Disentangling the lifespans of hepatitis C virus-infected cells and intracellular vRNA replication-complexes during direct-acting anti-viral therapy

The decay rate of hepatitis C virus (HCV)-infected cells during therapy has been used to determine the duration of treatment needed to attain a sustained virologic response, but with direct-acting anti-virals (DAA), this rate has been difficult to estimate. Here, we show that it is possible to estimate it, by simultaneously analysing the viral load and alanine aminotransferase (ALT) kinetics during combination DAA therapy. We modelled the HCV RNA and ALT serum kinetics in 26 patients with chronic HCV genotype 1b infection, under four different sofosbuvir-based combination treatments. In all patients, ALT decayed exponentially to a set point in the normal range by 1-3 weeks after initiation of therapy. The model indicates that the ALT decay rate during the first few weeks after initiation of therapy reflects the death rate of infected cells, with an estimated median half-life of 2.5 days in this patient population. This information allows independent estimation of the rate of loss of intracellular replication complexes during therapy. Our model also predicts that the final ALT set point is not related to the release of ALT by dying HCV-infected cells. Using ALT data, one can separately obtain information about the rate of 'cure' of HCV-infected cells versus their rate of death, something not possible when analysing only HCV RNA data. This information can be used to compare the effects of different DAA combinations and to rationally evaluate their anti-viral effects.

Dynamic imaging in patients with tuberculosis reveals heterogeneous drug exposures in pulmonary lesions

Tuberculosis (TB) is the leading cause of death from a single infectious agent, requiring at least 6 months of multidrug treatment to achieve cure¹. However, the lack of reliable data on antimicrobial pharmacokinetics (PK) at infection sites hinders efforts to optimize antimicrobial dosing and shorten TB treatments². In this study, we applied a new tool to perform unbiased, noninvasive and multicompartment measurements of antimicrobial concentration-time profiles in humans³. Newly identified patients with rifampin-susceptible pulmonary TB were enrolled in a first-in-human study⁴ using dynamic [¹¹C]rifampin (administered as a microdose) positron emission tomography (PET) and computed tomography (CT). [¹¹C]rifampin PET-CT was safe and demonstrated spatially compartmentalized rifampin exposures in pathologically distinct TB lesions within the same patients, with low cavity wall rifampin exposures. Repeat PET-CT measurements demonstrated independent temporal evolution of rifampin exposure trajectories in different lesions within the same patients. Similar findings were recapitulated by PET-CT in experimentally infected rabbits with cavitary TB and confirmed using postmortem mass spectrometry. Integrated modeling of the PET-captured concentration-time profiles in hollow-fiber bacterial kill curve experiments provided estimates on the rifampin dosing required to achieve cure in 4 months. These data, capturing the spatial and temporal heterogeneity of intralesional drug PK, have major implications for antimicrobial drug development.

Increased inflammation in sanctuary sites may explain viral blips in HIV infection

Combined antiretroviral therapy (cART) suppress HIV-1 viral replication, such that viral load in plasma remains below the limit of detection in standard assays. However, intermittent episodes of transient viremia (blips) occur in a set of HIV-patients. Given that follicular hyperplasia occurs during lymphoid inflammation as a normal response to infection, it is hypothesised that when the diameter of the lymph node follicle (LNF) increases and crosses a critical size, a viral blip occurs due to cryptic viremia. To study this hypothesis, a theoretical analysis of a mathematical model is performed to find the conditions for virus suppression in all compartments and different scenarios of LNF size changes are simulated. According to the analysis, blips with duration of around 30 days arise when the diameter rise rate is between 0.02 and 0.03 days(-1). Moreover, the final diameter of the site is directly related to the steady states of the virus load after the occurrence of a blip. When the value of R0 is around 2.1, to have a steady-state below the limit of detection after the viral blip, the maximum final diameters should be greater than 0.7 mm so that there is a relative loss of connection between compartments.

Treatment with integrase inhibitor suggests a new interpretation of HIV RNA decay curves that reveals a subset of cells with slow integration

The kinetics of HIV-1 decay under treatment depends on the class of antiretrovirals used. Mathematical models are useful to interpret the different profiles, providing quantitative information about the kinetics of virus replication and the cell populations contributing to viral decay. We modeled proviral integration in short- and long-lived infected cells to compare viral kinetics under treatment with and without the integrase inhibitor raltegravir (RAL). We fitted the model to data obtained from participants treated with RAL-containing regimes or with a four-drug regimen of protease and reverse transcriptase inhibitors. Our model explains the existence and quantifies the three phases of HIV-1 RNA decay in RAL-based regimens vs. the two phases observed in therapies without RAL. Our findings indicate that HIV-1 infection is mostly sustained by short-lived infected cells with fast integration and a short viral production period, and by long-lived infected cells with slow integration but an equally short viral production period. We propose that these cells represent activated and resting infected CD4+ T-cells, respectively, and estimate that infection of resting cells represent ~4% of productive reverse transcription events in chronic infection. RAL reveals the kinetics of proviral integration, showing that in short-lived cells the pre-integration population has a half-life of ~7 hours, whereas in long-lived cells this half-life is ~6 weeks. We also show that the efficacy of RAL can be estimated by the difference in viral load at the start of the second phase in protocols with and without RAL. Overall, we provide a mechanistic model of viral infection that parsimoniously explains the kinetics of viral load decline under multiple classes of antiretrovirals.

Modelling HIV-1 2-LTR dynamics following raltegravir intensification

A model of reservoir activation and viral replication is introduced accounting for the production of 2-LTR HIV-1 DNA circles following antiviral intensification with the HIV integrase inhibitor raltegravir, considering contributions of de novo infection events and exogenous sources of infected cells, including quiescent infected cell activation. The model shows that a monotonic increase in measured 2-LTR concentration post intensification is consistent with limited de novo infection primarily maintained by sources of infected cells unaffected by raltegravir, such as quiescent cell activation, while a transient increase in measured 2-LTR concentration is consistent with significant levels of efficient (R0 > 1) de novo infection. The model is validated against patient data from the INTEGRAL study and is shown to have a statistically significant fit relative to the null hypothesis of random measurement variation about a mean. We obtain estimates and confidence intervals for the model parameters, including 2-LTR half-life. Seven of the 13 patients with detectable 2-LTR concentrations from the INTEGRAL study have measured 2-LTR dynamics consistent with significant levels of efficient replication of the virus prior to treatment intensification.

Measurement error robustness of a closed-loop minimal sampling method for HIV therapy switching

We test the robustness of a closed-loop treatment scheduling method to realistic HIV viral load measurement error. The purpose of the algorithm is to allow the accurate detection of an induced viral load minimum with a reduced number of samples. Therapy must be switched at or near the viral-load minimum to achieve optimal therapeutic benefit; therapeutic benefit decreases logarithmically with increased viral load at the switching time. The performance of the algorithm is characterized using a number of metrics. These include the number of samples saved vs. fixed-rate sampling, the risk-reduction achieved vs. the risk-reduction possible with frequent sampling, and the difference between the switching time vs. the theoretical optimal switching time. The algorithm is applied to simulated patient data generated from a family of data-driven patient models and corrupted by experimentally confirmed levels of log-normal noise.

Robust closed-loop minimal sampling method for HIV therapy switching strategies

The emergence of drug-resistant strains of human immunodeficiency virus during antiretroviral therapy is a major cause of treatment failure and disease progression. Development of a resistant strain necessitates switching to a new antiretroviral regimen composed of novel drugs. Recent work has shown that current methods of switching antiviral therapies carry significant unnecessary risk of subsequent failures, and optimal switching schedules to minimize this risk have been proposed. These switching schedules require frequent sampling of viral load during an induced phase of transient viral load reduction, with the goal of switching to the new antiviral regimen at an induced viral load minimum. The proposed frequent sampling carries an unacceptable level of cost both in terms of measurement expense and inconvenience to the patient. In this paper, we propose a closed-loop sampling algorithm to reduce the number of samples required to achieve the desired reduction in risk. We demonstrate through the Monte-Carlo analysis that the proposed method is able to robustly achieve an average 50% reduction in the number of required samples while maintaining a reduction in the risk of subsequent failure to under 3%, despite experimentally verified levels of model and measurement uncertainty.