Increasing the use of second-line therapy is a cost-effective approach to prevent the spread of drug-resistant HIV: a mathematical modelling study

METHODS: We develop a deterministic mathematical model representing Kampala, Uganda, to predict the prevalence of TDR over a 10-year period. We then compare the impact on TDR and cost-effectiveness of: (1) introduction of pre-therapy genotyping; (2) doubling use of second-line treatment to 80% (50-90%) of patients with confirmed virological failure on first-line ART; and (3) increasing viral load monitoring from yearly to twice yearly. An intervention can be considered cost-effective if it costs less than three times the gross domestic product per capita per quality adjusted life year (QALY) gained, or less than $3420 in Uganda.RESULTS: The prevalence of TDR is predicted to rise from 6.7$1612 to $2234 (IQR$450-dominated) per QALY gained.CONCLUSIONS: While earlier treatment initiation will result in a predicted increase in the proportion of patients infected with drug-resistant HIV, the absolute numbers of patients infected with drug-resistant HIV is predicted to decrease. Increasing use of second-line treatment to all patients with confirmed failure on first-line therapy is a cost-effective approach to reduce TDR. Improving access to second-line ART is therefore a major priority.INTRODUCTION: Earlier antiretroviral therapy (ART) initiation reduces HIV-1 incidence. This benefit may be offset by increased transmitted drug resistance (TDR), which could limit future HIV treatment options. We analyze the epidemiological impact and cost-effectiveness of strategies to reduce TDR

Long term probability of detection of HIV-1 drug resistance after starting antiretroviral therapy routine clinical practice.

BACKGROUND: Little is known about the long term risk of development of HIV-1 drug resistance for patients starting antiretroviral therapy (ART) with three or four drug regimens in routine clinical practice. METHODS: We analysed a large cohort study of patients seen in one of six large HIV clinics in and around London, UK. The focus of this analysis was on patients who started ART with two nucleosides plus either a single protease inhibitor (PI), a PI with ritonavir, abacavir or a non-nucleoside reverse transcriptase inhibitor (NNRTI). RESULTS: 4306 patients were followed; 1436 (33%) started with a single PI, 279 (6%) with a PI plus ritonavir, 156 (4%) with triple nucleosides and 2435 (57%) with an NNRTI. The overall cumulative risk of viral load failure was 38% by 6 years. Risk of > or =1 major IAS-USA mutation was 27% by 6 years; risk of mutations from at least two of the three main drug classes was 20% over the same period. These are lower limit estimates as test results were not available for many with viral load failure. Risk of PI mutations being detected in people who started ART with regimens containing a PI with ritonavir was significantly lower than the risk of NNRTI mutations being detected in those starting with NNRTI-containing regimens (relative hazard 0.3195% CI 0.15-0.61; p = 0.0008). CONCLUSION: In routine practice, rates of viral load failure and of resistance detection in patients who started ART with three or four drugs are appreciable.The UK HIV Drug Resistance Database is partly funded by the Department of Health; additional support is provided by Bristol-Myers Squibb, Gilead, Pfizer, and Tibotec (a division of JanssenCilag Ltd). This project was supported by the European AIDS Treatment Network (NEAT; European Commission NEAT contract FP6/03757)

The population genetics of drug resistance evolution in natural populations of viral, bacterial and eukaryotic pathogens

Drug resistance is a costly consequence of pathogen evolution and a major concern in public health. In this review, we show how population genetics can be used to study the evolution of drug resistance and also how drug resistance evolution is informative as an evolutionary model system. We highlight five examples from diverse organisms with particular focus on: (i) identifying drug resistance loci in the malaria parasite Plasmodium falciparum using the genomic signatures of selective sweeps, (ii) determining the role of epistasis in drug resistance evolution in influenza, (iii) quantifying the role of standing genetic variation in the evolution of drug resistance in HIV, (iv) using drug resistance mutations to study clonal interference dynamics in tuberculosis and (v) analysing the population structure of the core and accessory genome of Staphylococcus aureus to understand the spread of methicillin resistance. Throughout this review, we discuss the uses of sequence data and population genetic theory in studying the evolution of drug resistance