Experimental data, pharmacokinetics

Experimental Data Pharmacokinetics - Absorption, Distribution, Metabolism, Excretion 153... 

Sheiner LB, Beal SL. Evaluation of methods for estimating population pharmacokinetic parameters. II. Biexponential model and experimental pharmacokinetic data. / Pharmacokinet Biopharm 1981 9 635-51. 

Coupling with its intravenous pharmacokinetic parameters, the extended CAT model was used to predict the observed plasma concentration-time profiles of cefatrizine at doses of 250, 500, and 1000 mg. The human experimental data from Pfeffer et al. [82] were used for comparison. The predicted peak plasma concentrations and peak times were 4.3, 7.9, and 9.3 qg/mL at 1.6, 1.8, and 2.0 hr, in agreement with the experimental mean peak plasma concentrations of... 

While these models simulate the transfer of lead between many of the same physiological compartments, they use different methodologies to quantify lead exposure as well as the kinetics of lead transfer among the compartments. As described earlier, in contrast to PBPK models, classical pharmacokinetic models are calibrated to experimental data using transfer coefficients that may not have any physiological correlates. Examples of lead models that use PBPK and classical pharmacokinetic approaches are discussed in the following section, with a focus on the basis for model parameters, including age-specific blood flow rates and volumes for multiple body compartments, kinetic rate constants, tissue dosimetry,... 

In spite of its limitations, the ACAT model combined with modeling of saturable processes has become a powerful tool in the study of oral absorption and pharmacokinetics. To our knowledge, it is the only tool that can translate in vitro data from early drug discovery experiments all the way to plasma concentration profiles and nonlinear dose-relationship predictions. As more experimental data become available, we believe that the model will become more comprehensive and its predictive capabilities will be further enhanced. 

Various attempts have been made to determine which scheme, or hiunan/animal scaling ratio (K,J, is correct based on comparative pharmacokinetic data, e.g., Dietz et al., 1983. In an NAS/NRC report on pesticides (1975) the authors assumed that Kh = 35, and they studied the limited human data to show that this assumption appeared to be correct within a factor of 100. Crouch and Wilson (1979) and Crouch (1983a, 1983b) have shown that the experimental data for 250 chemicals are consistent with = 1, with a variation in... 

Physiological toxicokinetic (or pharmacokinetic) models represent descriptions of biological systems and can be used to describe the behaviour of chemicals in the intact animal. Such models have been used to predict the disposition of butadiene and metabolites in rats, mice, and humans. For the case of rats and mice, these predictions can be compared with experimental data. In some cases (see below), the models successfully describe (and accurately predict) the disposition of butadiene and metabolites. Human physiological toxicokinetic model predictions normally cannot be verified due to lack of experimental data. 

A non-ribosomal biosynthetic pathway is clearly indicated for cyclosporin A, considering the uncommon structural elements MeBmt, L-a-aminobutyric acid and D-alanine as well as the plethora of isolated congeners [20,21]. Non-ribosomal biosynthesis directed by multienzyme thiotemplates have been reported for other small peptides of microbial origin, for example, gramicidin S [22] and enniatin [23]. Experimental data for cyclosporin A were obtained by feeding appropriate labelled precursors to cultures of T. inflation strains. The distribution profile of the labelled atoms in cyclosporin A was determined by 3H- or 13C-NMR spectroscopy. In preliminary trials with several tritium and carbon-14 labelled precursors, [met/y>/-3H]methionine proved to be the most suitable marker for the biosynthetic preparation of radiolabelled cyclosporin A for pharmacokinetic and metabolic studies [24],... 

The biexponential rate equation associated with this model was fitted to the experimental data using a nonlinear least squares procedure. Pharmacokinetic constants for the two-compartment model were calculated by standard methods. The fraction amount absorbed as a function of time was estimated by the Loo-Riegelman method using the macroscopic rate constants calculated from the intravenous data. The slope of the linear part of the Loo-Riegelman plot combined with the total amount absorbed (quantitated by depletion analysis of the saturated donor solution) was used to calculate the zero-order rate constant for buccal permeability. 

Experimental data arise from studies carried out specifically for pharmacokinetic investigations, under controlled conditions of drug dosing and extensive blood sampling. 

In this chapter, we describe four major protocols formulation of md-LErafAON, toxicology of systemically delivered md-LErafAON, pharmacokinetics and biodistribution of md-LErafAON, and anti-tumor efficacy of a combination of md-LErafAON and radiation. Representative examples of experimental data with detailed legends are provided to further clarify the methods described. Additional information, variations, and alternatives to the methods are described under Heading 4 under each protocol. For further information, the reader is referred to the earlier reports (13-15,17,26,27). 

For noncancer effects the use of PBTD models has elucidated the fundamental mechanisms of toxicological interactions. Such mechanistic knowledge linked with Monte Carlo simulations has initially been employed in in silico toxicology to develop models that predict the toxicity of mixtures in time. The combination of PBTK/TD models for individual compounds with binary PBTK/TD models can be achieved by incorporating key mechanistic knowledge on metabolism inhibitions and interactions through shared enzyme pathways. Simulations of such models can then be compared to experimental data and allow conclusions to be reached about their pharmacokinetics and the likelihood of effects being dose additive. 

Chemical data (e.g., physical and chemical properties, structureactivity relationships, and environmental fate and transport), basic toxicity data, and pharmacokinetic data (information on absorption, distribution (including placental and lactational transfer), metabolism, and excretion) should be reviewed. These data are particularly important because reproductive and developmental effects are interpreted in the context of general toxicity data in humans or experimental animals. Pharmacokinetic data for both animals and humans can be helpful in extrapolating exposure levels from one species to another. 

Where possible, an evaluation should use pharmacokinetic data, including metabolic and mechanism-of-action information, to determine the relevance of experimental data to humans. Should the available data for a particular species demonstrate a pharmacokinetic pattern similar to that found in humans, the data from that species will be considered relevant. But if, for example, an agent given to an experimental animal requires biotransformation to produce toxicity, and if humans are known to be incapable of that biotransformation pathway, then toxicity data from that experimental animal species would be considered irrelevant to humans. 

Where there are experimental data from more than one species, the default assumption is that humans are at least as sensitive as the most sensitive animal species. If the data indicate, however, that some particular species is a more relevant surrogate for humans, either because of physiological similarity at the site of action or because of the pharmacokinetic parameters associated with the substance under review, such information will preempt that general assumption. 

Reaction rate parameters required for the distributed pharmacokinetic model generally come from independent experimental data. One source is the analysis of rates of metabolism of cells grown in culture. However, the parameters from this source are potentially subject to considerable artifact, since cofactors and cellular interactions may be absent in vitro that are present in vivo. Published enzyme activities are a second source, but these are even more subject to artifact. A third source is previous compartmental analysis of a tissue dosed uniformly by intravenous infusion. If a compartment in such a study can be closely identified with the organ or tissue later considered in distributed pharmacokinetic analysis, then its compartmental clearance constant can often be used to derive the required metabolic rate constant. 

Baxter LT, Yuan F, Jain RK. Pharmacokinetic analysis of the perivascular distribution of bifunctional antibodies and haptens Comparison with experimental data. Cancer Res 1992 52 5838h14. 

Sheiner and Beal proposed the NAIVE pooled data (NPD) approach for the method in which all data from all individuals are considered as arising from one unique individual. Unlike the NAD approach, the NPD approach is far more general. It can easily deal with experimental data, non-standard data, and routine pharmacokinetic data. After a unique fitting of all data at once, parameter estimates are obtainable. It may perform well when variations between subjects are small. This is occasionally the case in a group of homogeneous laboratory animals from a given strain, but it is rarely true for humans. The drawbacks of NPD are the same as those of NAD, as has been repeatedly pointed The NPD approach... 

The performance of the FO approach for the analysis of observational and experimental data have been evaluated by Sheiner and Beal with the Michaelis-Menten pharmacokinetic modek and the one- and two-compartment models. In all instances, a comparison was made with the NPD and STS approaches for the analysis of the two types of data. The FO approach outperformed the NPD and the STS approaches on both data types. Despite the approximation, the FO approach provides good parameter estimates. If the residual error increases, the STS approach quickly deteriorates, especially with respect to variance parameters. However, the STS still performs reasonably well but the bias and imprecision of the estimates tend to increase with increasing residual error. Estimates of residual variability have been shown to deteriorate with the FO approach when residual error increases. " ... 

NAIVE pooled data Preclinical pharmacokinetics/ toxicokientics, experimental data Simplicity Very sensitive to imbalance and confounding correlations in the dataset... 

Standard two-stage Experimental (rich) pharmacokinetic data Fairly straightforward May overestimate parameter dispersion... 

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