Clinical Pk Flashcards
what is responsible for the variability in response to drugs between patients
different Cp (plasma concentration of drug)
the variability in drug plasma concentration of drugs is especially important for drugs with (3 words) because____
- narrow therapeutic index
- the dose that is effective is very close to the dose at which toxic effects occur, i.e. ratio of the minimum toxic plasma level to the minimum effective dose is very small
- little room for error
how are drugs with narrow therapeutic index managed medically
through drug plasma level monitoring
name 4 classes of drugs for which plasma level monitoring is common, provide examples for each class
- Antiarrhythmic Drugs
- e.g. Procainamide
- Antibiotics
- Vancomycin
- Anticonvulsants
- phenytoin
- Psychotropics
- lithium
what are the 2 key parameters that need to be known to determine the mean steady state plasma level (Cpss)
- volume of distribution (V)
- elimination rate constant (K)
what is this equation and what do each of the variables stand for
Cpss = FD/tau x Cl = FD/(tau x K x V) = FD t1/2/ (tau x 0.693 x V) where
- Drug plasma concentration at steady state
- F = bioavailability
- D = dose of drug administerd
- tau = dosage interval
- Cl = clearance
- V = volume of distribution
- K = loss rate constant
Dose adjustment may be warranted when the following physiological factors exist (3)
- weight issues
- chronic heart failure
- renal or liver issues
renal function is assessed or measured by
creatinine clearance (Clcr)
creatinine is eliminated almost exclusively by
glomerular filtraton
what will impairment of glomerular filtration result in
- decrease in excretion rate of creatinine,
- i.e. decrease in Clcr, and serum creatinine concentration rises
although creatinine clearance can be calculated, this biomarker is most often used to measure renal function
serum creatinine
Serum creatinine is inversely related or directly proportional to glomerular function?
Describe the equation
- inversely related
- Clcr = k0/SCC where
- SCC = serum creatinine
- ko is the constant rate of production of creatinine in the body
as it relates to the equation below, when renal or kidney function decline, serum creatinine decreases or increases
Clcr = Ko/Scc
where Scc = serum creatining
Ko = rate constant - production of creatinine in the body
decreases because serum creatinine levels are inversely proportional to glomerular filtration
these tools used by physicians (what are these?) relate creatinine clearance to serum creatinine, gender, body weight and height are are used to
- nomograms
- calculate the dose for the patient based upon the estimate of creatinine clearance from serum creatinine levels
biomarkers for metabolic (liver) function are readily available (true/false)
false
when liver function is reduced, drug metabolism is severly or mildly impacted
mildly
when studies are conducted in normal healthy volunteers, what are the issues with dosing and dosing regimen as it relates to use in real life. Why?
- the study doesn’t mimic the pt population that will use the drug i.e. healthy volunteers compared with disease state patients
- limited ethnicities or underrepresentation of gender
- comorbid conditions and con-meds excluded
- well controlled parameters for trials
Population pharmacokinetics looks at
ways to obtain more representative estimates of pK parameters in different pt populations
what are the three clinical study phases, what do they typically assess
- Phase I studies
- small study 12-100 subjects
- healthy volunteers, intensive sampling, calculate mean values
- safety study
- sub therapeutic dose but with dose ranging/ascending dose
- Phase II
- 100 - 300 subjets
- disease state
- safety and efficacy at therapeutic dose
- Phase III
- large study
- disease state, less sampling
- efficacy, effectiveness, safety
- estimate population parameters
What are 3 statistical approaches to estimation of pK parameters
- Conventional
- non-compartmental analysis
- model fitting by non-linear regression
- Multiple regression analysis
- non-linear mixed effect modelling
the conventional approach to estimation of pK parameters determines population parameters by
- intermediate estimation of parameters in each member of a group of individuals
what are common pk parameters measured or analyzed via the conventional approach
- mean and std deviation for each parameter
- e.g. gender, ethnic group, responders/non-responders
- Clearance (Cl); Volume of distribution (Vd); Plasma concentration (Cp)
how are pk parameters estimated in the conventional approach (2 statistical methods)
- non-compartmental analysis
- model fitting using non-linear least squares regression analysis
this statistical method does not require a specific model
non-compartmental analysis
what pK parameters is noncomparmental analysis based upon (4)
- AUC
- Cp vs time
- slope
- terminal beta phase
what 4 pK parameters are calculated in noncompartmental analysis
- AUC
- Vd
- Cl
- halflife
True/False:
Model fitting fits the selected pK model to pk response data (Cp and t) and attempts to provide parameter estimates that minimize differences between observed and predicted values
True
Error or variability in model fitting is a result of (3)
- random error relating to independent variables (time) e.g. errors in recording of drug administration time
- random errors related to dependent variables (Cp) e.g. assay measurement
- intraindividual variation
to account for errors and avoid bias the model is linked to a ____________model
variance
Nonlinear least squares regression analysis is an iterative process that modifies parameters until criterial for good fit are met. This is done by fitting non linear data to
- differential and integrated equations (NonLin)
- extended least squares method (Elsfit)
Multiple regression analysis using SAS or SPSS do this
determines the impact of demographic, phsiological and pathological factors on pK
SPSS multiple regression analysis assesses
- the relationship b/t pk parameters (Vd, Cl) and predictors (age, sex etc)
- calculates contribution of each predictor to the pK parameter
- tests signficance
SAS allows development of
- predictive models for pk parameters
- considers all regressions and generates model with highest R2
- uses predicted sum of squares for model selection
- max adjusted R2 models compared with othe models
- model with minimum predicted sum of squares with fewest independent variables chosen
the model that fits a pk model to all data obtained from pooled pts and includes randomly treated errors as well as unusual errors in analysis is called:
what is another name by which it is known as
- Non-linear fixed effects modeling (NonMem)
- First order method
what is an advantage and disadvantage of the first order method
- advantage - do not need a lot of pk data
- disadvantage - need large number of subjects
what are common sources of error that cause biased estimates in population pK parameters
- Fixed Effects
- pt characteristics (age, weight, height, gender)
- underlying pathology (comorbid conditions)
- concomitant therapy, alcohol intake, smoking
- Random Effects
- amount of pK variability
- interindividual variation (n)
- intraindividual variation (assay error)
- amount of pK variability
NonMEM analyzes poole data and deals with the concomitant effects on parameter estimation through determination of (3)
- mean values of pk parameters
- quantiative relationship to subjects
- variability across population
