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[NMusers] FDA-AACR: Oncology Dose-finding Workshop

From: Wang, Yaning <Yaning.Wang_at_fda.hhs.gov>
Date: Mon, 25 Apr 2016 01:13:44 +0000

FDA-AACR: Oncology Dose-finding Workshop
June 13, 2016 | 8 a.m. - 5 p.m.
Walter E. Washington Convention Center, Washington, D.C.
Co-sponsored by the FDA and the AACR
Registration for this workshop is now open.
Since the approval of imatinib in 2001, the FDA has approved 26 small-molecule kinase inhibitors for the treatment of oncology indications. Given the recent history of approvals based on the results of early phase trials driven by extraordinary efficacy data, the incentive for conducting rigorous dose-finding trials may not be overtly apparent. However, the increasing need for the development of combination therapy due to resistance to monotherapy and poor tolerance of approved dosing regimens underscores the need for a more efficient process of dose selection in the early stages of study design. Furthermore, the unknown efficacy in light of frequent dose reductions in the post-market setting begs the question of whether efficacy reported in early phase trials is accurate when applied to a real-world population.
Objectives of the workshop
1. To identify key "best practices" in the nonclinical evaluation of a compound, including, but not limited to: selectivity, pharmacology, secondary pharmacology and toxicology.
2. To identify disease- and mechanism-specific nonclinical models better able to predict efficacy.
3. To assess whether nonclinical information can be incorporated into the statistical assumptions of an adaptive dose finding trial.
4. To discuss the "best practices" of integrating human pharmacokinetic and pharmacometric data, including exposure-response analyses, into dose-finding studies.
5. To assess how drug exposure can be integrated into the statistical assumptions of an adaptive dose-finding trial and to assess whether evolving exposure data can be adapted into an ongoing trial.
6. To shift from conducting a large, single-arm drug trial with the MTD based on a 28-day window to identifying tolerable, biologically effective doses for confirmatory trials through prudent search of doses based on safety, efficacy and patient tolerability.
7. To discuss potential regulatory implications of dose-finding studies, including but not limited to: product labeling of dose ranges, dose titration and post-marketing studies.
Register for this workshop.

A full agenda will be posted soon.

This workshop is a follow-up to the successful FDA-AACR public workshop: Dose-finding of Small Molecule Oncology Drugs, which was held May 18-19, 2015. Full transcripts and select presentations from that workshop are also available.

Yaning Wang, Ph.D.
Deputy Director
Division of Pharmacometrics
Office of Clinical Pharmacology
Office of Translational Sciences
Center for Drug Evaluation and Research
Food and Drug Administration
Phone: 301-796-1624
Email: yaning.wang_at_fda.hhs.gov
Disclaimer: The contents of this message are mine personally and do not necessarily reflect any position of the Government or the Food and Drug Administration.

-----Original Message-----
From: owner-nmusers_at_globomaxnm.com [mailto:owner-nmusers_at_globomaxnm.com] On Behalf Of Nick Holford
Sent: Saturday, April 23, 2016 4:43 AM
To: nmusers
Subject: Re: [NMusers] Time varying volume of distribution implementation

Thorsten,

Time varying V is no different from time varying CL (or any other
parameter). You should use the variable T in $DES, not TIME, in order to
have the time at the instant the DEQ solver evaluates $DES. T may occur
anywhere in the interval between the previous record TIME and the
current record TIME. TIME in $PK, $DES and $ERROR is the time at the end
of the $DES solution interval.

The other thing that you may wish to do is to assign the random effect
expression for V and CL in $PK so that you can estimate the random
variability after accounting for the fixed effect variability in WT. An
expression involving ETA() cannot be used in $DES so it has to be
assigned in $PK.

e.g.

$PK
POP_V=THETA(1)
POP_CL=THETA(2)
WT_ZERO=THETA(3)
WT_ALPHA=THETA(4)
PPV_V=EXP(ETA(1)) ; random effect for V (PPV_V=population parameter
variability for V)
PPV_CL=EXP(ETA(2)) ; random effect for CLT (PPV_CL=population parameter
variability for CL)

$DES
;Variable names e.g. DWT_T are used in $DES because the same variable
names cannot be assigned in both $DES and in $ERROR

DWT_T=WT_ZERO + WT_ALPHA*T ; fixed effect prediction of WT at T

; Biology requires V and CL must both be functions of WT
DGRP_V=POP_V*DWT_T/70
DV=DGRP_V*PPV_V ; "individual" V at T using random effect for V

DGRP_CL=POP_CL*(DWT_T/70)**(3/4)
DCL=DGRP_CL*PPV_CL ; "individual" CL at T using random effect for CL

DADT(1)= -DCL*A(1)/DV

$ERROR

WT_T=WT_ZERO + WT_ALPHA*TIME ; fixed effect prediction of WT at the TIME
of the current record

GRP_V=POP_V*WT_T/70
GRP_CL=POP_CL*(WT_T/70)**(3/4)

V=VT*PPV_V ; "individual" V at the TIME of the current record
CL=GRP_CL*PPV_CL ; "individual" CL at the TIME of the current record

C=A(1)/V

You may, of course, add random effects to WT_ZERO and/or WT_ALPHA as
well as having random effects on V and CL.

BTW You should consider using the term postmenstrual age rather than
gestational age. Gestational age is a single value defined at the time
of delivery according to the American Academy of Pediatrics (Engle et al
2004). Postmenstrual age is a continuous variable which may be used
during pregnancy and after birth to represent the biological age of the
fetus/child.

Best wishes,

Nick

Engle WA. Age terminology during the perinatal period. Pediatrics.
2004;114(5):1362-4.


On 22-Apr-16 23:34, Thorsten Lehr wrote:
>
> Dear NMusers,
>
> I'm modeling a compound where body weight has a known impact on the
> volume of distribution. This compound is investigated in pregnant
> women over a long period (from gestational age of 8 weeks until they
> give birth). Consequently, the body weight changes over time and I
> have a decent formula to describe the individual body weight change.
> The PK model has to be coded by ODEs. Does anyone has experience how
> to integrate a time varying volume of distribution if differential
> equations are used?
>
> Best regards
>
> Thorsten
>
> --
>
> Thorsten Lehr, PhD
> Junior Professor of Clinical Pharmacy
> Saarland University
> Campus C2 2
> 66123 Saarbr├╝cken
> Germany
>
> Office: +49/681/302-70255
> Mobile: +49/151/22739489
> thorsten.lehr_at_mx.uni-saarland.de
> www.clinicalpharmacy.me

--
Nick Holford, Professor Clinical Pharmacology
Dept Pharmacology & Clinical Pharmacology, Bldg 503 Room 302A
University of Auckland,85 Park Rd,Private Bag 92019,Auckland,New Zealand
office:+64(9)923-6730 mobile:NZ+64(21)46 23 53 FR+33(6)62 32 46 72
email: n.holford_at_auckland.ac.nz
http://holford.fmhs.auckland.ac.nz/

"Declarative languages are a form of dementia -- they have no memory of events"

Holford SD, Allegaert K, Anderson BJ, Kukanich B, Sousa AB, Steinman A, Pypendop, B., Mehvar, R., Giorgi, M., Holford,N.H.G. Parent-metabolite pharmacokinetic models - tests of assumptions and predictions. Journal of Pharmacology & Clinical Toxicology. 2014;2(2):1023-34.
Holford N. Clinical pharmacology = disease progression + drug action. Br J Clin Pharmacol. 2015;79(1):18-27.

Received on Sun Apr 24 2016 - 21:13:44 EDT

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