Efthymios Manolis

Implementation of proteomic biomarkers: Making it work

Eur J Clin Invest 2012; 42 (9): 1027–1036

Proposals for model‐based paediatric medicinal development within the current European Union regulatory framework

The new paediatric European Union (EU) regulation and the consequent demand for paediatric studies on one hand and the ethical need for minimizing the burden of studies in children on the other hand necessitate optimal techniques in the assessment of safety/efficacy and use of drugs in children. Modelling and simulation (M&S) is one way to circumvent some difficulties in developing medicinal products in children. M&S allows the quantitative use of sparse sampling, characterization and prediction of pharmacokinetics/pharmacodynamics (PK/PD), extrapolation from adults to children, interpolation between paediatric age subsets, optimal use of scientific literature and in vitro/preclinical data. Together, industry, academia and regulators recognize the usefulness of modelling and simulation in this setting. However, even if M&S is an emerging science, its integration in the EU regulatory decision making is for the time being deficient and M&S expertise is concentrated in big pharmaceutical companies and academic institutions. The European Medicines Agency, acknowledging all the above conditions, organized and hosted a Workshop on Modelling in Paediatric Medicines. The article presents the personal views of the authors on the issues presented and discussed in the workshop.We attempt to identify the regulatory framework for the use of M&S in paediatric medicinal development and to make proposals for model-based paediatric medicinal development. The objective is to open the discussion between industry, academia, paediatricians and regulators on the optimal use of M&S in paediatric medicinal development.

Role of modeling and simulation in pediatric investigation plans.

Ethical and practical constraints encourage the optimal use of resources in pediatric drug development. Modeling and simulation has emerged as a promising methodology acknowledged by industry, academia, and regulators. We previously proposed a paradigm in pediatric drug development, whereby modeling and simulation is used as a decision tool, for study optimization and/or as a data analysis tool. Three and a half years since the Paediatric Regulation came into force in 2007, the European Medicines Agency has gained substantial experience in the use of modeling and simulation in pediatric drug development. In this review, we present examples on how the proposed paradigm applies in real case scenarios of planned pharmaceutical developments. We also report the results of a pediatric database search to further ‘validate’ the paradigm. There were 47 of 210 positive pediatric investigation plan (PIP) opinions that made reference to modeling and simulation (data included all positive opinions issued up to January 2010). This reflects a major shift in regulatory thinking. The ratio of PIPs with modeling and simulation rose to two in five based on the summary reports. Population pharmacokinetic (POP‐PK) and pharmacodynamics (POP‐PD) and physiologically based pharmacokinetic models are widely used by industry and endorsed or even imposed by regulators as a way to circumvent some difficulties in developing medicinal products in children. The knowledge of the effects of age and size on PK is improving, and models are widely employed to make optimal use of this knowledge but less is known about the effects of size and maturation on PD, disease progression, and safety. Extrapolation of efficacy from different age groups is often used in pediatric medicinal development as another means to alleviate the burden of clinical trials in children, and this can be aided by modeling and simulation to supplement clinical data. The regulatory assessment is finally judged on clinical grounds such as feasibility, ethical issues, prioritization of studies, and unmet medical need. The regulators are eager to expand the use of modeling and simulation to elucidate safety issues, to evaluate the effects of disease (e.g., renal or hepatic dysfunction), and to qualify mechanistic models that could help shift the current medicinal development paradigm.

New pathway for qualification of novel methodologies in the European Medicines Agency.

The European Medicines Agency (EMA) qualification process is a new, voluntary, scientific pathway leading to either a Committee for Medicinal Products for Human Use (CHMP) opinion or a Scientific Advice on innovative methods or drug development tools:

Pharmacometrics for Regulatory Decision Making

As a methodology to rationalize and inform drug development, pharmacometrics (modelling and simulation) is widely appreciated by industry, academia and regulators in general. However, the integration of pharmacometric analyses into regulatory decision making is not formally established and has been the subject of discussion in many scientific and regulatory fora. The article by Lee et al. published in this issue of the journal provides instructive case studies of how pharmacometrics can be used by regulators to help decision making, interaction with companies, reviewing and labelling. Their case studies indicate that pharmacometrics is not a tool reserved only for companies for internal decision making, but also is a powerful platform that regulators may use to compile and analyse data in order to support approval and labelling. This is considered to have beneficial effects for both industry, in terms of resource optimization, and for prescribers and patients, who obtain more precise labelling instructions and optimal therapeutic interventions, respectively. The increasing impact of pharmacometrics on US FDA approval and labelling (see table I in the article by Lee et al.) indicates the success of the methodology and the need to make best use of all available data during regulatory decisionmaking, especially in controversial cases or when data are scarce (e.g. in orphan diseases or paediatrics). In Europe, modelling and simulation was identified by the European Medicines Agency (EMA) Think-Tank Group on Innovative Drug Development and Committee for Medicinal Products for Human Use (CHMP) as one of the key methodologies to overcome bottlenecks in drug development. There is no EMA guideline that generally defines how pharmacometrics should be used in regulatory decision making. Indeed, it is difficult to discuss this methodology outside a specific context (e.g. the clinical condition, feasibility of trials, availability of good biomarkers for safety and efficacy, availability of clinical efficacy and safety data from other groups, and stage of development). In general, the hurdle for regulatory acceptance ofmodelling and simulation seems lower in the exploratory phases than in the confirmatory phases of medicine development. On the basis of information compiled from various clinical efficacy/safety and methodological EMA guidelines, and discussions by the EMA Scientific Advice Working Party (SAWP) and Paediatric Committee (PDCO), we have identified the following cases to exemplify the spectrum of current thinking in the European regulatory setting. Examples where the use of modelling and simulation is well appreciated are: hypothesis generation and learning throughout drug development; use of models to minimize the burden of pharmacokinetic/ pharmacodynamic evaluations in current studies and to optimize future experiments; use of models for selection of doses to be further tested in clinical trials. Examples where the use of modelling and simulation could be accepted if properly justified are: use of models for final recommendation of intermediate doses that were not specifically tested in phase II/III trials; population pharmacokinetic analysis in phase II/III to support regulatory claims (e.g. the absence of suspected drugdrug interactions and the effect of pharmacogenetics on exposure); modelling and simulation to bridge efficacy data. Examples where the use of modelling and simulation is generally seen as controversial are: model-based inference as the ‘sole’ evidence of efficacy/ safety, notwithstanding exceptional scenarios; approval based on simulated data for efficacy and safety. An important criterion that regulators check when assessing the weight of modelling and simulation in a given submission is the quality of the exercise. The principles of transparency, traceability, parsimony, external validity and internal validity, COMMENTARY Clin Pharmacokinet 2011; 50 (10): 625-626 0312-5963/11/0010-0625/

Hollow Fiber System Model for Tuberculosis: The European Medicines Agency Experience

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Advanced methods for dose and regimen finding during drug development: Summary of the EMA /EFPIA workshop on dose finding (London 4-5 December 2014)

The in vitro hollow fiber system model has been qualified by the European Medicines Agency as a methodology for use in support of selection and development of antituberculosis regimens. More data are expected to be generated in the future to further characterize its value.

The European Medicines Agency Experience with Biomarker Qualification

Inadequate dose selection for confirmatory trials is currently still one of the most challenging issues in drug development, as illustrated by high rates of late‐stage attritions in clinical development and postmarketing commitments required by regulatory institutions. In an effort to shift the current paradigm in dose and regimen selection and highlight the availability and usefulness of well‐established and regulatory‐acceptable methods, the European Medicines Agency (EMA) in collaboration with the European Federation of Pharmaceutical Industries Association (EFPIA) hosted a multistakeholder workshop on dose finding (London 4–5 December 2014). Some methodologies that could constitute a toolkit for drug developers and regulators were presented. These methods are described in the present report: they include five advanced methods for data analysis (empirical regression models, pharmacometrics models, quantitative systems pharmacology models, MCP‐Mod, and model averaging) and three methods for study design optimization (Fisher information matrix (FIM)‐based methods, clinical trial simulations, and adaptive studies). Pairwise comparisons were also discussed during the workshop; however, mostly for historical reasons. This paper discusses the added value and limitations of these methods as well as challenges for their implementation. Some applications in different therapeutic areas are also summarized, in line with the discussions at the workshop. There was agreement at the workshop on the fact that selection of dose for phase III is an estimation problem and should not be addressed via hypothesis testing. Dose selection for phase III trials should be informed by well‐designed dose‐finding studies; however, the specific choice of method(s) will depend on several aspects and it is not possible to recommend a generalized decision tree. There are many valuable methods available, the methods are not mutually exclusive, and they should be used in conjunction to ensure a scientifically rigorous understanding of the dosing rationale.

Changes and determination of dosing recommendations for medicinal products recently authorised in the European Union.

Since the launch of the qualification process in 2009, the CHMP reviewed/is reviewing 48 requests for qualification advice or opinion (as of Sept 2013) related to biomarkers (BM) or other novel drug development tools (e.g. patient reported outcome measures, modeling, and statistical methods). The qualification opinions are available on the EMA website (Qualification of novel methodologies for medicine development, http://www.ema.europa.eu/ema/index.jsp?curl=pages/regulation/document_listing/document_listing_000319.jsp&mid=WC0b01ac0580022bb0#section2 , 2013). Also there is a trend of increasing numbers of qualification requests to CHMP, indicative of the pace that targeted drug development and personalized medicine is gaining and the need to bring the new tools from research to drug development and clinical use. This chapter will focus on the regulatory experience gained so far from the CHMP qualification procedure. Basic qualification principles will be presented. Through qualification examples, we will elaborate on common grounds and divergences between the different stakeholders.

Chest imaging in CF studies - Commentary

Introduction: The quantity and quality of data for determining the dose and treatment schedule of medicinal products is directly related to how safe and efficacious these medicines are and how successful they can be used to treat patients. Areas covered: This review provides an analysis of dose-related label modifications of recently approved drugs. It shows which areas could benefit from a better dose–exposure–response understanding, both during initial assessment and after marketing authorisation. This analysis highlights regulators’ considerations in dosage evaluations and provides reflections for drug developers on how to ensure best possible dose selection in the interest of the patients. Expert opinion: Using modelling and simulation, pharmacogenomics, population pharmacokinetics, physiologically based pharmacokinetic models and drug–drug interaction studies in conjunction with well-designed clinical trials will improve the understanding of the pharmacology of medicines, of the physiology of the disease and of the dose–exposure–response relationship during drug development. More focus should be given to the investigation of dose and regimens for special populations before applying for marketing authorisation. Consequently, regulators could review dose–exposure–response data with more certainty and better define dose recommendations in the label.