Introduction to Clinical Pharmacology and Therapeutics – Module 1, Session 1

>> William Douglas Figg: We’re excited to have Dr. Juan Lertora to give today’s lecture Dr. Lertora received his medical degree from the National University of the Northeast in Argentina and received a Ph.D. in pharmacology from Tulane University Dr. Lertora is currently an adjunct professor of medicine at Duke University and Louisiana State University Previously, Dr. Lertora was a faculty member at the Clinical Pharmacology Center, Northwestern University Between 1981 and 2006, he was professor of medicine and pharmacology at Tulane University School of Medicine Dr. Lertora joined the NIH clinical center in 2006 as the director of clinical pharmacology program and led this course, The Principles of Clinical Pharmacology, until his retirement from the NIH in 2016 Please enjoy the presentation >> Juan Lertora: Welcome to Principles of Clinical Pharmacology My name is Juan Lertora, and today I will present an overview of the discipline of clinical pharmacology, and I also will introduce the basic concepts in pharmacokinetics and its clinical applications The focus of the course traditionally has been on the scientific basis of drug use, development, and evaluation We do not consider this to be a course in therapeutics, but of course, there will be relevant examples of applications of clinical pharmacology in therapeutics We will discuss general principles that are applicable to both old and new drugs There is a textbook that has been used for this course for a number of years, Principles of Clinical Pharmacology The lead editor is Dr. Arthur J. Atkinson, Junior So, let us see an outline of what I would like to cover for you today In the first part, we will have an overview addressing the general scope of the discipline, some brief historical notes We will talk about what do clinical pharmacologists engage in as professionals We will emphasize the topic of variability in drug response as an area of great interest in our field Also, adverse drug reactions and their impact both in terms of drug development and clinical use of drugs And finally, a brief overview of drug development So, let’s move on, then, and define pharmacology as the study of drugs and biologics and their actions in living organisms Generally, when we talk about drugs we think of small molecules, chemical agents When we talk about biologics we’re thinking about large molecules, peptides, and antibodies The most basic definition of our field is that clinical pharmacology is the study of drugs and biologics in humans The discipline really spans the spectrum of drug discovery, drug development, drug utilization, and drug regulation We aim — in clinical pharmacology, we aim at advancing therapeutics I humans with mechanistic understandings of drug actions — this is an area termed pharmacodynamics — and also drug disposition, and that is, of course, the subject of pharmacokinetics Now, you know, of course, the concept of translational sciences and how much it has been emphasized for the last decade or so Basically, we talk about knowledge that has been acquired in animal or in cylical [spelled

phonetically] models of disease, or through ex vivo studies in human tissues, or in vivo studies in healthy or diseased humans that then is translated into effective treatment for patients Clinical pharmacology is a translational discipline essential for drug development and therapeutics in humans Now, a bit of history focusing on the founders of American clinical pharmacology I’m talking about Dr. Harry Gold and Dr. Walter Modell at Cornell University, and this is a partial list of their accomplishments and fundamental contributions Introducing the double-blind clinical trial design in 1937; initiating the Cornell Conference on Therapy a couple of years later; and in the early ’50s, analyzing digoxin effect kinetics to estimate the absolute bioavailability as well as the time course of the chronotropic effects of digoxin We’ll come back to this example in the talk And in 1960, they founded the journal Clinical Pharmacology and Therapeutics, which is today, of course, the leading journal in the discipline Now, at the NIH we should mention Dr. Albert Sjoerdsma, who headed the experimental therapeutic branch at the National Heart Institute from 1958 through 1971 He trained individuals of the stature of Lou Gillespie, John Oates, Leon Goldberg, Richard Crout, Ken Melmon, and many others that subsequently became leaders in the discipline as well Their research focused on serotonin and the carcinoid syndrome, pheochromocytoma, antihypertensive drugs, and many other contributions Now, what are the professional goals of clinical pharmacologists? Well, we are interested in the discovery, development, evaluation of new medicines and how their use is regulated by the Food and Drug Administration in the United States and other regulatory agents in other countries We are also interested in optimizing the use of existing medicines and often finding new indications for old drugs But as I mentioned in our initial outline, a critical area of interest to clinical pharmacologists is to define the basis for variability in therapeutic and toxic responses to medicines, and this is an example looking at the exposure to two antidiabetic drugs, pioglitazone on the left side of the slide and metformin on the right-hand side And we’re looking at drug exposure in terms of the area under the plasma concentration time versus time curve And this is an AUC, Area Under the Curve, that has been normalized to a 15-milligram dose of pioglitazone and a 500-milligram dose of metformin, and also normalized to 70 kilograms of body weight for a human patient And you see the great variability that we see in drug exposure, both in females and males, in the case of both of these antidiabetic agents So, that’s one of the challenges clinical pharmacologists face in trying to understand the basis for this variability in drug exposure and how it may impact on the therapeutic actions of the drug Another source of variability in drug exposure may relate to underlying genetic variants

In this instance, we are using the example of nortriptyline, a tricyclic antidepressant that has been in use for many years, and the impact of cytochrome P450 II D6 polymorphism And here we are plotting plasma concentration of nortriptyline after a 25-milligram dose over time, and then we see the impact of the number of functional genes for CYP2D6 The first curve, on top, indicates a higher exposure for an individual that does not express CYP2D6, and actually, by definition, is a very slow metabolizer of this drug, and then the progressively smaller area under the curve with increasing numbers of CYP2D6-functional genes This over here at the bottom is an individual with 13 copies of the gene that is also an ultra-rapid metabolizer of this drug So, another source of variation in drug exposure and, of course, potentially untherapeutic efficacy of drugs in terms of these pharmacogenetically determine variation in drug exposure Now, let’s turn to another major area of interest in clinical pharmacology; namely, adverse drug reaction Some toxicities of drugs can be managed and may be acceptable, based on a risk/benefit ratio, but other adverse reactions and toxicities by their nature and severity are really unacceptable, and those drugs either have to be removed from clinical use or used with great caution and adherence to significant and close monitoring of the patients We need to understand, of course, that risk/benefit is contextual, depending on the drug and the disease that we intend to treat It is not the same to consider potentially serious toxicity for a drug intended to treat hypertension, which is a condition that needs lifelong therapy, compared to, say, treatment of cancer, a disease that is potentially lethal over the short term and that requires very intense treatment with combination of drugs that have very significant toxicity So, again, risk/benefit is contextual, and we must consider the drug in question and the disease that we are intending to treat Now, again, in terms of genetics as it may relate to severe drug toxicity, now, this is a condition or situations, if you will, where an underlying genetic variant may predispose individuals to severe toxicity from drugs Here we have the examples of HLA B5701 Individuals that carry this HLA variant are at very high risk of abacavir hypersensitivity Abacavir is a drug used in the treatment of HIV infections and AIDS, and prior to instituting treatment with abacavir, every patient is first tested for this variant, HLA B5701 If they have the variant, they cannot be treated with that drug; an alternative must be found The next example that we show here is that of HLA B1502, predisposing to severe carbamazepine-induced Stevens-Johnson syndrome This is a serious cutaneous adverse drug reaction that actually can be fatal

So, once again, the underlying genetic variants conferring predisposition to severe drug toxicity Another example of unacceptable drug toxicity is that of torsades de pointes What we’re showing here is an electrocardiographic record of heart rhythm in a patient that suffered from an episode of this polymorphic ventricular tachycardia This is a very abnormal rhythm You can see here a normal beat, if you will, in the electrocardiogram preceding these runs of polymorphic ventricular tachycardia That is actually drug-induced So, this is another example of a potentially life-threatening adverse reaction from drugs And here I’m showing terfenadine, which is historically was the first non-sedating antihistamine that was introduced in the United States market under the brand name of Seldane, but was subsequently withdrawn from the market because of the risk of drug-induced arrhythmias Now, look at the metabolic transformation of terfenadine in humans and the production of terfenadine carboxylate as a metabolite Very interestingly, this metabolite is active It also has this antihistamine pharmacological action, and it’s also a non-sedating antihistamine, but terfenadine, which is marketed as Allegra, does not have the risk of a drug-induced arrhythmia like torsades de pointes And this again brings us to consider and remember the importance of studying drug metabolism and assessing whether metabolites are also pharmacologically active or are otherwise inactive ones, whether transformation has taken place Let me bring you the example of thalidomide, again, in terms of unacceptable drug toxicities, but actually with a very interesting history, as I will show you in a moment Thalidomide was introduced in the 1960s as a sedative and actually was prescribed as an anti-nausea medication to pregnant women Unfortunately, in many countries — although not in the U.S. because thalidomide was not approved in the United States, and actually was not allowed to enter the market at the time because of the discovery of some severe toxicity to unborn children due to prenatal drug exposure This led to an epidemic worldwide of phocomelia, children born with severe defects in terms of their limbs And of course, this is a very unfortunate outcome of the use of that drug in pregnant women Now, there were consequences to this thalidomide crisis For one thing, the United States Congress approved the Kefauver Harris amendment in 1962 that instituted new and more strict FDA regulations to establish whether drugs were, on the one hand, effective but safe And the process has been modernized over the years, but, again, emphasizing safety and demonstrating efficacy of drugs before they’re allowed into the market The Institute of Medicine and the National Academy of Sciences began to review therapeutic claims at that time, and also more research on the causes of adverse drug reactions was encouraged And the National Institute of General Medical Sciences created a number of clinical pharmacology centers in the United States to, again, implement rational drug development to establish the

scientific basis of drug use in clinical medicine, and again, sadly, as a consequence of this major thalidomide crisis So, our discipline is eminently involved in the development and evaluation of new drugs We start with drug discovery, and this is a process in itself that we will be addressing in detail in another session of this course Then we have preclinical, meaning animal testing of candidate drugs, and eventually clinical evaluation to demonstrate safety in humans and whether or not the drug is effective in a given clinical condition But then we also have post-marketing studies Once the drug enters the market we continue to evaluate for the possibility of rare adverse drug reactions that were not discovered in the pre-approval stage, and also performing studies in special populations like the elderly and children Now, this is a schematic of pre-marketing drug development You see here the face of preclinical development We have animal models; we have assay development We study pharmacokinetics and pharmacodynamics in animals We of course begin to study animal toxicology in the short term and the long term if the drug is intended for chronic use And once a package of information is developed that indicates that the candidate drug may, in fact, be promising, an investigational new drug application, the IND, is filed with the Food and Drug Administration or other regulatory agencies, and then we begin the process of evaluating drugs in humans Typically considered as Phase I: First dose in human studies; dose escalations to assess tolerance Phase II, when we do the proof of concept studies, treating patients with the condition that may benefit, potentially, from the drugs And Phase III, the large randomized clinical trials comparing the new drug to a placebo or to a previously established therapy And that then leads to the submission of a new drug application, or NDA, where the sponsor asks the regulatory agents to review this body of evidence and request approval for marketing the drug and to begin using the drugs in clinical practice One way to look at the phases of drug development is with the “learn and confirm” paradigm The late Dr. Lou Scheiner and his colleagues advocated this approach Phase I and Phase II are the learning phases of drug development Phase III is the confirmatory phase, and Phase IV, again, is the post-marketing phase, but learning continues, focusing on rare adverse drug reactions and special populations, if required Now, let’s talk for a moment about drug repurposing This is an area where the National Institutes of Health and other academic investigators have been very interested in, and that has to do with finding new biological targets and new therapeutic indications for old drugs What are the potential advantages of this approach? Well, for one thing, it may shorten drug development time We already know a lot about the safety of the drug, and we also have data in terms of

the human pharmacokinetic behavior of the drug And drug repurposing then, and this is the concept of Dr. Austin at NCATS, is illustrated in this fashion Now, typically, we have a process of drug screening of thousands of compounds, and the whole process may take 10 years between identifying the target agent and performing all the preclinical and clinical phases of drug development that may then lead to drug approval What if, then, through repurposing of a much smaller number of drugs that have been in use for other indications — could shorten, perhaps, the period of drug development to a couple of years? Now, this is ideal, but conceptually, again, very important And we do have examples of a number of drugs that have been repurposed, and very interestingly, we have again thalidomide Extremely toxic and forbidden in pregnant females, but nevertheless, through the clinical observation of a physician in the 1960s, it became a very useful agent to lead — or, rather, to treat a complication of leprosy called erythema nodosum leprosa So, again, a drug that otherwise was banned from marketing becomes now useful in a clinical condition like erythema nodosum leprosa Years later, the drug was actually studied in the condition of multiple myeloma — again, a form of cancer — this time, through targeted drug development In any case, these are now two approved FDA indications This an immunomodulatory agent; marketing is done under a very special and very restricted distribution program referred to as System for Thalidomide Education and Prescribing, but a very good example of drug repurposing And in this slide, I show you a list of drugs that were approved originally for a different indication, but now are FDA-approved for indications that, for example, for sildenafil, include pulmonary hypertension; lamotrigine being used for bipolar disorder; and so forth So, again, repurposing as a viable and potentially very important way to look at finding new indications for old drugs Now, let us move to the second phase of our conversation today and introduce you to the basic concepts in pharmacokinetics and its clinical applications We will talk about the apparent volume of distribution and the clearance parameters These are two parameters that we call primary pharmacokinetic parameters Then we will address first-order kinetics The vast majority of drugs that we use in clinical medicine follow the pattern of first-order kinetics of drug elimination, but there are exceptions And that would lead us to discuss Michaelis-Menten kinetics for drug elimination So, pharmacokinetics: The quantitative analysis of the time course of drug absorption, drug distribution, drug metabolism, and excretion or elimination from the body Schematically, here we prescribe a dose or administer a dose of medication to a human subject, then we need to wait for the process of absorption to take place so that the drug

can be carried, typically from the gastrointestinal tract to the systemic circulation The drug in plasma may circulate as the free drug, but also may bind to plasma proteins like albumin And again, you have this reversible equilibrium between drug that is free in plasma and drug that is protein-bound The extent of protein-binding varies tremendously, depending on the drug in question Then, drug elimination will take place, but of course, drug distribution from the plasma compartment will take place The drug may actually distribute to most tissues, and you may find nonspecific binding of drug to tissues, but where we’re really interested is in the distribution of the drug to its cite of pharmacological action, what we call the bio phase, and of course, the study of receptor binding, and ultimately, the effect of the drug that we’re looking for Now, again, drug metabolism may contribute to elimination, and renal excretion is a pathway for elimination of drug metabolites, but also a significant pathway for elimination of the parent drug itself if the bio transformation is incomplete or actually does not take place And finally, here we want to measure the element of adherence Physicians prescribe medications to patients; ultimately, patients decide whether or not they will take their prescribed medication Monitoring for adherence is critical in the process of drug development If you are evaluating the efficacy of the drug you want to know that patients are actually taking the medication as prescribed before you make a statement like “The drug does not work.” Well, we need to have rigorous control for adherence in the context of clinical drug development So, what are the uses of pharmacokinetics? Pharmacokinetics provides the basis for rational dose selection in therapeutics It is essential for development and evaluation of new drugs We need to know how drugs are absorbed, to what extent they are absorbed if given orally, where does the drug distribute, and again, how is the drug eliminated and what is the rate of drug elimination Pharmacokinetics is also very important in basic studies of drug distribution in animals and humans, with the use of PET scanning, position emission tomography, where you can actually visualize the binding of drugs to its cite of action Now, a central tenet of pharmacology is the dose-response relationship We carefully study drug exposure-response relationships in order to find the right dose for a given therapeutic indication Now, exposure response, of course, applies to both drug efficacy and toxicity It is important to understand the range of doses that are useful therapeutically and the range of doses and resulting plasma concentrations that may lead to toxicity with the use of this drug Now, there are a number of pharmacokinetic/pharmacodynamic modelling approaches that have been used to define these drug exposure-response relationships, and you will deal with that in subsequent sessions of the course Now, linked to this notion of the dose-response relationship is the target concentration strategy

that has been very useful clinically for a number of drugs We already addressed the concern with individual variation in drug exposure when drugs are used in standard doses, as we saw with pioglitazone or metformin So, this approach, the target concentration strategy, attempts to individualize therapy when therapeutic and toxic ranges of drug concentrations in plasma have been established This is important to define a useful therapeutic range and then to target therapy to that range of therapeutic concentrations The ultimate goal is to optimize efficacy and minimize toxicity Now, the first description of therapeutic drug monitoring that we have on record is that of Dr. Wuth, using bromides and establishing ranges of therapeutic concentrations of bromide as a sedative Of course, this approach is now used for a number of drugs, and for example, lithium carbonate in bipolar disorder is administered with very strict attention to the resulting plasma levels of lithium so that you maintain efficacy and avoid some potentially serious toxicities with the use of this agent Now, what drugs are candidates for therapeutic drug monitoring? Generally, drugs with low therapeutic index, meaning that we can quickly move from concentrations that are therapeutic into ranges of concentrations that can cause toxicity The example of lithium is a very good example of a drug with a low therapeutic index, but there are many others, like digoxin and some antibiotics But in any event, that category of drugs is a good category of agents for therapeutic drug monitoring You may also be dealing with a clinical situation where you don’t have, if you will, physiologic endpoints that you can observe on an ongoing basis, or biomarkers to guide the dosage You may be dealing with patients with a seizure disorder — epilepsy — where the seizures are infrequent and, of course, undesirable So, you use the range of therapeutic concentrations, if you will, as your biomarker to guide dosage and hope that that would lead to a significant reduction in the frequency of seizures We already stated that pharmacokinetics vary widely between individuals, so if you have a target concentration, then you can adjust doses on an individual basis Occasionally, we may use a measurement of plasma-drug concentration to monitor adherence, but there are some issues with this approach as well So, let’s see, schematically, then, what happens when using the target concentration strategy We have an estimated initial dose that we administer with a target level in mind Some drugs need a loading dose to establish a therapeutic concentration quickly, followed by a maintenance dose Other drugs we begin simply with a maintenance dose Therapy is initiated, and then we have to evaluate the patient We need to see the response in the patient, and we may also measure a drug level And based on this assessment, then, we may refine the dose estimate, adjust the dose, and then continue on an iterative basis to optimize the range of concentrations that we want to maintain throughout therapy Now, how do we choose a target level?

Well, this is an empirical process in terms of defining what ranges of concentrations are therapeutic and where you have minimal or no toxicity So, we will have the example of digoxin to address this topic of how do you define a therapeutic range of concentrations This was a study conducted in Boston by Dr Smith and Haber [spelled phonetically] in patients that were being treated with digoxin because they had either congestive heart failure or atrial fibrillation requiring rate control And what they saw looking at a group of patients that were classified as being toxic or nontoxic, based on clinical characteristics and electrocardiographic characteristics, without knowledge of the resulting digoxin levels And this is a histogram of the distribution of concentrations of digoxin in the patients that were nontoxic, and then higher concentrations of digoxin being measured in patients that were clinically toxic So, on the basis of these empirical observations, then a therapeutic target range is proposed; in this instance, 0.8 to 1.6 nanograms per mL of plasma It was considered that levels in the range of 1.6 to 3.0 per mL were possibly toxic, and patients that had levels of 3.0 nanograms per mL or greater were probably already having digoxin toxicity But once again, based on further evaluation of the effects of digoxin not only on function in patients with congestive heart failure, but now in terms of survival after long-term treatment with digoxin This study that was published in the early 2000s looking at patients on therapy for congestive heart failure and receiving digoxin throughout this period of observation that lasted 48 months, and then looking at survival on the basis of the observed levels of digoxin Now, there was a placebo group here that you see with the continuous line These patients were receiving treatment for congestive heart failure, but were not receiving digoxin as part of their regimen And then patients that were receiving digoxin but now stratified based on their digoxin levels: Low levels of 0.5 to 0.8; intermediate levels of 0.9 to 1.1; and high levels greater than 1.2 nanograms per mL Now, you see that survival changed based on the digoxin levels and the range of digoxin levels that were measured actually at one month into the trial — one month into the trial The better survival is actually in patients that have low digoxin levels in plasma, and there is a disadvantage in terms of survival for patients that continue digoxin and maintain the level or at least had a level at one month after beginning the trial that exceeded 1.2 nanograms per mL So, of course, the question is, what were the digoxin levels well into the trial? We don’t have that data, but based on this survival analysis for the use of digoxin in patients with congestive heart failure, there is a new therapeutic range that has been proposed; namely, 0.5 to 0.9 nanograms per mL, much lower than what was usual in clinical practice

And the benefits may result from inhibition of sympathetic nervous system rather than improved inotropy or improved contractility of the myocardia There are limitations for this study — we already pointed that out — that no digoxin levels were done after one month in the study, and considering that the observations lasted for 48 months So, that’s how we estimate a target level And then, in the case of drugs that require a loading dose — and that was the practice, actually, with digoxin — we need to estimate the loading dose based on the concept of distribution volume Distribution volume or apparent volume of distribution, a primary pharmacokinetic parameter So, let us use the example of digoxin once again Here, we’re plotting the concentrations of digoxin in plasma — this is in logarithmic scale — versus time in a linear scale, and we’re showing the plasma concentration versus time curve for digoxin after intravenous administration of three quarters of a milligram; single dose This is a loading dose And now we see that the plasma concentration versus time, plotted semi-logarithmically, declines in a bio-exponential fashion We refer to this as the distribution phase, and then this terminal phase we call the elimination phase Now, the modeling here is plotting the tissue concentrations of digoxin over time, and we see that those tissue concentrations of digoxin rise as the plasma concentrations of digoxin are declining Now, in order to estimate the apparent volume of distribution for digoxin, one approach is that of the extrapolation method; namely, extrapolating from the terminal phase of this curve back times zero and estimated this C subzero [spelled phonetically] or initial concentration of the drug Now, that is, again, one approach to estimating the apparent volume of distribution, and we are using what we call a single compartmental model of drug distribution and elimination We administered the dose; in our example, we gave this dose intravenously Then we have this single body compartment, a hypothetical compartment, where the drug is distributed, and then we are showing here the parameter of elimination clearance And basically, what we’re doing in this example — the volume of distribution by extrapolation — is estimated as the ratio of the dose over that extrapolated initial concentration The assumption, of course, is that instantaneous distribution occurs We saw that that is not the case, but once again, this is one approach that has been useful in terms of estimating the apparent volume of distribution There are other approaches that you will discuss later in the course, the volume of distribution by area and the volume of distribution at steady state So, the example of digoxin Initial digitalization — this is a term referring to the loading dose of digoxin — a quarter of a milligram being administered, and that distributing into a single compartment, resulting in that initial concentration of 1.4 nanograms per mL You see here we are doing our proper dimensional analysis in terms of the dose that was administered The measure concentration in plasma in terms of nanograms per mL, and then applying that

principle — the delusion [spelled phonetically] principle, if you will — we have now our dose in nanograms per mL, our concentration in nanograms per mL, and we have this rather large volume, apparent volume of distribution of 536 liters for digoxin Of course, this does not agree with the reality of physiological body fluid compartments, but nevertheless, the apparent volume of distribution is a critical and very important pharmacokinetic parameter to determine Now, let’s go back to the process of drug distribution We saw that distribution, in fact, was not instantaneous, and that has an impact on the action of the drug — in this case, the chronotropic action of digoxin — in that digoxin slows the heart rate Here, we’re looking at ventricular rate in a group of patients with atrial fibrillation with rapid ventricular response, and we have both oral and intravenous administration This is from the classic work of Harry Gold and his coworkers in the early 1950s And what we’re seeing here is a significant reduction in heart rate after the intravenous administration of digoxin, but you see that the effect is not instantaneous The maximal effect, in fact, requires six hours before we can observe that significant slowing of the heart rate in patients with atrial fibrillation So, drug distribution may, in fact, impact the onset of drug action That is, the rate of drug distribution may impact the onset of drug action So, now, if we want to continue treatment, we have to select the maintenance dose So, what are the principles that apply here? Now, in order to estimate the maintenance dose, we need to understand the concept of elimination half-life and elimination clearance, clearance being the other primary pharmacokinetic parameter we referred to a moment ago So, simple definition Elimination half-life: The time required for the plasma concentration or the total body stores of the drug to fall to half of the concentration or amount present at some previous time It’s a very straightforward definition, but again, half-life applies strictly to drugs that follow first-order or exponential kinetics of elimination, and we will come back to these in a moment So, let’s look at some simple equations here referring to the half-life Again, assuming first-order kinetics of elimination And the half-life can be estimated, then, as the product of the natural logarithm of two times the apparent volume of distribution, divided by the clearance of elimination for that drug The first-order elimination rate constant can be estimated as the ratio of the natural logarithm of two over the observed half-life And finally, the elimination clearance can be calculated as the product of k times the apparent volume of distribution, but in fact, k does not determine clearance This is one way to estimate the clearance of elimination, but in fact, clearance determines both the half-life and the first-order rate constant Now, maintenance therapy in the case of digoxin Now, how much do we need to give in order to maintain that therapeutic level that we were looking for? In this case, 1.4 nanograms per mL Well, we need to estimate how much drug is lost over time In this case, it was estimated that one third of the total body stores of digoxin is lost

daily In the case of digoxin, the drug is eliminated primarily via the kidneys So, one third of the total body stores times zero; namely, a quarter — or rather, three quarters of a milligram One third of that is a quarter of a milligram, so that is the daily loss, and that is the loss that has to be replaced on a regular basis So, that’s how you establish what your maintenance dose should be Now, you may start treatment without giving a loading dose, and this is a brute force demonstration of the fact that drug accumulation will take place — will take place — over time until you reach or approach a plateau After seven doses, in this example, you’re pretty close to that total body stores of 0.75 milligrams that was established by giving a loading dose So, drug accumulation will take place exponentially when you have a constant dosing rate for maintenance and you have first-order kinetics of elimination for the drug Now, there is another approach, of course, to estimate the extent of drug accumulation using this cumulation factor that is shown here This parameter Tao [spelled phonetically] is the dosing interval — the dosing interval I mean, in the case of our example, it was 24 hours, or one day And then, of course, you need to know or have an estimation of the elimination rate constant, the first-order elimination rate constant, for that drug Now, you can find the derivation [spelled phonetically] of this and other equations in your textbook, and once again, the elimination rate constant that we showed as in the equation for the accumulation factor, estimated as the natural logarithm of two divided by the elimination half-life Now, let’s see graphically what happens in three different situations here The first one is that no digitalizing dose, no loading dose, was administered, and the drug is accumulating exponentially until it reaches a plateau The solid line here would be a situation where a loading dose was administered to establish a therapeutic level quickly, and then the optimal maintenance dose was administered over a period of time Actually, the maintenance dose here is the same as the maintenance dose here Now, let’s say that you gave a higher loading dose, twice the loading dose you gave before, but then administered the same maintenance dose that was used here and here Over a period of time, the concentration that will be achieved at the plateau or when steady state is achieved is the same So, this illustrates the fact that the loading dose that’s not determined what the concentration is going to be at steady state, and now we’re illustrating another useful estimation; namely, that 90 percent of the steady state level with continuous drug administration will be achieved in approximately 3.3 half-lives for that particular drug Now, practically, think about an individual with normal renal function that is receiving a quarter of a milligram of digoxin for maintenance and approaches the plateau concentration in approximately seven days, as we saw in our example Now, think of an individual with uremia, impaired renal function, and consequently, impaired elimination of digoxin

The drug will accumulate, again, using the same maintenance dose, and you will anticipate that the plateau concentration is going to be double if the clearance of elimination, say, is reduced by 50 percent But the other thing that is important is to recognize that you will not reach the plateau in the patient with impaired renal function until later This is normal renal function, normal half-life for digoxin This is impaired renal function and a prolonged half-life for digoxin Consequently, you will not achieve that steady state concentration until later; in this case, in this example, until 14 days of dosing have taken place So, now let’s discuss clearance as a primary parameter in pharmacokinetics And of course, we need to understand clearance in the context of drug evaluation and use in clinical medicine Now, this is a traditional creatinine clearance equation that you learn in your physiology courses that describes the clearance of creatinine — this is an endogenous product that can be measured in plasma — and the clearance of creatinine being used as an index of renal function And we have this relationship here that says that U times V over P determines what the creatinine clearance is in that context So, U refers to the urine concentration of the drug, or rather, of creatinine, in this case V is the urine volume produced over a period of time Typically, the creatinine clearance requires a 24-hour urine collection, so this is really a urine formation rate And then, P standing for plasma concentration of creatinine Now, let’s look at this again and think about the appearance of creatinine in the urine, the rate of appearance of creatinine in the urine: dE — think about excretion of creatinine — dE over dt And now this is equal to the clearance for creatinine and the plasma concentration at that time So, again, that equation that we had before is really a differential equation in disguise Now, let’s think about the rate of change of creatinine in the body, X being the creatinine in the body So, we have dX over dt now being equal to I, I being the rate of creatinine synthesis — this is an endogenous product — minus the clearance of creatinine, times the plasma concentration This would be the creatinine excretion rate At steady state, we can of course discard this term, dX over dt, such that the plasma concentration now is equal to the rate of creatinine synthesis or is directly proportional to the rate of creatinine synthesis and inversely proportional to the rate of creatinine clearance And let’s look at these steady state equations, because these are truly some of the most useful equations you’re going to use in pharmacokinetics So, if we look at continuous synthesis of creatinine, the steady state plasma concentration of creatinine equals the endogenous rate of production of creatinine over the clearance And if you think about a drug that is being given continuously — say, by intravenous infusion — the steady state concentration is going to be equal to the infusion rate over the elimination clearance for that drug

So, again, one of the most useful equations for you to keep in mind in addressing what are the determinants of the steady state concentration of the drug Now, we don’t often do creatinine clearance determinations and collect urine for 24 hours, and a number of equations have been developed over the years to estimate the clearance of creatinine in the case of the Cockcroft and Gault equation that has been in use since the 1970s And you have these parameters here that consider age, that consider weight of the individual, and of course, the serum creatinine concentration in milligrams per deciliter Now, this estimate, based on the Cockcroft and Gault equation, has to be reduced by 15 percent for women because, generally, they have a smaller body mass — specifically, skeletal muscle mass — and that leads to a reduced estimate for women when using this approach Now, in this equation — or rather, in this slide — what you see is that the terms that are shown in red are actually estimating the creatinine synthesis rate that we had in our basic equation previously An example of the importance of relying on the estimated clearance of creatinine, as opposed to simply measuring a serum concentration of creatinine is illustrated in this work by Piergies and colleagues in the early ’90s They had a group of individuals that were clinically toxic due to the use of digoxin, and what they were trying to see is what was the clearance of creatinine in these patients, as opposed to the serum concentration of creatinine And they grouped their patients into individuals that had creatinine and serum of 1.7 milligrams per deciliter or less, or individuals that had greater than 1.7 milligrams per deciliter of creatinine And these are their estimated clearances of creatinine, using the Cockcroft and Gault equation What you see here is that in the group of individuals with low serum creatinine concentration, relatively low serum creatinine concentration, 19 individuals out of 23 actually had an estimated clearance of creatinine that was less than 50 On the other hand, the majority of individuals with creatinine in serum greater than 1.7 had a clearance of creatinine less than 50 So, once again, it is important to estimate the clearance of creatinine Now, another approach to estimating renal function is based on this equation, the MDRD equation — many versions — that actually estimate the glomerular filtration rate Not the creatinine clearance, but the glomerular filtration rate normalized to body surface area Now, you’re going to have more discussions of this equation in addressing pharmacokinetics alternations in patients with renal disease A more modern equation is the CKD-EPI collaboration equation that is more accurate than the MDRD equation in estimating the glomerular filtration rate, and actually has less bias if the GFR is greater than 60 milliliters per meeting — per minute, rather Once again, normalized to body surface area So, back to our steady state equations If you have a continuous drug infusion, the steady state concentration is a function of

the infusion rate and the clearance of elimination for the drug If you’re using intermittent dosing, say, giving the drug once a day or twice a day or whatever the case may be, this is the estimated mean serum concentration over that dosing interval, now being equal to the dose over the dosing interval, and again, over the clearance of elimination for the drug So, the steady state concentration Let us emphasize it is not determined by the loading dose Now, once again, some drugs require the administration of a loading dose to establish a therapeutic concentration rapidly, but the loading dose does not determine what the steady state concentration will be with continuous administration of the drug Now, the mean steady state concentration with intermittent drug administration is not determined by the volume of distribution But on the other hand, we need to pay attention to peak and trough levels because they will be affected by the apparent volume of distribution, and this is shown in this example, where the volume of distribution is either large or small, and the same dose being administrated over a dosing interval And you see the variations in peaks and troughs, but the mean estimated concentration over the dosing interval is the same and corresponds, of course, to that dosing rate and the elimination clearance And an important element to highlight is that changes in maintenance dose for most drugs, when we’re dealing with first-order kinetics of elimination, result in directly proportional changes in the steady state concentration Once again, for most drug that follow first-order kinetics of elimination And we are reemphasizing our steady state equations because, truly, these are equations you should remember because of their conceptual and practical use But some drugs are not eliminated by first-order kinetics, and I’m giving you three examples here Phenytoin, ethyl alcohol, and aspirin — acetylsalicylic acid These are drugs that deviate from the general pattern of first-order kinetics of elimination And let’s focus on phenytoin; phenytoin undergoes metabolism in the liver via this main pathway of cytochrome CYP 2C9, and we have this parahydroxylated metabolite that is generated through this pathway And here we have an example, actually from Dr. Arthur J. Atkinson, Junior, who had the opportunity to study a patient with phenytoin toxicity Very high levels of phenytoin observed in this patient upon admission And which signs of toxicity — just to give you a reference, the therapeutic range for phenytoin is typically 10 to 20 micrograms per mill We’re near 60 micrograms per mL when this patient was admitted with signs of toxicity And what they did in this very elegant study is they followed the plasma concentrations of the drug over time, and at the same time, they started collecting urine to measure the appearance of this parahydroxylated metabolite of phenytoin And you see here, day after day after day, that the amount of this metabolite of phenytoin

that was recovered in the urine remained relatively constant The plasma concentrations are falling, as you see here, over time, but for a period of time, the amount of the metabolite that appears in the urine is constant And then we reach a point when the plasma concentrations begin to decline more rapidly, and also, we see that the amount of metabolite recovered in the urine also diminishes What this is indicating — and of course, they’re measuring urine creatinine to validate their urine collections, if you will, over time — and over here they started re-administering phenytoin once again, and they see the increase in the level, and then the subsequent decline of the level What this indicates is that the metabolic pathway, CYP2C9, that generates this hydroxylated metabolite of phenytoin is saturated over a significant period of time because of these very high concentrations of phenytoin that that metabolic pathway cannot handle, if you will Phenytoin kinetics actually follow the pattern of Michaelis-Menten kinetics Again, concentration over time; in this case, giving the drug intravenously This is the rate of change of phenytoin plasma concentration, which does not follow first-order kinetics, represented here — or rather, determined by the V-max — that is the maximum capacity of the metabolic pathway — the Michaelis constant, and here again the phenytoin concentration terms So, this is a deviation from first-order kinetics of elimination So, let’s look again at our steady state equations We’re giving the drug — a drug, if you will — at intervals orally, and this is the equation we described before for drugs that follow first-order kinetics: Clearance of elimination times the mean steady state concentration In the case of drugs that follow Michaelis-Menten kinetics, like phenytoin and ethyl alcohol and aspirin, you need to apply this equation and this term, if you will, in lieu of this clearance of elimination term A very important issue, because when you follow this type of kinetics you lose the element of dose proportionality Here, the patient receiving 300 milligrams per day of phenytoin has a concentration of 10 micrograms per mL in plasma; again, the mean steady state concentration We go up to 400 milligrams and the concentration already doubles We go to 500 milligrams per day; we have triple the concentration of phenytoin So, we do not have dose proportionality with drugs that follow Michaelis-Menten kinetics of elimination And again, this is another example of a patient that became toxic on a phenytoin dose of 300 milligrams per day A typical dose, if you will, but excessive in the case of this individual with slower rate of metabolism Defining, then, the therapeutic dose for this patient should really be 200 milligrams per day And one thing, of course, that arises as a question is, well, there is a large number of drugs that are metabolized in the liver, so an enzymatic pathway is involved, and yet we do not see Michaelis-Menten kinetics for those drugs So, we have apparent first-order kinetics of elimination, and what we can see here is that in situations where the KM, the Michaelis-Menten constant for that particular drug and that

enzyme, is much greater than the plasma concentrations that we will need or observe in a therapeutic context in the clinic If the KM is much greater than C, then we can neglect this term here in the denominator, such that now we have V-max over KM becoming a pseudo-first-order rate constant of elimination So, the ratio of two constants, of course, is a constant, so now this becomes the equivalent term, if you will, under conditions in which the KM for that drug and that enzymatic pathway is much greater than the concentrations that we need to obtain for a therapeutic response So, I will refer you to the practice problems that are provided at the end of chapter two in our textbook, with answers provided in appendix two, so that you can practice and become comfortable with these concepts All the equations that I have shown are derived, again, in the relevant chapters in the textbook And that will conclude our discussion today After doing your practice problems and reviewing the lecture material, if you have questions, please contact our course coordinator, who will in turn implement a consultation mechanism with the lecturers in the course so that you can get an answer that will be posted I hope I have provided you with an overview of our discipline, a critical discipline in the context of drug development and in the context of therapeutic drug utilization Thank you very much