![]() It differs from endogenous human vasopressin by the substitution of lysine for arginine at the eighth position of the endogenous molecule (lys 8) and the addition of 3 glycyl residues at the amino terminus (Jamil et al. 1980) with an average molecular mass of 1227.4 Da (as a free base). Terlipressin, a synthetic vasopressin analog, is a 12-amino-acid peptide with the chemical name N-8- l-lysinevasopressin (Jamil et al. 2007 European Association for the Study of the Liver 2018). An increasing body of knowledge of the pathophysiology of HRS has demonstrated that vasoconstrictive drug therapy may improve renal function in patients with HRS (Salerno et al. At present, there are no approved therapies available in the United States or Canada for the treatment of HRS. If left untreated, patients with HRS have a poor prognosis (Gines et al. ![]() Hepatorenal syndrome (HRS), a potentially reversible renal failure, is a serious, rapidly progressing disease complicating decompensated chronic liver disease associated with cirrhosis (Arroyo et al. PD response, change in MAP, and HR were well correlated to L-VP concentrations compared with baseline values, the estimated maximum decrease in HR would be 10.6 bpm and the estimated maximum increase in MAP would be 16.2 mm Hg. Therefore, no weight-based dose is needed for terlipressin to treat HRS patients. However, simulation suggested that body weight had no clinically meaningful effects on the exposure of L-VP through terlipressin. Body weight was identified as the only covariate for the clearance of terlipressin. The population PK modeling results showed that the estimated clearances for terlipressin and L-VP are 27.4 L/h and 318 L/h, respectively, for a typical patient with a body weight of 86 kg. L-VP was well characterized as the active metabolite of terlipressin by a one-compartment model with first-order elimination. A two-compartment model with first-order elimination adequately described the PK of terlipressin. In addition, mean arterial pressure (MAP) and heart rate (HR) from 40 patients with HRS were available to explore the relationship between terlipressin and L-VP plasma concentrations and pharmacodynamic (PD) response. Sparse PK samples from 69 patients with HRS who participated in terlipressin phase 3 clinical studies were used for model development. Use of parallel computations with 4-12 processors running on the same computer improved the speed proportionally to the number of processors with the efficiency (for 12 processor run) in the range of 85-95% for all methods except BAYES, which had parallelization efficiency of about 70%.The objective of this population pharmacokinetics (PK) analysis was to characterize the PK of terlipressin and its active metabolite, lysine-vasopressin (L-VP), in patients with hepatorenal syndrome (HRS), following intravenous administration of terlipressin 1 mg to 2 mg every 6 h. Use of lower computational precision requirements for the FOCEI method reduced the estimation time by 3-5 times without compromising the quality of the parameter estimates, and equaled or exceeded the speed of the SAEM and BAYES methods. The ITS, IMP, and IMPMAP methods with the convergence tester were the fastest methods, reducing the computation time by about ten times relative to the FOCEI method. ![]() Standard errors of the parameter estimates were in general agreement with the PFIM 3.2 predictions. In the examples of the one- and two-target quasi-steady-state TMDD models with rich sampling, the parameter estimates and standard errors of the new Nonmem 7.2.0 ITS, IMP, IMPMAP, SAEM and BAYES estimation methods were similar to the FOCEI method, although larger deviation from the true parameter values (those used to simulate the data) was observed using the BAYES method for poorly identifiable parameters. ![]() The paper compares performance of Nonmem estimation methods-first order conditional estimation with interaction (FOCEI), iterative two stage (ITS), Monte Carlo importance sampling (IMP), importance sampling assisted by mode a posteriori (IMPMAP), stochastic approximation expectation-maximization (SAEM), and Markov chain Monte Carlo Bayesian (BAYES), on the simulated examples of a monoclonal antibody with target-mediated drug disposition (TMDD), demonstrates how optimization of the estimation options improves performance, and compares standard errors of Nonmem parameter estimates with those predicted by PFIM 3.2 optimal design software.
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