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\begin{document}

\title{On precision dosing of oral small molecule drugs in oncology}



\author[1]{Alex Lyashchenko}%
\author[2]{Serge Cremers}%
\affil[1]{Columbia University Irving Medical Center}%
\affil[2]{Columbia University Medical Center}%


\vspace{-1em}



  
  \date{March 14, 2025}


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\selectlanguage{english}
\begin{abstract}
Personalization of oral small molecule anticancer drug doses based on
individual patient blood drug levels, also known as therapeutic drug
monitoring or TDM, has the potential to significantly improve the
effectiveness of treatment by maximizing drug efficacy and minimize
toxicity. However, this option has not yet been widely embraced by the
oncology community. Some reasons for this include increased logistical
complexity of dose individualization, the lack of clinical laboratories
that measure small molecule drug concentrations in support of patient
care, and the lack of reimbursement of costs. However, the main obstacle
may be the lack of studies clearly demonstrating that monitoring of oral
small molecule anticancer drug levels actually improves clinical
outcomes. Without unequivocal evidence in support of TDM-guided dose
individualization, especially demonstration of improved survival with
TDM in randomized controlled trials, wide acceptance of this approach by
oncologists and reimbursement by insurance companies is unlikely, and
patients may continue to suffer as a result of receiving incorrect drug
doses. This article reviews the current status of therapeutic drug
monitoring of oral small molecule drugs in oncology and intends to
provide strategic insights into the design of studies for evaluating the
utility of TDM in this clinical context.%
\end{abstract}%



\sloppy




\section*{Introduction}

{\label{introduction}}

Precision oncology approaches have enabled matching of the best
available anticancer drug to each individual patient's particular tumor
type. Nevertheless, treatment even with such targeted agents is often
short-lived or unsuccessful. One factor that may significantly
contribute to failure of treatment, despite successful selection of the
correct drug, is the failure to select the correct drug dose.

Oral small molecule anticancer drugs are typically prescribed at fixed
drug doses regardless of patient weight, age, or gender. Differences in
bioavailability, metabolism, and adherence further increase
pharmacokinetic (PK) variability from patient to patient. This means
that, despite being prescribed the same dose of the drug, individual
patients may have significant differences in the resultant blood drug
levels (\emph{i.e.} drug exposure), with some being under-dosed and
others over-dosed.

One efficient way to optimize drug dosing is through therapeutic drug
monitoring (TDM) of patient blood drug levels. TDM is most effective for
the subset of drugs for which the inter-patient variation in systemic
drug exposure (\emph{e.g.} blood drug levels) is high relative to the
therapeutic index (concentration range of adequate efficacy and minimal
toxicity), and for which the exposure is strongly correlated with
clinical response. For such drugs, a flat-fixed dose will be optimal for
only a fraction of patients. Numerous oral small molecule anticancer
drugs that are already in clinical use have been shown to exhibit such
high inter-patient PK variabilities and strong
pharmacokinetics-pharmacodynamics (PK-PD) relationships, and seem to be
strong candidates for benefiting from TDM as described in several
excellent reviews on this subject.\textsuperscript{1--5}

Candidate drugs for TDM can be actively identified throughout various
phases of drug development, which includes the post-marketing phase. For
newly approved drugs and those in clinical development, critical
analysis of PK and PD data may provide clues to potential benefits of
dose individualization. PK and PD data is typically available for every
anticancer drug on the market or in late phases of clinical development,
but the type of analysis of this data for the purpose of further drug
development differs widely from company to company. Regulatory
authorities usually only require non-compartmental analysis of PK data,
and the relationships with therapeutic and adverse drug effects are
often explored only with correlative analysis. Alternatively, PK and
PKPD modeling and simulation can provide additional insights into the
often complex relationship between dose and effects and, in fact, there
are several efforts from the regulatory authorities to encourage the use
of modeling and simulation in drug
development.\textsuperscript{6,7}Consequently, the results from such
additional analyses can be quite useful for selection of optimal dose
regimens for further clinical studies, identification of candidates for
TDM and, ultimately, for patient care.

Drug dosing regimens are typically established during early phase trials
involving a small number of participants, with dosing decisions based on
population level data rather than individual level data. Late phase
trials often do not even include a PK component, which in the context of
establishing dosing for optimal drug exposure may be a missed
opportunity. Compared to early phase trials, phase 3 studies are usually
much larger and longer (involving hundreds to thousands of participants
over months to years), and are therefore better suited for accurate
characterization of inter- and intra-patient PK variability as well as
for determination of the relationships between systemic exposure,
relevant clinical outcome parameters, and side effects. The PK and PD
results that could be obtained in such studies (\emph{e.g.} PK
variability and exposure-efficacy/toxicity relationships) would inform
the need for optimization and perhaps individualization of drug dosing
for subsequent studies and for patient care.

This article discusses some important considerations for evaluating the
utility of TDM for new oral anticancer drugs during clinical trials as
well as during patient care. Similar to other types of clinical
interventions, studies evaluating TDM of oral small molecule oncology
drugs are likely to progress through several stages of increasing
complexity, with additional levels of evidence in support of the
intervention being gathered at each stage. To help conceptualize this
progression as it relates to TDM of oral small molecule oncology drugs,
we have subjectively categorized it into four stages (Figure 1) that are
detailed in the following sections. We first focus on measuring systemic
drug exposure to assess inter- and intra-patient PK variability,
followed by correlating drug exposure to efficacy and toxicity, then
evaluating whether TDM can effectively optimize drug exposure, and
finally testing whether the implementation of TDM-guided precision
dosing, \emph{i.e.} adjusting an individual's drug dose based on
measured blood drug concentrations, might improve clinical outcomes
indeed.

\section*{Assessing Variability in Systemic Drug
Exposure}

{\label{assessing-variability-in-systemic-drug-exposure}}

Some drugs (\emph{e.g.} intravenously administered medications with
well-known pharmacokinetics) have relatively consistent and predictable
dose-exposure relationships. The blood levels of such drugs can be
estimated reasonably well and measuring them may not provide additional
valuable information. Here, TDM is of limited relevance, except perhaps
for cases of medication non-compliance or failure of organs involved in
drug metabolism and excretion. For many orally administered medications,
on the other hand, numerous additional variables such as bioavailability
and first-pass metabolism widen the range of possible blood level
concentrations and make predicting systemic drug exposure from the dose
much more difficult. Thus, the earliest evidence in support of TDM for a
new oral anticancer drug would be derived from observation of a large
range of not otherwise predictable blood drug levels between patients on
the same treatment regimen (Figure 1, Stage 1).

There are several ways of measuring the systemic exposure to a drug.
Examples include comprehensive sampling of numerous time points to
determine area under the concentration (AUC) time curves as well as
parsimonious or limited sampling strategies such as trough
(C\textsubscript{min}) levels, peak levels, or a combination thereof.
For routine monitoring of oral drugs that are taken once or twice daily
in an outpatient setting, measuring the drug concentration in a single
sample collected prior to the next dose (\emph{i.e.} trough level
monitoring) is often the only practical option. Even during phase 2 and
phase 3 trials, blood sampling is restricted and, if possible, sparse
sampling strategies (\emph{e.g.} trough concentrations) should be used
to study PK and PK/PD. We will therefore only focus on trough level
monitoring.

The relationship between a trough level and the systemic exposure, as
determined by area under the concentration (AUC) time curves, can often
be obtained from phase 1 and phase 2 clinical
studies.\textsuperscript{8--10} Although most early-phase trials collect
the data to derive this correlation, it may not be explicitly reported.
Using phase 1 and 2 study data, one can get a reasonable idea if trough
level monitoring could be used as a proxy for the more comprehensive AUC
analysis. It is important to keep in mind that a clinical drug
development PK study is usually much more controlled in terms of drug
intake and sample collection than routine patient care and that
parameters obtained in such studies may not translate to real world
patients. Therefore, there may be added value from assessing the
relationship between trough levels and AUC in a patient care setting.
Factors that may confound the relationship between
C\textsubscript{min}and AUC include PK drug-drug interactions,
alterations in PK as a result of (auto-)induction, and inhibition of
metabolizing enzymes and transporters, as well as inaccuracies in
determining the triad of time of drug intake, time of sample collection,
and half-life of a drug. All components of this triad are relevant for
an accurate assessment of the systemic exposure and all may differ from
patient to patient.\textsuperscript{11} Nevertheless, it is intriguing
that less-than-perfect correlation between trough levels and systemic
exposure can still be useful for assessing systemic exposure. Indeed,
even for some of the most monitored drugs that utilize trough levels,
such as cyclosporine and tacrolimus, the correlation coefficient between
trough levels and systemic exposure is in the 0.7-0.8
range.\textsuperscript{12,13}

For effective TDM, in addition to being able to measure systemic
exposure (\emph{e.g.} trough levels), one must also be able to predict
how changes in dosing will change the drug exposure. Consequently, the
dose-exposure relationships must be well-characterized. To this end,
serial sampling of drug exposure in the same individual over time
provides crucial information and should be incorporated into precision
dosing studies whenever possible.\textsuperscript{14} First, it enables
evaluation of intra-individual exposure variability over time. This
helps estimate how well the systemic exposure can be predicted from dose
alterations. Second, it improves estimation of the total systemic
exposure over the course of treatment. Finally, it allows for
determination of additional parameters, such as the maximum or minimum
blood drug concentrations, which may also be relevant for predicting
drug efficacy, resistance, and toxicity.

The inter- and intra-individual PK variability and the strength of
correlation between trough levels and AUC are important considerations
for calculation of sample sizes in clinical studies. These parameters
should guide not only the number of study participants but also the
number of samples per individual as well as the sampling frequency.

\section*{Correlating Drug Exposure with Drug Efficacy and
Toxicity}

{\label{correlating-drug-exposure-with-drug-efficacy-and-toxicity}}

Once it is established that the blood levels of a novel oral anticancer
drug vary significantly from one patient to another and cannot be
practically estimated by means other than direct measurement, the
relationship between systemic drug exposure and drug efficacy and drug
toxicity should be investigated (Figure 1, Stage 2). Such studies can
provide evidence in favor of TDM and define the target (therapeutic)
exposure range by demonstrating increased treatment failures at
sub-therapeutic exposures and increased toxicity at supra-therapeutic
exposures. After all, if low drug levels cannot predict treatment
failure and high drug levels cannot predict adverse drug effects, TDM
will be of limited value.

\subsection*{Measuring Efficacy}

{\label{measuring-efficacy}}

A drug's effect can be measured in various ways. In oncology, efficacy
endpoints such as response rates, progression free survival, or overall
survival are typically used.\textsuperscript{15} These endpoints are
commonly derived from histologic and/or radiologic tumor evaluations,
but other assessments such as circulating tumor cells, circulating
cell-free tumor DNA, microRNA, or protein markers can also be used. In
addition, pharmacodynamics markers (\emph{e.g.} measurable molecules
corresponding to drug target inhibition or downstream pathway activity)
may be available for some drugs, which may enable close to real-time PD
monitoring.\textsuperscript{16} Thus, for studying the relationship
between drug levels and effect, one or more efficacy endpoints, alone or
in combination with PD biomarkers, can be
used.\textsuperscript{1--3}Measures of drug effect can be represented by
dichotomous variables, such as frequency of occurrence, or by continuous
variables such as concentrations of tumor markers.

To help decrease methodological heterogeneity in measuring drug
response, an international multidisciplinary working group developed
RECIST (Response Evaluation Criteria in Solid Tumours) criteria for the
evaluation of tumor burden. \textsuperscript{17} These criteria describe
standardized approaches of solid tumor size measurement, primarily using
imaging techniques, and define the outcomes of complete response (CR),
partial response (CR), stable disease (SD), and progressive disease
(PD). \textsuperscript{17}

The duration of time it takes to achieve the chosen efficacy endpoints
is also important for study design. The lag in time between when drug
exposure is initially assessed and when clinical response can be
detected is typically on the order of weeks to months or even years. On
these timescales, the initial exposure assessment may no longer
accurately represent the total drug exposure over the course of
treatment. Thus, in studies with long treatment duration, serial
exposure assessments over time may be particularly useful for capturing
the overall drug exposure more accurately.

The pre-treatment dynamics of outcome measures is also important to
consider. For example, high heterogeneity in the pre-treatment rates of
tumor growth and trajectories of biomarker levels between individuals in
a study population is likely to result in high inter-individual
variability in these measures during treatment. Consequently, the
statistical power of the study suffers, requiring increased numbers of
participants. As a further example, a small decrease in the rate of
tumor growth after treatment initiation may be interpreted as disease
progression in a patient with a fast-growing tumor and as stable disease
in a patient with a slow-growing tumor. This suggests that several
pre-treatment assessments of the patient's baseline tumor size or
biomarker levels, as well as the use of a control group, may help more
accurately characterize the effect of the drug.

\subsection*{Measuring Toxicity}

{\label{measuring-toxicity}}

The side effects that occur during treatment can be a consequence a
drug's effect, related to a drug's unwanted but expected off-target
effect, or they can be idiopathic. Depending on the mechanism, side
effects can manifest relatively quickly, within hours or days, or can
take months to develop. Similar to the assessment of drug efficacy
discussed above, the prevalence and timing of drug toxicity will impact
the study design with respect to the number of participants required,
the frequency of toxicity assessments, and the duration of toxicity
monitoring.

Drug-related toxicity often correlates with drug dose and typically
subsides following dose decrease or interruption. However, the
occurrence of adverse drug events may also seem stochastic and they may
appear and disappear without temporally related dose adjustments. In
this context, variations in drug exposure (at the same prescribed dose)
may correlate with toxicity. Thus, serial exposure assessments over time
may be particularly helpful for relating fluctuations in drug trough
levels to toxicity symptoms, especially in individuals concurrently
treated with other drugs prone to interactions or toxicities of their
own.

The approach to capturing and quantifying adverse drug effect data must
also be considered. Self-administered patient questionnaires (patient
reported outcomes or PROs) may be used to supplement clinical
assessments.\textsuperscript{18} Toxicity may be represented as
dichotomous (either present or not), categorical (based on severity) or
even continuous (\emph{e.g.} elevation in blood pressure) variables. In
addition, the National Cancer Institute (NCI) provides Common
Terminology Criteria for Adverse Events (CTCAE) to help standardize the
description and grading of adverse events.\textsuperscript{19,20}

Of note, drug toxicity itself can sometimes be used to guide dose
optimization. Such ``dosing to toxicity'' strategies have long been used
for chemotherapy but may also have a role in dosing of oral targeted
small molecule drugs.\textsuperscript{16} A relevant review on the
susceptibility to adverse drug reactions was recently published in this
journal.\textsuperscript{21} The described susceptibility factors
included the type of immunological reaction, genetics, age, sex,
physiological changes (such as pregnancy), exogenous factors (such as
interacting drugs), and diseases. Notably, the authors highlight that
there may be significant inter-patient variability in the dose-response
curves not only for drug benefits but also for harm, providing an
illustration of how some (hypersusceptible) patients may experience
toxicity at drug concentrations insufficient for
efficacy.\textsuperscript{21} Importantly, this is one context in which
TDM has a clear advantage: using a dosing to toxicity approach for
hypersusceptible patients results in continued treatment with drug doses
that are ineffective, while TDM informs a change in therapy.

\subsection*{Standardization of Assays and
Methods}

{\label{standardization-of-assays-and-methods}}

For the vast majority of new drugs there are no FDA-approved
quantitative assays. Instead, new drugs are typically quantified by
assays developed in individual laboratories, known as laboratory
developed tests (LDTs). The required levels of quality assurance for
these tests vary widely, in part depending on the laboratory's local and
other regulations (\emph{e.g.} CLIA, GLP). In addition, LDTs developed
in different labs may employ distinct methodologies
(\emph{e.g.}immunoassays, liquid chromatography-tandem mass
spectrometry, etc.). Taken together, this can lead to significant
inter-laboratory and sometimes even intra-laboratory differences in
results. External proficiency testing programs can help minimize such
differences but, more often than not, such programs do not exist for new
drugs. Therefore, it is important to be aware that lab-to-lab
differences in the measurement of drug levels may be a significant
contributor of noise in TDM studies. Utilizing the same laboratory with
a thoroughly validated method for all drug level measurements for a
precision dosing study may be a worthwhile consideration.

The same holds true for methods and approaches for quantifying drug
effects. The challenges associated with bioanalytical measurements of
pharmacodynamics biomarkers are analogous to those for drug assays.
Similarly, there may be significant inter-institution and even
intra-institution variability in imaging or anatomical techniques used
for tumor assessments. Again, this variability may be a considerable
source of noise in TDM studies.

In order to improve experimental reproducibility as well as
applicability and translatability of results, attempts should be made to
standardize the assays and methods. As mentioned above, RECIST criteria
can help standardize solid tumor size measurements and NCI's CTCAE can
help standardize assessments of drug
toxicity.\textsuperscript{17,19}Similarly, guidance from the NCI also
exists for the development and incorporation of biomarkers studies in
drug trials.\textsuperscript{22}The standardization of assays for oral
small molecules for cancer is lagging, although some proficiency testing
programs have recently become available.\textsuperscript{23}

\subsection*{Study Design Considerations for Exposure-Response
Relationships}

{\label{study-design-considerations-for-exposure-response-relationships}}

In contrast to biomarker studies, which can obtain useful data through
retrospective analysis of repository samples collected during routine
patient care, TDM studies aiming to investigate the correlation of drug
levels with effects and toxicity will likely require prospective
collection of samples. This is because the relative timing of drug
intake and blood sampling is critically important to interpreting the
obtained drug level results. In samples without associated data on
timing of last drug intake (most repository samples), the drug levels
may represent trough, peak, or intermediate time points. In addition,
exposure-response relationship studies are typically observational (no
dose adjustment based on results) rather than interventional, because
dose adjustment after blood level measurement but before response
measurement would confound interpretation of results. A large number of
examples of such studies for oral small molecule anticancer drugs have
been summarized in numerous reviews.\textsuperscript{1--5}

Although the necessity to conduct such studies prospectively presents
certain challenges (\emph{e.g.} obtaining preliminary data for a grant
proposal and long accrual times), prospective studies tend to be less
prone to certain types of biases such as recall bias and non-recorded
confounders. Other types of bias, such as selection bias, can still
occur in prospective studies.\textsuperscript{24,25}

As discussed above, numerous choices are available with respect to the
frequency and duration of exposure sampling as well as the timing,
prevalence, and quantification of clinical endpoints and toxicity.
Consequently, study design and power calculations should take into
account the temporal relationships between drug levels and efficacy and
toxicity as well as the anticipated frequency of measured outcomes and
adverse events. Although there are numerous resources to guide power
calculations for PK studies, the literature on power calculations for
TDM studies seems to be lacking.\textsuperscript{26,27}

Data from the exposure-response relationship studies can be described
using various forms of regression analysis or more simply by comparing
the outcomes of patients stratified by, for example,
C\textsubscript{min} quartiles or
deciles.\textsuperscript{25,28--30}Ultimately, the goal of such studies
is to define a therapeutic exposure range below which there is increased
risk of lack of efficacy and above which there is increased risk of
toxicity.\textsuperscript{1--5}

It should be mentioned that \emph{in vitro} and
pre-clinical\emph{in-vivo} experiments may also demonstrate
concentration-effect relationships and can be used to supplement the
results obtained in clinical studies. \textsuperscript{4,5} For solid
tumors, blood level measurements may be complemented by \emph{in vivo}
studies that also measure drug concentrations in tumor
tissue.\textsuperscript{31}

\section*{Evaluating the Feasibility of
TDM}

{\label{evaluating-the-feasibility-of-tdm}}

Once drug exposure-response studies successfully define a target
therapeutic concentration range or efficacy cutoff, prospective
dose-adjustment trials intended to demonstrate that TDM can be
implemented should be initiated (Figure 1, Stage 3). The primary outcome
of this type of study is the proportion of patients starting the
treatment under- or over-exposed who successfully achieve therapeutic
blood drug levels following recommendations for dose
adjustments.\textsuperscript{1--5,32--35} Such TDM feasibility studies
may compare the prevalence of therapeutic exposure before and after dose
adjustment recommendation in the same group of subjects or, preferably,
with a separate control arm.

The data on feasibility of dose individualization generated by such
studies is crucial, as it can identify a number of reasons why a dose
adjustment recommendation is not actually carried out. A substantial
number of physicians will not follow the dosing
advice.\textsuperscript{24} For example, doctors may be hesitant to
increase the dose beyond the highest FDA-approved dose or the maximum
tolerated dose established in clinical trials. On the other hand, most
oncologists would be rightfully reluctant to reduce the dose in the
absence of observable toxicity. Lack of reimbursement for increased
doses of these often very expensive drugs may also stifle dose
optimization. Finally, medication compliance issues may further reduce
feasibility of optimizing drug exposure through dose individualization.

As mentioned above, numerous examples of such studies evaluating
feasibility of TDM for oral small molecule anticancer drugs are
available and many of them indeed succeed in increasing the proportion
of patients with drug levels in the therapeutic
range.\textsuperscript{1--5,32--35} An example that deserves special
mention is the recently described study of the Dutch Pharmacology
Oncology Group (DPOG).\textsuperscript{33} The primary objective of this
ongoing prospective multi-center trial involving 600 patients is to
halve the proportion of patients with a drug exposure below the TDM
target after 2 potential PK-guided interventions which, for most of the
23 oral anticancer drugs that are being monitored, will be after 12
weeks of treatment. The secondary objectives are to examine tolerability
and outcome parameters such as objective response rate, time to
progression, progression free survival, etc.

\section*{Testing Whether Precision Dosing Improves Clinical
Outcomes}

{\label{testing-whether-precision-dosing-improves-clinical-outcomes}}

TDM feasibility trials can demonstrate that TDM-guided dose
individualization is effective in optimizing drug exposure, but they are
not designed to prove that TDM actually improves treatment outcomes.
Nevertheless, these studies are essential for test-driving
dose-adjustment protocols, workflows, and infrastructure, as well as for
identifying unforeseen barriers to execution of dose-adjustment
recommendations. Consequently, the parameters derived from TDM
feasibility trials are critical for informing the design and
implementation of subsequent randomized controlled trials (RCTs) for
testing whether dose individualization improves treatment outcomes
(Figure 1, Stage 4). Based on TDM feasibility trial data, much
consideration during the design of the RCT should be given to defining
the comparison groups and the criteria for inclusion and exclusion of
subjects with respect to the population of patients most likely to
benefit from TDM. For example, it may be prudent to select only those
individuals who are over- or under-exposed as your study
population,\emph{i.e.} those at high risk of treatment failure due to
lack of efficacy or toxicity, rather than comparing outcomes for ``TDM''
vs ``no TDM'' in all patients receiving the drug.

One possible concern related to conducting a randomized controlled trial
comparing TDM with the standard of care (\emph{i.e.} no TDM) is that
some may consider it unethical to abstain from increasing the drug dose
in control patients with low systemic exposure. The underlying
assumption in such an argument is that increasing the drug dose in an
under-exposed patient will improve the clinical outcome. Based on this
assumption, however, it may further be argued that it would be unethical
not to measure drug levels in all patients to identify the underexposed
individuals who would benefit from dose increases, and the ethical
solution may be to apply TDM universally.\textsuperscript{33} A
``catch-22'' of sorts, this assumption is exactly what the randomized
controlled trial in attempting to prove! Without RCTs demonstrating
clear benefits of TDM versus no TDM for at least some oral small
molecule anticancer drugs, the current oncology practice will not have
enough incentive to change and all underexposed patients receiving the
standard of care will continue to be prescribed sub-therapeutic doses of
these drugs. For example, at our institution there are approximately 550
patients with active imatinib prescriptions. Despite evidence to suggest
that approximately 150 to 350 of these individuals may be under-dosed
(\textasciitilde{}25\%-65\%),\textsuperscript{29,36,37} our laboratory
receives fewer than 3 samples per week for imatinib level monitoring.

To the best of our knowledge, there is no randomized controlled trial,
either published in a peer-reviewed journal or ongoing, that uses a
clinical outcome as the primary endpoint and demonstrates the benefit of
TDM of targeted oral anticancer drugs. Nevertheless, results from
several trials have been published in abstract form only, and thus may
need to be considered with caution. Rousselot et al. reported the final
results of the randomized OPTIM Imatinib study, which demonstrated that
TDM increases the rates of molecular response in patients with chronic
myeloid leukemia (CML).\textsuperscript{36} Another abstract from the
same group reported that TDM of dasatinib resulted in reduced risk of
pleural effusions and high molecular response rates in
CML.\textsuperscript{38} Results of a trial by Gotta et al. were
published in a peer-reviewed journal, but this study did not compare TDM
versus no TDM. Instead, ``routine TDM'' was compared to ``rescue TDM''
for Imatinib in 57 CML patients.\textsuperscript{24} The primary
endpoint was a combined outcome (failure- and toxicity-free survival
with continuation on imatinib) over 1-year follow-up. This dose
individualization trial could not demonstrate additional benefit of
``routine TDM'' using intention-to-treat analysis, due to a small number
of study participants and, surprisingly, limited prescribers' adherence
to dosage recommendations. However, using as-treated subgroup analysis
of ``routine TDM'' group, 10 of 14 of patients receiving the recommended
dosage after one cycle of TDM remained event-free compared to 3 of 13
patients for whom dosage recommendations were not correctly adopted
(P=0.033).

Thus, despite numerous studies (primarily in Stages 1-3 of Figure 1)
providing evidence to suggest that TDM of oral small molecule anticancer
drugs may be beneficial to patients, and perhaps even
cost-effective,\textsuperscript{39} there seems to be no published study
that definitively demonstrates such benefit. Historically, there are
examples of drugs, such as calcineurin inhibitors, for which TDM became
the standard of care without demonstrated superiority of ``TDM'' vs ``no
TDM'' in RCTs. However, in the present culture emphasizing
evidence-based medicine, having more drugs reach stage 4 of Figure 1
must be the goal, as this will encourage requests for TDM by medical
oncologists as well as increase the chances for reimbursement of costs.

\section*{Precision Dosing in Clinical Studies Versus in Routine Clinical
Care}

{\label{precision-dosing-in-clinical-studies-versus-in-routine-clinical-care}}

It must be emphasized that the levels of treatment oversight as well as
the characteristics of the patient populations in clinical studies are
significantly different from those in real-world patient care, which is
highly relevant for implementation of TDM. In routine patient care, the
timing of sample collection for trough levels is unlikely to be exact,
and there is increased likelihood of drug-drug and drug-food
interactions as well as other factors that could alter the relationships
between drug dose, trough levels, systemic exposure, and, subsequently,
biomarkers and treatment outcome. Prospective studies comparing TDM with
no TDM using clinical outcomes as the primary endpoint will therefore
need to be followed up by analysis of real-world patient data.

\section*{Comprehensive Approaches to Precision
Dosing}

{\label{comprehensive-approaches-to-precision-dosing}}

Systemic exposure to a drug may be closely related to outcome as well as
side effects, but it would be na\selectlanguage{ngerman}ïve to think that exposure is the only
parameter that we should focus on optimizing. As previously mentioned,
there are other ways a drug's effect can be monitored and other
parameters may modulate the relationship between systemic exposure and
effect. Thus, an ideal precision dosing approach goes far beyond the use
of TDM reference ranges, TDM software, or even PKPD software with
population models and Bayesian forecasting. In reality, we need to be
able to monitor, aggregate, and derive conclusions from thousands of
data points, and continually adjust treatment recommendations as new
data points are collected. This calls for machine learning and
artificial intelligence approaches that have started to make their mark
on some areas of medicine, although not yet in precision dosing. Current
and future technology will make this a reality and help bridge the gap
between clinical studies and real-world patient
care.\textsuperscript{40}

\section*{Conclusion}

{\label{conclusion}}

In summary, although numerous studies provide a clear rationale for
monitoring drug levels of oral small molecule anticancer drugs, the lack
of trials clearly showing significant improvement in outcomes with TDM
prevents this approach from being embraced by the oncology community.
Well-designed randomized controlled trials comparing drug level
monitoring with the standard of care, which are sufficiently powered to
demonstrate an effect on survival are desperately needed. Numerous
completed and ongoing studies on TDM of oral small molecule anticancer
drugs have prepared the precision oncology field for such a trial. The
time has come to take the next step.

\section*{Conflict of Interest
Statement}

{\label{conflict-of-interest-statement}}

The authors declare that there is no conflict of interest.

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\section*{Figure Legends}

{\label{figure-legends}}

Figure 1: Stages of clinical studies with increasing levels evidence in
support of TDM of oral small molecule anticancer drugs.
(C\textsubscript{min}, trough level; TDM, therapeutic drug monitoring;
RCT, randomized controlled trial; PFS, progression-free survival.)\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure1/Figure1}

\end{center}
\end{figure}

\selectlanguage{english}
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\end{document}

