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DESCRIPTION:Use of extrapolation techniques is playing an increasingly impo
rtant role in the development of new medicines particularly with regard to
special populations such as paediatrics and rare diseases. This meeting w
ill include speakers from industry\, academia and regulatory (including Ro
b Hemmings from MHRA). \nSpeakers include: Peter Milligan - Pfizer Krist
in Karlsson - Medical Products Agency\, Sweden Rob Hemmings - MHRA Nicky B
est - GSK Dawn Edwards - GSK Adrian Mander \;- MRC Biostatistics Unit\
, University of Cambridge Lisa Hampson - AstraZeneca Ian Wadsworth - Lanca
ster University \nPlease click here \;to view the flyer. Abstracts
Rob Hemmings\, MHRA Extrapolation\; regulatory need\, examples and emerg
ing guidance. Abstract: Extrapolation is defined as &lsquo\;extending info
rmation and conclusions available from studies in one or more subgroups of
the patient population (source population(s))\, or in related conditions
or with related medicinal products\, to make inferences for another subgro
up of the population (target population)\, or condition or product\, thus
reducing the amount of\, or general need for\, additional information (typ
es of studies\, design modifications\, number of patients required) needed
to reach conclusions for the target population\, or condition or medicina
l product&rsquo\;. \; The talk will illustrate the potential need for\
, and benefits of\, this concept in regulatory work with a primary focus o
n extrapolation from adults to children. \; An overview of the EMA Ref
lection Paper on this topic will be presented and discussed\, highlighting
areas for further discussion and research. Ian Wadsworth\, Lisa V. Hampso
n\, Thomas Jaki and Graeme J. Sills Using historical data to inform extrap
olation decisions in children When developing a new medicine for children\
, the potential to extrapolate from adult efficacy data is well recognised
. However\, significant assumptions about the similarity of adults and chi
ldren are needed for extrapolations to be biologically plausible. One such
assumption is that pharmacokinetic-pharmacodynamic (PK-PD) relationships
are similar in these different groups. In this presentation\, we consider
how &lsquo\;source&rsquo\; data available from historical trials completed
in adults and adolescents treated with a test drug\, can be used to quant
ify prior uncertainty about whether PK-PD relationships are similar in adu
lts and younger children. A Bayesian multivariate meta-analytic model is u
sed to synthesise the PK-PD data available from the historical trials whic
h recruited adults and adolescents. The model adjusts for the biases that
may arise since these existing data are not perfectly relevant to the comp
arison of interest\, and we propose a strategy for eliciting expert prior
opinion on the size of these external biases. From the fitted bias-adjuste
d meta-analytic model we derive prior distributions which quantify our unc
ertainty about the similarity of PK-PD relationships in adults and younger
children. These prior distributions can then be used to calculate the pro
bability of similar PK-PD relationships in adults and younger children whi
ch\, in turn\, may be used to inform decisions as to whether complete extr
apolation of efficacy data from adults to children is currently justified\
, or whether additional data in children are needed to reduce uncertainty.
Properties of the proposed methods are assessed using simulation\, and th
eir application to epilepsy drug development is considered. Clara Dom&iacu
te\;nguez-Islas1\, Adrian Mander1\, Rebecca Turner2\, Nicky Best3 A Bayes
ian framework for extrapolation using mixture priors 1 MRC Biostatistics
Unit\, University of Cambridge\, UK. 2 MRC Clinical Trials Unit\, Univers
ity College London\, UK. 3 GlaxoSmithKline\, UK. As defined by the Europ
ean Medicines Agency (EMA)\, extrapolation refers to the extension of info
rmation and conclusions available from studies in a source population to m
ake inferences in a target population\, in order to reduce the amount of a
dditional information needed to reach conclusions for the latter. \; B
ayesian inference seems to provide a natural framework to implement the ex
trapolation principle\, as the information from the source population can
be used as the prior beliefs for the target population. However\, intrinsi
c to extrapolation principle\, there is also the belief that the source an
d target populations\, although similar enough to allow one of them to inf
orm the other\, are not exactly the same and important differences\, not k
nown a priori\, might exist. Therefore\, along with informative priors\, w
e also need to incorporate a certain degree of scepticism. This could be a
chieved by the use of mixture priors. \; Although mixture priors have
been already proposed in different extrapolation contexts (bridging studie
s\, historical controls\, paediatric extrapolation)\, we identify some gap
s in the research conducted and reported so far. In this presentation\, we
intend to further explore and better understand the potential of mixture
priors to provide a quantitative framework for extrapolation. First we pre
sent the mixture prior model with special emphasis on the interpretation a
nd type of inference that it allows\, providing a connection with Bayesian
model averaging. We then address some of the challenges that arise when c
onstructing a mixture prior\, including the choices to be made for each of
the components of the model\, as well as technical aspects of the estimat
ion and computation. Finally\, we discuss the frequentist operating charac
teristics of this approach and identify the trade-offs that come with the
flexibility and robustness of the mixture priors. \; Lisa V Hampson\
, Franz Koenig Use of frequentist and Bayesian approaches for extrapolatin
g from adult efficacy data to design and interpret confirmatory trials in
children New medicines for children should be subject to rigorous examinat
ion whilst taking steps to avoid unnecessary experimentation. Extrapolatin
g from adult data can reduce uncertainty about a drug&rsquo\;s effects in
younger patients meaning smaller trials may suffice. We consider how to de
sign a confirmatory trial in children intended to compare the efficacy of
a new drug\, E\, against control. Assuming that conduct of this trial is c
onditional on having demonstrated a significant beneficial effect in adult
s\, we adopt a Bayesian approach to incorporate these adult data into the
design and analysis of the paediatric trial. At each stage\, inferences ar
e made using all available data to update a Bayesian mixture model for pri
or opinion on the degree of similarities between adults and children. Usin
g this framework\, we propose designs for the paediatric trial which are s
pecified by calibrating the sample size and final decision rule to: a) ach
ieve a high frequentist power and high minimum (or average) Bayesian posit
ive predictive value of a significant result in children\; or b) ensure th
at a final decision to adopt (abandon) drug E in children is always associ
ated with a minimum positive (negative) predictive value. Operating charac
teristics of our Bayesian designs are evaluated and compared with those of
a recently proposed hybrid approach (Hlavin et al. Statistics in Medicine
2016\; 35: 2117) where the sample size and significance level of a freque
ntist confirmatory trial in children are set to achieve a high frequentist
power and high average positive predictive value of a significant result
in children. Nicky Best\, Dawn Edwards A case study using Bayesian metho
ds to leverage existing clinical efficacy data in paediatric trials Recent
ly there has been increased regulatory interest in partial extrapolation o
f adult efficacy information to paediatrics populations to reduce data col
lection requirements in children. \; In this talk we will present a ca
se study describing plans to use partial extrapolation of adult efficacy d
ata from a phase III trial of an experimental drug in adolescents with&nbs
p\;a respiratory disease. \; We will demonstrate how adult data on the
treatment difference for the endpoint of interest can be included via an
informative prior distribution to increase the probability of success of t
he study in adolescents and the precision of the estimated treatment diffe
rence. A method which incorporates dynamic borrowing will be used to defin
e the level of extrapolation using a 2-step approach whereby information f
rom the adult data is first incorporated into a prior distribution before
being integrated with the data from the adolescent population. We propose
a 3-component weighted robust mixture prior with the informative component
s based on (1) the adult efficacy data\, (2) rescaled adult efficacy data
to reflect the expected response for the adolescent population\, and \
;(3) a flat component to ensure that\, in the event the adolescent and adu
lt data are in clear conflict\, the latter will have minimal influence on
the posterior distribution of the treatment difference\, thus also prevent
ing excessive inflation of type 1 error. We will present results of a simu
lation study investigating operating characteristics for different choices
of success criteria and prior weights. Peter Milligan Utilizing a Quanti
tative Framework to support Extrapolation Peter A Milligan on behalf of th
e EFPIA MID3 Workgroup \; The 2016 white paper on Good Practices in Mo
del-Informed Drug Discovery and Development: Practice\, Application\, and
Documentation (1) defines Model Informed Drug Discovery and Development (M
ID3) as a &lsquo\;&lsquo\;quantitative framework for prediction and extrap
olation\, centred on knowledge and inference generated from integrated mod
els of compound\, mechanism and disease level data and aimed at improving
the quality\, efficiency and cost effectiveness of decision making&rsquo\;
&rsquo\;. MID3 in its simplest form embodies using &lsquo\;&lsquo\;fit-for
-purpose&rsquo\;&rsquo\; mathematical models\, implemented according to go
od practices\, in order to enhance the extraction of inference from both e
xisting information and data emanating from ongoing experiments. As the un
derpinning foundations for MID3 are based on robust scientific principles
derived from pharmacological\, physiological\, and pathological processes
(the domain sciences)\, MID3 can more effectively support translation acro
ss\, and extrapolation beyond\, the direct inference obtained from standar
d descriptive methods applied to experimental data. Conversion of the cur
rent knowledge captured within the &lsquo\;&lsquo\;fit-for-purpose&rsquo\;
&rsquo\; model into inference requires a prediction based either on partic
ular model parameter estimates or utilizing values generated through simul
ation. Predictions can either be interpolative or extrapolative with respe
ct to available evidence and the intended purpose. Extrapolations beyond
current experience often provide the greatest value to pharmaceutical comp
anies. The recent EMA concept paper on The Extrapolation of Efficacy and S
afety Data in Medicine Development (2) identified the approaches utilized
in MID3 as part of the extrapolation concept and for inclusion in an extra
polation plan. The use of extrapolations emanating from an appropriate qua
ntitative framework to bridge efficacy and safety in special populations w
as discussed in the 2011 joint workshop (3) with some of the resultant pro
posals subsequently published in greater detail (4). Replacement of direct
experimental evidence (including all or part of a clinical trial) in a de
velopment program is conceptually &lsquo\;&lsquo\;permissible&rsquo\;&rsqu
o\; but considered to be of &lsquo\;&lsquo\;high regulatory impact&rsquo\;
&rsquo\; (5) necessitating substantive a priori discussions with the regul
atory agencies to characterize the context of use for any resultant extrap
olations. \; SF Marshall et al\, CPT Pharmacometrics Syst. Pharmacol.
(2016) 5\, 93&ndash\;122\; doi:10.1002/psp4.12049 EMA/EFPIA European Medi
cines Agency/European Federation of Pharmaceutical Industries and Associat
ions workshop on the importance of dose finding and dose selection for the
successful development\, licensing and lifecycle management of medicinal
products. http://www.ema.europa.eu/ema/index.jsp?curl5pages/news_and_event
s/events/2014/06/event_detail_000993.jsp&\;mid5WC0b01ac058004d5c3 (2014
). EMA/EFPIA M&\;S workshop 2011. http://www.ema.europa.eu/ema/index.js
p?curl5pages/news_and_events/events/2011/07/event_detail_000440.jsp&\;m
id5WC0-b01ac058004d5c3 (2011). Harnisch\, L.\, Shepard\, T.\, Pons\, G.\,
Della Pasqua\, O. Modeling and Simulation as a Tool to Bridge Efficacy and
Safety Data in Special Populations CPT: Pharmacometrics &\; Systems Ph
armacology 2\, e28 (2013). Manolis\, E.\, Rohou\, S.\, Hemmings\, R.\, Sal
monson\, T.\, Karlsson\, M. &\; Milligan\, P.A. The role of modeling an
d simulation in development and registration of medicinal products: output
from the EFPIA/EMA Modeling and Simulation Workshop. CPT Pharmacometrics
Syst. Pharmacol. 2\, e31 (2013). Please click on the links below to view
the slides: Rob Hemmings Peter Milligan Kristin Karlsson Ian Wadsworth Li
sa Hampson\, Franz Koenig and Martin Posch Clara Domí\;nguez-Islas\,
Adrian Mander\, Rebecca Turner\, Nicky Best Nicky Best\, Dawn Edwards
DTEND;VALUE=DATE:20171123
DTSTAMP:20240329T093324Z
DTSTART;VALUE=DATE:20171122
LOCATION:UK\,Stevenage\,GSK
SEQUENCE:0
SUMMARY:PSI One Day Meeting: Extrapolation
UID:RFCALITEM638473016045551849
X-ALT-DESC;FMTTYPE=text/html:Use of extr
apolation techniques is playing an increasingly important role in the deve
lopment of new medicines particularly with regard to special populations s
uch as paediatrics and rare diseases. This meeting will include speakers f
rom industry\, academia and regulatory (including Rob Hemmings from MHRA).
\nSpeakers include:
R ob Hemmings\, MHRA
Extrapola tion\; regulatory need\, examples and emerging guidance.
Abstract: Extrapolation is defined as &lsquo\;extending information and conclusions available from studies in on e or more subgroups of the patient population (source population(s))\, or in related conditions or with related medicinal products\, to make inferen ces for another subgroup of the population (target population)\, or condit ion or product\, thus reducing the amount of\, or general need for\, addit ional information (types of studies\, design modifications\, number of pat ients required) needed to reach conclusions for the target population\, or condition or medicinal product&rsquo\;. \; The talk will illustrate t he potential need for\, and benefits of\, this concept in regulatory work with a primary focus on extrapolation from adults to children. \; An o verview of the EMA Reflection Paper on this topic will be presented and di scussed\, highlighting areas for further discussion and research. p>
Ian Wadsworth\, Lisa V. H ampson\, Thomas Jaki and Graeme J. Sills
Using historical data to inform extrapolation decisions i n children
When devel oping a new medicine for children\, the potential to extrapolate from adul t efficacy data is well recognised. However\, significant assumptions abou t the similarity of adults and children are needed for extrapolations to b e biologically plausible. One such assumption is that pharmacokinetic-phar macodynamic (PK-PD) relationships are similar in these different groups. I n this presentation\, we consider how &lsquo\;source&rsquo\; data availabl e from historical trials completed in adults and adolescents treated with a test drug\, can be used to quantify prior uncertainty about whether PK-P D relationships are similar in adults and younger children. A Bayesian mul tivariate meta-analytic model is used to synthesise the PK-PD data availab le from the historical trials which recruited adults and adolescents. The model adjusts for the biases that may arise since these existing data are not perfectly relevant to the comparison of interest\, and we propose a st rategy for eliciting expert prior opinion on the size of these external bi ases. From the fitted bias-adjusted meta-analytic model we derive prior di stributions which quantify our uncertainty about the similarity of PK-PD r elationships in adults and younger children. These prior distributions can then be used to calculate the probability of similar PK-PD relationships in adults and younger children which\, in turn\, may be used to inform dec isions as to whether complete extrapolation of efficacy data from adults t o children is currently justified\, or whether additional data in children are needed to reduce uncertainty. Properties of the proposed methods are assessed using simulation\, and their application to epilepsy drug develop ment is considered.
A Bayesian framework for extrapolation using mixture p riors
1 M RC Biostatistics Unit\, University of Cambridge\, UK.
2 MRC Clinical Trials Unit\, Univ ersity College London\, UK.
3 GlaxoSmithKline\, UK.
As defined by the European Medicines Agency (EMA)\, extrapo
lation refers to the extension of information and conclusions available fr
om studies in a source population to make inferences in a target populatio
n\, in order to reduce the amount of additional information needed to reac
h conclusions for the latter. \; Bayesian inference seems to provide a
natural framework to implement the extrapolation principle\, as the infor
mation from the source population can be used as the prior beliefs for the
target population. However\, intrinsic to extrapolation principle\, there
is also the belief that the source and target populations\, although simi
lar enough to allow one of them to inform the other\, are not exactly the
same and important differences\, not known a priori\, might exist. Therefo
re\, along with informative priors\, we also need to incorporate a certain
degree of scepticism. This could be achieved by the use of mixture priors
. \; Although mixture priors have been already proposed in different e
xtrapolation contexts (bridging studies\, historical controls\, paediatric
extrapolation)\, we identify some gaps in the research conducted and repo
rted so far. In this presentation\, we intend to further explore and bette
r understand the potential of mixture priors to provide a quantitative fra
mework for extrapolation. First we present the mixture prior model with sp
ecial emphasis on the interpretation and type of inference that it allows\
, providing a connection with Bayesian model averaging. We then address so
me of the challenges that arise when constructing a mixture prior\, includ
ing the choices to be made for each of the components of the model\, as we
ll as technical aspects of the estimation and computation. Finally\, we di
scuss the frequentist operating characteristics of this approach and ident
ify the trade-offs that come with the flexibility and robustness of the mi
xture priors. \;
Lisa V Hampson\, Franz Koenig
New medicin es for children should be subject to rigorous examination whilst taking st eps to avoid unnecessary experimentation. Extrapolating from adult data ca n reduce uncertainty about a drug&rsquo\;s effects in younger patients mea ning smaller trials may suffice.
We consider how to design a confirmatory trial in children intende
d to compare the efficacy of a new drug\, E\, against control. Assuming th
at conduct of this trial is conditional on having demonstrated a significa
nt beneficial effect in adults\, we adopt a Bayesian approach to incorpora
te these adult data into the design and analysis of the paediatric trial.
At each stage\, inferences are made using all available data to update a B
ayesian mixture model for prior opinion on the degree of similarities betw
een adults and children. Using this framework\, we propose designs for the
paediatric trial which are specified by calibrating the sample size and f
inal decision rule to: a) achieve a high frequentist power and high minimu
m (or average) Bayesian positive predictive value of a significant result
in children\; or b) ensure that a final decision to adopt (abandon) drug E
in children is always associated with a minimum positive (negative) predi
ctive value. Operating characteristics of our Bayesian designs are evaluat
ed and compared with those of a recently proposed hybrid approach (Hlavin
et al. Statistics in Medicine 2016\; 35: 2117) where the sample size and s
ignificance level of a frequentist confirmatory trial in children are set
to achieve a high frequentist power and high average positive predictive v
alue of a significant result in children.
Recently there has been increased regulatory interes
t in partial extrapolation of adult efficacy information to paediatrics po
pulations to reduce data collection requirements in children. \; In th
is talk we will present a case study describing plans to use partial extra
polation of adult efficacy data from a phase III trial of an experimental
drug in adolescents with \;a respiratory disease. \; We will demon
strate how adult data on the treatment difference for the endpoint of inte
rest can be included via an informative prior distribution to increase the
probability of success of the study in adolescents and the precision of t
he estimated treatment difference. A method which incorporates dynamic bor
rowing will be used to define the level of extrapolation using a 2-step ap
proach whereby information from the adult data is first incorporated into
a prior distribution before being integrated with the data from the adoles
cent population. We propose a 3-component weighted robust mixture prior wi
th the informative components based on (1) the adult efficacy data\, (2) r
escaled adult efficacy data to reflect the expected response for the adole
scent population\, and \;(3) a flat component to ensure that\, in the
event the adolescent and adult data are in clear conflict\, the latter wil
l have minimal influence on the posterior distribution of the treatment di
fference\, thus also preventing excessive inflation of type 1 error. We wi
ll present results of a simulation study investigating operating character
istics for different choices of success criteria and prior weights.
Peter Milligan
Utilizing a Quantitative Framework to support Extrapolation
Peter A Milligan on behalf of the EFPIA MID3 Workgroup  \;
The 2016 white paper on Good Practices in Model-Informed Drug Di scovery and Development: Practice\, Application\, and Documentation (1) de fines Model Informed Drug Discovery and Development (MID3) as a &lsquo\;&l squo\;quantitative framework for prediction and extrapolation\, centred on knowledge and inference generated from integrated models of compound\, me chanism and disease level data and aimed at improving the quality\, effici ency and cost effectiveness of decision making&rsquo\;&rsquo\;.
MID 3 in its simplest form embodies using &lsquo\;&lsquo\;fit-for-purpose&rsqu o\;&rsquo\; mathematical models\, implemented according to good practices\ , in order to enhance the extraction of inference from both existing infor mation and data emanating from ongoing experiments. As the underpinning fo undations for MID3 are based on robust scientific principles derived from pharmacological\, physiological\, and pathological processes (the domain s ciences)\, MID3 can more effectively support translation across\, and extr apolation beyond\, the direct inference obtained from standard descriptive methods applied to experimental data.
Conversion of the current k nowledge captured within the &lsquo\;&lsquo\;fit-for-purpose&rsquo\;&rsquo \; model into inference requires a prediction based either on particular m odel parameter estimates or utilizing values generated through simulation. Predictions can either be interpolative or extrapolative with respect to available evidence and the intended purpose.
Extrapolations beyond current experience often provide the greatest value to pharmaceutical com panies. The recent EMA concept paper on The Extrapolation of Efficacy and Safety Data in Medicine Development (2) identified the approaches utilized in MID3 as part of the extrapolation concept and for inclusion in an extr apolation plan. The use of extrapolations emanating from an appropriate qu antitative framework to bridge efficacy and safety in special populations was discussed in the 2011 joint workshop (3) with some of the resultant pr oposals subsequently published in greater detail (4). Replacement of direc t experimental evidence (including all or part of a clinical trial) in a d evelopment program is conceptually &lsquo\;&lsquo\;permissible&rsquo\;&rsq uo\; but considered to be of &lsquo\;&lsquo\;high regulatory impact&rsquo\ ;&rsquo\; (5) necessitating substantive a priori discussions with the regu latory agencies to characterize the context of use for any resultant extra polations. \;