Bioinformatics Advance Access originally published online on December 21, 2004
Bioinformatics 2005 21(8):1668-1677; doi:10.1093/bioinformatics/bti230
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Evaluation of HIV-1 kinetic models using quantitative discrimination analysis
Department of Chemical Engineering, University of Connecticut Storrs, CT 06269, USA
*To whom correspondence should be addressed.
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Abstract |
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Motivation: Since the identification of human immunodeficiency virus (HIV) over twenty years ago, many mathematical models of HIV dynamics have been proposed. The purpose of this study was to evaluate intracellular and intercellular scale HIV models that best described the dynamics of viral and cell titers of a person, where parameters were determined using typically available patient data. In this case, ‘best’ was defined as the model most capable of describing experimental patient data and was determined by Bayesian-based model discrimination analysis and the ability to provide realistic results.
Results: Twenty models of HIV-1 viral dynamics were initially evaluated to determine whether parameters could be obtained from readily available clinical data from established HIV-1 patients with stable disease. Based on this analysis, three models were chosen for further examination and comparison. Parameters were estimated using experimental data from a cohort of 338 people monitored for up to 2484 days. The models were evaluated using a Bayesian technique to determine which model was most probable. The model ultimately selected as most probable was overwhelmingly favored relative to the remaining two models, and it accounted for uninfected cells, infected cells and cytotoxic T lymphocyte dynamics. The authors developed a fourth model for comparison purposes by combining the features of the original three models. Parameters were estimated for the new model and the statistical analysis was repeated for all four models. The model that was initially favored was selected again upon model discrimination analysis.
Contact: srivasta{at}engr.uconn.edu
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INTRODUCTION |
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TOPAbstractINTRODUCTIONSYSTEMS AND METHODSALGORITHMRESULTSDISCUSSIONAPPENDIXREFERENCES |
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Since it was initially identified, significant progress has been made in treating human immunodeficiency virus (HIV) due to improved treatment regimens, earlier identification and increased public awareness (Fauci, 2003; Pomerantz and Horn, 2003). However, there is still disagreement and confusion concerning the disease on several fronts. For example, many researchers are trying to elucidate which cells can be infected and which contribute a significant number of viral particles to infection (Brenchley et al., 2004). Others are attempting to determine whether intermittent viral rebound arises from hidden viral reservoirs, from production of resistant viral strains or from drugs that are not completely effective (Cohen Stuart et al., 2001; Herz et al., 1996). The determination of exactly how HIV causes acquired immune deficiency syndrome (AIDS) is also still open to debate (Stebbing et al., 2004; Stevenson, 2003). Traditionally experimental data has been gathered independently of creating a kinetic model. When models are developed, they are frequently based on experimental data from only a few patients. By comparing multiple models to a large experimental dataset, it may be possible to characterize this disease more precisely. Kinetic modeling is an important tool for understanding the course of infection in an afflicted individual, as well as for optimizing treatment strategies and characterizing antivirals. The work presented here is a contribution to the global effort to provide better treatment to those infected with HIV.
A large number of deterministic and stochastic intercellular and intracellular models have been proposed to understand the dynamics of HIV (Bonhoeffer et al., 1997a,b; Nowak et al., 1997; Perelson et al., 1996; Phillips, 1996; Reddy and Yin, 1999; Ribeiro and Bonhoeffer et al., 1999; Tan and Wu, 1998; Tuckwell and Le Corfec, 1998). In addition, a large amount of experimental data has been gathered through clinical trials such as the AIDS Clinical Trials Groups of the National Institutes of Health. Although many kinetic models have been developed to simulate viral infection, little effort has been made to identify, quantitatively or qualitatively, which model can best predict the course of the disease in a specific person. One study compared three deterministic models of human immunodeficiency type 1 (HIV-1) infection, monitoring the differences in different cells and ascertaining the location of the cell populations (Snedecor, 2003). While the research yielded beneficial results on the quality of various models, no quantitative evaluation was performed among the models. The current work attempts to determine which model best describes HIV-1 dynamics based on available experimental data.
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SYSTEMS AND METHODS |
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TOPAbstractINTRODUCTIONSYSTEMS AND METHODSALGORITHMRESULTSDISCUSSIONAPPENDIXREFERENCES |
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Parameters were estimated based on data provided by the VA hospital in West Haven, Connecticut, for a cohort of 338 people monitored for up to 2484 days (Hamilton et al., 1992; O'Brien et al., 1996). This data set is suitable for the current study because of the large number of patients included and the length of time for which they were monitored. To qualify for the VA study, patients had to have symptoms or signs of HIV infection as well as a CD4+ titer between 200 and 500 cells/mm3. In addition, patients with AIDS-defining illnesses were screened out of the study, ensuring that no one had progressed to AIDS. Patients were generally stable in their disease, as those classified as unstable were omitted from observation. Additional characteristics of the patients involved are summarized by Hamilton et al. (1992). The patients were treated with AZT either immediately upon entering the study or when their CD4+ counts dropped <200 cells/mm3. The experimental data includes virus, CD4+ cell and white blood cell (WBC) titers, the percentage of WBCs that are lymphocytes and the number of days from baseline that the measurements were taken. Baseline was defined as the time point when the first reading was taken for a given study participant.
Twenty intercellular models, ranging from simple to complex, were initially examined for the current study (Bonhoeffer et al., 1997a,b; De Boer and Perelson, 1998; Essunger and Perelson, 1994; Funk et al., 2001; Nelson et al., 2001; Perelson, 2002; Perelson et al., 1996; Stafford et al., 2000; Wein et al., 1998; Wodarz and Nowak, 2002) with several papers proposing more than one model. Table 1 lists the features of HIV infection that were accounted for in each model. The initial criterion for model selection was the ability to estimate parameters from typical patient data. Models that compensate for drug treatment and the corresponding efficacy of reverse transcriptase and protease inhibitors provide useful detail; however, for a number of reasons for this study, only data collected from untreated patients were used. By studying untreated patients, it was possible to focus on the details of HIV dynamics without external interference from medication. Another reason for not evaluating drug-dependent models was that they often monitored variables such as non-infectious virus particles. While it is true that these particles are created during treatment, there is no simple way to determine the number of defective particles from available experimental data. It would also be difficult to compare the treatment regimen used in the VA study to present courses of therapy because monotherapy has been replaced by highly active antiretroviral therapy (HAART) and drug cocktails (Flint et al., 2004; Goldsby et al., 2003). Further, AZT resistance develops quickly thus limiting its use (Flint et al., 2004; Goldsby et al., 2003) and various HAART treatments rarely use the same combinations of reverse transcriptase inhibitors (RTI) and protease inhibitors (PI). As a result, drug-dependent models were excluded. Other models were not included for analysis because they monitored variables that could not be quantified without significant effort or genome sequencing. These variables included active or latent infection, infection with different viral strains and virus particles susceptible or resistant to therapy.
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Given that infected and uninfected cells are subpopulations of the total CD4+ count, it was necessary to approximate what proportion of a patient's CD4+ count could be attributed to infected cells. Many reports in the literature attempt to determine what fraction of the CD4+ population is infected with HIV-1, with results ranging from one infected cell per 150 000 CD4+ cells (6.7x 10–6%) to 69 infected cells per 100 CD4+ cells (69%) (Bagasra et al., 1992,1993; Bieniasz et al., 1993; Brinchmann et al., 1991; Chun et al., 1997; Harper et al., 1986; Ho et al., 1989; Hsia and Spector, 1991; Macatonia et al., 1990; Poznansky et al., 1991; Re et al., 1994; Schnittman et al., 1889,1990; Simmonds et al., 1990). In the present analysis an intermediate value of 2% infected CD4+ cells was assumed, which may be high for newly infected people and low for someone nearing fully-developed AIDS.
A further assumption was necessary to determine what percentage of WBCs were cytotoxic T lymphocytes (CTL), as shown in Figure 1. CTLs are the mature form of cytotoxic T (Tc) cells that mediate killing of infected cells. For each experimental measurement of WBCs, there was a corresponding percentage of lymphocytes. Between 48 and 67% of lymphocytes are T cells (Goldsby et al., 2003). For this study 58% of lymphocytes were assumed to be T cells. Further, 
30% of T cells have CD8+ on their surface (Goldsby et al., 2003), indicative of a Tc cell; however, to the knowledge of the authors, there is no known relation between the number of Tc cells and the number of active CTL effector cells at any given point in time. To simulate a minimal and a complete immune response, CTL cells were assumed to be either 1 or 100% of the Tc population in two different scenarios.
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Three literature models of cellular and viral interactions were chosen for detailed analysis. Table 2 summarizes the parameter values used for each model. Parameters having the same function in different models are referred to by the same name. For example, k1 refers to the rate constant characterizing uninfected cell production in all models. The value of the parameter, however, will differ from model to model based on what is and what is not accounted for. By presenting the parameters in this way, the authors felt a better reference for comparison was established.
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The first model, referred to as the basic model, monitors uninfected cells (X), infected cells (Y) and virus (V)
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The second model is a simpler version of the basic model and will be referred to as the simplified model. The number of uninfected cells is assumed to be constant,
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In the third model, or the immune model, a constant number of virions is assumed. The model also includes the response of the immune system (Z)
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A fourth model, referred to as the composite model, was developed during this study to incorporate aspects of the first three models presented. This was done with the ambition of creating a more comprehensive model that simultaneously accounted directly for the immune system response and for the viral dynamics.
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One should note that CTLs do not remove free virus particles; they only remove infected cells as indicated in Equation (10) by the rate k9YZ. The actions of CTLs are generally only against cells displaying viral antigen on their surface, not virus particles themselves (Goldsby et al., 2003).
To provide a common frame of reference, all parameters for each model were estimated from the VA data. Parameter estimation was carried out using non-linear least squares regression analysis. Minimization of the objective function was accomplished by using the Global Optimization package v.4.2 from Loehle Enterprises. Once values of rate constants were estimated, the posterior probability share for each model was calculated by using the Bayesian technique described in the Algorithm section.
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ALGORITHM |
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TOPAbstractINTRODUCTIONSYSTEMS AND METHODSALGORITHMRESULTSDISCUSSIONAPPENDIXREFERENCES |
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Model discrimination was carried out using the method of Stewart et al. (1998). For convenience, a brief description of the method is provided here. Measurements of experimental data can be expressed mathematically as
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and the parameter vector 
plus some error 
. There are i = 1, ..., m responses for each of the u = 1, ..., n events of j = 1, ..., J models. To account for variation in the precision of observations with experimental conditions, Equation (13) may be weighted by the square root of the inverse variance of yiu (Stewart et al., 1998).
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, respectively. The experimental dataset provided for discrimination analysis was highly irregular, as shown in Table 3, meaning that many responses were missing and there was no identifiable pattern of data collection. A ‘response’ was the reading of one patient for a specified event. An ‘event’, for the purposes of this study, was defined as any day on which a measurement was made relative to the baseline. Although methods exist for comparing models with highly irregular data, also described by Stewart et al. (1998) the methods require a known covariance matrix. The covariance matrix was not calculable from the data obtained for this study, so the only methods applicable are those for regular data. To force the data into a regular, rectangular form, as shown in Table 4, responses that had a missing variable, such as CD4+ or virus titer, were not included in the analysis. Events that had only one response were ignored, because calculating the variance requires a minimum of two responses.
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For full data with unknown covariance, the posterior probability for a given model is calculated with a Bayesian- based technique. The probability of the j-th model, given a set of observations, is
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is the matrix of weighted observations; pj is the number of parameters in model j; 
is the matrix of products of the deviation of Yiu from the predicted value for model j, evaluated at the maximum likelihood j; and ve is the number of degrees of freedom. p(Mj) is the prior probability of model j, and is assumed to be equal for all models unless there is reason to give a higher probability to a particular model. The ik-th element of is calculated according to (Stewart et al., 1992, 1998; Stewart and Sørenson, 1981).
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Once the posterior probability is calculated for each model, normalization to a sum of one gives the posterior probability share, 
, of model Mj: (Stewart et al., 1996, 1998).
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is considered the most probable. Although the methods detailed above will allow for the selection of the best model among a set of potential models, the model with the highest probability share is not necessarily a good model. If all models characterize the system poorly, calculating will simply choose the least poor model (Stewart et al., 1998). A good model must be able to use clinical data to accurately describe viral dynamics. |
RESULTS |
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TOPAbstractINTRODUCTIONSYSTEMS AND METHODSALGORITHMRESULTSDISCUSSIONAPPENDIXREFERENCES |
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After 17 of the original 20 models were excluded from consideration, non-linear least squares regression analysis was used to determine parameter values for the remaining models. Parameter values, units and definitions are displayed in Table 2. Figures 2–5 show the predictions of each model versus average experimental data with respect to the four variables: uninfected cells, infected cells, virus particles and CTL response. Results presented in all figures and tables were based on the assumption that CTLs comprised 1% of Tc cells because it is unlikely that every Tc cell would be an effector at any point in time.
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The three models that accounted for uninfected cells each reached a steady state shortly after baseline. A noticeable difference among the models was the steady state value, where the basic model predicted 180 cells/mm3 after 150 days, and the immune model predicted the largest value of 267 cells/mm3 after 100 days (Fig. 2). To be included in the VA Cooperative Study Group, patients had to have CD4+ concentrations ranging from 200–500 cells/mm3, which indicates that they were far along in the course of infection. The basic model was slightly different from the immune and composite models in that it predicted an initial decline and resurgence of uninfected cells, which is accurate for initial infection, but not for individuals that have had the disease for some time. The cause of the transient behavior may be the result of an elevated initial condition, which was calculated by averaging all baseline readings. On day zero 167 individuals had CD4+ readings, but few subsequent days included more than 10 readings on average. This fact may indicate that as the number of days from baseline increased, those with higher virus titers discontinued the study. Fewer recordings per day can also be attributed to not having all patients return on the same day from baseline for follow-up; many days had only one response and were thus not included in model discrimination.
All four models accounted for the change in the number of infected cells but their trajectories were very different. In Figure 3, the basic model generated an infected cell trajectory that had an initial abrupt increase followed shortly thereafter by a sharp decrease and maintenance of a steady state. This result correlated with the decrease and increase of uninfected cells, shown in Figure 2. As HIV proliferated, the pool of healthy cells decreased and the number of infected cells increased. Both the immune and composite models predicted a very low value of infected cells beginning at baseline and continuing throughout the course of infection. Because the number of infected cells went to zero after a short time, the uninfected cells reached steady state in both models. In the immune model k8XY dropped out of Equation (6) and k1 balanced k2X. Once infected cells had been eliminated in the composite model, the number of virus particles declined because of k6V of Equation (11) which then caused a decrease in the rate of change of X as represented by Equation (9). When virus was not present, k1 and k2X of Equation (9) balanced each other to achieve steady state. The simplified model trajectory decreased monotonically and appeared to best fit the infected cell data. From Equation (4) it is apparent that the decrease in infected cells is due to the fact that more infected cells die than are produced after each round of infection.
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The basic, simplified and composite models had very different trajectories for viral titer. The simplified model most accurately fits the viral experimental data upon visual inspection. The slow decrease mimicked the slow decline in the number of infected cells. The basic model predicted a dramatic increase in concentration followed by decline and achievement of steady state. The number of virions increased dramatically because the number of infected cells also rose, yielding more successful infections. It is easy to see from Figure 4 that the steady state of the basic model was significantly higher than the other model predictions (note the secondary axis on the right), and not very representative of the experimental data. A large production rate of new virions, k5, and low clearance rate, k6, most likely caused this trend. The composite model decreased from the initial condition until the eventual prediction of viral clearance. Steady-state analysis, shown in Table 5, indicated that although the simplified model also eventually predicted viral clearance, this was not reached until after approximately 30 000 days.
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The immune system response was a feature of only the immune and composite models. All results presented in Figure 5 are for the assumption that CTLs were 1% of Tc cells. However, it was interesting to note that, except for CTLs themselves, results were essentially the same when CTLs were assumed to be 100% of the Tc population (data not shown). This behavior suggested the system was robust with respect to a wide range of CTL values. The composite model appeared to reflect the data well, with a slight decrease in titer over time. This effect could be explained by recent evidence that CD4 on the surface of CD8+ cells causes infection of those cells by HIV (Kitchen et al., 2004), thus decreasing the population through the course of infection. The immune model also predicted values close to experimental titers, but the decrease in concentration of CTL cells was more dramatic and larger at the beginning of the experiment. The effect was an increasing discrepancy between the two models over the course of time. Consistent decline was seen because once infected cells were removed from the system, the only term present in Equation (8) was death of CTLs.
A summary of the steady-state values for all four models is presented in Table 5. A number of the steady states are reached beyond the life expectancy of a patient, and others are achieved beyond the life expectancy of a healthy human being. Although the simplified model predicts a trivial state of viral and infected cell clearance, this occurs after a very long time. On the other hand, though the immune and composite models take a long time to reach their steady-state values, it is easy to see that after a short time the predicted values are already approaching zero in Figures 3 and 4, which is known to be unrealistic.
The immune model was overwhelmingly favored as most likely, based on model discrimination analysis. Table 6 presents the probability share of the four models considered in the present analysis. While the results of Table 1 are for the entire range of data, the immune model still had a probability of 1.00 when all models were fit to the first 25, 50, 100, 250 and 500 days (data not shown). It was peculiar that with the parameters used in this study, the immune model predicted clearance of infected cells, which is not a biological reality. The immune model may have had the largest because it had a variance several orders of magnitude lower than that of all other models, and therefore a larger determinant in Equation (15). One can see from this equation that having a large variance will penalize the model. The end result was a probability share many orders of magnitude larger than that of the other three models considered.
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If the immune model was ignored and the probability share was recalculated over the entire course of the infection the simplified model was the most favored. If the immune model was disregarded and models were again fitted and evaluated for the first 25, 50, 100, 250 or 500 days of experimental data, the simplified model still retained the highest probability share (data not shown). This was expected because the composite model predicted clearance of virus and infected cells, both of which are incorrect in vivo. In addition, the basic model poorly fits the data in the first 100 days of all variables considered. As a result, the simplified model best fits the data over the range considered, yielding a probability share of 1.00. Although the simplified model eventually predicted the elimination of the virus, this did not occur within the time frame of the data presented.
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DISCUSSION |
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TOPAbstractINTRODUCTIONSYSTEMS AND METHODSALGORITHMRESULTSDISCUSSIONAPPENDIXREFERENCES |
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Three steps were employed to determine the best model of HIV-1 intercellular kinetics: estimating parameters from clinical data, using model discrimination analysis to mathematically determine the best model and utilizing steady-state analysis to conclude if cell titer and viral load predictions were logical and sensible. A prediction was considered logical and sensible if it agreed with clinical data. Twenty deterministic models of HIV-1 intercellular dynamics were narrowed to three based on the variables they accounted for and the available experimental data. Upon using model discrimination analysis, the immune model had the largest probability share of the three models evaluated, implying it was the most favorable. However, steady-state analysis made it clear that this result was unrealistic because, as shown in Figure 3, infected cells were falsely predicted to be cleared from the body. Interestingly, although the immune model was very similar to the composite model, and analogous parameters were assigned values within one order of magnitude to each other, the probability shares were very different. Both models incorporated immune-mediated cell killing in addition to infected and uninfected cells, and both erroneously predicted clearance of infected cells. A significant difference was that the immune model explicitly assumed a constant number of virions. However, if at steady state Equation (11) is set equal to zero, as in the immune model, Y = k6V/k5. Substituting this into the k8XY of Equations (6) and (7), one can see that both models are equivalent at steady state. The composite model's additional prediction of viral clearance was a possible reason why it had a lower probability share than the immune model. While it is true that being exposed to HIV will not definitely cause infection, once an individual has contracted the disease, it is not eliminated from the body.
Although it was initially surprising that the immune model was the most favored, this result can be explained mathematically. It is important to realize the immune model only accounted for infected/uninfected CD4+ T cells and CTLs. It did not explicitly account for viral dynamics and was not fitted to viral data; rather, it assumed a constant level of virus. Because of this assumption, a steady state was reached where virus and uninfected cells coexisted without the presence of infected cells. The constant viral load assumption may have been an oversimplification because although a steady state is reached within a single person, a uniform steady-state concentration is not achieved within a population (Bonhoeffer et al., 2003). More suitable data may have avoided such assumptions, and may have given different probability shares for the models considered.
Caution should be exercised if applying the immune model to the entire course of infection because the viral titer is not always at steady state. In the initial stage of the disease, the amount of virus has a precipitous increase and then declines to a steady-state value (Stafford et al., 2000). At the later stages immediately preceding and after the on-set of AIDS, virus titer increases, causing a decline in the number of healthy CD4+ T cells. Assuming that the immune model is most likely, it would be applicable to the experimental data because many of the patients in this study had HIV for a long enough time to have reached a steady viral titer. However, the number of infected cells would not be accurately accounted for, as the model predicts eventual clearance.
Even though the immune model was determined to be the most favorable under the conditions studied based on the Bayesian analysis, the model may in fact not be the best in general. The immune model ignored changes in virus concentration, and poorly predicted the number of infected cells (Fig. 3). Perhaps with the appropriate data this model would forecast titers more precisely. However, given the available data, the best model to use for clinical studies may be the simplified model because it only had infected cells and virus in measured quantities, and did not predict clearance of either quantity in the time frame of interest. In addition, this model was assigned the highest probability share when the immune model was disregarded.
It is apparent that as a model becomes more complex it is more difficult to accurately account for everything occurring in the system, which is evidenced by the fact that the most complex model had a low probability share. The composite model was an extension of the immune model because it explicitly accounted for viral dynamics. Yet when compared, the composite model had a significantly lower probability share then the immune model. Furthermore, the parameters estimated for the composite model not only predicted clearance of infected cells, but also predicted clearance of virus particles. Similarly, the basic model was an extension of the simplified model, with the basic model explicitly accounting for uninfected cells. Yet the simplified model was considered the better candidate based on the discrimination analysis, although the difference in the probability shares was not as dramatic.
Supplementary research can apply the same methods of model discrimination to systems that include terms, such as drug efficacy, that account for changes in the viral network as a result of drug treatment. Such research may provide a definitive decision of which model is most acceptable.
Although the immune model had the highest probability share, the simplified model appears to be the most accurate for the present. In general, the models considered were most applicable for the intermediate stages of HIV-1 infection, as that was the range of data used in this study. During the analysis it was necessary to make several assumptions, including how many Tc cells were CTLs. Better assays for measuring the number of CTLs in HIV patients would allow a more comprehensive analysis of the models. However, the feasibility of such assays remains to be determined. Appropriate CTL data may give a more accurate representation of titers over time and may lead to the definitive decision that the immune model is the best.
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APPENDIX |
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TOPAbstractINTRODUCTIONSYSTEMS AND METHODSALGORITHMRESULTSDISCUSSIONAPPENDIXREFERENCES |
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- = Error
- E = Weighted error
- fji = Prediction of response i by model j
- Fji = Weighted prediction of response i by model j
- i, k = Response number
- j = Model number
- M = Model
- p = Probability
- pj = Number of parameters in model j
= Posterior probability share
j = Vector of parameters for model j
- u = Event number
- ve = Degrees of freedom
= Maximum likelihood value of v(j)
- V = Virus particle
u = Vector of independent variables for event u
- X = Uninfected CD4+ T cell
- yiu = Observation of response i in event u
- Yiu = Weighted observation of response i for event u
- Y = Infected CD4+ T cell
- Z = Cytotoxic T lymphocyte (CTL) effector
- E = Weighted error
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Acknowledgments |
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We thank Dr Pamela Hartigan and Tenya Economou of the West Haven VA for providing the experimental data used in this research, as well as Dr Warren Stewart for helpful discussions regarding model discrimination. We also thank Alex Shutak for comments regarding the manuscript.
Received on August 24, 2004; revised on December 13, 2004; accepted on December 15, 2004
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TOPAbstractINTRODUCTIONSYSTEMS AND METHODSALGORITHMRESULTSDISCUSSIONAPPENDIXREFERENCES |
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