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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5195_Библиотеки_им_академика_М_И_Перельмана.pdf
tacrolimus TDM was published by the Immunosuppressive Drugs Scientific
Committee of the IATDMCT in 2019.
125
The relationship between tacrolimus
exposure and the risk of acute rejection and drug-related adverse events has
been well documented. Although AUC is considered the PK exposure
parameter best associated with clinical effects, no prospective studies of
clinical outcomes have been conducted in adult or pediatric transplant
recipients to properly investigate the potential benefits of AUC monitoring
compared with C0-guided therapy.
125
Therefore, predose trough concentration
is still being monitored in most transplant centers for routine tacrolimus TDM.
HIV THERAPY
HIV therapy has much to gain from properly applied TDM.
126–128
Protease
inhibitor monitoring can be an especially valuable tool as part of therapeutic
optimization as demonstrated by several groups, largely because even brief
exposure of the virus to low protease concentrations is associated with rapid
development of tolerance and poor therapeutic outcome. Additionally,
exposure to excessive protease concentrations is associated with increased
risk of toxicity. Measuring multiple protease concentrations can help
clinicians identify causes of low (including “none detected”) or excessive
concentrations, leading to more effective and less toxic individualized dosing
regimens, less resistance, better outcomes, fewer adverse effects, and some
cost savings. In adult patients, suggested target concentration ranges have also
been established for other antiretroviral drugs, such as non-nucleoside
reverse transcriptase inhibitors (NNRTIs) and integrase strand transfer
inhibitors (InSTIs).
129
More recently introduced antiretroviral drugs have
broader therapeutic windows with improved benefit–risk ratios and reduced
intersubject variability; thus, the need for TDM of these drugs is currently
limited to specific clinical scenarios.
129
The dosing recommendations for
HIV-infected children are not always uniform among regulatory agencies or
HIV management guidelines as the current pediatric doses are often based on
small sample size studies.
129
NEUROPSYCHIATRIC DRUGS
The use of TDM in support of neuropsychopharmacology treatments has been
increasing over the past few decades. The first TDM consensus guidelines in
psychiatry were issued by the TDM task force of the working group on
neuropsychopharmacology (Arbeitsgemeinschaft für
Neuropsychopharmakologie und Pharmakopsychiatrie, AGNP) in 2004.
130
The
guidelines were updated in 2011
131
and 2017.22 For TDM in psychiatry, the
regular monitoring of blood drug concentrations is recommended during
maintenance therapy and at least every 3 to 6 months to prevent relapses and
rehospitalizations, The frequency of TDM measurements may be increased in
patients suspected of being nonadherent, when there are changes in
comedications or when smoking is likely to affect the PK of the drugs
prescribed. Reference therapeutic concentration ranges have been published
for many psychopharmacological drugs (over 150 medications). The ranges
for older drugs are based on clinical studies which documented the
relationships between drug concentration and clinical improvement (e.g.,
lithium and tricyclic antidepressants).22 These reference ranges were derived
based on average population data and may not necessarily be applicable to all
patients. Any individual patient may benefit from using a different therapeutic
target, and treatment can best be further guided by identifying the exposure or
concentration to which the patient best responds while adverse event free. It
has also been suggested that the arithmetic mean and standard deviation of
blood concentration in responders could be used as an initial therapeutic
reference range or drugs for which further studies are needed to establish a
range.
BIOLOGICS
In recent years, therapies with biologics, including monoclonal antibodies and
therapeutic proteins, have had major impact on disease management and
prevention, improving overall clinical outcomes in a large number of disease
areas, especially in cancer, infectious diseases, and autoimmune-mediated
inflammatory diseases. TDM has emerged as a potential useful tool to
optimize the use of many biologic therapies. The benefits of TDM have been
especially well demonstrated in inflammatory bowel diseases (IBD),
including Crohn disease and ulcerative colitis.
24,25,132
Infliximab is a chimeric
monoclonal antibody against tumor necrosis factor α (TNF-α) that was
approved in 1998 by the U.S. Food and Drug Administration (FDA) for
treatment of adult IBD and subsequently approved in 2006 for pediatric IBD
patients. The labeled dose of infliximab is 5 mg per kg given at 0, 2, and 6
weeks followed by a maintenance dose of 5 mg per kg every 8 weeks.
Primary response to infliximab induction therapy was reportedly achieved in
75% to 90% of pediatric patients with IBD.
133,134
However, over 30% of
patients require dose intensifications because of the loss of initial response
(LOR).
135
Multiple studies have documented the association of LOR with low
infliximab trough concentrations during maintenance therapy in both adults
136–
139
and in children.
140–142
A meta-analysis demonstrated that lower infliximab
trough concentrations were found in nonremitters than in patients in clinical
remission (0.9 mg per L vs. 3.1 mg per L).
143
IBD practice guidelines suggest
a trough concentration of 3 to 10 mg per L as the target range for maintenance
therapy.
144
In patients with infliximab concentrations below this target, dose
escalation has been shown to result in a better clinical response rather than is
changing to another anti-TNF therapy.
145
Recent studies have indicated that
low drug exposure is also a risk factor for the development of antidrug
antibodies (ADAs). ADAs are associated with an increased risk of infusion
reactions and a reduced response to treatment.
146
In addition, there is
substantial evidence that disease progression and worsening of inflammation
increase infliximab clearance, which result in lower drug exposure.
147–153
Therefore, concentration monitoring and dose individualization of infliximab
have been recommended to prevent subtherapeutic exposure and help with the
achievement of a sustained and durable remission. Mould and colleagues
developed a promising model-informed dosing strategy using Bayesian
estimation for infliximab dose optimization.
154
Their Bayesian decision
support tool is designed to integrate a population PK model with clinical
TDM data to predict the individual patient’s PK profile and the dose required
to achieve the desired target concentration.
DRUG DETERMINATION IN ALTERNATIVE
FLUIDS
Blood, either serum or plasma, has been the preferred biologic matrix for
TDM.
155
However, a number of other fluids can and have been used, including
tears, saliva, and urine.
156
In addition, transcutaneous and continuous
microdialysis sampling techniques hold promise for the future, especially for
small children, where sampling can be problematic.
FUTURE DEVELOPMENTS
A number of recent developments promise to improve the use and broader
implementation of TDM. These include the development of therapeutic drug
management teams, decision support tools that are integrated with the
electronic medical record (EMR), cost-effectiveness studies, and emerging
data on new drug classes, such as biologics, antifungal, antiviral, and
anticancer drugs. TDM is also likely to become more effective and broadly
applied with the implementation of advanced analytical techniques, such as
less invasive microsampling devices, DBS technology, rapid drug
quantification assays with paper spray ionization technology, and more
sensitive liquid chromatography/tandem mass spectrometry, which can rapidly
and reliably quantify a large number of drugs and metabolites simultaneously
(e.g., metabolomics). The impact of these technologies will increase as the
cost and complexity of the instrumentation decreases and their reliability
increases. The high sensitivity of such methods promises to allow noninvasive
(e.g., sweat, transcutaneous, or respiratory) real-time monitoring of multiple
drug and metabolite concentrations simultaneously. When combined with
stable isotope techniques, these techniques make it possible to also
simultaneously measure absorption from multiple sites and quantify
bioavailability in individual patients. Linkage of such advanced analytical
techniques with powerful computer modeling, innovative wearable electronic
devices, and drug sensors, along with patient management software including
smartphone applications promises to revolutionize MIPD. Eventual linkage
with individual pharmacogenetic and pharmacogenomic information,
physiology- and mechanism-based PK/PD, and quantitative systems
pharmacology platforms could revolutionize how individual patients are
dosed, both initially and repetitively, prophylactically or therapeutically.
157,158
As indicated, MIPD approaches have already been successfully used to
personalize the drug treatment at the point of care in individual patients.
However, those efforts are mostly confined to relatively few academic
institutions and have not found broad application throughout the health care
systems.63 One of the biggest unmet needs which would facilitate the
implementation of MIPD at the bedside is the development and verification of
more user-friendly tools to integrate a patient’s clinical information (e.g.,
dosing history, laboratory results, PK measurements) with PK/PD estimations
to identify the optimal dosing regimen. Model-informed dose optimization
uses patient-specific data that are predictive of the target drug’s PK/PD in an
individual. Standalone clinical TDM software application requires collection
and manual entry of the clinical information into the program. This is often
time-consuming, prone to errors, and not always feasible in clinical settings
with limited resources. Given that clinical data as well as dosing histories are
now documented in the EMR in many hospitals, leveraging the EMR as part of
decision support systems should reduce the workload and time required to
generate model-based precision dosing guidance. This would facilitate the
availability of MIPD capabilities to all clinical care providers and increase
the number of patients who would benefit from more precise, personalized
drug treatment.
One of the first-generation integrated decision support tools for TDM was
a “Dashboard” system developed by Barrett et al.
159
at the Children’s
Hospital of Philadelphia (CHOP) in the 2000s for the management of
leucovorin rescue utilized for high-dose methotrexate (MTX) therapy in
pediatric oncology patients. This MTX dashboard system was designed to
integrate individual patient data from the medical record with a population
PK model and then graphically display the most relevant clinical data needed
for the management of methotrexate therapy. MTX plasma concentrations are
used as feedback to generate the individual MTX PK profile which is
depicted together with the leucovorin rescue nomogram to guide leucovorin
dose intensity based on MTX concentration cutoffs. The dashboard
forecasting algorithm was retrospectively evaluated and shown to be
reasonably accurate in predicting MTX concentrations while facilitating
leucovorin rescue dose management.
160
Another compelling example of a
“dashboard” for children is the busulfan PK decision support tool recently
developed by Abdel-Rahman et al.
161
at the Children’s Mercy Hospital,
Kansas City. Busulfan exhibits an NTI for which clinicians routinely employ
TDM. Abdel-Rahman et al.
161
developed and tested a clinical decision
support tool embedded in their EMR designed to streamline the TDM
process. Patient and busulfan concentration results automatically populate the
tool. Data are then visualized, fitted, and inspected utilizing color-coded
indicators signaling goodness of fit. The tool provides clinicians the ability to
seamlessly transition from patient assessment, to PK M&S, and subsequent
prescription order entry. The usability of the tool was tested by 28 content
experts as end users and produced a high level of satisfaction.
161
Another
Bayesian dashboard system for biologics therapy was the previously
mentioned first infliximab dashboard described by Mould et al.
154
for use in
patients with IBD. A small cohort study demonstrated that patients dosed
according to the Bayesian dashboard recommendations had longer disease
remissions than did those dosed according to standard of care (51.5 months
vs. 4.6 months).
162
Model-informed decision support tools have been utilized
not only for PK but also for PK/PD-guided dose individualization. Hamberg
et al.
163
developed a Bayesian decision support tool using a published
warfarin PK/PD model in adult and pediatric patients. The tool is designed to
estimate an initial dosage regimen based on patient’s body weight, age,
baseline, and target INR and optionally also including the presence of specific
genetic variants of cytochrome P450 (CYP) 2C9 and vitamin K epoxide
reductase (VKORC1). After institution of the model predicted optimal,
individual starting dose, subsequent dosing regimens can be optimized using
Bayesian, forecasting with INR results as feedback.
Another EMR-linked decision support platform for morphine precision
dosing in neonates treated in an NICU was developed and implemented at
Cincinnati Children’s Hospital Medical Center.
164
This platform is set up as a
dashboard and translates morphine dose into a predicted PK profile, which
allows the clinical team to follow morphine exposure together with pain
scores, heart rate, and breathing frequency in real time.
165
Figure 7.7 shows an
example of a morphine PK prediction using the platform based on the infant’s
weight, gestational and postnatal age, and dosing regimen administered
(continuous infusion plus bolus doses as needed, based on the pain scores). In
this case, two measured morphine concentrations (open circles) revealed the
concentration to be less than the population model–based (mean) predictions
(dotted curved line). Based on these measured morphine concentrations, a
new individual PK profile was predicted (blue line) using Bayesian
estimation. The lower observed concentrations compared with the population
model–based predictions suggest that this infant’s morphine clearance was
higher than clearance in the average neonate. The horizontal dotted lines
represent the tentative target range of 10 to 30 ng per mL (mean 20 ng per mL
in red) as suggested by Anderson and van den Anker.
166
Figure 7.7 Decision support tool using Bayesian estimation integrated within the electronic health
record (EHR) for morphine precision dosing in neonates.
164
The dotted curved line represents the
population model–based (average) predicted morphine concentration profile. The open circles indicate
measured morphine concentrations. The solid line represents the Bayesian estimated individual predicted
profile. The horizontal dotted lines represent the tentative target concentration range of 10 to 30 ng per
mL (mean 20 ng per mL) as suggested by Anderson and van den Anker.
166
TDM teams and model TDM services have been in existence for decades,
but in only a few institutions. However, as modern quality control methods are
applied (belatedly) to medicine and more data become available on the costeffectiveness of TDM in terms of outcomes rather than laboratory revenue,
there is hope that such services will become more common. An interesting
recent development in terms of data management, reporting of TDM results,
and interpretation has been the use of information technology and the design of
web-based tools that allow health care providers to access real-time data for
their patients 24/7 from any place in the world. An example of such a webbased approach is the ImmunoSuppressants Bayesian dose Adjustment (ISBA)
support platform offered through the Department of Pharmacology and
1.
2.
3.
4.
5.
Toxicology at the University Hospital of Limoges, France.
167
Through this
resource, population model–based data interpretation is being provided using
Bayesian estimators, which includes a numerical report and graphical
representation of the predicted exposure–time relationship. This program
provides support for several different immunosuppressive drugs and
transplant indications and uses drug- and transplant-specific validated sparse
sampling strategies to estimate dose–exposure relationships. Other Bayesian
estimators have been developed and validated for MPA therapeutic drug
management in the treatment of multiple diseases.
168–173
CONCLUSION
Properly done, TDM has been and will continue to be useful, especially in
pediatric populations. However, there are many knowledge and performance
deficits that must be corrected for TDM to reach its full potential. Simpleminded, reactionary TDM is often not useful and can even be dangerous.
However, modern modeling, prediction, and control when combined with
modern medical information and laboratory analytical technologies can
clearly provide better, more cost-effective precision medicine for pediatric
patients. In addition, in the future, the combination of analytical, PK/PD and
systems pharmacology modeling, pharmacogenetics, and information
technology techniques offers tremendous promise for truly individualized
optimization of therapy, beginning with the initial dose of a medication and the
continuation of tailored precision dosing thereafter.
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