation among genotyping, phenotyping and effective clinical treatments.

Indinavir resistance evolution in one human immunodeficiency virus type 1 infected patient revealed by single-genome amplification

Human Immunodeficiency Virus Type 1 exists in vivo as quasispecies, and one of the genome's characteristics is its diversity. During the antiretroviral therapy, drug resistance is the main obstacle to effective viral prevention. Understanding the molecular evolution process is fundamental to analyze the mechanism of drug resistance and develop a strategy to minimize resistance.

OBJECTIVE

The molecular evolution of drug resistance of one patient who had received reverse transcriptase inhibitors for a long time and had treatment which replaced Nevirapine with Indinavir was analyzed, with the aim of observing the drug resistance evolution pathway.

METHODS

The patient, XLF, was followed-up for six successive times. The viral populations were amplified and sequenced by single-genome amplification. All the sequences were submitted to the Stanford HIV Drug Resistance Database for the analysis of genotypic drug resistance.

RESULTS

149 entire protease and 171 entire reverse transcriptase sequences were obtained from these samples, and all sequences were identified as subtype B. Before the patient received Indinavir, the viral population only had some polymorphisms in the protease sequences. After the patient began Indinavir treatment, the variants carrying polymorphisms declined while variants carrying the secondary mutation G73S gained the advantage. As therapy was prolonged, G73S was combined with M46I/L90M to form a resistance pattern M46I/G73S/L90M, which then became the dominant population. 97.9% of variants had the M46I/G73S/L90M pattern at XLF6. During the emergence of protease inhibitors resistance, reverse transcriptase inhibitors resistance maintained high levels.

CONCLUSION

Indinavir-resistance evolution was observed by single-genome amplification. During the course of changing the regimen to incorporate Indinavir, the G73S mutation occurred and was combined with M46I/L90M.

HIV-1 protease mutations and protease inhibitor cross-resistance

The effects of many protease inhibitor (PI)-selected mutations on the susceptibility to individual PIs are unknown. We analyzed in vitro susceptibility test results on 2,725 HIV-1 protease isolates. More than 2,400 isolates had been tested for susceptibility to fosamprenavir, indinavir, nelfinavir, and saquinavir; 2,130 isolates had been tested for susceptibility to lopinavir; 1,644 isolates had been tested for susceptibility to atazanavir; 1,265 isolates had been tested for susceptibility to tipranavir; and 642 isolates had been tested for susceptibility to darunavir. We applied least-angle regression (LARS) to the 200 most common mutations in the data set and identified a set of 46 mutations associated with decreased PI susceptibility of which 40 were not polymorphic in the eight most common HIV-1 group M subtypes. We then used least-squares regression to ascertain the relative contribution of each of these 46 mutations. The median number of mutations associated with decreased susceptibility to each PI was 28 (range, 19 to 32), and the median number of mutations associated with increased susceptibility to each PI was 2.5 (range, 1 to 8). Of the mutations with the greatest effect on PI susceptibility, I84AV was associated with decreased susceptibility to eight PIs; V32I, G48V, I54ALMSTV, V82F, and L90M were associated with decreased susceptibility to six to seven PIs; I47A, G48M, I50V, L76V, V82ST, and N88S were associated with decreased susceptibility to four to five PIs; and D30N, I50L, and V82AL were associated with decreased susceptibility to fewer than four PIs. This study underscores the greater impact of nonpolymorphic mutations compared with polymorphic mutations on decreased PI susceptibility and provides a comprehensive quantitative assessment of the effects of individual mutations on susceptibility to the eight clinically available PIs.

Nucleic acid template and the risk of a PCR-Induced HIV-1 drug resistance mutation

BACKGROUND

The HIV-1 nucleoside RT inhibitor (NRTI)-resistance mutation, K65R confers intermediate to high-level resistance to the NRTIs abacavir, didanosine, emtricitabine, lamivudine, and tenofovir; and low-level resistance to stavudine. Several lines of evidence suggest that K65R is more common in HIV-1 subtype C than subtype B viruses.

METHODS AND FINDINGS

We performed ultra-deep pyrosequencing (UDPS) and clonal dideoxynucleotide sequencing of plasma virus samples to assess the prevalence of minority K65R variants in subtype B and C viruses from untreated individuals. Although UDPS of plasma samples from 18 subtype C and 27 subtype B viruses showed that a higher proportion of subtype C viruses contain K65R (1.04% vs. 0.25%; p<0.001), limiting dilution clonal sequencing failed to corroborate its presence in two of the samples in which K65R was present in >1.5% of UDPS reads. We therefore performed UDPS on clones and site-directed mutants containing subtype B- and C-specific patterns of silent mutations in the conserved KKK motif encompassing RT codons 64 to 66 and found that subtype-specific nucleotide differences were responsible for increased PCR-induced K65R mutation in subtype C viruses.

CONCLUSIONS

This study shows that the RT KKK nucleotide template in subtype C viruses can lead to the spurious detection of K65R by highly sensitive PCR-dependent sequencing techniques. However, the study is also consistent with the subtype C nucleotide template being inherently responsible for increased polymerization-induced K65R mutations in vivo.

High level of primary drug resistance in Mali

BACKGROUND

As access to antiretroviral drugs increases in developing countries, it will become increasingly important to monitor the emergence of resistance and to define the molecular pathways involved to identify optimal therapeutic regimens.

METHODS

We performed genotypic resistance testing on plasma obtained from 101 HIV-infected treatment-naïve individuals from Mali. Genotyping was carried out using the Virco protocols and HXB2 was used as the reference strain.

RESULTS

CRF02_AG was the most common subtype, present in 71.3% of our patient population. Other subtypes included B, C, G, CRF06_CPX, CRF09_CPX, CRF01_AE, A2/CRF16_A2D, A1 and CRF13_CPX. A total of 9.9% [95% confidence interval (CI) 6.9-12.9%] of patients had at least one resistance mutation. The prevalences of mutations conferring resistance to nucleoside reverse transcriptase inhibitors (NRTIs), nonnucleoside reverse transcriptase inhibitors (NNRTIs) and protease inhibitors (PIs) were 5% (95% CI 0.7-9.2%), 6% (95% CI 1.3-10.6%) and 0%, respectively. The most frequent mutations were T215A/Y for NRTIs and K103N/T for NNRTIs. One patient harboured three NRTI resistance mutations and one NNRTI mutation. This is the first reported case of multi-drug-resistant viral transmission in Mali. Polymorphisms at protease codons 10I/V and 33F potentially associated with resistance were observed in 18.8% and 1% of patients, respectively. Several polymorphisms in the C-terminal domain of reverse transcriptase were observed: A371V (in 63.4% of patients), G335D (76.2%), E399D (10.9%) and G333E (1%).

CONCLUSION

Primary resistance was seen in 9.9% of subjects, which is higher than previously reported in Mali. Taking into consideration other polymorphisms in protease such as L10I/V and 33F, primary resistance could reach 28.7% (95% CI 19.9-37.5%). Our study reflects the need to monitor the evolution of resistance on a regular basis and trends of transmitted resistance.

Detecting and understanding combinatorial mutation patterns responsible for HIV drug resistance

We propose a systematic approach for a better understanding of how HIV viruses employ various combinations of mutations to resist drug treatments, which is critical to developing new drugs and optimizing the use of existing drugs. By probabilistically modeling mutations in the HIV-1 protease or reverse transcriptase (RT) isolated from drug-treated patients, we present a statistical procedure that first detects mutation combinations associated with drug resistance and then infers detailed interaction structures of these mutations. The molecular basis of our statistical predictions is further studied by using molecular dynamics simulations and free energy calculations. We have demonstrated the usefulness of this systematic procedure on three HIV drugs, (Indinavir, Zidovudine, and Nevirapine), discovered unique interaction features between viral mutations induced by these drugs, and revealed the structural basis of such interactions.

A Rough Set-Based Model of HIV-1 Reverse Transcriptase Resistome

Reverse transcriptase (RT) is a viral enzyme crucial for HIV-1 replication. Currently, 12 drugs are targeted against the RT. The low fidelity of the RT-mediated transcription leads to the quick accumulation of drug-resistance mutations. The sequence-resistance relationship remains only partially understood. Using publicly available data collected from over 15 years of HIV proteome research, we have created a general and predictive rule-based model of HIV-1 resistance to eight RT inhibitors. Our rough set-based model considers changes in the physicochemical properties of a mutated sequence as compared to the wild-type strain. Thanks to the application of the Monte Carlo feature selection method, the model takes into account only the properties that significantly contribute to the resistance phenomenon. The obtained results show that drug-resistance is determined in more complex way than believed. We confirmed the importance of many resistance-associated sites, found some sites to be less relevant than formerly postulated and—more importantly—identified several previously neglected sites as potentially relevant. By mapping some of the newly discovered sites on the 3D structure of the RT, we were able to suggest possible molecular-mechanisms of drug-resistance. Importantly, our model has the ability to generalize predictions to the previously unseen cases. The study is an example of how computational biology methods can increase our understanding of the HIV-1 resistome.

Pairwise and higher-order correlations among drug-resistance mutations in HIV-1 subtype B protease

Background

The reaction of HIV protease to inhibitor therapy is characterized by the emergence of complex mutational patterns which confer drug resistance. The response of HIV protease to drugs often involves both primary mutations that directly inhibit the action of the drug, and a host of accessory resistance mutations that may occur far from the active site but may contribute to restoring the fitness or stability of the enzyme. Here we develop a probabilistic approach based on connected information that allows us to study residue, pair level and higher-order correlations within the same framework.

Results

We apply our methodology to a database of approximately 13,000 sequences which have been annotated by the treatment history of the patients from which the samples were obtained. We show that including pair interactions is essential for agreement with the mutational data, since neglect of these interactions results in order-of-magnitude errors in the probabilities of the simultaneous occurence of many mutations. The magnitude of these pair correlations changes dramatically between sequences obtained from patients that were or were not exposed to drugs. Higher-order effects make a contribution of as much as 10% for residues taken three at a time, but increase to more than twice that for 10 to 15-residue groups. The sequence data is insufficient to determine the higher-order effects for larger groups. We find that higher-order interactions have a significant effect on the predicted frequencies of sequences with large numbers of mutations. While relatively rare, such sequences are more prevalent after multi-drug therapy. The relative importance of these higher-order interactions increases with the number of drugs the patient had been exposed to.

Conclusion

Correlations are critical for the understanding of mutation patterns in HIV protease. Pair interactions have substantial qualitative effects, while higher-order interactions are individually smaller but may have a collective effect. Together they lead to correlations which could have an important impact on the dynamics of the evolution of cross-resistance, by allowing the virus to pass through otherwise unlikely mutational states. These findings also indicate that pairwise and possibly higher-order effects should be included in the models of protein evolution, instead of assuming that all residues mutate independently of one another.

[Lopinavir/ritonavir monotherapy for the treatment of HIV-1 infection: the emergence of resistance]

Treatment with lopinavir/ritonavir (LPV/r) monotherapy has been shown to be an effective alternative, especially in the maintenance of patients previously treated with combination therapy and prolonged virological suppression. LPV/r monotherapy is associated with a greater number of low-level viremia episodes than combination therapy, without resistance mutations being detected in the majority of patients. The incidence of the development of major resistance mutations in the OK pilot and OK04 studies was very low: 0.51 per 100 patients-year, and was mainly related to mutations in positions 46, 54 and 82, which have not compromised other therapeutic options. The contribution of low-level resistance mutations to loss of virological control seems small, and no different from that observed in combination therapy. However, this phenomenon should be studied in larger, long-term trials.

Predicting drug resistance of the HIV-1 protease using molecular interaction energy components

Drug resistance significantly impairs the efficacy of AIDS therapy. Therefore, precise prediction of resistant viral mutants is particularly useful for developing effective drugs and designing therapeutic regimen. In this study, we applied a structure-based computational approach to predict mutants of the HIV-1 protease resistant to the seven FDA approved drugs. We analyzed the energetic pattern of the protease-drug interaction by calculating the molecular interaction energy components (MIECs) between the drug and the protease residues. Support vector machines (SVMs) were trained on MIECs to classify protease mutants into resistant and nonresistant categories. The high prediction accuracies for the test sets of cross-validations suggested that the MIECs successfully characterized the interaction interface between drugs and the HIV-1 protease. We conducted a proof-of-concept study on a newly approved drug, darunavir (TMC114), on which no drug resistance data were available in the public domain. Compared with amprenavir, our analysis suggested that darunavir might be more potent to combat drug resistance. To quantitatively estimate binding affinities of drugs and study the contributions of protease residues to causing resistance, linear regression models were trained on MIECs using partial least squares (PLS). The MIEC-PLS models also achieved satisfactory prediction accuracy. Analysis of the fitting coefficients of MIECs in the regression model revealed the important resistance mutations and shed light into understanding the mechanisms of these mutations to cause resistance. Our study demonstrated the advantages of characterizing the protease-drug interaction using MIECs. We believe that MIEC-SVM and MIEC-PLS can help design new agents or combination of therapeutic regimens to counter HIV-1 protease resistant strains.

Clinical relevance of substitutions in the connection subdomain and RNase H domain of HIV-1 reverse transcriptase from a cohort of antiretroviral treatment-naïve patients

Some mutations in the connection subdomain of the polymerase domain and in the RNase H domain of HIV-1 reverse transcriptase (RT) have been shown to contribute to resistance to RT inhibitors. However, the clinical relevance of such mutations is not well understood. To address this point we determined the prevalence of such mutations in a cohort of antiretroviral treatment-naïve patients (n=123) and assessed whether these substitutions are associated with drug resistance in vitro and in vivo. We report here significant differences in the prevalence of substitutions among subtype B, and non-subtype B HIV isolates. Specifically, the E312Q, G333E, G335D, V365I, A371V and A376S substitutions were present in 2-6% of subtype B, whereas the G335D and A371V substitutions were commonly observed in 69% and 75% of non-B HIV-1 isolates. We observed a significant decline in the viral loads of patients that were infected with HIV-1 carrying these substitutions and were subsequently treated with triple drug regimens, even in the case where zidovudine (AZT) was included in such regimens. We show here that, generally, such single substitutions at the connection subdomain or RNase H domain have no influence on drug susceptibility in vitro by themselves. Instead, they generally enhance AZT resistance in the presence of excision-enhancing mutations (EEMs, also known as thymidine analogue-associated mutations, TAMs). However, N348I, A376S and Q509L did confer varying amounts of nevirapine resistance by themselves, even in the absence of EEMs. Our studies indicate that several connection subdomain and RNase H domain substitutions typically act as pre-therapy polymorphisms.

[Tenofovir DF in rescue regimens]

As with other nucleoside analogues, tenofovir (TDF) can be affected by several mutations in the reverse transcriptase gene. Most nucleoside analogue mutations (NAMs) are not induced specifically by TDF, although they can affect the activity of this drug. The impact of thymidine analogue mutations (TAMs) on tenofovir varies and, as with the remaining nucleoside analogue reverse transcriptase inhibitors, largely depends on the type and number present. Thus, the greater the number of TAMs, and the greater the number of type 1 TAMs, the more TDF activity will be affected. The 41L and 210W mutations have the greatest effect. The incidence of the 65R mutation was slight before the clinical introduction of TDF. This mutation was selected by treatments with zalcitabine monotherapy. However, after TDF came on to the market, the 65R mutation began to be more frequently reported and is currently the signature mutation of this drug. TDF has been shown to be safe and effective in patients with prior virological failure and resistance mutations in the reverse transcriptase gene. In these patients, the presence of the 41L and 210W mutations is associated with a worse response to rescue therapy containing TDF. In contrast, the presence of type 2 TAMs (67N, 70R and 219Q/E/N) has little effect on TDF activity in these patients. Importantly, in TDF therapy, the presence of the 184V mutation is associated with a more favorable virologic response than the absence of this mutation, with any of the distinct combinations of mutations present.

Biomarker discovery using targeted maximum-likelihood estimation: application to the treatment of antiretroviral-resistant HIV infection

Researchers in clinical science and bioinformatics frequently aim to learn which of a set of candidate biomarkers is important in determining a given outcome, and to rank the contributions of the candidates accordingly. This article introduces a new approach to research questions of this type, based on targeted maximum-likelihood estimation of variable importance measures.The methodology is illustrated using an example drawn from the treatment of HIV infection. Specifically, given a list of candidate mutations in the protease enzyme of HIV, we aim to discover mutations that reduce clinical virologic response to antiretroviral regimens containing the protease inhibitor lopinavir. In the context of this data example, the article reviews the motivation for covariate adjustment in the biomarker discovery process. A standard maximum-likelihood approach to this adjustment is compared with the targeted approach introduced here. Implementation of targeted maximum-likelihood estimation in the context of biomarker discovery is discussed, and the advantages of this approach are highlighted. Results of applying targeted maximum-likelihood estimation to identify lopinavir resistance mutations are presented and compared with results based on unadjusted mutation-outcome associations as well as results of a standard maximum-likelihood approach to adjustment.The subset of mutations identified by targeted maximum likelihood as significant contributors to lopinavir resistance is found to be in better agreement with the current understanding of HIV antiretroviral resistance than the corresponding subsets identified by the other two approaches. This finding suggests that targeted estimation of variable importance represents a promising approach to biomarker discovery.

Mutations at human immunodeficiency virus type 1 reverse transcriptase tryptophan repeat motif attenuate the inhibitory effect of efavirenz on virus production

HIV-1 virus particle processing is mediated by protease (PR), with enzymatic activation triggered by Gag-Pol/Gag-Pol interaction. We previously reported that truncation mutations at the reverse transcriptase (RT) connection subdomain markedly impair virus particle processing, suggesting an important role for the RT subdomain in PR-mediated virus processing. A highly conserved tryptophan (Trp) repeat motif of the HIV-1 RT connection subdomain is involved in RT dimerization. Our goal in this study was to determine whether mutations at the Trp repeat motif have any effect on PR-mediated virus processing. Our results indicate that even though alanine substitutions at W401 (W401A) or at both W401 and W402 (W401A/W402A) have no major effect on steady-state virus processing, the combined W401A/W402A mutations partially negate and the W401A mutation almost completely negates an efavirenz (EFV)-imposed barrier to virus production. The combination of RT instability and poor enzymatic activity reflects a RT dimerization defect incurred by the mutations. We also found that an artificial p66RT carrying the W401A or W401A/W402A mutations was packaged into virions more efficiently than wild-type p66RT, and that the viral incorporation of p66RT is significantly reduced by EFV, implying a novel effect of EFV on RT-Gag interaction. Our results suggest that the Trp repeat motif may play a role in the Gag-Pol/Gag-Pol interaction that contributes to subsequent PR activation.

A copper(I)-catalyzed 1,2,3-triazole azide-alkyne click compound is a potent inhibitor of a multidrug-resistant HIV-1 protease variant

Treatment with HIV-1 protease inhibitors, a component of highly active antiretroviral therapy (HAART), often results in viral resistance. Structural and biochemical characterization of a 6X protease mutant arising from in vitro selection with compound 1, a C 2-symmetric diol protease inhibitor, has been previously described. We now show that compound 2, a copper(I)-catalyzed 1,2,3-triazole derived compound previously shown to be potently effective against wild-type protease (IC 50 = 6.0 nM), has low nM activity (IC 50 = 15.7 nM) against the multidrug-resistant 6X protease mutant. Compound 2 displays similar efficacy against wild-type and 6X HIV-1 in viral replication assays. While structural studies of compound 1 bound to wild type and mutant proteases revealed a progressive change in binding mode in the mutants, the 1.3 A resolution 6X protease-compound 2 crystal structure reveals nearly identical interactions for 2 as in the wild-type protease complex with very little change in compound 2 or protease conformation.

False I50V resistance readings of HIV isolates: co-amplification of NASBA HIV-1 RNA QT internal calibrators and HIV-1 patient isolates may lead to a false I50V mutation resistance reading in genotypic tests

The I50V protease inhibitor (PI) resistance mutation was found in 87.4% of protease gene fragments sequenced from 199 nucleic acid isolates extracted using an NASBA virus load assay, performed between 1997 and 2001 in Brazil. This mutation is an amprenavir-related mutation, and at that particular time this PI was seldom used in Brazil. This mutation was found both in patients with and without therapeutic success. Q calibrators showed the PI resistance mutation I50V when directly amplified and sequenced from the 423-bp PCR product targeting protease gene. The majority of the patients' samples had a mixture of I50I and I50V; however, this artifact was nor seen when a 989-bp PCR product was used. These results show that RNA extracted using virus load kits need to be critically evaluated before being used in home-brew genotypic tests.

Genetic basis of variation in tenofovir drug susceptibility in HIV-1

OBJECTIVE

To develop an improved model for the genetic basis of reduced susceptibility to tenofovir in vitro.

METHODS

A dataset of 532 HIV-1 subtype B reverse transcriptase genotypes for which matched phenotypic susceptibility data were available was assembled, both as a continuous (transformed) dataset and a categorical dataset generated by imposing a cut-off on the basis of earlier studies of in-vivo response of 1.4-fold. Models were generated using stepwise regression, decision tree and random forest approaches on both the continuous and categorical data. Models were compared by mean squared error (continuous models), or by misclassification rates by nested crossvalidation.

RESULTS

From the continuous dataset, stepwise linear regression, regression tree and regression forest methods yielded models with MSE of 0.46, 0.48 and 0.42 respectively. Amino acids 215, 65, 41, 67, 184 and 151 in HIV-1 reverse transcriptase were identified in all three models and amino acid 210 in two. The categorical data yielded logistic regression, classification tree and forest models with misclassification rates of 26, 24 and 23%, respectively. Amino acids 215, 65 and 67 appeared in all; 41, 184, 210 and 151 were also included in the classification forest model.

CONCLUSION

The random forests approach has yielded a substantial improvement in the available models to describe the genetic basis of reduced susceptibility to tenofovir in vitro. The most important sites in these models are amino acid sites 215, 65, 41, 67, 184, 151 and 210 in HIV-1 reverse transcriptase.

HIV-1 drug resistance mutations: an updated framework for the second decade of HAART

More than 200 mutations are associated with antiretroviral resistance to drugs belonging to six licensed antiretroviral classes. More than 50 reverse transcriptase mutations are associated with nucleoside reverse transcriptase inhibitor resistance including M184V, thymidine analog mutations, mutations associated with non-thymidine analog containing regimens, multi-nucleoside resistance mutations, and several recently identified accessory mutations. More than 40 reverse transcriptase mutations are associated with nonnucleoside reverse transcriptase inhibitor resistance including major primary and secondary mutations, non-polymorphic minor mutations, and polymorphic accessory mutations. More than 60 mutations are associated with protease inhibitor resistance including major protease, accessory protease, and protease cleavage site mutations. More than 30 integrase mutations are associated with the licensed integrase inhibitor raltegravir and the investigational inhibitor elvitegravir. More than 15 gp41 mutations are associated with the fusion inhibitor enfuvirtide. CCR5 inhibitor resistance results from mutations that promote gp120 binding to an inhibitor-bound CCR5 receptor or CXCR4 tropism; however, the genotypic correlates of these processes are not yet well characterized.

Prevalence and impact of HIV-1 protease mutation L76V on lopinavir resistance

Besides I47A, mutation L76V at the HIV protease gene has recently been proposed to cause lopinavir resistance. This change was present in 37 (2.7%) out of 1376 patients failing protease inhibitor containing regimens. Although 26 (70%) were on lopinavir, most had previously failed other protease inhibitors and carried multiple protease inhibitor resistance mutations. Therefore, L76V does not appear to be a primary lopinavir resistance change when the drug is used in combination therapy.

Dual-reporter phenotypic assay for human immunodeficiency viruses

We have established a novel human immunodeficiency virus (HIV) tandem-reporter assay using HIV receptor-transduced NP-2 cells with long terminal repeat-controlled beta-galactosidase, inserted internal ribosome entry site, and secretary alkaline phosphatase genes. This assay allows users to detect replication of clinical isolates, indicating its useful application as an HIV phenotypic assay.

Factors associated with the selection of mutations conferring resistance to protease inhibitors (PIs) in PI-experienced patients displaying treatment failure on darunavir

The objective of this study was to characterize the mutations selected by darunavir (DRV) use in protease inhibitor (PI)-experienced patients and the associated factors. We analyzed treatment failure in 54 PI-experienced human immunodeficiency virus (HIV)-infected patients on a DRV- and ritonavir-containing regimen. Viral genotyping was carried out at the baseline, at between 1 and 3 months of treatment, and at between 3 and 6 months of treatment to search for the selection of mutations conferring resistance to PIs. The median baseline HIV RNA level was 4.9 log(10) copies/ml, and the median CD4 count was 87 cells/mm(3). At the baseline, the median numbers of resistance mutations were as follows: 3 DRV resistance mutations, 4 major PI resistance mutations, and 10 minor PI resistance mutations. The most common mutations that emerged at rebound included V32I (44%), I54M/L (24%), L33F (25%), I84V (21%), and L89V (12%). Multivariate analysis showed that higher baseline HIV RNA levels and smaller numbers of nucleoside reverse transcriptase inhibitor simultaneously used with DRV were associated with a higher risk of DRV resistance mutation selection. By contrast, L76V, a known DRV resistance mutation, was found to decrease the risk of selection of another DRV resistance mutation. The occurrence of virological failure while a patient was on DRV was associated with the selection of mutations that increased the level of DRV resistance without affecting susceptibility to tipranavir (TPV). In these PI-treated patients who displayed treatment failure while they were on a DRV-containing regimen, we confirmed the set of emerging mutations associated with DRV failure and identified the factors associated with the selection of these mutations. TPV susceptibility does not seem to be affected by the selection of a DRV resistance mutation.

An evolutionary-network model reveals stratified interactions in the V3 loop of the HIV-1 envelope

The third variable loop (V3) of the human immunodeficiency virus type 1 (HIV-1) envelope is a principal determinant of antibody neutralization and progression to AIDS. Although it is undoubtedly an important target for vaccine research, extensive genetic variation in V3 remains an obstacle to the development of an effective vaccine. Comparative methods that exploit the abundance of sequence data can detect interactions between residues of rapidly evolving proteins such as the HIV-1 envelope, revealing biological constraints on their variability. However, previous studies have relied implicitly on two biologically unrealistic assumptions: (1) that founder effects in the evolutionary history of the sequences can be ignored, and; (2) that statistical associations between residues occur exclusively in pairs. We show that comparative methods that neglect the evolutionary history of extant sequences are susceptible to a high rate of false positives (20%-40%). Therefore, we propose a new method to detect interactions that relaxes both of these assumptions. First, we reconstruct the evolutionary history of extant sequences by maximum likelihood, shifting focus from extant sequence variation to the underlying substitution events. Second, we analyze the joint distribution of substitution events among positions in the sequence as a Bayesian graphical model, in which each branch in the phylogeny is a unit of observation. We perform extensive validation of our models using both simulations and a control case of known interactions in HIV-1 protease, and apply this method to detect interactions within V3 from a sample of 1,154 HIV-1 envelope sequences. Our method greatly reduces the number of false positives due to founder effects, while capturing several higher-order interactions among V3 residues. By mapping these interactions to a structural model of the V3 loop, we find that the loop is stratified into distinct evolutionary clusters. We extend our model to detect interactions between the V3 and C4 domains of the HIV-1 envelope, and account for the uncertainty in mapping substitutions to the tree with a parametric bootstrap.

Predictive Factors for Response to a Boosted Dual HIV-Protease Inhibitor Therapy with Saquinavir and Lopinavir in Extensively Pre-Treated Patients

Objective

To evaluate predictive factors for therapy outcome of a boosted double-protease inhibitor (PI) regimen in 58 extensively pre-treated patients with HIV.

Methods

Patients received lopinavir/ritonavir 400/100 mg and saquinavir 1,000 mg twice daily without reverse transcriptase inhibitors (RTI). The primary outcome parameter was HIV RNA <400 copies/ml at week 48, secondary parameters were HIV-1 RNA and CD4+ T-cell count changes from baseline to week 48. Pharmacokinetics, genotypic resistance and clinical and individual parameters were correlated with the clinical outcome in regression analyses. Covariates for the analyses were minimum plasma concentration (Cmin), maximum plasma concentration, area under the concentration versus time curve, half-life and clearance of lopinavir and saquinavir, the genotypic inhibitory quotients (GIQ) of archived (GIQarch) and baseline PI resistance mutations, previously taken antiretrovirals, archived and baseline viral resistance mutations, baseline HIV-1 RNA and CD4+ T-cell count.

Results

The analyses detected correlations between the primary outcome parameter and several factors: baseline CD4+ T-cell count ( P=0.001); absence of mutations at V82T/A/F/I/S plus I54M/V/L ( P=0.002) or K20M/R ( P=0.010); and lopinavir CminGIQarch ( P=0.046). This regression model had a predictability of 97.0% for response to therapy. Covariates for the decrease of HIV-1 RNA from baseline to week 48 were baseline HIV-1 RNA ( P<0.001), lopinavir CminGIQarch ( P=0.013), presence/absence of mutations at V82T/A/F/I/S or I84A/V plus L10I/R/V/F, I54M/V/L or L63P ( P=0.018), and previously taken antiretrovirals ( P=0.034).

Conclusions

Baseline HIV-1 RNA <5.0 log10 and CD4+ T-cell count >200 cells/μl, lopinavir CminGIQarch >2,000 ng/ml and the absence of viral resistance mutations at V82T/A/F/I/S and I54M/V/L are highly predictive for therapeutic success of a regimen of saquinavir/lopinavir/ ritonavir without RTI in a heterogenic cohort of patients with an extensive pre-treatment history and highly variable pharmacokinetics.

Prevalence of darunavir resistance-associated mutations: patterns of occurrence and association with past treatment

Eleven protease mutations have been associated with reduced susceptibility to darunavir (DRV). We examined the prevalence and covariates of these mutations in 2 populations. Thirty percent of 1175 Northern California patients and 24% of 2744 non-California patients in the Stanford HIV Drug Resistance Database had viruses with 1 or more mutations associated with resistance to DRV. In multivariate analyses, the number of DRV resistance-associated mutations depended on the number of previous protease inhibitors (PIs) administered and on amprenavir/fosamprenavir treatment. Most PI-treated patients should respond favorably to DRV-based salvage therapy.

Mining complex genotypic features for predicting HIV-1 drug resistance

MOTIVATION

Human immunodeficiency virus type 1 (HIV-1) evolves in human body, and its exposure to a drug often causes mutations that enhance the resistance against the drug. To design an effective pharmacotherapy for an individual patient, it is important to accurately predict the drug resistance based on genotype data. Notably, the resistance is not just the simple sum of the effects of all mutations. Structural biological studies suggest that the association of mutations is crucial: even if mutations A or B alone do not affect the resistance, a significant change might happen when the two mutations occur together. Linear regression methods cannot take the associations into account, while decision tree methods can reveal only limited associations. Kernel methods and neural networks implicitly use all possible associations for prediction, but cannot select salient associations explicitly.

RESULTS

Our method, itemset boosting, performs linear regression in the complete space of power sets of mutations. It implements a forward feature selection procedure where, in each iteration, one mutation combination is found by an efficient branch-and-bound search. This method uses all possible combinations, and salient associations are explicitly shown. In experiments, our method worked particularly well for predicting the resistance of nucleotide reverse transcriptase inhibitors (NRTIs). Furthermore, it successfully recovered many mutation associations known in biological literature.

AVAILABILITY

http://www.kyb.mpg.de/bs/people/hiroto/iboost/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Characterization and structural analysis of novel mutations in human immunodeficiency virus type 1 reverse transcriptase involved in the regulation of resistance to nonnucleoside inhibitors

Resistance to antivirals is a complex and dynamic phenomenon that involves more mutations than are currently known. Here, we characterize 10 additional mutations (L74V, K101Q, I135M/T, V179I, H221Y, K223E/Q, and L228H/R) in human immunodeficiency virus type 1 (HIV-1) reverse transcriptase which are involved in the regulation of resistance to nonnucleoside reverse transcriptase inhibitors (NNRTIs). These mutations are strongly associated with NNRTI failure and strongly correlate with the classical NNRTI resistance mutations in a data set of 1,904 HIV-1 B-subtype pol sequences from 758 drug-naïve patients, 592 nucleoside reverse transcriptase inhibitor (NRTI)-treated but NNRTI-naïve patients, and 554 patients treated with both NRTIs and NNRTIs. In particular, L74V and H221Y, positively correlated with Y181C, were associated with an increase in Y181C-mediated resistance to nevirapine, while I135M/T mutations, positively correlated with K103N, were associated with an increase in K103N-mediated resistance to efavirenz. In addition, the presence of the I135T polymorphism in NNRTI-naïve patients significantly correlated with the appearance of K103N in cases of NNRTI failure, suggesting that I135T may represent a crucial determinant of NNRTI resistance evolution. Molecular dynamics simulations show that I135T can contribute to the stabilization of the K103N-induced closure of the NNRTI binding pocket by reducing the distance and increasing the number of hydrogen bonds between 103N and 188Y. H221Y also showed negative correlations with type 2 thymidine analogue mutations (TAM2s); its copresence with the TAM2s was associated with a higher level of zidovudine susceptibility. Our study reinforces the complexity of NNRTI resistance and the significant interplay between NRTI- and NNRTI-selected mutations. Mutations beyond those currently known to confer resistance should be considered for a better prediction of clinical response to reverse transcriptase inhibitors and for the development of more efficient new-generation NNRTIs.

The priorities for antiviral drug resistance surveillance and research

The number of available antiviral drugs is growing fast. The emergence of drug-resistant viruses is well documented as a cause for drug failure. Such viruses also carry the potential for transmission, the risks for which vary according to specific viral transmission dynamics. This potential is best described for HIV and influenza. Resistance to the new generation of hepatitis C virus inhibitors is also likely to become a cause for concern. The priorities for future action to limit resistance include application of sophisticated surveillance mechanisms linked to detailed virological data, development of optimal treatment regimens (e.g. combination therapies) to limit emergence of resistance, and a focus on prevention strategies to prevent transmission.

HIV-1 subtype B protease and reverse transcriptase amino acid covariation

Despite the high degree of HIV-1 protease and reverse transcriptase (RT) mutation in the setting of antiretroviral therapy, the spectrum of possible virus variants appears to be limited by patterns of amino acid covariation. We analyzed patterns of amino acid covariation in protease and RT sequences from more than 7,000 persons infected with HIV-1 subtype B viruses obtained from the Stanford HIV Drug Resistance Database (http://hivdb.stanford.edu). In addition, we examined the relationship between conditional probabilities associated with a pair of mutations and the order in which those mutations developed in viruses for which longitudinal sequence data were available. Patterns of RT covariation were dominated by the distinct clustering of Type I and Type II thymidine analog mutations and the Q151M-associated mutations. Patterns of protease covariation were dominated by the clustering of nelfinavir-associated mutations (D30N and N88D), two main groups of protease inhibitor (PI)-resistance mutations associated either with V82A or L90M, and a tight cluster of mutations associated with decreased susceptibility to amprenavir and the most recently approved PI darunavir. Different patterns of covariation were frequently observed for different mutations at the same position including the RT mutations T69D versus T69N, L74V versus L74I, V75I versus V75M, T215F versus T215Y, and K219Q/E versus K219N/R, and the protease mutations M46I versus M46L, I54V versus I54M/L, and N88D versus N88S. Sequence data from persons with correlated mutations in whom earlier sequences were available confirmed that the conditional probabilities associated with correlated mutation pairs could be used to predict the order in which the mutations were likely to have developed. Whereas accessory nucleoside RT inhibitor-resistance mutations nearly always follow primary nucleoside RT inhibitor-resistance mutations, accessory PI-resistance mutations often preceded primary PI-resistance mutations.

Contribution of recombination to the evolution of human immunodeficiency viruses expressing resistance to antiretroviral treatment

Viral recombination has been postulated to play two roles in the development of human immunodeficiency virus (HIV) resistance to antiretroviral drugs. First, recombination has the capacity to associate resistance mutations expressed by distinct viruses, thereby contributing to the development of viruses with improved drug resistance. In addition, recombination could preserve diversity in regions outside those subject to strong selective pressure. In this study, we sought direct evidence for the occurrence of these processes in vivo by evaluating clonal virus populations obtained from the same patient before and after a treatment change that, while unsuccessful in controlling viral replication, led to the emergence of viruses expressing a different profile of resistance mutations. Phylogenetic studies supported the conclusion that the genotype arising after the treatment change resulted from the emergence of recombinant viruses carrying previously existing resistance mutations in novel combinations, whereas alternative explanations, including convergent evolution, were not consistent with observed genotypic changes. Despite evidence for a strong loss of genetic diversity in genomic regions coding for the protease and reverse transcriptase, diversity in regions coding for Gag and envelope was considerably higher, and recombination between the emerging viruses expressing the new pattern of resistance mutations and viral quasispecies in the previously dominant population contributed to this preservation of diversity in the envelope gene. These findings emphasize that recombination can participate in the adaptation of HIV to changing selective pressure, both by generating novel combinations of resistance mutations and by maintaining diversity in genomic regions outside those implicated in a selective sweep.

Super learning: an application to the prediction of HIV-1 drug resistance

Many alternative data-adaptive algorithms can be used to learn a predictor based on observed data. Examples of such learners include decision trees, neural networks, support vector regression, least angle regression, logic regression, and the Deletion/Substitution/Addition algorithm. The optimal learner for prediction will vary depending on the underlying data-generating distribution. In this article we introduce the "super learner", a prediction algorithm that applies any set of candidate learners and uses cross-validation to select between them. Theory shows that asymptotically the super learner performs essentially as well as or better than any of the candidate learners. In this article we present the theory behind the super learner, and illustrate its performance using simulations. We further apply the super learner to a data example, in which we predict the phenotypic antiretroviral susceptibility of HIV based on viral genotype. Specifically, we apply the super learner to predict susceptibility to a specific protease inhibitor, nelfinavir, using a set of database-derived non-polymorphic treatment-selected mutations.