Literature DB >> 12933554

Assessing antiviral potency of anti-HIV therapies in vivo by comparing viral decay rates in viral dynamic models.

A A Ding1, H Wu.   

Abstract

A virologic marker, the number of HIV RNA copies or viral load, is currently used to evaluate anti-HIV therapies in AIDS clinical trials. This marker can be used to assess the antiviral potency of therapies, but is easily affected by noncompliance, drug resistance, toxicities, and other factors during the long-term treatment evaluation process. Recently it has been suggested to use viral dynamics to assess the potency of antiviral drugs and therapies, since viral decay rates in viral dynamic models have been shown to be related to the antiviral drug potency directly, and they need a shorter evaluation time. In this paper we first review the two statistical approaches for characterizing HIV dynamics and estimating viral decay rates: the individual nonlinear least squares regression (INLS) method and the population nonlinear mixed-effect model (PMEM) approach. To compare the viral decay rates between two treatment arms, parametric and nonparametric tests, based on the estimates of viral decay rates (the derived variables) from both the INLS and PMEM methods, are proposed and studied. We show, using the concept of exchangeability, that the test based on the empirical Bayes' estimates from the PMEM is valid, powerful and robust. This proposed method is very useful in most practical cases where the INLS-based tests and the general likelihood ratio test may not apply. We validate and compare various tests for finite samples using Monte Carlo simulations. Finally, we apply the proposed tests to an AIDS clinical trial to compare the antiviral potency between a 3-drug combination regimen and a 4-drug combination regimen. The proposed tests provide some significant evidence that the 4-drug regimen is more potent than the 3-drug regimen, while the naive methods fail to give a significant result.*To whom correspondence should be addressed.

Entities:  

Year:  2001        PMID: 12933554     DOI: 10.1093/biostatistics/2.1.13

Source DB:  PubMed          Journal:  Biostatistics        ISSN: 1465-4644            Impact factor:   5.899


  10 in total

1.  Rapid sample size calculations for a defined likelihood ratio test-based power in mixed-effects models.

Authors:  Camille Vong; Martin Bergstrand; Joakim Nyberg; Mats O Karlsson
Journal:  AAPS J       Date:  2012-02-17       Impact factor: 4.009

2.  Extension of NPDE for evaluation of nonlinear mixed effect models in presence of data below the quantification limit with applications to HIV dynamic model.

Authors:  Thi Huyen Tram Nguyen; Emmanuelle Comets; France Mentré
Journal:  J Pharmacokinet Pharmacodyn       Date:  2012-08-11       Impact factor: 2.745

3.  A Bayesian Approach in Differential Equation Dynamic Models Incorporating Clinical Factors and Covariates.

Authors:  Yangxin Huang
Journal:  J Appl Stat       Date:  2010-02-01       Impact factor: 1.404

4.  Initial viral decay to assess the relative antiretroviral potency of protease inhibitor-sparing, nonnucleoside reverse transcriptase inhibitor-sparing, and nucleoside reverse transcriptase inhibitor-sparing regimens for first-line therapy of HIV infection.

Authors:  Richard H Haubrich; Sharon A Riddler; Heather Ribaudo; Gregory Direnzo; Karin L Klingman; Kevin W Garren; David L Butcher; James F Rooney; Diane V Havlir; John W Mellors
Journal:  AIDS       Date:  2011-11-28       Impact factor: 4.177

5.  Maximum likelihood estimation of long-term HIV dynamic models and antiviral response.

Authors:  Marc Lavielle; Adeline Samson; Ana Karina Fermin; France Mentré
Journal:  Biometrics       Date:  2011-03       Impact factor: 2.571

6.  Hierarchical Bayesian methods for estimation of parameters in a longitudinal HIV dynamic system.

Authors:  Yangxin Huang; Dacheng Liu; Hulin Wu
Journal:  Biometrics       Date:  2006-06       Impact factor: 2.571

7.  Viral decay rates are similar in HIV-infected patients with and without TB coinfection during treatment with an Efavirenz-based regimen.

Authors:  Margaret Lartey; Kwamena W Sagoe; Hongmei Yang; Ernest Kenu; Fafa Xexemeku; Joseph Oliver-Commey; Vincent Boima; Markafui Seshie; Augustine Sagoe; Julius A A Mingle; Timothy P Flanigan; Hulin Wu; Awewura Kwara
Journal:  Clin Infect Dis       Date:  2011-01-20       Impact factor: 9.079

8.  Hierarchical Bayesian inference for HIV dynamic differential equation models incorporating multiple treatment factors.

Authors:  Yangxin Huang; Hulin Wu; Edward P Acosta
Journal:  Biom J       Date:  2010-08       Impact factor: 2.207

9.  Pharmacodynamics of antiretroviral agents in HIV-1 infected patients: using viral dynamic models that incorporate drug susceptibility and adherence.

Authors:  Hulin Wu; Yangxin Huang; Edward P Acosta; Jeong-Gun Park; Song Yu; Susan L Rosenkranz; Daniel R Kuritzkes; Joseph J Eron; Alan S Perelson; John G Gerber
Journal:  J Pharmacokinet Pharmacodyn       Date:  2006-04-01       Impact factor: 2.745

10.  Modulation of HCV replication after combination antiretroviral therapy in HCV/HIV co-infected patients.

Authors:  Kenneth E Sherman; Jeremie Guedj; Mohamed Tarek Shata; Jason T Blackard; Susan D Rouster; Mario Castro; Judith Feinberg; Richard K Sterling; Zachary Goodman; Bruce J Aronow; Alan S Perelson
Journal:  Sci Transl Med       Date:  2014-07-23       Impact factor: 17.956

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.