Pharmacodynamic Modeling of Anti-Cancer Activity of Tetraiodothyroacetic Acid in a Perfused Cell Culture System

Abstract

Unmodified or as a poly[lactide-co-glycolide] nanoparticle, tetraiodothyroacetic acid (tetrac) acts at the integrin αvβ3 receptor on human cancer cells to inhibit tumor cell proliferation and xenograft growth. To study in vitro the pharmacodynamics of tetrac formulations in the absence of and in conjunction with other chemotherapeutic agents, we developed a perfusion bellows cell culture system. Cells were grown on polymer flakes and exposed to various concentrations of tetrac, nano-tetrac, resveratrol, cetuximab, or a combination for up to 18 days. Cells were harvested and counted every one or two days. Both NONMEM VI and the exact Monte Carlo parametric expectation maximization algorithm in S-ADAPT were utilized for mathematical modeling. Unmodified tetrac inhibited the proliferation of cancer cells and did so with differing potency in different cell lines. The developed mechanism-based model included two effects of tetrac on different parts of the cell cycle which could be distinguished. For human breast cancer cells, modeling suggested a higher sensitivity (lower IC50) to the effect on success rate of replication than the effect on rate of growth, whereas the capacity (Imax) was larger for the effect on growth rate. Nanoparticulate tetrac (nano-tetrac), which does not enter into cells, had a higher potency and a larger anti-proliferative effect than unmodified tetrac. Fluorescence-activated cell sorting analysis of harvested cells revealed tetrac and nano-tetrac induced concentration-dependent apoptosis that was correlated with expression of pro-apoptotic proteins, such as p53, p21, PIG3 and BAD for nano-tetrac, while unmodified tetrac showed a different profile. Approximately additive anti-proliferative effects were found for the combinations of tetrac and resveratrol, tetrac and cetuximab (Erbitux), and nano-tetrac and cetuximab. Our in vitro perfusion cancer cell system together with mathematical modeling successfully described the anti-proliferative effects over time of tetrac and nano-tetrac and may be useful for dose-finding and studying the pharmacodynamics of other chemotherapeutic agents or their combinations.

Targeted delivery of paclitaxel and doxorubicin to cancer xenografts via the nanoparticle of nano-diamino-tetrac

“…While we did not intend to achieve additive or synergistic antitumor effects of NDAT with the other agents, such additive effects may exist. 38 Antitumor effectiveness of doxorubicin and paclitaxel in the current studies was verified by volume measurements of orthotopic xenografts of breast cancer or by IVIS scanning of the orthotopic xenografts of pancreatic cancer. IVIS scanning, in this study, also provided verification of the loss of viability of cells within the orthotopic tumors.…”

“…31,32 NDAT (Nanotetrac), alone, is an anticancer agent, but the dose of NDAT (0.3 mg/kg daily) used in the current studies to deliver generic chemotherapeutic agents was suboptimal in terms of NDAT chemotherapeutic efficacy. 38 Thus, the antitumor effectiveness measured in this study examines primarily the efficiency of NDAT in terms of delivery of paclitaxel and doxorubicin. While we did not intend to achieve additive or synergistic antitumor effects of NDAT with the other agents, such additive effects may exist.…”

Two Mechanisms of Killing of Pseudomonas aeruginosa by Tobramycin Assessed at Multiple Inocula via Mechanism-Based Modeling

“…1). Bacterial replication for each population was described by a life cycle growth model, with bacteria preparing for replication (state 1) and bacteria immediately before the replication step (state 2) (41,43,45,51). Each of these three populations contained bacteria in two states (e.g., CFU S1 and CFU S2 for the susceptible population), yielding six bacterial compartments in total.…”

“…Bacterial replication (i.e., doubling) was assumed to be 100% successful at low bacterial densities. At the maximum population size (CFU max ), the probability for successful bacterial replication (Prob Succ ) was 50%, yielding net stasis (41,43,45,51):…”

Quantifying Subpopulation Synergy for Antibiotic Combinations via Mechanism-Based Modeling and a Sequential Dosing Design

“…For each population, a life cycle growth model (22,23) was applied that accounts for the underlying biology of bacterial growth (24) and contains bacteria that are preparing for replication (state 1) and bacteria immediately before the replication step (state 2). The transition from state 1 to state 2 occurred via a first-order growth The combination of drug A and drug B will eradiate these bacterial populations due to the lack of a population resistant to both drugs.…”