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In the previous chapters, pharmacokinetics was used to develop dosing regimens for achieving therapeutic drug concentrations for optimal safety and efficacy. The interaction of a drug molecule with a receptor causes the initiation of a sequence of molecular events, resulting in a pharmacodynamic or pharmacologic response. The term pharmacodynamics refers to the relationship between drug concentrations at the site of action (receptor) and pharmacologic response. Pharmacodynamics includes the biochemical and physiologic effects that result from the interaction of the drug with the receptor. Early pharmacologic research demonstrates that the pharmacodynamic response produced by the drug depends on the chemical structure of the drug molecule and the affinity of the drug at the receptor site. The drug affinity for the receptor site and the resultant pharmacodynamic response is referred to as the intrinsic activity of the drug. Drug receptors interact only with drugs of specific chemical structure, and the receptors are classified according to the type of pharmacodynamic response induced. Drugs may be considered a full agonist, partial agonist, or antagonist, depending upon the type of drug interaction with the receptor and the resulting pharmacodynamic response (see discussion below).
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Drug Receptor and Occupancy Concept
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Drugs may react with receptors to form covalent or noncovalent bonds. Drugs that form covalent bonds with the receptor produce a nonreversible pharmacodynamic response. Most pharmacologic responses are due to weak, noncovalent bonds (eg, hydrogen bonding, ionic electrostatic bonds, van der Waals forces) between the drug and the receptor. These interactions between drug and receptor are assumed to be reversible and to conform to the law of mass action. One or more drug molecules may interact simultaneously with the receptor to produce a pharmacologic response. Typically, a single drug molecule interacts with a receptor with a single binding site to produce a pharmacologic response, as illustrated below.
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where the brackets [ ] denote molar concentrations.
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This scheme illustrates the occupation theory for the interaction of a drug molecule with a receptor molecule. More recent schemes consider a drug that binds to macromolecules as a ligand. Thus, the reversible interaction of a ligand (drug) with a receptor may be written as (Neubig et al, 2003).
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where L is generally referred to as ligand concentration (since many drugs are small molecules) and LR is analogous to the [drug–receptor complex]. LR* is the activated form which results in the effect.
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The last step is written to accommodate different modes of how LR leads to a drug effect. For example, the interaction of a subsequent ligand with the receptor may involve a conformation change of the receptor or simply lead to an additional effect. In this chapter, effect and response are used interchangeably.
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This model makes the following assumptions:
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The drug molecule combines with the receptor molecule as a bimolecular association, and the resulting drug–receptor complex disassociates as a unimolecular entity.
The binding of drug with the receptor is fully reversible.
The basic model assumes a single type of receptor binding site, with one binding site per receptor molecule. It is also assumed that a receptor with multiple sites may be modeled after this (Taylor and Insel, 1990).
The occupancy of the drug molecule at one receptor site does not change the affinity of more drug molecules to complex at additional receptor sites.
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Each receptor has equal affinity for the drug molecule. The model is not suitable for drugs with allosteric binding to receptors, in which the binding of one drug molecule to the receptor affects the binding of subsequent drug molecules, as in the case of oxygen molecules binding to iron in hemoglobin. As more receptors are occupied by drug molecules, a greater pharmacodynamic response is obtained until a maximum response is reached.
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The receptor occupancy concept was extended to show how drugs elicit a pharmacologic response as an agonist, or block the pharmacologic response as an antagonist through drug– receptor interactions. Basically, three types of related responses may occur at the receptor: (1) a drug molecule that interacts with the receptor and elicits a maximal pharmacologic response is referred to as an agonist; (2) a drug that elicits a partial (below maximal) response is termed a partial agonist; and (3) an agent that elicits no response from the receptor, but inhibits the receptor interaction of a second agent, is termed an antagonist. An antagonist may prevent the action of an agonist by competitive (reversible) or noncompetitive (irreversible) inhibition. A few drugs (eg, pentazocine) have mixedagonist-antagonist activity in that the initial drug binding to a receptor produces a pharmacologic effect followed by blocking the receptor from eliciting additional pharmacologic activity.
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Spare, unoccupied receptors are assumed to be present at the site of action, because a maximal pharmacologic response may be obtained when only a small fraction of the receptors are occupied by drug molecules. Equimolar concentrations of different drug molecules that normally bind to the same receptor may give different degrees of pharmacologic response. The term intrinsic activity is used to distinguish the relative extent of pharmacologic response between different drug molecules that bind to the same receptor. The potency of a drug is the concentration of drug needed to obtain a specific pharmacologic effect, such as the EC50 (see Emax model, below).
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The receptor occupation theory, however, was not consistent with all kinetic observations. An alternative theory, known as the rate theory, essentially states that the pharmacologic response is not dependent on drug–receptor complex concentration but rather depends on the rate of association of the drug and the receptor. Each time a drug molecule combines with a receptor, a response is produced, similar to a ball bouncing back and forth, to and from the receptor site. The rate theory predicts that an agonist will associate rapidly to form a receptor complex, which dissociates rapidly to produce a response. An antagonist associates rapidly to form a receptor–drug complex and dissociates slowly to maintain the antagonist response.
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Both theories are consistent with the observed saturation (sigmoidal) drug–dose response relationships, but neither theory is sufficiently advanced to give a detailed description of the “lock-and-key” or the more recent “induced-fit” type of drug interactions with enzymatic receptors. Newer theories of drug action are based on in vitro studies on isolated tissue receptors and on observation of the conformational and binding changes with different drug substrates. These in vitro studies show that other types of interactions between the drug molecule and the receptor are possible. However, the results from the in vitro studies are difficult to extrapolate to in vivo conditions. The pharmacologic response in drug therapy is often a product of physiologic adaptation to a drug response. Many drugs trigger the pharmacologic response through a cascade of enzymatic events highly regulated by the body.
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Unlike pharmacokinetic modeling, pharmacodynamic modeling can be more complex because the clinical measure (change in blood pressure or clotting time) is often a surrogate for the drug's actual pharmacologic action. For example, after the drug is systemically absorbed, it is then transported to site of action where the pharmacologic receptor resides. Drug-receptor binding may then cause a secondary response, such as signal transduction, which then produces the desired effect. Clinical measurement of drug response may only occur after many such biologic events, such as transport or signal transduction (an indirect effect), so pharmacodynamic modeling must account for biologic processes involved in eliciting drug-induced responses.
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The complexity of the molecular events triggering a pharmacologic response is less difficult to describe using a pharmacokinetic approach. Pharmacokinetic models allow very complex processes to be simplified. The process of pharmacokinetic modeling continues until a model is found that describes the real process quantitatively. The understanding of drug response is greatly enhanced when pharmacokinetic modeling techniques are combined with clinical pharmacology, resulting in the development of pharmacokinetic–pharmacodynamic models. Pharmacokinetic–pharmacodynamic models use data derived from the plasma drug concentration-versus-time profile and from the time course of the pharmacologic effect to predict the pharmacodynamics of the drug. Pharmacokinetic–pharmacodynamic models have been reported for antipsychotic medications, anticoagulants, neuromuscular blockers, antihypertensives, anesthetics, and many antiarrhythmic drugs (the pharmacologic responses of these drugs are well studied because of easy monitoring).
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The best-characterized drug receptors are regulatory proteins, which mediate the actions of endogenous chemical signals such as neurotransmitters, autacoids, and hormones. Other protein receptors are endogenous enzymes, which may be inhibited or activated by binding to a drug. For example, the enzyme dihydrofolate reductase is the receptor for the antineoplastic drug methotrexate. The transport protein could also be a class of receptor (eg, Na+, K+ ATPase, the membrane receptor for cardioactive digitalis glycosides).
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Various types of drug receptors and pharmacologic responses are described by Katzung et al (2009, Goodman & Gilman, 2006) and quantitative models for many are reviewed by Neubig et al (2003). A simple pharmacologic response measured may be perceived as the result of many receptors and biological processes working together. The whole series of events may be the interplay of (1) pharmacologic response due to drug–receptor interaction, (2) physiologic or compensatory action or adjustment by the body, (3) pharmacogenetics that modify the drug response, and (4) alteration in biological responses due to an intervention by a pathogenic process. A comprehensive model incorporating all aspects is ideally best but may be too complicated to measure and validate in practice. An alternative approach is the monitoring of a biologic marker(s) in which the events in the biological processes may be observed directly or indirectly. The general approach for applying markers or biomarkers in pharmacodynamics is deeply rooted in pharmacology and pharmacokinetics and is illustrated by the modeling of various drugs shown in this chapter. Biomarkers broadly include markers that characterize the disease and the physical/biological changes associated with its progress in or drug treatment response in the body. During drug development, biomarkers developed with appropriate PD models can greatly increase understanding of drug therapy as well as the underlying disease.
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Unlike pharmacokinetic modeling, pharmacodynamic modeling has a greater advantage in that the clinical response (eg, change in blood pressure or clotting time) is affected by disposition of the drug as well as many events at the receptor site that may modulate actual pharmacologic action. In a simple case, drug that is systemically absorbed is transported to the site of action (receptor site) in which the pharmacologic receptor resides. Drug–receptor binding may occur, causing a response directly or through signal transduction that leads to the response. Observation of a drug response may involve several biologic steps as listed above and therefore, pharmacodynamic modeling must quantitatively account for all the processes involved to be useful.
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Pharmacodynamic (PD) models involve complex mechanisms that may not be easily simplified. Earlier PD models in this chapter are empirical models since the pharmacodynamic mechanisms were not well understood. Empirical models may help eliminate an unlikely pharmacokinetic mechanism. Generally, PK–PD models should be mechanism based whenever possible for simulations to be predictive. The understanding of drug response is greatly enhanced when pharmacokinetic modeling techniques are combined with clinical pharmacology, resulting in the development of mechanism-based pharmacokinetic–pharmacodynamic models. Pharmacokinetic–pharmacodynamic models use data derived from the plasma drug concentration-versus-time profile and from the time course of the pharmacologic effect to predict the pharmacodynamics of the drug. Pharmacokinetic–pharmacodynamic models have been reported for virtually every category of drugs. Examples in this chapter include antipsychotics, anticoagulants, neuromuscular blockers, antihypertensives, antiinfectives, and sedatives.
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The relationship between drug response and pharmacokinetics of many drugs may be explained in simple models. The computer model–simulated response based on assumptions should be distinguished from those models that are based on results obtained from clinical trials. These models are far more sophisticated and objective. Clinical effects in drug development are usually well defined, multicentered, and may be performed with special patient inclusion/exclusion criteria, subject size, controls, and other considerations as described in the clinical study protocols. The FDA publishes regulatory guidances that describe various aspects of e-study design and data analysis. Pharmacodynamics is indispensible for understanding drug receptors and disease state variables. Disease progress and drug therapy are both intimately related to pharmacokinetics (Chapter 12).
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Biomarkers (BM), Pharmacodynamics (PD), and Clinical Endpoints (CE)
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Biomarkers have been used to monitor biologic or genetic changes associated with the progress of a disease or a pharmacologic action.1 Biomarkers may include gene variants, functional deficiencies, expression changes, chromosomal abnormalities, and others. For pharmacodynamic modeling of a drug, the biomarker (BM) selected should ideally indicate the biological/pathological processe(es) and/or pharmacological response(s) modified due to the therapeutic intervention.
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A biomarker is measured objectively and specifically evaluated as an indicator of normal biologic processes, pathogenic processes, or biological responses to a therapeutic intervention. A biomarker can also define a physiologic, pathologic, or anatomic characteristic or measurement that is thought to relate to some aspect of normal or abnormal biologic function. Data from genomic and proteomics differentiating healthy versus disease states can therefore lead to the discovery of new biomarkers. Rational use of BMs can accelerate drug development and decision making and can provide a bridge between mechanistic preclinical studies and empiric clinical testing. The development of BM science including its definition has evolved during the last two decades (Lesko and Atkinson, 2001; Rolan et al 2003; Colburn and Lee, 2003). The use of BMs may be defined and developed by different disciplines to address special needs (FDA draft guidance, 2011).
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During drug development, clinical data and special markers also may be developed to demonstrate safety and efficacy. Clinical endpoints (CE) measure how patients feel, function, or survive. Since clinical endpoint data may be somewhat subjective, the CE (eg, pain) is often categorized (eg, using a 0–5 scale) to reflect the intensity of the feeling for quantitation. These clinical observations or treatment results may be partly or totally related to a BM. The surrogate markers (SM) or surrogate endpoints may be more objective and are designed to replace CE for efficacy and/or toxicity under a set of well-defined criteria. Clinically, few biomarkers are developed as surrogate endpoints due to the complexity of the disease. For example, the surrogate endpoints blood pressure and serum cholesterol concentrations may be used in evaluation of cardiovascular drugs (Temple, 1999). For example, the most widely used surrogate biomarkers are plasma concentrations of drugs that reflect systemic exposure to drug including bioavailability, bioequivalence, and pharmacokinetic guide for dosage regimens in clinical practice (eg, therapeutic drug monitoring).
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Biomarkers may reduce uncertainty in drug development and evaluation by providing quantitative predictions about drug performance, and with PK–PD modeling simulation, can play a critical role in the drug development process. Biomarkers are broadly used to follow disease or drug treatment effects over time and thus are important for understanding drug mechanism (Lesko and Atkinson 2001; Rolan et al, 2003). However, because the range of biologic measurements that can be considered biomarkers is now so broad, some classification and stratification are needed to provide an understanding of what types of biologic measurements are used for which purposes. The following mechanistic classification of BMs was cited by the International committee on BM (Rolan et al, 2003) (Table 19-1).
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Clinical Considerations in the Use of Biomarkers
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Most biomarkers are endogenous macromolecules, which are measured in vivo in biological fluids. However, not all biomarkers reflect in vivo processes (Stern et al, 2003). To be most informative in drug development, a biomarker assay or assays should measure in vivo drug effects, not drug concentrations. Stern et al (2003) cited an example involving the clotting time and activated partial thromboplastin time measured for ecarin.2 These coagulation tests that seemingly meet the definition of a biomarker by prolongation of the coagulation times closely correlated with blood concentrations of the oral thrombin inhibitor. However, these tests reflect enzyme inhibition assays as a function of drug concentration. Changes in coagulation test results demonstrate only that ex vivo clot formation has been altered but do not indicate that an in vivo process has been affected. Reliable and selective assays could be validated under a GLP-like environment for quantitative methods (Colburn and Lee, 2003).
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In early-phase clinical drug development, biomarkers may be used to guide dose selection and escalation. For a few drugs, well-characterized pharmacokinetic–pharmacodynamic (PK–PD) relationships can support, without further clinical data, therapeutic potential in a new target population, or justification for a different formulation or dosing regimen. Several considerations are important for BM selection to improve drug development based on Colburn and Lee (2003): (1) mechanism-based biomarker selection and correlation to clinical endpoints; (2) quantification of drug and/or metabolites in biological fluids under good laboratory practices (GLP); (3) GLP-like biomarker method validation and measurements; and (4) mechanism-based PK/PD modeling and validation.
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Changes in biomarkers following drug treatment may predict or identify safety problems related to a drug candidate or reveal a pharmacological activity expected to predict an eventual benefit from treatment. These measurements may be related to the mechanism of response to treatments or may be useful to evaluate the therapeutic response or clinical benefit endpoints. According to some scientists (Temple, 1999; Lesko and Atkinson, 2001), biomarkers:
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- May be valid surrogates for clinical benefit (eg, blood pressure, cholesterol, viral load)
- May reflect the pathologic process and be at least candidate surrogates (eg, brain appearance in Alzheimer's disease, brain infarct size, various radiographic/isotopic function tests)
- Reflect drug action but are of uncertain relation to clinical outcome (eg, inhibition of ADP-dependent platelet aggregation, ACE inhibition)
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A pharmacodynamic biomarker may be described as a dynamic assessment that shows that a biological response has occurred in a patient after receiving a drug for treatment. A pharmacodynamic biomarker may be treatment-specific or more broadly informative of disease response. Examples include blood pressure, cholesterol, HbA1C, intraocular pressure, radiographic measures, and C-reactive protein. However, even if carefully chosen, BMs may fail to become surrogate endpoints.
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Pharmacogenomic Biomarkers in Drug Labels
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Pharmacogenomics can play an important role in identifying responders and non-responders to medications, avoiding adverse events, and optimizing drug dose. Drug labels (FDA 2011) may contain information on genomic biomarkers and can describe:
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- Drug exposure and clinical response variability
- Risk for adverse events
- Genotype-specific dosing
- Mechanisms of drug action
- Polymorphic drug target and disposition genes
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Drug Receptors and the Development of Pharmacokinetic–Pharmacodynamic (PK–PD) Models
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The description of drug receptors was historically based on observations of drug response using known drug agonists and antagonists. From both these drug response observations and from pharmacokinetic data, PK–PD models were developed to describe quantitative relationships. Advances in molecular biology now allow the molecular structures of many drug receptors to be characterized and their locations elucidated (Katzung et al, 2009). Receptors are generally protein or macromolecules located inside or outside cell membranes. A tabulation of various receptors is listed in Table 19-2.
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Receptors are responsible for selectivity of drug action. The nature of the receptors determines the quantitative relationship between the drug dose or drug concentration and the pharmacologic effect. This relationship is an important basis for PD. The receptor's affinity for drug binding and the total number of receptors available determine the concentration of drug required to exert a PD response and the number of drug–receptor complexes which may limit the attainment of a maximal effect. This relationship forms the basis of the Emax model discussed in later sections of this chapter.
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The molecular size, shape, and electrical charge of a drug determine whether a drug will bind to a particular receptor among the vast number of chemically and structurally different binding sites in a cell, tissue, or patient. The drug may be referred to as a ligand when the drug binds a macromolecule or as a substrate when the receptor is an enzyme. An adverse drug reaction can occur when a drug interacts with different, unintended receptors, thus affecting therapeutic efficacy and toxic effects. The drug's relationship to drug receptor binding explains drug classes such as agonists, partial agonists, and antagonists. Elucidating this relationship also greatly helps the development of new drug therapies. Drugs may be designed to mimic or reproduce natural ligands, such as hormones and neurotransmitters.
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Pharmacokinetic–Pharmacodynamic (PK–PD) Models
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As discussed, PK–PD models relate the pharmacological effect of a drug to the log concentration of the drug in the plasma or in other body fluids close to the receptor site. The model equations created predict the time course of drug action and help to more accurately estimate a therapeutic dosage regimen for the patient. Similarly, the PK–PD model may relate drug concentrations to side effects or adverse events, such as the reduction of QT interval or slowing of repolarization of the heart due to side effects of some drugs such as torsades de pointes. The association between torsades and a prolonged QT interval has long been known. Common drugs such as erythromycin, clarithromycin, terfenadine, and some antihistamines may cause prolongation of the QT interval without necessarily causing overt clinical problems. PK–PD models can provide a much better understanding of how the mechanism of the drug acts and improves safety of these drugs. For either new or newly approved drugs, clinical information may be limited until adequate Phase 4 studies are completed. Phase 4 post marketing studies provide more information on the incidence of side effects and drug efficacy in various subpopulations. Mechanistic studies of a drug often reveal that there are biological changes as measured using biomarkers in the body associated with drug use.
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During PK–PD modeling, it is important to describe the following prospectively:
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Statement of the Problem
The objectives of modeling, the study design, and the available PK and PD data
Statement of Assumptions
The assumptions of the model regarding dose-response, PK, PD, and/or one or more of the following:
- The mechanism of the drug actions for efficacy and adverse effects
- Immediate or cumulative clinical effects
- Development or absence of tolerance
- Drug-induced inhibition or induction of PK processes
- Disease state progression
- Response in a placebo group
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1A genomic biomarker is a measurable DNA and/or RNA characteristic that is an indicator of normal biologic processes, pathogenic processes, and/or response to therapeutic or other interventions (FDA guidance, 2008).
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2Ecarin is a metalloproteinase from snake venom that activates prothrombin to meizothrombin.