Towards a virtual research environment for paediatric endocrinology across Europe

Paediatric endocrinology is a medical specialty dealing with variations of physical growth and sexual development in childhood. Genetic anomalies that can cause disorders of sexual development in children are rare. Given this, sharing and collaboration on the small number of cases that occur is needed by clinical experts in the field. The EU-funded EuroDSD project (www.eurodsd.eu) is one such collaboration involving clinical centres and clinical and genetic experts across Europe. Through the establishment of a virtual research environment (VRE) supporting sharing of data and a variety of clinical and bioinformatics analysis tools, EuroDSD aims to provide a research infrastructure for research into disorders of sex development. Security, ethics and information governance are at the heart of this infrastructure. This paper describes the infrastructure that is being built and the inherent challenges in security, availability and dependability that must be overcome for the enterprise to succeed

The current landscape of European registries for rare endocrine conditions

Objective To identify cross-border international registries for rare endocrine conditions that are led from Europe and to understand the extent of engagement with these registries within a network of reference centres (RCs) for rare endocrine conditions. Methods Database search of international registries and a survey of RCs in the European Reference Network for rare endocrine conditions (Endo-ERN) with an overall response rate of 82%. Results Of the 42 conditions with orphacodes currently covered within Endo-ERN, international registries exist for 32 (76%). Of 27 registries identified in the Orphanet and RD-Connect databases, Endo-ERN RCs were aware of 11 (41%). Of 21 registries identified by the RC, RD-Connect and Orphanet did not have a record of 10 (48%). Of the 29 glucose RCs, the awareness and participation rate in an international registry was highest for rare diabetes at 75 and 56% respectively. Of the 37 sex development RCs, the corresponding rates were highest for disorders of sex development at 70 and 52%. Of the 33 adrenal RCs, the rates were highest for adrenocortical tumours at 68 and 43%. Of the 43 pituitary RCs, the rates were highest for pituitary adenomas at 43 and 29%. Of the 31 genetic tumour RCs, the rates were highest for MEN1 at 26 and 9%. For the remaining conditions, awareness and participation in registries was less than 25%. Conclusion Although there is a need to develop new registries for rare endocrine conditions, there is a more immediate need to improve the awareness and participation in existing registries.This publication is part of the project ‘777215/EuRRECa’ which has received funding from the European Union’s Health Programme (2014–2020)

The role of international databases in understanding the aetiology and consequences of differences/disorders of sex development

The International Disorders of Sex Development (I-DSD) and International Congenital AdrenalHyperplasia registry (I-CAH) Registries were originally developed over 10 years ago and have since supported several strands of research and led to approximately 20 peer-reviewed publications. In addition to acting as an indispensable tool for monitoring clinical and patient-centered outcomes for improving clinical practice, the registries can support a wide nature of primary and secondary research and can also act as a platform for pharmacovigilance, given their ability to collect real world patient data within a secure, ethics approved virtual research environment. The challenge for the future is to ensure that the research community continues to use the registries to improve our understanding of Disorders of Sex Development (DSD)

Data privacy by design: digital infrastructures for clinical collaborations

The clinical sciences have arguably the most stringent security demands on the adoption and roll-out of collaborative e-Infrastructure solutions such as those based upon Grid-based middleware. Experiences from the Medical Research Council (MRC) funded Virtual Organisations for Trials and Epidemiological Studies (VOTES) project and numerous other real world security driven projects at the UK e-Science National e-Science Centre (NeSC – www.nesc.ac.uk) have shown that whilst advanced Grid security and middleware solutions now offer capabilities to address many of the distributed data and security challenges in the clinical domain, the real clinical world as typified by organizations such as the National Health Service (NHS) in the UK are extremely wary of adoption of such technologies: firewalls; ethics; information governance, software validation, and the actual realities of existing infrastructures need to be considered from the outset. Based on these experiences we present a novel data linkage and anonymisation infrastructure that has been developed with close co-operation of the various stakeholders in the clinical domain (including the NHS) that addresses their concerns and satisfies the needs of the academic clinical research community. We demonstrate the implementation of this infrastructure through a representative clinical study on chronic diseases in Scotland

Development of a Composite Health Index in Children with Cystic Fibrosis: A Pipeline for Data Processing, Machine Learning, and Model Implementation using Electronic Health Records

Cystic Fibrosis (CF) is a heterogeneous multi-faceted genetic condition that primarily affects the lungs and digestive system. For children and young people living with CF, timely management is necessary to prevent the establishment of severe disease. Modern data capture through electronic health records (EHR) have created an opportunity to use machine learning algorithms to classify subgroups of disease to understand health status and prognosis. The overall aim of this thesis was to develop a composite health index in children with CF. An iterative approach to unsupervised cluster analysis was developed to identify homogeneous clusters of children with CF in a pre-existing encounter-based CF database from Toronto Canada. An external validation of the model was carried out in a historical CF dataset from Great Ormond Street Hospital (GOSH) in London UK. The clusters were also re-created and validated using EHR data from GOSH when it first became accessible in 2021. The interpretability and sensitivity of the GOSH EHR model was explored. Lastly, a scoping review was carried out to investigate common barriers to implementation of prognostic machine learning algorithms in paediatric respiratory care. A cluster model was identified that detailed four clusters associated with time to future hospitalisation, pulmonary exacerbation, and lung function. The clusters were also associated with different disease related variables such as comorbidities, anthropometrics, microbiology infections, and treatment history. An app was developed to display individualised cluster assignment, which will be a useful way to interpret the cluster model clinically. The review of prognostic machine learning algorithms identified a lack of reproducibility and validations as the major limitation to model reporting that impair clinical translation. EHR systems facilitate point-of-care access of individualised data and integrated machine learning models. However, there is a gap in translation to clinical implementation of machine learning models. With appropriate regulatory frameworks the health index developed for children with CF could be implemented in CF care

Obesity and COVID-19: The Two Sides of the Coin

The World Health Organization declared COVID-19, the infectious disease caused by the coronavirus SARS-CoV-2, a pandemic on March 12, 2020. COVID-19 is causing massive health problems and economic suffering around the world. The European Association for the Study of Obesity (EASO) promptly recognised the impact that the outbreak could have on people with obesity. On one side, emerging data suggest that obesity represents a risk factor for a more serious and complicated course of COVID-19 in adults. On the other side, the health emergency caused by the outbreak diverts attention from the prevention and care of non-communicable chronic diseases to communicable diseases. This might be particularly true for obesity, a chronic and relapsing disease frequently neglected and linked to significant bias and stigmatization. The Obesity Management Task Force (OMTF) of EASO contributes in this paper to highlighting the key aspects of these two sides of the coin and suggests some specific actions

Learning from Conect4children: A Collaborative Approach towards Standardization of Disease-Specific Paediatric Research Data

The conect4children (c4c) initiative was established to facilitate the development of new drugs and other therapies for paediatric patients. It is widely recognized that there are not enough medicines tested in all relevant ages of the paediatric population. To overcome this, it is imperative that clinical data from different sources are interoperable and can be pooled for larger post-hoc studies. c4c has collaborated with the Clinical Data Interchange Standards Consortium (CDISC) to develop the cross-cutting data resources that build on existing CDISC standards, in an effort to standardize paediatric data. The natural next step was an extension to disease-specific data items. c4c brought together several existing initiatives and resources relevant to disease-specific data and to analyse their use for standardizing disease-specific data in clinical trials. Several case studies that combined disease-specific data from multiple trials have demonstrated the need for disease-specific data standardization. We identified three relevant initiatives. These include European Reference Networks, European Joint Programme on Rare Diseases, and Pistoia Alliance. Other resources reviewed were: National Cancer Institute Enterprise Vocabulary Services, CDISC standards, pharmaceutical company-specific data dictionaries, Human Phenotype Ontology, Phenopackets, Unified Registry for Inherited Metabolic Disorders, Orphacodes, Rare Disease Cures Accelerator-Data and Analytics Platform (RDCA-DAP) and Observational Medical Outcomes Partnership. The collaborative partners associated with these resources were also reviewed briefly. A plan of action focussed on collaboration was generated for standardizing disease-specific paediatric clinical trial data. A paediatric data standards multistakeholder and multi-project user group was established to guide the remaining actions– FAIRification of metadata, a Phenopackets pilot with RDCA-DAP, applying Orphacodes to case report forms of clinical trials, introducing CDISC standards into European Reference Networks, testing of the CDISC Pediatric User Guide using data from the mentioned resources and organization of further workshops and educational materials

Optimisation of congenital adrenal hyperplasia therapy in paediatric and foetal populations by leveraging pharmacometrics

Congenital adrenal hyperplasia (CAH) is a rare form of adrenal insufficiency causing deficiency of the highly regulated hormone cortisol and accumulation of its precursors such as 17α-hydroxyprogesterone (17-OHP) and subsequent androgen overproduction. Symptoms associated with CAH are premature pseudo puberty, earlier ending of longitudinal growth, and in female patients, virilisation and hirsutism. CAH patients require life-long cortisol replacement therapy, and dose optimisation through therapy monitoring is crucial to avoid potentially serious adverse events due to cortisol over- or underexposure. Paediatric CAH patients receive hydrocortisone (HC, synthetic cortisol) for cortisol replacement due to its lower risk for adverse effects whereas adult patients receive more potent glucocorticoids, e.g., dexamethasone (Dex). Especially in paediatrics, dried blood spot (DBS) sampling represents a highly advantageous alternative to plasma sampling. The major advantages include minimal invasiveness, low required blood volumes, stability of the analyte and easy storage of the matrix. Thus, DBS sampling has a high potential for facilitating CAH therapy monitoring routine. However, target concentrations of CAH biomarkers such as 17-OHP indicating a successful cortisol replacement are still unknown in DBS. To prevent in utero virilisation of female foetuses with CAH, prenatal therapy with Dex, administered to the pregnant women, has been conducted for decades. Yet, prenatal CAH therapy is still considered experimental since the traditionally administered Dex dose of 20 μg/kg/day is not based on a scientific rationale and is assumed to be too high, causing potential harm to the mother and foetus. In this regard, quantitative approaches such as pharmacometric modelling and simulation are powerful tools to provide a better understanding on pharmacokinetic (PK) and pharmacodynamic (PD) processes and to contribute to the optimisation of drug therapies. This work aimed at paving the way towards an optimised CAH therapy in paediatric and foetal populations by (1) providing insights into the quantitative relationship between cortisol concentrations measured in plasma and in DBS, (2) identifying paediatric target DBS concentrations for the commonly used biomarker 17-OHP and (3) suggesting a rational Dex dose in prenatal CAH therapy. To quantitatively link plasma and DBS cortisol concentrations, a semi-mechanistic nonlinear mixed-effects (NLME) PK model was developed based on data from paediatric CAH patients. The model characterised a nonlinear relationship between cortisol in plasma and DBS with plasma/DBS concentration ratios decreasing from approximately 8 to 2 with increasing DBS cortisol concentrations up to 800 nmol/L. These ratios decreased due to saturation of cortisol binding to corticosteroid-binding globulin and thus higher cortisol fraction associated with red blood cells. In future, more data from neonates and infants can be used to investigate a possible age effect, on the nonlinearity between plasma and DBS cortisol, in addition to the observed concentration effect. For the first time, a target morning DBS 17-OHP concentration range was determined for monitoring paediatric CAH patients. The DBS target range of 2.1-8.3 nmol/L was derived from simulations by applying a developed PK/PD model linking cortisol in plasma to 17-OHP in DBS and by leveraging healthy paediatric cortisol profiles. By extending the PK/PD model, and using the same simulation approach, circadian target concentration profiles, providing DBS biomarker targets for any time of the day, can be derived in future. Furthermore, in Bland-Altman and Passing-Bablok analyses, it was shown that capillary and venous DBS concentrations, which are both commonly obtained in clinical practice, are comparable to each other for cortisol and 17-OHP in paediatric CAH patients. For determining a reduced Dex dose which simultaneously decreases the risk for adverse events in prenatal CAH therapy and still shows sufficient efficacy in the foetus, a target Dex concentration range was identified from literature and a NLME model describing maternal Dex PK was developed. The Dex PK model was used to simulate maternal Dex concentration-time profiles following traditional or reduced Dex doses and to evaluate the tested dosing regimens with regard to the lowest effective dose. Based on the simulation results, a Dex dose of 7.5 μg/kg/day was suggested as a rational dose for prenatal CAH therapy, representing approximately a third of the traditional Dex dose. The suggested rational Dex dose should be evaluated in future clinical trials. In summary, this work provides quantitative insights into DBS measurements for CAH therapy monitoring, presents first target DBS concentrations for the biomarker 17-OHP in paediatrics, and suggests a first model-based dose rationale for Dex in prenatal CAH therapy. Ultimately, this work can help to improve CAH treatment with HC and Dex and therapy monitoring in the highly vulnerable paediatric and foetal populations