TDMx unites attractive state-of-the-art pharmacometric techniques in a single, unique web application to potentially enhance Therapeutic Drug Monitoring.
- "Click-and-feel" user interfaceTDMx was developed using reactive programming with a server-client architecture: TDMx reactively visualises your input such as dosing history, patient covariates or drug measurements. Thereby, TDMx provides quick answers to clinically relevant questions. TDMx does not require any software to be installed on your PC or tablet computer. Simply access TDMx through a generic web browser!
- 'Patient' moduleWith this module, you tell TDMx everything you know about your patient such as patient covariates or susceptibility of the target pathogen. With this basic information you can already use the 'Probabilistic dosing' module of TDMx. Furthermore, you can enter any dosing regimen of your drug of interest along with drug measurements to obtain the individual pharmacokinetic profile based on Bayesian feedback in the 'Bayesian dosing' module.
- 'Probabilistic dosing' moduleTDMx can predict a likely successful a priori dosing regimen solely based on patient covariates and/or pathogen data without requiring drug measurements. This probability of target attainment analysis is potentially useful to guide therapeutic decisions already before initiating treatment or if no drug measurements are available.
- 'Bayesian dosing' moduleIf one or multiple drug measurements are available, TDMx makes most out of them. The mathematical core of TDMx relies on published peer-reviewed population pharmacokinetic models and uses the Bayesian approach to individualise the pharmacokinetics of an individual patient. Bayesian dosing makes use of available knowledge on how drugs behave in the body and is more accurate than conventional regression approaches.
- 'Optimise sampling' module TDMx tells you which sampling time points are most informative to describe the individual pharmacokinetics based on D-optimal design theory. Currently, the optimal design module can predict the most informative sampling time points for PK/PD relationships or even for the full pharmacokinetic profile.