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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5195_Библиотеки_им_академика_М_И_Перельмана.pdf
TABLE 7.3
Application of PK/PD models that relate drug exposure to changes in
biomarkers (whether microbiologic, immunologic, neoplastic, neurologic, or
other) and ultimately to clinical outcomes will provide a better rationale for
proper individualized dose selection and other therapeutic interventions in
multiple patient populations.
BAYESIAN FORECASTING
Bayesian forecasting is derived from Bayes’ theorem and is based on the
concept that prior PK knowledge of a drug, in the form of a population model,
can be combined with individual patient data, such as drug concentrations
(Bayesian feedback)
4,31,64,65
(Table 7.3). The idea is to make an individualized
model of the behavior of the drug in a particular patient to see how the drug
will be or has been handled and to obtain the necessary information to make
rational dose adjustments so as to best achieve the selected target goal(s).
Flow Scheme for Bayesian Goal-Oriented, Model-Informed Dosing
Figure 7.2 shows a diagram of the goal-oriented, model-informed
optimization process. Drug dosage optimization requires (a) population PK
parameters (PK model), defined as mean values, standard deviations,
covariates, and information on the statistical distribution necessary to select
the initial dosing regimen for that particular patient based on chosen goals; (b)
measurement of a performance index related to the therapeutic goal, generally
one or more plasma concentrations or effects as feedback information to
update the system; and (c) availability of reliable software for an adaptive
control strategy [maximum a posteriori probability (MAP) Bayesian fitting]
and calculation of the subsequent optimal dosage regimen.
Figure 7.2 Flow diagram of the goal-oriented, model-informed strategy. A computer program is used
with a patient-specific population model describing absorption, distribution, and elimination of the drug in
relation to patient-specific parameters. Patient data and desired target concentrations are entered into the
system. Next, a model-based loading dose and maintenance dosing regimen required to optimally achieve
the target concentrations is selected. This regimen is administered to the patient, and subsequent
concentration measurements and clinical effect observations are used as feedback to update the initial
model and design a new dosing regimen if necessary. PD, pharmacodynamic; PG, pharmacogenetic; PK,
pharmacokinetic.
Figure 7.3 illustrates an example of the use of a population MIPD with
Bayesian feedback. This process was successfully implemented in a
concentration-controlled clinical study of sirolimus in pediatric patients with
acute lymphoblastic leukemia.66 First, a population model–based predicted
concentration profile is generated based on patient-specific dosing data and
body weight as a covariate of clearance and volume of distribution (Fig.
7.3A). Next, individual PK parameter estimates are generated using Bayesian
estimation based on the measured concentrations (Fig. 7.3B). Lastly, the
dosing regimen(s) to best attain the concentration target is identified based on
simulation using the individual PK parameters (Fig. 7.3C). In this trial, the
initial loading and maintenance doses were identified based on the population
PK model–based simulation using the pediatric sirolimus PK model.
Figure 7.3 The process of Bayesian adaptive precision dosing. A: The concentration–time profile
represents population model–based prediction for an average patient treated with the initial loading (1.8
mg per m2 per dose three times daily) and maintenance doses (1.8 mg per m2 per dose twice daily).
Dashed lines indicate the target trough concentration range of 10 to 12 ng per mL. B: The
concentration–time profile (solid line) represents the individual pharmacokinetic (PK) profile estimates
generated using Bayesian estimation based on the measured concentration(s) (closed circle). The
predicted trough concentrations are below the target of 10 ng per mL, suggesting that the patient needs a
higher dose to attain the target concentration. C: The concentration–time profile (solid line) represents
the individual’s predicted PK profile with the new dosing regimen (2.8 mg per m2 per dose twice daily)
identified based on simulations using the individual PK parameters. (Reused from Mizuno T, O‘Brien
MM, Vinks AA. Significant effect of infection and food intake on sirolimus pharmacokinetics and
exposure in pediatric patients with acute lymphoblastic leukemia. Eur J Pharm Sci 2019;128:209–214
with permission from Elsevier.)
Bayesian methods can be more cost-effective than other techniques
because they require fewer drug measurements for individual PK parameter
estimation. They can also handle sparse, random and distribution phase
samples.67 TDM, when applied appropriately, can also be used to detect and
quantify clinically relevant drug–drug or drug–diet interactions
68,69
as well as
medication errors.
However, regardless of which PK dose individualization technique is
being used, all are superior to a simple reactionary comparison of a reported
result to a “therapeutic range.” Simply reporting results as “numbers” that are
below, within, or above a published range is usually uninformative, not cost
saving, and can lead to inappropriate or even dangerous actions.
REAL-TIME DOSE INDIVIDUALIZATION WITH
BAYESIAN ADAPTIVE CONTROL
Real-time MIPD can refer to the direct prospective implementation of M&S
in a patient care setting based on real-time feedback about the patient, such as
drug concentrations or biomarker effect data.63 The approach can be
particularly useful for treatments requiring continuous monitoring of
efficacy/toxicity to control for variability in drug response. Large
interindividual PK variability (variability between patients) has been
documented for many drugs. This variability is incorporated in many useful
population PK models. Intraindividual variability (the variability in the same
patient), however, is equally important, especially when interpreting TDM
data during lifelong treatments, such as for epilepsy, transplantation, and
HIV/AIDS patients. Causes of intraindividual PK variability have not all been
systematically studied but can be attributed to temporary changes in
physiology, drug absorption, enterohepatic recycling, or, sometimes, as part of
the clinical noise in the system. Population model–based methods, with the
additional use of a graphical presentation of the concentration–time profile,
can be of great help in the clinical management of patients. It can also identify
outliers, patients with unusual PK and other candidates who would benefit
from more intensive monitoring.
Figure 7.4 shows an example of MIPD process that was implemented in
the concentration-controlled phase 2 sirolimus study in pediatric patients with
complicated vascular anomalies.
70,71
The process includes data on patient
visits and PK sample collection, sample shipment, and the specific assay used
to measure blood concentrations. It generates interpretative reports for assay
results and a final model-based dosing recommendation. In this study,
sirolimus was initiated at a dose of 0.8 mg per m2 twice a day (BID).
Subsequent dosing was individually adjusted using the drug concentration
measurements in combination with population model–based Bayesian
forecasting to target a sirolimus trough concentration of 10 to 15 ng per mL.
The first sirolimus blood concentration was measured at 8 to 14 days after
start of treatment. This was followed by weekly measurements throughout the
rest of the 28-day treatment course. During subsequent courses, sirolimus
concentrations were measured weekly until stable, which was defined as two
subsequent concentration results within the target range. After any dose
modification, subsequent sirolimus concentrations were measured every 7 to
14 days until stable. During the study, patients kept a dosing diary, recording
exact dosing times and any missed doses for 5 days before each visit. This
diary was reviewed before the drawing of blood for sirolimus concentration
measurements to document adherence and provided the actual dosing time
information for the PK assessments.
Figure 7.4 Outline of the different steps in the sirolimus precision dosing process from patient visit
and sampling, sample shipment, analysis, reporting of assay results, and the final communication of the
model-based dosing recommendations. (Reused from Mizuno T, Emoto C, Fukuda T, et al. Model-based
precision dosing of sirolimus in pediatric patients with vascular anomalies. Eur J Pharm Sci
2017;109S:S124–S131 with permission from Elsevier.)
Figure 7.5 shows representative examples of the model-based predictions
and dosing recommendations reported for patients in the concentrationcontrolled phase 2 sirolimus study.
70,71
The profile depicted in Figure 7.5A
represents the concentration–time data for a 3-year-old male patient (11.9 kg,
90.5 cm, 0.54 m2) with a kaposiform hemangioendothelioma who was started
on 0.8 mg per m2 BID. The first sirolimus concentration was 4.9 ng per mL
after which the dose was increased to 0.9 mg BID (1.67 mg per m2) and
eventually to 1.3 mg BID (2.4 mg per m2). Figure 7.5B shows the sirolimus
concentration–time course for a 2-month-old patient born with a congenital
lymphaticovenous malformation who received sirolimus in the neonatal
intensive care unit (NICU). This 2-month-old (4.7 kg, 52 cm, 0.24 m2) was
started on 0.2 mg BID (also 0.8 mg per m2). The first sirolimus concentration
came back as 22 ng per mL after which the dose was empirically reduced by
50% (2 days later) and another sirolimus measurement was ordered. The
second sirolimus concentration result was 27 ng per mL after which
subsequent doses were held. A PK consult was performed, and a new
maintenance dose regimen of 0.06 mg (0.25 mg per m2) was suggested. A
loading dose (0.2 mg) was also suggested based on the model-based
simulation to reachieve the rapid target attainment. After this loading dose, the
new regimen provided sirolimus concentrations that remained on target. This
baby had a clearance of 3.4 L per h per 70 kg; approximately 35% lower than
the median predicted clearance for a newborn of this age. Based on a
maturation model, sirolimus clearance was predicted to increase by 50% over
the next month, requiring a dose increase to 0.09 mg BID.
Figure 7.5 Reporting of model-based pharmacokinetic (PK) profiles during the clinical trial and as
part of off-label treatment with subsequent dosing recommendations. This 3-year-old male patient was
enrolled, and sirolimus dosing was initiated at a dose of 0.8 mg per m2 twice a day. A: The model
predicted concentration–time profile resulting from the subsequent recommended dose increases (0.9,
1.2, and 1.3 mg twice daily) based on the measured sirolimus concentrations (closed circles) are shown.
B: The sirolimus concentration–time profile of a 2-month-old baby who was started on 0.8 mg per m
2
twice a day. The dose was reduced by 50% after the first sirolimus result came back (22 ng per mL),
and another concentration was checked. As the second blood concentration was higher than the first (27
ng per mL), subsequent doses were held, and another PK evaluation was performed based on the
measured sirolimus concentrations (closed circles). After a loading dose, subsequent sirolimus
concentrations were on target. (Reused from Mizuno T, Emoto C, Fukuda T, et al. Model-based precision
dosing of sirolimus in pediatric patients with vascular anomalies. Eur J Pharm Sci 2017;109S:S124–S131
with permission from Elsevier.)
OVERCOMING PRACTICAL PROBLEMS IN
NEONATAL AND PEDIATRIC THERAPEUTIC
DRUG MONITORING
INCOMPLETE INFORM ATION FOR INTERPRETATION
OF THE DOSE–CONCENTRATION–EFFECT
RELATIONSHIP
Unfortunately, incorrect sample collection, handling, or analysis as well as
improper interpretation of results can diminish the clinical value of TDM
results and have negative clinical and economic implications. This can, and
has, lead to incorrect attitudes about the usefulness of TDM. There are
multiple studies showing that, when properly ordered, assayed, and
interpreted, TDM can often be useful in all patients and is always useful in
specific clinical situations.17 Although there are few studies of the costeffectiveness of TDM, those that have been done have shown very positive
results.
58,59,72–76
When poorly done, TDM can be useless or even harmful.
Despite the fact that TDM concepts are relatively simple, most
laboratories are not set up to perform the necessary data collection that would
allow unambiguous clinical interpretation of drug concentration
measurements.77 For instance, simple information such as the time of sampling
in relation to the time of last dose needs to be known to properly interpret the
result. In addition, demographic data (date of birth, weight, height), route of
administration, and dosing regimen (time and dates of recent intake, duration
of use, whether loading doses were given, concomitant medications taken)
should also be available. Additional factors that may influence proper TDM
TABLE 7.4
interpretation are summarized in Table 7.4. Over the years, institutions have
struggled with ways of collecting and reporting this type of information. In the
outpatient setting, a useful method to collect these data is the use of a
questionnaire administered by laboratory staff or filled out by the patients,
parents, or guardians while waiting for phlebotomy. Figure 7.6 shows an
example of a questionnaire that has been successfully used in a PK consult
service at the author’s institutions.78 The TDM system should be set up in such
a way that information from the questionnaire is processed with the test
request and ultimately reported in a user-friendly format. An important item
on this questionnaire is space for patient, parent, or guardian feedback on any
issues related to medication effects. This is a valuable tool for adverse event
monitoring as well as documenting therapeutic response.
Practical Problems in Neonatal and Pediatric Therapeutic Drug Monitoring
Sample collection—access, volume, skin contamination, line or catheter draws
Interference—maternal (Cr, DLIS, maternal Rx)
Altered metabolic patterns as well as rates
Dietary differences, fasting, stomach emptying, gastrointestinal transit-prolonged release,
inappropriate dosage alteration
Position or infusion apparatus changes
Administration uncertainty—cooperation, spillage, measurement, extemporaneous formulation,
inappropriate concentrations, measurement errors
Analytical differences (e.g., phenobarbital glucuronide interference missed in adult samples)
Day-to-day variation—weight, pharmacokinetics
Intravenous administration problems, no dose or sample before dose
Cr, serum creatinine; DLIS, digoxin-like immunoreactive substances; maternal Rx, maternal drug
therapy.