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f Candida infections are a leading cause of infectious disease-related death in children supported by extracorporeal membrane oxygenation (ECMO). The ECMO circuit can alter drug pharmacokinetics (PK); thus, standard fluconazole dosing may result in suboptimal drug exposures. The objective of our study was to determine the PK of fluconazole in children on ECMO. Forty children with 367 PK samples were included in the analysis. The PK data were analyzed using nonlinear mixed-effect modeling (NONMEM). A one-compartment model best described the data. Weight was included in the base model for clearance ( Extracorporeal membrane oxygenation (ECMO) is life-saving in children with refractory cardiorespiratory failure. ECMO is a cardiopulmonary bypass device that provides complete respiratory and cardiac support. Mechanically, blood is drained from the venous system, pumped through an artificial lung membrane in which oxygen is added and carbon dioxide is removed, and then returned to either venous or arterial circulation. ECMO has been used successfully to support children with multiple disease processes, including meconium aspiration syndrome, fulminant myocarditis, and sepsis (1). Despite these successes, children supported by ECMO are at high risk for ECMO-related complications, especially nosocomial infections (2).Invasive candidiasis is common and fatal in children on ECMO. In this population, Candida species are the most common infectious organism (2). The incidence of infection varies by center, and rates as high as 10% have been reported (2, 3). Candida infections cause substantial morbidity and mortality (3) and are difficult to eradicate due to the ability of the organism to adhere to indwelling catheters. For this reason, routine management of candidiasis consists not only of the use of antifungal agents but also the removal of catheters (4). Catheter removal for children on ECMO is often impossible, because the ECMO cannulas connect the child to the ECMO circuit. Therefore, therapy on ECMO relies on either the prevention of invasive candidiasis or optimal therapeutic dosing in children with infection.Optimal dosing for prevention or treatment of candidiasis in children on ECMO can differ greatly from that with other populations due to the pharmacokinetic (PK) changes induced by the ECMO circuit. PK changes attributed to ECMO support include increased volume of distribution (V) and decreased clearance (CL), but these vary by drug and are not consistently predicted using drug physicochemical properties (5-8). This study describes the population PK of fluconazole in children supported by ECMO and provides rational dosing recommendations for the prevention and treatment of invasive candidiasis in this vulnerable population. MATERIALS AND METHODSStudy design. Fluconazole samples were obtained from three prospective trials. Study 1 was a single-center open-label PK study of fluconazole in children on ECMO (n ϭ 20) (9), study 2 was a single-center PK study of a fluconazole loading dose in critically ill children (n...

The i.p. delivery of murine monoclonal antibody was compared with i.v. delivery in normal mice and rats, in normal nude mice and in those with i.p. human ovarian carcinoma xenografts. In normal rats, all classes of antibodies and antibody fragments evaluated were cleared from the peritoneal cavity at comparable rates. The regional delivery (Rd1) advantage to the peritoneal cavity following i.p. delivery was thus most dependent on the rate of clearance of the antibody or fragment from the blood stream. Determining the exact i.p. delivery advantage was problematic due to the difficulty in reliably obtaining peritoneal fluid later than 9-10 h after i.p. injection in normal animals. During the first 9 h following i.p. injection, the Rd(0-9/0-9) was, for a murine IgG2ak Fab greater than F(ab')2 greater than IgG (at 13.6 greater than 10 greater than 7.9). Two murine IgMs evaluated differed in Rd(0-9) at 27.1 and 9.2 respectively. When blood levels were extrapolated to infinity, these Rd (0-9/affinity) values were considerably lower with the Fab having the highest Rd at 4.67. The i.p. Rd advantage was almost solely due to the i.p. antibody levels seen in the first 24 h after injection, as after that time, blood levels become comparable to those seen following i.v. injection. Normal tissues obtained at sacrifice 5-7 days after i.p. injection. Normal tissues obtained at sacrifice 5-7 days after i.p. or i.v. injection in rats showed comparable levels of radioantibody activity, whether the injection was i.p. or i.v. (except for higher diaphragmatic levels following i.p. delivery). In nude mice with i.p. human-derived ovarian tumors, intact IgG clearance from the peritoneal cavity to the blood was considerably slower than in normal animals, and early i.p. tumor uptake of specific antibody was significantly higher than that following i.v. antibody delivery. With higher early tumor uptake and lower systemic exposure, early tumor/nontumor ratios were significantly greater than those for i.v. delivery, though not beyond 48 h after i.p. injection. This study demonstrates the pharmacokinetic rationale for i.p. monoclonal antibody delivery, especially for agents cleared rapidly from the blood, such as antibody fragments. In addition, definite i.p. delivery benefit for antibody specific to i.p. tumors in the i.p. ovarian cancer system was shown soon after injection. These data regarding i.p. antibody delivery should be useful in rationally planning diagnostic and therapeutic studies involving the i.p. delivery of unmodified and immunoconjugated monoclonal antibodies.

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Mathematical biology and pharmacology models have a long and rich history in the fields of medicine and physiology, impacting our understanding of disease mechanisms and the development of novel therapeutics. With an increased focus on the pharmacology application of system models and the advances in data science spanning mechanistic and empirical approaches, there is a significant opportunity and promise to leverage these advancements to enhance the development and application of the systems pharmacology field. In this paper, we will review milestones in the evolution of mathematical biology and pharmacology models, highlight some of the gaps and challenges in developing and applying systems pharmacology models, and provide a vision for an integrated strategy that leverages advances in adjacent fields to overcome these challenges.