Whitepaper – Data Federation in a Trusted Research Environment. This whitepaper covers the concept of data federation within a trusted research environment by using the example of Aridhia’s work with the PHEMS consortium.

Also covered is the Federated Node, a software component for running federated tasks, which Aridhia recently released on an open source license.

Find out more Consideration of the root causes in candidate attrition during oncology drug development. Cancer remained the second-leading cause of death in the United States in 2020, based on the data from the US Centers for Disease Control and Prevention. The study for the success rate from first-in-human trials to registration for 10 big pharmaceutical companies in the United States and Europe indicated…

Find out more Artificial intelligence opportunities to guide precision dosing strategies. Precision dosing is an approach to use various patient-specific data sources to individualize pharmacotherapy of critical medicines used in the care of disease and other conditions for which drug therapy is recommended. Often the “data” in question refers to therapeutic drug monitoring of drug…

Find out more A modern curriculum for training scientists in model-informed drug development.

Progress report on FDA grant to train regulatory scientists

Under US Food and Drug Administration (FDA) grant (2U18FD005320-06), the Critical Path Institute (C-Path) and experienced private sector partners collaborated with global health organizations to create didactic video materials in an e-learning format on model-informed drug development (MIDD)…

Find out more Implementing precision medicine with the Liver Cancer Collaborative.

A blueprint for delivery using the Aridhia DRE

Liver cancer remains one of the most challenging cancers worldwide, with a high mortality rate due to late diagnosis and limited treatment options. The Liver Cancer Collaborative (LCC) is committed to transforming liver cancer research and treatment through a clinician-led biorepository and…

Find out more Piloting the use of federated analysis to enhance privacy and enable trustworthy access to COVID-19 research data. The use of federated networks can reduce the risk of disclosure for sensitive datasets by removing the requirement to physically transfer data. Federated networks support federated analytics, a type of privacy-enhancing technology (PET) enabling trustworthy data analysis without the movement of data…

Find out more Bringing platform trials closer to reality by enabling with digital research environment (DRE) connectivity. Platform trials are generally regarded as an innovative approach to address clinical valuation of early stage candidates, regardless of modality as the evidence evolves. As a type of randomized clinical trial (RCT) design construct in which multiple interventions are evaluated concurrently against…

Find out more Whitepaper: Assessing the Aridhia DRE against the SATRE specification. This paper scores the Aridhia DRE against the four sections of the SATRE specification, and summarises some of the features that we believe contribute toward that score. A full breakdown of the DRE’s score against the SATRE specification is available in the appendix.

Find out more Data challenges for international health emergencies: lessons learned from ten international COVID-19 driver projects. The COVID-19 pandemic highlighted the importance of international data sharing and access to improve health outcomes for all. The International COVID-19 Data Alliance (ICODA) programme enabled 12 exemplar or driver projects to use existing health-related data to address major research questions…

Find out more Advancing the utilization of real-world data and real-world evidence in clinical pharmacology and translational research — Proceedings from the ASCPT 2023 preconference workshop. Real-world data (RWD) and real-world evidence (RWE) are now being routinely used in epidemiology, clinical practice, and post-approval regulatory decisions. Despite the increasing utility of the methodology and new regulatory guidelines in recent years, there remains a lack of awareness of how…

Find out more An AI Approach to Generating MIDD Assets Across the Drug Development Continuum. Model-informed drug development involves developing and applying exposure-based, biological, and statistical models derived from preclinical and clinical data sources to inform drug development and decision-making. Most data integration and model development approaches are still reliant on…

Find out more Moving towards a Question-centric approach for regulatory decision-making in the context of drug assessment. The most intuitive question for market access for medicinal products is the benefit/risk (B/R) balance. The B/R assessment can conceptually be divided into sub questions related to establishing efficacy and safety. There are additional layers to the B/R ratio for medical products, including questions related to…

Find out more Opportunities for Systems Biology and Quantitative Systems Pharmacology to Address Knowledge Gaps for Drug Development in Pregnancy. Pregnant women are still viewed as therapeutic orphans to the extent that they are avoided as participants in mainstream clinical trials and not considered a priority for targeted drug research despite the fact that many clinical conditions exist during pregnancy for which pharmacotherapy is warranted.

Find out more Supporting Prospective Pregnancy Trials via Modeling and Simulation: Lessons From the Past and Recommendations for the Future. Despite the increasing awareness and guidance to support drug research and development in the pregnant population, there is still a high unmet medical need and off-label use in the pregnant population for mainstream, acute, chronic, rare disease, and vaccination/prophylactic use.

Find out more Digital Research Environment (DRE)-enabled Artificial Intelligence (AI) to facilitate early stage drug development. Early-stage drug discovery is highly dependent upon drug target evaluation, understanding of disease progression and identification of patient characteristics linked to disease progression overlaid upon chemical libraries of potential drug candidates.

Find out more The future of Rare Disease Drug development: the Rare Disease Cures Accelerator Data Analytics Platform. Rare disease drug development is wrought with challenges not the least of which is access to the limited data currently available throughout the rare disease ecosystem where sharing of the available data is not guaranteed. Most pharmaceutical sponsors seeking to develop agents to treat rare diseases…

Find out more Training the next generation of pharmacometric modelers: a multisector perspective. The current demand for pharmacometricians outmatches the supply provided by academic institutions and considerable investments are made to develop the competencies of these scientists on-the-job. Even with the observed increase in academic programs related to pharmacometrics, this need is unlikely to change in the foreseeable future…

Find out more The Precompetitive Space for drug or vaccine development: What does it look like now and what could it look like in the future? The pharmaceutical industry including small and large organizations and biotech as well as other stakeholders in the health arena are increasingly aware of the benefits of working together in the precompetitive phase to address common problems.

Find out more Tachykinin receptors in GtoPdb v.2023.1 Tachykinin receptors (provisional nomenclature as recommended by NC-IUPHAR [91]) are activated by the endogenous peptides substance P (SP), neurokinin A (NKA; previously known as substance K, neurokinin α, neuromedin L), neurokinin B (NKB; previously known as neurokinin β, neuromedin K), neuropeptide K and neuropeptide γ (N-terminally extended forms of neurokinin A).

Find out more Natural History and Real-World Data in Rare Diseases: Applications, Limitations, and Future Perspectives. Rare diseases represent a highly heterogeneous group of disorders with high phenotypic and genotypic diversity within individual conditions. Due to the small numbers of people affected, there are unique challenges in understanding rare diseases and drug development for these conditions, including patient identification and recruitment, trial design, and costs.

Find out more Engaging student opinions on vaccine development innovation: Experiences from a “Shark Tank” project. A graduate course on vaccine development challenged students to propose concepts to improve upon current vaccine development paradigms in the context of a “Shark Tank-style” format .

Find out more Security in the Aridhia DRE.

Five Safes

As the Aridhia Digital Research Environment (DRE) has developed from its early days as an on-premises implementation through to a modern cloud-native service, we have tried to balance two principal requirements from our user community. That is, to meet the functionality…

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