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Cisplatin is among the most widely used anticancer drugs and known to cause a dose-limiting nephrotoxicity, which is partially dependent on the renal uptake carrier OCT2. We here report a previously unrecognized, OCT2-independent pathway of cisplatin-induced renal injury that is mediated by the organic anion transporters OAT1 and OAT3. Using transporter-deficient mouse models, we found that this mechanism regulates renal uptake of a mercapturic acid metabolite of cisplatin that acts as a precursor of a potent nephrotoxin. The function of these two transport systems can be simultaneously inhibited by the tyrosine kinase inhibitor nilotinib through noncompetitive mechanisms, without compromising the anticancer properties of cisplatin. Collectively, our findings reveal a novel pathway that explains the fundamental basis of cisplatin-induced nephrotoxicity, with potential implications for its therapeutic management. [ABSTRACT FROM AUTHOR]

Quantitative model-based analyses are helpful to support decision-making in drug development. In oncology, disease progression/clinical outcome (DPCO) models have been used for early predictions of clinical outcome, but most of such approaches did not include adverse events or dose intensity. In addition, cost-effectiveness evaluations of investigational compounds are becoming increasingly important. Here, we developed an integrated model-based framework including relevant treatment effects for patients with castration-resistant prostate cancer treated with the anticancer agent eribulin. The framework included (i) a DPCO model relating prostate-specific antigen (PSA) dynamics to survival; (ii) models for adverse events including dose-limiting neutropenia and other graded toxicities; (iii) a model for Eastern Cooperative Oncology Group (ECOG) performance score; (iv) a model for dropout; (v) the consideration of cost effectiveness. The model allowed simulation of realistic treatment courses. Subsequently, simulations evaluating alternative treatment protocols or patient characteristics were performed in order to derive inferences on expected efficacy and cost effectiveness. [ABSTRACT FROM AUTHOR]

Frameworks that associate cancer dynamic disease progression models with parametric survival models for clinical outcome have recently been proposed to support decision making in early clinical development. Here we developed such a disease progression clinical outcome model for castration-resistant prostate cancer (CRPC) using historical phase II data of the anticancer agent eribulin. Disease progression was captured using the dynamics of prostate-specific antigen (PSA). For clinical outcome, overall survival (OS) was used. The model for PSA dynamics comprised parameters for baseline PSA (23.2 ng/ml, relative standard error (RSE) 16.5%), growth rate (0.00879 day−1, RSE 12.6%), drug effect (0.241 µg·h·l−1 day−1, RSE 32.6%), and resistance development (0.0113 day−1, RSE 44.3%). OS was modeled according to a Weibull distribution. Predictors for survival included model-predicted PSA time to nadir (TTN), PSA growth rate, Eastern Cooperative Oncology Group (ECOG) score, and baseline PSA. The developed framework can be considered to support informative design and analysis of drugs developed for CRPC. [ABSTRACT FROM AUTHOR]

Gefitinib (Iressa(R)); AstraZeneca Pharmaceuticals, Wilmington, DE, http://www.astrazeneca-us.com) and erlotinib (Tarceva(R)); OSI Pharmaceuticals, Inc., Melville, NY, http://www.osip.com) are so-called small molecules that selectively inhibit epidermal growth factor receptor (EGFR) tyrosine kinase activity. Both drugs received registration approval by the U.S. Food and Drug Administration (FDA) for the second- and third-line treatment of non-small cell lung cancer (NSCLC), but the failure of gefitinib to show a survival advantage over placebo has resulted in a discussion about the registration of gefitinib. Recently published results have revealed that mutations in the tyrosine kinase domain of EGFR are strongly associated with increased gefitinib and erlotinib sensitivity in patients with advanced NSCLC. Here, we present the current knowledge and the future directions of the EGFR tyrosine kinase inhibitors gefitinib and erlotinib. [ABSTRACT FROM AUTHOR]

Farnesyl transferase inhibitors are a new class of biologically active anticancer drugs. The exact mechanism of action of this class of agents is, however, currently unknown. The drugs inhibit farnesylation of a wide range of target proteins, including Ras. It is thought that these agents block Ras activation through inhibition of the enzyme farnesyl transferase, ultimately resulting in cell growth arrest. In preclinical models, the farnesyl transferase inhibitors showed great potency against tumor cells; yet in clinical studies, their activity was far less than anticipated. Reasons for this disappointing clinical outcome might be found in the drug-development process. In this paper, we outline an algorithm that is potentially useful for the development of biologically active anticancer drugs. The development of farnesyl transferase inhibitors, from discovery to clinical trials, is reviewed on the basis of this algorithm. We found that two important steps of this algorithm were underestimated. First, understanding of the molecular biology of the defective pathway has mainly been focused on H-Ras activation, whereas activation of K-Ras or other farnesylated proteins is probably more important in tumorigenesis. Inhibition of farnesylation is possibly not sufficient, because geranylgeranylation might activate K-Ras and suppress the effect of farnesyl transferase inhibitors. Furthermore, a well-defined proof of concept in preclinical and clinical studies has not been achieved. Integrating the proposed algorithm in future studies of newly developed biologically active anti-cancer drugs might increase the rate of success of these compounds in patients. [ABSTRACT FROM AUTHOR]

To determine the maximum-tolerated dose (MTD), dose-limiting toxicities, and pharmacokinetics of topotecan administered as a 30-min intravenous (i.v.) infusion over 5 days in combination with a 1-h i.v. infusion of ifosfamide (IF) for 3 consecutive days every 3 weeks. Patients with advanced malignancies refractory to standard therapy were entered into the study. The starting dose of topotecan was 0.4?mg?m-2?day-1 × 5 days. Ifosfamide was administered at a fixed dose of 1.2?g?m-2?day-1 × 3 days. In all, 36 patients received 144 treatment courses. Owing to toxicities, the schedule of topotecan administration was reduced from 5 to 3 days. The MTD was reached at topotecan 1.2?mg?m-2?day-1 × 3 days with IF 1.2?g?m-2?day-1 × 3 days. Haematological toxicities were dose limiting. Neutropenia was the major toxicity. Thrombocytopenia and anaemia were rare. Nonhaematological toxicities were relatively mild. Partial responses were documented in three patients with ovarian cancer dosed below the MTD. Topotecan and IF did not appear to interact pharmacokinetically. The relationships between the exposure to topotecan lactone and total topotecan, and the decrease in absolute neutrophil count and the decrease in thrombocytes, were described with sigmoidal-Emax models. The combination of 1.0?mg?m-2?day-1 topotecan administered as a 30-min i.v. infusion daily times three with 1.2?g?m-2?day-1 IF administered as a 1-h i.v. infusion daily times three every 3 weeks was feasible. However, the combination schedule of topotecan and IF did result in considerable haematological toxicity and in conjunction with previously reported pronounced nonhaematological toxicities and treatment related deaths, it may be concluded that this is not a favourable combination.British Journal of Cancer (2004) 90, 2268-2277. doi:10.1038/sj.bjc.6601861 www.bjcancer.com Published online 11 May 2004 [ABSTRACT FROM AUTHOR]

Genetics has significantly added to our understanding of variability in drug response, especially in cancer treatment. Pharmacogenetics, aimed at predicting a patient's chance for effective and safe drug treatment by interrogating germ line genetic variants, has moved from investigating a monogenetic candidate gene to examining complex phenotype-based genome-wide approaches. With the rapid advances in sequencing technologies, decline in costs, and swift turnaround times, large-scale genomic information will become available in the clinical setting, facilitating implementation of pharmacogenetics. [ABSTRACT FROM AUTHOR]