Immunotherapy treatments use parts of a person’s immune system to fight disease. In the recent past, immunotherapy has become an important part of treating some types of cancer e.g. melanoma, NSCLC. Studies of these treatments have resulted in a number of observations that have implications for the statistician e.g. delayed treatment effects, long term survivors etc. This joint Basel Biometric Society / PSI one day meeting will present an overview of the science and potential statistical challenges across a range of topics covering early and late phases of development, regulatory and health technology assessments. The speakers from Academia, Regulatory Bodies and Pharma will share their thoughts, ideas and experiences, including case studies. There will be plenty of time for questions and interactions with colleagues.
The meeting will take place on June 15th 2017 in Basel at the Roche IT Centre. To view the agenda, please click here.
A consultant biostatistician with Stone Biostatistics who has 24 years of experience in the pharmaceutical industry and held a senior leadership or management position in the oncology TA at AstraZeneca for nearly 10 years. Andrew has significant regulatory experience, leading biostatistics teams to the submission or approval of six oncology products. Furthermore, Andrew sat on governance committees that approved the design of > 50 pivotal trials. In addition, Andrew led the Statistical Innovation team at AZ for 9 months before his departure from AZ, due to a site closure, in May 2016.
Statistical issues in the development of cancer immunotherapy
With the advent of immunotherapy (IO), which seems to be contributing to a golden age in oncology, a lot has been discussed about non-proportional hazards (NPH). Some of this has the potential to lead to unwise alternatives, whereby increasing the survival of better prognosis patients is weighted as more important. In the presence of NPH, the hazard ratio (HR) produced by conventional analysis approaches is equal to the average HR, which remains a meaningful measure of overall benefit. We should though consider alternative, not replacement, measures of absolute benefit to better the describe any benefit. The emerging profile of IO questions whether we should grapple with the challenges of assessing cure rates, or long term survival rates, and also re-consider the role of non-inferiority in assessing the overall benefit/risk of therapies. Currently we maybe making it more difficult to make available better tolerated, equally efficacious alternatives most difficult in situations where there is the greatest need. Finally, broader issues will be discussed such as requirements for demonstration of contribution of components when seeking approval for combinations of unapproved therapies, and less reliance on single-arm trials that provide a rapid but an ultimately unreliable approach to assessing likely benefit.
EARLY DEVELOPMENT CHALLENGES
Daniel Sabanes Bove
Senior Statistical Scientist, Roche
Daniel joined Roche in October 2013, and has supported multiple early phase Oncology projects from the Basel headquarters, where he is currently Senior Statistical Scientist. Before that, he received a Master of Science in Statistics from the Ludwig-Maximilians-Universität München in 2009 and a PhD in Statistics from the University of Zurich in 2013. Daniel received the Bernd-Streitberg young researcher award from the German Region of the International Biometrical Society, and co-authored the book "Applied Statistical Inference" (Springer, 2014). He developed the R-package “crmPack” for model-based dose escalation designs and gave Roche-internal and also external (ICTMC 2017) tutorials. Other current research interests comprise endpoints in cancer immunotherapy and associated decision making. (For publications please see Google Scholar)
Bayesian Learning in Early Phase Cancer Immunotherapy: A Case Study
The early clinical stage of drug development is a learning phase: we are learning continuously about the drug’s safety, pharmacokinetics, pharmacodynamics and efficacy, building on our current knowledge. Therefore Bayesian inference, with its coherent concept of updating prior information with observed data to obtain the posterior information about quantities of interest, is a perfect match to early phase study designs and to broader clinical development questions.
This case study on a new cancer immunotherapeutic agent starts with the entry-into-human phase I dose escalation study. It is shown how the modified Continual Reassessment Method (CRM) design incorporated reasonable prior assumptions about the expected safety profile, and ensured maximum flexibility for study conduct. A separate dose escalation was then planned for the combination with another new drug, with the design building on the two compound’s information. As during the phase I it became apparent that a large proportion of patients developed anti-drug antibodies against the molecule, a small proof-of-concept study with a pretreatment aiming to diminish the immune response against the biologic was designed. Finally, the information gathered so far can be used to setup the entry-into-human phase I study for another molecule from the same platform.
The clinical development questions and Bayesian answers to them will be presented, with a focus on the decision making and practical considerations in developing a new cancer immunotherapy.
Development of Immunotherapies – challenges encountered at MHRA
Immunotherapies work very differently to other cancer therapies but this is often not taken into account when designing clinical trials to investigate their efficacy and safety. This talk will describe some of the issues this can lead to, from phase 1 to phase 3, by using anonymised examples of trials seen at MHRA.
LATE DEVELOPMENT: STATISTICAL AND REGULATORY CHALLENGES
Associate Director Biostatistics, Roche
Dr. Dominik Heinzmann is an Associate Director and Manager of Biostatistics in Roche Basel for Cancer Immunotherapy and HER2+ targeted therapies. Dominik is also acting as a Global Development Team Leader in HER2+ breast cancer. Dominik has about 7 years of experience in Roche in medical oncology and has a broad experience in interactions with regulators including multiple submissions of different oncology products. Prior to joining Roche, Dominik received a PhD in Biomathematics from the University of Zurich, and holds a M.Sc. in Mathematics & Statistics from the Swiss Federal Institute of Technology. He authored more than 20 publications in peer-reviewed journals in statistical, epidemiological and medical journals.
Statistical, clinical and ethical considerations when minimizing confounding for overall survival in cancer immunotherapy trials
Recent data on cancer immunotherapy (CIT) monotherapy suggest that PFS may not be an appropriate endpoint. If one considers overall survival (OS), a risk with this endpoint is that of confounding, due to cross-over, i.e. treatment switching from control patients to the experimental arm within the trial, enrolling into a subsequent trial with a similar agent as the experimental arm, or because a similar agent has become commercially available.
In this talk, we will discuss statistical measures to minimize confounding for OS and their implications for trial participants as well as the broader underlying population from a clinical and ethical perspective.
Associated Director Biologics Cell and Gene Therapy, Novartis
Sergio presently works as Associate Director RegCMC – Cell and Gene Therapy at Novartis (Basel). He joined Novartis in 2016.
Previously, he covered the position of Regulatory Affairs Manager in Molmed (milan), where he followed the global development through the entire life cycle from early clinical trial application to filing of different cell and gene therapy products intended to address highly unmet medical need in the area of oncohaematology, primary immunodeficiencies and. neurodegenerative diseases.
He started his professional life with 1 year post-doctoral research in biochemistry, followed by 15 years experience at Merck/Serono where he worked in a laboratory focussed on cell bank characterization.
Formal education includes a MSc in Biology and a PhD in Biotechnology.
Challenges in development and approval: the case of cell based therapeutic
The presentation covers the issues and challenges in the development of cell and gene therapy products, still representing a specific niche, still increasing in relevance, in the broader pharmaceutical arena.
Pharmaceutical based on cells have been in the last decade an area of intense investigation and in the last year the first have been approved for commerce. These products differ from large and small molecules in a number of features because of the extreme species specificity and often because a specific batch is produced for a single patient. In addition, manufacturing process and product characterization are non-conventional. Therefore, the approach and paradigms adopted for overall manufacturing, non-clinical and clinical development leading to registration is different from the one usually used large and small molecules in terms of the stud(ies) design and endpoint for safety and efficacy assessment, timing for execution during development and size.
The impact of cell based pharmaceutical peculiarities in terms manufacturing and in-vivo behavior are considered and compared with large and small molecules.
BEYOND APPROVAL (HTA)
Assistant Director, Global HEOR and Market Access, Xcenda®
Fred Sorenson is an Assistant Director for Global Health Economics, Outcomes Research, and Market Access at Xcenda. His work includes comparative effectiveness research, retrospective database studies, prospective studies and chart reviews, systematic reviews, global value dossiers, and contributing to posters and publications.
Before joining Xcenda, Mr. Sorenson led various teams responsible for research in health economics as well as heading the department of biostatistics at a clinical research organization and health care consultancy in Switzerland for more than 10 years. He remains active in biostatistics as an Executive Board Member of the Basel Biometric Society and formerly as representatives to the European Federation of Statisticians in the Pharmaceutical Industry (EFSPI).
Mr. Sorenson received Bachelor of Science degrees in both Psychology and Philosophy from the University of Southern Colorado and did post-graduate studies in Sociology and Economics at the University of Basel in Switzerland.
Cancer immunotherapy from the Health Technology Assessment (HTA) and payer perspective
Assessment of value by Health Technology Assessment (HTA) bodies for reimbursement of Immuno-oncology drugs is complex as few of this class of drugs have single indications, and divergence in clinical value by indication complicates assessments by payers. This is further complicated by the fact that only one EU market authorization submission is required, whereas HTA bodies adhere to their own health reimbursement policy, and therefore are not always in agreement. More recently, the development by different organizations of “Value Frameworks”, especially in Oncology and employing different algorithms for assessing value have entered the scene.
This presentation will provide some background concerning what statisticians need to know and the issues surrounding these developments, and more importantly, possible ways that statisticians can contribute to improving methods and processes for evaluating value to meet the needs of payers and reimbursement.
Nicholas R. Latimer
Senior Research Fellow in Health Economics, NIHR Post-Doctoral Fellow, University of Sheffield
Nicholas Latimer is a Senior Research Fellow in Health Economics at the School of Health and Related Research (ScHARR), University of Sheffield. He joined ScHARR in 2008, having previously worked as a health economist at NERA Economic Consulting, Queen Mary University of London (QMUL), and Roche Products Ltd.
His research expertise is in the area of survival analysis in economic evaluations – particularly the use of survival modelling techniques to extrapolate beyond clinical trial data, and the use of statistical methods for adjusting survival estimates in the presence of treatment switching. In 2012 Nick completed an National Institute for Health Research (NIHR) Doctoral Research Fellowship that focussed upon these topics and in 2015 he was awarded an NIHR Post-Doctoral Fellowship to continue this research. Nick has authored two National Institute for Health and Care Excellence (NICE) Decision Support Unit technical support documents, on survival analysis (TSD 14) and the use of treatment switching adjustment methods (TSD16).
Nick has considerable experience of analysing clinical trial data, and of conducting model-based and trial-based economic evaluation. He has led the Evidence Review Group (ERG) on NICE Technology Appraisals, has led the economic analysis on NICE Clinical Guidelines, has contributed to NICE Public Health guidelines, and has been the principal investigator on several research and consultancy projects. Nick drafted sections on extrapolation and treatment switching for the 2013 NICE Methods Guide and is an invited expert on the NICE Scientific Advice Programme.
Nick collaborates internationally, and has been involved in the development of technical guidance on survival analysis methods by the Pharmaceutical Benefits Advisory Committee (PBAC) in Australia. He has links with IQWiG (Germany), the Canadian Agency for Drugs and Technologies in Health (CADTH), the European Medicines Agency (EMA) and the US Food and Drug Administration (FDA).
Estimating survival benefit for health technology assessment: new challenges presented by immuno-oncology treatments?
Several new immuno-oncology (I-O) treatments appear to ‘cure’ a proportion of patients: survival for this group often continues beyond the trial observation period and a plateau is observed in the survival curve. Hazard functions appear to be non-proportional and complex. Standard parametric models which have commonly been used to estimate long-term survival for use in economic evaluations undertaken within health technology assessments may not be appropriate for modelling such data. Novel survival modeling methods, such as mixture cure models and flexible parametric models, have emerged as potentially useful alternative modelling approaches, and using these models can fundamentally change estimates of effectiveness and cost–effectiveness. In this session, standard modelling approaches will be summarised, as will their limitations given the apparent characteristics of new I-O treatments. Alternative modelling methods will be introduced and discussion will consider whether the issues raised are specific to I-O treatments, and whether HTA agencies are prepared to appraise the application of more complex survival models.
Joint PSI, EFSPI & ASA BIOP Webinar: Estimands
PSI, the European Federation of Statisticians in the Pharmaceutical Industry (EFSPI) and the Biopharmaceutical Section of the American Statistical Association (ASA) are jointly organising a webinar on Estimands in Practice. Speakers from regulatory authorities (FDA and EMA) and industry will present on their experience on this topic to date.
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