Report

A General Framework for Model-Based Drug Development Using Probability Metrics for Quantitative Decision Making Ken Kowalski, Ann Arbor Pharmacometrics Group (A2PG) Outline Population Models Basic Notation and Key Concepts Basic Probabilistic Concepts General Framework for Model-Based Drug Development (MBDD) Examples Final Remarks/Discussion Bibliography 2 PaSiPhIC 2012 A2PG Population Models Basic Notation General Form of a Two-Level Hierarchical Mixed Effects Model: y i f i h i , i , i ~ N 0 , i g , i (e.g., i i or i e ), i i ~ N 0 , Definitions: y i vector of observatio ns for individual i i vector of subject - specific parameters for individual i vector of fixed effects parameters i vector of inter - individual random effects for individual i i vector of intra - individual random effects for individual i 3 covariance matrix of inter - individual random effects covariance matrix of intra - individual random effects PaSiPhIC 2012 A2PG Population Models Key Concepts Typical Individual Prediction: E y i i 0 f Easy to compute, same functional form as f Population Mean Prediction: E y i E y i i p i d i Integral is often intractable when f is nonlinear Typically requires Monte-Carlo integration (simulation) The typical individual and population mean predictions are not the same when f is nonlinear 4 Cannot observe a ‘typical individual’ Can observe a sample mean PaSiPhIC 2012 A2PG Basic Probabilistic Concepts Statistical intervals (i.e., confidence and prediction intervals) Statistical power Probability of achieving the target value (PTV) Probability of success (POS) Probability of correct decision (POCD) 5 PaSiPhIC 2012 A2PG What’s the difference between a confidence interval and prediction interval? A confidence interval (CI) is used to make inference about the true (unknown) quantity (e.g., population mean) Reflects uncertainty in the parameter estimates Typically used to summarize the current state of knowledge regarding the quantity of interest based on all available data used in the estimation of the quantity A prediction interval (PI) is used to make inference for a future observation (or summary statistic of future observations) 6 Reflects both uncertainty in the parameter estimates as well as the sampling variation for the future observation PaSiPhIC 2012 A2PG Relationship Between CIs and PIs Prediction Limits if E(X ) Located Here Distribution of sampling variation Prediction Limits Recognizing Uncertainty in E(X ) X Confidence Limits for X Note: Prediction intervals are always wider than confidence intervals. 7 PaSiPhIC 2012 A2PG Confidence interval for the mean based on a sample of N observations X t1 s 2,N 1 Sample mean (parameter estimate) 8 N Standard error of the mean (parameter uncertainty) PaSiPhIC 2012 A2PG Prediction interval for a single future observation X t1 Sample mean (parameter estimate) s 2 , N 1 2 s 2 N Sample variance of the mean (parameter uncertainty) Sample variance of a future observation (sampling variation) Note: The sample mean based on N previous observations is the best estimate for a single future observation. 9 PaSiPhIC 2012 A2PG Prediction interval for the mean of M future observations X t1 Sample mean (parameter estimate) s 2 , N 1 2 N Sample variance of the mean (parameter uncertainty) s 2 M Sample variance of the mean of M future observations (sampling variation) Note 1: The sample mean based on N previous observations is the best estimate for the mean of M future observations. Note 2: A prediction interval for M=∞ future observations is equivalent to a confidence interval (see Slide 8). This will also be referred to as ‘averaging out’ the sampling variation. 10 PaSiPhIC 2012 A2PG A Conceptual Extension of Confidence and Prediction Intervals to Population Modeling Measure/Quantity Parameters Prediction Sampling Variation Simple Mean Model Population Model , , Ω, X yˆ f ˆ s ˆ ˆ , ˆ ˆ 2 I s ˆ , ˆ Cov ˆ , Parameter Uncertainty* sX Confidence Interval See Slide 8 Stochastic simulations with sufficiently large M Prediction Interval See Slide 10 Stochastic simulations with finite M N * Note for the simple mean model the standard error of the mean does not take into account uncertainty in the sampling variation (s) whereas in population models we typically take into account the uncertainty in Ω and . 11 PaSiPhIC 2012 A2PG Quantifying Parameter Uncertainty in Population Models – Nonparametric Bootstrap Randomly sample with replacement subject data vectors to preserve within-subject correlations to construct bootstrap datasets Re-estimate model parameters for each bootstrap dataset to obtain an empirical (posterior) distribution of the parameter estimates (, Ω, ) May require stratified-resampling procedure (by design covariates) for a pooled-analysis with very heterogeneous study designs 12 E.g., limited data at a high dose in one study may be critical to estimation of Emax PaSiPhIC 2012 A2PG Quantifying Parameter Uncertainty in Population Models – Parametric Bootstrap Draw random samples from multivariate normal distribution with Mean vector = [ ˆ ˆ ˆ ] ,, Covariance matrix = Cov(ˆ , ˆ , ˆ) Based on Fisher’s theory (Efron, 1982) Assumes asymptotic theory (large sample size) that maximum likelihood estimates converge to a MVN distribution Obtained from Hessian or other procedure (e.g., COV step in NONMEM) See Vonesh and Chinchilli (1997) Based on Wald’s approximation that likelihood surface can be approximated by a quadratic model locally around the maximum likelihood estimates 13 Approximations are dependent on parameterization Improved approximations may occur by estimating the natural logarithm of the parameter for parameters that must be positive PaSiPhIC 2012 A2PG Non-parametric Versus Parametric Bootstrap Procedures The non-parametric bootstrap procedure is widely used in pharmacometrics Often used as a back-up procedure to quantify parameter uncertainty when difficulties arise in estimating the covariance matrix (eg., NONMEM COV step failure) In this setting issues with a large number of convergence failures in the bootstrap runs may call into question the validity of the confidence intervals (i.e., Do they have the right coverage probabilities?) This form of parametric bootstrap procedure is less computationally intensive than other bootstrap procedures that require re-estimation 14 Requires successful estimation of the covariance matrix (NONMEM COV step) but can lead to random draws outside the feasible range of the parameters unless appropriate transformations are applied PaSiPhIC 2012 A2PG Non-parametric Versus Parametric Bootstrap Procedures (2) Developing stable models that avoid extremely high pairwise correlations (>0.95) between parameter estimates and have low condition numbers (<1000) can help Ensure successful covariance matrix estimation Reduce convergence failures in non-parametric bootstrap runs Choice of bootstrap procedure should focus on the adequacy of the parametric assumption 15 Random draws from MVN versus the more computationally intensive re-estimation approaches (e.g., non-parametric bootstrap) PaSiPhIC 2012 A2PG Simulation Procedure to Construct Statistical Intervals for Population Model Predictions Obtain random draw of , Ω, from bootstrap procedure for kth trial Summarize predictions (e.g., mean) stratified by design (dose ,time, etc.) Simulate subject level data Yi | , Ω, for M subjects k<K Note 1: To construct confidence interval consider sufficiently large M (e.g., ≥2000 subjects) to average out sampling variation in Ω and . Note 2: For prediction intervals, M is chosen based on observed or planned sample size. 16 PaSiPhIC 2012 Repeat for k=1,…,K trials k=K Use percentile method to obtain statistical interval from K predictions A2PG To describe other probabilistic concepts we need to define some additional quantities True (unknown) treatment effect or quantity () Target value (TV) A reference value for Data-analytic decision rule (e.g., Go/No-Go criteria) 17 Based on an observed treatment effect or quantity (T) PaSiPhIC 2012 A2PG Treatment Effect () is the true (unknown) treatment effect Models provide a prediction of ˆ , ˆ ˆ g ˆ , Uncertainty in the parameter estimates of the model provides uncertainty in the prediction of 18 P(ˆ ) denotes the distribution of predictions of PaSiPhIC 2012 A2PG Example of Model-Predicted Dose-Response Model for Fasted Plasma Glucose (FPG) Semi-mechanistic model of inhibition of glucose production D K out FPG , R 0 K in 1 ED 50 D dFPG dt Week 0 Week 2 Week 4 Week 6 Week 8 Week 12 D Model-Predicted Placebo 1 e K t R0 1 Corrected FPG Versus ED D 50 Dose at Week 12 o ut Dose (mg) Population Mean Prediction 5th Percentile (90% LCL) 95th Percentile (90% UCL) Dose (mg) Observed Mean Typical Individual Prediction (PRED) 19 Placebo-Corrected Delta FPG (mg/dL) Delta FPG (mg/dL) Mean Model Fit of FPG Versus Dose FPG (integrates data across dose and time) PaSiPhIC 2012 A2PG Target Value (TV) Suppose we desire to develop a compound if the true unknown treatment effect () is greater than or equal to some target value (TV) TV may be chosen based on: Target marketing profile Clinically important difference Competitor’s performance If we knew truth we would make a Go/No-Go decision to develop the compound based on: 20 Go: No-Go: ≥ TV < TV PaSiPhIC 2012 A2PG Data-Analytic Decision Rule But we don’t know truth… So we conduct trials and collect data to obtain an estimate of the treatment effect (T) T can be a point estimate or confidence limit on the estimate or prediction of (e.g., placebo-corrected change from baseline FPG) We might make a data-analytic Go/No-Go decision to advance the development of the compound if: 21 Go: No-Go: T ≥ TV T < TV PaSiPhIC 2012 A2PG Statistical Power Power is a conditional probability based on an assumed fixed value of the treatment effect () Power = P(T ≥ TV | ) where P(T ≥ TV | = TV) = (false positive) TV=0 for statistical tests of a treatment effect Power is an operating characteristic of the design based on a likely value of 22 No formal assessment that the compound can achieve the assumed value of PaSiPhIC 2012 A2PG Simulation Procedure to Calculate Power Based on a Population Model-Predicted Use the same final estimates (, Ω, ) for each simulated trial Simulate subject level data Yi | , Ω, for M subjects Analyze simulated data as per SAP to test Ho: = TV Ha: TV k<K Note 1: Typically TV=0 when assessing whether the compound has an overall treatment effect. Note 2: When using simulations to evaluate power it is good practice to also simulate data under the null (e.g., no treatment effect or placebo model) to verify that the Type 1 error () is maintained. 23 PaSiPhIC 2012 Repeat for k=1,…,K trials k=K Power is calculated as the fraction of the K trials in which Ho is rejected A2PG Probability of Achieving the Target Value (PTV) Probability of achieving the target value is defined as the proportion of trials where the true ≥ TV PTV = P( ≥ TV) PTV is a measure of confidence in the compound at a given stage of development Does not depend on design or sample size Based only on prior information through the model(s) and its assumptions Can change as compound progresses through development PTV can be calculated from the same set of simulations used to construct confidence intervals of the predicted treatment effect () 24 PaSiPhIC 2012 A2PG Simulation Procedure to Calculate PTV Based on Population Model Predictions Obtain random draw of , Ω, from bootstrap procedure for kth trial Summarize simulated data to obtain population mean predictions of Simulate subjectlevel data Yi | , Ω, for arbitrarily large M k<K Note: To calculate PTV use an arbitrarily large M (e.g., ≥2000 subjects) to average out sampling variation in Ω and . PTV should only reflect the parameter uncertainty based on all available data used in the model estimation. 25 PaSiPhIC 2012 Repeat for k=1,…,K trials k=K Calculate PTV as proportion of K trials in which ≥ TV A2PG Probability of Success (POS) Probability of success is defined as the proportion of trials where a data-analytic Go decision is made POS is an operating characteristic that evaluates both the performance of the compound and the design POS = P(Go) = P(T ≥ TV) In contrast to Power = P(T ≥ TV | ) which is an operating characteristic of the performance of the design for a likely value of POS is sometimes referred to as ‘average power’ where a Go decision is based on a statistical hypothesis test 26 P Go P T TV P T TV P d PaSiPhIC 2012 A2PG Simulation Procedure to Calculate POS Based on a Population Model-Predicted Obtain random draw of , Ω, from bootstrap procedure for kth trial Summarize simulated data to obtain estimate of (T) and perform hypothesis test Simulate subjectlevel data Yi | , Ω, for planned sample size (M) k<K Note: POS integrates the conditional probability of a significant result over the distribution of plausible values of reflected through the uncertainty in the parameter estimates for , Ω, and . 27 PaSiPhIC 2012 Repeat for k=1,…,K trials k=K Calculate POS as proportion of K trials in which T ≥ TV A2PG Probability of Correct Decision (POCD) A correct data-analytic Go decision is made when A correct data-analytic No-Go decision is made when T < TV and < TV Probability of a correct decision is calculated as the proportion of trials where correct decisions are made T ≥ TV and ≥ TV POCD = P(T ≥ TV and ≥ TV) + P(T < TV and < TV) POCD can only be evaluated through simulation where the underlying truth () is known based on the datageneration model used to simulate the data 28 PaSiPhIC 2012 A2PG Simulation Procedure to Calculate POCD Based on a Population Model-Predicted Obtain random draw of , Ω, from bootstrap procedure for kth trial Simulate subjectlevel data Yi | , Ω, for planned sample size (M) Summarize simulated data to obtain estimate of (T) Classify Classify Go: Go:≥TV ≥TV No No Go: Go:<TV <TV Under UnderTruth Truth Compare Truth Versus Data-Analytic Decision Classify Go: T≥TV No Go: T<TV Under Trial Data k<K Note: Classification of trial under truth is obtained from the 29 PTV simulations. Repeat for k=1,…,K trials PaSiPhIC 2012 k=K Calculate POS as proportion of K trials in which T ≥ TV A2PG General Framework for MBDD Basic assumptions of MBDD Six components of MBDD Clinical trial simulations (CTS) as a tool to integrate MBDD activities Table of trial performance metrics Improving POCD Setting performance targets Comparing performance targets between early and late stage clinical drug development 30 PaSiPhIC 2012 A2PG Basic Assumptions of MBDD Predicated on the assumptions: That we can and should develop predictive models That these models can be used in CTS to predict trial outcomes Think of MBDD as a series of learnpredict-confirm cycles 31 Update models based on new data (learn) Conduct CTS to predict trial outcomes (predict) Conduct trial to obtain actual outcomes and evaluate predictions (confirm) PaSiPhIC 2012 Learn Confirm Predict A2PG Six Components of MBDD Quantitative Decision Criteria PK/PD & Disease Models Trial Performance Metrics Evaluate probability of Leverage Implement SAP,and Evaluate designs Understand achieving Explicitly andof understanding evaluate alternative dose selection; competitive landscape target value (PTV), quantitatively defined pharmacology/disease analysis methods incorporate trial – from a dose-response success (POS), criteria – useful for such ANCOVA, MMRM, execution models perspective correct decisions (POCD) “draw line in the sand” regression, NLME as extrapolation dropout models MBDD Meta-Analytic Models (Meta-Data from Public Domain) Data-Analytic Models Design & Trial Execution Models 32 PaSiPhIC 2012 A2PG Clinical Trial Simulations (CTS) Just as a clinical trial is the basic building block of a clinical drug development program, clinical trial simulations should be the cornerstone of an MBDD program CTS allows us to assume (know) truth for a hypothetical trial Based on simulation model we know Mimic all relevant design features of a proposed clinical trial Analyze simulated data based on the proposed statistical analysis plan (SAP) Calculate T (test statistic for treatment effect) and apply data-analytic decision rule CTS should be distinguished from other forms of stochastic simulations Sample size, treatments (doses), covariate distributions, drop out rates, etc. E.g., CIs for dose predictions, PTV calculations, etc. CTS can be used to integrate the components of MBDD and the various probabilistic concepts (including POS and POCD) 33 PaSiPhIC 2012 A2PG Table of Trial Performance Metrics Trial No Go Trial Go Total “True” Correct No Go No Go Incorrect Go P(True No Go) “True” Go Incorrect No Go Correct Go P(True Go) P(Trial No Go) P(Trial Go) 1.0 Total POCD 34 POS PaSiPhIC 2012 PTV A2PG Improving the probability of making correct decisions Change the design Change the data-analytic model n/group Regression-based designs ( # of dose groups, n/group) Consider other design constraints (cross-over, titration, etc.) Regression versus ANOVA Longitudinal versus landmark analysis Change the data-analytic decision rule Alternative choices for T Point estimate, confidence limit, etc. All of the above can be evaluated in a CTS 35 PaSiPhIC 2012 A2PG Setting Performance Targets PTV will change over time as model is refined and new data emerge Bring forward compounds/treatments with higher PTV as compound moves through development Industry average Phase 3 failure rate is approximately 50% PTV may be low in early development It is difficult to improve on this average unless we can routinely quantify PTV Strive to achieve PTV>50% before entering Phase 3 Strive to achieve high POCD in decision-making throughout development 36 PaSiPhIC 2012 A2PG Comparing performance targets between early and late stage clinical drug development Trial No Go Trial Go Total True No Go High Low High True Go Low Low Low Total Low Low 100% Trial No Go Trial Go True No Go Low Low Low True Go Low High High Total Low High 100% Late Development POCD should be high Early Development POCD should be high PTV PTV may be low should be high Advance good compounds / treatments to registration Kill poor compounds / treatments early 37 Total PaSiPhIC 2012 A2PG Examples Rheumatoid Arthritis Example Urinary Incontinence Example Phase 3 development decision Potency-scaling for back-up to by-pass Phase 2a POC trial and proceed to a Phase 2b dose-ranging trial Acute Pain Differentiation Case Study 38 Decision to change development strategy to pursue acute pain differentiation hypothesis PaSiPhIC 2012 A2PG Example – Rheumatoid Arthritis Endpoints: DAS28 remission (DAS28 < 2.6) ACR20 response (20% improvement in ACR score) Models developed based on Phase 2a study: Continuous DAS28 longitudinal PK/PD model with Emax directeffect drug model ACR20 logistic regression PK/PD model with Emax drug model Conducted clinical trial simulations for a 24-week Phase 2b placebo-controlled dose-ranging study (placebo, low, medium and high doses) Both direct and indirect-response models evaluated At Week 12 non-responders assigned to open label extension at medium dose level Primary analysis at Week 24; Week 12 responses for non-responders carried forward to Week 24 Evaluated No-Go/Hold/Go criteria for Phase 3 development 39 PaSiPhIC 2012 A2PG Example – Rheumatoid Arthritis (2) ACR20 (Difference from placebo) DAS28-CRP Remission (Difference from placebo) <20% 20-25% 25-30% 30% <10% No Go No Go Hold Hold 10-16% No Go Hold Hold Hold 16-20% Hold Hold Hold Go 20% Hold Hold Go Go No Go: Hold: Go: 40 Stop development Wait for results of separate Phase 2b active comparator trial Proceed with Phase 3 development without waiting for results from comparator trial PaSiPhIC 2012 A2PG Example – Rheumatoid Arthritis (3) Treatment Probability (%) No Go Hold Go Low Dose 96.1 3.9 0.0 Medium Dose 28.1 62.9 9.0 High Dose 18.4 65.6 16.0 CTS results suggest a high probability that the team will have to wait for results from the Phase 2b active comparator trial before making a decision to proceed to Phase 3. Very low probability of taking low dose into Phase 3. 41 PaSiPhIC 2012 A2PG Example – Urinary Incontinence Endpoint: Daily micturition (MIC) counts Models developed: Longitudinal Poisson-Normal model developed for daily MIC counts for lead compound Potency scaling for back-up based on: Time-dependent Emax drug model using AUC0-24 as measure of exposure In vitro potency estimates for lead and back-up (back-up more potent than lead) Equipotency assumption between lead and back-up Conducted CTS to evaluate Phase 2b study designs for back-up compound (placebo and four active dose levels) 42 Evaluated various dose scenarios of low (L), medium #1 (M1), medium #2 (M2) and high (H) doses levels Implemented SAP (constrained MMRM analysis with step down trend tests) Quantified POS for the L, M1, M2 and H doses for the various dose scenarios and potency assumptions PaSiPhIC 2012 A2PG Example – Urinary Incontinence (2) Dose L Scenario M1 M2 H Comment 1 1X 2.5X 12.5X 25X 2 1X 2.5X 12.5X 37.5X Doses selected favor in vitro potency assumption (i.e., back-up more potent than lead compound) 3 1X 5X 25X 50X 4 2.5X 5X 25X 75X 5 2.5X 12.5X 37.5X 75X 6 5X 12.5X 50X 100X Doses selected favor equipotent assumption Note: Low (L) dose was selected to be a sub-therapeutic response. Design was not powered to detect a significant treatment effect at this dose. 43 PaSiPhIC 2012 A2PG Example – Urinary Incontinence (3) CTS results: High POS (>95%) demonstrating statistical significance at the H dose for all 6 dose scenarios High POS (>88%) demonstrating statistical significance at the M2 dose for all 6 dose scenarios Insensitive to potency assumptions POS varied substantially for demonstrating statistical significance of the M1 dose Insensitive to potency assumptions Depending on dose scenario and potency assumption POS < 60% for demonstrating statistical significance at the L dose Except for dose scenarios 4 – 6 for the in vitro potency assumption CTS results provided guidance to the team to select a range of doses that would have a high probability of demonstrating dose-response while being robust to the uncertainty in the relative potency between the back-up and lead compounds. Provided confidence to bypass POC and move directly to a Phase 2b trial for the back-up. 44 PaSiPhIC 2012 A2PG Case Study – Acute Pain Differentiation Background SC-75416 is a selective COX-2 inhibitor Capsule dental pain study conducted Poor pain response relative to active control (50 mg rofecoxib) Lower than expected SC-75416 exposure with capsule relative to oral solution evaluated in Phase 1 PK studies PK/PD models developed to assess whether greater efficacy would have been obtained if exposures were more like that observed for the oral solution 45 Pain relief scores (PR) modeled as an ordered-categorical logistic normal model Dropouts due to rescue therapy modeled as a discrete survival endpoint dependent on the patient’s last observed PR Assumes a missing at random (MAR) dropout mechanism PaSiPhIC 2012 A2PG Case Study – Acute Pain Differentiation Background (2) PK/PD modeling predicted greater efficacy with oral solution relative to capsules A 6-fold higher SC-75416 dose (360 mg) than previously studied predicted to have clinically relevant improvement in pain relief relative to active control (400 mg ibuprofen) Model extrapolates from capsules to oral solution and leverages in-house data from other COX-2s and NSAIDs Project team considers change in development strategy to pursue a high-dose efficacy differentiation hypothesis Original strategy was to determine an acute pain dose that was equivalent to an active control and then scale down the dose for chronic pain (osteoarthritis) 46 Based on well established relationships that chronic pain doses for NSAIDs tend to be about half of the acute pain dose PaSiPhIC 2012 A2PG Case Study – Acute Pain Differentiation Proposed POC Dental Pain Trial Proposed conducting a proof of concept oral solution dental pain study Demonstrate improvement in pain relief for 360 mg SC relative to 400 mg ibuprofen Perform ANOVA on observed LOCF-imputed TOTPAR6 response and calculate LS mean differences Primary endpoint is TOTPAR6 (SC vs. ibuprofen) TOTPAR6 = 3 (TV) is considered clinically relevant T = LS mean (SC) – LS mean (ibuprofen) LCL95 = 2-sided lower 95% confidence limit on T Compound and data-analytic decision rule: 47 Truth: Data: Go if ≥3, No-Go if <3 Go if T≥3 and LCL95>0, No-Go if T<3 or LCL95≤0 PaSiPhIC 2012 A2PG Case Study – Acute Pain Differentiation Simulation Procedure to Calculate PTV Simulate PR Model Parameters Simulate PR Scores M=2,000 patients per treatment (PR,2) ~ MVN Simulate Dropout Times M=2,000 patients per treatment Simulate Dropout Model Parameters DO ~ MVN k<K Summarize Distribution of TOTPAR6 () 48 k=K Repeat for k = 1,…,K=10,000 trials PaSiPhIC 2012 Perform LOCF Imputation and Calculate TOTPAR6 Calculate Population Mean TOTPAR6 & TOTPAR6 Across M=2,000 pts Determine True Decision Go: 3 No Go: <3 A2PG Case Study – Acute Pain Differentiation Posterior Distribution of TOTPAR6 360 mg SC-75416 vs 400 mg Ibuprofen 2500 PTV = P( 3) = 67.2% F re q ue nc y 2000 Mean Prediction = 3.2 1500 1000 500 0 0 1 2 3 4 5 Delta-TOTPAR6 PTV = 67.2% sufficiently high to warrant recommendation to conduct oral solution dental pain study to test efficacy differentiation hypothesis. 49 PaSiPhIC 2012 A2PG Case Study – Acute Pain Differentiation CTS Procedure to Evaluate POC Trial Designs Simulate PR Scores for k-th Trial n pts / treatment Perform LOCF Imputation & Calculate TOTPAR6 Calculate Mean TOTPAR6 (T), SEM & 95% LCL Compare Truth vs. Data-Analytic Decision Apply Decision Rule Simulate Dropout Times for k-th Trial n pts / treatment k<K 50 Repeat for k=1,…,K=10,000 trials Go: LCL>0 and T3 No Go: LCL0 or T<3 k=K Calculate Metrics POS POCD PaSiPhIC 2012 A2PG Case Study – Acute Pain Differentiation CTS Trial Performance Metrics Trial Truth Trial No Go LCL95 0 or T<3 Trial Go LCL95> 0 and T3 Total <3 20.81% 11.99% 32.80% 3 17.29% 49.91% 67.20% 61.90% 100% (out of 10,000 trials) Total 38.10% POCD = 70.72% POS = 61.90% PTV = 67.20% A sufficiently high POCD and POS supported the recommendation and approval to proceed with the oral solution dental pain study. 51 PaSiPhIC 2012 A2PG Case Study – Acute Pain Differentiation Comparison of Observed and Predicted (About 9 months later…) Placebo F re qu e nc y 400 60 mg SC-75416 Pred = -7.0 Obs = -9.6 300 200 100 0 -12 -8 -4 0 4 8 -12 -8 DELTA F re qu e nc y 400 0 4 8 4 8 360 mg SC-75416 Pred = 3.2 Obs = 3.3 Pred = 2.0 Obs = 2.6 300 -4 DELTA 180 mg SC-75416 200 100 0 -12 -8 -4 0 4 8 -12 DELTA 52 Pred = -0.9 Obs = -1.8 -8 -4 0 DELTA PaSiPhIC 2012 A2PG Case Study – Acute Pain Differentiation Summary of Results 360 mg SC-75416 met pre-defined Go decision criteria Confirmed model predictions Demonstrated statistically significant improvement relative to 400 mg ibuprofen MBDD approach provided rationale to pursue acute pain differentiation strategy that might not have been pursued otherwise Allowed progress to be made while reformulation of solid dosage form was done in parallel Validation of model predictions provided confidence to pursue alternative pain settings for new formulations without repeating dental pain study 53 Model could be used to provide predictions for new formulations PaSiPhIC 2012 A2PG Final Remarks/Discussion Some thoughts on implementing MBDD Challenges to implementing MBDD 54 PaSiPhIC 2012 A2PG Final Remarks/Discussion Some thoughts on implementing MBDD We need to clearly define objectives We need explicit and quantitatively defined decision criteria It’s difficult to know how to apply the models if decision criteria are ambiguous or ill-defined We need complete transparency in communicating model assumptions What questions are we trying to address with our models? Entertain different sets of plausible model assumptions Evaluate designs for robustness to competing assumptions We need to routinely evaluate the predictive performance of the models on independent data 55 Modeling results should be presented as ‘hypothesis generating’ requiring confirmation in subsequent independent studies PaSiPhIC 2012 A2PG Final Remarks/Discussion Some thoughts on implementing MBDD (2) Conduct CTS integrating information across disciplines Provide graphical summaries of CTS results for recommended design prior to the release of the actual trial results Implement key features of the design and trial execution (e.g., dropout) Implement statistical analysis plan (SAP) Perform quick assessment of predictive performance when actual trial reads out Update models and quantification of PTV after actual trial reads out 56 i.e., Begin new learn-predict-confirm cycle PaSiPhIC 2012 A2PG Final Remarks/Discussion Challenges to implementing MBDD Focus on timelines of individual studies and a ‘go-fast-at-risk’ strategy (i.e., minimizing gaps between studies) can be counterproductive to a MBDD implementation Integration of MBDD activities in project timelines will require focus on integration of information across studies M&S (learning phase) is a time-consuming effort Not just tracking of individual studies May need processes to allow modelers to be un-blinded to interim results to begin modeling activities earlier to meet aggressive timelines Insufficient scientific staff with programming skills to perform CTS 57 Pharmacometricians and statisticians with such skills should be identified CTS implementation often requires considerable customization to address the project’s needs (i.e., no two projects are alike) PaSiPhIC 2012 A2PG Final Remarks/Discussion Challenges to implementing MBDD (2) Insufficient modeling and simulation resources to implement MBDD on all projects Reluctance to be explicit in defining decision rules (i.e., reluctance to ‘draw line in the sand’) Due to complexities and tradeoffs in making decisions Can be difficult to achieve consensus http://www.ascpt.org/Portals/8/docs/Meetings/2012%20Annual%20Me eting/2012%20speaker%20presentations/ASOP%20TUE%20CHERRY% 20BLOS%20SESSION%201.pdf Reluctance to use assumption rich models 58 We make numerous assumptions now when we make decisions…we’re just not very explicit about them MBDD can facilitate open debate about explicit assumptions PaSiPhIC 2012 A2PG Bibliography Neter, J., and Wasserman, W. Applied Linear Statistical Models, Irwin Inc., IL, 1974, pp. 71-73. Efron, B. The Jackknife, the Bootstrap, and Other Resampling Plans, Society for Industrial and Applied Mathematics, PA, 1982, pp. 29-30. Vonesh, E.F., and Chinchilli, V.M. Linear and Nonlinear Models for the Analysis of Repeated Measurements, Marcel Dekker, Inc., NY, 1997, pp. 245-246. Kowalski, K.G., Ewy, W., Hutmacher, M.M., Miller, R., and Krishnaswami, S. “ModelBased Drug Development – A New Paradigm for Efficient Drug Development”. Biopharmaceutical Report 2007;15:2-22. Lalonde, R.L., et al. “Model-Based Drug Development”. Clin Pharm Ther 2007;82:2132. Chuang-Stein, C.J., et al. “A Quantitative Approach to Making Go/No Go Decisions in Drug Development”. DIJ 2011;45:187-202. Smith, M.K., et al. “Decision-Making in Drug Development – Application of a ModelBased Framework for Assessing Trial Performance”. Book chapter in Clinical Trial Simulations: Applications and Trends, Kimko H.C. and Peck C.C. eds. , Springer Inc., NY, 2011, pp. 61-83. Kowalski, K.G., Olson, S., Remmers, A.E., and Hutmacher, M.M. “Modeling and Simulation to Support Dose Selection and Clinical Development of SC-75416, a Selective COX-2 Inhibitor for the Treatment of Acute and Chronic Pain”. Clin Pharm Ther, 2008; 83: 857-866. 59 PaSiPhIC 2012 A2PG