A pooled analysis of CD4 response to zidovudine and zalcitabine treatment in patients with AIDS and AIDS-related complex

Superiority of directly administered antiretroviral therapy over self-administered therapy among HIV-infected drug users: a prospective, randomized, controlled trial

BACKGROUND

Directly administered antiretroviral therapy (DAART) is one approach to improve treatment adherence among human immunodeficiency virus (HIV)-infected drug users.

METHODS

In this randomized, controlled trial (ClinicalTrials.gov identifier, NCT00367172), the biological outcomes of a 6-month community intervention of DAART were compared with those of self-administered therapy among HIV-infected drug users. Patients randomized to receive DAART received supervised therapy 5 days per week from workers in a mobile health care van. The primary outcome, using an intention-to-treat approach, was the proportion of patients achieving either a reduction in HIV-1 RNA level of > or = 1.0 log10 copies/mL or an HIV-1 RNA level < or = 400 copies/mL at 6 months. Secondary outcomes included the mean change from baseline in HIV-1 RNA level and CD4+ T lymphocyte count.

RESULTS

Of the 141 patients who met the entry criteria, 88 were randomized to receive DAART, and 53 were randomized to receive self-administered therapy; 74 (84%) of 88 of the patients randomized to receive DAART accepted the intervention. Of the 74 patients who initiated DAART, 51 (69%) completed the full 6-month intervention. At the end of 6 months, a significantly greater proportion of the DAART group achieved the primary outcome (70.5% vs. 54.7; P=.02). Additionally, compared with patients receiving self-administered therapy, patients receiving DAART demonstrated a significantly greater mean reduction in HIV-1 RNA level (-1.16 log10 copies/mL vs. -0.29 log10 copies/mL; P=.03) and mean increase in CD4+ T lymphocyte count (+58.8 cells/microL vs. -24.0 cells/microL; P=.002).

CONCLUSIONS

This randomized, controlled trial was, to our knowledge, the first to demonstrate the effectiveness of DAART at improving 6-month virologic outcomes among drug users. These results suggest that DAART should be more widely available in HIV treatment programs that target drug users who have poor adherence to treatment.

Non-linear mixed effects modeling - from methodology and software development to driving implementation in drug development science

Few scientific contributions have made significant impact unless there was a champion who had the vision to see the potential for its use in seemingly disparate areas-and who then drove active implementation. In this paper, we present a historical summary of the development of non-linear mixed effects (NLME) modeling up to the more recent extensions of this statistical methodology. The paper places strong emphasis on the pivotal role played by Lewis B. Sheiner (1940-2004), who used this statistical methodology to elucidate solutions to real problems identified in clinical practice and in medical research and on how he drove implementation of the proposed solutions. A succinct overview of the evolution of the NLME modeling methodology is presented as well as ideas on how its expansion helped to provide guidance for a more scientific view of (model-based) drug development that reduces empiricism in favor of critical quantitative thinking and decision making.

Application and impact of population pharmacokinetics in the assessment of antiretroviral pharmacotherapy

Population pharmacokinetics has been an important technique used to explore and define relevant sources of variation in drug exposure and response in patient populations. This has been especially true in the area of antiretroviral therapy where the assurance of adequate and sustained drug exposure of multiple agents is highly correlated with therapeutic success. Population pharmacokinetic analyses across the four drug classes and 20 US FDA-approved products used to treat HIV have been published to date. The published reports were predominantly based on actual clinical trials conducted in HIV-infected patients with one or more agents administered. Modelling and simulation approaches have been used in the evaluation of antiretroviral agent outcomes incorporating problematic design and analysis factors such as sparse plasma sampling, data imbalance and censored data. Additional benefits of population modelling approaches applied to the investigation of antiretroviral agents include the ability to assess dosing compliance, understanding and quantifying drug-drug interactions in order to select dosing regimens and the screening of new drug candidates. Pharmacokinetic/pharmacodynamic models have been used to characterise the relationship between drug exposure and virological and immunological response, and to predict clinical outcome. These models offer the best opportunity for individualising and optimising patient therapy, particularly when adjusted for adherence/compliance. The impact of population pharmacokinetics in the area of antiretroviral therapy can be directly assessed by its role in the validation of surrogate markers such as viral RNA load, therapeutic drug monitoring and the management of individual patient outcomes via exposure-toxicity relationships. Each of these population pharmacokinetic outcomes has contributed to the current regulatory environment, specifically in the area of accelerated approval of new antiretroviral agents.

Systematic review and meta-analysis of evidence for increasing numbers of drugs in antiretroviral combination therapy

OBJECTIVE

To assess the evidence for the effectiveness of increasing numbers of drugs in antiretroviral combination therapy.

DESIGN

Systematic review, meta-analysis, and meta-regression of fully reported randomised controlled trials. All studies included compared quadruple versus triple therapy, triple versus double therapy, double versus monotherapy, or monotherapy versus placebo or no treatment.

PARTICIPANTS

Patients with any stage of HIV infection who had not received antiretroviral therapy.

MAIN OUTCOME MEASURES

Changes in disease progression or death (clinical outcomes); CD4 count and plasma viral load (surrogate markers).

SEARCH STRATEGY

Six electronic databases, including Medline, Embase, and the Cochrane Library, searched up to February 2001.

RESULTS

54 randomised controlled trials, most of good quality, with 66 comparison groups were included in the analysis. For both the clinical outcomes and surrogate markers, combinations with up to and including three (triple therapy) were progressively and significantly more effective. The odds ratio for disease progression or death for triple therapy compared with double therapy was 0.6 (95% confidence interval 0.5 to 0.8). Heterogeneity in effect sizes was present in many outcomes but was largely related to the drugs used and trial quality.

CONCLUSIONS

Evidence from randomised controlled trials supports the use of triple therapy. Research is needed on the effectiveness of quadruple therapies and the relative effectiveness of specific combinations of drugs.

Automated covariate model building within NONMEM

PURPOSE

One important task in population pharmacokinetic/pharmacodynamic model building is to identify the relationships between the parameters and demographic factors (covariates). The purpose of this study is to present an automated procedure that accomplishes this. The benefits of the proposed procedure over other commonly used methods are (i) the covariate model is built for all parameters simultaneously, (ii) the covariate model is built within the population modeling program (NONMEM) giving familiar meaning to the significance levels used, (iii) it can appropriately handle covariates that varies over time and (iv) it is not dependent on the quality of the posterior Bayes estimates of the individual parameter values. For situations in which the computer run-times are a limiting factor, a linearization of the non-linear mixed effects model is proposed and evaluated.

METHODS

The covariate model is built in a stepwise fashion in which both linear and non-linear relationships between the parameters and covariates are considered. The linearization is basically a linear mixed effects model in which the population predictions and their derivatives with respect to the parameters are fixed from a model without covariates. The stepwise procedure as well as the linearization was evaluated using simulations in which the covariates were taken from a real data set.

RESULTS

The covariate models identified agreed well with what could be expected based on the covariates that were actually supported in each of the simulated data sets. The predictive performance of the linearized model was close to that of the non-linearized model.

CONCLUSIONS

The proposed procedure identifies covariate models that are close to the model supported by the data set as well as being useful in the prediction of new data. The linearized model performs nearly as well as the non-linearized model.

Use of a pharmacokinetic/pharmacodynamic model to design an optimal dose input profile

A model for the pharmacodynamic effect of a drug (designated only X), and the use of the model to explore optimal input is described. The data analyzed here are from a crossover comparison study of the effect of 4 active treatments, yielding distinct concentration vs. time curves, plus placebo in 32 subjects. The model expresses total effect as the sum of placebo effect and (pure) drug effect. The latter allows for possible tolerance (found) and time effects (not found). Random effects allow interindividual differences to be expressed. Conditioning on the fitted model, a population optimal input profile is designed that obeys certain protocol constraints. The profile minimizes the expectation of a sum of squared differences between target effect and the resulting response over a given time interval. The target is a fixed constant, chosen to be either the individuals' maximum effect level in response to a baseline input regimen used in the study or the maximum effect level for the typical individual in the population in response to this regimen, as predicted from the model. The expectation is taken over the estimated nonparametric distribution of the 32 subjects' random effects. As one goal of early clinical studies of drugs may be to provide a basis for designing an optimal delivery profile (with respect to a specified loss function), we suggest this report as an example of a reasonable way to go about finding such a profile.

A semiparametric method for describing noisy population pharmacokinetic data

We propose a semiparametric method to estimate model-independent pharmacokinetic (PK) measures such as area under concentration-time, peak concentration and time to peak concentration (Tpeak), for noisy population PK data from a sparsely sampled prospectively designed trial. The method is developed within the mixed-effect model framework, for the single-dose and steady-state case. We describe individual concentration vs. time using a longitudinal spline, consisting of a template spline, common to all individuals, and an individual-specific distortion spline accounting for individual differences. We impose a number of constraints on the longitudinal spline, including (i) it has a decreasing tail, (ii) its typical Tpeak is near the modal Tpeak observed in the population data, and (iii) its value is zero at time zero (single dose), or the same nonzero value at the beginning and end of a dosing interval (steady state). We test our method using simulated data and compare its performance to that of a parametric and a nonparametric method. An actual data example is also shown. The performance of the method is as good or better than that of a standard nonparametric method, and when the analysis model is misspecified, the method is superior to a standard parametric one. Since it is often not apparent that an analysis model is correct, we propose this approach as a general method for analysis.