Alex Sparreboom

Napoleon Bonaparte (1769-1821) once remarked that “medicine is a collection of uncertain prescriptions, the results of which, taken collectively, are more fatal than useful to mankind (http://www.quotationspage.com/quote/ibog.html)”. Although this needs to be read in historic context, it is still true that, in most common cancer types, many of the currently available drugs have rather limited efficacy combined with a significant degree of toxicity. The major reasons for this have presumably been the lack of detailed knowledge in tumor cell biology and inappropriate preclinical models for the identification and testing of compounds. Moreover, the standard strategy still in use for dose selection of cytotoxic anticancer drugs is to establish a therapeutic dose in phase II trials and subsequently, at best, only modify it for individual differences in body-surface area. There is wealth of experimental data, however, indicating that both the efficacy and the safety of cytotoxic chemotherapeutic treatment might be optimized if dosing strategies would consider individual patient characteristics as they relate to the pharmacokinetic (e.g., measures of liver function), pharmacodynamic (e.g., timing and extent of neutropenia after dosing), or pharmacogenetic profiles of the agent (1). These individual patient characteristics could, depending on the extent of unexplainable drug variability, be characterized following the first dose of the drug and subsequent blood sampling.The study of population pharmacokinetics seeks to identify the measurable pathophysiologic factors that cause changes in the dose-concentration relationship and the extent of these alterations so that, if these are associated with clinically significant shifts in the therapeutic index, dosage can be appropriately modified in the individual patient. It is obvious that a careful collection of data during the development of drugs and subsequent analyses could be helpful to collect some essential information on the drug. Unfortunately, important information is often lost by failing to analyze these data or due to the fact that the relevant samples or data were never collected. Historically, this has resulted in the notion that tools for the identification of patient population subgroups are inadequate for most of the currently approved anticancer drugs (2).In the present issue of the Clinical Cancer Research, Joerger et al. describe the first comprehensive and long overdue population pharmacokinetic modeling analysis for paclitaxel (3), more than two decades after the clinical development of the drug was initiated. The authors are to be congratulated for meticulously collecting blood samples on 168 patients treated with paclitaxel over the last 15 years and for planning and executing this retrospective investigation. Using complex nonlinear mixed-effect modeling algorithms, the authors showed that among the variables evaluated sex, body-surface area, age, and serum bilirubin all independently and significantly correlated with the disposition and elimination pathways of paclitaxel. As pointed out by Joerger et al., similar associations between body-surface area, age, and bilirubin and the pharmacokinetic profile of paclitaxel were reported previously in smaller studies and are now confirmed. Of particular interest is the authors' novel finding that male gender was positively correlated with the maximum elimination capacity of paclitaxel, adding to a growing body of evidence that the pharmacokinetic profile of various anticancer drugs exhibits significant sexual dimorphism (4).A few issues need to be considered to appraise the authors' final conclusion that future utilization of their pharmacokinetic model may include prospective covariate-adapted paclitaxel dosing to lower interindividual variability of drug exposure and toxicity. First, the development of rational dose individualization strategies depends, with respect to pharmacokinetics, on choosing an adequate matrix to measure (e.g., blood, plasma, and plasma water), an adequate analyte to monitor (parent drug or metabolites), and an adequate way of summarizing exposure-effect relationships (e.g., area under the curve or time above a threshold concentration). It is further necessary that exposure is quantitatively linked to the effect and/or toxicity possibly through a suitable biomarker. Unfortunately, in the study by Joerger et al., efficacy and toxicity data were not considered because the data were obtained from patients treated with variable doses of paclitaxel, either given alone or in combination with other agents, and in a variety of diseases. The authors focused in their population analysis on finding predictors of the duration that concentrations of paclitaxel in plasma are >0.1 μmol/L, which variable is associated with the main side effect of paclitaxel, neutropenia, and overall survival in non–small cell lung cancer. It is noteworthy though that the use and generalizability of this empirically designed threshold model to describe pharmacokinetic-pharmacodynamic relationships for paclitaxel is somewhat controversial and that various alternative models have been proposed (5). In any event, the lack of a solid foundation based on pharmacodynamic data makes it unclear if the current findings by Joerger et al. already provide meaningful tools for medical decision-making in clinical practice. For example, body-surface area was found to be associated with paclitaxel elimination, as predicted previously, but flat-fixed dosing strategies have already been shown to be feasible (6, 7), although these studies were not randomized and hence a direct comparison with dose calculations based on body size measures cannot be made. It is of note that the identification of body-surface area in a population pharmacokinetic model as a significant covariate, with subsequent demonstration of the clinical feasibility of delivering a fixed dose independent of...

Members of the organic anion transporting polypeptide (OATP) family are responsible for the cellular uptake of a broad range of endogenous compounds and xenobiotics in multiple tissues. This review focuses on OATP1B1 and -1B3, which are specifically expressed in the liver and considered to be of particular importance for hepatic drug elimination and drug pharmacokinetics. Recent literature has indicated that inhibition of these transporters may result in drug–drug interactions. Furthermore, genetic polymorphisms in the genes encoding OATP1B1 and -1B3 have been described that increase or decrease transport in vitro and in vivo. Alteration of transporter function by either of these mechanisms may contribute to interindividual variability in drug disposition and response. In this review an update of this rapidly emerging field is provided.

Interindividual variability in paclitaxel and docetaxel pharmacokinetics, toxicity and response is extensive, and largely unexplained. We hypothesized that this is due to affinity of taxanes for an uptake transporter that indirectly regulates elimination pathways. Here, we studied accumulation of [3H]docetaxel and [3H]paclitaxel in Xenopus laevis oocytes injected with cRNA of the liver-specific organic anion transporting polypeptide (OATP) family members OATP1B1 (OATP2) or OATP1B3 (OATP8). Taxane transport by OATP1B1 expressing oocytes was not significantly different from that by water-injected controls, whereas uptake by OATP1B3 was 2.2-fold higher for docetaxel (P=0.0007) and 3.3-fold higher for paclitaxel (P

Purpose: To compare the preclinical and clinical pharmacokinetic properties of paclitaxel formulated as a Cremophor-free, albumin-bound nanoparticle (ABI-007) and formulated in Cremophor-ethanol (Taxol). Experimental Design: ABI-007 and Taxol were given i.v. to Harlan Sprague-Dawley male rats to determine pharmacokinetic and drug disposition. Paclitaxel pharmacokinetic properties also were assessed in 27 patients with advanced solid tumors who were randomly assigned to treatment with ABI-007 (260 mg/m², 30 minutes; n = 14) or Taxol (175 mg/m², 3 hours; n = 13), with cycles repeated every 3 weeks. Results: The volume of distribution at steady state and clearance for paclitaxel formulated as Cremophor-free nanoparticle ABI-007 were significantly greater than those for paclitaxel formulated with Cremophor (Taxol) in rats. Fecal excretion was the main elimination pathway with both formulations. Consistent with the preclinical data, paclitaxel clearance and volume of distribution were significantly higher for ABI-007 than for Taxol in humans [21.13 versus 14.76 L/h/m² (P = 0.048) and 663.8 versus 433.4 L/m² (P = 0.040), respectively]. Conclusions: Paclitaxel formulated as ABI-007 differs from paclitaxel formulated as Taxol, with a higher plasma clearance and a larger volume of distribution. This finding is consistent with the absence of paclitaxel-sequestering Cremophor micelles after administration of ABI-007. This unique property of ABI-007 could be important for its therapeutic effectiveness.

Purpose: Taxol® contains paclitaxel formulated in Cremophor EL-P (CrEL-P) and ethanol. Paxene® is similar to Taxol, except for the use of Cremophor EL (CrEL) and the addition of citric acid. Here, we investigated the physicochemical properties and clinical pharmacokinetics of the two paclitaxel formulations. Experimental Design: The size and modality of distribution of CrEL-P and CrEL micelles was determined by dynamic-light scattering. The effect of vehicle composition on the fraction unbound paclitaxel in vitro was determined by equilibrium dialysis. Paclitaxel pharmacokinetics was studied in 61 cancer patients receiving Taxol and 26 patients receiving Paxene. Comparative pharmacokinetics of CrEL-P and CrEL were obtained in 14 and 6 patients, respectively. Results: The size of micelles present in Taxol was slightly smaller (9 to 13%) than those present in Paxene. Surface tension and critical micellar concentration were also similar for the two formulations, with mean values of 37.0 and 38.1 mN/m and 0.0387 and 0.0307 mg/mL, respectively. The fraction unbound paclitaxel was not significantly different for Taxol and Paxene (P > 0.05). Over the tested dose range, the mean clearance of paclitaxel decreased from 45.1 to 16.9 L/h for Taxol, and from 50.7 to 16.4 L/h for Paxene (P > 0.05). Concentrations of the excipient following the administration of CrEL-P or CrEL were also similar. Conclusion: The differences in formulation between Taxol and Paxene do not significantly affect micelle formation and/or quantitative aspects of the vehicle-paclitaxel interaction in vitro and in vivo.

Background: Irinotecan is a topoisomerase I inhibitor that has been approved for use as a first- and second-line treatment for colorectal cancer. The response to irinotecan is variable, possibly because of interindividual variation in the expression of the enzymes that metabolize irinotecan, including cytochrome P450 3A4 (CYP3A4) and uridine diphosphate glucuronosyltransferase 1A1 (UGT1A1). We prospectively explored the relationships between CYP3A phenotype, as assessed by erythromycin metabolism and midazolam clearance, and the metabolism of irinotecan and its active metabolite SN-38. Methods: Of the 30 white cancer patients, 27 received at least two treatments with irinotecan administered as one 90-minute infusion (dose, 600 mg) with 3 weeks between treatments, and three received only one treatment. Before the first and second treatments, patients underwent an erythromycin breath test and a midazolam clearance test as phenotyping probes for CYP3A4. Erythromycin metabolism was assessed as the area under the curve for the flux of radioactivity in exhaled CO ₂ within 40 minutes after administration of [ N -methyl- ¹⁴ C]erythromycin. Midazolam and irinotecan were measured by high-performance liquid chromatography. Genomic DNA was isolated from blood and screened for genetic variants in CYP3A4 and UGT1A1. All statistical tests were two-sided. Results: CYP3A4 activity varied sevenfold (range = 0.223%–1.53% of dose) among patients, whereas midazolam clearance varied fourfold (range = 262–1012 mL/min), although intraindividual variation was small. Erythromycin metabolism was not statistically significantly associated with irinotecan clearance ( P = .090), whereas midazolam clearance was highly correlated with irinotecan clearance ( r = .745, P <.001). In addition, the presence of a UGT1A1 variant with a (TA) ₇ repeat in the promoter (UGT1A1*28) was associated with increased exposure to SN-38 (435 ng · h/mL, 95% confidence interval [CI] = 339 to 531 ng · h/mL in patients who are homozygous for wild-type UGT1A1; 631 ng · h/mL, 95% CI = 499 to 762 ng · h/mL in heterozygous patients; and 1343 ng · h/mL, 95% CI = 0 to 4181 ng · h/mL in patients who are homozygous for UGT1A1*28) ( P = .006). Conclusion: CYP3A4 phenotype, as assessed by midazolam clearance, is statistically significantly associated with irinotecan pharmacokinetics. Evaluation of midazolam clearance combined with UGT1A1*28 genotyping may assist with optimization of irinotecan chemotherapy.

The purpose of this study was to perform exploratory relationships between the pharmacokinetics of the farnesyl transferase inhibitor, tipifarnib (R115777, Zarnestra) and allelic variants of genes coding for ATP binding-cassette transporters and drug-metabolizing enzymes. Twenty-eight patients with advanced solid tumors were treated with tipifarnib administered orally at a dose of 200 or 300 mg. Blood samples were collected for pharmacokinetics and genotyping of 10 variants in genes encoding P-glycoprotein (ABCB1), cytochrome P450 isozymes CYP3A4 and CYP3A5, and UDP glucuronosyltransferase isozyme UGT1A1. The homozygous T-allele of ABCB1*8 (1236C > T) was associated with a trend for a higher area under the curve of tipifarnib as compared to patients with only one or no variant alleles [mean (±SD), 5,303 ± 1,620 ng·h/mL vs. 3,619 ± 1,275 ng·h/mL; P = 0.047). No statistically significant differences were observed with any other genetic variant (P > 0.15). Overall, this study indicates that ABCB1 genotype might be correlated with tipifarnib pharmacokinetics, although considerable overlap in exposure measures between genotype groups was observed.

The adenosine triphosphate-binding cassette transporter ABCG2 (breast cancer resistance protein [BCRP]) functions as an efflux transporter for many drugs, including the topoisomerase I inhibitor diflomotecan, and is expressed at high levels in the intestine and liver. We performed an exploratory analysis to evaluate the effects of the natural allelic variant ABCG2 421C>A on the pharmacokinetics of diflomotecan. The drug was administered to 22 adult white patients with cancer as a 20-minute infusion (dose, 0.10-0.27 mg), followed 2 weeks later by an oral solution (dose, 0.10-0.35 mg). The ABCG2 421C>A genotype significantly affected the pharmacokinetics of diflomotecan; in 5 patients heterozygous for this allele, plasma levels after intravenous drug administration were 299% (P =.015) of those in 15 patients with wild-type alleles, at mean values of 138 ng x h/mL x mg(-1) (95% confidence interval, 11.3-264 ng x h/mL x mg(-1)) versus 46.1 ng x h/mL x mg(-1) (95% confidence interval, 25.6-66.7 ng x h/mL x mg(-1)), respectively. Diflomotecan levels were not significantly influenced by 11 known variants in the ABCB1, ABCC2, cytochrome P450 (CYP) 3A4, and CYP3A5 genes. These findings provide the first evidence linking variant ABCG2 alleles to altered drug exposure and suggest that interindividual variability in substrate drug effects might be influenced, in part, by ABCG2 genotype.

Purpose: Interest in the use of herbal products has grown dramatically in the Western world. Recent estimates suggest an overall prevalence for herbal preparation use of 13% to 63% among cancer patients. With the narrow therapeutic range associated with most anticancer drugs, there is an increasing need for understanding possible adverse drug interactions in medical oncology.Methods: In this article, a literature overview is provided of known or suspected interactions of the 15 best-selling herbs in the United States with conventional allopathic therapies for cancer.Results: Herbs with the potential to significantly modulate the activity of drug-metabolizing enzymes (notably cytochrome P450 isozymes) and/or the drug transporter P-glycoprotein include garlic (Allium sativum), ginkgo (Ginkgo biloba), echinacea (Echinacea purpurea), ginseng (Panax ginseng), St John's wort (Hypericum perforatum), and kava (Piper methysticum). All of these products participate in potential pharmacokinetic interactions with anticancer drugs.Conclusion: It is suggested that health care professionals and consumers should be aware of the potential for adverse interactions with these herbs, question their patients on their use of them, especially among patients whose disease is not responding to treatments as expected, and urge patients to avoid herbs that could confound their cancer care.