Coding delayed covariate effects

Dear nonmemusers In my PK modeling project, a drug (elimination: 100 % hepatic metabolism) is given orally twice daily over a period of 10 weeks. 100 subjects contribute daily trough concentrations. During this period, covariates vary within subjects. Two covariates that are difficult to handle are changes in corticosteroid regime and changes in cytokine concentrations, which both are expected to alter metabolism (CYP enzymes) at a transcriptional level. This means that a covariate effect on CL is expected to be delayed and/or last for some time (unknown for how long). My first strategy is to code this in the normal way: TVCL=THETA(1) + THETA(2) *(cytokine concentration) or (corticosteroid dose) However, when coding the covariate as continuous with this method, it will not take into account that an effect does still have an impact on the parameter after the covariate value has returned to normal in the data set. Example 1 - Low dose steroids are typically administered daily. Then, high dose methylprednisolone is given I.V. for 3 days, before going back to the low dose. The inductive effect on CYP enzymes from the high dose could be delayed and then last for 1-2 weeks even though the high dose is not maintained. Example 2 - Cytokines marks an inflammation, which could have temporary impact on CL/F. Cytokine concentration returns to normal, but the impact on the parameter could be lasting for some days. Does anyone have experience of a strategy to code these kinds of covariate relationships in NONMEM? Could NONMEM estimate for how long time CL/F might be affected, or is this too much to ask for? :) Additional question: Is there any way to make the visual predictive check an appropriate validation method when only trough concentrations are known over a long period of time, when doses are frequently changing within and between subjects during the period? Thank you all in advance for your kind help :) Elisabet, pharmacy student, University of Bergen