Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type.

- JMP User Community
- :
- Discussions
- :
- Re: Modeling of dissolution profiles

- Subscribe to RSS Feed
- Mark Topic as New
- Mark Topic as Read
- Float this Topic for Current User
- Bookmark
- Subscribe
- Printer Friendly Page

Highlighted

- Mark as New
- Bookmark
- Subscribe
- Mute
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Aug 30, 2020 2:51 AM
(293 views)

Dear all,

How to fit kinetic models to dissolution profiles? The most common for pharma are referred to below. I tried the non-linear modeling with the weibull function and the linear (zero-order), but the rest is not available. Does anyone have a functioning script or a solution for this?

Many thanks in advance!

Marta

Marta Simões

2 ACCEPTED SOLUTIONS

Accepted Solutions

Highlighted

- Mark as New
- Bookmark
- Subscribe
- Mute
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

The nonlinear regression in Fit Curve and Nonlinear both require starting values. Fit Curve uses heuristics associated with each model to obtain starting values from the data. Nonlinear does not have this feature. You must specify the starting values when you define the parameters in the Formula Editor.

The heuristics simply consider the interpretation of the parameter and estimate the starting value. For example, a parameter might represent the central tendency of the curve, so the mean or median are good choices. Or another formula might have an inflection point, so the mid-point might be a good choice.

The regression then searches for estimates (minimizes loss function) until any of the convergence criteria are met. It is not a closed-form exact solution.

Yes, some functions are particularly difficult and demand more careful selection of the starting values.

Learn it once, use it forever!

Highlighted

- Mark as New
- Bookmark
- Subscribe
- Mute
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Thanks Mark! It is clear now!

I was able to put all the equations working with some adjustments in those parameters.

Thanks for your help!

Marta

Marta Simões

4 REPLIES 4

Highlighted
##

- Mark as New
- Bookmark
- Subscribe
- Mute
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Re: Modeling of dissolution profiles

Your description is brief, so perhaps you already know this answer and tried it. The Fit Curve platform provides a limited number of PK models to choose from. It makes such models easy to use and interpret. The Nonlinear platform is more powerful and flexible, but it requires more set up. You can define any one of these models as a 'custom model' for the Nonlinear platform. See this JMP Help entry for more information.

Learn it once, use it forever!

Highlighted
##

- Mark as New
- Bookmark
- Subscribe
- Mute
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Re: Modeling of dissolution profiles

Dear Mark,

Thanks for your feedback. The issue with the non-linear platform is that we need to define the parameters and provide them a value, exactly as described in JMP help you mentioned. But I don't know what these parameters are, they are meant to be determined by JMP (I was expecting so!). Depending on the values I provide for the parameters, the function may not work. For instance, in my jmp file the gompertz function in the non-linear platform is not working, but the Fit Curve provides a good fit! Same with Weibull function.

How can I solve this?

Many thanks for your support.

Marta

Marta Simões

Highlighted

- Mark as New
- Bookmark
- Subscribe
- Mute
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

The nonlinear regression in Fit Curve and Nonlinear both require starting values. Fit Curve uses heuristics associated with each model to obtain starting values from the data. Nonlinear does not have this feature. You must specify the starting values when you define the parameters in the Formula Editor.

The heuristics simply consider the interpretation of the parameter and estimate the starting value. For example, a parameter might represent the central tendency of the curve, so the mean or median are good choices. Or another formula might have an inflection point, so the mid-point might be a good choice.

The regression then searches for estimates (minimizes loss function) until any of the convergence criteria are met. It is not a closed-form exact solution.

Yes, some functions are particularly difficult and demand more careful selection of the starting values.

Learn it once, use it forever!

Highlighted

- Mark as New
- Bookmark
- Subscribe
- Mute
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Thanks Mark! It is clear now!

I was able to put all the equations working with some adjustments in those parameters.

Thanks for your help!

Marta

Marta Simões