Dear Shytaj

Most common parameters in any PK study includes T1/2, T1/2(abs), K, Ka, Tmax, Cmax, Vd/F, Cl/F, AUC0-24, AUC 0-∞ and MRT. You can go through the following article for information on PK/PD differences among species.

"Species Differences in Pharmacokinetics and Pharmacodynamics"

http://www.ncbi.nlm.nih.gov/pubmed/20204582

Hope it helps.

Best

Rohitash

You are supposed to have established a concentration-response curve with your drug first. If you want to establish the relationships between drug concentrations and PD endpoints (efficacy, toxicity), standard PK/PD modeling with Emax sounds the best option. The most used appraoch in PD modeling is the Hill equation, which postulates the relationship between drug concentration and drug effect. Basically, E = Emax (C^γ/(C^γ+C50^γ)) with Emax = maximal effect, C50 = drug concentration that results 50% of the maximal effect, and γ = slope parameter that determines the slope of the concentration-response curve. Just seek further details in the appropriate literature - and plenty of softwares do the job next, including as freewares. Depending your data scarcity, you may need to use some population approach to establish the PK/PD too, but this is more complex then. I hope this helps.

I hope you may be aware,that before starting your experiment during study protocols preparation need to decide the hypothesis and statistical methods to be used. This is a GLP followed by many scientists across the world.

Here I understand your concern and question. In general to compare the PK/PD parameters descriptive statistics (mean, SD, %CV) is very much important. To compare between treatments/groups you may need to apply statistical test with confidence interval (95 %CI) to test the statistical significance. Whether to choose T-test or ANOVA depends on the number of groups being tested. There are plenty of literature available to seek further inputs.

I hope this helps you.

I hope I find you well. From the general definition of PK (what the body does to the drug) and PD (what the drug does to the body), I recommend that first look at the amount of data you have to establish whether you have sparse or rich sampling for both PK and PD. If you have sparse sampling, you may consider non-linear mixed effects modeling approach, this will cater for both individual and population PK/PD parameters. The best model will be the most parsimonious one, though it feels like you will discarding important information. The other thing is this for publication or just reporting, say regulatory requirements. The mimimum and important primary PK parameters are ka, CL and Vd while other like t1/2, ke, etc are derived. If you use a transit compartment to cater for delay absorption other than aLag, mean-trannsit time (MTT) and number of hypothetical transit compartment can be required for all your PK parameters, Tmax, Cmax, AUCs, time above "mimimum" threshold also may come into play. For PD I normally use Emax, EC50, Eo (baseline) with variabilities both intra and interindividual being driven by your PK. I hope this helps. Feel free if you need more help!

Regarads

Simba

You should have a look into compartmental PK-PD modelling approaches, which allow you to quantitatively describe the PK-PD relationship in various ways, also allowing to account for delays between concentration and effect. It is probably best to do this in a nonlinear mixed effect modelling framework to allow differentiation between different levels of variability. An overview of different PK-PD models for instance in Pharmacokinetic & Pharmacodynamic Dta Analysis from Gabrielsson Weiner. Good introduction to NLME in Pharmacokinetic-pharmacodynamic modeling and simulation from Peter Bonate. But also various comprehensive review articles can be found that explain these concepts.

The statistical approach is often available within the software package that you use to examine the potential relationship. The major driver will be whether or not your data are normally distributed. If uncertain, then a nonparametric nonlinear modeling program is preferred. Fitting of the data is evaluated based on established quality of fit data. Usually this assessment is then tested statistically, often with correlation matrices. Hope this is helpful. P.S. There are already papers in the scientific literature on this issue that you may want to read as a basis for your approach to analyzing your data.

Thank you for your help!

Plz see this paper, may be interesred

Regards