Monte Carlo simulations help you gain confidence in your design by allowing you to run parameter sweeps, explore your design space, test for multiple scenarios, and use the results of these simulations to guide the design process through statistical analysis. The design and testing of these complex systems involves multiple steps, including identifying which model parameters have the greatest impact on requirements and behavior, logging and analyzing simulation data, and verifying the system design. You can model and simulate multidomain systems in Simulink ® to represent controllers, motors, gains, and other components. Risk Management Toolbox™ facilitates credit simulation, including the application of copula models.įor more control over input generation, Statistics and Machine Learning Toolbox™ provides a wide variety of probability distributions you can use to generate both continuous and discrete inputs. Financial Toolbox™ provides stochastic differential equation tools to build and evaluate stochastic models. In financial modeling, Monte Carlo Simulation informs price, rate, and economic forecasting risk management and stress testing. MATLAB is used for financial modeling, weather forecasting, operations analysis, and many other applications. IdCrCLGrp = false(nPatients, nDoseGrps) į.MATLAB ® provides functions, such as uss and simsd, that you can use to build a model for Monte Carlo simulation and to run those simulations. See the lognstat documentation for more information. You can use the following definitions to calculate them. Here and throughout the example, mu and sigma were calculated from the reported typical value and coefficient of variation of the lognormal distribution. The inputs to the lognrnd function are the mean ( mu) and standard deviation ( sigma) of the associated normal distribution. The creatinine clearance rates ( CrCL) were calculated using the Cockcroft-Gault equation. 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 Develop a PK/PD model to describe the antibacterial effect of doripenem against several Pseudomonas aeruginosa strains
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