ABSTRACT
The viral particles must contact, bind, and then interact with distinct host cells in order to initiate viral replication and the onset of infection. Those target cells reside within the vaginal and rectal mucosal tissue (CD4+ T cells, macrophages) and in the lumenal fluids and epithelial surfaces of the tissue (namely in dendritic cells). The APIs can act by a variety of mechanisms, via directly contacting and neutralizing virions (e.g., in the fluids of the vaginal and colorectal lumens) or by contacting HIV target cells and thence interfering with the virion-cell interaction process (Fig. 5.1). The introduced API may require a chemical conversion in vivo in order to exhibit bioactivity (e.g., phosphorylation of tenofovir-TFV-to TFV diphosphate-TDF-in host cells). The overall phenomenon of HIV deposition and infection, and microbicide API introduction, distribution, possible chemical conversion, and interaction with the virus, can be regarded as a transport process of two distinct interacting particles (HIV and API), migrating throughout a complex environment (the “vehicle” that introduces the API, and vaginal/ rectal mucosal tissues and fluids, including semen, through which it transits). As such, biomedical engineers and biophysicists can conceptualize the HIV-microbicide gestalt in terms of the principles and methods of mass transport theory [1]. This can provide an objective, mechanism-based, in silico framework within which to understand cause and effect for HIV and API movement, interaction, and biological activity. That is, this theoretical framework (which we call “modeling”) can characterize the pharmacokinetics (PK) of a particular product (namely drug delivery) and, in principle, inform us about the pharmacodynamics (PD) of the product (namely viral dynamics that are the consequence of the PK). Indeed, there have been some models that directly focus on the probability of infection in relation to the presence of microbicides [2]. In this chapter, we review the nature and application of modeling in the microbicides field, with a focus primarily upon drug transport. In doing so, we build upon our earlier review of such modeling [3], revisiting key topics, updating the scope of work and providing, we hope, interpretations that enhance our understanding of the value and use of modeling in the microbicides field. Our presentation of the models tends to illustrate their predictions and we limit specific comments to the governing equations per se. Details of the mathematics and computational schemas are found in the source papers.
