Trial simulations Technique

Clinical trial simulation or computer-assisted trial design or biosimulation is not a revolutionary technique. Computer simulation has been used in the automotive... 

Even though most stomach ulcers are treated by gastric acid secretory inhibitors or H2-receptor (histamine) blockers such as cimetidine, the simulation technique has led to formulations that produce bismuth patches on ulcers at other sites having other pH values. However, the optimization of conditions for ulcer healing is very difficult using clinical trials because of the vagaries of ulceration and of responses to therapy, some 80% of ulcers reoccurring within a year. 

It is important to note that in order for the MC simulation technique to work, the sequence of configurations must form a Markov Chain. A Markov Chain is a sequence of trials that satisfy the following conditions ... 

Vapor-Liquid Equilibrium Plus Chemical Reaction. To conclude this discussion of VLE calculations, I d like to mention that we have initiated a program to allow VLE plus chemical reaction in column calculations. A modification of Frank s dynamic simulation method ( ) is being used. The initial trials with complex VLE and kinetic models have been moderately successful, but further testing is required. Other simulation techniques are also being investigated. 

Most flow sheets have one or mote recycles, and trial-and-ettot becomes necessary for the calculation of material and energy balances. The calculations in a block sequential simulator ate repeated in this trial-and-ettot process. In the language of numerical analysis, this is known as convergence of the calculations. There ate mathematical techniques for speeding up this trial-and-ettot process, and special hypothetical calculation units called convergence, or recycle, units ate used in calculation flow diagrams that invoke special calculation routines. 

Monte Carlo simulation is a numerical experimentation technique to obtain the statistics of the output variables of a function, given the statistics of the input variables. In each experiment or trial, the values of the input random variables are sampled based on their distributions, and the output variables are calculated using the computational model. The generation of a set of random numbers is central to the technique, which can then be used to generate a random variable from a given distribution. The simulation can only be performed using computers due to the large number of trials required. 

Figure 22.4 Monte Carlo techniques were used to simulate different hypothetical individuals for different instances of the trial design, using variability and uncertainty distributions from the model analysis. The result is a collection of predicted outcomes, shown as a binned histogram (top figure). Success was defined as a difference in end point measurement of X or smaller between drug and comparator. Likelihood of success (shown in the bottom figure as a cumulative probability) for this example (low/medium drug dose and high comparator dose) is seen to be low, about 33%.

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