Systems pharmacology-based optimization of postoperative morphine treatment
Previous research has found important inter-individual differences in the pharmacokinetics (PK) of morphine in special populations such as children, the morbidly obese or the critically ill.
- Catherijne Knibbe
As a result, different doses are required to achieve comparable drug exposure as in the reference adult population. With population PK models, these pharmacokinetic differences have been quantified. Using these models and patient’s characteristics like weight, age, organ function and other covariates, the morphine treatment can be personalized to reach a certain target concentration of morphine.
To optimally use PK models to guide morphine dosing, we have to know which morphine concentration to target. This target concentration could be different for different patient populations. It’s also important to quantify the pharmacodynamic (PD) differences between patients of the same population, and within the same patient. For example, the morphine concentration required for adequate analgesia might change over time in the postoperative period.
We aim to find clinically relevant covariates to explain part of the pharmacodynamic variability of morphine. These would allow us to personalize the dose further, adding to the advances achieved with the population PK models. For postoperative pain, we might find these predictive covariates in patient characteristics (age, gender, type of surgery, disease history). Such covariates would be readily available in the clinic. This would make it easier to use them for morphine dose personalization if they are found to be relevant covariates for morphine pharmacodynamics.
However, demographics and treatment characteristics might not provide enough information on the individual differences in the nociception and the response to drug treatment. In this project we also aim to use metabolomic profiles to find biomarkers for morphine requirements. While this approach would certainly face practical difficulties (invasive sampling, analytical turnaround time), it might give us more insight in individual variability than patient characteristics could provide.
This project is carried out in collaboration with the department of Analytical BioSciences and the Erasmus MC-Sophia Children’s hospital in Rotterdam.