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The Future is Now: CPAD’s Modeling Strategy
CPAD Annual Meeting – October 29, 2019
Klaus Romero MD MS FCPExecutive Director, Clinical Pharmacology and Quantitative Medicine
CONFIDENTIAL
www.c-path.org/cpad2
Varying personalities, interests, skills, backgrounds, but one same belief…
Klaus Romero JD PodichettyJackson Burton
Meet C-Path’s QuantMed Program Team
Varun Aggarwal
Samantha Kariolich
Nathan Hanan
Sakshi Sardar Rhoda Muse
www.c-path.org/cpad3
What is Model-Informed Drug Development?
Development and application of pharmaco-statistical models of drug efficacy and safety from preclinical and clinical data to improve drug development knowledge management and decision-making1
Quantitative framework for prediction and extrapolation, centered on knowledge and inference generated from integrated models of compound-, mechanism-, and disease-level data and aimed at improving the quality, efficiency and cost effectiveness of decision making2
1 Lalonde 2007 [PMID 17522597] | 2 Marshall 2016 [PMID 27069774]
www.c-path.org/cpad4
Critical questions for trial design
• How many patients should be recruited to properly power the trial?
• What should be the inclusion criteria?
• Can the control arm be optimized?
• What types of progression rates are expected for different subpopulations?
• What measures of progression are most adequate, at which stages of the disease continuum?
• How long should the trial duration be?
• How often should I assess?
• What is the time-varying probability of dropouts, and what are their predictors?
How should one go about providing sound quantitative answers to these questions?
www.c-path.org/cpad5
Answer 1: Quantifying variability
Quantifying multiple sources of variability simultaneously within the patient population reduces overall unexplained variability
Unexplained variability in patients with
AD
Unexplained variability in patients with
AD
patient feature 1
patient feature 2
patient feature 3
Result: The ability to predict more accurate progression rates for heterogeneous subpopulations of patients in clinical trials
patient feature 1
Unexplained variability in patients with
AD
www.c-path.org/cpad6
Answer 2: Multiple data sources
Understanding the ‘universe’ of a given disease’s heterogeneity
Result: The ability to more accurately account for the heterogeneity in rare diseases and avoid biased conclusions on few data sources
Unexplained variability in ALL patients
with rare diseases
Unexplained variability in patients with rare diseases from a single
study
patient feature 1
patient feature 2
patient feature 1
patient feature 2
www.c-path.org/cpad7
Answer 3: Drug-trial-disease modeling
Disease
Disease progression and characterization model
Drug
Drug effects model / Disease modifying
effect
Trial
Placebo effects model, dropout model
ClinicalTrial
Simulator
Longitudinal observational, registries, RWD & clinical trial data
RWD & clinical trial data
www.c-path.org/cpad8
Putting it altogether
• Start with an understanding of what sponsors can practically use to design clinical trials, and reverse engineer
Execution
𝑆𝑆 𝑡𝑡 =𝑆𝑆0
𝑆𝑆0𝛽𝛽 + 1 − 𝑆𝑆0
𝛽𝛽 𝑒𝑒−𝛽𝛽𝛽𝛽𝛽𝛽1/𝛽𝛽
𝑓𝑓 𝑆𝑆; 𝛼𝛼,𝛽𝛽
=𝛤𝛤 𝛼𝛼 + 𝛽𝛽
𝛤𝛤 𝛼𝛼 + 𝛤𝛤 𝛽𝛽� 𝑆𝑆 𝛼𝛼−1 � 1 − 𝑆𝑆 𝛽𝛽−1
𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖 = 𝜃𝜃𝑖𝑖𝑐𝑐𝑐𝑐𝑐𝑐𝑡𝑡𝑖𝑖𝑟𝑟𝑒𝑒𝑓𝑓
𝜃𝜃𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝜃𝜃𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝛽𝛽𝑖𝑖
www.c-path.org/cpad9
Disease Progression Model
ModelingInput Output
www.c-path.org/cpad10
Disease Progression Model
ModelingInput Output
Patient-level data
www.c-path.org/cpad11
Disease Progression Model
ModelingInput Output
Age
Demographics
Dropouts
Sex
Genetics
Longitudinal biomarkers
Baseline severity
Longitudinal endpoints
Medications Baseline biomarkers
Patient-level data
www.c-path.org/cpad12
Disease Progression Model
Clinicalstudies
Understanding of disease worsening
Trajectory
Rate
Predictors
Web Clinical Trial Simulator
ModelingInput Output
Age
Demographics
Dropout
Sex
Genetics
Longitudinal biomarkers
Baseline severity
Longitudinal endpoints
Medications Baseline biomarkers
www.c-path.org/cpad13
DISEASE PROGRESSION MODEL ACROSS THE ENTIRE AD CONTINUUM
CONFIDENTIAL
www.c-path.org/cpad14
Disease Progression Model
DATA
ModelingInput Output
KNOWLEDGE
TRANSFORMATION
www.c-path.org/cpad15
Disease Progression Model
DATA
ModelingInput Output
KNOWLEDGE
TRANSFORMATION
Use
OPTIMIZETrial Design
www.c-path.org/cpad16
C-Path’s impact in MIDD
TB model-based meta-analysis to identify predictors that can help identify subpopulations more likely to respond to treatment.
The model is helping optimize the design of clinical trials and are also being used to optimize programmatic deployment of standard of care treatment in high-burden countries.
www.c-path.org/cpad17
Scor
e
Spee
d
AgeAge
Spee
d
Age
Age
Spee
d
Age
Volu
me
Age
Scor
e
C-Path’s impact in MIDDMultiple endpoints over time in Duchenne Muscular Dystrophy (DMD).
www.c-path.org/cpad18
So…
Models, trials and biomarkers?• It’s all about the sources of variability• Unless dealing with safety or diagnosis, biomarkers are either:
• Covariates in a model• One of many endpoints in a model
• Quantitatively understanding disease progression helps improve theunderstanding of biomarkers and other relevant sources of variability,and can streamline the pathway towards regulatory acceptance ofquantitative solutions to improve clinical trial design efficiency
Thank you!
CONFIDENTIAL