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Feb 12, 2018 3:35 AM
(708 views)

Hi,

I have a set of tablets in which one component determines how slowly the active is released (measured at 1, 2, 3, 4, 5 and 6hours).

If I have the results for tablets containing 10, 15, 20 and 25% of this component how can I create a model that will tell me how much of that component is needed to obtain a target profile (target release values at target timepoints)?

Thanks

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Feb 13, 2018 6:29 AM
(1124 views)

Also, you might try saving the model and creating a new response to optimize. A good choice would be the root mean square error (RMSE). A column formula could compare the predicted response with the target response using something like this

```
Sqrt(
Sum(
(P(t1,X) - T(t1))^2 +
(P(t2,X) - T(t2))^2 +
(P(t3,X) - T(t3))^2 +
(P(t4,X) - T(t4))^2 +
(P(t5,X) - T(t5))^2
)
)
```

where the P(t,X) is the fitted model of the predicted response at time t and excipient level X. T(t) is the target level at time t. You could use the Graph > Profiler with this column to optimize X (minimize response with desirability function).

Learn it once, use it forever!

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Feb 12, 2018 4:34 AM
(703 views)

One JMP platform you may want to take a look at is the Fit Curve platform for the type of problem you are trying to address. Here is the link to the JMP online documentation for the Fit Curve platform.

https://www.jmp.com/support/help/13-2/Fit_Curve.shtml

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Feb 12, 2018 4:59 AM
(693 views)

I have no problems with fitting the curves. My problem is that each concentration/fit has a different slope/equation and I'd like to have a model that would tell me what concentration of the excipient is needed to obtain a given profile – that is to say I have a target fit of curve and I'd like to know how I can achieve that based on the results of the other fits.

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Feb 12, 2018 8:39 AM
(680 views)

I guess I'm a bit confused then. For your example, are you trying to predict any one curve as a function of the slope/intercept of the other 3 curves?

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Feb 12, 2018 8:52 AM
(675 views)

You might also try using Analyze > Reliability > Degradation. Use the excipient level as X. You can try transforms on Y and X with a linear path or use a nonlinear path.

Learn it once, use it forever!

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Feb 13, 2018 2:46 AM
(659 views)

I probably do not fully understand how the degradation model works but...

When I have a target profile it means that I have target values for the various timepoints (e.g. timepoint 100: 10%, 200: 15%, 400: 45%, etc.) so not just one point. And I would need a model that would tell me what concnetration of X would give me a closest match.

My best shot so far was to create a single factor DOE where I set the timepoints as the responses and simply overwrote the parameters in the provided table with the parameters and results of my experiments and used the profilers.

But there must be a better way to do that, I guess.

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Feb 13, 2018 6:21 AM
(646 views)

Please don't be put off by the terminology. The 'degradation path' in the Degradation platform is just a model of the change in the response (dissolution) over time under the influence of a factor (excipient level). You can then profile the response under different conditions to find the target profile. You can use inverse prediction to find the condition that achieves a desired response level, too.

Learn it once, use it forever!

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Feb 13, 2018 6:29 AM
(1125 views)

Also, you might try saving the model and creating a new response to optimize. A good choice would be the root mean square error (RMSE). A column formula could compare the predicted response with the target response using something like this

```
Sqrt(
Sum(
(P(t1,X) - T(t1))^2 +
(P(t2,X) - T(t2))^2 +
(P(t3,X) - T(t3))^2 +
(P(t4,X) - T(t4))^2 +
(P(t5,X) - T(t5))^2
)
)
```

where the P(t,X) is the fitted model of the predicted response at time t and excipient level X. T(t) is the target level at time t. You could use the Graph > Profiler with this column to optimize X (minimize response with desirability function).

Learn it once, use it forever!