Presentation on theme: "A General Framework for Model-Based Drug Development Using Probability Metrics for Quantitative Decision Making Ken Kowalski, Ann Arbor Pharmacometrics."— Presentation transcript:
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 PaSiPhIC 20122A2PG
Population Models Basic Notation A2PGPaSiPhIC General Form of a Two-Level Hierarchical Mixed Effects Model: Definitions:
Population Models Key Concepts A2PGPaSiPhIC Typical Individual Prediction: Easy to compute, same functional form as f Population Mean Prediction: 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 Cannot observe a ‘typical individual’ Can observe a sample mean
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) PaSiPhIC 20125A2PG
What’s the difference between a confidence interval and prediction interval? A2PGPaSiPhIC 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) Reflects both uncertainty in the parameter estimates as well as the sampling variation for the future observation
Relationship Between CIs and PIs A2PGPaSiPhIC Confidence Limits for Prediction Limits Recognizing Uncertainty in E( ) Distribution of sampling variation Prediction Limits if E( ) Located Here Note: Prediction intervals are always wider than confidence intervals.
Confidence interval for the mean based on a sample of N observations A2PGPaSiPhIC Sample mean (parameter estimate) Standard error of the mean (parameter uncertainty)
Prediction interval for a single future observation A2PGPaSiPhIC Sample mean (parameter estimate) 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.
Prediction interval for the mean of M future observations A2PGPaSiPhIC Sample mean (parameter estimate) Sample variance of the mean (parameter uncertainty) 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.
A Conceptual Extension of Confidence and Prediction Intervals to Population Modeling A2PGPaSiPhIC Measure/QuantitySimple Mean ModelPopulation Model Parameters , , Ω, Prediction Sampling Variation Parameter Uncertainty* Confidence IntervalSee Slide 8 Stochastic simulations with sufficiently large M Prediction IntervalSee Slide 10 Stochastic simulations with finite M * 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 .
Quantifying Parameter Uncertainty in Population Models – Nonparametric Bootstrap A2PGPaSiPhIC 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 E.g., limited data at a high dose in one study may be critical to estimation of Emax
Quantifying Parameter Uncertainty in Population Models – Parametric Bootstrap A2PGPaSiPhIC Draw random samples from multivariate normal distribution with Mean vector = [ ] Covariance matrix = Cov( ) Obtained from Hessian or other procedure (e.g., COV step in NONMEM) Based on Fisher’s theory (Efron, 1982) Assumes asymptotic theory (large sample size) that maximum likelihood estimates converge to a MVN distribution 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 Approximations are dependent on parameterization Improved approximations may occur by estimating the natural logarithm of the parameter for parameters that must be positive
Non-parametric Versus Parametric Bootstrap Procedures A2PGPaSiPhIC 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 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
Non-parametric Versus Parametric Bootstrap Procedures (2) A2PGPaSiPhIC 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 Random draws from MVN versus the more computationally intensive re-estimation approaches (e.g., non-parametric bootstrap)
Simulation Procedure to Construct Statistical Intervals for Population Model Predictions A2PGPaSiPhIC Obtain random draw of , Ω, from bootstrap procedure for k th trial Simulate subject level data Y i | , Ω, for M subjects Summarize predictions (e.g., mean) stratified by design (dose,time, etc.) Repeat for k=1,…,K trials Use percentile method to obtain statistical interval from K predictions k
To describe other probabilistic concepts we need to define some additional quantities A2PGPaSiPhIC True (unknown) treatment effect or quantity ( ) Target value (TV) A reference value for Data-analytic decision rule (e.g., Go/No-Go criteria) Based on an observed treatment effect or quantity (T)
Treatment Effect ( ) A2PGPaSiPhIC is the true (unknown) treatment effect Models provide a prediction of Uncertainty in the parameter estimates of the model provides uncertainty in the prediction of P( ) denotes the distribution of predictions of
Example of Model-Predicted Dose-Response Model for Fasted Plasma Glucose (FPG) A2PGPaSiPhIC Semi-mechanistic model of inhibition of glucose production Mean Model Fit of FPG Versus Dose (integrates data across dose and time) Model-Predicted Placebo- Corrected FPG Versus Dose at Week 12 Dose (mg) Placebo-Corrected Delta FPG (mg/dL) Population Mean Prediction 5th Percentile (90% LCL) 95th Percentile (90% UCL)
Target Value (TV) A2PGPaSiPhIC 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: Go: ≥ TV No-Go: < TV
Data-Analytic Decision Rule A2PGPaSiPhIC 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: Go:T ≥ TV No-Go:T < TV
Statistical Power A2PGPaSiPhIC 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 No formal assessment that the compound can achieve the assumed value of
Simulation Procedure to Calculate Power Based on a Population Model-Predicted A2PGPaSiPhIC Use the same final estimates ( , Ω, ) for each simulated trial Simulate subject level data Y i | , Ω, for M subjects Analyze simulated data as per SAP to test Ho: = TV Ha: TV Repeat for k=1,…,K trials Power is calculated as the fraction of the K trials in which Ho is rejected k
Probability of Achieving the Target Value (PTV) A2PGPaSiPhIC Probability of achieving the target value is defined as the proportion of trials where the true ≥ TV PTV = P( ≥ TV) Does not depend on design or sample size Based only on prior information through the model(s) and its assumptions PTV is a measure of confidence in the compound at a given stage of development 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 ( )
Simulation Procedure to Calculate PTV Based on Population Model Predictions A2PGPaSiPhIC Obtain random draw of , Ω, from bootstrap procedure for k th trial Simulate subject- level data Y i | , Ω, for arbitrarily large M Summarize simulated data to obtain population mean predictions of Repeat for k=1,…,K trials Calculate PTV as proportion of K trials in which ≥ TV k
Probability of Success (POS) A2PGPaSiPhIC Probability of success is defined as the proportion of trials where a data-analytic Go decision is made POS = P(Go) = P(T ≥ TV) POS is an operating characteristic that evaluates both the performance of the compound and the design 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
Simulation Procedure to Calculate POS Based on a Population Model-Predicted A2PGPaSiPhIC Obtain random draw of , Ω, from bootstrap procedure for k th trial Simulate subject- level data Y i | , Ω, for planned sample size (M) Summarize simulated data to obtain estimate of (T) and perform hypothesis test Repeat for k=1,…,K trials Calculate POS as proportion of K trials in which T ≥ TV k
Probability of Correct Decision (POCD) A2PGPaSiPhIC A correct data-analytic Go decision is made when T ≥ TV and ≥ TV 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 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 data- generation model used to simulate the data
Simulation Procedure to Calculate POCD Based on a Population Model-Predicted A2PGPaSiPhIC Obtain random draw of , Ω, from bootstrap procedure for k th trial Simulate subject- level data Y i | , Ω, for planned sample size (M) Summarize simulated data to obtain estimate of (T) Repeat for k=1,…,K trials Calculate POS as proportion of K trials in which T ≥ TV k
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 PaSiPhIC A2PG
Basic Assumptions of MBDD A2PGPaSiPhIC 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 learn- predict-confirm cycles Update models based on new data (learn) Conduct CTS to predict trial outcomes (predict) Conduct trial to obtain actual outcomes and evaluate predictions (confirm) Learn Predict Confirm
Six Components of MBDD A2PGPaSiPhIC MBDD PK/PD & Disease Models Meta-Analytic Models (Meta-Data from Public Domain) Design & Trial Execution Models Data-Analytic Models Quantitative Decision Criteria Trial Performance Metrics Explicitly and quantitatively defined criteria “draw line in the sand” Leverage understanding of pharmacology/disease – useful for extrapolation Understand competitive landscape from a dose-response perspective Evaluate designs and dose selection; incorporate trial execution models such as dropout models Implement SAP, evaluate alternative analysis methods – ANCOVA, MMRM, regression, NLME Evaluate probability of achieving target value (PTV), success (POS), correct decisions (POCD)
Clinical Trial Simulations (CTS) A2PGPaSiPhIC 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 Sample size, treatments (doses), covariate distributions, drop out rates, etc. 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 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)
Table of Trial Performance Metrics A2PGPaSiPhIC Trial No GoTrial GoTotal “True” No Go “True” Go Total Correct No Go P(Trial Go) Incorrect GoP(True No Go) Incorrect No Go P(Trial No Go) 1.0 P(True Go)Correct Go PTV POCDPOS
Improving the probability of making correct decisions A2PGPaSiPhIC Change the design n/group Regression-based designs ( # of dose groups, n/group) Consider other design constraints (cross-over, titration, etc.) Change the data-analytic model 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
Setting Performance Targets A2PGPaSiPhIC 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 PTV may be low in early development Industry average Phase 3 failure rate is approximately 50% 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
Comparing performance targets between early and late stage clinical drug development A2PGPaSiPhIC Low High LowHigh100% Total True No Go Trial No Go True Go Trial Go Late Development POCD should be high PTV should be high Advance good compounds / treatments to registration HighLowHigh Low 100% Total Trial No Go Trial Go Early Development POCD should be high P TV may be low Kill poor compounds / treatments early True No Go True Go
Examples A2PGPaSiPhIC Rheumatoid Arthritis Example Phase 3 development decision Urinary Incontinence Example 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 Decision to change development strategy to pursue acute pain differentiation hypothesis
Example – Rheumatoid Arthritis A2PGPaSiPhIC 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 direct- effect drug model ACR20 logistic regression PK/PD model with Emax drug model Both direct and indirect-response models evaluated Conducted clinical trial simulations for a 24-week Phase 2b placebo-controlled dose-ranging study (placebo, low, medium and high doses) 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
Example – Rheumatoid Arthritis (2) A2PGPaSiPhIC DAS28-CRP Remission (Difference from placebo) ACR20 (Difference from placebo) <20% 20-25% 25-30% 30% <10%No Go Hold 10-16% No GoHold 16-20% Hold Go 20% Hold Go No Go:Stop development Hold: Wait for results of separate Phase 2b active comparator trial Go: Proceed with Phase 3 development without waiting for results from comparator trial
Example – Rheumatoid Arthritis (3) A2PGPaSiPhIC Treatment Probability (%) No GoHoldGo Low Dose Medium Dose High Dose 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.
Example – Urinary Incontinence A2PGPaSiPhIC Endpoint: Daily micturition (MIC) counts Models developed: Longitudinal Poisson-Normal model developed for daily MIC counts for lead compound Time-dependent Emax drug model using AUC 0-24 as measure of exposure Potency scaling for back-up based on: 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) 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
Example – Urinary Incontinence (2) A2PGPaSiPhIC 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. Dose Scenario LM1M2HComment 11X2.5X12.5X25XDoses selected favor in vitro potency assumption (i.e., back-up more potent than lead compound) 21X2.5X12.5X37.5X 31X5X25X50X 42.5X5X25X75X 52.5X12.5X37.5X75XDoses selected favor equipotent assumption 65X12.5X50X100X
Example – Urinary Incontinence (3) A2PGPaSiPhIC CTS results: High POS (>95%) demonstrating statistical significance at the H dose for all 6 dose scenarios Insensitive to potency assumptions 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 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.
Case Study – Acute Pain Differentiation Background A2PGPaSiPhIC SC 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 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 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
Case Study – Acute Pain Differentiation Background (2) A2PGPaSiPhIC PK/PD modeling predicted greater efficacy with oral solution relative to capsules A 6-fold higher SC 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) Based on well established relationships that chronic pain doses for NSAIDs tend to be about half of the acute pain dose
Case Study – Acute Pain Differentiation Proposed POC Dental Pain Trial A2PGPaSiPhIC 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 Primary endpoint is TOTPAR6 (SC vs. ibuprofen) TOTPAR6 = 3 (TV) is considered clinically relevant Perform ANOVA on observed LOCF-imputed TOTPAR6 response and calculate LS mean differences T = LS mean (SC) – LS mean (ibuprofen) LCL 95 = 2-sided lower 95% confidence limit on T Compound and data-analytic decision rule: Truth: Go if ≥3, No-Go if <3 Data:Go if T≥3 and LCL 95 >0, No-Go if T<3 or LCL 95 ≤0
Case Study – Acute Pain Differentiation Simulation Procedure to Calculate PTV A2PGPaSiPhIC Simulate PR Model Parameters ( PR, 2 ) ~ MVN Simulate Dropout Model Parameters DO ~ MVN Simulate Dropout Times M=2,000 patients per treatment Simulate PR Scores M=2,000 patients per treatment Perform LOCF Imputation and Calculate TOTPAR6 Calculate Population Mean TOTPAR6 & TOTPAR6 Across M=2,000 pts Determine True Decision Go: 3 No Go: <3 Summarize Distribution of TOTPAR6 ( ) k=K Repeat for k = 1,…,K=10,000 trials k
Case Study – Acute Pain Differentiation Posterior Distribution of TOTPAR6 A2PGPaSiPhIC PTV = P( 3) = 67.2% Mean Prediction = 3.2 PTV = 67.2% sufficiently high to warrant recommendation to conduct oral solution dental pain study to test efficacy differentiation hypothesis.
Case Study – Acute Pain Differentiation CTS Procedure to Evaluate POC Trial Designs A2PGPaSiPhIC Simulate PR Scores for k-th Trial n pts / treatment Simulate Dropout Times for k-th Trial n pts / treatment Perform LOCF Imputation & Calculate TOTPAR6 Calculate Mean TOTPAR6 (T), SEM & 95% LCL Apply Decision Rule Go: LCL>0 and T 3 No Go: LCL 0 or T<3 Compare Truth vs. Data-Analytic Decision Calculate Metrics POS POCD k=K Repeat for k=1,…,K=10,000 trials k
Case Study – Acute Pain Differentiation CTS Trial Performance Metrics A2PGPaSiPhIC Trial Truth Trial No Go LCL 95 0 or T<3 Trial Go LCL 95 > 0 and T 3 Total < %11.99%32.80% 3 %49.91%67.20% Total 38.10%61.90% 100% (out of 10,000 trials) 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.
Case Study – Acute Pain Differentiation Comparison of Observed and Predicted (About 9 months later…) A2PGPaSiPhIC Pred = 3.2 Pred = 2.0 Pred = -0.9Pred = -7.0 Obs = -9.6 Obs = -1.8 Obs = 3.3 Obs = 2.6
Case Study – Acute Pain Differentiation Summary of Results A2PGPaSiPhIC 360 mg SC 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 Model could be used to provide predictions for new formulations
Final Remarks/Discussion A2PGPaSiPhIC Some thoughts on implementing MBDD Challenges to implementing MBDD
Final Remarks/Discussion Some thoughts on implementing MBDD A2PGPaSiPhIC We need to clearly define objectives What questions are we trying to address with our models? 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 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 Modeling results should be presented as ‘hypothesis generating’ requiring confirmation in subsequent independent studies
Final Remarks/Discussion Some thoughts on implementing MBDD (2) A2PGPaSiPhIC Conduct CTS integrating information across disciplines Implement key features of the design and trial execution (e.g., dropout) Implement statistical analysis plan (SAP) Provide graphical summaries of CTS results for recommended design prior to the release of the actual trial results Perform quick assessment of predictive performance when actual trial reads out Update models and quantification of PTV after actual trial reads out i.e., Begin new learn-predict-confirm cycle
Final Remarks/Discussion Challenges to implementing MBDD A2PGPaSiPhIC Focus on timelines of individual studies and a ‘go-fast-at-risk’ strategy (i.e., minimizing gaps between studies) can be counter- productive to a MBDD implementation M&S (learning phase) is a time-consuming effort Integration of MBDD activities in project timelines will require focus on integration of information across studies 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 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)
Final Remarks/Discussion Challenges to implementing MBDD (2) A2PGPaSiPhIC 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 eting/2012%20speaker%20presentations/ASOP%20TUE%20CHERRY% 20BLOS%20SESSION%201.pdf eting/2012%20speaker%20presentations/ASOP%20TUE%20CHERRY% 20BLOS%20SESSION%201.pdf Reluctance to use assumption rich models 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
Bibliography A2PGPaSiPhIC Neter, J., and Wasserman, W. Applied Linear Statistical Models, Irwin Inc., IL, 1974, pp Efron, B. The Jackknife, the Bootstrap, and Other Resampling Plans, Society for Industrial and Applied Mathematics, PA, 1982, pp Vonesh, E.F., and Chinchilli, V.M. Linear and Nonlinear Models for the Analysis of Repeated Measurements, Marcel Dekker, Inc., NY, 1997, pp Kowalski, K.G., Ewy, W., Hutmacher, M.M., Miller, R., and Krishnaswami, S. “Model- Based 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: Chuang-Stein, C.J., et al. “A Quantitative Approach to Making Go/No Go Decisions in Drug Development”. DIJ 2011;45: Smith, M.K., et al. “Decision-Making in Drug Development – Application of a Model- Based 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 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: