[NMusers] RE: Using MCP-MOD in dose finding for Phase 3

From: Standing Joseph (GREAT ORMOND STREET HOSPITAL FOR CHILDREN NHS FOUNDATION TRUS <joseph.standing_at_nhs.net>
Date: Mon, 23 Mar 2015 09:03:30 +0000

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 wh=
en you get the wrong dose in Phase 3. Agree that you should be concerned w=
hen people want to use use dose rather than PK (not symmetrically distribut=
ed) when you can get it, and anyone suggesting they are not interested in b=
iomarker 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 dream=
t up but can be derived from the law of mass action. Biomarker trajectorie=
s with time predicted using turnover models linked to Emax models will be m=
ore useful than an empirical function. Mechanistic pharmacometric models a=
re trying to go beyond describing observed data, and may be used for extrap=
olating to places where we don't have data. I therefore have two general c=
oncerns about the "any old model" approach:

1. Highly data reliant and assumes you have covered the whole dose-respons=
e range - picks up on your point about adding in physiological knowledge. =
Perhaps some Bayesians might comment on how to sensibly incorporate prior i=
nformation from earlier phases?
2. Difficult to see how resulting models can be used to extrapolate to spe=
cial populations where we will never have Phase 2B type data.

For pharma company strategic decision makers, what is needed is a systemati=
c comparison of mechanistic PKPD dose recommendations versus MCPmod across =
a large range of compounds. Perhaps this has already been done (I don't kn=
ow the literature on this), but if not and before putting all eggs in one b=
asket then it would seem sensible to try to perform such a comparison. Als=
o this needs to account for the fact that good mechanistic Phase 2B PKPD mo=
dels will help support paediatric and other special population development,=
 and in dose individualisation (personalised medicine) which as we leave th=
e 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
________________________________________
From: owner-nmusers_at_globomaxnm.com [owner-nmusers_at_globomaxnm.com] On Behalf=
 Of Åstrand, Magnus [Magnus.Astrand_at_astrazeneca.com]
Sent: 20 March 2015 17:47
To: Mueller-Plock, Nele; nmusers_at_globomaxnm.com
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 re=
sponse either by model selection or model averaging among the significant c=
andidate models.

I think you could use the principles of MCPmod even if you use a longitudin=
al 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 respo=
nse 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 gi=
ven 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, com=
bined with a assumed true dose response curve.
An interesting thought that I actually may explore! (I think the output wou=
ld 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 se=
e 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 t=
he 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 improv=
es 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 in=
to 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
magnus.astrand_at_astrazeneca.com

Please consider the environment before printing this e-mail



From: owner-nmusers_at_globomaxnm.com [mailto:owner-nmusers_at_globomaxnm.com] On=
 Behalf Of Mueller-Plock, Nele
Sent: den 20 mars 2015 13:02
To: nmusers_at_globomaxnm.com
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 th=
e MCP-MOD approach for dose finding based on Phase 2B results and would lik=
e to hear your opinion on this approach. It would be good to get feedback f=
rom 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 wh=
at I understood, I can see the following issues:
MCP-MOD

· Only uses trial endpoints, i.e. it ignores the time course of t=
he treatment effect. I have a problem with this because there might be nois=
e in the endpoint (e.g. if the effect has reached a plateau), which might p=
otentially lead to the selection of the wrong model structure. Including th=
e time-course like in PKPD modeling approaches would detect that the deviat=
ion is just noise, and thus probably be able to identify the right model st=
ructure 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 g=
iven the data we have. What happens if the true model is not part of the te=
sted ones? What if we have new physiological insights that tell us about th=
e model structure after we have seen the data? Do we then ignore what we kn=
ow 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 prespecificat=
ion and only have a few dose strength, wouldn’t the model with more param=
eters be more likely to give a good fit (e.g. when comparing Emax to logist=
ic), with the consequence that a wrong dose might be selected?

Colleagues from statistics recommend to cover all potential models with dif=
ferent shapes in the candidate set to avoid potential bias in dose selectio=
n, but they argue that post-hoc model fitting leads to data-dredging and ov=
er-fitting, does not account for model uncertainty and gives overly-optimis=
tic results. I am wondering however what the difference in the approach is =
if anyway ALL potential models are considered (which can lead to overfittin=
g 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 receiv=
ing 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: nele.mueller-plock_at_takeda.com<mailto:nele.kaessner_at_nycomed.com>
http://www.takeda.com<http://www.takeda.com/>


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Received on Mon Mar 23 2015 - 05:03:30 EDT

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