Distribution, drug without elimination

Lead generation offers an opportunity to initiate the drug discovery process with the best possible chemical starting points. In this phase, a project can survey a wide diversity of compoimds to identify novel compounds that balance potency with appropriate physicochemical, absorption, distribution, metabolism, and elimination (ADME) properties and a low risk of toxicity. Later in the optimization process, when locked in to a small number of series, it may be much more difficult to improve one property without having a negative effect on another. 

It should be noted that the development of nanomedicine was not without challenge nanobased products present a variety of safety issues, including their absorption, distribution, metabolism and elimination, which may differ from those of pharmaceuticals. In addition, biomedical nanoscale materials are at present neither well characterized nor standardized, and their interactions with traditional drugs/devices/metaboHc pathways are largely unknown. The toxicological aspects of nanocopper materials should, therefore, always be borne in mind when developing new ideas or invasive devices. 

Simple diffusion is another mechanism by which substances cross membranes without the active participation of components in the membranes. Generally, lipid-soluble substances employ this method to enter cells. Both simple diffusion and filtration are dominant factors in most drug absorption, distribution, and elimination. 

PK modeling can take the form of relatively simple models that treat the body as one or two compartments. The compartments have no precise physiologic meaning but provide sites into which a chemical can be distributed and from which a chemical can be excreted. Transport rates into (absorption and redistribution) and out of (excretion) these compartments can simulate the buildup of chemical concentration, achievement of a steady state (uptake and elimination rates are balanced), and washout of a chemical from tissues. The one- and two-compartment models typically use first-order linear rate constants for chemical disposition. That means that such processes as absorption, hepatic metabolism, and renal excretion are assumed to be directly related to chemical concentration without the possibility of saturation. Such models constitute the classical approach to PK analysis of therapeutic drugs (Dvorchik and Vesell 1976) and have also been used in selected cases for environmental chemicals (such as hydrazine, dioxins and methyl mercury) (Stem 1997 Lorber and Phillips 2002). As described below, these models can be used to relate biomonitoring results to exposure dose under some circumstances. 

At this point, the lead is chemically modified to improve potency, selectivity, bioavailability, and its pharmacokinetic profile. This means a drug candidate should be potent enough to bind properly to the target. It needs to bind specifically to the target or else it will generate potential side effects. It needs to have proper absorption (preferably orally) and distribution throughout the body, and it should be metabolized without generating any toxic substances. Lastly, it should be easily eliminated from the body. 

Drug that is distributed to the peripheral space and eliminated there without returning will not make a contribution to the positive distribution effect. Such distribution is kinetically indistinguishable from a central irreversible elimination. 

In contrast to other modeling approaches, the disposition decomposition technique enables the elimination kinetics to be isolated and determined nonparametrically without the need for a specific structured modeling of the distribution kinetics. Such objective determination of the nonlinear elimination kinetics is of significant value in the areas of nonlinear drug level predictions and absorption evaluations. Moreover, the nonparametric determination provides a useful visualization of the nonlinear elimination kinetics that may be quite helpful in determining the mechanism of the elimination process. 

Noncompartmental pharmacokinetics has been developed as an alternative to data-intensive compartmental and physiologic models. While the latter techniques are useful in pharmacokinetic predictions if sufficient data are available, drugs with complex distribution and elimination may be difficult to properly model without additional experimental data. The noncompartmental techniques do not rely on specific distribution characteristics of a drug and therefore become useful when data are limited. 

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