REVIEW
`
` Dose Escalation Methods in Phase I Cancer Clinical Trials
` Christophe Le Tourneau , J. Jack Lee , Lillian L. Siu
`
` Phase I clinical trials are an essential step in the development of anticancer drugs. The main goal of these studies is to establish the
`recommended dose and/or schedule of new drugs or drug combinations for phase II trials. The guiding principle for dose escalation
`in phase I trials is to avoid exposing too many patients to subtherapeutic doses while preserving safety and maintaining rapid
`accrual. Here we review dose escalation methods for phase I trials, including the rule-based and model-based dose escalation meth-
`ods that have been developed to evaluate new anticancer agents. Toxicity has traditionally been the primary endpoint for phase I
`trials involving cytotoxic agents. However, with the emergence of molecularly targeted anticancer agents, potential alternative end-
`points to delineate optimal biological activity, such as plasma drug concentration and target inhibition in tumor or surrogate tissues,
`have been proposed along with new trial designs. We also describe specific methods for drug combinations as well as methods
`that use a time-to-event endpoint or both toxicity and efficacy as endpoints. Finally, we present the advantages and drawbacks of
`the various dose escalation methods and discuss specific applications of the methods in developmental oncotherapeutics.
`
` J Natl Cancer Inst 2009;101: 708 – 720
`
` Phase I trials represent the first application of a new drug or drug
`combination to humans and as such are the foundation of a success-
`ful clinical drug development process. Because the early clinical
`development of a novel agent may unduly influence its ultimate fate,
`a careful and thoughtful approach to the design of phase I trials is
`essential. Phase I clinical trials in oncology are typically small, sin-
`gle-arm, open-label, sequential studies that include patients with a
`good performance status whose cancers have progressed despite
`standard treatments. A principal goal of such trials is to establish the
`recommended dose and/or schedule of an experimental drug or
`drug combination for efficacy testing in phase II trials. A phase I trial
`design has many components, including starting dose, dose incre-
`ment, dose escalation method, number of patients per dose level,
`specification of dose-limiting toxicity, target toxicity level, definition
`of the maximum tolerated dose (MTD) and recommended dose for
`phase II trials, patient selection, and number of participating centers
`( see definitions of basic concepts in Table 1 ). Although all of these
`components are relevant for the design of a phase I trial, this review
`will focus on selecting the dose escalation method that will yield an
`optimal balance of safety, efficiency, and ethical conduct.
` The guiding principle for dose escalation in phase I trials is to
`avoid unnecessary exposure of patients to subtherapeutic doses of
`an agent (ie, to treat as many patients as possible within the thera-
`peutic dose range) while preserving safety and maintaining rapid
`accrual. Dose escalation methods for phase I cancer clinical trials
`fall into two broad classes: the rule-based designs, which include
`the traditional 3+3 design and its variations, and the model-based
`designs. The rule-based designs assign patients to dose levels
`according to prespecifi ed rules based on actual observations of
`target events (eg, the dose-limiting toxicity) from the clinical data.
`Typically, the MTD or recommended dose for phase II trials is
`determined by the prespecifi ed rules as well. On the other hand,
`the model-based designs assign patients to dose levels and defi ne
`the recommended dose for phase II trials based on the estimation
`of the target toxicity level by a model depicting the dose – toxicity
`
`relationship. However, because of safety concerns, most model-
`based designs are modifi ed such that specifi c restrictions are set as
`safeguards for elements such as dose increments to avoid over-
`shooting of the MTD and thus exposing patients to undue harm.
`All of these methods were developed in the era of cytotoxic drugs,
`during which time it was assumed that both effi cacy and toxicity
`increase with dose. These relationships are typically represented
`by dose – toxicity and dose – effi cacy curves in which toxicity and
`effi cacy increase monotonically with increasing dose ( Table 1 and
` Figure 1 ). Consequently, these methods have used toxicity as the
`primary endpoint. For molecularly targeted agents, the dose –
`effi cacy and dose – toxicity curves may differ from those for cyto-
`toxic agents, and effi cacy may occur at doses that do not induce
`clinically signifi cant toxicity ( 1 – 4 ). Thus, for trials involving these
`agents, the occurrence of drug-related biological effects has been
`suggested as an alternate primary endpoint besides toxicity ( 1 – 4 ).
` Here we review the different dose escalation methods for phase I
`cancer clinical trials of single agents and drug combinations and
`discuss their pros and cons. Recent reviews ( 2 , 5 ) of phase I clinical
`trials including this update reveal that new dose escalation designs
`have been incorporated into phase I trials infrequently, and we
`
` Affiliations of authors: Division of Medical Oncology and Hematology,
`Princess Margaret Hospital, University Health Network, University of
`Toronto, Toronto, ON, Canada (CLT, LLS); Department of Biostatistics, The
`University of Texas M. D. Anderson Cancer Center, Houston, TX (JJL ) .
` Correspondence to: Lillian L. Siu, MD, FRCPC, Division of Medical Oncology
`and Hematology, Princess Margaret Hospital, University Health Network,
`University of Toronto, 610 University Ave, Ste 5-718, Toronto, ON, Canada
`M5G 2M9 (e-mail: lillian.siu@uhn.on.ca )
` See “Funding” and “Notes” following “References.”
` DOI: 10.1093/jnci/djp079
` © 2009 The Author(s).
`This is an Open Access article distributed under the terms of the Creative Com -
`mons Attribution Non-Commercial License (http://creativecommons.org/licenses/
`by-nc/2.0/uk/), which permits unrestricted non-commercial use, distribution, and
`reproduction in any medium, provided the original work is properly cited.
`
`708 Review | JNCI
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`Vol. 101, Issue 10 | May 20, 2009
`
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`

`
` Table 1 . Glossary of terms
`
` Term
`
` Cohort
` Starting dose
` Dose increment (decrement)
` Dose-limiting toxicity (DLT)
`
` Dose – efficacy curve
`
` Dose – toxicity curve
`
` Target toxicity level
`
` Maximum tolerated dose (MTD)
`
` Optimal biological dose (OBD)
`
` Recommended phase II dose
`
` Pharmacokinetics
`
` Pharmacodynamics
`
` Therapeutic index
`
`Definition
`
`Group of patients treated at a dose level.
`The dose chosen to treat the first cohort of patients in a phase I trial.
`The percent increase (or decrease) between dose levels.
`Toxic effects that are presumably related to the drugs that are considered unacceptable (because
` of their severity and/or irreversibility) and that limit further dose escalation. DLTs are defined before
` beginning the trial and are protocol specific. They are typically defined based on toxic effects seen in
`
`the first cycle and specified using a standardized grading criteria, for example, Common Terminology
` Criteria for Adverse Events.
`The dose – efficacy curve reflects the relationship between dose and probability of efficacy for an
` anticancer agent. A logistic function is commonly assumed to describe the dose – efficacy curve for
` cytotoxic agents and is characterized by a parameter, ␪ , which represents the slope of the
` dose – efficacy curve. Small values of ␪ indicate that the probability of efficacy increases very slowly
` with increasing dose levels, whereas large values of ␪ indicate a sharp increase in efficacy with
`
`increasing dose levels ( see Figure 1 ).
`The dose – toxicity curve reflects the relationship between dose and probability of toxicity for an
` anticancer agent. A logistic function is commonly assumed to describe the dose – toxicity curve for
` cytotoxic agents and is characterized by a parameter, ␪ , which represents the slope of the
` dose – toxicity curve. Small values of ␪ indicate that the probability of toxicity increases very slowly
` with increasing dose levels, whereas large values of ␪ indicate a sharp increase in toxicity with
`
`increasing dose levels ( see Figure 1 ).
`The maximum probability of DLT that is considered acceptable in the trial. The target toxicity level in
` phase I trials is typically between 20% and 33%.
`Phase I trials conducted in the United States: the highest dose level at which ≤ 33% of patients
` experience DLT.
` Phase I trials conducted in Europe and Japan: the lowest dose level at which ≥ 33% of patients
` experience DLT (a misnomer in the sense that the MTD is actually not a tolerable dose).
` Phase I trials that use model-based methods: the dose that produces the target toxicity level.
`Dose associated with a prespecified most desirable effect on a biomarker among all doses studied
`
`(eg, inhibition of a key target in tumor or surrogate tissue or achievement of a prespecified
`
`immunologic parameter).
`Phase I trials with a toxicity endpoint that are conducted in the United States: the MTD.
` Phase I trials with a toxicity endpoint that are conducted in Europe and Japan: one dose level below
`
`the MTD.
` Phase I trials in which the endpoint is a prespecified biological endpoint: the OBD.
`Pharmacologic effects of the body on the drug (ie, the time course of drug absorption, distribution,
` metabolism, and excretion).
`Pharmacologic effects of the drug on the body (eg, nadir neutrophil or platelet count, nonhematologic
`
`toxicity, molecular correlates, imaging endpoints).
`The dosage or range of dosages of a drug that is required to produce a given level of damage to critical
` normal tissues (toxicity) divided by the dosage or range of dosages that yields a defined level of
` antitumor effect (efficacy) ( see Figure 1 ).
`
`explore the reasons for this disconnect. Finally, we recommend
`ways to assign dose escalation methods to evaluate new drugs or
`drug combinations. These recommendations are based on pre-
`clinical information, existing knowledge of agents that target the
`same or similar molecular pathways, and the availability of
`resources to execute such methods.
`
` Rule-Based Designs
` The main characteristic of rule-based designs is that they do
`not stipulate any prior assumption of the dose – toxicity curve.
`These designs comprise the so-called “up-and-down” designs
`because they allow dose escalation and de-escalation. The first up-
`and-down design was introduced in the late 1940s by Dixon and
`Mood ( 6 ), and Storer ( 7 ) described implementation of this design
`in clinical practice half a century later. The general principle of
`this design is to escalate or de-escalate the dose with diminishing
`fractions of the preceding dose depending on the absence or
`
` presence of severe toxicity in the previous cohort of treated
`patients ( Figure 2, A ). The simple up-and-down design converges
`to a dose that corresponds to a probability of severe toxicity of
`approximately 50%, which is higher than the 33% threshold com-
`monly accepted in most phase I cancer clinical trials. Although
`variations of this up-and-down design have been developed in an
`attempt to increase patient safety and to use toxicity data collected
`in real time ( 8 , 9 ), these designs have not been used much in clinical
`practice because they risk exposing patients to unacceptable levels
`of toxicity. The first rule-based design to be used widely in clinical
`practice was the traditional 3+3 design. Variations of the tradi-
`tional 3+3 design that have been put into clinical use include the
`accelerated titration designs and the pharmacologically guided
`dose escalation (PGDE) method.
`
` Traditional 3+3 Design
` The traditional 3+3 design remains the prevailing method for con-
`ducting phase I cancer clinical trials ( 7 ). It requires no modeling of
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`three, one of four, or two of fi ve patients, but the trial will termi-
`nate if three or more dose-limiting toxicities are observed.
` The main advantages of the traditional 3+3 design are that it is
`simple to implement and safe ( Table 2 ). In addition, the accrual of
`three patients per dose level provides additional information about
`pharmacokinetic interpatient variability. However, a disadvantage
`of this design is that it involves an excessive number of escalation
`steps, which results in a large proportion of patients who are
`treated at low (ie, potentially subtherapeutic) doses while few
`patients actually receive doses at or near the recommended dose
`for phase II trials. This latter point is illustrated in Table 3 , which
`presents the dose escalation method used as well as the number of
`dose levels in recent fi rst-in-human single-agent phase I trials for
`anticancer agents that were eventually (1992 – 2008) approved by
`the US Food and Drug Administration (FDA) for the treatment of
`solid tumors. Among 21 trials that used the traditional 3+3 design,
`more than half involved six or more dose levels.
`
` Accelerated Titration Designs
` Accelerated titration designs combine features from variations of
`the traditional 3+3 design and the model-based design. Because the
`patient assignment to doses is based on prespecified rules, we clas-
`sify accelerated titration designs as rule-based designs. Through
`simulations based on a stochastic model fit to data from 20 actual
`phase I trials of nine different drugs, Simon et al. ( 36 ) described
`one control design and three accelerated titration designs. The
`control design, design 1, is a standard 3+3 design with a 40% dose
`increment between successive cohorts of patients. Although the
`three accelerated titration designs, designs 2, 3, and 4, were created
`based on a statistical model as described ( 36 ), the assignment of
`patients to dose levels follows specific rules according to the
`observed toxicities at each dose level. Designs 2 and 3 allow 40%
`and 100% dose escalations, respectively, between single-patient
`cohorts until a dose-limiting toxicity or two moderate toxicities are
`observed during cycle 1, at which point dose escalation reverts to
`the more conservative one used in design 1. In design 4, the 100%
`dose escalation between single-patient cohorts in the accelerated
`phase reverts to design 1 when one dose-limiting toxicity or two
`moderate toxicities are observed during any cycle (not just during
`cycle 1). Intrapatient dose escalation is allowed during the acceler-
`ated phase of designs 2, 3, and 4 ( Figure 2, C ). In all three acceler-
`ated titration designs, the standard 3+3 design is used after the
`accelerated phase as a stopping rule, and then the described model
`is recommended to estimate the MTD with all toxicity data col-
`lected during the trial. In addition, the model recommended for
`use included a parameter for cumulative toxicity as well as a para-
`meter for interpatient variability, such that the accelerated titra-
`tion designs would provide information in these aspects. In
`practice, investigators often determine the MTD based on the
`conventional 3+3 escalation rule without fitting trial data to the
`model at the end of the trial. Consequently, the original model-
`based accelerated titration designs have been adapted primarily as
`rule-based designs in clinical practice.
` The accelerated phase in accelerated titration designs — in
`which only one patient is included per dose level — along with the
`possibility of intrapatient dose escalation theoretically reduce the
`number of patients who are treated at subtherapeutic doses
`
`Dose–efficacy curve
`Dose–toxicity curve
`
`1
`
`0.3
`
`0.1
`
`efficacy
`
`Probability of toxicity or
`
`0
`
`x
`
`1
`
`
`
` Figure 1 . Typical dose – toxicity and dose – effi cacy curves for cytotoxic
`agents. This example illustrates that at dose x , the probability of effi -
`cacy is 30% and the probability of toxicity is 10%; hence, the therapeutic
`index of the drug at dose x is 10% divided by 30% = 1/3.
`
`Dose
`
`the dose – toxicity curve beyond the classical assumption for cyto-
`toxic drugs that toxicity increases with dose. This rule-based
`design proceeds with cohorts of three patients; the first cohort is
`treated at a starting dose that is considered to be safe based on
`extrapolation from animal toxicological data, and the subsequent
`cohorts are treated at increasing dose levels that have been fixed in
`advance ( Figure 2, B ). Historically, dose escalation has followed a
`modified Fibonacci sequence in which the dose increments become
`smaller as the dose increases (eg, the dose first increases by 100%
`of the preceding dose, and thereafter by 67%, 50%, 40%, and
`30% – 35% of the preceding doses). In most cases, the prespecified
`dose levels do not fit the exact Fibonacci sequence as described in
`the 12th century ( 5 ). If none of the three patients in a cohort expe-
`riences a dose-limiting toxicity, another three patients will be
`treated at the next higher dose level. However, if one of the first
`three patients experiences a dose-limiting toxicity, three more
`patients will be treated at the same dose level. The dose escalation
`continues until at least two patients among a cohort of three to six
`patients experience dose-limiting toxicities (ie, ≥ 33% of patients
`with a dose-limiting toxicity at that dose level). The recommended
`dose for phase II trials is conventionally defined as the dose level
`just below this toxic dose level.
` Alternative rules besides “3+3” have been proposed, including
`the “2+4,” “3+3+3,” and “3+1+1” (also referred as “best of fi ve”)
`rules ( 10 ). In the “2+4” design, an additional cohort of four patients
`is added if one dose-limiting toxicity is observed in a fi rst cohort of
`two patients. The stopping rule is the same as in the traditional 3+3
`design. In the “3+3+3” design, a third cohort of three patients is
`added if two of six patients in the fi rst two cohorts experience a
`dose-limiting toxicity at a certain dose level. The trial terminates if
`at least three of nine patients experience a dose-limiting toxicity.
`The “best of fi ve” design is more aggressive than the traditional
`3+3 design in that one additional patient is added if one or even
`two dose-limiting toxicities are observed among the fi rst three
`patients. Another patient is added if two dose-limiting toxicities
`are observed among the four treated patients. Dose escalation is
`allowed if dose-limiting toxicities are observed among none of
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`

`
`Dose
`
`A
`
`Dose
`
`B
`
`DLT
`
`1
`
`DLT
`
`DLT
`
`1
`
`1
`
`1
`
`SD
`
`1
`
`1
`
`1
`
`1
`
`RD
`
`DLT DLT
`
`3 3
`
`3
`
`3
`
`3
`
`RD
`
`3
`
`3
`
`3
`
`SD
`
`Time
`
`Time
`
`Dose
`
`C
`
`Dose
`
`D
`
`DLT
`
`DLT DLT
`
`3
`
`3
`
`1
`
`2
`
`3
`
`RD
`
`DLT DLT
`
`3 3
`
`3
`
`3
`
`RD
`
`3
`
`1
`
`Plasma drug AUC >
`prespecified treshold
`
`Determination of
`plasma drug AUC
`
`1
`
`1
`
`SD
`
`Intrapatient dose escalation
`
`1
`
`1
`
`SD
`
`Time
`
`Time
`
`Dose
`
`E
`
`Dose
`
`F
`
`DLT
`
`DLT
`
`1
`
`DLT
`
`DLT
`
`1
`
`1 1 1
`
`3
`
`RD
`
`1 1 1
`
`3
`
`RD
`
`1
`
`1
`
`Computation of p(DLT at next DL)
`= target toxicity level
`
`1
`
`SD
`
`1
`
`1
`
`1
`
`SD
`
`Computation of p(DLT at next DL)
`= target toxicity level
`Computation of p(DLT at next DL)
`= overdosing or excessive overdosing
`
`
` Figure 2 . Graphical depiction of dose escalation methods for phase I
`cancer clinical trials. Each box represents a cohort comprising the indi-
`cated number of patients treated at a given dose level. A ) Simple
`up-and-down design. B ) Traditional 3+3 design. C ) Accelerated titra-
`tion design. Dashed arrows represent intrapatient dose escalation.
` D ) Pharmacologically guided dose escalation. E ) Modifi ed continual
`
`Time
`
`Time
`
`
`reassessment method. F ) Escalation with overdose control. “Overdosing
`or excessive overdosing” refers to doses that exceed the MTD. DLT =
`dose-limiting toxicity; SD = starting dose; RD = recommended dose; DL
`= dose level; AUC = area under the curve for drug concentration as a
`function of time; p(DLT at next DL) = probability of dose-limiting toxicity
`at the next dose level.
`
`( Table 2 ). Permitting intrapatient dose escalation in accelerated
`titration designs is appealing because it gives some patients the
`opportunity to be treated at higher and presumably more effective
`doses. For example, in the fi rst-in-human phase I trial of ixabepi-
`lone, which used an accelerated titration design with intrapatient
`dose escalation, all patients received the drug at the eventually
`established recommended dose for phase II trials ( 35 ). On the
`other hand, unless the model recommended in the original publi-
`cation (36) using parameters for cumulative toxicity and interpa-
`tient variability is applied and fi ts the data well, one drawback of
`
`intrapatient dose escalation is that it may mask the cumulative
`effects of treatment or, at the very least, would make them harder
`to differentiate from chronic or delayed toxic effects. However,
`regardless of the trial design used, chronic, delayed, or cumulative
`toxic effects are generally not well captured by most phase I trials
`because most patients with advanced cancers do not remain on
`study for extended periods of time. Furthermore, it can be diffi cult
`to present and interpret results of trials that allow intrapatient dose
`escalations because a single patient may contribute data for several
`dose levels.
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` Table 2 . Theoretical main advantages and drawbacks of dose escalation methods for phase I cancer clinical trials *
`
`Advantages
`
`Drawbacks
`
` Dose escalation method
`
` Rule-based designs
`
` Traditional 3+3 design
`
`
`Easy to implement and safe
`
` Provide some data on PK interpatient variability
`
`
`Many patients treated at subtherapeutic doses
` Slow dose escalation
`Uncertainty about the RP2D
` Only the result from the current dose is used
`
`for determining the dose of next cohort of
` patients. Information on other doses is ignored.
`If model fitting is not performed (as is often the
` case in clinical practice):
`
` Intrapatient dose escalation may mask
`
` cumulative or delayed toxicities
`
` Difficult interpretation of the results when
`
`
`intrapatient dose escalation is allowed
`
` Uncertainty about the RP2D
`Need to obtain real-time PK results
`Interpatient variability may hamper dose
` escalation
`
`Need to have a prior guess of the RP2D
`
`Computations after each patient or cohort of
` patients
`Need real-time biostatistical support for dose
` escalation decisions (may also be an advantage)
`
`
`
` Accelerated titration designs
`
`More rapid dose escalation
`
`
`
`
` Pharmacologically guided dose
` escalation
`
` Model-based designs
`
` Modified continual reassessment
`
` method, escalation with overdose
`
` control, time-to-event continual
`
`
`reassessment method, EffTox,
`
` TriCRM
`
` May expose a greater proportion of patients at
` higher doses
` Data from all patients, cumulative toxicity, and
`
`interpatient variability can be fit to a model to
` establish the RP2D
`More rapid dose escalation
` Provide some data on PK interpatient variability
`
`
`Target toxicity level is explicitly defined
` More rapid dose escalation
` Use all available information from all patients
` Estimate of the RP2D with a confidence interval
` Take into account late-onset toxicities (time-to-event
` continual reassessment method)
` Take into account both toxicity and efficacy
`
`(EffTox + TriCRM)
`
` * PK = pharmacokinetic; RP2D = recommended phase II dose; EffTox = efficacy and toxicity method; TriCRM = an adaptative continual reassessment method that
`considers three potential trial outcomes: no efficacy and no toxicity, efficacy only, and toxicity only .
`
` Pharmacologically Guided Dose Escalation
` The PGDE method is another variation of the traditional 3+3
`design that has not been widely used in clinical practice. This
`approach assumes that dose-limiting toxicities can be predicted
`by plasma drug concentrations and that animal models can
`accurately reflect this relationship in humans ( 37 ). The PGDE
`method has two stages. A prespecified plasma exposure defined
`by the area under the curve for drug concentration as a function
`of time (AUC) is extrapolated from preclinical data. Then,
`pharmacokinetic data are obtained for each patient in real time
`to determine the subsequent dose level. As long as the prespeci-
`fied plasma exposure is not reached, dose escalation proceeds
`with one patient per dose level and typically at 100% dose
`increments (stage 1, Figure 2, D ). When the target AUC is
`reached or if dose-limiting toxicities occur, dose escalation
`switches to the traditional 3+3 design with smaller (usually
`around 40%) dose increments (stage 2).
` The PGDE method has not been widely adopted due to prac-
`tical obstacles, including: 1) logistic diffi culties in obtaining real-
`time pharmacokinetic results, which are required to determine
`the safety of the subsequent dose escalation; 2) problems in
`extrapolating preclinical pharmacokinetic data to phase I studies
`with different treatment schedules; and 3) risk of exposing the
`next patient to a highly toxic dose if the AUC obtained in the
`preceding patient was atypically low due to interpatient variabil-
`ity in drug metabolism. In clinical practice, the PGDE method
`has reliably defi ned the recommended dose for phase II trials for
`some cytotoxic agents such as certain anthracyclines and platinum
`
`compounds but has been found to be inappropriate for other
`classes of cytotoxic agents such as the antifolates, which display a
`high interpatient pharmacokinetic heterogeneity ( 38 ).
`
` Other Rule-Based Designs
` Several other rule-based designs have been proposed, including
`the isotonic regression model ( 39 ), the biased coin design ( 9 )
`and its variations ( 40 , 41 ), and the “rolling six” design ( 42 ). The
`rolling six design was originally proposed as a way to shorten
`the timeline of pediatric phase I trials by reducing the number
`of times a study is suspended to accrual ( 42 ). This method
`allows accrual of two to six patients concurrently onto a dose
`level based on the numbers of patients who are currently
`enrolled and evaluable, who experience a dose-limiting toxicity
`and who remain at risk of developing a dose-limiting toxicity.
`Because pediatric trials are typically conducted only after com-
`pletion of adult phase I trials, this design is intended to shorten
`the study duration in situations in which there is prior informa-
`tion about the dose range to be evaluated.
` Ji et al. ( 43 ) developed a rule-based design in which subse-
`quent patients are assigned to doses according to the toxicity
`outcome at the current dose by calculating the toxicity probabil-
`ity interval under the beta-binomial model. The authors also
`developed a freely available macro in Microsoft Offi ce Excel
`software that can be downloaded to facilitate the study conduct.
`Simulations have shown that the performance of this dose-
`fi nding design is better than the traditional 3+3 design and com-
`parable to some model-based designs.
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`

`
` Table 3 . Characteristics of first-in-human phase I clinical trials for recent anticancer agents that were eventually approved by the
`US FDA*
`
` Agent
`
` Molecular targeted agents
`
` Trastuzumab
`
` Imatinib
`
` Gefitinib
`
` Erlotinib
`
` Cetuximab
`
` Bevacizumab
`
` Sorafenib
`
` Sunitinib
`
` Panitumumab
`
` Lapatinib
`
` Temsirolimus
` Cytotoxic agents
`
` Paclitaxel
`
` Vinorelbine
`
` Docetaxel
`
` Gemcitabine
`
` Topotecan
`
` Irinotecan
`
` Capecitabine
`
` Liposomal doxorubicin
`
` Temozolomide
`
` Oxaliplatin
`
` Pemetrexed
`
` Trabectedin
`
` Albumin-bound paclitaxel
`
` Ixabepilone
`
`Class or mechanism
`of action
`
`Year of FDA
`approval
`
`Dose escalation
`method
`
`Reason for
`stopping trial
`
`No. of
`patients
`
`No. of
`dose levels Reference
`
`
`
`
`Mab
`TKI
`TKI
`TKI
`Mab
`Mab
`TKI
`TKI
`Mab
`TKI
`STKI
`
`Anti-tubulin
`Alkaloid agent
`Anti-tubulin
`Antimetabolite
`Anti – topoisomerase I
`Anti – topoisomerase I
`Antimetabolite
`Anti – topoisomerase II
`DNA alkylating
`DNA alkylating
`Antimetabolite
`Alkaloid agent
`Anti-tubulin
`Anti-tubulin
`
`1998
`2001
`2003
`2004
`2004
`2004
`2005
`2006
`2006
`2007
`2007
`
`1992
`1994
`1996
`1996
`1996
`1998
`1998
`1999
`1999
`2002
`2004
`2004
`2005
`2007
`
`
`
`PK
`Traditional
`PK
`Traditional
`Toxicity
`Traditional
`Toxicity
`Traditional
`PK
`Traditional
`Target inhibition
`Traditional
`Toxicity
`Traditional
`Toxicity
`Traditional
`PK
`Traditional
`NR
`NR
`Toxicity
`Modified CRM
`
`
`Toxicity
`Traditional
`Traditional + IPDE Toxicity
`ATD
`Toxicity
`Traditional + IPDE Toxicity
`Traditional
`Toxicity
`Traditional
`Toxicity
`Traditional
`Toxicity
`Traditional
`Toxicity
`Traditional + IPDE Toxicity
`Traditional + IPDE Toxicity
`Traditional
`Toxicity
`Traditional
`Toxicity
`Traditional
`Toxicity
`ATD
`Toxicity
`
`
`
`
`
`
`
`18
`83
`64
`40
`52
`25
`69
`28
`96
`81
`24
`
`34
`20
`39
`47
`28
`17
`34
`26
`51
`23
`38
`21
`19
`21
`
`4
`14
`8
`5
`6
`5
`>5
`6
`13
`NR
`10
`
`11
`7
`6
`12
`5
`4
`5
`4
`15
`9
`10
`4
`4
`4
`
`(11)
`(12)
`(13)
`(14)
`(15)
`(16)
`(17)
`(18)
`(19)
`(20)
`(21)
`
`(22)
`(23)
`(24)
`(25)
`(26)
`(27)
`(28)
`(29)
`(30)
`(31)
`(32)
`(33)
`(34)
`(35)
`
` * FDA = US Food and Drug Administration; Mab = monoclonal antibody; TKI = tyrosine kinase inhibitor; STKI = serine/threonine kinase inhibitor; NR = not reported;
`PK = pharmacokinetic data; CRM = continual reassessment method; IPDE = intrapatient dose escalation; ATD = accelerated titration design.
`
` Summary of Rule-Based Designs
` The main advantages of rule-based methods are that they are
`easy to implement and do not require special software. However,
`their performance (operating characteristics) is not guaranteed and
`they have some drawbacks. For example, these designs may be
`inefficient in establishing the dose that meets a specific target tox-
`icity level. In addition, the decision of dose allocation for future
`patients as well as the definition of the recommended dose for
`phase II trials rely on information from the current dose level and
`do not use all available information. As such, the recommended
`dose for phase II trials is then selected from the prespecified dose
`levels depending on which one best fits the definition of acceptable
`toxicity set a priori. However, although not ideal, the rule-based
`methods have been successful in establishing safe recommended
`doses for phase II trials during the past several decades for antican-
`cer agents that were eventually used worldwide in clinical practice
`( Table 3 ).
`
` Model-Based Designs
` An alternative dose escalation method for phase I clinical trials is
`to use statistical models that actively seek a dose level that pro-
`duces a prespecified probability of dose-limiting toxicity by using
`toxicity data from all enrolled patients to compute a more precise
`dose – toxicity curve. This method can be conveniently carried
`out using Bayesian models. Bayesian models require an initial
`
` estimation of ␪ (also called prior distribution of ␪ ; Table 1 ), which
`characterizes the shape of the dose – toxicity curve. The occurrence
`of toxicity (or not) in patients enrolled at each dose level provides
`additional information for the statistical model and results in an
`adjustment of ␪ (also called posterior distribution of ␪ ) according
`to Bayes ’ theorem. The posterior distribution is then evaluated to
`identify the dose closest to the target toxicity level, and this dose is
`used to treat future patients and to set the recommended dose for
`phase II trials. These model-based designs use all of the available
`data to model the dose – toxicity curve, and they provide a confi-
`dence interval for the recommended dose for phase II trials at the
`end of the trial.
`
` Continual Reassessment Method and Modifications
` The continual reassessment method was the first Bayesian model –
` based method proposed for adoption in phase I trial designs ( 44 ).
`The initial estimate of ␪ required for this method is generally elic-
`ited from experts who are familiar with the preclinical data or who
`have experience with similar drugs if any exist. Although this initial
`estimate may not be accurate, it provides guidance about dose
`escalation. In the original description of the continual reassess-
`ment meth