Predicting drug pharmacokinetics and effect in vascularized tumors using computer simulation

Abstract

In this paper, we investigate the pharmacokinetics and effect of doxorubicin and cisplatin in vascularized tumors through two-dimensional simulations. We take into account especially vascular and morphological heterogeneity as well as cellular and lesion-level pharmacokinetic determinants like P-glycoprotein (Pgp) efflux and cell density. To do this we construct a multi-compartment PKPD model calibrated from published experimental data and simulate 2-h bolus administrations followed by 18-h drug washout. Our results show that lesion-scale drug and nutrient distribution may significantly impact therapeutic efficacy and should be considered as carefully as genetic determinants modulating, for example, the production of multidrug-resistance protein or topoisomerase II. We visualize and rigorously quantify distributions of nutrient, drug, and resulting cell inhibition. A main result is the existence of significant heterogeneity in all three, yielding poor inhibition in a large fraction of the lesion, and commensurately increased serum drug concentration necessary for an average 50% inhibition throughout the lesion (the IC50 concentration). For doxorubicin the effect of hypoxia and hypoglycemia (“nutrient effect”) is isolated and shown to further increase cell inhibition heterogeneity and double the IC50, both undesirable. We also show how the therapeutic effectiveness of doxorubicin penetration therapy depends upon other determinants affecting drug distribution, such as cellular efflux and density, offering some insight into the conditions under which otherwise promising therapies may fail and, more importantly, when they will succeed. Cisplatin is used as a contrast to doxorubicin since both published experimental data and our simulations indicate its lesion distribution is more uniform than that of doxorubicin. Because of this some of the complexity in predicting its therapeutic efficacy is mitigated. Using this advantage, we show results suggesting that in vitro monolayer assays using this drug may more accurately predict in vivo performance than for drugs like doxorubicin. The nonlinear interaction among various determinants representing cell and lesion phenotype as well as therapeutic strategies is a unifying theme of our results. Throughout it can be appreciated that macroscopic environmental conditions, notably drug and nutrient distributions, give rise to considerable variation in lesion response, hence clinical resistance. Moreover, the synergy or antagonism of combined therapeutic strategies depends heavily upon this environment.

Multiscale Cancer Modeling

“…Sinek, Frieboes, Cristini and coworkers developed a cell-scale compartmental model of chemotherapeutic drug (doxorubicin) transport within a cell's cytoplasm and nucleus, which they combined with a mechanistic model of DNA-drug adduct formation, repair, and apoptosis (89)(90)(91). Their model accounted for cell cycling effects by varying the probability of apoptosis with the substrate level via a Hill-type function.…”

Integrated nanotechnology platform for tumor-targeted multimodal imaging and therapeutic cargo release

“…To substantiate our findings in vivo, we used a mechanistic mathematical model (SI Materials and Methods) of drug transport through the tumor interstitium and fitted it to predict and verify the corresponding tumor response. The underlying biophysical processes can be arithmetically defined by the exponential death rate equation (e x death rate = λ K · σ U ) to determine the potential efficacy of a given chemotherapy drug, where λ K is the biological property of the pair cell type/drug type, and σ U is the physical property of the tumor parenchyma, stroma, and its associated microenvironment, in which processes such as convection, diffusion (29)(30)(31), and cell membrane crossing (32) determine how much drug is available for uptake by the tumor.…”

Development and Engineering of CSαβ Motif for Biomedical Application

“…Computer aided drug screening methods have been established to screen drugs with small molecule weights that can fit into active sites of target proteins (Barrons, 2004;Weideman et al, 1999). Drug dynamics also can be in silico simulated (Sinek et al, 2009;Zunino et al, 2009). An antibody can be as a simple binder and its binding targets usually are short fragments exposed to the surface of the proteins.…”