help button home button The Oncologist
HOME HELP CONTACT US SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
 Click here to read this article as a CME course

The Oncologist, Vol. 12, No. 8, 913-923, August 2007; doi:10.1634/theoncologist.12-8-913
© 2007 AlphaMed Press

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow eLetters: Submit a response to this article
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow E-mail this article link to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow Reprints/Permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Mathijssen, R. H.J.
Right arrow Articles by Sparreboom, A.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Mathijssen, R. H.J.
Right arrow Articles by Sparreboom, A.

Clinical Pharmacology

Flat-Fixed Dosing Versus Body Surface Area–Based Dosing of Anticancer Drugs in Adults: Does It Make a Difference?

Ron H.J. Mathijssen, Floris A. de Jong, Walter J. Loos, Jessica M. van der Bol, Jaap Verweij, Alex Sparreboom

Department of Medical Oncology, Erasmus University Medical Center Rotterdam – Daniel den Hoed Cancer Center, Rotterdam, The Netherlands

Key Words. BSA • Flat-fixed dosing • Pharmacokinetics • Pharmacodynamics • Individualized dosing

Correspondence: Ron H.J. Mathijssen, M.D., Ph.D., Erasmus University Medical Center Rotterdam – Daniel den Hoed Cancer Center, Department of Medical Oncology, Groene Hilledijk 301, 3075 EA Rotterdam, The Netherlands. Telephone: 31-10-4391-112; Fax: 31-10-4391-053; e-mail: a.mathijssen{at}erasmusmc.nl

Received November 14, 2006; accepted for publication February 12, 2007.


   Learning Objectives
Top
 Learning Objectives
Abstract
 The Current Practice
 Dosing Alternatives
 Conclusions and Future...
 Disclosure of Potential...
 References
 
After completing this course, the reader will be able to:

  1. Describe how and why BSA-based dosing was implemented into oncology.
  2. Discuss if flat-fixed dosing of adults has advantages over BSA-based dosing in terms of interpatient pharmacokinetic variation of anticancer drugs, efficiency, and costs.
  3. Explain which alternative dosing strategies for BSA-based dosing may have potential, leading to a minimum of adverse events and superior therapeutic outcome.

Access and take the CME test online and receive 1 AMA PRA Category 1 CreditTM at CME.TheOncologist.com


   ABSTRACT
Top
 Learning Objectives
 Abstract
The Current Practice
 Dosing Alternatives
 Conclusions and Future...
 Disclosure of Potential...
 References
 
The current practice of using body-surface area (BSA) in dosing anticancer agents was implemented in clinical oncology half a century ago. By correcting for BSA, it was generally assumed that cancer patients would receive a dose of a particular cytotoxic drug associated with an acceptable degree of toxicities without reducing the agent's therapeutic effect. More recently, doubt has arisen to this hypothesis, and for many drugs, the effects of BSA on the pharmacokinetics of these agents have therefore been studied retrospectively. In (by far) most cases, use of BSA does not reduce the interindividual variation in the pharmacokinetics of adults, and thus, a logical rationale for further use of this tool in dosing adults is lacking. As a result, alternative dosing strategies have been proposed in order to replace BSA-based dosing. Flat-fixed dosing regimens have been suggested, thereby avoiding potential dose calculation mistakes. As flat-fixed dosing does not typically lead to greater pharmacokinetic variability, it does not seem worse than using BSA-based dosing. While it provides a simplification, it can, however, be questioned whether to call this an improvement or not. The implementation of so-called genotyping and phenotyping strategies, and therapeutic drug monitoring, may probably be of more clinical value. In the end, the nonscientifically based BSA-based dosing strategy should be replaced by alternative strategies. Despite the lack of basic fundamentals, BSA-based dosing still seems "untouchable" in clinical oncology. Even when alternatives will be shown to be indisputably better, many hurdles will probably have to be overcome before physicians will be willing to ban BSA-based dosing.

Disclosure of potential conflicts of interest is found at the end of this article.


   THE CURRENT PRACTICE
Top
 Learning Objectives
 Abstract
 The Current Practice
Dosing Alternatives
 Conclusions and Future...
 Disclosure of Potential...
 References
 
Historic Development of Body Surface Area–Based Dosing
Classic cytotoxic agents are known for their relatively narrow therapeutic window. This means that "low" doses of these drugs may not be effective, while "high" doses may be (very) toxic for the patient. Therefore, the optimal dose should be somewhere in between, thereby leading to the best possible treatment, which means yielding maximal therapeutic effect given tolerable and manageable toxicities. Based on the theory that large patients have a larger volume of distribution and a higher metabolizing capacity, it is assumed that those patients need to be dosed higher than smaller patients to reach equal drug concentrations. For this reason, traditionally, the administered dose is typically adjusted to the body-surface area (BSA) of the individual patient. BSA was originally calculated using a formula based on length and weight developed by DuBois and DuBois in 1916 [1] from an investigation that involved only nine individuals (Table 1) [1–5]. Although validation of the derived formula was not performed initially [3, 4], the use of BSA was incorporated into animal studies for the purpose of allometric scaling, and in the 1950s, BSA-based dosing was introduced into pediatric oncology [6, 7]. Without further study, its use was also incorporated into drug dose calculation in adults (to get a safe starting dose in phase I trials). Currently it is still the standard for most agents used [8].


View this table:
[in this window]
[in a new window]

  		Table 1. Frequently used BSA formulas (in chronological order)

 
According to a study by Gehan and George in more than 400 individuals, the original formula was found to overestimate the real BSA by >15% in about 17% of people, while underestimating BSA in a much lower number (1%) of cases [3, 8]. This however, did surprisingly not lead to acceptance of the Gehan and George BSA formula as the medical standard. More recently, Mosteller changed the original formula to BSA (m2) = {surd}([length (cm) x weight (kg)]/3,600), which appeared to mimic the original formula quite closely and simplified its use (Table 1) [5]. Although today BSA can even be determined by a three-dimensionally derived formula [9], the correlation among all these formulas remains high (r > 0.97), suggesting only nonsubstantial differences among the formulas used [10]. An exception should be made, however, for overweight and obese patients, where values for BSA appear to differ significantly between the DuBois and DuBois formula and the other formulas [10]. In this specific group, BSA prediction according to the original formula underestimated BSA by 3% for male and 5% for female patients, compared with Mosteller's formula.

With the exception of carboplatin [11, 12], it was a long time before concerns were raised on the relevance of using BSA in oncology. Currently, many studies have been performed, which clearly show that (most frequently) BSA-based dosing does not yield the desired minimization in interindividual variation in exposure in adults. In this review article, the value of BSA-based dosing of adults for individual anticancer agents is questioned and potential alternatives are discussed. The dosing of children, however, is not comparable, and reaches beyond the scope of this manuscript.

Interindividual Variability
From daily experience, any physician knows that patients may respond differently to prescribed drugs. Many factors can influence systemic exposure to a given drug (Fig. 1), which include physiological factors (e.g., age, performance status score), intrinsic factors (e.g., genetic alterations), and environmental factors (e.g., comedication, use of herbal supplements) [13–15]. As a consequence, the pharmacokinetic parameters (exposure, i.e., area under the plasma concentration–time curve [AUC], and clearance, i.e., dose divided by exposure) of a particular agent may vary substantially among patients, which is expressed by the term interindividual variation. On the basis of the pharmacologic tenet that pharmacokinetic parameters can be used as a surrogate for pharmacodynamic outcome, this interindividual variation leads to an unpredictable variety in clinical response and adverse effects. For cytotoxic agents, interindividual variability, expressed as a coefficient of variation (which is the standard deviation divided by the mean times 100), of 25%–70% is very common (Fig. 2) [16–20], despite the use of BSA for dose calculation.


Figure 1
View larger version (42K):
[in this window]
[in a new window]

  		Figure 1. Factors affecting interindividual variability of drug therapy.

Abbreviations: AAG, alpha-1-acid glycoprotein; CAM, complementary and alternative medicine.

 


Figure 2
View larger version (17K):
[in this window]
[in a new window]

  		Figure 2. Effect of body-surface area (BSA) on the interindividual variability of anticancer agents. (A): Percentage difference without BSA dose adjustment. (B): Absolute variability in plasma clearance, without correction for BSA (blue bars) and after correction for BSA (red bars).

Symbols: ##, data from orally administered drug; #, data from unbound fraction of drug. Data are derived from [17–20].

 
Back in 1990, relationships between pharmacokinetic parameters of several cytotoxic agents under development in phase II trials and body size measures (height, weight, and BSA) were described by Grochow et al. [21]. Of these drugs, paclitaxel clearance showed a relationship with height (r = 0.70; p = .003), while other relationships were surprisingly scant. At the same time, Ratain et al. studied etoposide [17] and found no correlation between clearance and BSA, and correction for BSA did not lead to a change in interindividual variability. Later, this lack of relationship was confirmed by others [22].

In addition, for epirubicin, no relationship between clearance and BSA was seen, although the small number of patients may have influenced outcome [23]. Also, Gurney et al. wondered if parameters other than length and weight might be better predictors for epirubicin pharmacokinetics [24]. In an exploratory study, 20 patients received a "flat-fixed" dose of 150 mg of epirubicin, meaning that the given dose was independent of body size. In a multivariate analysis, only liver function tests predicted the pharmacokinetic behavior and hematological adverse effects of epirubicin, while BSA (and weight) did not. Retrospectively, Dobbs and Twelves [25] studied the same drug in patients who received a BSA-adjusted dose. By correcting epirubicin clearance for BSA, the coefficient of variation was determined in both the adjusted and unadjusted situations. This percentage appeared to be similar in both groups, suggesting that BSA does not lead to less variability. Both studies, although not comparable in study design, clearly demonstrated that BSA is an unimportant factor in epirubicin disposition. In addition, Ralph et al. performed a population pharmacokinetic analysis, including 109 cancer patients treated with this drug [26]. They found only a weak (negative) relationship between BSA and the clearance of epirubicin, clearly insignificant to base dose adjustments to this parameter. In contrast, aspartate aminotransferase reduced the interindividual variability from 49% to 39% [26].

In the years that followed, the strategy used by Dobbs and Twelves was also used to test the role of BSA in other cytotoxic compounds, including topotecan, cisplatin, and irinotecan [18, 27, 28]. It was noted that the variability in clearance of orally administered topotecan did not decrease as a result of BSA-based dosing [27], while just a small, and clinically unimportant, reduction in the coefficient of variation of unbound cisplatin clearance was found after BSA correction [18]. For irinotecan, the absolute coefficient of variation for the unadjusted dose was even lower than for the BSA-adjusted dose (32.1% versus 34.0%), although this difference was not statistically significant [28]. Hence, for these three compounds it also should be concluded that the role of BSA in explaining interindividual variability is most likely insignificant. Next, Baker et al. [19] retrospectively studied the pharmacokinetics of 33 anticancer agents tested in phase I trials during the 1991–2001 period in 1,650 adult patients. For only five agents (including paclitaxel), BSA-based dosing did reduce the interindividual pharmacokinetic variability. In contrast, for the other 28, the variability was not statistically significantly reduced by BSA, and the rationale for using BSA in dosing these (and other) compounds could therefore be questioned [19].

The Role of Other Body-Size Measures
Other, less commonly used in clinical oncology, body-size measures have been investigated as well, because it was once suggested that they might be better predictors of drug clearance, and therefore of systemic exposure and the occurrence of adverse effects. Aside from BSA, the "direct" body-size measures weight and height have been tested as predictors of anticancer drug metabolism. Other "indirect" body-size measures involve lean body mass (LBM), ideal body weight (IBW), adjusted ideal body weight (AIBW), and body mass index (BMI).

As discussed before, Grochow et al. described a relationship between height and clearance of paclitaxel [21]. In a prospective evaluation by Smorenburg et al. [29], in addition to correlations with BSA, paclitaxel clearance correlated with weight, LBM, (A)IBW, and BMI, indicating that there is a relation with body size. LBM, (A)IBW, BMI, height, and weight have also been correlated with irinotecan pharmacokinetics, although all showed a higher extent of variability than BSA-adjusted clearance [28]. However, nonadjusted clearance in this group of 82 patients showed the lowest interindividual variability of all [28].

Drug dosing recommendations are usually based on results from clinical trials that have included patients who are considered typical of those likely to receive the drug in clinical practice. In many cases, however, the morbidly obese patient may not be well represented, and therefore extrapolation of dosing recommendations to this group can only be performed arbitrarily when the dose is required to be standardized to a particular patient demographic like BSA [30]. For extreme weights, the relationships with clearances of cyclophosphamide, doxorubicin, etoposide plus cisplatin, and ifosfamide were studied in obese cancer patients [31–34]. Correlations between extreme weights and clearances of these compounds appeared to be somewhat contradictory, possibly as a result of the small number of patients. In two studies of only 16 patients each, no effect of body weight on the total clearances of, respectively, cyclophosphamide and ifosfamide was noticed [31, 33]. Rodvold et al., studying 21 patients equally distributed over three classes of IBW, found a decrease in doxorubicin clearance, but only between the normal and the severely obese group [32]. In a much larger study (n = 262) by Georgiadis et al., it was shown that doses of cyclophosphamide alone or etoposide plus cisplatin based on total body weight can be given without increasing toxicity or decreasing survival in small-cell lung cancer patients [34, 35]. A recent study even showed a lower degree of febrile neutropenia in severely obese breast cancer patients treated with doxorubicin and cyclophosphamide [36]. As a result, in general practice, obese patients are often underdosed, with serious consequences for treatment outcome [36, 37]. This effect may even be enlarged as a result of "dose capping" (using the patient's actual weight or BSA, up to an arbitrary cutoff value), which is very common and may be the result of fear of overdosing the patient [30].

Similar to studies involving obese patients, it is clear that the usefulness of formulas used to calculate BSA has also not been appropriately evaluated in cancer patients who are very small and thin. Furthermore, the original formulas for calculating BSA-based drug dosages were typically developed in a population with an average BSA. The majority of studies of alternative dosing strategies have also not taken into account patients at the upper or lower ends of BSA. Hence, much remains to be understood about appropriate dosing strategies for severely obese or frail patients, not only for the purpose of evaluating alternative dosing strategies but also for drugs that are already dosed in daily clinical practice according to BSA.

Why Do Some Drugs Relate to BSA?
It has been suggested that, for drugs primarily eliminated by the kidneys and for drugs confined to the central compartment (blood volume), BSA-based dosing might reduce interindividual pharmacokinetic variability, because BSA is associated with both glomerular filtration rate (GFR) and blood volume [19]. However, the association between BSA and GFR is weak [38]. Therefore, in the case of carboplatin, a drug almost completely excreted by the kidneys, dosing based on BSA may result in serious patient under- and overdosing [12]. This urged the wish for better predictors of pharmacokinetic measures than BSA. As discussed later in this review, a dosing strategy based on GFR, using a simple formula, would become the first generally accepted alternative for BSA-based dosing in clinical oncology.

In the previously mentioned study by Baker et al. [19], for docosahexaenoic acid (DHA)-paclitaxel, paclitaxel, 5-fluorouracil/eniluracil, temozolomide, and troxacitabine, significantly less variability in drug clearance was seen after BSA-based dosing. For 5-fluorouracil and troxacitabine, renal elimination contributes to the positive relationship between BSA and drug clearance. A kinetic restriction to the central compartment may be the reason for the relationship found in case of temozolomide [19]. With respect to paclitaxel, which binds extensively to its pharmaceutical excipient Cremophor EL within the central compartment following i.v. administration, it should be pointed out that circulating concentrations of Cremophor EL have been shown to be related to total blood volume, and hence to BSA [29].

Should BSA Be Used if It Leads to a Reduction in Pharmacokinetic Variability?
The relationship between paclitaxel pharmacokinetics and BSA was further explored by Smorenburg et al. [29]. In a randomized crossover design, a BSA-based dose was compared with a flat-fixed dose in the same patients, resulting in a >50% lower coefficient of variation in the exposure of unbound paclitaxel in the BSA-based courses. Also, in a recently published U.S. study, a similar relationship between systemic paclitaxel exposure (AUC) and clearance versus BSA was seen [39]. So, is there a rationale for using an adjustment to BSA in this case? Insights differ, so this question is apparently not easy to answer. Smorenburg et al. [29] are generally positive about BSA-based dosing, because a flat-fixed dose of 300 mg (3-weekly, 3-hour infusion), compared with an identical regimen with a dose of 175 mg/m2, led to a significant difference in variability in favor of the BSA-based dosing strategy. In contrast, Miller et al. reported that, although BSA is related to paclitaxel pharmacokinetics, it is not related to adverse effects, and therefore has no clinical utility and should be replaced by other strategies (i.e., flat-fixed dosing) [39, 40]. In a population analysis, BSA, bilirubin, age, and gender all appeared to affect paclitaxel pharmacokinetics [41]. Unfortunately, in that retrospective study, toxicity was not studied, and therefore the statement by Miller et al. was not tested. Obviously, additional studies are required to further elucidate the complexity of this topic, and in order to obtain a definitive answer.

Another problem is introduced if BSA does not seem to be related in a clinically relevant extent to the clearance of a drug in the usual normal-weighted population, but has potential for patients with extremely high or low BSA values, which has been shown for cisplatin [42]. Patients with extremely low or high BSA values were randomly exposed to one course of cisplatin based on a fixed dose, followed by one course of cisplatin based on a BSA-based regimen, or vice versa [42]. The use of fixed doses led to a significantly lower exposure to the drug in large patients, while a significantly higher exposure to cisplatin in small patients was seen, compared with a BSA-adjusted dose. Clearance was significantly related to BSA in that study, but nonetheless, interindividual pharmacokinetic variability was only slightly lower after BSA adjustment (interindividual variability, 20.8% versus 17.1%). A retrospective analysis was performed in the same study. Here, the clearance of (unbound) cisplatin was, although weakly related to BSA, significantly lower in patients with a low BSA value (≤1.65 m2) and significantly higher in patients with a high BSA value (≥2.05 m2), compared with the clearance in the average BSA group (Fig. 3) [18, 42]. Taking into account the small reduction in interindividual pharmacokinetic variability after BSA correction and the huge differences in clearance between patients with extremely high and those with extremely low BSA values, the authors recommended fixed dosing regimens per BSA cluster (≤1.65 m2, 1.66–2.04 m2, and ≥2.05 m2) [42]. Obviously, BSA clusters such as those developed for cisplatin could also be developed for other commonly used drugs [43]. However, it should be noted that such clustering and subsequent dose fixation based on the average BSA in each cluster may be dependent on the population (e.g., ethnicity) and region (e.g., prevalence of obesity).


Figure 3
View larger version (13K):
[in this window]
[in a new window]

  		Figure 3. (A): Linear relationship (r2 = 0.12) between body-surface area (BSA) and clearance of unbound cisplatin-derived platinum: (B): Same data clustered in three BSA clusters. Data are derived from [18, 42].

Abbreviation: CL, clearance.

 

   DOSING ALTERNATIVES
Top
 Learning Objectives
 Abstract
 The Current Practice
 Dosing Alternatives
Conclusions and Future...
 Disclosure of Potential...
 References
 
From a pharmacological point of view, it is not surprising that body-surface measures based on height and weight alone do not explain all of the interindividual pharmacokinetic variability. Even for drugs with renal elimination or confinement to the central compartment (the intravascular volume), there is often no straightforward relationship between body-surface measures and clearance. As shown in Figure 1, systemic exposure is influenced by many factors. Although such influences may be represented in part by differences in body-surface measures, most of these factors may affect drug exposure independently (as well). For example, it is well known that polymorphisms in genes encoding metabolizing enzymes involved in the elimination of a particular drug can affect its clearance and exposure [44, 45]. Likewise, use of comedication, food substances, supplements, and herbals may result in (unexpected) serious effects [46]. Moreover, pharmacodynamic effects are not predicted by pharmacokinetic parameters alone, resulting in even more variability in toxicity and therapeutic outcome.

To find successful alternative dosing regimens, apart from body-surface measures, other and more relevant parameters should be included. Which parameters are most relevant depends on the specific kinetic characteristics (i.e., elimination pathways, hydrophilicity, distribution pattern) of the drug in question. In addition, therapeutic drug monitoring (see later) using pharmacokinetic and pharmacodynamic outcome parameters of earlier treatment cycles might be another useful dosing strategy for certain drugs.

Flat-Fixed Dosing
Flat-fixed dosing refers to dosing strategies without correction for body size or other (pharmacological) parameters. This is very common in the medical world, outside oncology. At first sight, this strategy does not look very elegant, but a "standard" dose may be a realistic alternative for BSA-based dosing as long as no tailored or individualized dosing strategy for a specific BSA-unrelated anticancer drug (characterized by a narrow therapeutic window and high interindividual variability in exposure) is introduced. For drugs characterized by a broad therapeutic window and/or small interindividual variability in exposure and/or limited toxicity (e.g., targeted drugs), flat-fixed dosing seems the best option. In addition, the use of this approach may have positive economic implications, because only a unit dose has to be produced and stored, while no time-consuming dosing method is involved. There are also safety implications, because fewer errors will be made in calculating, preparing, and administering the proper individual dose. Moreover, patient adherence to oral anticancer drugs could be improved if, for instance, only one standard tablet had to be taken instead of, for example, two large ones and a smaller one [47]. There is one other, more subtle, advantage: BSA dosing may give prescribers the false feeling of being precise.

Flat-fixed dosing regimens have been studied with and without comparison with BSA-based regimens (Table 2), and in most of these cases flat-fixed dosing did not lead to significantly different interindividual pharmacokinetic variability in drug exposure [24, 29, 39, 42, 48–54]. For instance, in the case of irinotecan, dosing based on BSA did not reduce clearance variability [28], compared with an unadjusted dose. Patients who received a flat-fixed irinotecan dose of 600 mg did not show greater interindividual pharmacokinetic variability than a control group who received the registered dose of 350 mg/m2 [50]. As toxicity did not significantly differ either, it was concluded that flat-fixed dosing could safely be used to supplant the BSA dosing strategy of irinotecan.


View this table:
[in this window]
[in a new window]

  		Table 2. Evaluation of flat-fixed dosing strategies for anticancer agents

 
For vinorelbine, a fixed dose was tested in a group of 41 cancer patients, leading to a 4.3-fold variation in clearance [53]. BSA was the only pretreatment predictor of fractional survival of neutrophils, independent of clearance. The authors concluded that a fixed dose of vinorelbine, guided by indicators of clearance and tissue reserve, is thought to be an appropriate option to dose this drug [53, 55]. However, in our opinion, additional data are required to exclude (or verify) the role of BSA in dosing vinorelbine. Moreover, results need to be confirmed in larger cohorts of patients. Until that time, there is no good reason for stop using BSA in dosing this anticancer drug.

GFR-Based Dosing
Carboplatin was the first anticancer drug for which an alternative dosing strategy was adopted on a worldwide scale. After early exploratory studies [10, 56], Childs et al. [57] compared a BSA-based dosing regimen with a GFR-based regimen (using 51Cr-EDTA clearance, developed by Calvert et al.) [58], which appeared to be superior over the first one, as a result of a closer prediction of toxicity and efficacy. Development of this dosing strategy is described in more detail elsewhere [12]. In routine practice in many hospitals, the original Calvert formula for carboplatin dosing is adapted by measurement of the GFR in an alternative way, using cheaper, less invasive, and easier methods [59]. This, however, may have serious consequences for the validity of this method, as was recently found by Ekhart et al. [59]. In a population pharmacokinetic analysis, they found no relevant correlations between carboplatin pharmacokinetics and modifications of the Calvert formula. They, therefore, advise using the original formula for targeted carboplatin exposures. In patients with normal renal function, however, a flat dose based on the mean population carboplatin clearance seems justified [59].

Genotyping
In contrast to flat-fixed dosing, a dosing strategy based on a genetic variant involved in an agent's metabolic pathway may be a more patient-tailored strategy in cases in which this genetic variation has been shown to affect the pharmacokinetic and/or pharmacodynamic profile. Many polymorphisms have already been found to be related to anticancer drug metabolism, and probably many more will be found to be related in the (near) future. However, only a few of these have shown clinical relevance. Moreover, depending on the relationship, with the severity of its pharmacokinetic and/or pharmacodynamic consequences, the prevalence of the polymorphisms may be considered a factor for the value of this strategy.

For example, patients with functional variant alleles in genes coding for the enzymes dihydropyrimidine dehydrogenase (DPD), thiopurine-methyltransferase (TPMT), and uridine-diphosphate glucuronidase 1A1 (UGT1A1) are supposed to be at risk for unacceptable adverse effects when treated with 5-fluorouracil, 6-mercaptopurine, and irinotecan, respectively [45, 60, 61]. A priori genetic screening for polymorphisms in these genes subsequently followed by adequate action can identify patients at risk to optimize their anticancer treatment before harm is done.

A textbook example of a useful germline variant in predicting clinical outcome in anticancer drug therapy is TPMT. 6-Mercaptopurine is a prodrug that needs to be activated into thioguanine nucleotides, which inhibit DNA and RNA synthesis. TPMT inactivates 6-mercaptopurine, thus preventing formation of its active metabolites. Three different single nucleotide polymorphisms account for almost all dysfunctional TPMT alleles, resulting in higher formation of its active metabolites [62, 63]. Patients who carry one dysfunctional allele are at risk for nausea and myelosuppression, whereas those with two disfunctional alleles are highly at risk for severe, life-threatening myelosuppression. It has been clearly demonstrated that TPMT genotyping can identify patients at risk before starting therapy with thiopurines, and that integration of this genetic screening strategy into the clinical management of 6-mercaptopurine therapy can optimize its outcome [64]. Therefore, in 2003, the U.S. Food and Drug Administration (FDA) met to consider the role of TPMT genotyping in the administration of this treatment to pediatric leukemia patients.

Likewise, a dinucleotide polymorphism (UGT1A1*28) in UGT1A1, which is involved in inactivation of the active irinotecan metabolite SN-38, has been shown to significantly influence the pharmacokinetics and pharmacodynamics (levels of diarrhea and neutropenia) of irinotecan therapy [44, 65–68]. As this UGT1A1*28 allele has been related to greater hematological toxicity in particular, the FDA recently advised performing a genetic test for this polymorphism prior to irinotecan treatment as well [44]. Unfortunately, good dose advice in cases of patients who carry this mutation homozygously was not provided. Therefore, although relevance has been made clear, most clinicians still dose irinotecan based on BSA without a priori genetic testing [69].

Phenotyping
Helpful genotyping options are currently lacking for most anticancer agents, because polymorphic variants with functional consequences within the principal metabolizing enzymes have not been determined yet. Not only because of limited functional consequences of most polymorphisms, substrate overlap between metabolizing enzymes and drug transporters, and complicated metabolic pathways, but also because of the influence of other, nongenetic factors on drug exposure, genotyping may be insufficient in predicting interindividual pharmacokinetic variability. The development of an indirect enzyme activity measurement (inside the patient), using compounds metabolized the same way as the drug in question, may have additional value here [45]. Probe drugs, which are ideally cheap, safe, and easily available and determined agents, are given prior to the anticancer therapy. Because the metabolism, distribution, and elimination of the probe are related to the pharmacokinetic behavior of the anticancer drug in question, the subsequent given chemotherapy dose can be adjusted to probe-drug elimination. Such phenotyping strategies take the patient as a whole, including all factors affecting pharmacokinetic behavior of both the probe drug and the anticancer drug. Relationships have been studied for several cytochrome P450 probes (e.g., midazolam) and anticancer drugs [45]. Also, for other enzymes, like DPD, a phenotypic test is in development [70]. In addition, for drug transporting proteins (like ABCB1, P-glycoprotein), activity can be determined using drugs shown to be substrates for these proteins. For example, 99mTc-sestamibi is a substrate for ABCB1 excretion [71], and hepatic nuclear imaging using a 99mTc-sestamibi scan has been correlated with the pharmacokinetics of several agents, including vinorelbine [53] and irinotecan [72]. However, only very small numbers of patients have been studied yet, so more research is warranted to establish the findings and potentials of these exploratory studies.

Therapeutic Drug Monitoring
Therapeutic drug monitoring (TDM) implies that, at regular intervals, systemic drug concentrations are determined to achieve and maintain a relatively constant circulating level. In general, administration of a standard dose is much easier than the dosing strategy of continuous systemic drug level determination and subsequent dose adjustment. Generally speaking, drugs that are monitored have a narrow therapeutic window and are supposed to be taken for a long time. Because factors affecting pharmacokinetic exposure may change during this time, exposure may change as well. Integrating TDM into clinical management helps to compensate for such influences to maintain optimal treatment, that is, the combination of stable levels, acceptable adverse effects, and therapeutic benefit. Through the years, in many drug therapies, such as antibiotics, immunosuppressive drugs, antipsychotics, and antiepileptics, TDM has earned a place in clinical practice. Likewise, in oncology, for drugs that are generally also prescribed based on pre-established dosing schedules, TDM can be an option. After determining the pharmacokinetic profile (exposure) of the first cycle, the dose to administer can be adjusted to these results to achieve optimal drug exposure and clinical results. This strategy may improve patient outcome and survival [73]. However, in daily clinical practice, this strategy has limitations, because drug levels need to be determined quickly, accurately, and precisely. In addition, pharmacokinetic sampling should be done at the right time points, and interacting drugs should be evaluated. Moreover, a clear relationship between toxicity/efficacy and drug exposure should exist, intrapatient variation should be limited, and it should be known how dose adjustments should be made. Costs, logistics, and patient inconvenience are frequently encountered potential problems as well.

Although many factors can be overcome, time will tell if TDM in routine clinical practice for chemotherapy administration in adults is feasible [74]. It has been suggested that TDM should be reserved for only a proportion of patients, depending on factors such as tumor type, anticancer drug, and patient characteristics [74]. However, despite its limitations, more and more research is being done on the use of TDM in oncology [73–75]. For example, Ratain et al. [17] introduced a (complex) formula to dose etoposide based on pretreatment WBC, among others, whereas a population-based Bayesian methodology to routinely adjust etoposide therapy when given as a 5-day infusion, based on its pharmacokinetic profile as determined on the first day, was proposed by others [76]. In addition, de Jonge et al. [77] evaluated the performance and technical feasibility of pharmacokinetically guided dosing of paclitaxel. Twenty-five lung cancer patients, monitored for pharmacokinetics, were treated for a total of 92 courses. In 43% of the individualized courses, the dose was increased to achieve a predefined target value. Individualization of the dose led to a higher percentage of patients who attained the target value, compared with the control group. Currently, it is unclear if this dose individualization leads to better efficacy, and a randomized study should be performed first. In pediatric oncology as well, several good examples can be given for TDM-based therapy [78–80]. As this topic falls outside the scope of our review, we refer to other work for a detailed discussion [81].

In general, controlling systemic exposure based on TDM should maximize treatment efficacy, while at the same time prevent serious adverse effects, thus resulting in an optimal balance between efficacy and adverse effects.


   CONCLUSIONS AND FUTURE PERSPECTIVES
Top
 Learning Objectives
 Abstract
 The Current Practice
 Dosing Alternatives
 Conclusions and Future...
Disclosure of Potential...
 References
 
A statistically significant correlation between a given demographic characteristic (i.e., BSA) and measures of a drug's pharmacokinetic profile does not necessarily guarantee that a meaningful relationship exists [82]. This is important to realize, because it largely explains why dosing based on BSA does not have to lead to significantly less variability in pharmacokinetic parameters for a given drug. The other way around, characteristics that do relate to a drug's clearance have a better chance to accurately predict the toxicities and activity of an agent, because a good correlation between pharmacokinetics and pharmacodynamics is likely for most anticancer drugs. Because most anticancer drugs have a narrow therapeutic index, it is clinically of utmost importance to control and even predict their exposure a priori. In the last decade, alternative strategies aimed at truly individualizing anticancer treatment and optimizing its efficacy have been considered, tested, and slowly brought into clinical practice. For some drugs, clear and unbiased relationships with genetic polymorphisms have been described, and a priori genotyping for one or a few mutations seems to improve treatment. In other cases, phenotyping is capable of predicting the pharmacokinetic behavior beforehand. For specific anticancer drugs, TDM may have potential, because it is possible to change the dose as a result of earlier experience in that patient. Unfortunately, except for some examples, such dose-individualization strategies have not yet been able to show their additive value in routine clinical practice, because they are mostly applied in experimental settings so far.

Probably for this reason, and despite the growing amount of scientific evidence regarding the (poor) utility of BSA-based dosing in oncology, clinical practice "anno 2007" has not changed that much [40, 43, 47, 55, 74, 82–84]. In addition to the lack of good alternatives, the lack of willingness of clinicians to change their policies may be part of the explanation for this phenomenon. Fortunately, for an increasing number of novel drugs, the use of BSA is already no longer included in dose calculation in the early phases of drug development, unless a significant role in reducing interpatient variability in exposure is assumed. In general, flat-dosing strategies are advised in the development of investigational drugs for the advantages mentioned earlier, and as long as no better alternatives exist. Flat-dosing has been shown to be feasible for many anticancer compounds and may, in due time, fluently be replaced by more individual (or tailored) drug-dosing strategies. Phase I and II trials with flat-fixed dosing strategies, including registered drugs, are performed at present to implement non–BSA-based dosing regimens [48, 54]. Ultimately, registration of anticancer drugs may take place without BSA-based dose adjustment. As a consequence, clinical practice would be unable to stick to one (irrational) dosing strategy, but would have to consider alternatives.


   DISCLOSURE OF POTENTIAL CONFLICTS OF INTEREST
Top
 Learning Objectives
 Abstract
 The Current Practice
 Dosing Alternatives
 Conclusions and Future...
 Disclosure of Potential...
References
 
The authors indicate no potential conflicts of interest.


   ACKNOWLEDGMENTS
Top
 Learning Objectives
 Abstract
 The Current Practice
 Dosing Alternatives
 Conclusions and Future...
 Disclosure of Potential...
 References
 
R.H.J.M. and F.A.de J. contributed equally to this manuscript.


   REFERENCES
Top
 Learning Objectives
 Abstract
 The Current Practice
 Dosing Alternatives
 Conclusions and Future...
 Disclosure of Potential...
 References
 

  1. DuBois D, DuBois EF. A formula to estimate the approximate surface area if height and weight be known. Arch Intern Med 1916;17:863–871.
  2. Boyd E. The Growth of the Surface Area of the Human Body. Minneapolis: University of Minnesota Press, 1935.
  3. Gehan EA, George SL. Estimation of human body surface area from height and weight. Cancer Chemother Rep 1970;54:225–235.[Medline]
  4. Haycock GB, Schwartz GJ, Wisotsky DH. Geometric method for measuring body surface area: A height-weight formula validated in infants, children, and adults. J Pediatr 1978;93:62–66.[Medline]
  5. Mosteller RD. Simplified calculation of body-surface area. N Engl J Med 1987;317:1098.[Medline]
  6. Crawford JD, Terry ME, Rourke GM. Simplification of drug dosage calculation by application of the surface area principle. Pediatrics 1950;5:783–790.[Abstract/Free Full Text]
  7. Pinkel D. The use of body surface area as a criterion of drug dosage in cancer chemotherapy. Cancer Res 1958;18:853–856.[Abstract/Free Full Text]
  8. Sawyer M, Ratain MJ. Body surface area as a determinant of pharmacokinetics and drug dosing. Invest New Drugs 2001;19:171–177.[CrossRef][Medline]
  9. Yu C-Y, Lo Y-H, Chiou W-K. The 3D scanner for measuring body surface area: A simplified calculation in the Chinese adult. Appl Ergon 2003;34:273–278.[CrossRef][Medline]
  10. Verbraecken J, Van de Heyning P, De Backer W et al. Body surface area in normal-weight, overweight, and obese adults. A comparison study. Metabolism 2006;55:515–524.[CrossRef][Medline]
  11. Egorin MJ, Van Echo DA, Olman EA et al. Prospective validation of a pharmacologically based dosing scheme for the cis-diamminedichloroplatinum(II) analogue diamminecyclobutanedicarboxylatoplatinum. Cancer Res 1985;45:6502–6506.[Abstract/Free Full Text]
  12. Alberts DS, Dorr RT. New perspectives on an old friend: Optimizing carboplatin for the treatment of solid tumors. The Oncologist 1998;3:15–34.[Abstract/Free Full Text]
  13. Lichtman SM, Skirvin JA, Vemulapalli S. Pharmacology of antineoplastic agents in older cancer patients. Crit Rev Oncol Hematol 2003;46:101–114.[CrossRef][Medline]
  14. Wilkinson GR. Drug metabolism and variability among patients in drug response. N Engl J Med 2005;352:2211–2221.[Free Full Text]
  15. Scripture CD, Figg WD. Drug interactions in cancer therapy. Nat Rev Cancer 2006;6:546–558.[CrossRef][Medline]
  16. Felici A, Verweij J, Sparreboom A. Dosing strategies for anticancer drugs: The good, the bad and body-surface area. Eur J Cancer 2002;38:1677–1684.[CrossRef][Medline]
  17. Ratain MJ, Mick R, Schilsky RL et al. Pharmacologically based dosing of etoposide: A means of safely increasing dose intensity. J Clin Oncol 1991;9:1480–1486.[Abstract]
  18. De Jongh FE, Verweij J, Loos WJ et al. Body-surface area-based dosing does not increase accuracy of predicting cisplatin exposure. J Clin Oncol 2001;19:3733–3739.[Abstract/Free Full Text]
  19. Baker SD, Verweij J, Rowinsky EK et al. Role of body surface area in dosing of investigational anticancer agents in adults, 1991–2001. J Natl Cancer Inst 2002;94:1883–1888.[Abstract/Free Full Text]
  20. Rudek MA, Sparreboom A, Garrett-Mayer ES et al. Factors affecting pharmacokinetic variability following doxorubicin and docetaxel-based therapy. Eur J Cancer 2004;40:1170–1178.[CrossRef][Medline]
  21. Grochow LB, Baraldi C, Noe D. Is dose normalization to weight or body surface area useful in adults? J Natl Cancer Inst 1990;82:323–325.[Free Full Text]
  22. Nguyen L, Chatelut E, Chevreau C et al. Population pharmacokinetics of total and unbound etoposide. Cancer Chemother Pharmacol 1998;41:125–132.[CrossRef][Medline]
  23. Cosolo WC, Morgan DJ, Seeman E et al. Lean body mass, body surface area and epirubicin kinetics. Anticancer Drugs 1994;5:293–297.[Medline]
  24. Gurney HP, Ackland S, Gebski V et al. Factors affecting epirubicin pharmacokinetics and toxicity: Evidence against using body-surface area for dose calculation. J Clin Oncol 1998;16:2299–2304.[Abstract]
  25. Dobbs NA, Twelves CJ. What is the effect of adjusting epirubicin doses for body surface area? Br J Cancer 1998;78:662–666.[Medline]
  26. Ralph LD, Thomson AH, Dobbs NA et al. A population model of epirubicin pharmacokinetics and application to dosage guidelines. Cancer Chemother Pharmacol 2003;52:34–40.[Medline]
  27. Loos WJ, Gelderblom H, Sparreboom A et al. Inter- and intrapatient variability in oral topotecan pharmacokinetics: Implications for body-surface area dosage regimens. Clin Cancer Res 2000;6:2685–2689.[Abstract/Free Full Text]
  28. Mathijssen RH, Verweij J, de Jonge MJ et al. Impact of body-size measures on irinotecan clearance: Alternative dosing recommendations. J Clin Oncol 2002;20:81–87.[Abstract/Free Full Text]
  29. Smorenburg CH, Sparreboom A, Bontenbal M et al. Randomized cross-over evaluation of body-surface area-based dosing versus flat-fixed dosing of paclitaxel. J Clin Oncol 2003;21:197–202.[Abstract/Free Full Text]
  30. Sparreboom A. BSA-based dosing and alternative approaches. Clin Adv Hematol Oncol 2005;3:448–450.[Medline]
  31. Powis G, Reece P, Ahmann DL et al. Effect of body weight on the pharmacokinetics of cyclophosphamide in breast cancer patients. Cancer Chemother Pharmacol 1987;20:219–222.[CrossRef][Medline]
  32. Rodvold KA, Rushing DA, Tewksbury DA. Doxorubicin clearance in the obese. J Clin Oncol 1988;6:1321–1327.[Abstract/Free Full Text]
  33. Lind MJ, Margison JM, Cerny T et al. Prolongation of ifosfamide elimination half-life in obese patients due to altered drug distribution. Cancer Chemother Pharmacol 1989;25:139–142.[CrossRef][Medline]
  34. Georgiadis MS, Steinberg SM, Hankins LA et al. Obesity and therapy-related toxicity in patients treated for small-cell lung cancer. J Natl Cancer Inst 1995;87:361–366.[Abstract/Free Full Text]
  35. Baker SD, Grochow LB, Donehower RC. Should anticancer drug doses be adjusted in the obese patient? J Natl Cancer Inst 1995;87:333–334.[Free Full Text]
  36. Griggs JJ, Sorbero ME, Lyman GH. Undertreatment of obese women receiving breast cancer chemotherapy. Arch Intern Med 2005;165:1267–1273.[Abstract/Free Full Text]
  37. Colleoni M, Li S, Gelber RD et al. Relation between chemotherapy dose, oestrogen receptor expression, and body-mass index. Lancet 2005;366:1108–1110.[CrossRef][Medline]
  38. Dooley MJ, Poole SG. Poor correlation between body surface area and glomerular filtration rate. Cancer Chemother Pharmacol 2000;46:523–526.[CrossRef][Medline]
  39. Miller AA, Rosner GL, Egorin MJ et al. Prospective evaluation of body surface area as a determinant of paclitaxel pharmacokinetics and pharmacodynamics in women with solid tumors: Cancer and Leukemia Group B Study 9763. Clin Cancer Res 2004;10:8325–8331.[Abstract/Free Full Text]
  40. Egorin MJ. Horseshoes, hand grenades, and body-surface area-based dosing: Aiming for a target. J Clin Oncol 2003;21:182–183.[Free Full Text]
  41. Joerger M, Huitema AD, van den Bongard DH et al. Quantitative effect of gender, age, liver function, and body size on the population pharmacokinetics of paclitaxel in patients with solid tumors. Clin Cancer Res 2006;12:2150–2157.[Abstract/Free Full Text]
  42. Loos WJ, de Jongh FE, Sparreboom A et al. Evaluation of an alternative dosing strategy for cisplatin in patients with extreme body surface area values. J Clin Oncol 2006;24:1499–1506.[Abstract/Free Full Text]
  43. Gurney H. Developing a new framework for dose calculation. J Clin Oncol 2006;24:1489–1490.[Free Full Text]
  44. De Jong FA, de Jonge MJ, Verweij J et al. Role of pharmacogenetics in irinotecan therapy. Cancer Lett 2006;234:90–106.[CrossRef][Medline]
  45. Mathijssen RH, van Schaik RH. Genotyping and phenotyping cytochrome P450: Perspectives for cancer treatment. Eur J Cancer 2006;42:141–148.[CrossRef][Medline]
  46. Tascilar M, de Jong FA, Verweij J et al. Complementary and alternative medicine during cancer treatment: Beyond innocence. The Oncologist 2006;11:732–741.[Abstract/Free Full Text]
  47. Miller AA. Body surface area in dosing anticancer agents: Scratch the surface! J Natl Cancer Inst 2002;94:1822–1823.[Free Full Text]
  48. Sharma R, Rivory L, Beale P et al. A phase II study of fixed-dose capecitabine and assessment of predictors of toxicity in patients with advanced/metastatic colorectal cancer. Br J Cancer 2006;94:964–968.[CrossRef][Medline]
  49. Engels FK, de Jong FA, Sparreboom A et al. Medicinal cannabis does not influence the clinical pharmacokinetics of irinotecan and docetaxel. The Oncologist 2007;12:291–300.[Abstract/Free Full Text]
  50. de Jong FA, Mathijssen RH, Xie R et al. Flat-fixed dosing of irinotecan: Influence on pharmacokinetic and pharmacodynamic variability. Clin Cancer Res 2004;10:4068–4071.[Abstract/Free Full Text]
  51. Mross K, Holländer N, Unger C et al. Flat dose (175 mg/weekly) paclitaxel: Pharmacokinetics and clinical implications. Int J Clin Pharmacol Ther 2005;43:601–602.[Medline]
  52. Mross K, Holländer N, Frost A et al. PAC fixed dose: Pharmacokinetics of a 1-hour paclitaxel infusion and comparison to BSA-normalized drug dosing. Onkologie 2006;29:444–450.[CrossRef][Medline]
  53. Wong M, Balleine RL, Blair EY et al. Predictors of vinorelbine pharmacokinetics and pharmacodynamics in patients with cancer. J Clin Oncol 2006;24:2448–2455.[Abstract/Free Full Text]
  54. Schott AF, Rae JM, Griffith KA et al. Combination vinorelbine and capecitabine for metastatic breast cancer using a non-body surface area dosing scheme. Cancer Chemother Pharmacol 2006;58:129–135.[CrossRef][Medline]
  55. Baker SD, Sparreboom A. Predicting vinorelbine disposition and toxicity: Does BSA provide more than a "Bad Statistical Association"? J Clin Oncol 2006;24:2412–2413.[Free Full Text]
  56. Egorin MJ, Jodrell DI. Utility of individualized carboplatin dosing alone and in combination regimens. Semin Oncol 1992;19:132–138.[Medline]
  57. Childs WJ, Nicholls EJ, Horwich A. The optimisation of carboplatin dose in carboplatin, etoposide and bleomycin combination chemotherapy for good prognosis metastatic nonseminomatous germ cell tumours of the testis. Ann Oncol 1992;3:291–296.[Abstract/Free Full Text]
  58. Calvert AH, Newell DR, Gumbrell LA et al. Carboplatin dosage: Prospective evaluation of a simple formula based on renal function. J Clin Oncol 1989;17:1748–1756.
  59. Ekhart C, de Jonge ME, Huitema AD et al. Flat dosing of carboplatin is justified in adult patients with normal renal function. Clin Cancer Res 2006;12:6502–6508.[Abstract/Free Full Text]
  60. Relling MV, Dervieux T. Pharmacogenetics and cancer therapy. Nat Rev Cancer 2001;1:99–108.[CrossRef][Medline]
  61. Marsh S, McLeod HL. Cancer pharmacogenetics. Br J Cancer 2004;90:8–11.[CrossRef][Medline]
  62. Yates CR, Krynetski EY, Loennechen T et al. Molecular diagnosis of thiopurine S-methyltransferase deficiency: Genetic basis for azathioprine and mercaptopurine intolerance. Ann Intern Med 1997;126:608–614.[Abstract/Free Full Text]
  63. Schaeffeler E, Fischer C, Brockmeier D et al. Comprehensive analysis of thiopurine S-methyltransferase phenotype-genotype correlation in a large population of German-Caucasians and identification of novel TPMT variants. Pharmacogenetics 2004;14:407–417.[CrossRef][Medline]
  64. Evans WE, Hon YY, Bomgaars L et al. Preponderance of thiopurine S-methyltransferase deficiency and heterozygosity among patients intolerant to mercaptopurine and azathioprine. J Clin Oncol 2001;19:2293–2301.[Abstract/Free Full Text]
  65. Ando Y, Saka H, Ando M et al. Polymorphisms of UDP-glucuronosyltransferase gene and irinotecan toxicity: A pharmacogenetic analysis. Cancer Res 2000;60:6921–6926.[Abstract/Free Full Text]
  66. Iyer L, Das S, Janisch L et al. UGT1A1*28 polymorphism as a determinant of irinotecan disposition and toxicity. Pharmacogenomics J 2002;2:43–47.[CrossRef][Medline]
  67. Innocenti F, Undevia SD, Iyer L et al. Genetic variants in the UDP-glucuronosyltransferase 1A1 gene predict the risk of severe neutropenia of irinotecan. J Clin Oncol 2004;22:1382–1388.[Abstract/Free Full Text]
  68. de Jong FA, Kehrer DF, Mathijssen RH et al. Prophylaxis of irinotecan-induced diarrhea with neomycin and potential role for UGT1A1*28 genotype screening: A double-blind, randomized, placebo-controlled study. The Oncologist 2006;11:944–954.[Abstract/Free Full Text]
  69. O'Dwyer PJ, Catalano RB. Uridine diphosphate glucuronosyltransferase (UGT) 1A1 and irinotecan: Practical pharmacogenomics arrives in cancer therapy. J Clin Oncol 2006;24:4534–4538.[Free Full Text]
  70. Mattison LK, Fourie J, Hirao Y et al. The uracil breath test in the assessment of dihydropyrimidine dehydrogenase activity: Pharmacokinetic relationship between expired 13CO2 and plasma [2-13C]dihydrouracil. Clin Cancer Res 2006;12:549–555.[Abstract/Free Full Text]
  71. Wong M, Evans S, Rivory LP et al. Hepatic technetium Tc 99m-labeled sestamibi elimination rate and ABCB1 (MDR1) genotype as indicators of ABCB1 (P-glycoprotein) activity in patients with cancer. Clin Pharmacol Ther 2005;77:33–42.[CrossRef][Medline]
  72. Michael M, Thompson M, Hicks RJ et al. Relationship of hepatic functional imaging to irinotecan pharmacokinetics and genetic parameters of drug elimination. J Clin Oncol 2006;24:4228–4235.[Abstract/Free Full Text]
  73. de Jonge ME, Huitema AD, Schellens JH et al. Individualised cancer chemotherapy: Strategies and performance of prospective studies on therapeutic drug monitoring with dose adaptation: A review. Clin Pharmacokinet 2005;44:147–173.[CrossRef][Medline]
  74. Gurney H. Dose calculation of anticancer drugs: A review of the current practice and introduction of an alternative. J Clin Oncol 1996;14:2590–2611.[Abstract]
  75. Rousseau A, Marquet P. Application of pharmacokinetic modelling to the routine therapeutic drug monitoring of anticancer drugs. Fundam Clin Pharmacol 2002;16:253–262.[CrossRef][Medline]
  76. Ciccolini J, Monjanel-Mouterde S, Bun SS et al. Population pharmacokinetics of etoposide: Application to therapeutic drug monitoring. Ther Drug Monit 2002;24:709–714.[CrossRef][Medline]
  77. de Jonge ME, van den Bongard HJ, Huitema AD et al. Bayesian pharmacokinetically guided dosing of paclitaxel in patients with non-small cell lung cancer. Clin Cancer Res 2004;10:2237–2244.[Abstract/Free Full Text]
  78. Santana VM, Furman WL, Billups CA et al. Improved response in high-risk neuroblastoma with protracted topotecan administration using a pharmacokinetically guided dosing approach. J Clin Oncol 2005;23:4039–4047.[Abstract/Free Full Text]
  79. Evans WE, Relling MV, Rodman JH et al. Conventional compared with individualized chemotherapy for childhood acute lymphoblastic leukemia. N Engl J Med 1998;338:499–505.[Abstract/Free Full Text]
  80. Bleyzac N, Souillet G, Magron P et al. Improved clinical outcome of paediatric bone marrow recipients using a test dose and Bayesian pharmacokinetic individualization of busulfan dosage regimens. Bone Marrow Transplant 2001;28:743–751.[CrossRef][Medline]
  81. Hon YY, Evans WE. Making TDM work to optimize cancer chemotherapy: A multidisciplinary team approach. Clin Chem 1998;44:388–400.[Abstract/Free Full Text]
  82. Sparreboom A, Figg WD. Identifying sources of interindividual pharmacokinetic variability with population modeling. Clin Cancer Res 2006;12:1951–1953.[Free Full Text]
  83. Ratain MJ. Body-surface area as a basis for dosing of anticancer agents: Science, myth, or habit? J Clin Oncol 1998;16:2297–2298.[Medline]
  84. Canal P, Chatelut E, Guichard S. Practical treatment guide for dose individualisation in cancer chemotherapy. Drugs 1998;56:1019–1038.[CrossRef][Medline]



This article has been cited by other articles:


Home page
 The OncologistHome page
G. Hempel and J. Boos
Flat-Fixed Dosing Versus Body Surface Area Based Dosing of Anticancer Drugs: There Is a Difference
Oncologist, August 1, 2007; 12(8): 924 - 926.
[Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow eLetters: Submit a response to this article
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow E-mail this article link to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow Reprints/Permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Mathijssen, R. H.J.
Right arrow Articles by Sparreboom, A.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Mathijssen, R. H.J.
Right arrow Articles by Sparreboom, A.

HOME HELP CONTACT US SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
THE ONCOLOGIST STEM CELLS CME ALPHAMED PRESS JOURNALS