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Using modeling to help understand vaginal microbicide functionality and create better products

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Abstract

A summary is presented of a range of mathematical models that relate to topical microbicidal molecules, applied vaginally to inhibit HIV transmission. These models contribute to the fundamental understanding of the functioning of those molecules, as introduced in different delivery systems. They also provide computational tools that can be employed in the practical design and evaluation of vaginal microbicide products. Mathematical modeling can be implemented, using stochastic principles, to understand the probability of infection by sexually transmitted HIV virions. This provides a frame of reference for the deterministic models of the various processes that underlie HIV transmission and its inhibition, including: the temporal and spatial history of HIV migration from semen to vaginal epithelial surfaces and thence to the underlying stroma; the time and spatial distribution of microbicidal drugs as delivered by various vehicles (e.g., gels, rings, films, and tablets)—this is central to understanding microbicide product pharmacokinetics; and the time and space history of the drug interactions with HIV directly and with host cells for HIV within the vaginal environment—this informs the understanding of microbicide pharmacodynamics. Models that characterize microbicide functionality and performance should and can interface with both in vitro and in vivo experimental studies. They can serve as a rapidly applied, inexpensive tool, to facilitate microbicide R&D, in advance of more costly and time consuming clinical trials.

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Acknowledgments

We gratefully acknowledge the contributions of our co-workers Meredith Clark, David Friend, Anthony Geonnotti, Alex Gorham, Marcus Henderson, Sarah Kieweg, Patrick Kiser, Bonnie Lai, Alamelu Mahalingam, Derek Owen, Su Chan Park, Jennifer Peters, Audra Plenys, Andrew Szeri, Savas Tasoglu, and Stephane Verguet in the creation of the models described here. Support is gratefully acknowledged from the CONRAD Program MC-08-498, NIH U19 AI 077289, and NIH R21 AI 076019

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Authors and Affiliations

  1. Department of Biomedical Engineering, Duke University, Durham, NC, USA

    David F. Katz, Yajing Gao & Meng Kang

  2. Department of Biomedical Engineering, Duke University, Box 90281, Durham, NC, 27708, USA

    David F. Katz

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  1. David F. Katz
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Correspondence to David F. Katz.

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Katz, D.F., Gao, Y. & Kang, M. Using modeling to help understand vaginal microbicide functionality and create better products. Drug Deliv. and Transl. Res. 1, 256–276 (2011). https://doi.org/10.1007/s13346-011-0029-z

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