Drug Development in Rare Diseases: Need for Innovation in - PowerPoint PPT Presentation

Drug Development in Rare Diseases: Need for Innovation in Statistical Thinking Kannan Natarajan Nov 6, 2019 1

Seeking New Treatment in Rare Disease Source: PhRMA 2013 report on Rare Diseases 2

Current Landscape https://www.pfizer.com/science/rare-diseases 3

Challenges with Traditional Development in Rare Diseases • Few patients available to participate – Multi-center, multi-country trials • Unmet medical need • Phenotypic diversity and genetic subsets – heterogeneity at presentation / late diagnosis – highly variable disease course • Lack of well defined and validated endpoints, outcome measures/tools, and biomarkers • Natural histories are often not well understood/characterized 4

Changing Regulatory Perspectives 21 st Century Cures Act, • December 2016 – includes provisions that will improve the development, for rare disease patients – further expansion of the Patient-Focused Drug Development • New FDA guidance – use of natural history data – human Gene Therapy for Rare Diseases 5

Need for Innovative Study Designs • Design considerations – endpoint: asking the right question – Replace or augment control arm with available information in standard of care – Extrapolation to other demographic subgroups – modeling disease from natural history data – use of real world data (RWD) Other considerations • collaboration and data sharing • registry development 6

Innovative Design in Drug Development • An innovative trial design uses all available evidence for better and efficient drug development without undermining the validity and integrity. The goal is to provide fast access of good drugs to patients. Validity Integrity  providing correct statistical  Pre-planning appropriately inference:   providing convincing results to maintaining confidentiality of a broader scientific community data  minimizing statistical bias  minimizing operational bias 7

Use of Historical Control Data 8

Use of Historical Control • More than 220 000 registered trials in the electronic database – typically more than one clinical study investigating the same treatment • Using available information in design and analysis may lead to – increase efficiency: fewer patients – ethical – decreased costs and trial duration • It is useful particularly when – information is sparse (e.g. rare disease) – new information is difficult to obtain • Recommendations and considerations are provided in ICH E10 9

Historical Data Augmentation Design • Use of available information in the design – Use information for control worth n* patients and allocate n- n* patients SS=n – saves sample size S E • Choice of relevant historical data? c r – Requires judgment about similarity of SS=n – n * e historical and current setting e C n • Inclusion/exclusion criteria, endpoint, time- trends etc. HC – Requires interaction with non-statistician End of Study pESS=n * • If information available after trial starts – Indirect comparison: evidence synthesis 10

Choice of “Relevant” Historical Data • Proper choice of historical data: justification of “similarity” – prior to start of trial – choice must be “science” based not “result” based – avoiding publication bias: often requires KOL and independent groups – inter-disciplinary collaboration – data gathering can be time-consuming • Pocock’s (1976) “six criteria” 11

Key Statistical Considerations • Both frequentist and Bayesian methods available – Few frequentist methods include Test and Pool, Propensity Score • Bayesian methods provide a natural way to incorporate historical data in the form of prior – handles the between trial heterogeneity • Various available methods: – Meta-analytic approaches are well established to handle both (Neuenschwander et. al 2010) • Meta-analytic framework provides a flexible framework to incorporate relevant data from different sources 12

Checklist for Practical Implementation • Statistically principled approach – Incorporating between trial heterogeneity – Quantify «borrowing»: how much to borrow – What if historical data and actual data are in conflict?: requires robust statistical approach (Schmidli et al. 2014, Neuenschwander, Roychoudhury and Schmidli 2016) • Evaluation of frequentist operating characteristics (Type-I error, Power) Wide number of scenarios – • Protocol and manuscript writing • Communication with key customers 13

Meta-analytic Approaches Meta-Analytic Combined (MAC ) Meta-Analytic Predictive(MAP) Combined analysis of historical Derivation of informative prior data and current data: one using historical data: two-step approach analysis MAP and MAC are equivalent : “exchangeability” is the key assumption • Methodology is general enough to be extended for different endpoints (continuous, binary, count data, time to event) 14

Robustness to Handle Prior-data Conflict Prior-data Conflict Scenario Robustness and more rapid adaptation to prior-data conflicts by adding extra weakly-informative mixture component De Groot always carried an ε of probability for surprises in his pocket! 15

Current Regulatory Consideration • Recently released regulatory guidelines encourage the efficient use of historical data – Primary concerns theoretical strong control of Type-I error, bias etc. – Need upfront discussion with regulatory authorities • Few exceptions: regulatory approval using historical control – Brineura for Batten Disease – APTIOM as monotherapy for Seizures – Venetoclax in Relapsed / Refractory Chronic Lymphocytic Leukemia (CLL) – Eteplirsen in Duchenne Muscular Dystrophy (DMD) – Suvodirsen in DMD: submitted for FDA CID program 16

Example: Progressive Supranuclear Palsy (PSP) • Progressive supranuclear palsy (PSP) is a degenerative neurologic disease due to damage to nerve cells in the brain • 20,000 PSP patients have been diagnosed with the disease (6·5 cases per 100 000 individuals) • No effective drug halting the progression of the disease 17

Example: Phase II Trial in PSP • Disease – PSP • Experimental treatment – Monoclonal antibody (E) • Endpoint – PSPRS (A clinical rating scale) change from baseline assessed at week 52 Golbe and Ohman-Strickland 2007 • Traditional clinical trial design – New treatment (n=80) vs. Placebo (n=80) Can historical placebo – Z test information be used? 18

Example: Use of Historical Placebo Data • Double-blind, randomized, Study N Y se placebo-controlled study for E • Primary endpoint: Change from baseline in Tolosa14 21 10.4 6.5 PSPRS at 52 weeks – 4 points from placebo clinically meaningful Boxer14 123 10.9 11.0 • Planned sample size 120 – 2:1 in favor of E – Z test: 73% power for δ= 4 • 2 historical trial data for placebo (n=144)) – Tideglusib vs. placebo (Tolosa et. al. 2014) – Davunetide vs. placebo (Boxer et. al. 2014) 19

Example: Informative Prior for Placebo Arm • Historical data for placebo is homogeneous in two studies – Sample size varies: poses uncertainty • MAP prior reflects this uncertainties – apriori placebo effect varies 7.8-13.3 – prior worth 52 subject information for placebo – non-informative prior for E • New trial is successful if – P( δ < 0 | data) > 97.5% 20

Example: Robustification of MAP Prior • A mixture of MAP and weakly informative prior becomes a heavy-tailed version of the historical prior – Mixture prior- 75% informative, 25%non-informative MAP prior Robust MAP prior (100%-0%) (75%-25%) 21

Example: Robust Prior Handles Prior Data Conflict Scenario: No Conflict Scenario: Conflict Weights Weights • aprior informative 75% weak 25% aprior informative 75% weak 25% • • postrior informative 1% weak 99% • postrior informative 90% weak 10% Note: Weights are fix apriori but posterior weights get updated using standard Bayesian calculus (Schmidli et. al 2014) 22

Example: Operating Characteristics • Robust prior provides a nice balance between Type-I error and power − Type-I error: well controlled when prior and data are aligned − Type-I error: max 8% under prior-data conflict − Power= 87% for δ = 4: considerable gain over traditional frequentist design • Type-I error inflation is much higher with informative prior only under prior-data conflict 23

Extrapolation to Demographic subgroups 24

Extrapolation of Efficacy from Adult to Pediatric Population • Drug development for pediatric rare disease faces substantial hurdles, including economic, logistical, technical, and ethical barriers • An efficient design for rare pediatric population may “extrapolate” – from adults to pediatric patients, between pediatric subpopulations • Extrapolation can be considered as an extension of “borrowing historical control data” – extrapolation from adult population to pediatric refers to borrowing “treatment effect” information • Assumes that there is no need for formal proof of efficacy in the pediatric population – no substantial difference in proof of mechanism between adult and children (supporting PK/PD information) 25