Pharmacokinetics assumptions

This approach is robust because it does not rely on any pharmacokinetic assumptions and allows the characterization of absorption processes among different drugs if IV data are available. For example, differences in the absorption profiles between fluticasone propionate and budesonide can easily be identified with this method, while differences in fmax were not able to readily provide this information. The mean residence time without availability of intravenous data should not be used to compare absorption profiles of different drug entities, because it is also determined by the systemic elimination of the drug. This approach is, however, suitable for evaluating the differences of different formulations of the same drug. 

Clinical Pharmacokinetics. Clinical pharmacokinetics attempts to define the relationship between dmg concentration and therapeutic response. The underlying assumption is that response is proportional to dmg concentration at the site of action. This concentration is dependent on many... 

Analysis of most (perhaps 65%) pharmacokinetic data from clinical trials starts and stops with noncompartmental analysis (NCA). NCA usually includes calculating the area under the curve (AUC) of concentration versus time, or under the first-moment curve (AUMC, from a graph of concentration multiplied by time versus time). Calculation of AUC and AUMC facilitates simple calculations for some standard pharmacokinetic parameters and collapses measurements made at several sampling times into a single number representing exposure. The approach makes few assumptions, has few parameters, and allows fairly rigorous statistical description of exposure and how it is affected by dose. An exposure response model may be created. With respect to descriptive dimensions these dose-exposure and exposure-response models... 

The same assumptions apply to CoMFA as to ordinary Hansch analysis. These are additivity of effects and the availability of structurally similar (congeneric) molecules. The method does not account for pharmacokinetic effects, such as distribution, elimination, transport and metabolization. A prospective drug may appear to bind well to the receptor or enzyme, but may not reach the target site due to undesirable pharmacokinetic properties [8]. 

Pharmacokinetics is closely related to pharmacodynamics, which is a recent development of great importance to the design of medicines. The former attempts to model and predict the amount of substance that can be expected at the target site at a certain time after administration. The latter studies the relationship between the amount delivered and the observable effect that follows. In some cases the observable effect can be related directly to the amount of drug delivered at the target site [2]. In many cases, however, this relationship is highly complex and requires extensive modeling and calculation. In this text we will mainly focus on the subject of pharmacokinetics which can be approached from two sides. The first approach is the classical one and is based on so-called compartmental models. It requires certain assumptions which will be explained later on. The second one is non-compartmental and avoids the assumptions of compartmental analysis. 

Major questions that arise whenever a pesticide exposure evaluation is completed are how good are the data and how close to the real answer have we gotten For most commercially sold insecticides, there are no appreciable pharmacokinetic data in human systems, although some data normally exist for animal models. Because such pharmacokinetic data do not exist for most active insecticides, passive dosimetry measurements must be used to estimate the exposure and eventually dose. Once such passive dosimetry data exist, certain assumptions must be made to arrive at an estimate of dose. 

The advantage of the mixing tank model approach is its relative simplicity, intuitive accessibility, and easy correlation with pharmacokinetic models. However, the physical basis for considering a segment of the small intestine as one or more serial mixing tanks is limited, although such an assumption has been commonly and successfully utilized in the physical and biological sciences. 

Estimation of the volume of distribution in man may be carried out in a number of ways. These methods have recently been reviewed by Obach et al. [60], who carried out a wide-ranging evaluation of a large number of different ways of predicting the human pharmacokinetics of 50 compounds that entered development at Pfizer. One of the simplest methods was reported to be the most reliable. It is based on the assumption that the free-fraction of drug in the plasma in dog and human and... 

Further development in the chemistry of oxazolidinone antibacterials was based mainly on the assumption that the 4-pyridyl moiety of one of Dupont s lead compounds, E-3709, might be amenable to replacement by suitably saturated heterocyclic bioisosteres [48]. This assumption was based on an example in which successful replacement of the piperazine ring system in the quinolone antibacterials, such as ciprofloxacin, with a pyridine fragment, such as seen in Win-57273, results in improvement of both the antibacterial and the pharmacokinetic profiles of the compounds. Similarly, as in the case of ciprofloxacin and Win-57273, it was predicted that the presence of a small but highly electron-withdrawing fluorine atom would be tolerated at the meta position(s) of the central phenyl ring, and would confer enhanced antibacterial activity and/or other desirable properties to the targeted oxazolidinones, as shown in Fig. 3. 

Prediction methods based on animal pharmacokinetic data can be categorized into three types (1) allometric scaling, (2) proportionality methods, and (3) correlative approaches. All three make a basic underlying assumption that the types... 

Noncompartmental analysis is limited in that it is not descriptive or predictive concentrations must be interpolated from data. The appeal of noncompartmental analysis is that the shape of the blood concentration-versus-time curve is not assumed to be represented by an exponential function and, therefore, estimates of metabolic and pharmacokinetic parameters are not biased by this assumption. In order to minimize errors in parameter estimates that are introduced by interpolation, a large number of data points that adequately define the concentration-versus-tie curve are needed. 

Further refinements of methods require more elaborate models with explicit or implicit assumptions and defaults, e.g.. Physiologically Based Pharmacokinetic/Toxicokinetic (PBPK/PBTK) models, which may also provide modeled information on the target concentrations/amounts (Section 4.3.6). 

Overall, this study indicated that generic simulation of pharmacokinetics at the lead optimization stage could be useful to predict differences in pharmacokinetic parameters of threefold or more based upon minimal measured input data. Fine discrimination of pharmacokinetics (less than twofold) should not be expected due to the uncertainty in the input data at the early stages. It is also apparent that verification of simulations with in vivo data for a few compounds of each new compound class was required to allow an assessment of the error in prediction and to identify invalid model assumptions. 

Some caution is required with some chemical classes and compound properties related to low solubility, high lipophihcity, major impact of active transport processes on elimination and distribution. It is therefore recommended that PBPK models should only be applied after verification of the simulations with in vivo pharmacokinetics for a few compounds of a given chemical class. Such verification will help to identify invalid model assumptions or missing processes where additional data is needed. 

For FTIH trials, all applications should include a summary of projected free plasma concentrations of the new active substance (NAS) in humans and a brief description of any pharmacokinetic modelling programs used to generate the estimates. A comparison with the concentrations obtained in the nonclinical toxicity studies and projected safety margins should be given. In the same section, an estimate of the extent of the intended pharmacological or pharmacodynamic response at the expected plasma concentrations should be included, with a list of the assumptions used in deriving that estimate. 

Lorazepam. Lorazepam has been increasingly studied for control of psychotic aggressivity ( 157,158, 159,160, 161,162, 163,164, 165,166 and 167). One reason is that, of all the BZDs available in parenteral form, lorazepam has a pharmacokinetic profile (quick, reliable absorption) that makes it particularly suitable for this type of use. Open, retrospective, and controlled studies indicate that oral or parenteral lorazepam added to an antipsychotic controls disruptive behavior safely and effectively for most patients. The combination may also permit an overall reduction of the antipsychotic dose, although this assumption requires further study ( 162, 164, 166). 

There are almost no data available concerning the pharmacokinetics (i.e., the uptake, distribution, metabolisms, and excretion) of chemical carcinogens in humans. Nevertheless, it is possible to make limited assumptions about the pharmacokinetics of carcinogens, based on the results of animal studies conducted with various chemicals, notably polycyclic hydrocarbons such as benzo[a]p3nene. 

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... 

This definition remains model-dependent, but VDSS can be calculated using a non-compartmental approach, which does not require any assumptions about the pharmacokinetic model concerned (see below). 

Estimates of dosing rate and average steady-state concentrations, which may be calculated using clearance, are independent of any specific pharmacokinetic model. In contrast, the determination of maximum and minimum steady-state concentrations requires further assumptions about the pharmacokinetic model. The accumulation factor (equation... 

The commonsense approach to the interpretation of drug concentrations compares predictions of pharmacokinetic parameters and expected concentrations to measured values. If measured concentrations differ by more than 20% from predicted values, revised estimates of Vd or CL for that patient should be calculated using equation (1) or equation (2). If the change calculated is more than a 100% increase or 50% decrease in either Vd or CL, the assumptions made about the timing of the sample and the dosing history should be critically examined. 

Once a chemical is in systemic circulation, the next concern is how rapidly it is cleared from the body. Under the assumption of steady-state exposure, the clearance rate drives the steady-state concentration in the blood and other tissues, which in turn will help determine what types of specific molecular activity can be expected. Chemicals are processed through the liver, where a variety of biotransformation reactions occur, for instance, making the chemical more water soluble or tagging it for active transport. The chemical can then be actively or passively partitioned for excretion based largely on the physicochemical properties of the parent compound and the resulting metabolites. Whole animal pharmacokinetic studies can be carried out to determine partitioning, metabolic fate, and routes and extent of excretion, but these studies are extremely laborious and expensive, and are often difficult to extrapolate to humans. To complement these studies, and in some cases to replace them, physiologically based pharmacokinetic (PBPK) models can be constructed [32, 33]. These are typically compartment-based models that are parameterized for particular... 

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