Papers & Publications
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. Even when clinical trials are conducted in pregnant women, they are often underpowered and absent biomarkers and exclude evaluation across multiple stages of pregnancy where relevant development risk could have been assessed.
Quantitative systems pharmacology model development has been proposed as one solution to fill knowledge gaps, make earlier and perhaps more informed risk assessment, and design more informative trials with better recommendations for biomarker and end point selection including design and sample size optimality. Opportunities exist for further advances in quantitative systems pharmacology model development with the inclusion of real-world data sources and complimentary artificial intelligence/machine learning approaches.
The successful coordination of the approach reliant on these new data sources will require commitments to share data and a diverse multidisciplinary group that seeks to develop open science models that benefit the entire research community, ensuring that such models can be used with high fidelity. New data opportunities and computational resources are highlighted in an effort to project how these efforts can move forward.
Data sharing is a core component of the Aridhia DRE and is the backbone of several highly successful multi-institutional collaborations including the RDCA-DAP and neonatal DAP managed by the Critical Path Institute (CPATH) and the International Neonatal Consortium (INC) respectively. Several current ”works in progress” seek to establish data sharing platforms in other therapeutic areas and we have added QSP analytical capabilities to the workspace environment making possible the forward thinking alluded to in this paper.
You can find the full text for the publication below.
Barrett JS, Azer K
J Clin Pharmacol. 2023 Jun;63 Suppl 1:S96-S105
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