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 will initiate data landscaping efforts to identify various data sources that might be informative with respect to disease prevalence, patient selection and identification, disease progression and any data projecting likelihood of patient response to therapy including any genetic data. Such data are often difficult to come by for highly prevalent, mainstream disease populations let alone for the 8000 rare disease that make up the pooled patient population of rare disease patients. The future of rare disease drug development will hopefully rely on increased data sharing and collaboration among the entire rare disease ecosystem. One path to achieving this outcome has been the development of the rare disease cures accelerator, data analytics platform (RDCA-DAP) funded by the US FDA and operationalized by the Critical Path Institute. FDA intentions were clearly focused on improving the quality of rare disease regulatory applications by sponsors seeking to develop treatment options for various rare disease populations. As this initiative moves into its second year of operations it is envisioned that the increased connectivity to new and diverse data streams and tools will result in solutions that benefit the entire rare disease ecosystem and that the platform becomes a Collaboratory for engagement of this ecosystem that also includes patients and caregivers.

(2007). Model-based drug development applied to oncology. Expert Opinion on Drug Discovery: Vol. 2, No. 2, pp. 185-209. doi: 10.1517/17460441.2.2.185

One of the biggest challenges in pediatric drug development is defining a safe and effective dose in pediatric populations, which span across a wide age and development range from neonates to adolescents. Model‐informed drug development approaches are particularly suited to address knowledge gaps including data leveraging to increase the success of pediatric studies. Considering the often limited number of patients available for study and logistic difficulties to collect the necessary data in pediatric populations, the application of pharmacometrics and modeling and simulation techniques can improve clinical trial efficiency, increase the probability of regulatory success, and optimize therapeutic individualization in support of dedicated trials. This review describes the state of pediatric model‐informed drug development to define the right dose for children and provides suggestions for future development.

A course on vaccine development asked students to write a blog addressing general anti‐vaccination strategies and their significance today, in the context of the resistance seen against novel SARS‐CoV‐2 mRNA vaccines. This perspective explores how and why these efforts are successful at reducing vaccine uptake and why, for the most part, efforts to combat the movement have been unsuccessful. This summary of the collective view of the class provides recommendations for combatting current and future campaigns of misinformation. It is hoped that this perspective will serve as a call to action for clinical pharmacologists and translational scientists to do their part to educate the lay community and promote the science in an open and transparent manner to ensure that current and future vaccines fulfill their potential.