(1) At present, anyone can bring a legal action for an indefinite period over a posted article. (a.) Too numerous or variable to make a particular enumeration important - said of the parts of a flower, and the like. (a.) Having no determined or certain limits large and unmeasured, though not infinite unlimited as indefinite space the indefinite extension of a straight line. (a.) Not definite not limited, defined, or specified not explicit not determined or fixed upon not precise uncertain vague confused obscure as, an indefinite time, plan, etc. Thus, I made this comment as you mentioned the last sampling time but neither the number of blood draws taken nor the value of the terminal elimination half-life of your compound. Unfortunately, I have seen "mathematical model optimizations" which have not been justified by the number of available data. I would like to mention an additional aspect (besides starting wth the easiest model first) when deciding on the number of compartiments which can be included in a model: Every compartment (elimnination rate) need to be based on a sufficient number of time points (should be at least 3) - and the fraction of the partial AUC in relation to the total AUC should be meaningful for each compartment. Keep also in mind that NCA is usually the method to assess toxicokinetic data and it is often useful to be able to compare parameters over studies based on the same evaluation method. I agree on the approach suggested by Marcus and Jorge performing NCA first. It is a recomendation to check before waht is the best model because the estimated values for PK parameters could be very differents and we want the values more adjusted to real points If we use the best adjustment we find the best estimated parameters. All estimated parameter wuill be different in value than parameters estimated using the compartmental model. As example if the plasma concentration versus time curve had 2 phases (with 2 different slopes in the log adjustment) and at first we assume (without check) that it is non compartmental = only pone solpe, the slope give us the half-live, only 1 half life, when realy the experimental points defined 2 solpes with 2 half lives (alpha and beta). If we check what is the best model to define the PK behaviour we are more proxim to real values. If we don't check if our drug describes or not a compartmental behaiviour we can do more errors in the estimated parameters. Then for the rest of parameters: A pharmacokinetic adjustment to find the best model to define the experimental data helps to estimate better results for PK parameters. I agree, first Cmax and AUC can be calculated without any adjustments. I have uploaded the following video, I hope that helps somehow: 
How does Matlab Simbiology calculate pharmacokinetic (PK) non-compartmental (NCA) area under the curve (AUC)? ()."So just want to know is my study is compartmental or non-compartmental." In case of doubt, apply both, they are complementary non compartmental is easier. " try also simBiology, they have very nice tools.
Non compartmental analysis with winnonlin software#
"Now I have to do the PK analysis using software Kinetica. "I calculated the serum concentration using HPLC." I have no idea what is HPLC, but must be a measuring technique. "I have inject single dose my compound in mice IP and collected serum from 0 to 36 Hr. What may happen is that you can apply non compartmental analysis to estimate the parameters for a compartmental analysis () I have selected some references that might help to understand better.īased on your comments: "Im working on some of the pharmacokinetic parameters of our new drug. It is generally applied when you do not need internal details of your system, dynamics. area under the curve it sets no boundaries on your system, it is not modeling based, rather, analysis based. Non compartmental analysis gives you generic information, e.g.
clearance rate, the best is compartment analysis. The simplest one to know is: if you need to estimate parameters, e.g.
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