Biomedical subjects

Hulin Wu

Publications and source records attributed to Hulin Wu.

22 records · Page 2Linked to original sources

PubMed2003

The relationship between virologic and immunologic responses in AIDS clinical research using mixed-effects varying-coefficient models with measurement error.

In this article we study the relationship between virologic and immunologic responses in AIDS clinical trials. Since plasma HIV RNA copies (viral load) and CD4+ cell counts are crucial virologic and immunologic markers for HIV infection, it is important to study their relationship during HIV/AIDS treatment. We propose a mixed-effects varying-coefficient model based on an exploratory analysis of data from a clinical trial. Since both viral load and CD4+ cell counts are subject to measurement error, we also consider the measurement error problem in covariates in our model. The regression spline method is proposed for inference for parameters in the proposed model. The regression spline method transforms the unknown nonparametric components into parametric functions. It is relatively simple to implement using readily available software, and parameter inference can be developed from standard parametric models. We apply the proposed models and methods to an AIDS clinical study. From this study, we find an interesting relationship between viral load and CD4+ cell counts during antiviral treatments. Biological interpretations and clinical implications are discussed.

Acquired Immunodeficiency Syndrome↗

PubMed2003

Indinavir, efavirenz, and abacavir pharmacokinetics in human immunodeficiency virus-infected subjects.

Adult AIDS Clinical Trials Group (AACTG) Protocol 886 examined the dispositions of indinavir, efavirenz, and abacavir in human immunodeficiency virus-infected subjects who received indinavir at 1,000 mg every 8 h (q8h) and efavirenz at 600 mg q24h or indinavir at 1,200 mg and efavirenz at 300 mg q12h with or without abacavir 300 at mg q12h. Thirty-six subjects participated. The median minimum concentration in plasma (C(min)) for indinavir administered at 1,200 mg q12h was 88.1 nM (interquartile range [IR], 61.7 to 116.5 nM), whereas the median C(min) for indinavir administered at 1,000 mg q8h was 139.3 nM (IR, 68.8 to 308.7 nM) (P = 0.19). Compared to the minimum C(min) range for wild-type virus (80 to 120 ng/ml) estimated by the AACTG Adult Pharmacology Committee, the C(min) for indinavir administered at 1,200 mg q12h (54 ng/ml) is inadequate. The apparent oral clearance (CL/F) (P = 0.28), apparent volume of distribution at steady state (V(ss)/F) (P = 0.25), and half-life (t(1/2)) (P = 0.80) of indinavir did not differ between regimens. The levels of efavirenz exposure were similar between regimens. For efavirenz administered at 600 mg q24h and 300 mg q12h, the median maximum concentrations in plasma (C(max)s) were 8,968 nM (IR, 5,784 to 11,768 nM) and 8,317 nM (6,587 to 10,239 nM), respectively (P = 0.66), and the C(min)s were 4,289 nM (IR, 2,462 to 5,904 nM) and 4,757 nM (IR, 3,088 to 6,644 nM), respectively (P = 0.29). Efavirenz pharmacokinetic parameters such as CL/F (P = 0.62), V(ss)/F (P = 0.33), and t(1/2) (P = 0.37) were similar regardless of the dosing regimen. The median C(max), C(min), CL/F, V(ss)/F, and t(1/2) for abacavir were 6,852 nM (IR, 5,702 to 7,532), 21.0 nM (IR, 21.0 to 87.5), 43.7 liters/h (IR, 37.9 to 55.2), 153.9 liters (IR, 79.6 to 164.4), and 2.0 h (IR, 1.8 to 2.8), respectively. In summary, when indinavir was given with efavirenz, the trough concentration of indinavir after administration of 1,200 mg q12h was inadequate. Abacavir did not influence the pharmacokinetics or exposure parameters of either indinavir or efavirenz. The levels of efavirenz exposure were similar in subjects receiving efavirenz q12h or q24h.

Administration, Oral↗

PubMed2002

The study of long-term HIV dynamics using semi-parametric non-linear mixed-effects models.

Modelling HIV dynamics has played an important role in understanding the pathogenesis of HIV infection in the past several years. Non-linear parametric models, derived from the mechanisms of HIV infection and drug action, have been used to fit short-term clinical data from AIDS clinical trials. However, it is found that the parametric models may not be adequate to fit long-term HIV dynamic data. To preserve the meaningful interpretation of the short-term HIV dynamic models as well as to characterize the long-term dynamics, we introduce a class of semi-parametric non-linear mixed-effects (NLME) models. The models are non-linear in population characteristics (fixed effects) and individual variations (random effects), both of which are modelled semi-parametrically. A basis-based approach is proposed to fit the models, which transforms a general semi-parametric NLME model into a set of standard parametric NLME models, indexed by the bases used. The bases that we employ are natural cubic splines for easy implementation. The resulting standard NLME models are low-dimensional and easy to solve. Statistical inferences that include testing parametric against semi-parametric mixed-effects are investigated. Innovative bootstrap procedures are developed for simulating the empirical distributions of the test statistics. Small-scale simulation and bootstrap studies show that our bootstrap procedures work well. The proposed approach and procedures are applied to long-term HIV dynamic data from an AIDS clinical study.

Acquired Immunodeficiency Syndrome↗

PubMed2002

Identification of significant host factors for HIV dynamics modelled by non-linear mixed-effects models.

Non-linear mixed-effects models are powerful tools for modelling HIV viral dynamics. In AIDS clinical trials, the viral load measurements for each subject are often sparse. In such cases, linearization procedures are usually used for inferences. Under such linearization procedures, however, standard covariate selection methods based on the approximate likelihood, such as the likelihood ratio test, may not be reliable. In order to identify significant host factors for HIV dynamics, in this paper we consider two alternative approaches for covariate selection: one is based on individual non-linear least square estimates and the other is based on individual empirical Bayes estimates. Our simulation study shows that, if the within-individual data are sparse and the between-individual variation is large, the two alternative covariate selection methods are more reliable than the likelihood ratio test, and the more powerful method based on individual empirical Bayes estimates is especially preferable. We also consider the missing data in covariates. The commonly used missing data methods may lead to misleading results. We recommend a multiple imputation method to handle missing covariates. A real data set from an AIDS clinical trial is analysed based on various covariate selection methods and missing data methods.

Acquired Immunodeficiency Syndrome↗