Organoids are innovative three-dimensional and self-organizing cell cultures of various lineages that can be used to study diverse tissues and organs. Human organoids have dramatically increased our understanding of developmental and disease biology. They provide a patient-specific model to study known diseases, with advantages over animal models, and can also provide insights into emerging and future health threats related to climate change, zoonotic infections, environmental pollutants or even microgravity during space exploration. Furthermore, organoids show potential for regenerative cell therapies and organ transplantation. Still, several challenges for broad clinical application remain, including inefficiencies in initiation and expansion, increasing model complexity and difficulties with upscaling clinical-grade cultures and developing more organ-specific human tissue microenvironments. To achieve the full potential of organoid technology, interdisciplinary efforts are needed, integrating advances from biology, bioengineering, computational science, ethics and clinical research. In this Review, we showcase pivotal achievements in epithelial organoid research and technologies and provide an outlook for the future of organoids in advancing human health and medicine.

In 2022, worldwide mpox outbreaks have called attention to mpox virus infection and treatment opportunities using the drugs cidofovir and tecovirimat, which target different stages of in-host viral proliferation, respectively production and shedding. We propose a new model of in-host viral infection dynamics that distinguishes between the two stages, so as to explore the distinct effects of the two drugs, and we analyse the model properties and behaviour. Reducing the model order via timescale separation is shown to lead to the classical target-cell limited model, with a lumped viral proliferation rate depending on both production and shedding. We explicitly introduce the effect of the two drugs and we exemplify how to formulate and solve an optimal control problem that leverages the model dynamics to schedule optimal combined treatments.