From Rational Design of Drug Crystals to Understanding of Nucleic Acid Structures: Lamivudine Duplex

A DNA-like duplex of nucleosides is probable to exist even without the 5`-phosphate groups needed to assemble the chain backbone. However, double-stranded helical structures of nucleosides are unknown. Here, we report a duplex of nucleoside analogs that is spontaneously assembled due to stacking of the neutral and protonated molecules of lamivudine, a nucleoside reverse transcriptase inhibitor (NTRI) widely used in anti-HIV drug combinatory medication. The left-handed lamivudine duplex has features similar to those of i-motif DNA, as the face-to-face base stacking and the helix rise per base pair. Furthermore, the protonation pattern on alternate bases expected for it DNA-like duplex stabilized by pairing of neutral and protonated cytosine fragments was observed for the first time in the lamivudine double-stranded helix. This structure demonstrates that hydrogen bonds can substitute for covalent phosphodiester linkage in the stabilization of the duplex backbone. This interesting example of spontaneous molecular self-organization indicates that the 5`-phosphate group could not be a requirement for duplex assembly.

Multi-dimensional classification using Bayesian networks for stationary and evolving streaming data

Hoy en día, con la evolución continua y rápida de las tecnologías de la información y los dispositivos de computación, se recogen y almacenan continuamente grandes volúmenes de datos en distintos dominios y a través de diversas aplicaciones del mundo real. La extracción de conocimiento útil de una cantidad tan enorme de datos no se puede realizar habitualmente de forma manual, y requiere el uso de técnicas adecuadas de aprendizaje automático y de minería de datos. La clasificación es una de las técnicas más importantes que ha sido aplicada con éxito a varias áreas. En general, la clasificación se compone de dos pasos principales: en primer lugar, aprender un modelo de clasificación o clasificador a partir de un conjunto de datos de entrenamiento, y en segundo lugar, clasificar las nuevas instancias de datos utilizando el clasificador aprendido. La clasificación es supervisada cuando todas las etiquetas están presentes en los datos de entrenamiento (es decir, datos completamente etiquetados), semi-supervisada cuando sólo algunas etiquetas son conocidas (es decir, datos parcialmente etiquetados), y no supervisada cuando todas las etiquetas están ausentes en los datos de entrenamiento (es decir, datos no etiquetados). Además, aparte de esta taxonomía, el problema de clasificación se puede categorizar en unidimensional o multidimensional en función del número de variables clase, una o más, respectivamente; o también puede ser categorizado en estacionario o cambiante con el tiempo en función de las características de los datos y de la tasa de cambio subyacente. A lo largo de esta tesis, tratamos el problema de clasificación desde tres perspectivas diferentes, a saber, clasificación supervisada multidimensional estacionaria, clasificación semisupervisada unidimensional cambiante con el tiempo, y clasificación supervisada multidimensional cambiante con el tiempo. Para llevar a cabo esta tarea, hemos usado básicamente los clasificadores Bayesianos como modelos. La primera contribución, dirigiéndose al problema de clasificación supervisada multidimensional estacionaria, se compone de dos nuevos métodos de aprendizaje de clasificadores Bayesianos multidimensionales a partir de datos estacionarios. Los métodos se proponen desde dos puntos de vista diferentes. El primer método, denominado CB-MBC, se basa en una estrategia de envoltura de selección de variables que es voraz y hacia delante, mientras que el segundo, denominado MB-MBC, es una estrategia de filtrado de variables con una aproximación basada en restricciones y en el manto de Markov. Ambos métodos han sido aplicados a dos problemas reales importantes, a saber, la predicción de los inhibidores de la transcriptasa inversa y de la proteasa para el problema de infección por el virus de la inmunodeficiencia humana tipo 1 (HIV-1), y la predicción del European Quality of Life-5 Dimensions (EQ-5D) a partir de los cuestionarios de la enfermedad de Parkinson con 39 ítems (PDQ-39). El estudio experimental incluye comparaciones de CB-MBC y MB-MBC con los métodos del estado del arte de la clasificación multidimensional, así como con métodos comúnmente utilizados para resolver el problema de predicción de la enfermedad de Parkinson, a saber, la regresión logística multinomial, mínimos cuadrados ordinarios, y mínimas desviaciones absolutas censuradas. En ambas aplicaciones, los resultados han sido prometedores con respecto a la precisión de la clasificación, así como en relación al análisis de las estructuras gráficas que identifican interacciones conocidas y novedosas entre las variables. La segunda contribución, referida al problema de clasificación semi-supervisada unidimensional cambiante con el tiempo, consiste en un método nuevo (CPL-DS) para clasificar flujos de datos parcialmente etiquetados. Los flujos de datos difieren de los conjuntos de datos estacionarios en su proceso de generación muy rápido y en su aspecto de cambio de concepto. Es decir, los conceptos aprendidos y/o la distribución subyacente están probablemente cambiando y evolucionando en el tiempo, lo que hace que el modelo de clasificación actual sea obsoleto y deba ser actualizado. CPL-DS utiliza la divergencia de Kullback-Leibler y el método de bootstrapping para cuantificar y detectar tres tipos posibles de cambio: en las predictoras, en la a posteriori de la clase o en ambas. Después, si se detecta cualquier cambio, un nuevo modelo de clasificación se aprende usando el algoritmo EM; si no, el modelo de clasificación actual se mantiene sin modificaciones. CPL-DS es general, ya que puede ser aplicado a varios modelos de clasificación. Usando dos modelos diferentes, el clasificador naive Bayes y la regresión logística, CPL-DS se ha probado con flujos de datos sintéticos y también se ha aplicado al problema real de la detección de código malware, en el cual los nuevos ficheros recibidos deben ser continuamente clasificados en malware o goodware. Los resultados experimentales muestran que nuestro método es efectivo para la detección de diferentes tipos de cambio a partir de los flujos de datos parcialmente etiquetados y también tiene una buena precisión de la clasificación. Finalmente, la tercera contribución, sobre el problema de clasificación supervisada multidimensional cambiante con el tiempo, consiste en dos métodos adaptativos, a saber, Locally Adpative-MB-MBC (LA-MB-MBC) y Globally Adpative-MB-MBC (GA-MB-MBC). Ambos métodos monitorizan el cambio de concepto a lo largo del tiempo utilizando la log-verosimilitud media como métrica y el test de Page-Hinkley. Luego, si se detecta un cambio de concepto, LA-MB-MBC adapta el actual clasificador Bayesiano multidimensional localmente alrededor de cada nodo cambiado, mientras que GA-MB-MBC aprende un nuevo clasificador Bayesiano multidimensional. El estudio experimental realizado usando flujos de datos sintéticos multidimensionales indica los méritos de los métodos adaptativos propuestos. ABSTRACT Nowadays, with the ongoing and rapid evolution of information technology and computing devices, large volumes of data are continuously collected and stored in different domains and through various real-world applications. Extracting useful knowledge from such a huge amount of data usually cannot be performed manually, and requires the use of adequate machine learning and data mining techniques. Classification is one of the most important techniques that has been successfully applied to several areas. Roughly speaking, classification consists of two main steps: first, learn a classification model or classifier from an available training data, and secondly, classify the new incoming unseen data instances using the learned classifier. Classification is supervised when the whole class values are present in the training data (i.e., fully labeled data), semi-supervised when only some class values are known (i.e., partially labeled data), and unsupervised when the whole class values are missing in the training data (i.e., unlabeled data). In addition, besides this taxonomy, the classification problem can be categorized into uni-dimensional or multi-dimensional depending on the number of class variables, one or more, respectively; or can be also categorized into stationary or streaming depending on the characteristics of the data and the rate of change underlying it. Through this thesis, we deal with the classification problem under three different settings, namely, supervised multi-dimensional stationary classification, semi-supervised unidimensional streaming classification, and supervised multi-dimensional streaming classification. To accomplish this task, we basically used Bayesian network classifiers as models. The first contribution, addressing the supervised multi-dimensional stationary classification problem, consists of two new methods for learning multi-dimensional Bayesian network classifiers from stationary data. They are proposed from two different points of view. The first method, named CB-MBC, is based on a wrapper greedy forward selection approach, while the second one, named MB-MBC, is a filter constraint-based approach based on Markov blankets. Both methods are applied to two important real-world problems, namely, the prediction of the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase and protease inhibitors, and the prediction of the European Quality of Life-5 Dimensions (EQ-5D) from 39-item Parkinson’s Disease Questionnaire (PDQ-39). The experimental study includes comparisons of CB-MBC and MB-MBC against state-of-the-art multi-dimensional classification methods, as well as against commonly used methods for solving the Parkinson’s disease prediction problem, namely, multinomial logistic regression, ordinary least squares, and censored least absolute deviations. For both considered case studies, results are promising in terms of classification accuracy as well as regarding the analysis of the learned MBC graphical structures identifying known and novel interactions among variables. The second contribution, addressing the semi-supervised uni-dimensional streaming classification problem, consists of a novel method (CPL-DS) for classifying partially labeled data streams. Data streams differ from the stationary data sets by their highly rapid generation process and their concept-drifting aspect. That is, the learned concepts and/or the underlying distribution are likely changing and evolving over time, which makes the current classification model out-of-date requiring to be updated. CPL-DS uses the Kullback-Leibler divergence and bootstrapping method to quantify and detect three possible kinds of drift: feature, conditional or dual. Then, if any occurs, a new classification model is learned using the expectation-maximization algorithm; otherwise, the current classification model is kept unchanged. CPL-DS is general as it can be applied to several classification models. Using two different models, namely, naive Bayes classifier and logistic regression, CPL-DS is tested with synthetic data streams and applied to the real-world problem of malware detection, where the new received files should be continuously classified into malware or goodware. Experimental results show that our approach is effective for detecting different kinds of drift from partially labeled data streams, as well as having a good classification performance. Finally, the third contribution, addressing the supervised multi-dimensional streaming classification problem, consists of two adaptive methods, namely, Locally Adaptive-MB-MBC (LA-MB-MBC) and Globally Adaptive-MB-MBC (GA-MB-MBC). Both methods monitor the concept drift over time using the average log-likelihood score and the Page-Hinkley test. Then, if a drift is detected, LA-MB-MBC adapts the current multi-dimensional Bayesian network classifier locally around each changed node, whereas GA-MB-MBC learns a new multi-dimensional Bayesian network classifier from scratch. Experimental study carried out using synthetic multi-dimensional data streams shows the merits of both proposed adaptive methods.

Crystal structure of the RNA-dependent RNA polymerase of hepatitis C virus

We report the crystal structure of the RNA-dependent RNA polymerase of hepatitis C virus, a major human pathogen, to 2.8-Å resolution. This enzyme is a key target for developing specific antiviral therapy. The structure of the catalytic domain contains 531 residues folded in the characteristic fingers, palm, and thumb subdomains. The fingers subdomain contains a region, the “fingertips,” that shares the same fold with reverse transcriptases. Superposition to the available structures of the latter shows that residues from the palm and fingertips are structurally equivalent. In addition, it shows that the hepatitis C virus polymerase was crystallized in a closed fingers conformation, similar to HIV-1 reverse transcriptase in ternary complex with DNA and dTTP [Huang H., Chopra, R., Verdine, G. L. & Harrison, S. C. (1998) Science 282, 1669–1675]. This superposition reveals the majority of the amino acid residues of the hepatitis C virus enzyme that are likely to be implicated in binding to the replicating RNA molecule and to the incoming NTP. It also suggests a rearrangement of the thumb domain as well as a possible concerted movement of thumb and fingertips during translocation of the RNA template-primer in successive polymerization rounds.

Recognition by viral and cellular DNA polymerases of nucleosides bearing bases with nonstandard hydrogen bonding patterns.

The ability of DNA polymerases (pols) to catalyze the template-directed synthesis of duplex oligonucleotides containing a nonstandard Watson-Crick base pair between a nucleotide bearing a 5-(2,4-diaminopyrimidine) heterocycle (d kappa) and a nucleotide bearing either deoxyxanthosine (dX) or N1-methyloxoformycin B (pi) has been investigated. The kappa-X and kappa-pi base pairs are jointed by a hydrogen bonding pattern different from and exclusive of those joining the AT and GC base pairs. Reverse transcriptase from human immunodeficiency virus type 1 (HIV-1) incorporates dXTP into an oligonucleotide opposite d kappa in a template with good fidelity. With lower efficiency and fidelity, HIV-1 reverse transcriptase also incorporates d kappa TP opposite dX in the template. With d pi in the template, no incorporation of d kappa TP was observed with HIV reverse transcriptase. The Klenow fragment of DNA pol I from Escherichia coli does not incorporate d kappa TP opposite dX in a template but does incorporate dXTP opposite d kappa. Bovine DNA pols alpha, beta, and epsilon accept neither dXTP opposite d kappa nor d kappa TP opposite d pi. DNA pols alpha and epsilon (but not beta) incorporate d kappa TP opposite dX in a template but discontinue elongation after incorporating a single additional base. These results are discussed in light of the crystal structure for pol beta and general considerations of how polymerases must interact with an incoming base pair to faithfully copy genetic information.

Analysis of HIV- type 1 protease and reverse transcriptase in Brazilian children failing highly active antiretroviral therapy (HAART)

The aim of this study was to evaluate the genotypic resistance profiles of HIV-1 in children failing highly active antiretroviral therapy (HAART). Forty-one children (median age = 67 months) receiving HAART were submitted to genotypic testing when virological failure was detected. cDNA was extracted from PBMCs and amplified by nested PCR for the reverse transcriptase and protease regions of the pol gene. Drug resistance genotypes were determined from DNA sequencing. According to the genotypic analysis, 12/36 (33.3%) and 6/36 (16.6%) children showed resistance and possible resistance, respectively, to ZDV; 5/36 (14%) and 4/36 (11.1%), respectively, showed resistance and possible resistance to ddI; 4/36 (11.1%) showed resistance to 3TC and D4T; and 3/36 (8.3%) showed resistance to Abacavir. A high percentage (54%) of children exhibited mutations conferring resistance to NNRTI class drugs. Respective rates of resistance and possible resistance to PIs were: RTV (12.2%, 7.3%); APV (2.4%, 12.1%); SQV(0%, 12.1%); IDV (14.6%, 4.9%), NFV (22%, 4.9%), LPV/RTV (2.4%, 12.1%). Overall, 37/41 (90%) children exhibited virus with mutations related to drug resistance, while 9% exhibited resistance to all three antiretroviral drug classes.

In silico screening of HIV-1 non-nucleoside reverse transcriptase and protease inhibitors

Two targets, reverse transcriptase (RT) and protease from HIV-1, were used during the past two decades to the discovery of non-nucleoside reverse transcriptase inhibitors (NNRTI) and protease inhibitors (PI) that belong to the arsenal of the antiretroviral therapy. Herein these enzymes were chosen as templates for conducting a computer-aided ligand design. Ligand and structure-based drug designs were the starting points to select compounds from a database bearing more than five million compounds by means of cheminformatic tools. New promising lead structures are retrieved from the database, which are open to acquisition and test. Classes of molecules already described as NNRTI or PI in the literature also came out and were useful to prove the reliability of the workflow, and thus validating the work carried out so far. (c) 2007 Elsevier Masson SAS. All rights reserved.

Critical role of reverse transcriptase in the inhibitory mechanism of CNI-H0294 on HIV-1 nuclear translocation.

HIV-1 replication requires the translocation of viral genome into the nucleus of a target cell. We recently reported the synthesis of an arylene bis(methyl ketone) compound (CNI-H0294) that inhibits nuclear targeting of the HIV-1 genome and thus HIV-1 replication in monocyte cultures. Here we demonstrate that CNI-H0294 inhibits nuclear targeting of HIV-1-derived preintegration complexes by inactivating the nuclear localization sequence of the HIV-1 matrix antigen in a reaction that absolutely requires reverse transcriptase. This drug/reverse transcriptase interaction defines the specificity of its antiviral effect and is most likely mediated by the pyrimidine side-chain of CNI-H0294. After binding to reverse transcriptase, the carbonyl groups of CNI-H0294 react with the nuclear localization sequence of matrix antigen and prevent its binding to karyopherin alpha, the cellular receptor for nuclear localization sequences that carries proteins into the nucleus. Our results provide a basis for the development of a novel class of compounds that inhibit nuclear translocation and that can, in principle, be modified to target specific infectious agents.

Evaluation of Primary Resistance to HIV Entry Inhibitors Among Brazilian Patients Failing Reverse Transcriptase/Protease Inhibitors Treatment Reveal High Prevalence of Maraviroc Resistance-Related Mutations

Entry inhibitor is a new class of drugs that target the viral envelope protein. This region is variable; hence resistance to these drugs may be present before treatment. The aim of this study was to analyze the frequency of patients failing treatment with transcriptase reverse and protease inhibitors that would respond to the entry inhibitors Enfuvirtide, Maraviroc, and BMS-806. The study included 100 HIV-1 positive patients from one outpatient clinic in the city of Sao Paulo, for whom a genotype test was requested due to treatment failure. Proviral DNA was amplified and sequenced for regions of gp120 and gp41. A total of 80 could be sequenced and from those, 73 (91.3%), 5 (6.3%) and 2 (2.5%) were classified as subtype B, F, and recombinants (B/F and B/C), respectively. CXCR4 co-receptor use was predicted in 30% of the strains. Primary resistance to Enfuvirtide was found in 1.3%, following the AIDS Society consensus list, and 10% would be considered resistant if a broader criterion was used. Resistance to BMS-806 was higher; 6 (7.5%), and was associated to non-B strains. Strikingly, 27.5% of samples harbored one or more mutation among A316T, I323V, and S405A, which have been related to decreased susceptibility of Maraviroc; 15% of them among viruses predictive to be R5. A more common mutation was A316T, which was associated to the Brazilian B strain harboring the GWGR motif at the tip of V3 loop and their derivative sequences. These results may be impact guidelines for genotype testing and treatment in Brazil.

Individual contributions of mutant protease and reverse transcriptase to viral infectivity, replication, and protein maturation of antiretroviral drug-resistant human immunodeficiency virus type 1.

Human immunodeficiency virus type 1 (HIV-1) variants resistant to protease (PR) and reverse transcriptase (RT) inhibitors may display impaired infectivity and replication capacity. The individual contributions of mutated HIV-1 PR and RT to infectivity, replication, RT activity, and protein maturation (herein referred to as "fitness") in recombinant viruses were investigated by separately cloning PR, RT, and PR-RT cassettes from drug-resistant mutant viral isolates into the wild-type NL4-3 background. Both mutant PR and RT contributed to measurable deficits in fitness of viral constructs. In peripheral blood mononuclear cells, replication rates (means +/- standard deviations) of RT recombinants were 72.5% +/- 27.3% and replication rates of PR recombinants were 60.5% +/- 33.6% of the rates of NL4-3. PR mutant deficits were enhanced in CEM T cells, with relative replication rates of PR recombinants decreasing to 15.8% +/- 23.5% of NL4-3 replication rates. Cloning of the cognate RT improved fitness of some PR mutant clones. For a multidrug-resistant virus transmitted through sexual contact, RT constructs displayed a marked infectivity and replication deficit and diminished packaging of Pol proteins (RT content in virions diminished by 56.3% +/- 10.7%, and integrase content diminished by 23.3% +/- 18.4%), a novel mechanism for a decreased-fitness phenotype. Despite the identified impairment of recombinant clones, fitness of two of the three drug-resistant isolates was comparable to that of wild-type, susceptible viruses, suggestive of extensive compensation by genomic regions away from PR and RT. Only limited reversion of mutated positions to wild-type amino acids was observed for the native isolates over 100 viral replication cycles in the absence of drug selective pressure. These data underscore the complex relationship between PR and RT adaptive changes and viral evolution in antiretroviral drug-resistant HIV-1.

Resistance to nucleoside analog reverse transcriptase inhibitors mediated by human immunodeficiency virus type 1 p6 protein.

Resistance of human immunodeficiency virus type 1 (HIV-1) to antiretroviral agents results from target gene mutation within the pol gene, which encodes the viral protease, reverse transcriptase (RT), and integrase. We speculated that mutations in genes other that the drug target could lead to drug resistance. For this purpose, the p1-p6(gag)-p6(pol) region of HIV-1, placed immediately upstream of pol, was analyzed. This region has the potential to alter Pol through frameshift regulation (p1), through improved packaging of viral enzymes (p6(Gag)), or by changes in activation of the viral protease (p6(Pol)). Duplication of the proline-rich p6(Gag) PTAP motif, necessary for late viral cycle activities, was identified in plasma virus from 47 of 222 (21.2%) patients treated with nucleoside analog RT inhibitor (NRTI) antiretroviral therapy but was identified very rarely from drug-naïve individuals. Molecular clones carrying a 3-amino-acid duplication, APPAPP (transframe duplication SPTSPT in p6(Pol)), displayed a delay in protein maturation; however, they packaged a 34% excess of RT and exhibited a marked competitive growth advantage in the presence of NRTIs. This phenotype is reminiscent of the inoculum effect described in bacteriology, where a larger input, or a greater infectivity of an organism with a wild-type antimicrobial target, leads to escape from drug pressure and a higher MIC in vitro. Though the mechanism by which the PTAP region participates in viral maturation is not known, duplication of this proline-rich motif could improve assembly and packaging at membrane locations, resulting in the observed phenotype of increased infectivity and drug resistance.

Use of nucleoside reverse transcriptase inhibitors and risk of myocardial infarction in HIV-infected patients enrolled in the D:A:D study: a multi-cohort collaboration.

BACKGROUND: Whether nucleoside reverse transcriptase inhibitors increase the risk of myocardial infarction in HIV-infected individuals is unclear. Our aim was to explore whether exposure to such drugs was associated with an excess risk of myocardial infarction in a large, prospective observational cohort of HIV-infected patients. METHODS: We used Poisson regression models to quantify the relation between cumulative, recent (currently or within the preceding 6 months), and past use of zidovudine, didanosine, stavudine, lamivudine, and abacavir and development of myocardial infarction in 33 347 patients enrolled in the D:A:D study. We adjusted for cardiovascular risk factors that are unlikely to be affected by antiretroviral therapy, cohort, calendar year, and use of other antiretrovirals. FINDINGS: Over 157,912 person-years, 517 patients had a myocardial infarction. We found no associations between the rate of myocardial infarction and cumulative or recent use of zidovudine, stavudine, or lamivudine. By contrast, recent-but not cumulative-use of abacavir or didanosine was associated with an increased rate of myocardial infarction (compared with those with no recent use of the drugs, relative rate 1.90, 95% CI 1.47-2.45 [p=0.0001] with abacavir and 1.49, 1.14-1.95 [p=0.003] with didanosine); rates were not significantly increased in those who stopped these drugs more than 6 months previously compared with those who had never received these drugs. After adjustment for predicted 10-year risk of coronary heart disease, recent use of both didanosine and abacavir remained associated with increased rates of myocardial infarction (1.49, 1.14-1.95 [p=0.004] with didanosine; 1.89, 1.47-2.45 [p=0.0001] with abacavir). INTERPRETATION: There exists an increased risk of myocardial infarction in patients exposed to abacavir and didanosine within the preceding 6 months. The excess risk does not seem to be explained by underlying established cardiovascular risk factors and was not present beyond 6 months after drug cessation.

Avaliação do perfil de mutações e resistência aos inibidores de transcriptase reversa e protease em pacientes pediátricos infectados pleo HIV-1

Pós-graduação em Pesquisa e Desenvolvimento (Biotecnologia Médica) - FMB

Relevant drug to drug interactions between itraconazole and non-nucleoside reverse transcriptase inhibitors in HIV-infected patients on maintenance therapy for disseminated histoplasmosis

Objective. Itraconazole is recommended life-long for preventing relapse of disseminated histoplasmosis in HIV-infected patients. I sought to determine if serum itraconazole levels are affected by the type of Highly Active Anti-Retroviral Therapy (NNRTI or PI) being taken concomitantly to treat HIV. ^ Design. Retrospective cohort. ^ Methods. De-identified data were used from an IRB-approved parent study which identified patients on HAART and maintenance itraconazole for confirmed disseminated histoplasmosis between January 2003 and December 2006. Available itraconazole blood levels were abstracted as well as medications taken by each patient at the time of the blood tests. Mean itraconazole levels were compared using the student's t-test. ^ Results. 11 patients met study criteria. Patient characteristics were: median age 36, 91% men, 18% white, 18% black, 55% Hispanic and 9% Asians, median CD4 cell count 120 cells/mm3. 14 blood levels were available for analysis—8 on PI, 4 on NNRTI and 2 on both. 8/8 itraconazole levels obtained while taking concomitant PI were therapeutic (>0.4 μg/mL) in contrast to 0/4 obtained while taking NNRTI. Two patients switched from NNRTI to PI and reached therapeutic levels. Mean levels on NNRTI (0.05 μg/mL, s.d. 0.0) and on PI (2.45 μg/mL, s.d. 0.21) for these two patients were compared via a paired t-test (t = 16.00, d.f. = 1, P = 0.04). Remaining patient levels were compared using an unpaired t-test. Mean itraconazole on concomitant PI (n = 6) was 1.37 μg/mL (s.d. 0.74), while the mean on concomitant NNRTI was 0.05 μg/mL (s.d. 0.0), t = 2.39, d.f. = 6, P = 0.05. ^ Conclusions. Co-administration of NNRTI and itraconazole results in significant decreases in itraconazole blood levels, likely by inducing the CYP3A4 enzyme system. Itraconazole drug levels should be monitored in patients on concomitant NNRTI. PI-based HAART may be preferred over NNRTI-based HAART when using itraconazole to treat HIV-infected patients with disseminated histoplasmosis. ^

Concomitant combination therapy for HIV infection preferable over sequential therapy with 3TC and non-nucleoside reverse transcriptase inhibitors

Exposure to 3TC of HIV-1 mutant strains containing non-nucleoside reverse transcriptase inhibitor (NNRTI)-specific mutations in their reverse transcriptase (RT) easily selected for double-mutant viruses that had acquired the characteristic 184-Ile mutation in their RT in addition to the NNRTI-specific mutations. Conversely, exposure of 3TC-resistant 184-Val mutant HIV-1 strains to nine different NNRTIs resulted in the rapid emergence of NNRTI-resistant virus strains at a time that was not more delayed than when wild-type HIV-1(IIIB) was exposed to the same compounds. The RTs of these resistant virus strains had acquired the NNRTI-characteristic mutations in addition to the preexisting 184-Val mutation. Surprisingly, when the 184-Ile mutant HIV-1 was exposed to a variety of NNRTIs, the 188-His mutation invariably occurred concomitantly with the 184-Ile mutation in the HIV-1 RT. Breakthrough of this double-mutant virus was markedly accelerated as compared with the mutant virus selected from the wild-type or 184-Val mutant HIV-1 strain. The double (184-Ile + 188-His) mutant virus showed a much more profound resistance profile against the NNRTIs than the 188-His HIV-1 mutant. In contrast with the sequential chemotherapy, concomitant combination treatment of HIV-1-infected cells with 3TC and a variety of NNRTIs resulted in a dramatic delay of virus breakthrough and resistance development.

Host-parasite dynamics and outgrowth of virus containing a single K70R amino acid change in reverse transcriptase are responsible for the loss of human immunodeficiency virus type 1 RNA load suppression by zidovudine.

The association between human immunodeficiency virus type I (HIV-1) RNA load changes and the emergence of resistant virus variants was investigated in 24 HIV-1-infected asymptomatic persons during 2 years of treatment with zidovudine by sequentially measuring serum HIV-1 RNA load and the relative amounts of HIV-1 RNA containing mutations at reverse transcriptase (RT) codons 70 (K-->R), 41 (M-->L), and 215 (T-->Y/F). A mean maximum decline in RNA load occurred during the first month, followed by a resurgence between 1 and 3 months, which appeared independent of drug-resistance. Mathematical modeling suggests that this resurgence is caused by host-parasite dynamics, and thus reflects infection of the transiently increased numbers of CD4+ lymphocytes. Between 3 and 6 months of treatment, the RNA load returned to baseline values, which was associated with the emergence of virus containing a single lysine to arginine amino acid change at RT codon 70, only conferring an 8-fold reduction in susceptibility. Despite the relative loss of RNA load suppression, selection toward mutations at RT codons 215 and 41 continued. Identical patterns were observed in the mathematical model. While host-parasite dynamics and outgrowth of low-level resistant virus thus appear responsible for the loss of HIV-1 RNA load suppression, zidovudine continues to select for alternative mutations, conferring increasing levels of resistance.