HIV Model Parameter Estimates from Interruption Trial Data including Drug Efficacy and Reservoir Dynamics

Abstract

Mathematical models based on ordinary differential equations (ODE) have had significant impact on understanding HIV disease dynamics and optimizing patient treatment. A model that characterizes the essential disease dynamics can be used for prediction only if the model parameters are identifiable from clinical data. Most previous parameter identification studies for HIV have used sparsely sampled data from the decay phase following the introduction of therapy. In this paper, model parameters are identified from frequently sampled viral-load data taken from ten patients enrolled in the previously published AutoVac HAART interruption study, providing between 69 and 114 viral load measurements from 3–5 phases of viral decay and rebound for each patient. This dataset is considerably larger than those used in previously published parameter estimation studies. Furthermore, the measurements come from two separate experimental conditions, which allows for the direct estimation of drug efficacy and reservoir contribution rates, two parameters that cannot be identified from decay-phase data alone. A Markov-Chain Monte-Carlo method is used to estimate the model parameter values, with initial estimates obtained using nonlinear least-squares methods. The posterior distributions of the parameter estimates are reported and compared for all patients.

Modeling the three stages in HIV infection

“…The tested ranges in which the model provide the full behaviour of HIV infection are shown in Table 2; some ranges are consistent with other works [48], [49], [50]. We can notice in Fig.4 that the model may reproduce long term behaviour despite high variations of the parameter k T , which may range from 10% below nominal values and 220% above.…”

Autologous Stem Cell Transplantation Disrupts Adaptive Immune Responses during Rebound Simian/Human Immunodeficiency Virus Viremia

“…To that end, we initialized at logical guesses based on literature and then used model fitting to determine the remaining six best-fit parameters for each animal. The susceptible cell rates appear to vary widely between animals; in a human treatment interruption trial, Luo et al fit a model with the same formulation for viral-free target cell dynamics, so we chose similar values (49). Estimates from mouse models of lymphocytic choriomeningitis virus (LCMV) and some human studies can be used to estimate rates of the adaptive immune response.…”

A two-loop robust controller for HIV infection models in the presence of parameter uncertainties

“…A deterministic model of HIV infection therapy can be expressed by the following differential equations [3,9,10,13,14,16]:…”

“…The parameter values of an HIV-infection model depend on the patient's infection condition [13,14]. That is, parameter uncertainties (or parameter variations) may exist in the model.…”

“…Many references [7,8,[13][14][15][16] employed the total concentration of CD4+ T cells and the viral load as measurable state variables. As will be seen later, on the other hand, the proposed controller requires only the measurement of the total concentration of CD4+ T cells.…”