Factors associated with virological response in HIV-infected patients failing antiretroviral therapy: a prospective cohort study

Multiple Molecular Dynamics Simulations of the Inhibitor GRL-02031 Complex with Wild Type and Mutant HIV-1 Protease Reveal the Binding and Drug-Resistance Mechanism

Human immunodeficiency virus type 1 (HIV-1) protease is regarded as a fascinating target for drug development against HIV infection. However, mutations causing drug resistance severely limit the efficiency of the recently marketed drugs in the treatment of HIV replication. To elucidate the binding mechanism of HIV-1 protease with promising inhibitor GRL-02031 and further to probe the resistance mechanism associated with mutations (I47V, L76V, V82A, and N88D) to the inhibitor, we applied multiple molecular dynamics (MMD) simulations along with energy analysis by the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) and solvated interaction energy (SIE) methodology on specific HIV-1 protease with GRL-0231 complexes. On the basis of detail analysis of the simulations, we revealed key characteristics that constitute the drug resistance of four mutation HIV-1 proteases toward GRL-02031: substitution of the side chain in these four mutation residues leads to a change in the distances between the flaps and catalytic sites, thereby reducing the affinity for GRL-02031 with these four mutation proteases, even though the L76V and N88D residues cannot directly contact GRL-02031. The results of energy analysis according to the MM-PBSA and SIE methods further indicated that hydrophobic interaction was considered to be the prime driving force for inhibitor GRL-02031 binding to protease and the decrease in van der Waals interactions between inhibitor GRL-02031 and mutant proteases as the primary cause of the drug resistance. Analyses of the hydrogen bonds and atomic interactions further provided detailed explanations for the resistance of these four mutation proteases toward inhibitor GRL-02031. The present study provides potential guidance on the structure-based inhibitors' design targeting HIV-1 protease.

Patients' demographic and clinical characteristics and level of care associated with lost to follow-up and mortality in adult patients on first-line ART in Nigerian hospitals

Introduction

Clinical outcome is an important determinant of programme success. This study aims to evaluate patients’ baseline characteristics as well as level of care associated with lost to follow-up (LTFU) and mortality of patients on antiretroviral treatment (ART).

Methods

Retrospective cohort study using routine service data of adult patients initiated on ART in 2007 in 10 selected hospitals in Nigeria. We captured data using an electronic medical record system and analyzed using Stata. Outcome measures were probability of being alive and retained in care at 12, 24 and 36 months on ART. Potential predictors associated with time to mortality and time to LTFU were assessed using competing risks regression models.

Results

After 12 months on therapy, 85% of patients were alive and on ART. Survival decreased to 81.2% and 76.1% at 24 and 36 months, respectively. Median CD4 count for patients at ART start, 12, 18 and 24 months were 152 (interquartile range, IQR: 75 to 242), 312 (IQR: 194 to 450), 344 (IQR: 227 to 501) and 372 (IQR: 246 to 517) cells/µl, respectively. Competing risk regression showed that patients’ baseline characteristics significantly associated with LTFU were male (adjusted sub-hazard ratio, sHR=1.24 [95% CI: 1.08 to 1.42]), ambulatory functional status (adjusted sHR=1.25 [95% CI: 1.01 to 1.54]), World Health Organization (WHO) clinical Stage II (adjusted sHR=1.31 [95% CI: 1.08 to 1.59]) and care in a secondary site (adjusted sHR=0.76 [95% CI: 0.66 to 0.87]). Those associated with mortality include CD4 count <50 cells/µl (adjusted sHR=2.84 [95% CI: 1.20 to 6.71]), WHO clinical Stage III (adjusted sHR=2.67 [95% CI: 1.26 to 5.65]) and Stage IV (adjusted sHR=5.04 [95% CI: 1.93 to 13.16]) and care in a secondary site (adjusted sHR=2.21 [95% CI: 1.30 to 3.77]).

Conclusions

Mortality was associated with advanced HIV disease and care in secondary facilities. Earlier initiation of therapy and strengthening systems in secondary level facilities may improve retention and ultimately contribute to better clinical outcomes.

Optimal timing and duration of induction therapy for HIV-1 infection

The tradeoff between the need to suppress drug-resistant viruses and the problem of treatment toxicity has led to the development of various drug-sparing HIV-1 treatment strategies. Here we use a stochastic simulation model for viral dynamics to investigate how the timing and duration of the induction phase of induction–maintenance therapies might be optimized. Our model suggests that under a variety of biologically plausible conditions, 6–10 mo of induction therapy are needed to achieve durable suppression and maximize the probability of eradicating viruses resistant to the maintenance regimen. For induction regimens of more limited duration, a delayed-induction or -intensification period initiated sometime after the start of maintenance therapy appears to be optimal. The optimal delay length depends on the fitness of resistant viruses and the rate at which target-cell populations recover after therapy is initiated. These observations have implications for both the timing and the kinds of drugs selected for induction–maintenance and therapy-intensification strategies.

Clinicians treating HIV infection must balance the need to suppress viral replication against the harmful side effects and significant cost of antiretroviral therapy. Inadequate therapy often results in the emergence of resistant viruses and treatment failure. These difficulties are especially acute in resource-poor settings, where antiretroviral agents are limited. This has prompted an interest in induction–maintenance (IM) treatment strategies, in which brief intensive therapy is used to reduce host viral levels. Induction is followed by a simplified and more easily tolerated maintenance regimen. IM approaches remain an unproven concept in HIV therapy. We have developed a mathematical model to simulate clinical responses to antiretroviral drug therapy. We account for latent infection, partial drug efficacy, cross-resistance, viral recombination, and other factors. This model accurately reflects expected outcomes under single, double, and standard three-drug antiretroviral therapy. When applied to IM therapy, we find that (1) IM is expected to be successful beyond 3 y under a variety of conditions; (2) short-term induction therapy is optimally started several days to weeks after the start of maintenance; and (3) IM therapy may eradicate some preexisting drug-resistant viral strains from the host. Our simulations may help develop new treatment strategies and optimize future clinical trials.

Utility of CD4 cell counts for early prediction of virological failure during antiretroviral therapy in a resource-limited setting

BACKGROUND

Viral load monitoring is not available for the vast majority of patients receiving antiretroviral therapy in resource-limited settings. However, the practical utility of CD4 cell count measurements as an alternative monitoring strategy has not been rigorously assessed.

METHODS

In this study, we used a novel modelling approach that accounted for all CD4 cell count and VL values measured during follow-up from the first date that VL suppression was achieved. We determined the associations between CD4 counts (absolute values and changes during ART), VL measurements and risk of virological failure (VL > 1,000 copies/ml) following initial VL suppression in 330 patients in South Africa. CD4 count changes were modelled both as the difference from baseline (DeltaCD4 count) and the difference between consecutive values (CD4 count slope) using all 3-monthly CD4 count measurements during follow-up.

RESULTS

During 7093.2 patient-months of observation 3756 paired CD4 count and VL measurements were made. In patients who developed virological failure (n = 179), VL correlated significantly with absolute CD4 counts (r = - 0.08, P = 0.003), DeltaCD4 counts (r = - 0.11, P < 0.01), and most strongly with CD4 count slopes (r = - 0.30, P < 0.001). However, the distributions of the absolute CD4 counts, DeltaCD4 counts and CD4 count slopes at the time of virological failure did not differ significantly from the corresponding distributions in those without virological failure (P = 0.99, P = 0.92 and P = 0.75, respectively). Moreover, in a receiver operating characteristic (ROC) curve, the association between a negative CD4 count slope and virological failure was poor (area under the curve = 0.59; sensitivity = 53.0%; specificity = 63.6%; positive predictive value = 10.9%).

CONCLUSION

CD4 count changes correlated significantly with VL at group level but had very limited utility in identifying virological failure in individual patients. CD4 count is an inadequate alternative to VL measurement for early detection of virological failure.

Comparative biological and clinical outcomes after a switch from a virologically unsuccessful first protease inhibitor-containing antiretroviral combination to a 3-drug regimen containing efavirenz, nevirapine, or abacavir

BACKGROUND

Incomplete adherence is the main cause of antiretroviral therapy failure during initial combination antiretroviral therapy (cART). A switch to a protease inhibitor-sparing cART may be useful when a patient does not tolerate a first protease inhibitor-containing cART regimen.

METHODS

To compare the biological and clinical outcomes of patients in whom a first protease inhibitor-containing cART regimen failed to control viral replication and whose treatment was switched to cART containing efavirenz, nevirapine, or abacavir, we studied 1440 patients from the French Hospital Database on HIV whose treatment was changed from a first protease inhibitor-containing cART to a 3-drug regimen with either efavirenz, nevirapine, or abacavir while their plasma viral load was detectable.

RESULTS

Kaplan-Meier 12-month probabilities of virological suppression were 73.6%, 53.9%, and 66.1% among patients whose treatment was switched to efavirenz-cART, nevirapine-cART, and abacavir-cART, respectively. Factors associated with a lower likelihood of virological suppression were antiretroviral exposure before the first cART, higher plasma viral load values at the treatment switch, a stavudine-lamivudine backbone after the switch (instead of a zidovudine-lamivudine backbone), and a switch to nevirapine (adjusted hazard ratio, 0.63 [95% CI, 0.54-0.74], compared with efavirenz) or abacavir (adjusted hazard ratio, 0.84 [95% CI, 0.68-1.04] compared with efavirenz). There was no difference among the 3 groups with regard to immunological gain (>50 CD4+ T cells/mm3) or clinical outcome.

CONCLUSION

When virological rebound occurs from receipt of protease inhibitor-containing cART, virological suppression can be obtained after a switch to a protease inhibitor-free cART--efavirenz-cART yielding the highest rate of virological suppression.

Drug-resistance mutations in antiretroviral-naive patients with established HIV-1 infection in Mexico

OBJECTIVES

To describe the prevalence of baseline drug-resistance mutations, resistance to antiretroviral drugs, and the subsequent virological response to therapy in treatment-naïve patients from Mexico with established HIV-1 infection.

METHODS

Resistance testing was performed on plasma samples from antiretroviral-naïve patients. Data on mutations associated with antiretroviral drug resistance were obtained using Stanford software (http://hivdb.stanford.edu).

RESULTS

Ninety-six treatment-naïve individuals were enrolled in the study during 2002-2003. Of these, 83 patients (86%) had at least one resistance mutation and 15 (16%) had drug resistance. At baseline, the mean plasma viral load was 299 834 HIV-1 RNA copies/mL, and at follow-up it was 37 620 copies/mL (P<0.0001). Primary mutations in the reverse transcriptase region were observed in 15% of patients. For nucleoside inhibitors, mutations T215Y/C and F77L (3%) and D67N/S, T69N and M184V (2%), were detected. For nonnucleoside inhibitors, mutations K103N/R (6%), Y181C (3%) and G190A (2%) were detected. Overall, 6% of patients showed resistance to delavirdine and nevirapine, 4% to efavirenz, and 2% to lamivudine and nelfinavir. Twelve patients showed no response to treatment and three of these patients had antiretroviral drug resistance.

CONCLUSIONS

The prevalence of baseline drug-resistance mutations found in this study was similar to that found in previous reports for newly HIV-infected individuals, although access to and management of antiretrovirals in Mexico are different.