Meemansa Sood 1, Karen Sinclair 1, Gregory Pinault 1, Marina Savelieva 1
1 Novartis Pharma Ag (Basel, Switzerland)
Objectives: Drug development in rare diseases and paediatrics is challenging due to limited disease understanding, small sample sizes, single-arm trials and recruitment difficulties, resulting in scarce clinical data that makes robust estimation of efficacy, safety or disease progression from a single study difficult. While modelling and simulation tools such as PKPD and disease progression modelling or Virtual Patient Cohorts can address these evidence gaps, regulatory acceptance remains limited due to uncertainty around data quality and model qualification.
The Horizon EU-funded INVENTS Consortium aims to overcome these challenges by improving model-based extrapolation capability and enhancing regulatory acceptance of approaches such as Virtual Patient Cohorts in orphan and paediatric diseases. This effort relies on access to good-quality, securely anonymised clinical data. To support this, Novartis shared large, high-quality anonymised individual-level data (including PK, biomarkers and clinical endpoints) along with disease area expertise and population PK/PKPD models for two compounds: Fingolimod and Secukinumab. Pooled anonymised datasets were created to support development, testing and validation of new modelling approaches while maintaining high information content. This work describes the anonymisation and data-sharing process, primarily focusing on Fingolimod, providing a foundation for advancing implementation and acceptance of new methodologies with regulatory bodies.
Methods: All data were processed within EU GDPR-compliant secure environments and deployed on controlled high-performance computing platforms accessible to consortium partners. The process ensured datasets met regulatory requirements while retaining sufficient utility for robust modelling, simulation and virtual patient generation. Tailored anonymisation workflows balanced privacy protection with preservation of key clinical, biomarker and PKPD variables.
For Fingolimod, four Phase III clinical trials in patients with relapsing-remitting multiple sclerosis (RRMS)—three in adults and one in paediatrics—were pooled. Variables that were feasibly recognisable, replicable and distinguishable were labelled as identifiers: direct identifiers (items that alone allow identification, e.g. subject ID) or indirect identifiers (items that can identify participants when combined, e.g. race). Variables underwent transformations including randomisation of subject identifiers, suppression of free text, PHUSE-compliant date shifting, generalisation of race, suppression of demographics such as height and ethnicity, and generalisation of MS duration into three-year intervals. Both direct and indirect identifiers were transformed iteratively to reduce re-identification risk while balancing data utility.
Results: The implemented framework enabled consortium members to securely access individual patient-level data from 12 Phase III RCTs containing over 4,000 adult and paediatric patients treated with secukinumab for plaque-type psoriasis, psoriatic arthritis and juvenile idiopathic arthritis, and four Phase III trials for fingolimod comprising over 3,500 adults and over 200 paediatric patients with RRMS.
Certain changes occurred during anonymisation while preserving analytical utility. In one adult RRMS study, mean MS duration changed from 8.18 years (SD 6.6) pre-anonymisation to 6.79 years (SD 6.58) post-anonymisation, with minimal impact on median values. Correlation structures critical for PKPD and disease progression modelling were largely retained; for example, the correlation between MS duration and age decreased only modestly (45% to 41%). In the paediatric study, broader shifts in MS duration were observed reflecting the smaller sample size, but core covariate relationships remained stable (correlation with age shifted from −60% to −70%). Importantly, modelling and simulation results derived from anonymised datasets were consistent with analyses from original data, demonstrating that utility for model-based decision-making was preserved.
Conclusion: This work demonstrates that well-designed, risk-based anonymisation enables advanced quantitative sciences respectful of privacy regulations, and highlights the importance of strong coordination, robust governance and technical infrastructure. It provides a transferable model for future public–private data-sharing initiatives in clinical research. The INVENTS project has implemented a collaborative framework between industrial and academic stakeholders, enabling development of novel clinical trial methodologies in rare and paediatric diseases. The anonymised data was further used to perform credibility evaluation of the PKPD model to support future generation of virtual patient cohorts for paediatric RRMS.
Reference: PAGE 34 (2026) Abstr 12153 [www.page-meeting.org/?abstract=12153]
Poster: Methodology - Other topics