Connecting the dots: Accelerating clinical development by integrating non-clinical aspects in the plan

Transcription

1 Connecting the dots: Accelerating clinical development by integrating non-clinical aspects in the plan Bruno Boulanger Exploratory Statistics UCB, Belgium Lou, living with epilepsy 18/5/09

2 2 Clinical study Objective: Connecting the Dots CM&C Non-Clinical

3 Agenda 3 1. Objectives of Connecting the dots 2. Non-clinical impacting Clinical 1. Ex1: LLOQ in a MBDD strategy 2. Ex2: Biomarker Assay precision in an Adaptive Design 3. Clinical impacting non-clinical 1. Ex3: Formulation development 4. Non-clinical informing Clinical 1. Ex4: Prior information on Assay Precision 5. Conclusions

4 Why Connecting the dots? 4 Performances (methods, processes, products) in Non-clinical impact the performances of clinical trials. Need to define specifications limits based on the intended use of the results/products not based on the performance of process or State of the art Let s feedback clinical observations/measures to non-clinical labs. Need to measure directly in patients Need smart DOEs and strategies to make the link a reality Leverage non-clinical information available Non-clinical information provide prior information This information has great value for decrease costs and improving decision making.

5 Examples Laboratories performances Clinical Performances 5 Example 1 Lower LOQ of a bioassay for dosing a compound in a PKPD study to support a MBDD strategy Example 2 Performances of an efficacy Biomarker assay to find the optimal dose in a clinical trial using an adaptive design

6 Example 1 Study Objectives in an MBDD paradigm 6 Finding study design to ensure... Small bias of IC50 estimate Precise IC50 estimate Small bias of Slope estimate Precise Slope estimate... For the various scenarios envisaged Scenario 1 (w=0.2) Scenario 2 (w=0.2) Scenario 3 (w=0.6) Biomarker log(crp) log(concentration)... To provide reliable predictions

7 Example 1 Design elements and constraints 7 Impact of Biomarker levels at baseline On the ability to obtain a predictive model On the recruitment rate Doses to envisage Range of doses Number of doses Impact of LLOQ of UCBXXXXX assay Precision of Biomarker assay Sample size 1152 possible study designs (DOE+RS) have been simulated 500 times each. Interactions and trade-off between design elements are investigated.

8 Finding optimal design Making optimal trade-offs 8 Assuming LOQ = 0.1 SS=15pts/dose Max dose=5 %CV Biomarker assay=15% Design Global Efficacy=0.45 By changing the LOQ LOQ = SS=10pts/dose Max dose=3 Design Global Efficacy=0.63 Dosemax #doses #subj LLOQ Min_basl Max_basl Dosemax #doses #subj LLOQ Min_basl Max_basl With an accurate model for UCBXXXXX, predictions can be made to support: - Decision (Go/Non Go, Dose, Regimen,...) - Optimizing next study designs

9 Conclusions Example 1 9 Without the simulations: The trial may have failed and PKPD not accurately estimated. Nobody would had realized that LLOQ was in the case really on the critical path. Improving the LLOQ lead to decrease the SS by 33%. The way the different pieces propagate can be envisaged The pieces to improve can be easily located. New specifications limits can be established by simulation. QbD thinking

10 Example 2 Laboratory Performance and Adaptive Design 10 The objective: To determine the optimal dose (ED80) within +-10mg in a Dose-Ranging study based on a biomarker. A Bio-analytical procedure measures a biomarker. The experiment: Adaptive design, cohort of 16 patients, 4 on placebo. Bayesian Emax model to optimally allocate the patients. The question: What Total Error should be accepted to ensure accurate estimate of optimal dose using an Adaptive Design?

11 Example 2 Laboratory Performance and Adaptive Design 11 Simulations: True optimal dose : 93 mg [83mg 103mg] True Performance of the analytical method (biomarker): No bias CV : 10% 20% 25%

12 Example 2 Laboratory Performance and Adaptive Design 12 Acceptance limits and number of cohorts/patients True CV=10% 2 cohorts 32 patients True CV=20% 3 cohorts 48 patients True CV=25% 6-8 cohorts patients

13 Example 2 Laboratory Performance and Adaptive Design 13 cohort 10 cohort 7 cohort 4 cohort dose dose dose dose cohort 2 cohort 5 cohort dose dose dose cohort 3 cohort 6 cohort CV=20% 3 cohorts 48 patients dose dose dose Depending on the objective (ED80 vs DR) and the allocation rule, the patients are rapidly allocated at the doses of interest. Adaptive Designs, when logistic permits, are preferred for this type of purpose. Is it worth the tremendous efforts to setup an adaptive design (logistic, simulations,...)? NB: it depends on inter-individual variability relatively to assay precision Knowing min. true performance allowed, the acceptance limits and decision rules can be derived to ensure performance will be met.

14 Clinical impacting NonClinical 14 Design Space thinking: What are the Specifications settings in processes/products that: Guarantee best reliable clinical results? Guarantee specifications will be met according to best practices? Example: Finding the formulation space: That gives the suitable PK profile Whose specifications limits can unambiguously established That can be reliably produced NB: A PQRI group was initiated to come with proposals/methodologies to tackle this type of questions.

15 Clinical impacting NonClinical 15 Proposal 1: The responses of interest are directly obtained in a clinical trial. E.g. PK profiles for a DOE-defined set of formulations are obtained in dedicated clinical trial. Non-Clinical Design Exc API Ex1 Ex2 Form Form Form Form Form Form Form Form Form Form Exc2 10 (8) Formulations/treatments To be tested in a Clinical trial API Variance estimates needed.

16 Clinical impacting NonClinical 16 For example a Balanced Incomplete Block Design (BIB) can be envisaged to measure the PK profile in volunteers.1 Non-Clinical Design Exc Exc2 Clinical Design: BIB, 2 subjects/seq Period1 Period2 Sequence 1 Form 4 Form 3 Sequence 2 Form 9 Form 7 Sequence 3 Form 8 Form 1 Sequence 4 Form 7 Form 6 Sequence 5 Form 2 Form 5 Sequence 6 Form 6 Form 10 Sequence 7 Form 5 Form 9 Sequence 8 Form 1 Form 4 Sequence 9 Form 10 Form 8 Sequence 10 Form 3 Form 2 API

17 Specifications Settings derived from Clinical results. 17 Assuming preliminary PKPD models have shown that having pauc>65 is appropriate to ensure efficacy, results from Clinical Trial can be mapped down into the space of formulation process Design Space Formulation Exc Exc2 PK Availability ± API 0.4 Results from Clinical Study Exc Exc API In the Design Space E[P(AUC>65 data]>95%

18 More Clinical Designs 18 Challenge is to find an effective low cost Clinical Trial Design More periods Use radio-labeled formulation in cassette dosing. Use Parametric (PK) models to accurately estimate response of interest and better model error. Bayesian Design Space based on Predictive distribution is the safest route to pursue to guarantee success, if existing. QbD: defining specifications limits based on clinical results.

19 Non-clinical informing Clinical 19 A lot of valuable information has been generated in the nonclinical areas. Some elements remain uncertain once moving into man Eg. the EC50 in human, Clearance... Some elements are independent from the species or the material, but are well characterized Eg: the precision of the assay Leverage pieces of information that can be reliable

20 Example 4: estimating a PK model 20 A PK study is conducted to estimate the PK of a new compound is patients. The PK(PD) model will be used in a MBDD strategy to simulate future trials and propose dose range, etc... The better the precision on the PK parameters, the better the prediction. The very objective of the study is to estimate the parameters as accurately as possible. Method: The nonlinear model can be fitted without informative prior on the assay precision, as in ML with informative prior on the assay, as in Bayesian

21 21 The data 6 patients in the study 8 sampling times X Y

22 Results Non-Informative 22 On all parameters: non-informative priors, like a ML approach Informative Prior only on the precision of the assay. Density Density Alpha Density Density Beta Informative Density Density Gamma Alpha Beta Gamma Param. Variance Non-infor Informative Ratio Alpha Beta Gamma

23 Value of non-clinical information 23 To achieve about the same precision without informative prior, about twice the number of patients. This is the value of this information The precision of the assay is rather well known, why to pay that much to estimate it? This information if for free. Many such information is utilizable and can be easily justified. Param. Variance Non-infor Informative Ratio Alpha Beta Gamma

24 Conclusions 24 Current design, simulations and modeling capacities allow to connect non-clinical and clinical domains Cross-fertilization is needed; we need to learn each other. The gain and benefit can be tremendous This benefit patients, regulatory bodies and companies This is fully part of the Quality by Design concept.

25 Acknowlegments 25 Exploratory Statistics Astrid Jullion Modeling & Simulations Kosmas Krestos Ruth Oliver Université de Louvain Jonathan Jaeger

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