Ted Whittem1, Thierry Beths2 and Sébastien H. Bauquier3 1 College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, Queensland, Australia 2 Melbourne Veterinary School, University of Melbourne, and Greencross Veterinary Hospital, Werribee, Victoria, Australia 3 Melbourne Veterinary School, University of Melbourne, Werribee, Victoria, Australia, and Southpaws Specialty, Emergency and Referral Hospital, Malvern East, Victoria, Australia Anesthesia and pain management are two branches of veterinary clinical medicine that inarguably are inextricably intertwined with clinical pharmacology. A thorough, deep, and workable understanding of the principles of clinical pharmacology is fundamental to successful clinical practice in these disciplines. The primary objective of this chapter is to introduce these fundamental pharmacological concepts as they relate to the practice of anesthesia, and to do so in sufficient detail to provide the basis for which sound clinical decisions can be made, even when specific evidence to support the decision is unavailable or unknown. This chapter also strives to provide the nascent anesthetic scientist with an elementary understanding of some of the commonly used research methods and tools for pharmacokinetic research in veterinary clinical pharmacology. To allow communication between organs and between cells, the body uses messengers or signals. The role of the biological receptors is to convert signals into different forms of stimuli that will induce a reaction from the cell or organ. The French scientist Claude Bernard (1813–1878) was the first to demonstrate the existence of circulating messengers allowing communication between different parts of the body [1]. This discovery was the prelude to the receptor theory. A receptor is a component of a cell, usually a protein or glycoprotein, which interacts with the signaling messenger substance. Classically, the signaling messenger substance is called a “ligand.” Ligands can be, for example, hormones or drugs. The combination of a drug–ligand with its receptor induces pharmacological effects; the initial effect is called the “action” of the drug, whereas succeeding effects are called drug “effects” [2]. The relationship between a ligand and its receptor follows the law of mass‐action: where where Ka is the rate constant of association of the ligand with the receptor, Kd is the rate constant of dissociation of the ligand with the receptor, [L] is the concentration of unbound ligand, [R] is the concentration of unbound receptor, and [LR] is the concentration of bound receptors. The affinity describes the relationship between a particular receptor and its ligand: from eqn 19.1, if the amount of ligand administered is just enough to occupy 50% of the receptors, then A high Ka (and a low Kd) implies that at equilibrium the number of unbound ligand molecules is low, showing a high affinity of the ligand for the receptor. Conversely, a low Ka (and a high Kd) indicates a poor affinity of the ligand for the receptor. However, a ligand can have a strong affinity to a receptor without producing an effect. The activity will describe the ability of a ligand to induce an action. For example, the mu (μ) and kappa (κ) opioid receptors are the main opioid receptors involved in opioid‐modulation of pain. Buprenorphine is classified as a partial μ‐opioid receptor agonist. It has a high affinity for the μ‐opioid receptor and as a consequence its effects, although moderate, are difficult to antagonize [3]. Butorphanol is classified as an agonist–antagonist opioid [2]. It produces its effect by activating κ‐opioid receptors and also binds to μ‐opioid receptors. However, even though butorphanol has a strong affinity for μ‐opioid receptors, it is unable to activate them and will cause no action from the association with those receptors. The selectivity of a ligand determines its capacity to produce a particular effect. A highly selective drug will only produce one effect through activity at only one class or subclass of receptor. Dopamine is a catecholamine that produces different cardiovascular effects at different doses; when given at a relatively low dose it will increase myocardial contractility (β‐adrenergic receptor‐mediated); however, when the dose is increased, dopamine will induce some peripheral vasoconstriction (α‐adrenergic receptor‐mediated). This lack of selectivity occurs because dopamine acts as a ligand at different subclasses of adrenergic receptors with varying affinity. In contrast, dobutamine is a more selective catecholamine (β‐adrenergic receptor‐mediated only) and its main effect will be to increase myocardial contractility without inducing major peripheral vasoconstriction [4]. The specificity of a ligand describes its capacity to associate with only one specific type of receptor. The effects of a highly specific ligand can be numerous but are due to only one type of receptor–ligand interaction. For example, atropine associates with one specific type of receptor even though these receptors are present in different tissues and the effects are diverse [5]. In comparison, inhalant anesthetics will interact with several different receptors to induce their broad effect, unconsciousness [6]. It can be difficult to directly assess the interaction of a receptor with its ligand by observing the number of bound or unbound receptors or even by looking at drug action. As a consequence, investigators often measure the effects of the drugs to try to quantify the receptor occupancy. The effect of the drug is proportional to the concentration of ligand molecules available to bind, which is a function of the dose and method of administration of the ligand, physicochemical and pharmacokinetic properties of the ligand, and location of the receptor. Delays from dosing to onset of pharmacological effects are common and are often referred to as the “lag‐time.” The delays can be due to the relative difficulty of a ligand reaching the receptor (pharmacokinetics) or can be due to post‐transduction delay (pharmacodynamics). For example, glucocorticoid receptors are nuclear receptors, which, when not bound to a ligand such as cortisol or another glucocorticoid, are located in the cytosol. Once activated by ligand binding, the glucocorticoid receptor–drug complex is actively transported into the nucleus where it induces the transcription of genes coding for anti‐inflammatory proteins and inhibits the transcription of genes usually upregulated by inflammatory mediators [7]. In this case, the onset of activity is post‐transduction and the long lag‐time explains why glucocorticoids fail to show good results as emergency drugs. The term “agonist” comes from the Greek agōnistēs meaning “contestant” or “champion.” An agonist ligand is a ligand that binds to the receptor and usually activates it the same way that endogenous molecules would. An agonist ligand can be a full agonist, fully activating the receptor, or a partial agonist that does not fully activate the receptor and therefore produces a less intense maximum effect (see below). It is unknown why some molecules work as full agonists and others work as partial agonists; however, the beginning of the explanation involves the receptor state concept (see below). For example, morphine and buprenorphine are full μ‐opioid receptor agonists and partial μ‐opioid receptor agonists, respectively. Accordingly, morphine can provide better analgesic effects than buprenorphine, even though buprenorphine’s affinity for μ‐opioid receptors is higher than that of morphine. A ligand can also be classified as a neutral antagonist. In this case, the ligand will bind to the receptor but will be unable to activate it. No action following the association of the ligand with the receptor will be seen. This association is usually competitive but can also be noncompetitive. The competitive inhibition of the receptor can be overcome by administering a large enough amount of an agonist ligand, whereas the same action will have no effect if the inhibition is noncompetitive. Reverse agonists are ligands that will activate the receptor but will induce opposite effects compared to the agonist ligands. Another way to see it is to consider that a receptor has a baseline agonist effect that is not nil, and the administration of the reverse agonist will decrease the baseline effect. The explanation of this phenomenon is based on the receptor state theory discussed below. If the agonist effect is to provide analgesia, the reverse agonist effect will be to increase the sensation of pain. Flumazenil and Ro 19‐4063 are examples of competitive neutral antagonist and reverse agonist, respectively. Mandema et al. studied the averaged concentration‐EEG effect relationships of all individual rats that had received an intravenous bolus administration of midazolam, flumazenil, or Ro 19‐4603. Midazolam was followed by a positive effect, whereas flumazenil and Ro 19‐4063 were followed by neutral and negative effects, respectively [8]. Confusion between neutral antagonist and reverse agonist can occur when the baseline agonist effect is minimal. Also, the classification of agonist, partial agonist, neutral antagonist, and reverse agonist applies to a ligand in relation to a particular receptor, and the overall effect of a drug may be different from the effect expected from its action on a particular receptor. Although naloxone is a well‐known opioid receptor antagonist, low doses of naloxone can induce analgesic effects. The mechanism by which an opioid antagonist can enhance the opioid agonist effect is unknown, although it could be explained by its effects on: increasing the release of endogenous ligands, upregulation of postsynaptic receptors, inhibiting counteraction by Gs proteins, uncoupling of filament A, and attenuating the increase in the expression of glial fibrillary acidic protein (GFAP) [9]. The classic receptor theory implies that the receptor is by default in a nonactivated form and needs an agonist ligand in order to be activated. Although the nonactivated receptor form represents most of the receptors, experiments have shown that without the presence of an agonist ligand, some receptors can exist in their activated form. The role of the ligand would then be not to activate the receptor but to stabilize the receptor’s activated form. The two major implications of this theory are the existence of a baseline agonist effect for the receptor and the differentiation between antagonist drugs and inverse agonist drugs. Receptors are mainly multiprotein entities and so have four levels of structure. The primary structure is a linear sequence of amino acids, the secondary structure is a regular local substructure (α‐helix or β‐sheets), the tertiary structure is the three‐dimensional structure of a single peptide molecule, and the quaternary structure is the combination of the multiple tertiary structures of different proteins linked together. All these levels of structure are of particular importance, as a ligand will have to fit with these four levels of structure to be able to associate with and activate the receptor. The aim of this chapter is to illustrate the study of receptor structures using a few examples only of receptors that have a particular importance in the field of anesthesia. The discussion will be limited to some ion channels, G‐protein receptors, and the angiotensin‐converting enzyme 2 (ACE2) receptor. One key role of the sodium channel is to allow the genesis of action potentials [10]. Indeed, sodium channels are voltage‐gated ion channels that conduct the sodium cation into the cell, generating an action potential. When the membrane potential equals the resting potential of the cell, the channels are closed. However, as the membrane potential increases, the conformation of the central pore of the channel changes, increasing sodium permeability and allowing influx of sodium ions and consequently initiating or propagating an action potential. This change of conformation is made possible by the presence of particular transmembrane‐spanning segments (α‐helices) called “voltage sensors.” The sodium channel is comprised of three peptide subunits: one α glycoprotein subunit and two accessory β subunits [10] (Fig. 19.1). The α subunit is composed of four homologous domains and each domain contains six transmembrane‐spanning segments. The primary structure of one of these segments includes a high number of positively charged amino acids, and when the membrane potential increases, the positively charged segments move toward the extracellular side of the membrane changing the conformation of the channel. Although their mechanism of action is not completely understood, the local anesthetics work by blocking these channels, thus preventing the formation of action potentials. Figure 19.1 Structure of the sodium channel. A. Schematic of a sodium channel composed of one α subunit and two accessory β subunits. B. Schematic of the α glycoprotein subunit showing the four homologous domains (DI–DIV) containing six membrane‐spanning segments numbered 1–6. The primary structure of segment 4 includes positively charged amino acids placed at every fourth position. When the membrane potential increases, the positively charged segments move toward the extracellular side of the membrane, changing the conformation of the channel, and allowing more sodium cations to enter the cell. Figure 19.2 Structure of the GABAA receptor. A. Schematic of a GABAA receptor composed of two α, two β, and one γ subunits showing the binding site of the agonist GABA and of the allosteric modulators: benzodiazepines, inhalant anesthetics, ethanol, propofol, and alfaxalone. B. Schematic of one glycoprotein subunit showing the four membrane‐spanning segments numbered 1–4. Activated by the neurotransmitter ligand γ‐aminobutyric acid (GABA), the neuro‐inhibitory GABA receptors (GABAA and GABAB) are mainly present in the central nervous system in mammals. The GABAA (fast response) receptors are anion channels, whereas the GABAB (slow response) receptors are G‐protein‐coupled receptors. Of this ligand’s receptor types, only the GABAA receptor will be addressed in this chapter. The GABAA receptors are ligand‐gated anion channels that allow the passage of chloride anions into the cell. The hyperpolarization of the neuron following activation of the receptor inhibits subsequent depolarization of the neuron, reducing central nervous system activity. GABA is the main agonist of this receptor. Most of the drugs used in anesthesia that bind to the GABAA receptor do not directly activate the receptor but induce an allosteric change (i.e., change of the quaternary structure) of the conformation of the receptor and are called “allosteric modulators.” Barbiturates, benzodiazepines, propofol, etomidate, alfaxalone, inhalant anesthetics, and ethanol are examples of ligands that increase the efficiency of the receptor, allowing greater hyperpolarization of the neuron and are thus called “positive allosteric modulators” [11]. The benzodiazepine reversal agent flumazenil decreases the efficiency of the receptor and is therefore a negative allosteric modulator. The GABAA receptor is a heteropentamer (five subunits) comprising α, β, and γ subunits, with many combinations of those subunits being possible [11]. Agonist and antagonist ligands and allosteric modulators bind to specific sites of these subunits as illustrated in Figure 19.2A. Also, each subunit has four transmembrane‐spanning (α‐helix) segments creating the chloride channel (Fig. 19.2B). The α‐aminohydroxymethylisoxazolepropionic acid (AMPA) and N‐methyl‐D‐aspartate (NMDA) receptors are both cation channels. Their endogenous agonist is glutamate [12] and they usually co‐exist on the same postsynaptic membranes. Their interaction is thought to be one key element of central sensitization. The AMPA receptor is a ligand‐gated ionotropic receptor. This cation channel allows the entrance of Na+ into the cell and the exit of K+ from the cell. The AMPA receptor is composed of four subunits, with each subunit having four membrane‐spanning segments, which create the cation channel [12]. Like the GABAA receptor, the association with an agonist ligand induces a conformational change allowing the channel to open. The amount of glutamate being released in the synapse will dictate the amount of cation transfer, and consequently the degree of depolarization of the postsynaptic neuron induced by the AMPA receptor–ligand interaction. The NMDA receptor is both a ligand‐gated and voltage‐gated ionotropic receptor [12] (Fig. 19.3). This cation channel allows the entrance of Na+ and Ca2+ into the cell, and the exit of K+ out of the cell. In contrast to the AMPA receptor, a molecule of Mg2+ keeps the channel closed until a strong enough depolarization of the postsynaptic membrane occurs. Although weak activation of the AMPA receptor is not enough to activate the NMDA receptor, multiple or stronger activations of the AMPA receptor will induce a strong enough depolarization of the postsynaptic membrane to release the Mg2+ from inside the channel of the NMDA receptor. Glutamate and aspartate are the main endogenous agonists for the NMDA receptor and glycine is a co‐agonist required to efficiently open the channel. NMDA is a partial agonist and amantidine, gabapentin, ketamine and phencyclidine derivatives, ethanol, xenon, nitrous oxide, and some opioids (methadone and tramadol) are NMDA receptor antagonists. Magnesium, sodium, calcium, potassium, zinc, and copper are modulators of the NMDA receptor. Figure 19.3 Structure of the NMDA receptor. A. Schematic of an NMDA receptor composed of four subunits (two NR1 subunits and two NR2 subunits) showing the binding site of the agonist glutamate, the co‐agonist glycine, the antagonist ketamine, and the modulators zinc and copper. Other axillary subunits have also been described. B. Schematic of one glycoprotein subunit showing the four membrane‐spanning segments numbered 1–4. It should be noted that segment numbered 2 does not cross the membrane but kinks back on itself toward the intracellular side of the membrane. To transduce extracellular signals, some transmembrane receptors use intermediaries such as guanine nucleotide‐binding proteins (G‐proteins). A series of intracellular biochemical events (or second messengers) is initiated by the receptor–ligand interaction and ultimately leads to the observation of the clinical effect. Guanosine triphosphate (GTP) supplies the energy required by such complex G‐protein receptors. These receptors are some of the most common and important receptors. The β‐adrenergic receptor will be discussed as an example of a G‐protein‐coupled receptor. There are three types of β‐adrenergic receptors: β1 found in the cardiac sarcolemma, β2 found in the bronchial and vascular smooth muscle, and β3 found in adipose tissue. The β‐adrenergic receptors are linked to the adenylate cyclase (AC) enzyme system through the G‐proteins and are activated by the endogenous β‐adrenergic receptor agonists, epinephrine, and norepinephrine. The stimulatory G‐protein (Gs or Gαs), linked to the β‐adrenergic receptor, is composed of α, β, and γ subunits and is located on the intracellular side of the cellular membrane. By default, the α subunit is associated with guanosine diphosphate (GDP), and the protein is considered switched off. However, interaction of the β‐adrenergic receptor with norepinephrine or epinephrine changes its conformation allowing GTP to replace the GDP on the α‐subunit, and the protein is switched on. The associated AC is stimulated, inducing the production of cyclic AMP (cAMP), which in turn activates protein kinase A (PKA) and numerous downstream enzymes (Fig. 19.4). The AC can also be inhibited by the inhibitory G‐proteins (Gi or Gαi), which have a similar mechanism of activation to Gs. Indeed, Gi is linked to a β2‐adrenergic receptor and is a likely mechanism of modulation. Most anesthesiologists are familiar with angiotensin‐converting enzyme (ACE), which has an important role in blood pressure homeostasis through conversion of angiotensin I to angiotensin II – a potent vasoconstrictor within the renin–angiotensin–aldosterone system (RAAS). However, angiotensin‐converting enzyme 2 (ACE2) plays a key balancing role in this system principally through generation of the vasodilator angiotensin‐(1–7) from angiotensin II. More recently, the human ACE2 has been identified as an entry receptor for SARS‐CoV‐2 [13]. ACE2 is abundantly present in the lung and small intestinal epithelia and in most organs’ arterial and venous endothelial cells and arterial smooth muscle cells [14]. It is a transmembrane mono‐carboxypeptidase zinc‐containing metalloprotease receptor that binds to the spike proteins of SARS‐CoV‐2 and blocks it in place when the virus comes in contact with the respiratory epithelium [13,15]. Furthermore, cell surface proteases and lysosomal proteases will both activate SARS‐CoV‐2 entry [13]. The protease cuts the SARS‐CoV‐2 spike protein at a specific location, allowing the spike protein to remodel and insert itself into the epithelium cell’s membrane. The spike protein then pulls the cell and virus membranes toward each other. After fusion, the viral RNA is transferred into the host cell. To study pharmacodynamics, or the effects of a drug on the whole body, one can evaluate the efficacy, potency, concentration–response relationship, effective dose, lethal dose, and the therapeutic index. Although one general pharmacological principle remains – the more drug, the more effects (with effects being measured either within an individual or within a population) – the occurrences of U‐shaped (or inverted U‐shaped) dose–response curves are widely and independently observed phenomena [16]. “Hormesis” defines dose–response relationships characterized by stimulatory effects at low doses and inhibitory effects at higher doses, resulting in an inverted U‐shaped dose–response curve. Naloxone is one example of this hormetic response (see above paragraph on receptor agonist and antagonist), and all receptors assessed to date display dose–response relationships with identifiable mechanisms regulated by agonist concentration gradients [16]. Figure 19.4 Illustration of the association between the β‐adrenergic receptor (βAR), G‐proteins (GP), and adenylate cyclase (AC). The βAR and AC glycoproteins are showing 7 and 12 membrane‐spanning segments, respectively. The GP is composed of α, β, and γ subunits and is located on the intracellular side of the cellular membrane. The conformation of the βAR and the GP are closely linked together. When an agonist ligand binds to the βAR and changes its conformation, the α subunit of the GP also changes conformation, allowing a GTP (guanosine triphosphate) to bind instead of a GDP (guanosine diphosphate). The α subunit of the GP is switched on and will stimulate the AC, inducing the production of cAMP (cyclic AMP) from ATP (adenosine triphosphate). Earlier in the chapter, it was described how the association of a ligand with a receptor follows the laws of mass‐action (eqn 19.1). The total concentration of receptors [RT] is equal to the sum of [LR] plus [R], and if [R] is replaced by [RT] – [LR] in eqn 19.1, the relationship becomes: which rearranges to: Also, knowing that the maximum efficacy (Emax) represents the maximal pharmacological effect of a drug or a ligand, and that the pharmacological effect (E) of the drug is directly proportional to the percentage of activated receptors [17], then eqn 19.4 becomes: This equation indicates a sigmoid relationship between the logarithm of the concentration of ligand and the effect (concentration–response relationship) and that in the absence of ligand, the effect approaches nil. However, the receptor state theory implies the existence of a baseline agonist effect (E0) of the receptor, and this baseline effect is taken into consideration in Hill’s equation: The concentration–response relationship is usually illustrated showing the effect as a function of the ligand’s concentration (or other measure of exposure). The term “potency” characterizes the concentration of drug needed to obtain a pharmacological effect that equals 50% of Emax, that is, the EC50. The lower the EC50, the less drug that is needed to achieve the required effect and the higher the potency of the drug (Fig. 19.5). The effective dose (ED50) corresponds to the dose of drug necessary to induce the desired effect in 50% of the animals to which it is administered. Similarly, the lethal dose (LD50) corresponds to the dose of drug necessary to induce death in 50% of the animals to which the drug is administered. In most human trials, the lethal dose is not established, and is replaced by the toxic dose (TD50). The TD50 corresponds to the dose of drug necessary to induce toxic effects in 50% of the patients to whom it is administered. The therapeutic index is the ratio LD50/ED50 or TD50/ED50. The higher the therapeutic index, the safer the drug is considered to be. However, the therapeutic index does not take into consideration the slope of the concentration–response curve. Two drugs, A and B, with the same therapeutic index and given at ED90 (the effective dose in 90% of the population) can induce a significantly different prevalence of side effects, and therefore the therapeutic index is not useful as a measure of a drug’s clinical safety (Fig. 19.6). Figure 19.5 Illustration of the concentration–response relationships of the two opioids morphine and buprenorphine, the considered measured effect being analgesia. A. The maximal analgesic effect of buprenorphine (Emax B) is lower than that of morphine (Emax M). Indeed, morphine is a full μ‐opioid receptor agonist, whereas buprenorphine is a partial μ‐opioid receptor agonist. B. The concentration of drug needed to obtain a pharmacological effect that equals 50% of the maximal effect is lower for buprenorphine (EC50 B) than for morphine (EC50 M). Figure 19.6 Illustration of the limitation of the therapeutic index to establish the safety profile of a drug. Drugs A and B have the same therapeutic index (ED50/TD50, the ratio of the effective dose in 50% of the population to the toxic dose in 50% of the population) but different dose–response curve slopes. A. The EC90 (effective dose in 90% of the population) of drug A is significantly different from the TD50, implying that most of the population will benefit from drug A without encountering side effects. B. The EC90 of drug B is similar to the TD50, implying that a large part of the population that benefits from drug B will encounter toxic effects. It is fundamental to recognize that variability between the sensitivity of individuals to drug effects results in a range of doses that may be effective or toxic in some individuals, and not in others. It is less self‐evident, but equally important, to recognize that between‐occasion variability in response to a fixed dose exists within an individual. The sum of between‐individual and between‐occasion variability causes uncertainty in the dose–response correlation, necessitating attention to the individual response on each occasion of drug administration. When drugs are administered to animals two interactions are initiated. The animal’s body initiates its actions on the drug; absorption, distribution, metabolism, and elimination (ADME). These processes are described by the discipline called “pharmacokinetics.” At the same time, the drug initiates its actions on the body and this process is described by the discipline called “pharmacodynamics.” As discussed above, the interaction of free drug with its receptor stimulates the effect response by the organism. The association between receptor–drug concentration and response can differ between drugs, receptor systems, and occasions. As discussed above, the effect response is regarded as directly proportional to the ratio of the receptor–drug concentration and the total receptor concentration [17]. Experimental description of the effect response helps us to illustrate and understand the nature and extent of drug responses. One of the purposes of drug effect response modeling is to allow one to make predictions of the outcome from clinical use of the drug. Consequently, a good pharmacodynamic model is one that closely describes the data and allows the development and evaluation of hypotheses about the drug’s effect response at doses not directly tested. Several routine approaches are made to describe pharmacodynamic effect response data mathematically. Where the effect response being measured is induced by a drug–receptor complex between 20 and 80% of maximal binding capacity at a single receptor class, then the effect response is often approximately log‐linear in relation to drug concentration. Therefore, in the absence of a maximal effect being achieved, where E is the effect response measure, C is the drug concentration, and E0 is a constant which represents baseline activity, the effect response measure can be approximated by a straight line: In single receptor systems where a maximal response is measured so that the effect response measure approaches an asymptote, the shape of the response curve may be described in the same form as for receptor binding (eqn 19.5). Where Emax is the maximum achievable response and EC50 is the concentration at which a response is achieved that equals 50% of the Emax, the effect response can sometimes be described as: Sometimes, the relationship between drug concentration and effect response reflects changes in the sensitivity of the signaling pathway to the presence of the drug. This may be a function of biological processes such as receptor tachyphylaxis or may be unrelated to known processes. These cases are not well approximated by the Emax model shown above, but can often be better described by the following related equation, where h (the Hill coefficient) modifies the steepness of the effect response curve: when h = 1, this equation reduces to the standard Emax model of eqn 19.8. For example, the dose–response curves illustrated in Fig. 19.6A versus Fig. 19.6B are examples of curves with the same Emax and EC50 but with different slopes, hence they would have differing values for h. More complex models are needed for drugs, which achieve the measured effect response through multiple signaling pathways, and for effect responses, which are not objectively measured on continuous distributions. During the 1950s, Robins and Rall formulated the “free hormone hypothesis,” which is now generally accepted to be true both for hormones and for drugs [18]. Robins and Rall presented a convincing argument that, where a hormone (or drug) is present in protein‐bound and unbound (or free) forms in equilibrium, the activity of the drug or hormone at its site of action is proportional to the concentration of the free drug or free hormone in the plasma, irrespective of the total drug or hormone concentration. Based on the assumption that free drugs can diffuse readily to their sites of action, or that there are alternative facilitated or active processes that assist drugs to reach their site of action, the free hormone hypothesis allows the linkage of the plasma concentration to the drug’s effect response. Consequently, to examine the degree, extent, and duration of drug effect, a drug’s pharmacokinetics is usually sufficiently described by its disposition in blood, plasma, or serum. Most pharmacokinetic processes within mammalian bodies occur as saturable processes (Michaelis‐Menton kinetics): the speed at which a process occurs has an upper limit, which can be reached, at least in theory. However, we can simplify the mathematics for pharmacokinetics by assuming that processes occur either at constant rates (zero‐order) or at rates constant in proportion to the concentration of administered drug (first‐order). This simplification is usually acceptable because usually, the concentration range of drugs used is narrow. Zero‐order processes are those in which the change of drug concentration in a body fluid such as plasma or urine occurs at a constant rate, irrespective of the concentration of the drug present in that body fluid. Since this process is constant, the formula for a straight line can be used to describe the plasma drug disposition curve. Consequently, the equation for a zero‐order elimination has the following form: where C(t) is the concentration of the drug in the sample at any time t and the zero‐order elimination rate (k0) is the slope of the curve when concentration is plotted as a function of time and is called the “zero‐order rate equation.” This is the equation for a straight line, that is, y = b + mx, where y is the concentration in the sample, C(0) is the y‐intercept when x = 0, and x is time. Sometimes, drug delivery devices or formulations are designed purposefully to achieve zero‐order delivery (constant rate delivery) to maintain a constant effect of the drug for a prolonged period. Constant rate delivery is discussed later in this chapter. Figure 19.7 For most drugs, the elimination processes obey saturable kinetics, but at the concentrations used clinically most drugs approximate linear “first‐order” pharmacokinetics because the elimination processes are not approaching saturation. In contrast to zero‐order processes, a first‐order process is one where the rate of change in concentration of drug in a body fluid is proportional to the concentration of the drug in that fluid at that time. In this case, to describe the plasma drug disposition curve, an exponential function is appropriate, conforming to a straight line on a semi‐logarithmic plot: where ln(C) is the natural logarithm of the concentration of drug in the sample at any time t and the first‐order elimination rate constant (kel) is the slope. This reduces to which is called the “first‐order rate equation.” Many drugs used in anesthesia and analgesia adhere to first‐order pharmacokinetics with respect to their elimination from the body, and there are several drugs which are absorbed or eliminated by zero‐order processes (Fig. 19.7). The plasma elimination half‐life of a drug is the time interval needed for the plasma concentration to be reduced to 50% (half) of its initial value [19]. Drugs eliminated by zero‐order kinetics have half‐lives, which are a function of the initial plasma concentration of the drug at the beginning of the time interval. Therefore, plasma half‐life is not constant for these drugs and consequently is of no clinical value. In contrast, drugs eliminated by first‐order kinetics have plasma elimination half‐lives, which are fixed at a constant rate per unit time. The half‐life is inversely proportional to the rate of elimination (kel) as derived in the following sequence from the first‐order rate equation: Superficially, the concept of elimination half‐life appears easy to understand. However, this is a hybrid parameter and is influenced by many factors. Both metabolism and elimination commonly affect the elimination half‐life of a drug. Some factors that affect the rate of metabolism of a drug include the species, the age, the gender, the bodyweight, disease states (especially cardiac, renal, and hepatic diseases), and drug interactions. The time one must wait after administering a drug or changing a drug dose rate before attaining a new steady‐state plasma concentration, or before attaining complete elimination of a drug, is a function solely of the half‐life of that drug. If the fraction of drug that is eliminated is FE from the first‐order rate equation, we can derive: For practical purposes in clinical veterinary practice, approximately 4–5 half‐lives must pass before a change in dose achieves a new steady‐state plasma concentration (at about 97% of total). Similarly, approximately 4–5 half‐lives must pass after cessation of administration before all drug is effectively eliminated. The mass of drug in the body at any time is proportional to the concentration measured in the sample space (often the plasma) at any time and can be expressed as the following equality: where XB is the mass of drug in the body and Vd is the proportionality constant. This proportionality constant Vd is known as the “apparent volume of distribution” [20]. The reason that the word “apparent” is used in the parameter is, although it has units of volume, it does not actually represent any particular physiological or anatomical space and cannot be attributed to any such space from any simple physiological or pharmacokinetic analysis. It is clear that each sample space will have its own volume parameter, so in complex models, each “compartment” will have an individual volume, for example, the volume of the circulation is often referred to as the volume of the “central compartment” (Vc). The sum of all volumes, central and peripheral, is often referred to as the “volume of distribution at steady state” (Vdss). Understanding and having an estimate for volume parameters facilitates dose calculations, for example: Just as there are several different volume parameters, there are also different methods to calculate them. It is important to understand that estimates of a volume parameter might be differently derived and therefore have different absolute values. Particular volume parameters are better suited to particular uses; for example, the apparent volume of the central compartment (Vc) is usually used for calculating loading doses for rapidly acting or toxic drugs such as an induction dose of an anesthetic, whereas the apparent volume of distribution at steady state (Vdss) is often used for calculating constant rate infusions (CRIs) or repeated analgesic drug dosing regimens (see discussion below in the section on Constant rate therapy). Within a species, the apparent volume of distribution varies from individual to individual. Individual factors which affect the volume of distribution include the state of hydration, the age and gender of the animal, the bodyweight and body composition (fat, muscle, and water ratios), the protein content of serum especially for protein‐bound drugs, the presence of plasma expanders such as lipids after a meal, and drug interactions (rarely, due to competition for binding sites). Total body clearance is defined as the volume of blood from which drug is completely removed, per unit of time. It is commonly referred to simply as “clearance” (Cl) and the units for clearance are volume per time (e.g., mL/min or L/h) often normalized by body weight (e.g., mL/min/kg). Cl is a measure of the efficiency of drug elimination and is often directly compared or contrasted with cardiac output (CO) in the species under examination. As Cl describes the relationship between the rate of excretion of a drug and its plasma concentration, if XE is the mass of drug eliminated from the body and equals −XB if no further dose is administered, it can be described algebraically as follows: However, we have already seen that XB = CVd (eqn 19.19). Therefore, in this simplified example: Cl can thereby be understood as a function of both the volume of distribution and the concentration of drug present in the sample space. While this is demonstrated to be true for total body Cl, it is useful to foresee that this can be extrapolated to clearance from individual compartments in complicated multi‐compartment systems. Total body Cl is the sum of the clearance achieved by all mechanisms. Although some anesthetic drugs are cleared by the lungs, the most important two components are usually attributed to hepatic and renal clearance. Thus, total body Cl can be defined as follows: where ClH and ClR are the clearances due to hepatic and renal mechanisms, respectively. Total body Cl is a very important pharmacokinetic parameter and must be comprehensively understood [21]. Consider the flow diagram in Fig. 19.8, where QH is the rate of organ blood flow through the liver, CA is the concentration of drug in the arterial blood, CV is the concentration of drug in the venous blood, and QH(CA – CV) is the rate of elimination by the liver. Figure 19.8 Hepatic clearance of drugs is estimated from hepatic blood flow (QH) and the concentration difference between the drug in the hepatic blood inflow (CA) and outflow (CV). Then, since Now, since the fraction of drug removed from the blood on each pass of the blood through the liver is the extraction ratio (ER), it can be seen that: and hence by substitution: Because most body systems and processes are saturable, the liver in each individual animal has a maximum capacity for removing a drug from the blood when there are no blood flow constraints. This capacity for drug elimination by the combination of biotransformation and by excretion in the bile is a function of liver mass and of the amount and activity of the enzymes of drug metabolism that are present in that individual’s hepatocytes. This maximum capacity for drug removal from the blood is the intrinsic ability of the liver to remove drug; the innate capacity of the liver to clear drug is known as its “intrinsic clearance” (ClI). An understanding of the concept of ClI allows us to redefine the ER in terms of ClI: Therefore, by substitution: A corollary to the free hormone hypothesis is that the availability of a hormone or drug for binding to receptors or to enzymes of biotransformation is proportional to the free concentration. If fu is the fraction of drug not bound to plasma proteins (i.e., the free fraction) and ClI′ is the ClI for free drug only, it can be seen that: For each drug in each individual, a relationship will exist between the intrinsic hepatic clearance and that animal’s particular hepatic blood flow. This relationship between drugs is likely to be a continuum from one extreme, where ClI is very much less than hepatic blood flow to the other extreme, where hepatic ClI is very much greater than hepatic blood flow. If ClI is very much less than hepatic blood flow, then ClI is not a significant contributor to the denominator in eqn 19.31 above. Therefore, in this special case, where ClI is much less than hepatic blood flow, by cancellation it can be seen that: Since intrinsic hepatic clearance is the product of the fraction unbound (fu) and the ClI for free drug only, it is clear that in this special case, hepatic clearance is proportional to the fraction of drug unbound [22]. It is with drugs for which this special set of conditions is true that alterations to plasma protein binding of the drug can significantly affect the rate of that drug’s excretion. Similarly, for these drugs, induction of hepatic enzymes for biotransformation can have a large effect on their elimination rate, as discussed further below in this chapter. Now let us consider the alternate case where the intrinsic hepatic clearance is very much greater than hepatic blood flow. In this case, it is the contribution of hepatic blood flow to the denominator of eqn 19.31 that is insignificant. Consequently, in this alternate special case: For drugs that conform to this set of special conditions, it is clear that alterations in hepatic blood flow will profoundly influence the rate of hepatic clearance. The elimination of such drugs will be dependent upon factors such as CO and normal structure and function of hepatic blood vessels. Anesthesiologists understand that many of the drugs used have significant effects on CO and regional blood flow and that surgical procedures and pathology can also lead to marked changes in hepatic blood flow. Therefore, the rate of elimination of many common anesthetic drugs can differ from that predicted due to changes in hepatic blood flow induced by the animal’s pathophysiology or by the procedure itself. It can be seen in Example 1 (Box 19.1) that doubling the intrinsic hepatic clearance for a high ClI drug resulted in an increase in the rate of hepatic clearance by a factor of only 1.09, whereas in Example 2 (Box 19.2) the doubling of the intrinsic hepatic clearance rate for a low intrinsic hepatic clearance drug has resulted in a doubling of its hepatic clearance.
19
General Pharmacology of Anesthetic and Analgesic Drugs
Introduction
Classical receptor theory
Affinity and activity of a ligand
Selectivity and specificity of a ligand
Assessment of the ligand–receptor interaction
Receptor agonists and antagonists: definition and examples
Receptor state theory
Receptor structure
Sodium ion channels
GABA receptors
GABAA receptors
AMPA and NMDA receptors
AMPA receptors
NMDA receptors
G‐protein receptors – second messengers
β‐Adrenergic receptors
Angiotensin‐converting enzyme 2 – SARS‐CoV‐2 receptors
Clinical evaluation of drug effects
Efficacy, potency, and concentration–response relationship
Effective dose, lethal dose, toxic dose, and therapeutic index
Pharmacokinetics and pharmacodynamics
Fundamentals of pharmacodynamics
Fundamentals of pharmacokinetics
Half‐life of elimination
Apparent volume of distribution
Total body clearance
Hepatic clearance
Stay updated, free articles. Join our Telegram channel
Full access? Get Clinical Tree
