Deducing Mucosal Pharmacokinetics and Pharmacodynamics of the Anti-HIV Molecule Tenofovir from Measurements in Blood

Abstract

Microbicide pharmacokinetic (PK) studies typically sample drug in luminal fluid, mucosal tissue, and blood. Blood measurements can be conducted most frequently, serially within subjects. Antiretroviral drugs, however, act against HIV in mucosal tissue/cells. We computationally modeled the extent measurements in blood can predict concentrations in tissue, focusing on the antiretroviral drug tenofovir delivered by a vaginal gel. Deterministic PK models input host and product factors and output spatiotemporal drug concentrations in luminal fluid, epithelium, stroma/host cells, and blood. Pharmacodynamic (PD) analysis referenced stroma/host cell concentrations to prophylactic values; summary metrics were time from product insertion to protection (t_lag) and degree of protection (PP_max). Results incorporated host factors characteristic of population variability. Neural nets (NN) linked simulated blood PK metrics (C_max, t_max, AUC, C₂₄) to mucosal PK/PD metrics. The NNs delivered high-performance mapping of these multiparametric relationships. Given multi-log variability typical of biopsy data for tenofovir and other topical microbicides, results suggest downstream but higher fidelity measurements in blood could help improve determination of PK and create inferences about PD. Analysis here is for a tenofovir gel, but this approach offers promise for application to other microbicide modalities and to topical drug delivery to vaginal mucosa more generally.

Figure 1

Structure of vaginal mucosa. Histology of the vaginal mucosa depicting epithelial and stromal layers (left). Schematic of model geometry depicting the gel layer above the mucosal surface (right).

Figure 2

Feedforward neural net architecture. Mapping C_max, t_max, AUC, and C₂₄ for TFV in blood (PK metrics) to C_max, t_max, AUC, and C₂₄ for TFV in stroma (PK metrics) and t_lag and PP_max in stroma (PD metrics) using machine learning.

Figure 3

Example PK curves of volume averaged concentrations in different compartments vs. time. Compartments are blood, gel, epithelium, and stroma. Tenofovir (TFV) is shown in all compartments and tenofovir diphosphate (TFV-DP) is shown in the stroma, where it acts against target HIV-infectible cells.

Figure 4

Sample effect of anatomical and physiological parameter variation on TFV PK metrics in blood. The variation involves epithelial thickness (h_E), dilution rate in the gel (k_D), and drug transport rate from the stroma to the blood (k_B).

Figure 5

Example curve of the time history of the percent protected (PP) measure of mucosal protection by tenofovir diphosphate against HIV infection. The PP is the instantaneous fraction of stromal volume within which TFV-DP concentration is greater than or equal to a reference EC₅₀ value. Here the reference EC₅₀ is 224 ng/mL (500 fmol/mg).

Figure 6

Methodology process diagram. Schematic of steps in implementing neural net to predict mucosal PK metrics and, if data on drug potency are available, deduce resulting mucosal protection against infection (PD).