Running large population PK/PD analyses on laptops and desktops often requires long computational times. This is quite tedious. In addition, when using parallel computing on your machine, it can slow it down for a while, creating further nuisances.
Outsourcing computation to the cloud is a solution to this problem. Among the various cloud providers, Amazon Web Service (AWS) is one of the most famous and used by industries in various fields. AWS elastic compute cloud (EC2) is a service that allows the user to easily create his/her own “machines”, called instances, with a certain hardware and software configuration. It is interesting to note that it is possible to scale up and down those instances whenever the user wants, by choosing the most suitable hardware configuration for a given analysis. Broadly speaking, the user can change the type of CPU, the number of cores (up to 192!) and the amount of RAM according to the need. It is possible to see the vast choice of configurations offered by AWS EC2 here. The pay-per-use pricing model is really interesting, see this link to get an idea.
AWS services are already exploited in pharmacometrics. In 2015, an interesting paper published in CPT:PSP explained how to configure NONMEM on AWS (https://doi.org/10.1002/psp4.12016).
At Systems Forecasting we commonly use R, RStudio and the open source package nlmixr to perform PKPD and other data analyses. In order to speed up our own analyses we recently explored how to set-up nlmixr on AWS EC2. In this presentation by Lorenzo Chiudinelli, Nicola Melillo and Hitesh Mistry we explain, step by step, how to configure R, RStudio and nlmixr on AWS EC2. Feel free to check it out and provide feedback.