A5055 Long-Term Viral Dynamic Data (including PK, Adherence, and Drug Resistance): Hierarchical Bayesian (NLME) Models

1. The data were generated from AIDS Clinical Trials Group, ACTG 5055 study, which is sponsored by NIAID/NIH. More details on this clinical study can be found in Acosta et al (2004, JAIDS). If you have any question regarding the data, you may contact Dr. Hulin Wu by email: hwu@bst.rochester.edu.

2. Among total 44 patients accrued in this study, 42 subjects were included in the analysis; of the remaining two subjects, one was excluded from the analysis because the PK parameters were not obtained and the other was excluded because the phenotype assay could not be completed on this subject.

3. Detection limit of the viral load (HIV RNA copies) assay is 50 copies per ml blood. If it is below detectable, we imputed as 25 in the data set.

4. The complete data are used in the following data analysis including rebounded viral load data (Huang et al 2005, Biometrics). See the following references for more related analyses and models.

5. For model fitting we used data of viral load, along with three factors data: IC50, adherence and PK. For Fortran code, we used "open" commend to read datasets of viral load (A5055-V42.dat) and measurement time (A5055-day42.dat), and IC50, adherence and PK data were directly integrated with Fortran code (A5055.for). However, All of these datasets are available on this website.

6. Please cite the following references if appropriate when you use the data in your paper:


1. Acosta EP, Wu H, Walawander A, Eron J, Pettinelli C, Yu S, Neath D, Ferguson E, Saah AJ, Kuritzkes DR, Gerber JG, for the Adult ACTG 5055 Protocol Team (2004). Comparison of two indinavir/ritonavir regimens in treatment-experienced HIV-infected individuals. Journal of Acquired Immune Deficiency Syndromes. 37:1358-1366

2. Huang Y. and Wu, H. (2004). Mechanistic PK/PD modeling of antiretroviral therapies in AIDS clinical trials. In: Biomedical Simulations Resource PK/PD. New York: Kluwer Academic Publishers. 221-231.

3. Wu, H., Huang, Y., Acosta, P. E., Rosenkranz S.L., et al. (2005). Modeling antiretroviral response: effects of drug potency, pharmacokinetics, adherence and drug resistance. JAIDS 39, 272-283.

4. Huang Y, Liu D, Wu H.(2005), Hierarchical Bayesian methods for estimation of parameters in a longitudinal HIV dynamic system. Biometrics. (In press)

5. Huang Y. and Wu H. (2005). A Bayesian approach for estimating antiviral efficacy in HIV dynamic model. Journal of Applied Statistics (In press)

6. Huang, Y. and Wu, H. (2005). Bayesian estimation of individual parameters in an HIV dynamic model using long-term viral load data. Chapter 15 in Deterministic and Stochastic Models of AIDS Epidemics and HIV Infections with Intervention. Edited by W.Y. Tan and H. Wu. Singapore: World Scientific. 361-383.

7. Wu, H., Huang, Y., Acosta, P. E, Rosenkranz S.L., et al. (2005). Pharmacodynamics of antiretroviral agents in HIV-1 infected patients using viral dynamic models with consideration of drug susceptibility and adherence. Journal of Pharmacokinetics and Pharmacodynamics (submitted)


1. A5055-rna42.txt (Original dataset)
2. A5055-ic50.txt (IC50 data)
3. A5055-pk.txt (PK data)
4. A5055-adh.txt (Adherence data)
5. A5055-V42.dat (Viral load data)
6. A5055-day42.dat (Time)
7. A5055.for (Fortran code)