RE: Using MCP-MOD in dose finding for Phase 3
Dear Nele,
One advantage of the biologically blind multiple model approach in Phase 2B is
that it shifts the blame from the pharmacologist to the statistician when you
get the wrong dose in Phase 3. Agree that you should be concerned when people
want to use use dose rather than PK (not symmetrically distributed) when you
can get it, and anyone suggesting they are not interested in biomarker or
response time courses clearly needs get out more.
My advice is to keep in mind that PK models are parameterised specifically to
link to physiological processes: e.g. CL scales with organ function, and the
Emax model is not just some function that a crazy pharmacologist dreamt up but
can be derived from the law of mass action. Biomarker trajectories with time
predicted using turnover models linked to Emax models will be more useful than
an empirical function. Mechanistic pharmacometric models are trying to go
beyond describing observed data, and may be used for extrapolating to places
where we don't have data. I therefore have two general concerns about the "any
old model" approach:
1. Highly data reliant and assumes you have covered the whole dose-response
range - picks up on your point about adding in physiological knowledge.
Perhaps some Bayesians might comment on how to sensibly incorporate prior
information from earlier phases?
2. Difficult to see how resulting models can be used to extrapolate to special
populations where we will never have Phase 2B type data.
For pharma company strategic decision makers, what is needed is a systematic
comparison of mechanistic PKPD dose recommendations versus MCPmod across a
large range of compounds. Perhaps this has already been done (I don't know the
literature on this), but if not and before putting all eggs in one basket then
it would seem sensible to try to perform such a comparison. Also this needs to
account for the fact that good mechanistic Phase 2B PKPD models will help
support paediatric and other special population development, and in dose
individualisation (personalised medicine) which as we leave the blockbuster era
will become increasingly important.
Joe
Joseph F Standing
MRC Fellow, UCL Institute of Child Health
Antimicrobial Pharmacist, Great Ormond Street Hospital
Tel: +44(0)207 905 2370
Mobile: +44(0)7970 572435
Quoted reply history
________________________________________
From: [email protected] [[email protected]] On Behalf Of
Åstrand, Magnus [[email protected]]
Sent: 20 March 2015 17:47
To: Mueller-Plock, Nele; [email protected]
Subject: [NMusers] RE: Using MCP-MOD in dose finding for Phase 3
Dear Nele, here are some thoughts:
The idea with the MCPmod is twofold,
a) provide a procedure for testing for a treatment effect and in that test
incorporate all doses studies and still maintain control of type I error.
b) If significance in a) continue with framework for estimating the dose
response either by model selection or model averaging among the significant
candidate models.
I think you could use the principles of MCPmod even if you use a longitudinal
model with a time course of your treatment effect.
You could for example use the same time profile for the treatment effect in all
doses, but estimate different magnitude for each dose. (indirect response model
with effect on kin, one level for each dose)
The estimated magnitudes would then replace the mean effect in each dose in the
standard MCPmod application.
The theory of MCPmod builds on the existence of a optimal contrast for a given
true effect profile across your set of doses.
Potentially there is a way to derive optimal tests but instead base that on a
assumed distribution of the exposure across all your doses included, combined
with a assumed true dose response curve.
An interesting thought that I actually may explore! (I think the output would
be a weight function w(exposure) so that you would get a test based on
w(exposure)*observed_effect, sum across all your data.
There is no limit on how many candidate models you can use, so I don’t see that
as a problem.
Planning of your analysis across a wide range of potential DR functions to make
sure you have good power whatever the true DR is recommended.
(And actually by selecting a smart set of candidate models can improve on the
power)
You can include several emax, but with different set of parameters, combine
that with other types of functions, sigmod emax.
On your last bullet, a good way around is to use model averaging instead of
model selection. If your model with more parameters only marginally improves
the fit, the weight for that model will not be so high.
My experience is that model averaging generally performs better than model
selection. A big advantage is also if you end up with 2 equally good models,
instead of presenting 2 results to your project, you combine them both into one.
Kind regards
Magnus Åstrand
Senior Clinical Pharmacometrician, Ph.D.
_____________________________________________________________________________________________
AstraZeneca
Innovative Medicines | Quantitative Clinical Pharmacology
SE-431 83 Mölndal, Sweden
T: +46 (0)31 776 23 41
Mob: +46 (0)708 467 667
[email protected]
Please consider the environment before printing this e-mail
From: [email protected] [mailto:[email protected]] On
Behalf Of Mueller-Plock, Nele
Sent: den 20 mars 2015 13:02
To: [email protected]
Subject: [NMusers] Using MCP-MOD in dose finding for Phase 3
Dear all,
I am writing to you as we are currently discussing the implementation of the
MCP-MOD approach for dose finding based on Phase 2B results and would like to
hear your opinion on this approach. It would be good to get feedback from both
statisticians and classical modelers.
I have thought about the approach, and have a few problems about seeing the
advantage of the approach over complete population-PK/PD modeling. From what I
understood, I can see the following issues:
MCP-MOD
· Only uses trial endpoints, i.e. it ignores the time course of the
treatment effect. I have a problem with this because there might be noise in
the endpoint (e.g. if the effect has reached a plateau), which might
potentially lead to the selection of the wrong model structure. Including the
time-course like in PKPD modeling approaches would detect that the deviation is
just noise, and thus probably be able to identify the right model structure
despite this.
· Uses dose-response models instead of exposure-response models
· Pre-specifies the model structure. While I understand that for
pivotal trials prespecification is crucial, I would assume that Phase 2 is
performed to allow exploration of the data to come up with the best model given
the data we have. What happens if the true model is not part of the tested
ones? What if we have new physiological insights that tell us about the model
structure after we have seen the data? Do we then ignore what we know and fit
all bad models, and if none gives a good description we do model averaging of
bad models?
· If we include a model with many parameters in the prespecification
and only have a few dose strength, wouldn’t the model with more parameters be
more likely to give a good fit (e.g. when comparing Emax to logistic), with the
consequence that a wrong dose might be selected?
Colleagues from statistics recommend to cover all potential models with
different shapes in the candidate set to avoid potential bias in dose
selection, but they argue that post-hoc model fitting leads to data-dredging
and over-fitting, does not account for model uncertainty and gives
overly-optimistic results. I am wondering however what the difference in the
approach is if anyway ALL potential models are considered (which can lead to
overfitting as well)?
Might a good solution be to combine PKPD modeling with MCP-Mod?
Your opinion will be highly appreciated, and I am looking forward to receiving
comments both in favour and against the approach :-)
Best
Nele
______________________________________________________________
Dr. Nele Mueller-Plock, CAPM
Associate Scientific Director Pharmacometrics
Global Pharmacometrics
Translational Medicine
Takeda Pharmaceuticals International GmbH
Thurgauerstrasse 130
8152 Glattpark-Opfikon (Zürich)
Switzerland
Visitor address:
Alpenstrasse 3
8152 Glattpark-Opfikon (Zürich)
Switzerland
Phone: (+41) 44 / 55 51 404
Mobile: (+41) 79 / 654 33 99
mailto: [email protected]<mailto:[email protected]>
http://www.takeda.com/
--------------------------------------------------------------------
The content of this email and of any files transmitted may contain
confidential, proprietary or legally privileged information and is intended
solely for the use of the person/s or entity/ies to whom it is addressed. If
you have received this email in error you have no permission whatsoever to use,
copy, disclose or forward all or any of its contents. Please immediately notify
the sender and thereafter delete this email and any attachments.
--------------------------------------------------------------------
________________________________
Confidentiality Notice: This message is private and may contain confidential
and proprietary information. If you have received this message in error, please
notify us and remove it from your system and note that you must not copy,
distribute or take any action in reliance on it. Any unauthorized use or
disclosure of the contents of this message is not permitted and may be unlawful.
********************************************************************************************************************
This message may contain confidential information. If you are not the intended
recipient please inform the
sender that you have received the message in error before deleting it.
Please do not disclose, copy or distribute information in this e-mail or take
any action in reliance on its contents:
to do so is strictly prohibited and may be unlawful.
Thank you for your co-operation.
NHSmail is the secure email and directory service available for all NHS staff
in England and Scotland
NHSmail is approved for exchanging patient data and other sensitive information
with NHSmail and GSi recipients
NHSmail provides an email address for your career in the NHS and can be
accessed anywhere
********************************************************************************************************************