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Use of Linear Retention Indices in GC-MS Libraries for Essential Oil Analysis
Published in K. Hüsnü Can Başer, Gerhard Buchbauer, Handbook of Essential Oils, 2020
Emanuela Trovato, Giuseppe Micalizzi, Paola Dugo, Margita Utczás, Luigi Mondello
The magnitude of retention depends on the partition coefficient, or distribution constant (K), which is defined as the ratio of the equilibrium concentrations of a solute in the stationary (CS) and mobile phases (Cm) during partitioning in the column:
Use of Covariates in Randomization and Analysis of Clinical Trials
Published in John Crowley, Antje Hoering, Handbook of Statisticsin Clinical Oncology, 2012
Garnet L. Anderson, Michael LeBlanc, P.Y. Liu, John J. Crowley
For each hypothetical trial, the basic setting was a two-arm trial with 400 patients, 200 per arm. The underlying survival models were derived from Cox models assuming the existence of up to three binary covariates using the hazard models where h0(t) is the hazard function from the exponential distribution for models A and B. For all models, x = 0, 1 represents the randomization assignment and z is the vector of binary covariates that jointly define membership into eight strata. For model C, with nonproportional covariate effects, the eight baseline hazard functions h0(t; z1,z2,z3) were generated from Weibull (λj, κj) distribution functions where λj and κj are the scale and shape parameters for stratum j, j = 1,…, 8. The Weibull family of distributions was chosen because of the degree of flexibility it allows in describing nonproportional hazard functions. Values of (λj,κj) used were (0.2, 0.7), (0.2, 0.8), (0.6, 1), (0.1, 1.2), (0.2, 1.5), (0.5, 2), and (0.2, 3). Note that for κj = 1, the Weibull model reduces to the exponential (λj) distribution (constant hazard), and all covariate effects are proportional. When κ > 1 (κ < 1), the baseline hazard functions are decreasing (increasing) with time. Hazard functions associated with a covariate will be nonproportional when values of κj differ across levels of the covariates. Moderate covariate effects were defined as α1 = ln(0.33), α2 = ln(1.5), and α3 = ln(2.0). Larger covariate effects used hazard ratios of α1 = ln(0.2), α2 = ln(3), and α3 = ln(4). To examine the setting of highly stratified allocation, model B was expanded to include five independent binary covariates (32 strata) with coefficients in the data generating model of ln(0.33), ln(1.5), ln(2), ln (0.67), and ln(1.5).
Square root law model for the delivery and intestinal absorption of drugs: a case of hydrophilic captopril
Published in Drug Delivery, 2021
Valentina Anuta, Constantin Mircioiu, Victor Voicu, Ion Mircioiu, Roxana Sandulovici
The calculated elimination constant differed for the two models. The constant that resulted from the monocompartmental model was close to the ‘distribution constant’ calculated from analysis of the regression line fitting the data on the tail of the plasma level curve, as presented above. In fact, a second compartment, following the hydrophilicity of CPT is not the lipid compartment, but rather disulfide conjugation products (Savu et al., 2016). The final elimination constant associated with small amounts of residual CPT could be connected with the partial reversibility of conjugation reactions.
Time matters – in vitro cellular disposition kinetics help rationalizing cellular potency disconnects
Published in Xenobiotica, 2022
Birk Poller, Sophie Werner, Norbert Domange, Lina Mettler, Richard R. Stein, Jacqueline Loretan, Markus Wartmann, Bernard Faller, Felix Huth
The parameter Kp represents the distribution between the cells and aqueous buffer, analogous to the octanol water distribution constant logD. Accordingly, data were plotted as logKp vs. logD (Figure 3). Increasing logKp values were observed in line with increasing lipophilicity. Measured logKp values of bases were distinctly higher compared to the other ionisation classes at corresponding logD values. Benzbromarone was identified as an outlier with a low logKp of 1.0 despite high lipophilicity (logD: 3.9).