Midazolam Sedation Models: Why a Medication Association Does Not Prove Antagonism
A medication can be associated with a different drug response because of the condition it was prescribed to treat. That possibility matters when interpreting a concentration-response model as evidence of a pharmacodynamic interaction.
Franken and colleagues examined this problem in a study of midazolam sedation in terminally ill adults. Their population model described probabilities of observed sedation categories, with substantial variation between patients.
Repeated scores from 43 patients
The analysis used 941 Ramsay sedation observations from 43 patients. These were repeated observations within individuals, not 941 independent patients. The investigators linked the scores to individual concentration profiles predicted using a previously developed pharmacokinetic model from the same study population.
The Ramsay scale describes states ranging from agitation to absence of a response to specified stimuli. Because relatively few observations fell in categories 3 and 4, the investigators combined categories 3, 4 and 5 for modeling.
The model estimated probabilities of sedation categories at a given modeled concentration. A population probability does not determine one person’s observed state with certainty.
The haloperidol association
Concomitant haloperidol use was associated with a greater probability of lower Ramsay scores, corresponding to a less sedated state. It was the only tested covariate retained after the model’s selection procedure.
That does not establish a direct antagonistic drug interaction. Haloperidol was used to treat agitation or delirium, and patients with those conditions may be more difficult to sedate. The clinical reason for prescribing the medication could therefore explain part of the association.
The study was not a randomized comparison of adding haloperidol versus withholding it under otherwise identical conditions. Its findings cannot isolate a causal effect of haloperidol from the characteristics of patients who received it.
A missing model effect does not establish biological inactivity
Adding the two major midazolam metabolites did not improve the model. The authors discussed reasons why their effects could be difficult to separate from the parent drug’s effects, including correlated concentration profiles and limits of the sedation scale.
The appropriate conclusion concerns what this dataset and model could resolve. It is not proof that the metabolites have no sedative activity or that they cannot matter in a different clinical setting.
What the measurement schedule could not resolve
The authors noted that the Ramsay scale had not been validated for terminally ill patients. Assessments occurred at selected times, and few observations were available shortly after dosing. They could not estimate a separate effect-compartment rate constant to characterize delay between concentration and response.
The grouped sedation categories also limit how finely the analysis can distinguish intermediate states. Observed sedation depth is not identical to patient comfort, the clinical purpose of palliative sedation.
Applying the model carefully
The study supports describing variable concentration-response relationships in its palliative-care population. It does not establish a universal sedative concentration threshold, prove medication antagonism or provide an individual dosing rule.
A sound interpretation preserves the measured outcome, repeated-observation structure and possibility of confounding before turning an association into a mechanism.
Source: Franken LG and colleagues. Population pharmacodynamic modelling of midazolam induced sedation in terminally ill adult patients. British Journal of Clinical Pharmacology. 2018;84:320-330. PubMed record.
