Bringing Model-Based Prediction to Oncology Clinical Practice: A Review of Pharmacometrics Principles and Applications
Buil-Bruna N (1), López-Picazo JM (2), Martín-Algarra S (2), Trocóniz IF (3).
Despite much investment and progress, oncology is still an area with significant unmet medical needs, with new therapies and more effective use of current therapies needed.
The emergent field of pharmacometrics combines principles from pharmacology (pharmacokinetics [PK] and pharmacodynamics [PD]), statistics, and computational modeling to support drug development and optimize the use of already marketed drugs.
Although it has gained a role within drug development, its use in clinical practice remains scarce. The aim of the present study was to review the principal pharmacometric concepts and provide some examples of its use in oncology.
Integrated population PK/PD/disease progression models as part of the pharmacometrics platform provide a powerful tool to predict outcomes so that the right dose can be given to the right patient to maximize drug efficacy and reduce drug toxicity. Population models often can be developed with routinely collected medical record data; therefore, we encourage the application of such models in the clinical setting by generating close collaborations between physicians and pharmacometricians.
IMPLICATIONS FOR PRACTICE:
The present review details how the emerging field of pharmacometrics can integrate medical record data with predictive pharmacological and statistical models of drug response to optimize and individualize therapies.
In order to make this routine practice in the clinic, greater awareness of the potential benefits of the field is required among clinicians, together with closer collaboration between pharmacometricians and clinicians to ensure the requisite data are collected in a suitable format for pharmacometrics analysis.