Unmodified or as a polylactide-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.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Antibiotic resistance is seen in both the hospital and community settings. Approaches are required to minimize the increase in resistant strains, such as good antibiotic stewardship and the limiting ...of antibiotic use to appropriate circumstances. There are instances when drug dose and/or schedule can be used to minimize the probability that mutants will take over the bacterial population. Over the past several years, significant advances have been made in understanding the relationship between drug concentrations and amplification of resistant mutant subpopulations. In this review, we examine the use of preclinical models for facilitating this understanding. We also use mathematical techniques, including Monte Carlo simulation, to bridge between the identification of exposures to minimize resistance and the examination of candidate drug doses to achieve this end. Examples are provided for Pseudomonas aeruginosa, Streptococcus pneumoniae, Staphylococcus aureus, and Mycobacterium tuberculosis. In each instance, quinolone antimicrobials were examined. More investigations with other pathogens and drug classes are required.
Efficacious therapy is of utmost importance to save lives and prevent bacterial resistance in critically ill patients. This review summarizes pharmacokinetic (PK) and pharmacodynamic (PD) modeling ...methods to optimize clinical care of critically ill patients in empiric and individualized therapy. While these methods apply to all therapeutic areas, we focus on antibiotics to highlight important applications, as emergence of resistance is a significant problem. Nonparametric and parametric population PK modeling, multiple-model dosage design, Monte Carlo simulations, and Bayesian adaptive feedback control are the methods of choice to optimize therapy. Population PK can estimate between patient variability and account for potentially increased clearances and large volumes of distribution in critically ill patients. Once patient- specific PK data become available, target concentration intervention and adaptive feedback control algorithms can most precisely achieve target goals such as clinical cure of an infection or resistance prevention in stable and unstable patients with rapidly changing PK parameters. Many bacterial resistance mechanisms cause PK/PD targets for resistance prevention to be usually several-fold higher than targets for near-maximal killing. In vitro infection models such as the hollow fiber and one-compartment infection models allow one to study antibiotic-induced bacterial killing and emergence of resistance of mono- and combination therapies over clinically relevant treatment durations. Mechanism-based (and empirical) PK/PD modeling can incorporate effects of the immune system and allow one to design innovative dosage regimens and prospective validation studies. Mechanism-based modeling holds great promise to optimize mono- and combination therapy of anti-infectives and drugs from other therapeutic areas for critically ill patients.
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