Development of clinically relevant models for tumor growth

Recent advances in molecular biology and biotechnology have helped identify novel therapeutic targets that have led to the development of many new anticancer agents. However, only a handful of them show efficacy on human tumors due to a wide range genetic and epigenetic factors. As tumors grow in size, tumor cells and micro-environments in different regions become heterogeneous. Tumor tissue is heterogeneous with respect to metabolic milieu, micro-vascular density and permeability, drug susceptibility of local cell populations, all of which directly affects chemotherapeutic efficacy. Cancer cells are known to have altered metabolism and tumor metabolic microenvironment is characterized by hypoxia, high levels of lactate and lower pH. We incorporated a model of primary energy metabolism with glucose, oxygen, and lactate as substrates within a reaction-convection model of tumor spheroid growth to better understand and explain observed nutrient concentration profiles and tumor physiology. The spheroid model was extended further to include cell-cycle progression, cellular drug effects, and drug pharmacokinetics; thereby quantifying the interaction between drug and tumor micro-environment. We found that oxygen transport has a greater effect than glucose transport on the distribution of drug-resistant quiescent cells. Model simulations showed the existence of an optimum drug diffusion coefficient: a low diffusivity prevents effective penetration before the drug is cleared from the blood and a high diffusivity limits drug retention. The simulations also showed that fast growing tumors are less responsive to therapy than are slower tumors with more quiescent cells, demonstrating the competing effects of regrowth and cytotoxicity. Dynamic contrast enhanced Magnetic Resonance Images of breast tumors are routinely used in clinics to size tumors. These contain dynamic information that reveal the spatial distribution of vasculature and vascular permeability, and can be used to characterize the accessibility of different regions in tumors. We have developed a predictive framework integrating functional patient specific information about tumor micro-environment from DCE-MRI’s into a dynamic mathematical model of tumor growth that predicts the outcome of a treatment modality. Our model predictions for the tumor growth and tumor drug response for patient tumors were correlated to the average tumor trans-vascular transport rate. Drug response predictions for tumors with heterogeneity incorporated were found to differ significantly those for homogeneous tumors suggesting that the transport heterogeneity present within a tumor must be taken into account for generating drug response predictions. The interaction of drug/nutrient transport dynamics and cell growth/death dynamics is central to the efficacy of chemotherapy and these models can be developed further and used as a tool to predict therapeutic outcome

Propofol Pharmacokinetics and Estimation of Fetal Propofol Exposure during Mid-Gestational Fetal Surgery: A Maternal-Fetal Sheep Model.

Measuring fetal drug concentrations is extremely difficult in humans. We conducted a study in pregnant sheep to simultaneously describe maternal and fetal concentrations of propofol, a common intravenous anesthetic agent used in humans. Compared to inhalational anesthesia, propofol supplemented anesthesia lowered the dose of desflurane required to provide adequate uterine relaxation during open fetal surgery. This resulted in better intraoperative fetal cardiac outcome. This study describes maternal and fetal propofol pharmacokinetics (PK) using a chronically instrumented maternal-fetal sheep model.Fetal and maternal blood samples were simultaneously collected from eight mid-gestational pregnant ewes during general anesthesia with propofol, remifentanil and desflurane. Nonlinear mixed-effects modeling was performed by using NONMEM software. Total body weight, gestational age and hemodynamic parameters were tested in the covariate analysis. The final model was validated by bootstrapping and visual predictive check.A total of 160 propofol samples were collected. A 2-compartment maternal PK model with a third fetal compartment appropriately described the data. Mean population parameter estimates for maternal propofol clearance and central volume of distribution were 4.17 L/min and 37.7 L, respectively, in a typical ewe with a median heart rate of 135 beats/min. Increase in maternal heart rate significantly correlated with increase in propofol clearance. The estimated population maternal-fetal inter-compartment clearance was 0.0138 L/min and the volume of distribution of propofol in the fetus was 0.144 L. Fetal propofol clearance was found to be almost negligible compared to maternal clearance and could not be robustly estimated.For the first time, a maternal-fetal PK model of propofol in pregnant ewes was successfully developed. This study narrows the gap in our knowledge in maternal-fetal PK model in human. Our study confirms that maternal heart rate has an important influence on the pharmacokinetics of propofol during pregnancy. Much lower propofol concentration in the fetus compared to maternal concentrations explain limited placental transfer in in-vivo paired model, and less direct fetal cardiac depression we observed earlier with propofol supplemented inhalational anesthesia compared to higher dose inhalational anesthesia in humans and sheep

Pharmacokinetic Model.

<p>The maternal- fetal pharmacokinetic model of propofol was best fitted using a 2 maternal compartment with a separate fetal compartment model. Vc = maternal central volume of distribution (L), Vp = maternal peripheral volume of distribution (L), Q = inter-compartmental clearance (L/min), CL = clearance from the maternal central compartment (L/min), Q_M-F = transfer rate between maternal and fetal compartment (L/min), V_Fetus = volume of distribution of fetal compartment (L).</p

Visual predictive check of prediction-corrected concentration of propofol in ewe and fetus for the final model.

<p>Circles demonstrate prediction corrected observations. Red lines demonstrate 5th, 50th and 95th prediction percentiles.</p

The mean difference between maternal and fetal propofol plasma concentration in sheep after a bolus of propofol 3 mg/kg via the maternal femoral venous line, followed by an intravenous infusion of propofol (450 mcg/kg/min) for 60 minutes and then propofol infusion (75 mcg/kg/min) for 90 more minutes.

<p>The mean difference between maternal and fetal propofol plasma concentration in sheep after a bolus of propofol 3 mg/kg via the maternal femoral venous line, followed by an intravenous infusion of propofol (450 mcg/kg/min) for 60 minutes and then propofol infusion (75 mcg/kg/min) for 90 more minutes.</p

Between-subject random effects (η) for maternal clearance versus heart rate (HR) from the base (A) and final models (B).

<p>Each box represents data from one sheep. The lines in the box correspond to median values; the bottom and top of the box are the first and third quartiles (the 25th and 75th percentiles); the upper whiskers extend from the box to the highest value within 1.5 times of inter-quartile range (IQR); the lower whisker extend from the box to the lowest value within 1.5 times of IQR. The individual variability (Random effect, η) for maternal clearance (ηCL) is narrower in the final model than in the base model.</p

Propofol concentration time profiles for each fetal-maternal sheep unit (n = 8).

<p>Propofol was administered to the ewes as a bolus of 3 mg/kg, followed by an infusion of 450 μg/kg/min for 60 minutes. After that, propofol infusion rate was decreased to 75 μg/kg/min for 90 more minutes, and then stopped.</p

Goodness-of-fit plots for the final PK model.

<p>(A) Population prediction versus observed concentration. (B) Individual prediction versus observed concentration. (C) Conditional weighted residuals (CWRES) versus population prediction. (D) Conditional weighted residuals (CWRES) versus time. Dashed red line, a locally weighted least-squares regression; solid black line, line of identity.</p