PubMed Health⌕ Search

PubMed · 10799355

Current developments and future prospects for HIV gene therapy using interfering RNA-based strategies.

Abstract

Acquired immunodeficiency syndrome (AIDS) is a slow, progressive, degenerative disease of the human immune system, ultimately leading to premature death of the patient. This disease is primarily caused by human immunodeficiency virus type-1 (HIV-1). The major targets of HIV infection are blood cells, namely lymphocytes and macrophages. While the immune response fails to eliminate the infected cells, the virus continues to spread. The purpose of HIV gene therapy is to provide "anti-HIV" genes to cells that are susceptible to HIV infection. Anti-HIV genes may be designed to express RNAs or proteins that interfere with the function of viral or cellular RNA(s)/protein(s), thereby inhibiting virus replication. Whereas interfering proteins may be cytotoxic and/or immunogenic, interfering RNAs are not. Interfering protein-based strategies requiring inducible gene expression (under the control of HIV regulatory proteins) can only be designed to block steps subsequent to the viral regulatory protein production. In contrast, interfering RNAs can be produced in a constitutive manner, which further enhances their antiviral activity and allows one to design strategies to inhibit virus replication before viral regulatory protein production occurs. Thus, interfering RNAs are of particular interest and are the focus of this review. Genes expressing interfering RNAs were designed to inhibit syncytium formation to prevent the death of the gene-modified cells. Strategies may also be developed to prevent gene-modified cells from becoming infected by HIV or from supporting HIV replication. Genes expressing interfering RNAs have been designed to inhibit HIV-1 entry and to cleave the incoming virion RNA, thus blocking virus replication before provirus DNA synthesis can be completed. A number of genes were also designed to express interfering RNAs that inhibit HIV replication at a post-integration step, by inhibiting the function of HIV RNAs or proteins produced in the infected cell. Also in development are anti-HIV genes that produce RNAs that would not only inhibit HIV replication in the gene-modified cell, but also prevent HIV RNA packaging and/or reverse transcription such that the progeny virus produced would be non-infectious. Further refinements to these strategies may lead to the development of "self-propagating" anti-HIV genes. These genes would express interfering RNAs that not only inhibit virus replication in the cell and prevent HIV RNA packaging and/or reverse transcription in the progeny virus, but also make use of the HIV itself to deliver the anti-HIV gene(s) to other cells. Thus, more and more cells susceptible to HIV infection would become resistant. Such "self-propagation" of anti-HIV-1 genes would only occur in cells that are susceptible to HIV infection, and would continue to take place for as long as HIV exists in the body.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

B Lamothe, S Joshi. 2000-05-01. Current developments and future prospects for HIV gene therapy using interfering RNA-based strategies.. https://doi.org/10.2741/lamothe

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

On the exact interval estimation for the difference in paired areas under the ROC curves.

An important measure for comparison of accuracy between two diagnostic procedures is the difference in paired areas under the receiver operating characteristic (ROC) curves. Non-parametric and maximum likelihood methods have been proposed for interval estimation for the difference in paired areas under ROC curves. However, these two methods are asymptotic procedures and their performance in finite sample sizes has not been thoroughly investigated. We propose to use the concept of generalized pivotal quantities (GPQs) to construct an exact confidence interval for the difference in paired areas under ROC curves. A simulation study is conducted to empirically investigate the probability coverage and expected length of the three methods for various combinations of sample sizes, values of the area under the ROC curve and correlations. Simulation results demonstrate that the exact confidence interval based on the concept of GPQs provides not only sufficient probability coverage but also reasonable expected length. Numerical examples using published data sets illustrate the proposed method.

Clinical Trials as Topic↗

An efficient test for the analysis of dichotomized variables when the reliability is known.

A difference in an outcome variable between the treatment groups in a trial does not necessarily mean that there is a difference in the number of patients who experience relevant improvement on that variable. When the relevant improvement corresponds with an outcome or change in outcome that exceeds a certain threshold, the outcome variable can be dichotomized. A responder is a patient whose outcome exceeds the threshold. Comparisons can be made between the number of responders in the two treatment groups using logistic regression, or some other method to evaluate binary outcomes. An important disadvantage of this approach is the loss of power. In general, it is more efficient to test the difference between the mean values. We developed a statistical test that compares response rates for a dichotomized variable. It requires that an estimate of the reliability of the outcome variable is available. Simulations showed that the test was valid and robust over a wide range of distributions and sample sizes. The power was greater than the power of a chi(2) test, which would enable substantial reduction in the sample size.

Clinical Trials as Topic↗

Sample size determination for logistic regression revisited.

There is no consensus on the approach to compute the power and sample size with logistic regression. Some authors use the likelihood ratio test; some use the test on proportions; some suggest various approximations to handle the multivariate case. We advocate the use of the Wald test since the Z-score is routinely used for statistical significance testing of regression coefficients. The null-variance formula became popular from early studies, which contradicts modern software, which utilizes the method of maximum likelihood estimation (MLE), when the variance of the MLE is estimated at the MLE, not at the null. We derive general Wald-based power and sample size formulas for logistic regression and then apply them to binary exposure and confounder to obtain a closed-form expression. These formulas are applied to minimize the total sample size in a case-control study to achieve a given power by optimizing the ratio of controls to cases. Approximately, the optimal number of controls to cases is equal to the square root of the alternative odds ratio. Our sample size and power calculations can be carried out online at www.dartmouth.edu/ approximately eugened.

Clinical Trials as Topic↗