Models for Computer-Aided Trial & Program Design Terrence - PowerPoint PPT Presentation

Models for Computer-Aided Trial & Program Design Terrence Blaschke, M.D. VP, Methodology and Science Pharsight Corporation & Professor of Medicine & Molecular Pharmacology Stanford University

“MODELS” What is a model and Why do we use them?

MODELS: what are they? A mathematical representation of the relationship between an input and an output – Is expressed in terms of equations – Is quantitative – May also contain representations of variability

MODELS: why do we use them? • For analysis of data – Condense data and provide summary views – Explore relationships using various models • Understand factors (covariates) which affect output/outcome • For interpolation & extrapolation from data – Predict the range of possible outcomes of various untested inputs into a model derived from data from other inputs • Models may be empirical, mechanistic or a combination of both • Interpolate from empirical models, extrapolate from mechanistic models • As a tool for communication

Models used in improving the efficiency of drug development • Drug and Disease Models • Trial Models • Predictive Market Models • Dynamic Financial Models

Models are the foundation to optimize the drug development process Drug & Disease Predictive Models Market Models Trial Models Dynamic Financial Models Models integrate all available information on the drug, analogues and markets to predict outcomes, quantify uncertainty, and understand trade-offs

Different views of models in drug development • Models are used to predict the outcome of the next trial • Models aid in planning of the next trial by predicting the probability distribution of trial outcomes conditional on current knowledge, assumption and trial execution uncertainties. The use of those predictions is to evaluate the ability of the trial to support a certain decision • Two aspects of prediction: – Probability distribution. – Context. The model is a mixture of abstractions from data (what we already know) and assumptions (what we don’t already know, but have some ideas about based on scientific judgments or experience)

Drug-Disease Models • Usually composed of 3 submodels – A Pharmacokinetic model • Relates dose to concentration at site(s) of action – A Pharmacodynamic Model • Relates concentration at site of action to effect – A Disease progress model • Describes natural history of the disease in the absence of treatment, or, preferably, in the presence of a placebo

Drug-Disease Models A drug-disease model predictively characterizes the distribution of treatment outcomes (safety, efficacy, surrogate outcomes) for the NCE and related compounds as a function of dosing strategy, disease, patient, and trial characteristics. 200 180 160 140 120 Efficacy 100 80 60 Adverse 40 Effects 20 0 -20 0 2 4 6 8 10 12 14 16 Dose

Drug-Disease Models A drug-disease model predictively characterizes the distribution of treatment outcomes (safety, efficacy, surrogate outcomes) for the NCE and related compounds as a function of dosing strategy, disease, patient, and trial characteristics. Drug-Disease Models Integrates all available 200 180 160 information on NCE and 140 Efficacy 120 analogues to predict 100 80 60 Adverse outcomes and quantify 40 Effects 20 uncertainty 0 -20 0 2 4 6 8 10 12 14 16 Dose

Trial Models A trial model predicts outcomes and reductions in uncertainty around the trial as a function of dosing strategy, number of treatment arms, type of control, sample population characteristics, sample size, and treatment duration. Trial Models 120 Probability of Successful Outcome 100 80 60 40 Subject 20 0 Variations in design and performance

Trial Models A trial model predicts outcomes and reductions in uncertainty around the trial as a function of dosing strategy, number of treatment arms, type of control, sample population characteristics, sample size, and treatment duration. • Integrates all available information on a • Integrates all available information on a Trial Models possible trial market in dynamic form to possible trial market in dynamic form to quantify uncertainties and sensitivities quantify uncertainties and sensitivities 120 Outcome 100 • Quantifies impact of trial design choices 80 • Quantifies impact of trial design choices 60 on outcome and uncertainty on outcome and uncertainty 40 20 • Creates the basis for simulations to 0 • Creates the basis for simulations to Subject optimize trial design within clinical, optimize trial design within clinical, regulatory, commercial, and financial regulatory, commercial, and financial constraints constraints

Predictive Market Models Predictive Market Models A market model characterizes the demand for products under different feature sets and different competitive and innovation scenarios. Predictive Market Models 120 100 e r 80 a h S t e 60 k r a M 40 Time 20 0 Various competitive and innovation scenarios

Predictive Market Models Predictive Market Models A market model characterizes the demand for products under different feature sets and different competitive and innovation scenarios. • Integrates all available information on a Predictive Market Models market in dynamic form to quantify 120 uncertainty and make trade-offs Market Share 100 • Quantifies impact of product features on 80 60 market share (individual and groups) 40 20 • Identifies key uncertainties in the market 0 that could have major consequences for Time product success

Dynamic Financial Models A dynamic financial model incorporates scientific, clinical, and commercial insights to create a dynamic understanding of the value of a program. This is the foundation for assessing the cost and value of assets, specific program strategy elements, and trial designs. Integrated Valuation Models Expected Net 120 Present Value 100 (ENPV) 80 60 40 20 0 -20 1 3 5 7 9 11 13 15 17 -40 Cumulative Investment -60 -80

Dynamic Financial Models A dynamic financial model incorporates scientific, clinical, and commercial insights to create a dynamic understanding of the value of a program. This is the foundation for assessing the cost and value of assets, specific program strategy elements, and trial designs. Integrated Valuation Models Translates all scientific, clinical and Expected Net 120 Present Value commercial information into 100 80 60 common language of uncertainty, 40 20 cost, and value 0 -20 1 3 5 7 9 11 13 15 17 -40 Cumulative -60 Investment -80

Models are the foundation to optimize the drug development process Drug & Disease Predictive Models Market Models Quantify how a certain trial or sequence can reduce uncertainty around Product Profile: safety, efficacy Market share impact — Dose of various product profiles — Efficacy Trial — Side-effects Models Dynamic Financial ENPV at various market shares Models Models integrate all available information on the drug, analogues and markets to predict outcomes, quantify uncertainty, and understand trade-offs

Model-Based Integrated View of NCE The Models together create an integrated, uncertainty-based view of an NCE that can support all key decisions in drug development. Sample Questions Requiring Dynamic, Integrated View � What is the value of a trial that � What is the expected value of reduces that uncertainty by each product feature or group 20%? 40%? 60%? What is the of features in the Target cost? Product Profile (TPP)? � How confident do we need to � By how much does value be before: decline vs. TPP if feature X is – In-Licensing a compound? 25% lower than TPP? – Killing a program? – Moving into Full � What is the probability that Development NCE will achieve target safety? Efficacy?

How does it work in practice? • Step 1: Build Models, • Late • Drug/Disease Models • Trial Models Quantify Discovery • Predictive Market Models Information • Integrated Valuation Models • Late • Step 2: Design Asset • Target Product Profile • Alternative Development Plans Discovery Strategy • Downstream Options, Scenarios • Value-Maximizing Asset Strategy • Step 3: Design • Late • Optimize Trial Sequence Program Discovery • Optimize Trial Design Strategy and • Define decision points Trials • Phase I • Step 4: Re-Assess/ • New data • Phase II • Market changes Modify • Post-Approval Strategy • Phase III Program • Phase IV Strategy These models can be used in two basic ways to optimize value – Asset Strategy and These models can be used in two basic ways to optimize value – Asset Strategy and Program Strategy/Trial Optimization. The combination, begun in Late Discovery, can Program Strategy/Trial Optimization. The combination, begun in Late Discovery, can guide value maximizing decisions throughout development. guide value maximizing decisions throughout development.