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Pharmacodynamics and Individualized Drug Therapy
Population Pharmacokinetics/ Pharmacodynamics and Individualized Drug Therapy Paratneters describing pharmacologic effect as a function of drug concentration can help phartnacists develop individualized doses and dosing schedules. by MatthelN M. Riggs
Introduction Today's pharmacy practice settings are placing more emphasis on tailoring patient care to each individual's needs. To provide an example of how pharmaceutical research is affecting phannaceutical care, this article demonstrates how patient-specific medication doses and dosing schedules may be achieved through population pharmacokinetic and pharmacodynamic modeling. Pharmacokinetics (PK) has been described as the action of the body on the drug-that is, how the drug is absorbed, distributed, metabolized, and excreted during its journey into, through, and out of the body. TIlls process is also called drug disposition. Pharmacodynamics (PO) has been described as the action of the drug on the body. 1 PD characterizes the
relationship between the effect of the drug and the amount of drug in the body. PK and PD can be used together to help the pharmacist develop an understanding of the effect of concentration ranges on response and toxicity. Parameters describing pharmacologic effect as a function of drug concentration may then be used to develop an appropriate dose and a dosing schedule that achieves optimal concentration for maximal therapeutic benefit to an individual patient. If people were alike in the way their bodies handled drugs, this task would be simple. Unfortunately, an appropriate dose for one patient often proves to be less than ideal for another patient or for that same patient at a later time.
Characteristics That Alter Drug Effect
Abstract Drug concentration and effect vary among patients for any given dose. A goal of population pharmacokinetics (PK) and pharmacodynamics (PD) is to identify patientspecific factors such as weight, creatinine clearance, and age, and associate them with the observed PK/PD differences. Once associated with differences in concentration or response, these distinguishing factors may then be used to better individualize drug therapy. Studies of the population PK of teicoplanin and the population PK and PD of felodipine are summarized as examples of this approach.
Vol. NS36, No.2
February 1996
Individualized drug therapy, the nucleus of pharmaceutical care, requires that the distinguishing characteristics of each patient be identified and the magnitude with which they alter drug concentration and effect be described. These characteristics, or covariates, include weight, age, gender, creatinine clearance, concurrent medications, fed versus fasted state, and social habits such as smoking or alcohol consumption. For example, if it can be shown that clearance for a particular drug is doubled for persons who smoke, that the volume of distribution of drug into body tissues increases with total body weight, and that the therapeutic index is dependent upon age, then a 180-pound, 60year-old smoker and a 120-pound, 21-year-old nonsmoker journal of the American Pharmaceutical Association
will benefit from different dosing regimens. Obviously, given the many differences among people, individualization of dosing can become quite complex.
Traditional PK/PD Studies Traditional PKjPD studies have not allowed for adequate determination of which factors among a population give rise to differences in the overall effect of a drug. These studies have typically used 15 to 20 healthy male subjects of "ideal" weight and age. Numerous blood samples are obtained from each subject and parameters describing drug disposition for each individual are determined. These separate estimates are combined, and the mean and standard deviation are reported. 2 However, because of our inherent heterogeneity, PK and PD often vary greatly fr~m these "ideal" values when the drug is actually administered. Furthermore, because of the necessity for intensive sampling, ethical and logistiC concerns preclude drug disposition studies in elderly, critically ill, and pediatric patients. 3 Therefore, using traditional methodology, it is difficult to account qualitatively and quantitatively for differences in response within the population that receives the drug.
Population PK/PD Studies
concentration and effect. Information obtained from preclinical trials may be useful in narrowing the possibilities. For example, knowing how the drug is administered (intramuscularly, intravenously, or orally) and eliminated (renally and/or hepatic ally) will influence which patient characteristics are considered pertinent for inclusion in the study. Table 1 pro- , vides examples of relevant covariates. From this compilation of possible covariates, it is necessary to assess which are clinically and/or statistically relevant. This relevance is tested by statistical methods through modeling of the data. 5 Modeling is an attempt to describe the observations of interest with respect to time or the covariates, using mathematical expressions. Several computer software packages are available (e.g. , NONMEM, P-Pharm) to aid in the process of building and testing a model. These programs start with a simple model that does not include any of the covariates. This is termed the base PK/PD model, and it should properly describe dnlg disposition. 5 This model provides mean estimates of the PK/PD parameters and the variability among subjects that is associated with these estimates. However, this model does not yet describe the sources of variability. From the base model, individual characteristics are tested stepwise on the relevant PK/PD parameters. Statistical analy- , ses are performed on the model results to determine whether the inclusion of a covariate decreases the unexplained variability observed in a relevant parameter within the study p opulation. In essence, this process reduces unexplained intersubject variability by association with a patient characteristic, accounting for some of the difference observed among individuals. A fmal model is constructed to include only those covariates that significantly- improve the ability of the model
Assessment of concentration and response variability is more feasible through a population PKjPD approach, which employs advanced statistical and computational techniques. Population PK/PD studies may involve hundreds of patients with only a few blood samples collected from each individual. 2 Therefore, studies in a target population are tenable. Data may be collected directly from large-scale, Table 1 multiple-center (Phase III) and postmarketing (phase IV) Parameters and Patient Characteristics (Covariates) that trials or from other ongoing Affect an Individual's Drug Concentration and Response clinical trials with minimal or no changes in protocol Patient Characteristics PKJPD Parameter design. 4 Information about Fed versus fasted, dietary influences, gender distinguishing patient characteristics, or covariates, may CL (Renal) Concurrent medications (e.g., NSAIDs, probenecid), creatini'ne clearance, age, weight, gender now be collected from patients receiving a drug in CL (Hepatic) Concurrent medications (e.g., phenytoin, cimetidine), these studies. Additionally, smoking, ethanol, age, weight, ethnicity/polymorphism (fast versus slow acetylators), gender because of the increase in study group size, a broader Weight, body surface area, height, age, gender range of potential covariates Weight, age, ethnicity, concurrent medications, disease may be gathered. 2 state(s), gender The potential for these data to be collected and anaKa = -Oral absorption rate constant; CL = clearance; V d = volume of distribution; Emax = maximal lyzed accentuates the overeff~ct; EC50 = concentration producing 112 maximal effect; NSAIDs = nonsteroidal antiinflammatory drugs. whelming number of factors that conceivably may be associated with variability irt drug 0
journal of the American Pharmaceutical Association
m
February 1996
Vol. NS36, No.2
to predict drug concentration and effect. Improvement is measured by a decreased difference between the predicted and observed values and smaller intersubject error. 5 Overall, the fmal model will not explain all of the variability, but will improve the confidence with which predictions of drug concentration and effect are made.
A population PKJPD model for felodipine was developed from a study of 239 patients with essential hypertension. 7 Results of the PK model show decreased drug clearance as patient age increases and increased drug clearance in Mrican American patients. PD modeling revealed an increased antihypertensive effect in elderly patients, leading to the recommendation of a low daily starting dose (2.5 mg extended release).
Individual Dosing Algorithms Meaningful covariates from the fmal population model may then be used for the development of a dosing algorithm or nomogram. Coupled with patient assessment and history, the physician and pharmacist can use the nomogram to obtain an individualized dosing regimen for each medication. For example, if from the final model, CL = 100 mL/min + (0.2 mLJk~min) x Weight - (0.5 mL/yr_min) x Age, then younger, heavier people will clear the drug from their bodies most quickly, resulting in lower concentrations. Subtherapeutic concentrations may result when members of this group are given a standard dose. Likewise, mature and slender people will clear the drug more slowly. Higher drug concentrations may lead to increased incidences of adverse drug reactions when members of this group are given a standard dose. Application of the population approach is illustrated in recent studies of the drugs teicoplanin (Targocid-MarionMerrell Dow) and felodipine (Plendil-Astra-Merck). Teicoplanin, a new glycoprotein antibiotic for the treatment of gram-positive infections, was studied in 197 patients with endocarditis. 6 The results of this population PK study indicated that clearance of teicoplanin increased with increased body weight, whereas clearance decreased in association with concomitant gram-positive treatment and increased serum creatinine. Additionally, volume of distribution of this antibiotic increased with increased body weight. To reduce the pharmacokinetic variability, it is recommended that the dose be normalized for body weight and adjusted in patients with severe renal impairment. Furthermore, the potential for interaction with other gram-positive medications warrants further investigation.
Vol. NS36, No.2
February 1996
Conclusion The ability of the pharmacist and physician to customize and individualize drug therapy for patients is crucial to the optimization of outcomes. Critical to this process of individualization is the degree to which we can explain the differences in drug disposition and effect among individuals. Population PK/PD, therefore, fulfills an important need in the customization of pharmaceutical care. Matthew M Riggs, BS, is a graduate student at The University of Connecticut School of Pharmacy, Storrs, Conn., where he is pursuing a PhD in pharmacokinetics. He also serves as postgraduate officer of the APhA Academy of Pharmaceutical Research and Science, Basic Pharmaceutical Sciences Section.
References
m
1. Gloff CA, Benet LZ. Pharmacokinetics and protein therapeutics. Adv Drug Delivery Rev. 1990;4:359-86. 2. Sheiner LB. The population approach to pharmacokinetic data analysis: rationale and standard data analysis methods. Drug Metab Rev. 1984;15( 1-2): 153-71. 3. Aarons L. Population pharmacokinetics: theory and practice. Br J Clin Pharmacol. 1991;32:669-70. 4. Antal EJ, Grasela TH, Smith RB. The application of population pharmacokinetic analysis to large scale clinical efficacy trials. J Pharmacokinet Biopharm. 1991;19:37S-46S. 5. Sheiner LB, Ludden TM. Population pharmacokinetics/dynamics. Annu Rev Pharmacal Toxicol. 1992;32:185-209. 6. Yu DK Nordbrock E Hutcheson SJ, et al. Population pharmacokinetics of teidoplanin in p~tients with endocarditis. J Pharmacokinet Biopharm.1995;23:25-39. 7. Wade JR, Sambol NC. Felodipine population dose-response and concentration-response relationships in patients with essential hypertension. Clin Pharmacol Ther. 1995;57:569-81.
journal of the American PhannaceUtica1 Association
Introduction Today's pharmacy practice settings are placing more emphasis on tailoring patient care to each individual's needs. To provide an example of how pharmaceutical research is affecting phannaceutical care, this article demonstrates how patient-specific medication doses and dosing schedules may be achieved through population pharmacokinetic and pharmacodynamic modeling. Pharmacokinetics (PK) has been described as the action of the body on the drug-that is, how the drug is absorbed, distributed, metabolized, and excreted during its journey into, through, and out of the body. TIlls process is also called drug disposition. Pharmacodynamics (PO) has been described as the action of the drug on the body. 1 PD characterizes the
relationship between the effect of the drug and the amount of drug in the body. PK and PD can be used together to help the pharmacist develop an understanding of the effect of concentration ranges on response and toxicity. Parameters describing pharmacologic effect as a function of drug concentration may then be used to develop an appropriate dose and a dosing schedule that achieves optimal concentration for maximal therapeutic benefit to an individual patient. If people were alike in the way their bodies handled drugs, this task would be simple. Unfortunately, an appropriate dose for one patient often proves to be less than ideal for another patient or for that same patient at a later time.
Characteristics That Alter Drug Effect
Abstract Drug concentration and effect vary among patients for any given dose. A goal of population pharmacokinetics (PK) and pharmacodynamics (PD) is to identify patientspecific factors such as weight, creatinine clearance, and age, and associate them with the observed PK/PD differences. Once associated with differences in concentration or response, these distinguishing factors may then be used to better individualize drug therapy. Studies of the population PK of teicoplanin and the population PK and PD of felodipine are summarized as examples of this approach.
Vol. NS36, No.2
February 1996
Individualized drug therapy, the nucleus of pharmaceutical care, requires that the distinguishing characteristics of each patient be identified and the magnitude with which they alter drug concentration and effect be described. These characteristics, or covariates, include weight, age, gender, creatinine clearance, concurrent medications, fed versus fasted state, and social habits such as smoking or alcohol consumption. For example, if it can be shown that clearance for a particular drug is doubled for persons who smoke, that the volume of distribution of drug into body tissues increases with total body weight, and that the therapeutic index is dependent upon age, then a 180-pound, 60year-old smoker and a 120-pound, 21-year-old nonsmoker journal of the American Pharmaceutical Association
will benefit from different dosing regimens. Obviously, given the many differences among people, individualization of dosing can become quite complex.
Traditional PK/PD Studies Traditional PKjPD studies have not allowed for adequate determination of which factors among a population give rise to differences in the overall effect of a drug. These studies have typically used 15 to 20 healthy male subjects of "ideal" weight and age. Numerous blood samples are obtained from each subject and parameters describing drug disposition for each individual are determined. These separate estimates are combined, and the mean and standard deviation are reported. 2 However, because of our inherent heterogeneity, PK and PD often vary greatly fr~m these "ideal" values when the drug is actually administered. Furthermore, because of the necessity for intensive sampling, ethical and logistiC concerns preclude drug disposition studies in elderly, critically ill, and pediatric patients. 3 Therefore, using traditional methodology, it is difficult to account qualitatively and quantitatively for differences in response within the population that receives the drug.
Population PK/PD Studies
concentration and effect. Information obtained from preclinical trials may be useful in narrowing the possibilities. For example, knowing how the drug is administered (intramuscularly, intravenously, or orally) and eliminated (renally and/or hepatic ally) will influence which patient characteristics are considered pertinent for inclusion in the study. Table 1 pro- , vides examples of relevant covariates. From this compilation of possible covariates, it is necessary to assess which are clinically and/or statistically relevant. This relevance is tested by statistical methods through modeling of the data. 5 Modeling is an attempt to describe the observations of interest with respect to time or the covariates, using mathematical expressions. Several computer software packages are available (e.g. , NONMEM, P-Pharm) to aid in the process of building and testing a model. These programs start with a simple model that does not include any of the covariates. This is termed the base PK/PD model, and it should properly describe dnlg disposition. 5 This model provides mean estimates of the PK/PD parameters and the variability among subjects that is associated with these estimates. However, this model does not yet describe the sources of variability. From the base model, individual characteristics are tested stepwise on the relevant PK/PD parameters. Statistical analy- , ses are performed on the model results to determine whether the inclusion of a covariate decreases the unexplained variability observed in a relevant parameter within the study p opulation. In essence, this process reduces unexplained intersubject variability by association with a patient characteristic, accounting for some of the difference observed among individuals. A fmal model is constructed to include only those covariates that significantly- improve the ability of the model
Assessment of concentration and response variability is more feasible through a population PKjPD approach, which employs advanced statistical and computational techniques. Population PK/PD studies may involve hundreds of patients with only a few blood samples collected from each individual. 2 Therefore, studies in a target population are tenable. Data may be collected directly from large-scale, Table 1 multiple-center (Phase III) and postmarketing (phase IV) Parameters and Patient Characteristics (Covariates) that trials or from other ongoing Affect an Individual's Drug Concentration and Response clinical trials with minimal or no changes in protocol Patient Characteristics PKJPD Parameter design. 4 Information about Fed versus fasted, dietary influences, gender distinguishing patient characteristics, or covariates, may CL (Renal) Concurrent medications (e.g., NSAIDs, probenecid), creatini'ne clearance, age, weight, gender now be collected from patients receiving a drug in CL (Hepatic) Concurrent medications (e.g., phenytoin, cimetidine), these studies. Additionally, smoking, ethanol, age, weight, ethnicity/polymorphism (fast versus slow acetylators), gender because of the increase in study group size, a broader Weight, body surface area, height, age, gender range of potential covariates Weight, age, ethnicity, concurrent medications, disease may be gathered. 2 state(s), gender The potential for these data to be collected and anaKa = -Oral absorption rate constant; CL = clearance; V d = volume of distribution; Emax = maximal lyzed accentuates the overeff~ct; EC50 = concentration producing 112 maximal effect; NSAIDs = nonsteroidal antiinflammatory drugs. whelming number of factors that conceivably may be associated with variability irt drug 0
journal of the American Pharmaceutical Association
m
February 1996
Vol. NS36, No.2
to predict drug concentration and effect. Improvement is measured by a decreased difference between the predicted and observed values and smaller intersubject error. 5 Overall, the fmal model will not explain all of the variability, but will improve the confidence with which predictions of drug concentration and effect are made.
A population PKJPD model for felodipine was developed from a study of 239 patients with essential hypertension. 7 Results of the PK model show decreased drug clearance as patient age increases and increased drug clearance in Mrican American patients. PD modeling revealed an increased antihypertensive effect in elderly patients, leading to the recommendation of a low daily starting dose (2.5 mg extended release).
Individual Dosing Algorithms Meaningful covariates from the fmal population model may then be used for the development of a dosing algorithm or nomogram. Coupled with patient assessment and history, the physician and pharmacist can use the nomogram to obtain an individualized dosing regimen for each medication. For example, if from the final model, CL = 100 mL/min + (0.2 mLJk~min) x Weight - (0.5 mL/yr_min) x Age, then younger, heavier people will clear the drug from their bodies most quickly, resulting in lower concentrations. Subtherapeutic concentrations may result when members of this group are given a standard dose. Likewise, mature and slender people will clear the drug more slowly. Higher drug concentrations may lead to increased incidences of adverse drug reactions when members of this group are given a standard dose. Application of the population approach is illustrated in recent studies of the drugs teicoplanin (Targocid-MarionMerrell Dow) and felodipine (Plendil-Astra-Merck). Teicoplanin, a new glycoprotein antibiotic for the treatment of gram-positive infections, was studied in 197 patients with endocarditis. 6 The results of this population PK study indicated that clearance of teicoplanin increased with increased body weight, whereas clearance decreased in association with concomitant gram-positive treatment and increased serum creatinine. Additionally, volume of distribution of this antibiotic increased with increased body weight. To reduce the pharmacokinetic variability, it is recommended that the dose be normalized for body weight and adjusted in patients with severe renal impairment. Furthermore, the potential for interaction with other gram-positive medications warrants further investigation.
Vol. NS36, No.2
February 1996
Conclusion The ability of the pharmacist and physician to customize and individualize drug therapy for patients is crucial to the optimization of outcomes. Critical to this process of individualization is the degree to which we can explain the differences in drug disposition and effect among individuals. Population PK/PD, therefore, fulfills an important need in the customization of pharmaceutical care. Matthew M Riggs, BS, is a graduate student at The University of Connecticut School of Pharmacy, Storrs, Conn., where he is pursuing a PhD in pharmacokinetics. He also serves as postgraduate officer of the APhA Academy of Pharmaceutical Research and Science, Basic Pharmaceutical Sciences Section.
References
m
1. Gloff CA, Benet LZ. Pharmacokinetics and protein therapeutics. Adv Drug Delivery Rev. 1990;4:359-86. 2. Sheiner LB. The population approach to pharmacokinetic data analysis: rationale and standard data analysis methods. Drug Metab Rev. 1984;15( 1-2): 153-71. 3. Aarons L. Population pharmacokinetics: theory and practice. Br J Clin Pharmacol. 1991;32:669-70. 4. Antal EJ, Grasela TH, Smith RB. The application of population pharmacokinetic analysis to large scale clinical efficacy trials. J Pharmacokinet Biopharm. 1991;19:37S-46S. 5. Sheiner LB, Ludden TM. Population pharmacokinetics/dynamics. Annu Rev Pharmacal Toxicol. 1992;32:185-209. 6. Yu DK Nordbrock E Hutcheson SJ, et al. Population pharmacokinetics of teidoplanin in p~tients with endocarditis. J Pharmacokinet Biopharm.1995;23:25-39. 7. Wade JR, Sambol NC. Felodipine population dose-response and concentration-response relationships in patients with essential hypertension. Clin Pharmacol Ther. 1995;57:569-81.
journal of the American PhannaceUtica1 Association