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Drug supply in clinical trials is an increasingly complex operation, trying
to balance the conflicting requirements of:
- Ensuring there are always adequate supplies at centers to support recruitment and the
re-supply of existing subjects
- Minimizing the amount of material required to run a trial
- Avoiding forced randomization of patients
These requirements have been made harder to achieve by the introduction of
Adaptive Clinical Trial designs, which require central randomization and vary the proportion
of allocation of subjects to the different treatment arms during the trial in an
unpredictable way.
Modeling your supply planning with SFT reduces costs and risks associated
with a Clinical Trial in any Phase. For an example of how modeling works, and the business
benefit it can bring, click here.
Tessella SFT employs sophisticated statistical models to generate realistic
and scientifically accurate forecasts of:
- The likelihood of losing subjects due to the lack of supply
- The number of subjects that can be recruited for a given amount of drug
- The amount of drug required to reliably recruit a given number of subjects
At the core of Tessella SFT is an advanced simulation engine, based on a
mixture of statistics including the Poisson distribution, Binomial distribution and
Bayesian analysis techniques.
SFT is the only tool in the market place to combine all of these techniques
into a sophisticated simulation engine.
The result of this combination allows advanced modeling and multiple
simulations therefore increasing the accuracy of your results.
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Tessella SFT allows users to optimize drug supply for trials.
During Trial Planning, they can:
- Forecast drug supply needs and costs based on study design, recruitment rates, drug
formulation and packaging, manufacturing and logistics
- Plan optimal production and delivery schedules
- Explore the possible consequences on supply and cost of different recruitment rates or
delivery times
- Consider different packaging options
- Test the effects on both supply and cost of different re-supply algorithms and
methodologies, including fixed ceiling/floor models as well as adaptive re-supply
algorithms
And in the future, during Trial Execution, SFT will support :
- Import current trial data to accurately forecast needs, based on up-to-the-minute
supply inventory information
- Recommend shipments needed to ensure adequate supply at depots and centers
- Reduce unnecessary waste in production, distribution use and re-supply
- Determine the amount of materials needed to reliably supply the remainder of the trial
- Optimize the drug supply chain, regardless of the number of ‘arms’
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