Population kinetics of parasitemia.
The buildup of the kinetic models started with inspection of the natural logarithms of the parasite count,
P(
t), excluding the negative blood smears. It appeared that for most individuals the decline of ln
P(
t) was more or less constant over all 8-h intervals, with the exception of the first interval. In a plot of the geometric mean values (Fig.
FA1), this is less clear, because the tail of the mean curve is distorted by blood smears becoming negative. So it seemed rational to start with a simple log-linear (exponential) decline of the parasitemia as the basic model and build from this. In the formula
or
A is the estimated initial parasitemia and
k is the elimination rate constant.
k can be recalculated into a more conventional elimination half-life,
t1/2el, according to the formula
In a mixed-effects population kinetics model, parameters can be entered as fixed, which means that they have a certain value, or be nonfixed, which means that they can vary at random with a mean value of zero. Parameters can also vary depending on another factor, for example, the treatment regimen. The formula of such an exponential decline of the parasitemia looks like
In this way, several models can be applied to the data from all patients. Models were compared by using the Bayesian information criterion (BIC) (
12). The method of maximum likelihoods was used when different models were to be compared. After choosing the best model, restricted maximum likelihood was used to estimate the parameters.
A model with random intercepts and slopes and with independent residuals was tested as well as a comparable model in which the residuals, ɛ, are normal with autocorrelation, ρ. Autocorrelation in this context means that the repeated measurements over time are correlated; in other words, the value of parasitemia at a certain time predicts the following value. A third model, model I, in whichk was allowed to change per treatment regimen, was also constructed, and this model gave the best fit (BIC = 4,720.6). Negative blood smears, with a value of zero, were not included in the models. The three models were also fitted to ln [P(t) + 0.5], with zero values included, but this did not have a significant effect on the BIC and estimates. In further modeling, zero values were excluded from the data set.
Although the initial parasite counts were not different among the three treatment groups, a modification of model I was made in which the intercept, ln A, was allowed to change per regimen. As expected (treatment groups were similar with respect to baseline parasite count), this did not improve the fit, so that the variation of this term could be interpreted as a random effect.
In model I, the estimate of
A was greater than the observed initial parasitemia,
P(0), or put simply, after drug intake, it takes some time before the parasitemia starts to decline. This time can conceptually be simplified to a lag phase,
tlag. Although the lag phase is not readily explained in biologically plausible terms, in clinical experience, a lag phase is usually interpreted as the time until
P(
t) has decreased to values lower than
P(0). Moreover, the definitions of the in vivo response to drug treatment are based on decrease of the parasite count relative to the initial parasitemia, thereby ignoring that, in many patients, the parasitemia increases initially and that the lag times may be different for individuals. When the lag time is incorporated into the kinetic models, these have the form
From this formula it can easily be seen that in log-linear models,
A and
tlag are interdependent, which means that a difference in
tlag also affects the value of
A. Nevertheless, the models were fitted, again incorporating
tlag, and this did not yield better fits than model I.
To investigate further if a mathematical function could give an better description of the initial part of the curve, irrespective of biological interpretation, a quadratic term was added to the basic model:
This quadratic model was worked out analogously to the simple (log)linear model with normal residuals ɛ with autocorrelation, with
c and
k changing per regimen, or with only
k changing per regimen. The latter two models were also investigated with
k as a fixed factor. Model II, the model with a fixed quadratic term,
c, for every patient and a linear term,
k, variable per patient, but depending on regimen, yielded the best fit (BIC = 4,719.4). In both models I and II, the estimates of
k were comparable for regimens AQ3 and AQ5 and different from those for regimen Q. The difference in BIC for models I and II was small, and model II had 1 df more than model I. Another approach to describe the initial rise in the parasite count was to build models in which the logarithm of
t was factored in. The basic model of this approach looks like
This model did not improve the fits.
Alternatively, a model with an exponential term added to this logarithm of
t is described by
or written as
Model III, with
s fixed for all patients, independent of treatment regimen, and
k, with random variation, but with a value depending on the regimen, yielded the best fits (BIC = 4,640) of this group of models.
The problem with this model was that there was no plausible biological explanation behind it. Since antimalarial drugs are parasite stage specific, and since replication may continue in the lag phase, a term for parasite stage was incorporated in the model. For this purpose, we adopted a model constructed by White, who assumed a normal distribution in four patients who failed to respond to treatment was applied (
17). It was assumed that the distribution of the parasites' age has a mean value, μ, with a standard deviation (SD). For the convenience of calculations, a logistic distribution of the parasites' age was assumed. This is a small difference from White's model. This model, model IV, which is basically an extension of model III, but with a biologically plausible interpretation of the lag phase, can be described by
simplified to
where ς = (SD · π)/ ∥√3 and
s is the stage in the parasites' cycle.
However, the fits of model IV were not better than those of model III, but also were not better than those of model I or II. In addition, the lag phase is probably determined not only by parasite factors, but also by pharmacokinetic and other factors. Because model IV did not improve the fits, and because models II and III lacked any plausible biological explanation of the parameters c and s, respectively, it was decided that the simplest model, model I, would be taken as the best description for the data. In this model, the concept of Pterm, fitted by this model, gave a plausible explanation of the mechanism of recrudescence.