Multiple dosing models concentrations

As in single-dose models, the plasma concentrations can be substantially affected by changes in the values of the model parameters. The general effect of model parameter changes on multiple dosing plasma concentrations are summarized in Table 10.4. 

Effects of Model Parameters Value Changes on Multiple Dosing Plasma Concentrations... 

Various PK parameters such as CL, Vd, F%, MRT, and T /2 can be determined using noncompartmental methods. These methods are based on the empirical determination of AUC and AUMC described above. Unlike compartmental models (see below), these calculation methods can be applied to any other models provided that the drug follows linear PK. However, a limitation of the noncompartmental method is that it cannot be used for the simulation of different plasma concentration-time profiles when there are alterations in dosing regimen or multiple dosing regimens are used. 

As previously discussed, compartmental models can be effectively used to project plasma concentrations that would be achieved following different dosage regimens and/or multiple dosing. However, for these projections to be accurate, the drug PK profile should follow first-order kinetics where various PK parameters such as CL, V,h T /2, and F% do not change with dose. 

The statistical submodel characterizes the pharmacokinetic variability of the mAb and includes the influence of random - that is, not quantifiable or uncontrollable factors. If multiple doses of the antibody are administered, then three hierarchical components of random variability can be defined inter-individual variability inter-occasional variability and residual variability. Inter-individual variability quantifies the unexplained difference of the pharmacokinetic parameters between individuals. If data are available from different administrations to one patient, inter-occasional variability can be estimated as random variation of a pharmacokinetic parameter (for example, CL) between the different administration periods. For mAbs, this was first introduced in sibrotuzumab data analysis. In order to individualize therapy based on concentration measurements, it is a prerequisite that inter-occasional variability (variability within one patient at multiple administrations) is lower than inter-individual variability (variability between patients). Residual variability accounts for model misspecification, errors in documentation of the dosage regimen or blood sampling time points, assay variability, and other sources of error. 

THERdbASE contains two major modules, namely a Database Module and a Model Base Module. The Database Module relates information from exposure, dose and risk-related data files, and contains information about the following population distributions, location/activity patterns, food-consumption patterns, agent properties, agent sources (use patterns), environmental agent concentrations, food contamination, physiological parameters, risk parameters and miscellaneous data files. The Model Base Module provides access to exposure dose and risk-related models. The specific models included with the software are as follows Model 101, subsetting activity pattern data Model 102, location patterns (simulated) Model 103, source (time application) Model 104, source (instantaneous application) Model 105, indoor air (two zones) Model 106, indoor air (n zones) Model 107, inhalation exposure (BEAM) Model 108, inhalation exposure (multiple chemicals) Model 109, dermal dose (film thickness) Model 110, dose scenario (inhalation/dermal) Model 201, soil exposure (dose assessment). 

MRTC in a one-compartment body model is the inverse of the rate constant for elimination. In a multiple-compartment model, where the multiple dosing plasma half-life is useful, MTRC is given by the volume of the central compartment where drug concentrations are measured divided by clearance. MRT in Equation 17.32 is the ratio of AUMC/AUC. 

PK models for multiple dosing situations can be based on the principle of superposition as long as drug elimination follows linear kinetics. Superposition allows the concentration for each dose to be determined from single-dose PK models, and the overall resulting plasma concentrations for all combined doses to be taken as the sum of... 

Figure 10.2 Pharmacokinetics of intravenous T-20 administration. The graphs show the expected changes in plasma concentration vs time after intravenous administration of a single injection (dashed line) or multiple injections (solid line) of intravenous T-20 (lOOmg/dose). The curves are based on the half-life (1.8 h) and volume of distribution (4.7 L) measured in 17 human volunteers [4] using a one-compartment model (see Equation 7-3). Because of its rapid elimination, multiple doses are needed to maintain the peptide level in the effective range.

Compartmentel models can be used to simulate or to analyze multiple dose date. When uniform doses are given at uniform dosing intervals, T, the concentration after the fourth IV bolus dose can be calculated using Equation 12.9. 

Even the most sophisticated risk assessment has limitations. It involves numerous assumptions about both exposure and hazard. Exposure assessments typically reflect modeled concentrations or extrapolations from measured data. The degree of exposure by different individuals may vary, and their response can depend on factors such as general health, genetic predisposition, or other factors. Dose-response factors are typically extrapolated from animal studies and thus inherently introduce the imcertainty of relating the response of laboratory animals to that of humans or one of the many species in an ecosystem. The endpoints characterized may not include all of the potential effects for example, the potential for endocrine disruption has not been considered in many risk assessments and in fact standardized testing methods were not published until approximately 2007 or later [90]. And risk assessment tools only model relatively simple scenarios. They rarely account for exposure to multiple chemicals, or fully accoimt for the effects on a complex web of organisms in an ecosystem. 

Fig. 39.11. (a) One compartment open model for repeated intravenous injection of the same dose D at constant intervals 0. (b) Time course of the plasma concentration Cp with peaks C and troughs C at multiples of the time interval 0. Tlie peaks and troughs tend asymptotically toward the... 

Etoposide causes multiple DNA double-strand breaks by inhibiting topoisomerase II. The pharmacokinetics of etoposide are described by a two-compartment model, with an a half-life of 0.5 to 1 hour and a (5 half-life of 3.4 to 8.3 hours. Approximately 30% of the dose is excreted unchanged by the kidney.16 Etoposide has shown activity in the treatment of several types of lymphoma, testicular and lung cancer, retinoblastoma, and carcinoma of unknown primary. The intravenous preparation has limited stability, so final concentrations should be 0.4 mg/mL. Intravenous administration needs to be slow to prevent hypotension. Oral bioavailability is approximately 50%, so oral dosages are approximate two times those of intravenous doses however, relatively low oral daily dosages are used for 1 to 2 weeks. Side effects include mucositis, myelosuppression, alopecia, phlebitis, hypersensitivity reactions, and secondary leukemias. 

As a general rule, in vivo assays are more challenging than in vitro assays because the matrices for the samples are more complex. The most common use for in vivo assays is to measure the concentration of NCE dosed into a laboratory animal by collecting multiple sample time points, one can use the analytical results to plot the PK profile of the NCE and also obtain various PK parameters that help determine a test compound s PK properties. Preclinical PK parameters of a test compound are then used to predict its human PK parameters. Another use of in vivo assays is combining the results with pharmacodynamic (PD) observations to perform PK/PD modeling.77 82 PK/PD modeling is an important aspect of new drug discovery because it can be used to predict the exposures and durations required to determine clinical efficacy of a NCE. 

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