INTRODUCTION: Assessment of analgesic drugs includes comparative studies to other analgesics and local anesthesia blockade, number needed to treat estimates and opioid sparing descriptions. An additional methodology is to define the concentration-response relationship using pharmacokinetic/pharmacodynamic (PK/ @entity1726 ) modeling. Areas covered: A concentration-response relationship allows analgesic effect comparison between drugs for different acute @entity158 types. Covariates such as size, age and organ function impact greatly on PK in @entity1 . The cumulative effect of confounding factors (e.g., pharmacogenetics, placebo and changes in baseline @entity158 over time) complicates @entity1726 . Other factors (outcome measures, method of measurement, failure to account for study attrition) impact on outcome. Population PK/ @entity1726 modeling approaches allow us to account for these various factors to some extent. Expert opinion: Nonlinear mixed effects models help interpret analgesic data and their use is increasing. The PK is relatively well understood. The next investigative step will involve investigation into covariate effects for @entity1726 . Mathematical functions for both placebo models and dropout models are well described and should be incorporated into analgesic effectiveness studies that investigate a range of doses. Improvements in @entity158 assessment tools and a greater understanding of pharmacogenomics factors will help individualize analgesic therapy.

Considerations when using pharmacokinetic/pharmacodynamic modeling to determine the effectiveness of simple analgesics in XXXX .

@entity1 :: ('9606', 'Species') :: ['children'] @entity1726 :: ('OMIM:168600', 'Disease') :: ['PD'] @entity158 :: ('MESH:D010146', 'Disease') :: ['pain']
@entity1 :: (9606,Species) :: ['children']
