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The Perks of Using Monte Carlo Simulations in Drug Development

Overall, everything in an organization, and especially a profit driven organization, is possible to quantify. If you have a tough decision to make it’s a good idea to, at least as part of the decision material, create a quantitative model and see what it tells you. It might confirm what you initially thought, or it might go against your thoughts. Either way, it will reveal hidden answers to the overall problem, because the hardship of decision-making is not what you know that you don't know. It's what you don't know that you don't know that will eventually hit, as you weren’t able to prepare for it. Any way that helps you find questions that you don't know will be worth the effort.

A drug development process holds a significant amount of uncertainty in its inputs, such as predictions of failure, cost and schedule overruns and variations in revenue. From a forecasting point of view, it’s unrealistic to expect anyone to come up with a fixed cost for any activity during a drug development process. Most likely, any number selected will be wrong. A better approach is to come up with a range, an interval between two numbers, in which you are almost certain to find the real value. By doing so, you’re no longer guessing, but rather quantifying the limits of your knowledge. In practice, the range should be chosen so that you are 90% confident in the limits.

In theory, any problem that has any level of probabilistic interpretation can be solved using Monte Carlo simulation. Developed during World War II by John von Neumann and Stanislaw Ulam, and named after a casino district in Monaco, the Monte Carlo Method is a class of computational algorithms with repeated random sampling to obtain numerical results. The concept is based on the use of randomness in order to solve deterministic problems, and are mainly applied when investigating questions pertaining to optimization, numerical integration, or probability distribution.

One of the main reasons why Captario has chosen to use Monte Carlo simulation is that more or less everything that's fed from reality into a model has some uncertainty. Monte Carlo simulation can handle these uncertainties. A risk always has a probability of occurring, and will, if happening, have some consequence. With Monte Carlo simulation, you can map out the whole chain of events, analyze the whole development process, and see if there is perhaps a need to add activities to mitigate occurrences that would reduce the risk of non-favorable outcomes. Additionally, when addressing risk at all stages of the development process, it allows you to build a recovery plan into the initial project model, which in turn will be ”executed” automatically during the simulation procedure when the risk occurs. This way, you can simulate both favorable and non-favorable outcomes, allowing you to compare the resulting likelihood and consequence of each.

Commercial modeling tends to focus only on the market phase. However, Monte Carlo modeling allows for a dynamic connection from preclinical stages all the way through product launch. As such, if there is something that shortens the development process time, then that will automatically be reflected in the launch dates, which in turn impact the market model, the sales curve, and the associated costs due to the new launch date.

A portfolio expands the complexity of the model, especially if there are dependencies between projects. An example is if three projects (Project 2, Project 3, and Project 4) are dependent on whether another (Project 1) succeeds in the registration phase and intends to pursue an FDA approval. If Project 1 goes all the way, that would then be a signal to the other project models to go ahead. Evidently, any time savings you do for the first project will be multiplied in value because it will affect the other three projects to be launched earlier as well.

The merits of using Monte Carlo simulation include the ability to handle very complex models with dependencies within and across models. In addition, only viable outcomes from risks and opportunities are produced ensuring that process improvements are validated. A Monte Carlo simulation can thus capture the value of project dependencies, and provide a solid base of information for how to mitigate and reap the benefits of any potential outcome of the development process, spanning all the way from research to commercialization.


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