![]() Serum creatinine levels ( Scr) were sampled from a lognormal distribution with the typical value (geometric mean) of 0.78 mg/dL, and coefficient of variation (CV) of 32.8%. 26% of the population was assumed to be female. Weight ( Wt) and age ( Age) were sampled from a normal distribution with a mean of 51.6 kg and 71.8 years, respectively, and a standard deviation of 11.8 kg and 11.9 years, respectively. NDoseGrps = 4 % Number of tested dosage regimens NPatients = 1000 % Number of patients per dosage group The antibacterial effect of the drug was included in the killing rate of the bacteria via a simple Emax type model: For the bacterial growth model, they assumed that the total bacterial population is comprised of drug-susceptible growing cells and drug-insensitive resting cells. assumed a two-compartment infusion model with linear elimination from the central compartment to describe the pharmacokinetics of the doripenem. Pharmacokinetic/pharmacodynamic modeling and simulation to determine effective dosage regimens for doripenem. in SimBiology®, and replicate the results of the Monte Carlo simulation described in their work. In this example, we will implement the antibacterial PK/PD model developed by Katsube et al. Investigate the effect of renal function on the antibacterial efficacy of the treatments Use Monte Carlo simulations to compare the efficacy of four common antibiotic dosage regimes, and to determine the most effective dosing strategy These values come from Roy.Develop a PK/PD model to describe the antibacterial effect of doripenem against several Pseudomonas aeruginosa strains The neutral density near the thruster exit is approximately 10 18 m -3 while the density of the ion beam is around 10 16 m -3. To illustrate this, let’s consider the typical 30 cm ion thruster. It is quite limited compared to the Finite Volume, however, in applications where it works, it works great.Īnd one application where MCC works is in modeling the charge exchange (CEX) process in electric propulsion thruster plumes. In this sense, MCC is analogous to the Finite Difference Method. MCC is also much faster since particle pairs do not need to be selected. The benefit of MCC over DSMC is that MCC is much simpler to implement. If the collision frequency is not particularly high, it is reasonable to assume that the target species is affect by collisions to such a small extend as to be negligible. In many scenarios, the density of the target is many orders of magnitude greater than the density of the source species. MCC Applicationsĭoes this mean that MCC is not physically sound and should not be used? Not really. ![]() Since in MCC there is no target particle, energy is not conserved unless some non-standard adjustments are made to the background population. This allows DSMC to conserve energy and momentum through the collision. In DSMC both the source and the target are actual simulation particles. It differs from the commonly used DSMC (Direct Simulation Monte Carlo) in that in the MCC method, the source particles are collided with a target “cloud”. MCC works by looping through all source particles, testing each particle for a collision, and if a collision occurs, performing the appropriate action. It can be coupled with the PIC method to obtain the PIC-MCC algorithm. ![]() MCC (Monte Carlo Collisions) is a simple method for modeling particle collisions. The two primary drivers that control movement of individual particles in plasmas are the electric field and the inter-particle collisions. ![]()
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