Fluoropyrimidines (FU) are still the most prescribed anticancer drugs for the treatment of solid cancers. However, fluoropyrimidines cause severe toxicities in 10 to 40% of patients and toxic deaths in 0.2 to 0.8% of patients, resulting in a real public health problem. The main origin of FU-related toxicities is a deficiency of dihydropyrimidine dehydrogenase (DPD), the rate-limiting enzyme of 5-FU catabolism. DPD deficiency may be identified through pharmacogenetics testing including phenotyping (direct or indirect measurement of enzyme activity) or genotyping (detection of inactivating polymorphisms on the DPYD gene). Approximately 3 to 15% of patients exhibit a partial deficiency and 0.1 to 0.5% a complete DPD deficiency. Currently, there is no regulatory obligation for DPD deficiency screening in patients scheduled to receive a fluoropyrimidine-based chemotherapy. Based on the levels of evidence from the literature data and considering current French practices, the Group of Clinical Pharmacology in Oncology (GPCO)-UNICANCER and the French Network of Pharmacogenetics (RNPGx) recommend the following: (1) to screen DPD deficiency before initiating any chemotherapy containing 5-FU or capecitabine; (2) to perform DPD phenotyping by measuring plasma uracil (U) concentrations (possibly associated with dihydrouracil/U ratio), and DPYD genotyping (variants *2A, *13, p.D949V, HapB3); (3) to reduce the initial FU dose (first cycle) according to DPD status, if needed, and further, to consider increasing the dose at subsequent cycles according to treatment tolerance. In France, 17 public laboratories currently undertake routine screening of DPD deficiency.

5-FU therapeutic drug monitoring as a valuable option to reduce toxicity in patients with gastrointestinal cancer 5-FU therapeutic drug monitoring as a valuable option to reduce toxicity in patients with gastrointestinal cancer

Purpose: It has recently been shown that it is possible to improve the prediction of carboplatin clearance by adding plasma cystatin C level (cysC), an endogenous marker of glomerular filtration rate, to the other patient characteristics routinely used for carboplatin individual dosing, namely serum creatinine (Scr), actual body weight (ABW), age, and sex. This multicenter pharmacokinetic study was done to evaluate prospectively the benefit of using cysC for carboplatin individual dosing. Experimental Design: The 357 patients included in the study were receiving carboplatin as part of established protocols. A population pharmacokinetic analysis was done using NONMEM program. Seven covariates studied were as follows: Scr, cysC, age, sex, ABW, ideal body weight, and lean body mass. Results: The best covariate equation was as follows: carboplatin clearance (mL/min) = 117.8. (Scr/75)−0.450. (cysC/1,00)−0.385. (ABW/65)+0.504. (age/56)−0.366. 0.847sex, with Scr in μmol/L, cysC in mg/L, ABW in kilograms, age in years, and sex = 0 for male. Using an alternative weight descriptor (ideal body weight or lean body mass) did not improve the prediction. This final covariate model was validated by bootstrap analysis. The bias (mean percentage error) and imprecision (mean absolute percentage error) were +1% and 15%, respectively, on the total population, and were of a similar magnitude in each of the three subgroups of patients defined according to their body mass index. Conclusion: For the first time, a unique formula is proposed for carboplatin individual dosing to patients, which is shown to be equally valid for underweight, normal weight, and obese patients.

Purpose: Area under the curve (AUC) dosing is routinely carried out for carboplatin, but the chosen target AUC values remain largely empirical. This multicenter pharmacokinetic-pharmacodynamic (PK-PD) study was performed to determine the covariates involved in the interindividual variability of carboplatin hematotoxicity that should be considered when choosing individual target AUCs. Patients and Methods: Three hundred eighty-three patients received carboplatin as part of established regimens. A semi-physiologic population PK-PD model was applied to describe separately the time course of absolute neutrophil and platelet counts using NONMEM software. The plasma ultrafiltrable carboplatin concentration (C_Carbo) was assumed to inhibit the proliferation of blood cell precursors through a linear model: drug effect = slope × C_Carbo. The slope corresponds to the patients' sensitivity to carboplatin hematotoxicity. The relationships between the patients' sensitivity to the neutropenic or thrombopenic effects of carboplatin and various covariates, including associated chemotherapies, demographic, biologic, and pharmacogenetic data, were studied. Results: The sensitivity of carboplatin-induced thrombocytopenia decreased in the case of concomitant paclitaxel chemotherapy (slope decreased by 24%), whereas it increased with coadministration of etoposide and gemcitabine (slope increased by 45% and 133%, respectively). For neutropenia, the sensitivity increased when carboplatin was combined with other cytotoxics (slope increased by 76%). Conclusion: This study provides useful information to clinicians to better estimate the hematopoietic toxicity of carboplatin and thus choose more rationally carboplatin target AUCs as a function of pretreatment or concomitantly administered chemotherapies. For example, an AUC of 5 mg/mL · min is associated with a risk of grade 3 or 4 thrombocytopenia of 2% in combination with paclitaxel versus 38% with gemcitabine in a non-pretreated patient.