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Purpose The pharmacokinetic profiles of bendamustine and active metabolites were defined in patients with rituximab-refractory, relapsed indolent B-cell non-Hodgkin’s lymphoma (NHL), and supported understanding of exposure-response relationships for efficacy and safety. Methods Bendamustine was administered as a 60-minute 120 mg/m2 intravenous infusion on Days 1 and 2 of six 21-day cycles. Pharmacokinetic models were developed, with covariate assessment. Correlations between bendamustine exposure and responder status or occurrence of neutropenia, thrombocytopenia, fatigue, nausea, and vomiting were examined. Results Following a single dose of bendamustine HCl, concentrations declined in a triphasic manner, with rapid distribution, intermediate, and slow terminal phases. The intermediate t1/2 (40 minutes) was considered the pharmacologically relevant (beta elimination) t1/2 since the initial phases accounted for 99% of the AUC. Age, sex, mild/moderate renal, or mild liver impairment did not alter pharmacokinetics. Metabolite concentrations were low relative to parent. No correlation was observed between exposure and safety or efficacy measures because of the limited range of exposures after 120 mg/m2 administration, except bendamustine Cmax was a significant (p-value = 0.013) predictor of the probability of nausea in patients, most of whom were pretreated with antiemetics. Conclusions The BSA-based dosing regimen for bendamustine achieved the targeted exposure and was associated with a high incidence of therapeutic response. Given the short t1/2 and low concentrations of bendamustine observed by 12 hours after dosing, the single-dose profile for bendamustine described by these analyses is expected to be representative of the multiple-dose profile. The occurrence of nausea was significantly related to bendamustine exposure, with the probability of nausea increasing as bendamustine Cmax increases.

It is shown that the class of elliptical distributions extend the Tobin [14] separation theorem, Bawa's [2] rules of ordering uncertain prospects, Ross's [12] mutual fund separation theorems, and the results of the CAPM to non-normal distributions, which are not necessarily stable. Further, the mean-covariance matrix framework is generalized to a mean-characteristic matrix framework in which the characteristic matrix is the basis for a spread or risk measure, and a generalized equilibrium pricing equation is arrived at. The implications to empirical testing of the CAPM and modeling the empirical distribution of speculative prices are discussed. THE PURPOSE OF THIS paper is to describe the class of elliptical distributions and delineate their relevance to portfolio theory and its empirical applications. The class of elliptical distributions contains the multivariate normal (multinormal, henceforth) distribution as a special case; as well as many non-normal multivariate distributions including the multivariate Cauchy, the multivariate exponential, a multivariate elliptical analog of Student's t-distribution (the multivariate t, henceforth1), and non-normal variance mixtures of multinormal distributions. We show that some of the general characteristics of all members in the class of elliptical distributions make them admissible candidates for generalizing the theory of portfolio choice and equilibrium in capital asset markets, as well as attractive alternatives for use in empirical studies. More specifically, elliptical distributions are characterized by two parameters and extend the Tobin's [14] separation theorem, Bawa's [2] rules of ordering uncertain prospects, Ross's [13] mutual fund separation theorems, and the results of the CAPM to non-normal distributions, which are not necessarily stable. The definitions and properties of elliptical distributions are in Section I. The main results are in Section II and the importance of elliptical distributions in empirical works is discussed in Section III. are greatly appreciated. Special thanks are extended to Stephen Brown for his comments and suggestions. Errors remain ours. 'This is the so called Dunnett-Sobel multivariate t; it is also called the central multivariate t; see Johnson and Kotz [9] p. 134. 2 This section relies on Kelker [10], Devlin et al., [6], and the references therein. 745 746 The Journal of Finance definite symmetric matrix. Then, a p-component random vector X = (X1, X2, *. . , Xp)' is said to be distributed elliptically, X e (A, Q): Definition (a) if and only if the characteristic function of X is of the form: EIEXP(it'X)} cx(t) = I(t'Qt)EXP(it'A); (E denotes the expectation operator, i = , and a prime denotes transpose, throughout). Thus, cx(t) depends only on the quadratic form t 'Q t. Definition (b) if and only if for all nonzero p-component scalar vectors a, all the univariate random variables a'X such that VAR(W'X) is constant, follow the same distribution.

Tigecycline, a first-in-class expanded glycylcycline antimicrobial agent, has demonstrated efficacy in the treatment of complicated skin and skin structure infections (cSSSI) and complicated intra-abdominal (cIAI) infections. A population pharmacokinetic (PK) model for tigecycline was developed for patients with cSSSI or cIAI enrolled in two phase 2 clinical trials, and the influence of selected demographic factors and clinical laboratory measures was investigated. Tigecycline was administered as an intravenous loading dose followed by a 0.5-or 1-h infusion every 12 h for up to 14 days. Blood samples were collected the day before or the day of hospital discharge for the determination of serum tigecycline concentrations. Patient covariates were evaluated using stepwise forward (␣ ‫؍‬ 0.05) and backward (␣ ‫؍‬ 0.001) procedures. The predictive performance of the model was assessed separately using pooled data from either two phase 3 studies for patients with cSSSI or two phase 3 studies for patients with cIAI. A two-compartment model with zero-order input and first-order elimination adequately described the steady-state tigecycline concentration-time data. Tigecycline clearance was shown to increase with increasing weight, increasing creatinine clearance, and male gender (P < 0.001). The final model provided a relatively unbiased fit to each data set. Individual predicted values of the area under the concentration-time curve from 0 to 12 h (AUC 0-12 ) were generally unbiased (median prediction error, ؊1.60% to ؊3.78%) and were similarly precise (median absolute prediction error, <4%) when compared across data sets. The population PK model provided the basis to obtain individual estimates of steady-state AUC 0-12 in later exposure-response analyses of tigecycline safety and efficacy in patients with cSSSI or cIAI.