Re: Visual predictive check!

Dear Nick, I do not claim to have much real life experience with HPLC or LC-MS/MS either. However, I think both these methods can yield negative concentrations and both have some measurement noise. As far as I know, for HPLC-UV and HPLC-Fluorescence methods photons are converted into an electronic signal that then becomes the chromatogram. For LC-MS/MS, ions of a specific mass/charge ratio hit a photomultiplier tube and thereby create an electric signal. Some MS have a resolution in the mass/charge ratio of 10^(-5) atomic units. All those devices that provide an electric signal will most likely have some level of random background noise. I think the most likely reason to measure negative concentrations might not be a negative area or negative peak height in a chromatogram. Negative conc. might more likely come from the regression equation of the calibration row itself: Drug_conc = intercept + (area_drug / area_IS) * slope The area denotes the area of the drug of interest and of the internal standard (IS) in the chromatogram. An IS is often spiked to all samples at a known concentration. If you have a chromatogram without noise, then area_drug will be zero and area_IS will be a large number (otherwise e.g. the HPLC-injection went wrong and the sample is likely to be correctly discarded). A truly blank sample will then have a negative concentration, if the intercept is negative. As usually not all samples are measured in one analytical run, one will have several regression equations in a clinical trial with random intercepts which may be negative and should be centered around zero. Hope we will see more of these negative numbers in our datasets (-: Best wishes Juergen ----------------------------------------------- Jurgen Bulitta, PhD, Post-doctoral Fellow ID - Pharmacometrics, University at Buffalo, NY, USA Phone: +1 716 645 2855 ext. 281, [EMAIL PROTECTED] Fax: +1 716 645 3693 ----------------------------------------------- -----Ursprüngliche Nachricht----- Von: "Nick Holford" <[EMAIL PROTECTED]> Gesendet: 23.05.08 17:54:26 An: [email protected] Betreff: Re: [NMusers] Visual predictive check! Ken, First of all -- I have almost no real world experience of modern analytical laboratory methods. But I have seen chromatograms from HPLC machines which have baseline noise. One way to quantitate the sample is to integrate over an interval at the expected retention time after a true zero specimen had passed through the system then the resulting area could be either positive or negative. Another method would be to search for a positive peak around the expected retention time and center the integration around that peak -- this would of course lead to a positive bias. So if the first (potentially unnbiased) method is used for a series of pre-dose concs then the resulting distribution should have both negative and positive values. Whether it was symmetrical or even normal would depend on the factors that cause the baseline noise. I suspect that commonly used methods today rely on software that will have a truncation bias built into it (e.g. using the second method) even before the LLOQ bias is added. I have even less experience of mass spectroscopy methods - my naive understanding is that the mass lines are measured within one atomic weight unit of resolution so it is unlikely even for true zero samples that a negative mass would be obtained. So for mass spec assays the assumption that measurements are non-negative may be true. Best wishes, Nick Ken Kowalski wrote: > Nick, > > Yes, I'm making the assumption that a measured concentration cannot be > negative. Educate me about chemical assays. Can you get troughs rather > than peaks in a chromatogram such that the area below zero is integrated and > reported as a negative concentration? If so, what would happen if you > assayed a bunch of pre-dose samples (before drug is administered) where the > true mean concentration is zero? Would we get measured concentrations > symmetrically distributed about zero (with about 50% of the measured > concentrations reported as negative and 50% positive)? If so, then a normal > residual error model may indeed be appropriate. > > Ken >