modeling for decision making in clinical programs - case studies … events... · deling for...
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Modeling for decision making in clinical programs- Case Studies
Rolf Burghaus – Bayer Schering PharmaClinical Pharmacology / Modeling & Simulation
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Modeling & Simulation - Positioning
• Modeling & Simulation is a means to integrate knowle dge and data in order to:
– Check for consistency of different (types of) data sets and preexisting or derived knowledge – i.e. challenge hypotheses
– Generate in depth understanding of pharmacological processes– Provide predictions in accordance with all related information and data– Analyze and understand unexpected findings
• The overall goal of Modeling & Simulation is to provi de a basis for best informed decisions to comply with regulatory requi rements and create economic value
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Case 1
Female Cycle Simulationfor Clinical Development in Women’s Health
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Female Cycle Simulator (FCS)General Design
The Female Cycle Simulator comprises:• Physiology processes in following organs
– Hypothalamus– Pituitary– Ovaries– Blood
• Dynamic representation of– Hormones (e.g. progesterone (P4), estradiol (E2), F SH
and LH) – Enzymes– Receptors– Follicles/follicular states– GnRH pulse generating system
• Basic simulator is an academic tool integrating physiological/biological knowledge extracted from literature sources
• Simulator is adapted for industrial Clinical Pharmacology use by incorporating internal expertise and specific data
Basic FCS:Reinecke I, Deuflhard P, .J Theor Biol. 2007 Jul 21;247(2):303-30. Epub 2007 Mar 14
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Female Cycle Simulator (FCS)Implementation
MoBI™/ PKSim®Implementation of FCS in standard software package:• allows for complex and efficient simulation work• enables quality management
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Female Cycle Simulator (FCS)Model Establishment using Clinical Data
e.g. Gonadotropins (FSH), Progesterone (P4)
FCS adequately describes mean biological processes in great detail.
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Female Cycle Simulator (FCS)Model Establishment using Clinical Data
e.g. Follicle Growth Pattern
• FCS adequately describes processes up to relevant clinical endpoints.
• How to qualify for prediction of drug actions?
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Female Cycle Simulator (FCS)Qualification by Simulation of Clinical Study Data
Exo
geno
us h
orm
one
expo
sure
Exposure-
population
quantiles
FCS
Study population pharmacokinetics as described by compartmental non-linear mixed
effects model based on clinical study data
Hoogland score limits(clinical endpoint)
Follicle size-
population
quantiles
Study Population clinical endpointpredicted using
Female Cycle Simulator
• FCS adequately predicts:– Qualitative pharmacodynamic response pattern– Statistical distribution of response classes (Hoogland scores)
• FCS predicts effect of artificial exogenous hormones not used during simulator establishment!
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Female Cycle Simulator (FCS)Application for Optimization of Dosing Schedules
Hoogland score limit 10 mmHoogland score limit 13 mm
Efficacy Classification System
quantiles quantiles
quantiles quantiles
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Female Cycle Simulator (FCS)Application for Optimization of Dosing Schedules
(novel) hormones /
hormone combinations and dosages
FCS supports identification of promising treatment schemes
Effi
cacy
Cla
ssifi
catio
n C
lass
Non-trivial treatment schemes
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Female Cycle Simulator (FCS)Application for Optimization of Dosing Schedules
varia
bilit
yof
popu
latio
n
variabilityof
population
Population
quantiles
Numeric treatment scheme property
extremecase
extremecase
covariates ?
Analysis of diverse set of virtual trials helps to gain understanding about the origin of differences in (expected) clinical performance.
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Female Cycle Simulator (FCS)Summary
• FCS is a detailed mechanistic simulator of academic origin
• FCS is continuously extended with expert knowledge v ia data from– Research studies– (clinical) development
studies
• FCS was integrated into company platform to support so phisticated simulation programs
• FCS serves as a tool to– Identify promising research and development options– Predict pharmacodynamic study results – Analyze and understand clinical development data
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Case 2
Modeling & Simulation to support Pediatric Developm ent
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Case Study 2: Pediatric Development
Pediatric Development
• Prerequisite for registration or patent life extension EU /US legislation for any submission of novel and marketed drugs
• Pediatric development is especially challenging as– PK or PD studies in healthy children are discouraged fo r ethical
reasons�First pediatric application are performed in diseased c hildren�Pediatric starting dose needs to be safe and efficac ious
• Pediatric dose selection requires consideration of all drug specific as well as relevant pediatric information.
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Pediatric DevelopmentKnowledge Management – Data Integration
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A physiology based pharmacokinetic model•implements mechanistic hypotheses•integrates data from
•in vitro experiments•different preclinical species•clinical study data•data from different application routes
•Thus challenges pharmacokinetic understanding
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Pediatric DevelopmentUtilization of Pediatric Knowledge
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Kea
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M&S enables prediction of drug exposure in pediatric populationsincorporating all relevant preexisting data and pediatric physiology
knowledge.
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Pediatric StudiesClinical Trial SimulationBSP Standard Workflow
establish kinetic population model(PBPK/population module)
simulate virtual study population
define / refine study designi.e. sampling schema and power
apply study design to virtualpopulation and establish
NLME model
derive study endpoints forvirtual study population
Study design established
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Figure 23-31: Age-dependence of AUC(6-7)days[mg*h/l] for female. Graph (A) presents data in a linear graph, and (B) in a semi-log graph.
1 2 3 6 9 1 1.5 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
100
200
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ays
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Pediatric DevelopmentSummary
• Recent EU/US legislations require pediatric developmen t for every drug to be registered.
• Due to the special conditions of pediatric developme nt an extrapolation of available scientific knowledge (drug and pediatric conditions) for dose selection is mandatory.
• Bayer Schering Pharma has implemented a workflow combin ing mechanistic and statistical modeling.
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Challenges
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Comparison of PB and NLME modelingFeatures
V2/F
K20
K23V3/F
KA
K32
DADT(2) = KA*A(2)-K23*A(2)+K32*A3-K20*A(2)DADT(3) = K23*A(2)-K32*A(3)
Time
Con
cent
rati
on
NLME Modeling:• Approach to
�characterize (pre-) clinical study data
�identify structural properties�generate individual (post-hoc)
estimates from sparse data�simulate defined sub-
populations• Statistically sound procedure• High level of standardization• Good authority acceptance
PB Modeling:• Approach to
�integrate scientific knowledge�analyze/understand
pharmacological processes, mechanistically
�extrapolate to populations/ conditions/ properties not covered by data
�challenge consistency of hypotheses and data
• Means of knowledge management
How to combine?
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Combined statistical and physiologically-based mode ling Theophylline
PK-Sim® and MoBi®
Structure PBPK-Model for theophylline
intestinal permeability (Pint)
clearance (cl)
intestinal transit time (ITT)
per patient
Lipophilicity (Lip)per drug Error (σ)
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Combined statistical and physiologically-based mode lingBayesian approach
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PK-Sim® and MoBi®
� ?
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Conclusions
• Modeling and simulation serves as prospective tool f or decision making
– integrating in vitro and in vivo data to translatio nal pharmacology– to anticipate new clinical study data
• Mechanistic and classical compartmental population modeling are complementary in terms of
– Capacity for extrapolation (prediction)– Statistical performance (retrospective analysis)
• Both technologies are technically feasible and matu re– need for innovation in bridging the gap is recogniz ed
• Mechanistic modeling is shifting the paradigm from ret rospective data evaluation to predictive pharmacology
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Acknowledgements
Female Cycle Simulation• Hartmut Blode• Peter Deuflhard - Zuse Institute Berlin (ZIB)• Christoph Gerlinger• Stefanie Reif• Isabel Reinecke
Pediatric Simulations• Corina Becker• Martin Blunck• Matthias Frede• Wolfgang Mück• Stefan Willmann – Bayer Technology Services GmbH
Mechanistic population modeling• Michael Block – Bayer Technology Services GmbH• Linus Görlitz – Bayer Technology Services GmbH• Jörg Lippert – Bayer Technology Services GmbH
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