This model predicted myelosuppression after administration of one of several different chemotherapeutic drugs. In addition, with fixed system-related parameters to proposed values, and only drug-related parameters estimated, myelosuppression can be predicted. We propose that this model can be a useful tool in the development of anticancer drugs and therapies.

Purpose: To retrospectively evaluate the effects of six known allelic variants in the CYP2C8, CYP3A4, CYP3A5, and ABCB1 genes on the pharmacokinetics of the anticancer agent paclitaxel (Taxol). Experimental Design: A cohort of 97 Caucasian patients with cancer (median age, 57 years) received paclitaxel as an i.v. infusion (dose range, 80-225 mg/m 2 ). Genomic DNA was analyzed using PCR RFLP or using Pyrosequencing. Pharmacokinetic variables for unbound paclitaxel were estimated using nonlinear mixed effect modeling. The effects of genotypes on typical value of clearance were evaluated with the likelihood ratio test within NONMEM. In addition, relations between genotype and individual pharmacokinetic variable estimates were evaluated with oneway ANOVA. Results: The allele frequencies for the CYP2C8*2, CYP2C8*3, CYP2C8*4, CYP3A4*3, CYP3A5*3C, and ABCB1 3435C>T variants were 0.7%, 9.2%, 2.1%, 0.5%, 93.2%, and 47.1%, respectively, and all were in Hardy-Weinberg equilibrium.The population typical value of clearance of unbound paclitaxel was 301L/h (individual clearance range, 83.7-1055 L/h). The CYP2C8 or CYP3A4/5 genotypes were not statistically significantly associated with unbound clearance of paclitaxel. Likewise, no statistically significant association was observed between the ABCB1 3435C>T variant and any of the studied pharmacokinetic variables. Conclusions: This study indicates that the presently evaluated variant alleles in the CYP2C8, CYP3A4, CYP3A5, and ABCB1 genes do not explain the substantial interindividual variability in paclitaxel pharmacokinetics.

Earlier PK models for paclitaxel have been empirical. This study shows that a mechanistic model can be used to describe the nonlinear PK of paclitaxel. There is an indication that the PK/PD relationship is not the same for unbound and total plasma concentrations.

Purpose: Cancer chemotherapy, although based on body surface area, often causes unpredictable myelosuppression, especially severe neutropenia. The aim of this study was to evaluate qualitatively and quantitatively the influence of patient-specific characteristics on the neutrophil concentration-time course, to identify patient subgroups, and to compare covariates on systemrelated pharmacodynamic variable between drugs. Experimental Design: Drug and neutrophil concentration, demographic, and clinical chemistry data of several trials with docetaxel (637 patients), paclitaxel (45 patients), etoposide (71 patients), or topotecan (191 patients) were included in the covariate analysis of a physiologybased pharmacokinetic-pharmacodynamic neutropenia model. Comparisons of covariate relations across drugs were made.Results: A population model incorporating four to five relevant patient factors for each drug to explain variability in the degree and duration of neutropenia has been developed. Sex, previous anticancer therapy, performance status, height, binding partners, or liver enzymes influenced system-related variables and a 1 -acid glycoprotein, albumin, bilirubin, concomitant cytotoxic agents, or administration route changed drug-specific variables. Overall, female and pretreated patients had a lower baseline neutrophil concentration. Across-drug comparison revealed that several covariates (e.g., age) had minor (clinically irrelevant) influences but consistently shifted the pharmacodynamic variable in the same direction. Conclusions: These mechanistic models, including patient characteristics that influence drugspecific parameters, form the rationale basis for more tailored dosing of individual patients or subgroups to minimize the risk of infection and thus might contribute to a more successful therapy. In addition, nonsignificant or clinically irrelevant relations on system-related parameters suggest that these covariates could be negligible in clinical trails and daily use.In cancer chemotherapy, despite dose adaptation to body surface area, the degree of interpatient variability in effects is large (1, 2). Some patients fail to respond, whereas others experience unacceptable toxicity (3). Myelosuppression is the most common, often dose-limiting toxicity. Neutropenia makes patients highly susceptible to pathogens resulting in lifethreatening infections or even death.Empirical pharmacodynamic models accounting for the entire concentration-time profile of neutrophils have been developed (4,5). Recently, also (semi-)mechanistic models (6, 7) and a physiology-based pharmacokinetic-pharmacodynamic model describing neutropenia for several drugs (8) have been introduced. Mechanistic models have the great advantage that estimated variables may be attributed and compared with physiologic values.The contribution of pharmacokinetic or pharmacodynamic variability to the variable clinical outcome has clearly been shown (9, 10). A more rational approach for optimal dosing is based on elucidating the sources of variabil...

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For many oncological agents, myelosuppression is the dose-limiting toxicity and the quantitative characterisation of the relationship between drug dose, plasma concentration and haematological toxicity is of importance in the drug development. Mechanism-based population pharmacokinetic-pharmacodynamic models have been developed for this purpose and the applications of these in candidate selection, first-in-man studies, prodrug and formulation development, dose finding, schedule optimisation, assessing influence of modifying agents, drug combination studies, subgroup identification and feedback individualisation are reviewed.Population pharmacokinetic analysis of (sparse) concentration-time data have for a decade provided useful and sometimes crucial information during oncological drug development whereas there are far fewer reports on the use of population pharmacodynamic models for the same purpose. Although pharmacokinetic/pharmacodynamic models have been identified as having a potentially important role in oncological drug development (Aarons et al. 2001a), the only area where there has been considerable use of the methodology is in characterising haematological toxicity. In these models, the time course of leukocytes or absolute neutrophil counts after chemotherapy is characterised. A clear trend in time has been that the mechanistic basis for haematological toxicity ( fig. 1) has been increasingly considered in the structure of the models developed, as described in a recent review of the evolution of pharmacokinetic/pharmacodynamic models for myelosuppression (Friberg & Karlsson 2003). Although generally developed on data from treatment with drugs already on the market, there is, partially realised, usefulness of such predictive models for a dose-limiting toxicity, such as frequently occurring myelosuppression. We have used one of the recently published models (Friberg et al. 2002) (fig. 2) in clinical drug development. In this presentation, we will first briefly present the model, thereafter discuss the different uses of it and finally make some general remarks. Mechanism-based population pharmacokinetic/ pharmacodynamic model for haematological toxicityThe mechanism-based model describes the system in terms of five sequential compartments ( fig. 2). The first compartment in the chain represents the pool of proliferative cells, which are capable of self-replication. These cells are susceptible to drug-induced cell death but will otherwise generate non-mitotic cells that after maturation will be released to the systemic circulation and subsequently taken up by tissues. The physiological system is quantified in terms of the baseline circulating cell count (Baseline), the mean maturation time and a parameter governing the increase in self-replication that occurs when circulating cells are depleted (g), mimicking the stimulating endogenous granulocyte colony stimulating factor (G-CSF) interaction. An additional parameter, the first-order rate constant for loss from the circulating pool was found n...

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Background and Objective Women with postpartum depression (PPD) may expose their infants to antidepressants via breast milk. Brexanolone is the only FDA-approved antidepressant specifically indicated for the treatment of PPD. This open-label, phase Ib study of healthy lactating volunteers assessed pharmacokinetic (PK) properties of brexanolone and a population PK (PopPK) model determined the relative infant dose (RID) in breastfeeding mothers. Methods Twelve participants received a 60-h infusion of brexanolone (titration up to 90 µg/kg/h). Allopregnanolone concentration was measured in breast milk and plasma. The RID was computed using a nonlinear mixed-effects PopPK model of patients with PPD and healthy women (N = 156). Model results were extended across an integrated dataset of participants through day 7.Results Allopregnanolone concentration-time profiles were similar between breast milk and plasma (partition coefficient for concentration gradient [milk : plasma] 1.36). Mean (95% CI) C max was 89.7 ng/mL (74.19-108.39), and median (95% CI) t max was 47.8 h (47.8-55.8) in plasma. The overall PK profile was best described by a two-compartment model with linear elimination and distribution. Body weight was the only significant covariate identified. There were no apparent differences in PopPK AUC and C max between participants with or without concomitant antidepressant treatment. Maximum RID was 1.3%. Conclusion The PopPK model successfully described the variability and concentration-time profiles of allopregnanolone in breast milk and plasma in healthy participants and in the plasma of brexanolone-treated patients with PPD. The rapid elimination of allopregnanolone from plasma and breast milk, and low RID, suggests the appropriateness of brexanolone weight-based dosing and supports other PK-related labeling recommendations.