Principles of pharmacodynamics and their applications in veterinary pharmacology.
ABSTRACT Pharmacodynamics (PDs) is the science of drug action on the body or on microorganisms and other parasites within or on the body. It may be studied at many organizational levels--sub-molecular, molecular, cellular, tissue/organ and whole body--using in vivo, ex vivo and in vitro methods and utilizing a wide range of techniques. A few drugs owe their PD properties to some physico-chemical property or action and, in such cases, detailed molecular drug structure plays little or no role in the response elicited. For the great majority of drugs, however, action on the body is crucially dependent on chemical structure, so that a very small change, e.g. substitution of a proton by a methyl group, can markedly alter the potency of the drug, even to the point of loss of activity. In the late 19th century and first half of the 20th century recognition of these facts by Langley, Ehrlich, Dale, Clarke and others provided the foundation for the receptor site hypothesis of drug action. According to these early ideas the drug, in order to elicit its effect, had to first combine with a specific 'target molecule' on either the cell surface or an intracellular organelle. It was soon realized that the 'right' chemical structure was required for drug-target site interaction (and the subsequent pharmacological response). In addition, from this requirement, for specificity of chemical structure requirement, developed not only the modern science of pharmacology but also that of toxicology. In relation to drug actions on microbes and parasites, for example, the early work of Ehrlich led to the introduction of molecules selectively toxic for them and relatively safe for the animal host. In the whole animal drugs may act on many target molecules in many tissues. These actions may lead to primary responses which, in turn, may induce secondary responses, that may either enhance or diminish the primary response. Therefore, it is common to investigate drug pharmacodynamics (PDs) in the first instance at molecular, cellular and tissue levels in vitro, so that the primary effects can be better understood without interference from the complexities involved in whole animal studies. When a drug, hormone or neurotransmitter combines with a target molecule, it is described as a ligand. Ligands are classified into two groups, agonists (which initiate a chain of reactions leading, usually via the release or formation of secondary messengers, to the response) and antagonists (which fail to initiate the transduction pathways but nevertheless compete with agonists for occupancy of receptor sites and thereby inhibit their actions). The parameters which characterize drug receptor interaction are affinity, efficacy, potency and sensitivity, each of which can be elucidated quantitatively for a particular drug acting on a particular receptor in a particular tissue. The most fundamental objective of PDs is to use the derived numerical values for these parameters to classify and sub-classify receptors and to compare and classify drugs on the basis of their affinity, efficacy, potency and sensitivity. This review introduces and summarizes the principles of PDs and illustrates them with examples drawn from both basic and veterinary pharmacology. Drugs acting on adrenoceptors and cardiovascular, non-steroidal anti-inflammatory and antimicrobial drugs are considered briefly to provide a foundation for subsequent reviews in this issue which deal with pharmacokinetic (PK)-PD modelling and integration of these drug classes. Drug action on receptors has many features in common with enzyme kinetics and gas adsorption onto surfaces, as defined by Michaelis-Menten and Langmuir absorption equations, respectively. These and other derived equations are outlined in this review. There is, however, no single theory which adequately explains all aspects of drug-receptor interaction. The early 'occupation' and 'rate' theories each explain some, but not all, experimental observations. From these basic theories the operational model and the two-state theory have been developed. For a discussion of more advanced theories see Kenakin (1997).
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ABSTRACT: Drug delivery systems (DDSs) face several challenges including site-specific delivery, stability, and the programmed release of drugs. Engineered nanoparticle (NP) surfaces with responsive moieties can enhance the efficacy of DDSs for in vitro and in vivo systems. This triggering process can be achieved through both endogenous (biologically controlled release) and exogenous (external stimuli controlled release) activation. In this review, we will highlight recent examples of the use of triggered release strategies of engineered nanomaterials for in vitro and in vivo applications.Nano Today 08/2013; 8(4):439-447. · 17.69 Impact Factor
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ABSTRACT: For many decades, researchers are aware of the importance of circadian rhythm in physiological/biochemical properties and drug metabolism. Chronopharmacology is the study of how the effects of drugs vary with biological timing and endogenous periodicities. It has been attaching substantial attention in the last years. Chronopharmacodynamics mainly deals with the biochemical and physiological effects of drugs on the body, the mechanisms of drug action, the relationship between drug concentration and effect in relation to circadian clock. In this review, we will focus on mammalian circadian pharmacodynamics and discuss new chronotherapy approaches. Moreover, we will try to highlight the chronopharmacodynamics of cardiovascular drugs, anti-cancer drugs, analgesics and non-steroidal anti-inflammatory drugs (NSAIDs) and give some practical concerns for clinical pharmacists and pharmacy practitioners, concerning this issue.Journal of Pharmacy Practice and Research 10/2012; 1(2):41-7.
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ABSTRACT: In this paper, we developed a wavelet neural network (WNN) algorithm for electroencephalogram (EEG) artifact. The algorithm combines the universal approximation characteristics of neural networks and the time/frequency property of wavelet transform, where the neural network was trained on a simulated dataset with known ground truths. The contribution of this paper is two-fold. First, many EEG artifact removal algorithms, including regression based methods, require reference EOG signals, which are not always available. The WNN algorithm tries to learn the characteristics of EOG from training data and once trained, the algorithm does not need EOG recordings for artifact removal. Second, the proposed method is computationally efficient, making it a reliable real time algorithm. We compared the proposed algorithm to the independent component analysis (ICA) technique and an adaptive wavelet thresholding method on both simulated and real EEG datasets. Experimental results show that the WNN algorithm can remove EEG artifacts effectively without diminishing useful EEG information even for very noisy datasets.Neurocomputing 01/2012; 97(1):374-389. · 2.01 Impact Factor