透视图(图形)
药物开发
重症监护医学
医学
开发(拓扑)
药品
药理学
计算机科学
数学分析
数学
人工智能
作者
Rajesh Krishna,Satyendra Suryawanshi,Juliane Rascher,Sylvia Hartmann,Bernard Sébastien,Jeffrey S. Barrett
摘要
Emerging innovations in pediatric rare diseases are offering up the opportunity to fundamentally change the way therapeutic development in pediatric rare diseases is enabled, largely through the application of model‐informed drug development (MIDD). Pediatric rare diseases, often characterized by small patient populations, patient heterogeneity that is compounded by differences in adult and pediatric diseases, and limited development options, pose significant challenges in drug development. The ICH E11(R1) addendum particularly calls out the value of modeling and simulation and other statistical approaches in extrapolation and filling the gaps in knowledge and/or reducing uncertainties. Therefore, MIDD provides a powerful solution by enabling more efficient, data‐driven decision‐making, reducing the need for large, costly trials while ensuring that clinical endpoints are both relevant and feasible. MIDD approaches have been able to extrapolate the treatment responses from adults to pediatrics, making decisions around the viability of targets and dose selection simpler. In this whitepaper, we build on our previous results by critically examining the role of biomarkers and surrogate endpoints, statistical innovations, and modeling and simulation best practices as they apply to pediatric rare diseases therapeutic development. We posit that the effective integration of digital biomarkers, patient‐reported outcomes, and quality of life methodologies into the development of therapies for pediatric rare diseases will catalyze a significant shift towards more personalized, patient‐centered approaches in this vulnerable population.
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