At the risk of being tiresome about this topic, absent specific
differences between Phase 1 and Phase 2/3 data , e.g., renal function
due to age or disease states, etc., I’d argue that most of the
differences seen between Phase 1 and Phase 2/3 data are due to
adherence. In a sense, then, much of the differences in PK between these
two groups is artificial, and due to the fact that patients do not
reliably take their medication as prescribed, as opposed to Phase 1
volunteers, where adherence is near 100%. Bernard Vrijens has published
a lot on this topic as it relates to PPK analyses. We, as a discipline,
need to start pushing hard for adherence measures in clinical trials.
As an n=1 case study , a few years ago, I was involved with an analysis
of a large Phase 2 study which consisted of an in-house phase, followed
by discharge to home and an out-patient phase. The patients were
significantly older and sicker than Phase 1 volunteers, so one might
expect some PK differences. When we analyzed the data from the in-house
portion of the study, we got results nearly identical to Phase 1.
However, when we added in the out-patient phase, IIV on many of the
parameters increased dramatically, and the residual error became
extremely large. Clearly, patients were not taking their medication as
prescribed ( and as they wrote in their patient diaries). We ended up
not using the out-patient portion of the data, which represents a huge
waste of resources.
This irritates people when I say this, but we as a discipline are so
enamored of finding that magical covariate(s) which will explain
variability, but we neglect the most important one of all: Did they take
the medicine when they say they did? No biological covariate can have as
big of an effect as adherence. Accounting for adherence routinely
results in up to a 50% decrease in residual variability – few standard
covariates have this effect.
*Fossler M.J.*Commentary: Patient Adherence: Clinical Pharmacology’s
Embarrassing Relative. /Journal of Clinical Pharmacology/ (2015) 55(4):
365-367.
Mike
Michael J. Fossler, Pharm. D., Ph. D., F.C.P.
VP, Quantitative Sciences
Trevena, Inc
[email protected] <mailto:[email protected]>
Office: 610-354-8840, ext. 249
Cell: 610-329-6636
*From:*[email protected]
[mailto:[email protected]] *On Behalf Of *Denney, William S.
*Sent:* Wednesday, January 06, 2016 8:33 AM
*To:* <[email protected]>
*Cc:* Zheng Liu; [email protected]
*Subject:* Re: [NMusers] unbalanced data set
Hi Zheng,
I'll take an intermediate view between Joachim and Nick.
The rich data from Phase 1 provides the ability to define the structural
model and a few of the important covariates. The control of Phase 1
gives precision that cannot be achieved in Phase 2 or 3 studies. But,
there are usually important differences between Phase 1 and later phase
populations that makes the later phase separately important.
With later phase trials, the range of covariates is expanded [1]. On
top of the expanded covariate range, sometimes late-phase patient
populations are categorically different than early phase [2].
In practice, this means that I fit a single model to all data. The
model will allow for the dense data from Phase 1 with more
inter-individual variability (IIV) terms (fix the IIV to 0 for sparse
data) and the expanded covariate range with a richer set of fixed
effects as the model is expanded for later phase. Finally, due to
typical differences in data quality, I will often include a different
residual error structure for sparse data. This approach allows the
complexity of the Phase 1 structural model to carry into the richness of
the late phase covariate model.
[1] A specific example is that typically renal function is allowed to be
lower especially when Phase 1 is in healthy subjects.
[2] My true belief is that there may be unobserved covariates causing
what appears to be a categorical difference. The functional impact of
that belief is semantic only. In practice, the model would include a
categorical parameter.
Thanks,
Bill
On Jan 6, 2016, at 4:09, "Joachim Grevel" <[email protected]
<mailto:[email protected]>> wrote:
Dear Zheng,
This is indeed a fundamental and recurring problem in drug development.
You have rich data from Phase 1 studies (single ascending dose, multiple
ascending dose, others e.g. QTc) and sparse data from Phase 3 studies.
Should you mix them all in one large meta-analysis and derive the
definitive popPK model for that drug/project?
After years of experience, I tend to not mix Phase 1 with Phase 3 data.
Phase 1 can be used to establish the first popPK model which may contain
special features such as nonlinearities/saturation effects as a
consequence of the wide range of doses studied. This can be the starting
point for the building of a fit-for purpose model using Phase 3 data
only. I have come to believe that the specific patient population(s) of
Phase 3 require their own popPK model that predicts exposure without
bias. This is then used in the exposure-response (E-R) modelling that is
important for market approval. Only a dedicated Phase 3 popPK model,
that does not carry unnecessary legacies of Phase 1 development, is fit
for E-R modelling and can give the important answers about the dose
rate(s) to be put in the drug label.
I would be interested to hear some other opinions.
Good luck,
Joachim
*Joachim Grevel, PhD*
Scientific Director
BAST Inc Limited
Science & Enterprise Park
Loughborough University
Loughborough, LE11 3AQ
United Kingdom
Tel: +44 (0)1509 222908
www.bastinc.eu
<https://urldefense.proofpoint.com/v2/url?u=http-3A__www.bastinc.eu_&d=CwMFAg&c=UE1eNsedaKncO0Yl_u8bfw&r=4WqjVFXRfAkMXd6y3wiAtxtNlICJwFMiogoD6jkpUkg&m=wrsdorQ-9eTdtCeqy58cKOuX_NzLV7qeQgXnv6Rs89U&s=3ER4IQI_zP2M4rkqPEVwQseSkXSfoC6ux5FHzM7qeSs&e=>
*From:*[email protected]
<mailto:[email protected]>
[mailto:[email protected]] *On Behalf Of *Zheng Liu
*Sent:* 06 January 2016 02:03
*To:* [email protected] <mailto:[email protected]>
*Subject:* [NMusers] unbalanced data set
Dear all,
I recently have a data set for pk parameters fitting. The issue is some
patients have far more measurement points than others (i.e. a few
patients have ~15 points, other patients have only 1 or 2). I speculate
in the fitted parameters, those patients with many points would
contribute much more than those with less points. Then the
population "average" values of fitted pk parameters are not
anymore average from all the patients, but more biased to those patients
with many points. This is not what I expect.
Of course I could take away some points from the patients with many
points, in order to be comparable to less-points patients. Then I will
be forced to lose some information from the data set. I just wonder are
there anyone who have better proposal to solve this problem? I
appreciate your help very much!
Best regards,
Zheng
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