A Double Selection Approach to Achieve Specific Expression of Toxin Genes for Ovarian Cancer Gene Therapy
Abstract
Gene therapy is a novel treatment modality which offers great potential for the control of carcinoma of the ovary. The efficacy of such approaches, however, is currently limited due to the inability of available gene delivery vehicles (vectors) to achieve efficient and selective gene transfer to target tumor cells. Proposed herein is a strategy to modify one candidate vector, recombinant adenovirus, such that it embodies the requisite properties of efficacy and specificity required for ovarian cancer gene therapy. This approach is based on targeting the delivered anti-cancer gene to tumor via two complimentary approaches. This approach is based upon restricting the expression of the anti-cancer gene exclusively to ovarian cancer tumor cells ("transcriptional targeting") plus directing the binding of the viral vector particle exclusively to tumor cells ("transductional targeting"). This "double targeting" approach is highly novel. We hypothesize that the vector improvements we propose herein will allow an improvement in the therapeutic index achievable by ovarian cancer gent therapy. Further, these strategies, if shown to be efficient efficacious here, have the potential to be rapidly translated into the clinical context. In this regard, our group has gained NIH regulatory approval and support for the employment of targeted vectors for ovarian cancer. We are thus familiar with the upscaling and regulatory aspects of clinical translation of novel gene therapy approaches. The double targeting approach we propose will allow us to illustrate key "proof-of-principle" in a stringent animal model of cancer of the ovary. This data will provide the rationale to endeavor a human clinical trial on this basis. In this event, our experience and infrastructure can function to foster the most rapid possible transitioning of this strategy to the context of Phase 1 human clinical trials for carcinoma of the ovary.
Document Details
- Document Type
- Technical Report
- Publication Date
- Nov 01, 2005
- Accession Number
- ADA454984
Entities
People
- David T. Curiel
- Gene Siegal
- Minghui Wang
Organizations
- University of Alabama