Senior Vice President and Head of Early Clinical Development, Pfizer
About the Speaker
Sandeep Menon is Senior Vice President and Head of Early Clinical Development (ECD). During his nine years at Pfizer he has held several positions of increasing responsibility, most recently as Vice President and Head of ECD, Biostatistics. Prior to joining Pfizer, he held late-phase leadership roles at Biogen Idec and Aptiv Solutions (now ICON).
Sandeep has extensive experience with regulatory interactions, including with the FDA, EMA and PMDA (Japan Agency). He is internationally known for his technical expertise especially in the area of adaptive designs, translational biomarkers, multi-regional trials, and small populations. He has managed global multi-functional groups including statistics and programming responsible for successfully delivering NDA’s and PMA’s. Within Pfizer, he has led groups responsible from discovery through POC/Phase 2b and has worked extensively on different therapeutic areas including Oncology, Immunology, Rare Disease, Cardio Vascular and Metabolic Diseases, Neuroscience, and Ophthalmology.
Sandeep is an elected fellow of the American Statistical Association and was recently awarded the Young Scientist Award by the International Indian Statistical Association. Sandeep received his medical degree from Bangalore (Karnataka) University, India, and later completed his Masters and Ph.D. in Biostatistics at Boston University and research assistantship at Harvard Clinical Research Institute. He holds adjunct faculty positions at Boston University School of Public Health, Tufts University School of Medicine, and the Indian Institute of Management. He has received several awards for academic, teaching and research excellence.
SVP, Global Head of Biometrics and Data Management, Pfizer
Traditional statistical principles are often not ideal to establish the benefit risk of a new drug in rare disease. With a significant unmet medical need in some of the diseases, it is unethical and often not feasible to conduct large trials with either placebo or active controls. The talk will focus on the use of historical control information, natural disease history records, and Real World Data (RWD) to enrich the design and analysis of clinical trials in rare disease. Proper use of all available evidence allows a robust assessment of the benefit risk of experimental therapies. Different strategies of using a synthetic control in clinical trial design will be highlighted with possible pros and cons. The talk will focus on potential use of "platform trials" in rare diseases with a bio marker-driven, modern design approach that has been widely used in Oncology, to help expedite the development of drug candidates or combinations through proof of concept to phase 3. In addition, the talk will elaborate an example on the need for replacing traditional study endpoints and testing procedures with innovative methods that could be significantly efficient in assessing the treatment benefit. Lastly, the talk will propose thoughts on potential "estimands" in the context of rare disease.
About the speaker
Prior to joining Pfizer, Kannan was Senior Vice President and Global Head of Oncology Biometrics and Data Management at Novartis Pharmaceuticals. Kannan has been in the pharmaceutical industry for over 20 years, working across various therapeutic areas and in particular Oncology, over the last 10 years. At Novartis, Kannan was part of the Oncology Development Leadership Team managing the oncology development portfolio, contributing to the global development strategy and to the approvals of several major drugs. Kannan also served as the co-chair of the Protocol Review Committee, in conjunction with the Head of Development. During Kannan's tenure at Novartis, he was instrumental in managing the growth of Development Operations within India, consisting of multiple line functions within global developments. Prior to Novartis, Kannan worked at Bristol-Myers Squibb where he served as the Biostatistics and Statistical Programming therapeutic area head for Immunology, Cardiovascular and Metabolics/Endocrinology, supporting sever major global submissions and approvals. Kannan holds a PhD. Degree in Statistics from the University of Florida.
Head of Early Clinical Development Clinical Pharmacology, Pfizer
Rare Disease offers a unique opportunity for Model Informed Drug Development (MIDD) as an area that, in addition to the challenge of new modalities, there is a clear need to complement limited data with all available quantitative information to inform decisions. The talk will introduce how we have taken advantage of Model Informed Drug Development in general at Pfizer to both instill a cultural shift and to drive drug discovery and development decisions and how this has improved initially Ph3 success and later, with the addition of Pillars and QSP, is starting to impact Ph2 success. Examples of quantitative influence in rare disease early clinical development programs will be discussed together with the challenges encountered by clinical pharmacology in Gene Therapy.
About the Speaker
Gianluca Nucci, Ph.D is the Head of Early Clinical Development Clinical Pharmacology at Pfizer. His group provides clinical pharmacology leadership to integrate PK, PD, patient characteristics pharmacology and mechanistic disease understanding to inform translational and clinical development strategies, driving robust and successful signal of clinical activity & proof of concept delivery via quantitative methodologies, innovative study/program design and model informed drug discovery and development to facilitate knowledge based decisions. Gianluca has > 15 years of experience in quantitative Clinical Pharmacology across multiple therapeutic areas and two major Pharma companies (GSK and Pfizer).
Neuromyelitis optica spectrum disorder (NMOSD) is a rare, severe, disabling autoimmune inflammatory disorder of the central nervous system (CNS) that targets aquaporin-4 (AQP4)-expressing cells, and predominately affects the optic nerves and spinal cord. In patients with NMOSD, disease progresses secondary to the disabilities that are accrued with each relapse. The PREVENT Study was a Phase 3, randomized, double-blind, placebo-controlled, multicenter registration study designed to evaluate the safety and efficacy of eculizumab for the treatment of patients with relapsing NMOSD. Eligible patients were randomized 2:1 to one of two parallel treatment arms: 1) eculizumab or 2) placebo. The primary efficacy endpoint was time to first adjudicated relapse. The study was designed to continue until the occurrence of 24 positively adjudicated relapses in 24 distinct patients. The trial was stopped after 23 of the 24 prespecified adjudicated relapses, given the uncertainty in estimating when the final event would occur.
This presentation will discuss the challenges of doing a time to event study in a rare disease and the rigorous blinded review of the totality of information, which assessed that the study was unlikely to achieve the final (ie 24th) adjudicated event in a reasonable timeframe, while terminating at 23 adjudicated events (96% complete) would have a low risk of impacting the study outcome. On June 27, 2019, the FDA approved Soliris (eculizumab) as the first approved treatment of NMOSD in adult patients who are AQP4 antibody positive.
About the Speakers
Amy Pace is a Director of Biostatistics within the neurology franchise at Alexion Pharmaceuticals and most recently was the statistical lead for the Eculizumab NMOSD program. Prior to Alexion, Amy worked at Biogen across development and life cycle management in Multiple Sclerosis and Hemophilia and headed up the Worldwide Medical Biostatistics group. Amy has an ScD in Biostatistics from the Harvard School of Public Health and is the statistical author on numerous publications in the field of neurology.
Co-speaker: Fanny O’Brien is the Senior Director of Biostatistics heading up the Neurology franchise group of biostatisticians at Alexion. Fanny has her Ph.D. in Statistics from Princeton University. She has worked in the Biotech/Pharmaceutical industry for over 23 years. During her whole career she has been involved in the design and analysis of clinical trials in rare diseases and ultra-rare diseases leading to several regulatory submissions and approvals worldwide.
Associate Director, Takeda
When designing a clinical trial, borrowing historical control information can provide a more efficient trial by reducing the necessary control arm sample size while still yielding an increase in power. Several Bayesian methods for incorporating historical information via a prior distribution have been proposed, e.g. (modified) power prior, (robust) meta-analytic predictive prior. However, when utilizing historical control borrowing, the prior setting must be correctly pre-specified before the current data is observed. Thus, a flexible prior is needed in case of either heterogeneity between historic trials or prior data conflict with the current trial.
To incorporate the ability to selectively borrow historic information, we propose a Bayesian semiparametric meta-analytic-predictive prior. Using a Dirichlet process mixture prior allows for relaxation of parametric assumptions, and lets the model adaptively learn the relationship between the historic and current control data. Additionally, we give a method for estimating the prior effective sample size (ESS) for the proposed prior. This gives an intuitive quantification of the amount of information borrowed from historic trials, and aids in tuning the prior to the specific task at hand. Simulation studies and an illustration example with proof-of-concept study on ankylosing spondylitis (AS) were conducted to compare the performance of existing commonly used methods with the proposed method in terms of power, type I error, calibrated power and average prior ESS.
Overall, our proposed robustification of the meta-analytic-predictive prior alleviates the need for pre-specifying the amount of borrowing, providing a more flexible and robust method to integrate historical data in the design and analysis of clinical trials.
About the Speaker
Jianchang Lin is a group lead in Statistical and Quantitative Sciences (SQS) of Takeda Pharmaceuticals supporting several pipeline programs in oncology, immunology and rare disease. In addition, his team also provides statistical methodology and solution across Takeda therapeutic areas to enable innovative design and analysis. He has published more than 20 papers in statistical and clinical peer-reviewed journals/book chapters (e.g. NEJM, Biometrics, Statistical in Medicine, Statistics in Biopharmaceutical Research, Journal of Biopharmaceutical Statistics, Contemporary Clinical Trials) and served as editors for two books published in Springer. His research interest includes Adaptive designs, Bayesian methods and leveraging data science including AI/ML, real-world evidence in clinical development.
Senior Product Manager
About the Speaker
Geoffrey Grove, Ph.D. has worked in the pharmaceutical industry for 18 years focusing on software and instrumentation development. He currently manages software products at Cytel Corporation, including East, Xact, EnForeSys, FlexRandomizer, Aces, and OKGO. He is also involved in new product research and development efforts.
Director of DMC Services, Axio
Relatively few people have sat alongside Data Monitoring Committees (DMCs) in closed sessions. In these sessions, DMCs develop recommendations based on review of interim data to protect the safety of clinical trial participants and ensure the scientific integrity of the trial. The decisions made behind closed doors by the DMC regarding the future of the trial can be very challenging. This session will be helpful context for those who work alongside DMCs and receive their recommendations. It will cover general considerations and options that the DMC has when reviewing the data presentations during their closed sessions. Four specific case studies will highlight the data provided to the DMC from meeting to meeting and discuss how the DMC arrived at their recommendations for each meeting.
About the Speaker
David Kerr received his Master’s in Statistics from the University of Washington and has worked at Axio Research, now part of Cytel, in Seattle for the past 23 years. He currently holds the title of Director of DMC Services where he provides leadership, direction and oversight for all DMC services provided by Axio Research - which facilitates over 200 DMC meetings per year. He was instrumental in developing Axio’s SOPs which govern the company’s DMC Services as well as the specialized statistical programs which produce the materials reviewed by the DMC. In addition to his duties as Director of DMC Services, David has himself provided statistical support as the reporting statistician for more than 100 DMCs covering 150 individual clinical trials, with particular emphasis in disease areas such as oncology, cardiology, infectious disease, respiratory disease, and rheumatology. He has personally attended over 600 DMC meetings, and seen a wide variety of situations from those experiences. He is a strong advocate for improvements in the DMC process and has presented at JSM and SCT and BASS, as well as given tutorials to industry. His goal is to make sure the DMC has the best information available to help them make educated recommendations for the success of the trial and the protection of the study participants.
Senior Director and Metabolic Franchise Lead for Biostatistics, Alexion
A disease is generally considered to be ultra-rare if it affects one patient per 50,000 people and most ultra-rare diseases affect far fewer than this - as few as one per million or less. When designing studies and performing analyses that deliver therapies for life-threatening and ultra-rare diseases, there is usually no opportunity to evaluate survival data against a contemporary, concurrent dataset. I will present a bootstrap survival analysis that matches historical control patients to treated patients by key risk factors. For each treated patient a match was identified among the historical control patients by randomly selecting from the historical controls subset with corresponding risk factor(s). Once a historical control match was identified for each treated patient, Kaplan-Meier survival statistics comparing treated and control patients were obtained using this set of controls. This process was repeated to obtain a bootstrap estimate of the variability associated with the survival rates in the historical control group as well as the log-rank test p-values. These results show that when matching historical control patients with treated patients by key risk factors and conservatively selecting matches that survived at least to the age of the treated patient, the survival curves differed between treated and controls with better survival among treated patients.
About the Speaker
Clare Elkins is Senior Director and Metabolic Franchise Lead for Biostatistics within the Quantitative Sciences group at Alexion Pharmaceuticals. With over 25 years in industry, she has experience with global drug, biologics and medical device development including multiple rare disease studies. Prior to Alexion, Clare worked at Genzyme, Abt Associates, Serono Labs and Glaxo Group Research in the UK. Clare holds a Masters in Mathematical Statistics from Cambridge University.
Using Historical Control for Regulatory Approvals in US – A Practical Review Focusing on Diseases in Small Population
In life-threatening/rare diseases, randomized controlled trial often runs into feasibility and/or ethical issue. Single-arm interventional trial with historical control (HC) as the comparator, highlighted in the recent FDA Real World Evidence framework, provides an alternative approach to assess the effectiveness of the investigative therapy in these challenging scenarios. In recent years, quite a few novel therapies for rare diseases and in oncology for small population were developed utilizing historical data and several gained approvals. A roadmap of designing clinical trials with HC proposed by DIA NEED team will be introduced first, followed by case studies of real world drug development programs/filings using HC. The discussions will focus on challenges/disadvantages when using HC as the only comparator in clinical trial and strategies to minimize them.
About the Speakers
Ziliang Li is an Associate Director in the Biostatistics department at Vertex, serving as the statistical lead on (early) clinical development of various rare diseases. Previously, Ziliang worked in the late development statistics in Merck leading/supporting late phase development (regulatory filings/FDA Advisory Committee meetings, Phase 2/3 execution) for several respiratory compounds. His research interest includes clinical trial design, regression modeling and data visualization.
Chenkun Wang is a Principal Statistician in the Biostatistics department at Vertex with four-year experience in the rare disease of Cystic Fibrosis. Chenkun is also a group member of the DIA adaptive design working group on historical borrowing. Her research interest includes repeated measure analysis, historical borrowing, and composite endpoint.
Principal, Strategic Consulting, Cytel
Pantelis will be speaking about existing and new developments in East during the dedicated software training sessions on Thursday, November 7. The training will cover an overview of Cytel software and East modules, phase 1 dose escalation, phase 2 statistical methodology and dose-response, multiplicity in clinical studies, phase 3 group sequential designs, phase 2/3 multi-arm multi-stage designs, and phase 3 adaptive designs.
About the Speaker
Pantelis is Director/Strategic Consultant for Cytel, Inc. based in Geneva. He joined the company in January 2013. Before that, he was a Principal Biostatistician at Merck Serono as well as a Professor of Statistics at Carnegie Mellon University for 12 years. His research interests lie in the area of adaptive designs, mainly from a Bayesian perspective, as well as hierarchical model testing and checking although his secret passion is Text Mining. He has served as Managing Editor of the journal “Bayesian Analysis” as well as editorial boards of several other journals and online statistical data and software archives.