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Discovery and Insights in Health Research

Papers & Publications

Here at Aridhia, we feel it’s important to maintain a high level of thought leadership in our field, providing insight and guidance to the health science domain in order to better serve the interests of our customers and, ultimately, patients.

Below you will find published works by members of our team on a variety of relevant topics.

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…

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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…

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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.

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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.

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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.

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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…

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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…

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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.

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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).

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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.

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