Pharmacometrics-based decision tools facilitate mHealth implementation

ABSTRACT

Introduction: The healthcare system is experiencing a paradigm shift in delivering its services, evolving from a reactive ‘one size-fits-all’ structure to a patient-centric model focusing on individualized medicine. This change is driven by scientific progress, including quantitative evaluation and optimization of treatment strategies through pharmacometric approaches, harnessing the power of the digital revolution.

Areas covered: This review describes four main steps to apply pharmacometrics-based decision support tools, consisting of validated scientific components, available technical options, consideration of regulatory aspects, and achievement of efficient commercialization. Examples of pharmacometrics-based decision tools that support monitoring of patients and individualization of treatment strategies in neonates, children and adults are presented.

Expert commentary: We envision that user-friendly decision support tools will facilitate implementation of mobile health approaches (mHealth) realizing benefits to paediatric and adult patients and their caregivers.

KEYWORDS:

• mHealth
• pharmacometrics
• precision medicine
• computer modeling
• clinical pharmacology
• bench-to-bed-translation
• personalised health
• decision support tools
• digital technology

Acknowledgments

The authors would like to thank Nicola Pharaj, Rudraya Corporation, North Brunswick, USA, and Johannes N. van den Anker, University Children’s Hospital Basel (UKBB), Basel, Switzerland, for their critical review, feedback and expertise in reviewing this manuscript. Nadine Schrutt, UKBB, Basel, Switzerland, is also thanked for her rewarding help in reviewing and editing this manuscript.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Additional information

Funding

This work was partially supported by the NanoTera IsyPeM II project (SNF 20NA21145955), NSERC Industrial Research Chair in pharmacometrics, co-funded by inVentiv Health Clinical, Pfizer and Novartis, and the Canada Foundation for Innovation (#32702).