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Biomedical subjects

Klaus Korn

Publications and source records attributed to Klaus Korn.

17 recordsLinked to original sources

Dual selection pressure by drugs and HLA class I-restricted immune responses on human immunodeficiency virus type 1 protease.

To determine the influence of human immunodeficiency virus type 1 (HIV-1)-specific CD8+ T cells on the development of drug resistance mutations in the HIV-1 protease, we analyzed protease sequences from viruses from a human leukocyte antigen class I (HLA class I)-typed cohort of 94 HIV-1-positive individuals. In univariate statistical analyses (Fisher's exact test), minor and major drug resistance mutations as well as drug-associated polymorphisms showed associations with HLA class I alleles. All correlations with P values of 0.05 or less were considered to be relevant without corrections for multiple tests. A subset of these observed correlations was experimentally validated by enzyme-linked immunospot assays, allowing the definition of 10 new epitopes recognized by CD8+ T cells from patients with the appropriate HLA class I type. Several drug resistance-associated mutations in the protease acted as escape mutations; however, cells from many patients were still able to generate CD8+ T cells targeting the escape mutants. This result presumably indicates the usage of different T-cell receptors by CD8+ T cells targeting these epitopes in these patients. Our results support a fundamental role for HLA class I-restricted immune responses in shaping the sequence of the HIV-1 protease in vivo. This role may have important clinical implications both for the understanding of drug resistance pathways and for the design of therapeutic vaccines targeting drug-resistant HIV-1.

Alleles↗

The calculated genetic barrier for antiretroviral drug resistance substitutions is largely similar for different HIV-1 subtypes.

BACKGROUND: The genetic barrier, defined as the number of mutations required to overcome drug-selective pressure, is an important factor for the development of HIV drug resistance. Because of high variability between subtypes, particular HIV-1 subtypes could have different genetic barriers for drug resistance substitutions. This study compared the genetic barrier between subtypes using some 2000 HIV-1 sequences (>600 of non-B subtype) isolated from anti-retroviral-naive patients in Europe. METHODS: The genetic barrier was calculated as the sum of transitions (scored as 1) and/or transversions (2.5) required for evolution to any major drug resistance substitution. In addition, the number of minor protease substitutions was determined for every subtype. RESULTS: Few dissimilarities were found. An increased genetic barrier was calculated for I82A (subtypes C and G), V108I (subtype G), V118I (subtype G), Q151M (subtypes D and F), L210W (subtypes C, F, G, and CRF02_AG), and P225H (subtype A) (P < 0.001 compared with subtype B). A decreased genetic barrier was found for I82T (subtypes C and G) and V106M (subtype C) (P < 0.001 vs subtype B). Conversely, minor protease substitutions differed extensively between subtypes. CONCLUSIONS: Based on the calculated genetic barrier, the rate of drug resistance development may be similar for different HIV-1 subtypes. Because of differences in minor protease substitutions, protease inhibitor resistance could be enhanced in particular subtypes once the relevant major substitutions are selected.

Adult↗

Computational methods for the design of effective therapies against drug resistant HIV strains.

The development of drug resistance is a major obstacle to successful treatment of HIV infection. The extraordinary replication dynamics of HIV facilitates its escape from selective pressure exerted by the human immune system and by combination drug therapy. We have developed several computational methods whose combined use can support the design of optimal antiretroviral therapies based on viral genomic data.

Database Management Systems↗

Prevalence of drug-resistant HIV-1 variants in untreated individuals in Europe: implications for clinical management.

BACKGROUND: Infection with drug-resistant human immunodeficiency virus type 1 (HIV-1) can impair the response to combination therapy. Widespread transmission of drug-resistant variants has the disturbing potential of limiting future therapy options and affecting the efficacy of postexposure prophylaxis. METHODS: We determined the baseline rate of drug resistance in 2208 therapy-naive patients recently and chronically infected with HIV-1 from 19 European countries during 1996-2002. RESULTS: In Europe, 1 of 10 antiretroviral-naive patients carried viruses with > or = 1 drug-resistance mutation. Recently infected patients harbored resistant variants more often than did chronically infected patients (13.5% vs. 8.7%; P=.006). Non-B viruses (30%) less frequently carried resistance mutations than did subtype B viruses (4.8% vs. 12.9%; P<.01). Baseline resistance increased over time in newly diagnosed cases of non-B infection: from 2.0% (1/49) in 1996-1998 to 8.2% (16/194) in 2000-2001. CONCLUSIONS: Drug-resistant variants are frequently present in both recently and chronically infected therapy-naive patients. Drug-resistant variants are most commonly seen in patients infected with subtype B virus, probably because of longer exposure of these viruses to drugs. However, an increase in baseline resistance in non-B viruses is observed. These data argue for testing all drug-naive patients and are of relevance when guidelines for management of postexposure prophylaxis and first-line therapy are updated.

Adult↗

Characterization of virus isolates by particle-associated nucleic acid PCR.

Diagnostic virus isolation is still frequently used, particularly from respiratory tract secretions. Testing positive virus cultures for all possible viruses is time-consuming, and unexpected or unknown viruses may escape detection. Therefore, a novel random PCR approach was developed that allows sequence-independent amplification of viral nucleic acids from virus isolation-positive cultures. Selectivity for viral sequences is obtained by preferential isolation of nucleic acids that are particle associated and resistant to nucleases. Using primers with a degenerated 3' end, the isolated nucleic acids are amplified and the randomly amplified PCR products are cloned and sequenced. As proof of the concept, the PAN-PCR approach was applied to supernatants of coxsackievirus B3 and murine adenovirus type 1-infected cells. Enterovirus and adenovirus sequences were obtained, demonstrating that the random PCR approach allows detection of RNA and DNA viruses. As a first application of this PAN-PCR approach, we characterized a virus isolate from mouth-washing material of a patient with chronic fatigue syndrome and high antibody titers to coxsackievirus B2. The virus isolate had tested negative for enteroviruses and respiratory viruses (influenza viruses A and B, parainfluenza virus types 1 to 3, respiratory syncytial virus, and adenovirus) by immunofluorescence and PCR. Particle-associated, nuclease-resistant RNA and DNA were prepared from the supernatant of infected cells. The DNA and the reverse-transcribed RNA were randomly amplified, and PCR products were cloned and sequenced. Of 25 sequences obtained from the DNA preparation, 24 contained herpes simplex virus type 1 (HSV-1) sequences from 14 different loci spread over the HSV-1 genome. This result was confirmed by using a standard diagnostic HSV-PCR, demonstrating that the PAN-PCR correctly identified the virus isolate. Although the identification of HSV-1 in mouth-washing material is not surprising in retrospect, it clearly demonstrates the applicability of the PAN-PCR approach. This method should be particularly useful for characterizing virus isolates that have tested negative for all expected viruses and for identifying unknown viruses.

Adenoviridae↗

Safety and efficacy of a NRTI-sparing HAART regimen of efavirenz and lopinavir/ritonavir in HIV-1-infected children.

We studied a nucleoside reverse transcriptase inhibitor (NRTI)-sparing regimen for the treatment of children infected with NRTI-resistant HIV-1. The combination of lopinavir/ritonavir and efavirenz suppressed HIV-1 levels for a prolonged period and resulted in a significant increase in CD4+ T cell numbers despite an extensive prior treatment with NRTI (>4 years). Observed side effects were transient with the exception of dyslipidaemia.

Adolescent↗

Geno2pheno: Estimating phenotypic drug resistance from HIV-1 genotypes.

Therapeutic success of anti-HIV therapies is limited by the development of drug resistant viruses. These genetic variants display complex mutational patterns in their pol gene, which codes for protease and reverse transcriptase, the molecular targets of current antiretroviral therapy. Genotypic resistance testing depends on the ability to interpret such sequence data, whereas phenotypic resistance testing directly measures relative in vitro susceptibility to a drug. From a set of 650 matched genotype-phenotype pairs we construct regression models for the prediction of phenotypic drug resistance from genotypes. Since the range of resistance factors varies considerably between different drugs, two scoring functions are derived from different sets of predicted phenotypes. Firstly, we compare predicted values to those of samples derived from 178 treatment-naive patients and report the relative deviance. Secondly, estimation of the probability density of 2000 predicted phenotypes gives rise to an intrinsic definition of a susceptible and a resistant subpopulation. Thus, for a predicted phenotype, we calculate the probability of membership in the resistant subpopulation. Both scores provide standardized measures of resistance that can be calculated from the genotype and are comparable between drugs. The geno2pheno system makes these genotype interpretations available via the Internet (http://www.genafor.org/).

Anti-HIV Agents↗

Distinct cross-resistance profiles of the new protease inhibitors amprenavir, lopinavir, and atazanavir in a panel of clinical samples.

A panel of 245 clinical samples with known treatment histories was retrospectively evaluated for cross-resistance to new protease inhibitors (PI). Samples with resistance to previously approved PI displayed high cross-resistance to atazanavir, whereas cross-resistance to amprenavir was considerably lower. A similar cross-resistance profile was observed for lopinavir, if a higher cut-off for resistance (9.5-fold) was applied. The enhanced efficacy of boosted PI is discussed with respect to clinically relevant cut-offs for drug resistance.

Atazanavir Sulfate↗

Methods for optimizing antiviral combination therapies.

MOTIVATION: Despite some progress with antiretroviral combination therapies, therapeutic success in the management of HIV-infected patients is limited. The evolution of drug-resistant genetic variants in response to therapy plays a key role in treatment failure and finding a new potent drug combination after therapy failure is considered challenging. RESULTS: To estimate the activity of a drug combination against a particular viral strain, we develop a scoring function whose independent variables describe a set of antiviral agents and viral DNA sequences coding for the molecular targets of the respective drugs. The construction of this activity score involves (1) predicting phenotypic drug resistance from genotypes for each drug individually, (2) probabilistic modeling of predicted resistance values and integration into a score for drug combinations, and (3) searching through the mutational neighborhood of the considered strain in order to estimate activity on nearby mutants. For a clinical data set, we determine the optimal search depth and show that the scoring scheme is predictive of therapeutic outcome. Properties of the activity score and applications are discussed.

Algorithms↗

Tenofovir resistance and resensitization.

Human immunodeficiency viruses in 321 samples from tenofovir-naïve patients were retrospectively evaluated for resistance to this nucleotide analogue. All virus strains with insertions between amino acids 67 and 70 of the reverse transcriptase (n = 6) were highly resistant. Virus strains with the Q151M mutation were divided into susceptible (n = 12) and highly resistant (n = 8) viruses. This difference was due to the absence or presence of the K65R mutation, which was confirmed by site-directed mutagenesis. Viral clones with various combinations of the mutations M41L, K70R, L210W, and T215F or T215Y were analyzed for cross-resistance induced by thymidine analogue mutations (TAMs). The levels of increased resistance induced by single, double, and triple mutations at the indicated positions could be ranked as follows: for mutants with single mutations, mutations at positions 41 > 215 > 70; for mutants with double mutations, mutations at positions 41 and 215 > 70 and 215 = 210 and 215 > 41 and 70; for mutants with triple mutations, mutations at positions 41, 210, and 215 > 41, 70, and 215. Viral clones with M184V or M184I exhibited slightly increased susceptibilities to tenofovir (0.7-fold). Almost all clones with TAM-induced resistance were resensitized when M184V was present (P < 0.001). Among the viruses in the clinical samples, the rate of tenofovir resistance significantly increased with the number of TAMs both in the samples with 184M and in those with 184V (P = 0.005 and P = 0.003, respectively). A resensitizing effect of M184V was confirmed for all samples exhibiting at least one TAM (P = 0.03). However, accumulation of at least two TAMs resulted in more than 2.0-fold reduced susceptibility to tenofovir, irrespective of the presence of M184V. Decision tree building, a classical machine learning technique, was used to generate models for the interpretation of mutations with respect to tenofovir resistance. The application of previously proposed cutoffs for a reduced response to therapy and treatment failure demonstrated the central roles of positions 215 and 65 for 1.5- and 4.0-fold reduced susceptibilities, respectively. Thus, clinically relevant resistance may be conferred by the accumulation of TAMs, and the resensitizing effect of M184V should be considered only minor.

Adenine↗

Quality control trial for human immunodeficiency virus type 1 drug resistance testing using clinical samples reveals problems with detecting minority species and interpretation of test results.

Between January and March 2000, a quality control panel for human immunodeficiency virus (HIV) drug resistance testing was analyzed by 20 laboratories in five countries. The panel consisted of three clinical samples with different drug resistance genotypes and phenotypes and one HIV-negative plasma. Participants were asked to report the methods used for amplification and sequencing, a list of drug resistance-associated mutations that were detected in the protease and reverse transcriptase of each sample, and an interpretation concerning the susceptibility or resistance to 14 antiretroviral drugs. A total of 22 genotypic data sets were generated, which showed an overall good technical quality except for three participants, who failed to report key mutations for drug resistance. Problems were encountered in three respects: (i). resistant minorities of L90M in the protease, which were determined to about 12% by real-time amplification, were only detected by one-fourth of the participants; (ii). newly described resistance mutations were frequently not reported; and (iii). interpretations of drug resistance-associated mutations varied widely, in particular for protease inhibitors. In some cases, different interpretations were caused by differences in the detection of resistant minorities, but even for the same genotypic profile, interpretations varied considerably. Similar discrepancies were revealed if current Web-based interpretation systems were used to predict drug resistance for samples of the proficiency panel. This indicates that a consensus for the interpretation of drug resistance-associated mutations is urgently needed.

Antiviral Agents↗

No evidence for persistence of multidrug-resistant viral strains after a 7-month treatment interruption in an HIV-1-infected individual.

The number of HIV-1-infected patients harboring multidrug-resistant viruses is increasing. Since new antiretroviral drugs with favorable resistance profiles are limited, innovative strategies are urgently needed. Treatment interruptions can lead to a loss in HIV resistance followed by improved response to reinitiated therapy. The authors report the case of a patient with sustained antiretroviral response for 3.5 years after a 7-month treatment interruption. Concomitant with an increase in replication capacity, multidrug-resistant viruses gradually disappeared during treatment interruption. Resistance to protease inhibitors (PI) was completely lost, and resistance to reverse transcriptase inhibitors was still present when therapy was reinitiated. PI-resistant variants were not detected at four time points after treatment reinitiation. The alignment of the nucleic acid sequences from all different time points suggested that the viruses obtained after treatment reinitiation evolved from less-resistant variants prior to treatment interruption. This was supported by in vitro propagation of the viral plasma population and an individual clone derived from the time point of treatment interruption. This is consistent with a model favoring reversible binding of HIV-1 to reservoirs, as has recently been proposed for follicular dendritic cells. Understanding of this process could help to exploit the reduced fitness of drug-resistant viruses for treatment interruptions.

Adult↗

Diversity and complexity of HIV-1 drug resistance: a bioinformatics approach to predicting phenotype from genotype.

Drug resistance testing has been shown to be beneficial for clinical management of HIV type 1 infected patients. Whereas phenotypic assays directly measure drug resistance, the commonly used genotypic assays provide only indirect evidence of drug resistance, the major challenge being the interpretation of the sequence information. We analyzed the significance of sequence variations in the protease and reverse transcriptase genes for drug resistance and derived models that predict phenotypic resistance from genotypes. For 14 antiretroviral drugs, both genotypic and phenotypic resistance data from 471 clinical isolates were analyzed with a machine learning approach. Information profiles were obtained that quantify the statistical significance of each sequence position for drug resistance. For the different drugs, patterns of varying complexity were observed, including between one and nine sequence positions with substantial information content. Based on these information profiles, decision tree classifiers were generated to identify genotypic patterns characteristic of resistance or susceptibility to the different drugs. We obtained concise and easily interpretable models to predict drug resistance from sequence information. The prediction quality of the models was assessed in leave-one-out experiments in terms of the prediction error. We found prediction errors of 9.6-15.5% for all drugs except for zalcitabine, didanosine, and stavudine, with prediction errors between 25.4% and 32.0%. A prediction service is freely available at http://cartan.gmd.de/geno2pheno.html.

Computational Biology↗

Prediction of abacavir resistance from genotypic data: impact of zidovudine and lamivudine resistance in vitro and in vivo.

Abacavir is frequently used in antiretroviral combination therapies as a potent nucleoside reverse transcriptase inhibitor (NRTI). Four mutations are selected for by abacavir in vitro and in vivo: K65R, L74V, Y115F, and M184V. Abacavir resistance has also been observed in NRTI multidrug-resistant samples. Furthermore, abacavir resistance has been described in the context of zidovudine resistance. To evaluate the genetic basis of abacavir resistance, the viral genotype and phenotypic resistance were analyzed for 307 patient samples. Low- and high-level resistances were defined as 2.5- to 5.5-fold- and >5.5-fold-reduced susceptibility, respectively. If all samples with abacavir-selected and NRTI multidrug resistance-associated mutations were scored as resistant, 27.6% of the samples were misclassified, mainly due to samples falsely scored as susceptible. Therefore, the relative frequencies of other mutations were evaluated. Mutations at codons 44 and 118 were rarely detected in abacavir-susceptible samples but were overrepresented in resistant samples. Site-directed mutagenesis of E44D, V118I, and M184V resulted in low-level resistance for the double mutant 44/184 and the triple mutant. Low-level abacavir resistance was also detected for a viral clone carrying zidovudine mutations only. Additional insertion of M184V into the zidovudine background doubled the resistance, whereas 44/118 did not lead to a further increase. Incorporating combinations of zidovudine mutations and M184V into the scoring system markedly reduced the number of misclassified samples, whereas 44/118 did not improve the prediction. In conclusion, the combination of M184V with zidovudine mutations gives rise to high-level abacavir resistance, which may be clinically relevant. Thus, options for useful sequential combinations of NRTI are limited.

Algorithms↗

Technologies for measuring HIV-1 drug resistance.

Drug resistance testing significantly improves response to antiretroviral treatment in HIV-1-infected patients, therefore it has recently been implemented into current guidelines for the management of antiretroviral therapy. Knowledge about technologies for measuring drug resistance is important for several reasons: (a) differences exist between different technologies and also between assays based on the same technology; (b) the results of resistance testing are strongly dependent on the reliability and precision of the technology used; and (c) technical aspects have to be considered for a clinically relevant interpretation of drug resistance. The spectrum of genotypic and phenotypic technologies as well as the technical quality is increasing, which shifts the emphasis to the interpretation of resistance profiles. The interpretation is based on the knowledge of drug resistance-associated mutations as well as correlations between genotype and phenotype and clinical response, which are incorporated into rules-based systems. Bioinformatic techniques are used to generate mathematical models for the prediction of drug resistance from genotype. Both approaches are converging toward the prediction of clinical response. Because therapy response is dependent on many additional variables, further efforts are required for the generation of a large clinical database. This will be the basis of a prediction system that will optimize the antiretroviral therapy for each individual patient.

Anti-HIV Agents↗

Genotypic drug resistance interpretation systems--the cutting edge of antiretroviral therapy.

The technical quality of genotypic and phenotypic drug resistance testing has considerably improved, and therefore the major challenge now lies in the interpretation of drug resistance. This is due to several facts: (i) in times of combination therapy, the effect of drug resistance-associated mutations cannot be considered independently, (ii) many additive and subtractive interactions between mutations exist, and resistant strains may exhibit varying degrees of cross-resistance, (iii) the phenotype cannot adequately determine slight, but clinically relevant, differences for those drugs with a narrow range of resistance, and (iv) pharmacokinetic interactions may shift relevant levels of drug resistance. Genotypic drug resistance interpretation systems are designed to solve these problems. Rule-based systems incorporate current knowledge about correlations between genotype, phenotype and clinical response. Database-driven systems use the information provided by paired geno- and phenotypic data, applying database matching search or bioinformatic approaches. For detailed comparison, 11 interpretation systems were selected which present a comprehensive system for most of the available drugs, can easily be accessed via the Internet and are regularly updated. The systems were characterized for the source data, access, input, output, and availability of clinical studies. For further comparison, existing clinical databases should be merged into one large database to allow competition between the systems. This may also solve the burning problem of clinically relevant cut-offs. Head-to-head comparisons of interpretation systems require large prospective randomized trials in which only the interpretation system is different between groups, before a consensus can be achieved for the best antiretroviral therapy of the individual patient.

Algorithms↗