Physiologically Based Pharmacokinetic (PBPK) Models are routinely used in drug development and therefore appear frequently in marketing authorization applications (MAAs) to the European Medicines Agency (EMA). For a model to be a key source of evidence for a regulatory decision, it must be considered qualified for the intended use. Advice on the data expected to allow qualification of a PBPK model or platform is provided in the EMA Guideline on the reporting of PBPK modeling and simulation. The present study is an EMA review of the use of PBPK models in submitted MAAs in 2022 and 2023 focussing on the concept of qualification and the reasons why models were not considered qualified. A review of the 95 MAAs with a "full" legal basis approved during these years showed that 25 of them contained PBPK modeling. There were 65 proposed general areas of intended use for PBPK modeling identified across the applications, with the most common being a prediction of drug-drug interactions with enzymes or transporters (69%). Finally, this review showed that most of the models submitted in applications to EMA were not considered qualified for the intended use(s). The reasons identified for this are reported and the need for further EMA guidance, particularly around requirements for qualification of PBPK models, are discussed.

Except for medicines intended for use in pregnancy, pregnant and breastfeeding individuals are routinely excluded from drug development programs. Consequently, when medicines are needed to treat disorders that occur in pregnancy or during the breastfeeding period, or when unintended exposure to medicines occurs in unplanned pregnancies, there is uncertainty regarding the choice of treatment and the potential impact on the child. This article outlines the need for this to change, and opportunities in that direction offered by MIDD (Model Informed Drug Development). At the time of marketing authorization, pregnancy/breastfeeding labeling typically relies mainly on preclinical data. While post-authorization studies are sometimes requested to collect safety data in pregnant and breastfeeding individuals, usually, routine pharmacovigilance (signal detection and post-authorization safety update reports) is relied upon for generating information in this population once the products are on the market.1, 2 The overall consequence is possible under-prescription of medicines in these individuals and missing or ambiguous pregnancy-specific dosing recommendations in the SmPC (Summary of Product Characteristics).3, 4 Hence, before, and long after real-world evidence is available, the use of medicinal products tends to be discouraged during pregnancy and breastfeeding. This has been recognized as an unhelpful situation by regulators around the world,5 leading within the European Medicines Agency (EMA) to the development and implementation of a strategy6 to enhance the SmPC information on the benefits and risks of medicines in pregnancy and breastfeeding. Central to reaching this objective is to improve the related data collection (breadth and informativeness) during the product lifecycle. Important milestones include the agreement achieved by the International Conference of Harmonization (ICH) to draft a guideline for responsibly including, or permitting to remain, pregnant and breastfeeding individuals in clinical trials,7 and the reopening of the Committee for Human Medicinal products (CHMP) guideline on labeling in pregnancy and breastfeeding.8 We anticipate that regulatory developments such as those mentioned above, coupled with the significant development and innovation in nonclinical drug development methodologies and MIDD over the last decades, will shift the current labeling paradigms, to improve accessibility and safe use of medicines during pregnancy and breastfeeding. Several regulatory initiatives are underway, and these will be publicized on a dedicated webpage on the EMA public website in summer 2024. MIDD comprises the strategic use of computational modeling and simulation approaches that integrate data, prior information, and knowledge, including drug, nonclinical, clinical, and disease characteristics, to generate evidence. When adequately implemented, modeling and simulation is considered a powerful tool for characterizing the efficacy and safety of drugs in subgroups underrepresented in clinical studies such as pregnant and breastfeeding participants, who also deserve timely access to safe and effective medicines. From a physiology and pharmacology point of view, pregnant and breastfeeding individuals represent complex and dynamic systems.9 MIDD approaches, including population pharmacokinetics/pharmacodynamics, physiologically-based pharmacokinetic modeling, and quantitative systems pharmacology, can integrate available knowledge on the drug pharmacology, in vitro or in vivo nonclinical data and clinical data to quantify these complex systems and enable predictions of drug exposure and clinical response during pregnancy and lactation. These models can be continuously improved based on new data collected in clinical studies that enroll pregnant and breastfeeding individuals. Depending on the remaining uncertainty in the models and related parameters, they can be used to either improve operating characteristics of clinical trials enrolling pregnant and lactating participants or support regulatory claims by complementing (non) clinical evidence for benefit/risk, labeling and need for potential additional risk mitigation measures in these special populations. Among the different MIDD approaches, physiology-based pharmacokinetic (PBPK) modeling is expected by drug developers and regulators alike to have a prominent role in drug development in pregnant and breastfeeding individuals, given its ability to distinctly describe physiological and pharmacological processes.10, 11 Already, different software providers are including pregnancy and lactation modules in their platforms.12-14 PBPK can serve for early-in-development PK prediction by integrating drug parameters, systems knowledge (i.e., changes in absorption, distribution, metabolism, excretion (ADME) in pregnancy, lactating mother, neonate-infant, transplacental or/and mammary gland drug transfer), in vitro data (permeability, metabolism, active transporters, etc.) and in vivo data from nonclinical and clinical experiments. This prediction of drug exposure in special populations, coupled with an understanding of nonclinical toxicology exposure margins and exposure-response relationships (depending on the stage of drug development), can inform decision-making regarding the enrolment of pregnant and lactating participants in clinical studies and contribute to the weight-of-evidence approach advocated in the EMA guidelines. Even in cases where it is not possible to enroll pregnant and lactating individuals, this quantitative framework can facilitate decisions regarding labeling and risk management in these populations. Despite these developments being welcomed at EMA, the current experience in EMA submissions with PBPK and broader MIDD in this context remains limited. In addition, the MIDD framework proposed above does not come without challenges. Focusing on PBPK, the model predictions in pregnancy and lactation are associated with high uncertainty because of poor understanding and quantification of system parameters and mechanisms involved in transplacental and mammary gland transfer, physiological changes in pregnancy, lactation, and the maturing child. Likewise, the in vitro methods to enable reliable PBPK predictions in these special populations are not well characterized. The current uncertainties with PBPK in pregnancy and lactation impede their unconditional use and regulatory acceptance. Regulators would expect to be able to quantify the risk of making a wrong decision, for example, in this case agreeing on a dose that leads to under- or overexposure in pregnant women or breastfed infants/neonates with associated risks. The science is still evolving, but promising efforts are underway to improve knowledge by systematic collection of physiology data, development, and characterization of new in vitro cell lines for passive and active transport, nonclinical and clinical data generation.15, 16 PBPK is emerging as a tool of choice in this context. However, there is limited regulatory experience with these methods in the specific lactation/pregnancy context of use. In this regard, regulatory activities are ongoing to facilitate use of MIDD approaches and improve pregnancy/lactation labeling for medicines. European regulators are willing to engage early in discussions with platform developers and consortia, via the qualification procedure,17 to agree on development and application of PBPK models in pregnancy and lactation. No funding was received for this work. The authors declared no competing interests for this work. The views expressed in this article are the personal views of the authors and may not be understood or quoted as being made on behalf of or reflecting the position of the regulatory agencies or other organizations with which the authors are affiliated. The authors are employees of the European Medicines Agency or of a National Competent Authority.

Model-informed drug development (MIDD) approaches receive wide regulatory acceptance in the European Medicines Agency (EMA) to support new drug development. For generic drugs, the European regulators have not reached a common position on how to use these methods. This commentary expands on the existing EMA regulatory framework for bioequivalence and physiological based pharmacokinetic (PBPK) modeling to propose conditions where mechanistic models could support or potentially waive clinical bioequivalence (BE)/bioavailability (BA) studies.

Global alignment of expectations is required to achieve consistency in the planning, conduct, reporting, and regulatory review of model-informed drug development (MIDD) applications. An International Council for Harmonization (ICH) MIDD general principles guideline has been positioned to provide a common standard of practice including a framework for risk-based assessment of MIDD-derived evidence within the context of global regulatory decision-making. This perspective provides the background, our viewpoints, and the next steps in the development of this guideline.

The value of in silico methods in drug development and evaluation has been demonstrated repeatedly and convincingly. While their benefits are now unanimously recognized, international standards for their evaluation, accepted by all stakeholders involved, are still to be established. In this white paper, we propose a risk-informed evaluation framework for mechanistic model credibility evaluation. To properly frame the proposed verification and validation activities, concepts such as context of use, regulatory impact and risk-based analysis are discussed. To ensure common understanding between all stakeholders, an overview is provided of relevant in silico terminology used throughout this paper. To illustrate the feasibility of the proposed approach, we have applied it to three real case examples in the context of drug development, using a credibility matrix currently being tested as a quick-start tool by regulators. Altogether, this white paper provides a practical approach to model evaluation, applicable in both scientific and regulatory evaluation contexts.

Getting the right dose regimen for children and adolescents is important but poses great scientific, practical, and ethical challenges. At the same time, the availability of data in adults is a huge advantage and needs to be used optimally when designing studies in children and analyzing pediatric data. Furthermore, the processes of maturation and growth are always key when selecting doses for children. All the above make study adaptations and model-informed approaches imperative for dose exposure-response characterization and dose selection in children. This article summarizes the experience gained in the European Medicines Agency on this topic and proposes some general guiding principles for defining objectives, study designs, and methodology tools for pediatric dose selection.

Background Population pharmacokinetic evaluations have been widely used in neonatal pharmacokinetic studies, while machine learning has become a popular approach to solving complex problems in the current era of big data. Objective The aim of this proof-of-concept study was to evaluate whether combining population pharmacokinetic and machine learning approaches could provide a more accurate prediction of the clearance of renally eliminated drugs in individual neonates. Methods Six drugs that are primarily eliminated by the kidneys were selected (vancomycin, latamoxef, cefepime, azlocillin, ceftazidime, and amoxicillin) as ‘proof of concept’ compounds. Individual estimates of clearance obtained from population pharmacokinetic models were used as reference clearances, and diverse machine learning methods and nested cross-validation were adopted and evaluated against these reference clearances. The predictive performance of these combined methods was compared with the performance of two other predictive methods: a covariate-based maturation model and a postmenstrual age and body weight scaling model. Relative error was used to evaluate the different methods. Results The extra tree regressor was selected as the best-fit machine learning method. Using the combined method, more than 95% of predictions for all six drugs had a relative error of < 50% and the mean relative error was reduced by an average of 44.3% and 71.3% compared with the other two predictive methods. Conclusion A combined population pharmacokinetic and machine learning approach provided improved predictions of individual clearances of renally cleared drugs in neonates. For a new patient treated in clinical practice, individual clearance can be predicted a priori using our model code combined with demographic data.

The added value of in silico models (including quantitative systems pharmacology models) for drug development is now unanimously recognized. It is, therefore, important that the standards used are commonly acknowledged by all the parties involved. On April 25 and 26, 2019, a multistakeholder workshop on the validation challenges for in silico models in drug development was organized in Belgium. As an outcome, a White Paper is foreseen in 2020 on standards for in silico model verification and validation. Drug research, design, and development has a long-standing tradition in the use of in silico methodologies. In the context of clinical drug development Quantitative Structure-Property Relationship models in general and Quantitative Structure-Activity Relationship (QSAR) methods in particular, as well as pharmacometric approaches like population pharmacokinetics, pharmacokinetics (PKs)/pharmacodynamics, exposure-response, and physiology-based pharmacokinetics (PBPK) models are well-known. However, the in silico toolbox is rapidly expanding beyond these traditional/historical modeling technologies and new ones have emerged the last decades, including multiphysics simulations, the so-called systems medicine/pharmacology models (QSP) and clinical trial simulation tools (in silico clinical trials). In the remainder of this document, the term in silico models will be used to describe the collection of all the aforementioned modeling technologies. The added value of in silico models for drug development is now unanimously recognized by the scientific community.1, 2 Irrespective of the model used and the concerned part of the drug development pipeline, the evidence generated from these models, also called digital evidence, might eventually be included in regulatory submissions. In that case, the incorporation of digital evidence needs to follow standards of data/evidence generation, analysis, and reporting to enable the regulatory bodies to efficiently perform an adequate assessment of the submitted material. It is, therefore, of utmost importance that the standards to be considered are commonly acknowledged by all the involved parties (regulators, health technology assessment (HTA) agencies, academia, industry, regulators, and patients) and are relevant for all the types of models that can be included in regulatory submissions. The endorsement of these standards by regulators is particularly valuable because regulators generally provide guidance for data generation and reporting back to sponsors (industry or academia) thereby accelerating the uptake of the standards in the entire community and in the healthcare systems. Specific guidance documents on the reporting, verification, and validation of in silico models (including QSP models) for drug development/approval are, therefore, currently an unmet growing need. One of the prerequisites for the development of such regulatory guidance documents, in addition to some skills and experience from the concerned assessors, is the agreement on standards among relevant aforementioned stakeholders and further described hereafter. Of interest is the standard recently published by the American Society of Mechanical Engineers (ASME) on assessment of credibility of computational modeling through Verification and Validation, applied to medical devices (V&V40).9 The application of this framework to PBPK modeling was published the same year.10 In the current situation, a similar initiative oriented to drug development exceeding PBPK would be of great value. Lessons learned from regulatory guidelines on QSAR and traditional pharmacometric models reflect the general philosophy that model evaluation starts with the regulatory impact assessment closely related to the context of use.5-10 Two important points should be considered: (i) what the impact is of the model prediction on the identification of the appropriate research and development strategy and (ii) what the impact is of the research and development strategy in the regulatory submission. If both impacts are rated as high (e.g., model predictions used to replace a therapeutic study for extension of an indication in children), the requirements regarding overall quality of the model and related data are much more stringent than if both impacts are rated low (e.g., population pharmacokinetic model to describe data from a well-designed phase I PK study). Moreover, for in silico models, good tracking and adequate reporting of knowledge and data sources, analytical and statistical tools, as well as decision criteria to move to the next step/component, or to assess the whole model should also be part of such guidance documents. In view of the currently unmet need for specific guidance and complexity of the task, tackling the validation challenge of the growing amount of digital evidence is, therefore, not something that any stakeholder should be left alone with, be it the regulators, academia, industry, patients, payers, HTA agencies, or healthcare professionals. On April 25 and 26, 2019, a workshop was organized in Belgium gathering regulators, academics, and industry to start working on tackling the validation challenge for in silico models in drug development. This successful meeting clearly showed common interest of the participating stakeholders. In a next phase, started September 2019, the initiative was extended to a larger number of stakeholders from the entire European Union (as detailed below) interested in this transdisciplinary inter-stakeholder project, aiming to provide a roadmap document (White Paper) on standards for assessment of in silico models dedicated to regulatory submission. This White Paper will discuss in detail all the gaps and challenges for in silico models verification and validation as well as the proposed approaches for moving forward illustrated by examples. They act as policymakers regarding drug assessment and need to ensure not only that suboptimal models are not being used for decision making but also that good and innovative models are not disregarded, all in the interest of public health. Adequate standards are, therefore, needed by regulators to make proper and consistent assessments in order to play their roles as both gatekeepers and enablers. Given the rapidly evolving field, the training of regulatory experts is made easier when clear standards and related up-to-date guidance documents are available. HTA agencies, as regulators, need clear standards and related up-to-date guidance documents to ensure a correct assessment of the novel drugs developed with the support of in silico models. One of the main drivers of innovation, academia, is regrettably not visible enough in the current scene of drug development or evaluation, if not under the umbrella of industry (as external consultants) or regulatory agencies (as external experts). By being part of the reflection on adequate standards for in silico modeling and by adopting these rules (and related terminology), it can be expected that the distance between academia and industry/regulators/patients can be narrowed. Furthermore, academia is the main producer of the data and knowledge on which knowledge-based models are built. The quality of this production needs to be improved, as shown by the reproducibility crisis,10 and verified if incorporated in a model. Without hampering innovation and flexibility inherent to academic research, the developed set of guidelines for verification and validation can also be applied to research models published by academia. Altogether, this will increase the robustness and repeatability of the published body of work and will align methodologies among all the stakeholders of tomorrow. Being the current key players for data and related model generation for drug development, it is essential that the industry is involved in the reflection to ensure that the proposed standards are realistic and implementable in practice. The transparency on the criteria and standards on which the produced models would be assessed by the regulator will permit better design and conduct of in-house modeling related activities and ultimately saving time and resources toward marketing of drugs. Having verification and validation guidelines means that in silico models can more readily be used by sponsors, and, per se, this will allow quicker and safer delivery of products to patients. Specifically, for niche populations (pediatrics and rare diseases), in silico might be the only way to obtain sufficient evidence to make rational decisions. In all domains, it should result in less patients enrolled in failed development as well as in successful ones. Despite the unanimous recognition of the added value of the in silico models for drug development, including systems medicine/pharmacology models and clinical trial simulations tools, the availability of specific guidance documents related to these models is currently an unmet growing need. There is an ongoing initiative in the European Union space, bringing together relevant stakeholders (academia, industry, and regulators) to agree on standards for assessment of these in silico models that will be considered as a premise of dedicated regulatory guidelines. A White Paper is planned for early 2020. F.T. Musuamba received funding from European commission: CSA EUSTAND4PM. The authors declared no competing interests for this work.

There is a widely recognized need for patients’ faster access to better and safer medicines. One way to achieve that is by building and qualifying an in silico clinical pharmacology backbone. The obvious challenge is the scientific complexity of the task. What is missing is a consolidated framework to enable progress in this domain. Regulatory agencies need to take a more active role in this direction to make the clinical pharmacology backbone a reality by 2030.

Adopted guidelines reflect a harmonised European approach to a specific scientific issue and should reflect the most recent scientific knowledge. However, whilst EU regulations are mandatory for all member states and EU directives must be followed by national laws in line with the directive, EMA guidelines do not have legal force and alternative approaches may be taken, but these obviously require more justification. This new series of the BJCP, developed in collaboration with the EMA, aims to address this issue by providing an annotated version of some relevant EMA guidelines and regulatory documents by experts. Hopefully, this will help in promoting their diffusion and in opening a forum for discussion with our readers.

Good practices around model-informed drug discovery and development (MID3) aim to improve the implementation, standardization, and acceptance of these approaches within drug development and regulatory review. A survey targeted to clinical pharmacology and pharmacometric colleagues across industry, the US Food and Drug Administration (FDA), and the European Medicines Agency (EMA) was conducted to understand current and future roles of MID3. The documented standards were generally affirmed as a "good match" to current industry practice and regulatory expectations, with some identified gaps that are discussed. All have seen at least a "modest" step forward in MID3 implementation associated with greater organizational awareness and share the expectation for a future wider use and impact. The priority within organizations was identified as a limitation with respect to the future of MID3. Finally, potential solutions, including a global overarching MID3 regulatory guideline, to facilitate greater acceptance by industry and regulatory decision makers are discussed.

This chapter discusses some basic principles of D–E–R characterization and describes how regulatory assessors may use this information to assess the suitability of dose for Phase III, inform the dosing strategy as stated in the Summary of Product Characteristics (SmPC). The analysis of D–E–R data and dose selection for confirmatory trials can be strengthened by longitudinal, model-based analysis. The model-based evaluation facilitates the assessment of cutoff glomerular filtration (GFR) values that require dose adjustment. Longitudinal exposure–response analyses can enable more efficient use of data than cross-sectional analyses. A cross-sectional analysis of dose or exposure versus response is usually performed by nonlinear regression and represents a straightforward way to assess the therapeutic window of a medicinal product. Cross-sectional analyses are sensitive to rate and severity of disease progression, which can impact apparent magnitude of drug effects. Guidelines on investigation of drug interactions emphasize that worst-case scenario with respect to magnitude of drug–drug interactions (DDI) effects should be evaluated.

During the last 10 years the European Medicines Agency (EMA) organized a number of workshops on modeling and simulation, working towards greater integration of modeling and simulation (M&S) in the development and regulatory assessment of medicines. In the 2011 EMA - European Federation of Pharmaceutical Industries and Associations (EFPIA) Workshop on Modelling and Simulation, European regulators agreed to the necessity to build expertise to be able to review M&S data provided by companies in their dossier. This led to the establishment of the EMA Modelling and Simulation Working Group (MSWG). Also, there was agreement reached on the need for harmonization on good M&S practices and for continuing dialog across all parties. The MSWG acknowledges the initiative of the EFPIA Model-Informed Drug Discovery and Development (MID3) group in promoting greater consistency in practice, application, and documentation of M&S and considers the paper is an important contribution towards achieving this objective.

Recently, the EMA concept paper on the need for revision of the “guideline on the clinical development of medicinal products for the treatment of cystic fibrosis” [[1]Concept paper on the need for revision of the guideline on the clinical development of medicinal products for the treatment of cystic fibrosis (CHMP/EWP/9147/08).http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2016/08/WC500211478.pdfGoogle Scholar] was put for public consultation. A revision of the current guideline is deemed necessary due to advances in the understanding of the pathophysiology and in the field of CF therapeutics with a shift from symptomatic treatment towards disease modifying approach. These advances render several elements of the guideline outdated. The clinical trial design, choice of comparator, duration, and endpoints in the existing guideline are no longer adequately covered for all clinical developments. Furthermore, novel treatments offer the prospect of early intervention to prevent the progression of the disease with clinical trials expected to be performed progressively in younger children. Most young children have well preserved lung function and normal anthropometric measurements. Thus, the clinical endpoints suitable for older children and adults would not be adequate. Current and future successes in the management of CF disease may further postpone the clinical manifestations of CF to older age groups, increasing the complexity around endpoints and effect size. A sensitive test of early CF lung disease reliably and reproducibly reflecting severity of lung disease and response to treatment is highly warranted. The review series on efficacy measures for clinical trials starting with reviews on physiologic endpoints [[2]Stanojevic S. Ratjen F. Physiologic endpoints for clinical studies for cystic fibrosis.J Cyst Fibros. 2016; 15: 416-423Abstract Full Text Full Text PDF PubMed Scopus (63) Google Scholar] and biomarkers for cystic fibrosis drug development [[3]Muhlebach M.S. Clancy J.P. Heltshe S.L. Ziady A. Kelley T. Accurso F. et al.Biomarkers for cystic fibrosis drug development.J Cyst Fibros. 2016; 15: 714-723Abstract Full Text Full Text PDF PubMed Scopus (49) Google Scholar] is therefore a timely initiative to summarise current evidence and to stimulate discussions among clinicians, regulators and patients and their families [[4]Flume P.A. VanDevanter D.R. Efficacy measures for clinical trials: a review series.J Cyst Fibros. 2016; 15: 415Abstract Full Text Full Text PDF PubMed Scopus (1) Google Scholar]. The article in this issue “Chest Imaging in CF Studies: What counts, and can be counted” [[5]Szczesniak R. Turkovic L. Andrinopoulou E.R. Tiddens H.A.W.M. Chest imaging in CF studies: what counts, and can be counted.J Cyst Fibros. 2016; ([in this issue])PubMed Google Scholar] provides useful insight into the current state of knowledge as well as important statistical aspects for the utility of imaging markers, i.e. high resolution Chest Tomography (CT) and magnetic resonance imaging (MRI), as outcome measures for clinical studies. While the knowledgebase for the use of MRI is still in its infancy, it may emerge as useful radiation-free technique not only quantifying structural but also functional aspects of the CF lung. At present, CT is considered the most sensitive and accurate imaging modality to detect and monitor structural changes of early CF lung disease, and to be more sensitive and accurate than spirometry in the assessment of disease severity. While other lung function measurements such as the lung clearance index (LCI) have been reported to provide comparable sensitivity and accuracy to CT in the detection of CF lung disease in children older than 2 years of age, LCI may not be sensitive to mild structural abnormalities in infancy [[6]Ramsey K.A. Rosenow T. Turkovic L. Skoric B. Banton G. Adams A.M. et al.Lung clearance index and structural lung disease on computed tomography in early cystic fibrosis.Am J Respir Crit Care Med. 2016; 193: 60-67Crossref PubMed Scopus (129) Google Scholar]. However, before the use of CT scores can be recommended as outcome measures for clinical studies several challenges, particularly in but not restricted to very young children, have to be addressed [[7]Calder A.D. Bush A. Brody A.S. Owens C.M. Scoring of chest CT in children with cystic fibrosis: state of the art.Pediatr Radiol. 2014; 44: 1496-1506Crossref PubMed Scopus (47) Google Scholar]. The use of different CT modalities and scoring systems limits the comparability of CT results between studies. CT protocols, image quality and radiation dose usage differ largely among different CF centres [[8]Kuo W. Kemner-van de Corput M.P. Perez-Rovira A. de Bruijne M. Fajac I. Tiddens H.A. et al.Multicentre chest computed tomography standardisation in children and adolescents with cystic fibrosis: the way forward.Eur Respir J. 2016; 47: 1706-1717Crossref PubMed Scopus (30) Google Scholar]. In the absence of a universally agreed automated scoring system there is a need for either rigorous training of scorers or centralised scoring by highly trained scorers to ensure good reproducibility. Identification of very mild changes is prone to higher measurement bias and poor reproducibility. In addition, at present, the prognostic relevance of very mild bronchial dilatation in very young children is unclear. While bronchiectasis is defined as irreversible bronchial dilatation [[9]Hansell D.M. Bankier A.A. MacMahon H. et al.Fleischner society: glossary of terms for thoracic imaging.Radiology. 2008; 246: 697-722Crossref PubMed Scopus (2705) Google Scholar], mild bronchial dilatation has been reported to be reversible in some infants [[10]Mott L.S. Park J. Murray C.P. et al.Progression of early structural lung disease in young children with cystic fibrosis assessed using CT.Thorax. 2012; 67: 509-516Crossref PubMed Scopus (224) Google Scholar], raising the question, whether such finding should be scored as bronchiectasis. CT resolution may limit evaluation of small airways in infants and very young/small children. Even if standardisation and improvement in CT modalities enable acquisition of reproducible results in children, the clinical case definitions for bronchiectasis and other structural lung disease in children are not well established. The airway-artery ratio cut-off of 1 to define normal versus dilated bronchus might be too high for infants [[11]Thia L.P. Calder A. Stocks J. et al.Is chest CT useful in newborn screened infants with cystic fibrosis at 1 year of age?.Thorax. 2014 Apr; 69: 320-327Crossref PubMed Scopus (51) Google Scholar]. In multicomponent scores like PRAGMA CF [[12]Rosenow T. Oudraad M.C.J. Murray C.P. et al.PRAGMA-CF. a quantitative structural lung disease computed tomography outcome in young children with cystic fibrosis.Am J Respir Crit Care Med. 2015; 191: 1158-1165Crossref PubMed Scopus (161) Google Scholar], the weight of individual score components and their value for predicting long-term outcome is not known. Data on the power of CT to show response to treatment and changes over time are scarce and conflicting [[7]Calder A.D. Bush A. Brody A.S. Owens C.M. Scoring of chest CT in children with cystic fibrosis: state of the art.Pediatr Radiol. 2014; 44: 1496-1506Crossref PubMed Scopus (47) Google Scholar]. The time window for a repeat CT best suited to demonstrate changes in disease severity is unknown. Although radiation dose has been substantially reduced in new CT units, the radiation risk of repeat CT scans, particularly when started in infancy, cannot be disregarded. The review by Szczesniak summarises work currently undertaken in Australia, Europe and the US to address the manifold challenges and gives rise for optimism that CT markers could become a useful and accepted outcome measure reproducibly reflecting severity of lung disease and response to treatment. However, we are not there, yet. Regulatory authorities, beyond their role as gate keepers, have an important mandate as enablers in facilitating development and access to medicines. Many ongoing activities in EMA work towards achieving this objective. Especially the qualification of novel methodologies for medicine development is a platform created to enhance dialogue and data sharing between industry, academia, healthcare professionals, patients/carers and regulators on future plans to generate evidence (qualification advice), or to confirm adequacy of evidence (in the case of a qualification opinion), for the use of an innovative method in drug development. Several proposals for qualification of novel biomarkers or scores have been successfully concluded. A letter of support based on qualification advice may be issued by EMA as an option, when the novel methodology under evaluation cannot yet be qualified but is shown to be promising based on preliminary data [[13]EMA 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=WC0b01ac0580022bb0Google Scholar]. “May you live in interesting times” is purported to be the first of three Chinese curses of increasing severity, the other two being: “May you come to the attention of those in authority” and “May you find what you are looking for.” Let's not understand it as a curse but as an exciting opportunity and clear call to all stakeholders to work closely together and to join efforts in the development of safe, standardised scoring systems with high precision and accuracy that can be used as surrogate outcome measure for clinical studies. In the end the CF community may find what it is looking for, namely better medicines that address the primary defect in CF. The views presented in this correspondence are those of the authors and should not be understood or quoted as being made on behalf of the European Medicines Agency or its scientific Committees.

For a paediatric development to be rationally informed by all available knowledge, it is necessary to systematically collect and learn from available data, expert knowledge and prior developments. Aspects such as drug formulation, bioanalytical methodology, pharmacokinetics/pharmacodynamics (PK/PD), study design and statistics should be considered when defining a development plan. In this context, model informed drug discovery and development (MID3) methodology [1] is likely to prove useful.

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.

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.

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.

Eur J Clin Invest 2012; 42 (9): 1027–1036AbstractWhile large numbers of proteomic biomarkers have been described, they are generally not implemented in medical practice. We have investigated the reasons for this shortcoming, focusing on hurdles downstream of biomarker verification, and describe major obstacles and possible solutions to ease valid biomarker implementation. Some of the problems lie in suboptimal biomarker discovery and validation, especially lack of validated platforms with well‐described performance characteristics to support biomarker qualification. These issues have been acknowledged and are being addressed, raising the hope that valid biomarkers may start accumulating in the foreseeable future. However, successful biomarker discovery and qualification alone does not suffice for successful implementation. Additional challenges include, among others, limited access to appropriate specimens and insufficient funding, the need to validate new biomarker utility in interventional trials, and large communication gaps between the parties involved in implementation. To address this problem, we propose an implementation roadmap. The implementation effort needs to involve a wide variety of stakeholders (clinicians, statisticians, health economists, and representatives of patient groups, health insurance, pharmaceutical companies, biobanks, and regulatory agencies). Knowledgeable panels with adequate representation of all these stakeholders may facilitate biomarker evaluation and guide implementation for the specific context of use. This approach may avoid unwarranted delays or failure to implement potentially useful biomarkers, and may expedite meaningful contributions of the biomarker community to healthcare.