We invite you to read a selection of highly interesting articles recently published in the journal.
Garnett C et al. Scientific white paper on concentration-QT modeling. JOPA 45; 383-397, 2018; and correction 45; 399.
With the release of the Q & A document from ICH E14 Committee, concentration-QT analyses may be used in lieu of conducting thorough QT studies. This white paper, which was written through a collaboration of FDA and industry modelers, dicsusses recommendations for how these analyses should be conducted. This article was from a Themed Issue on Recent Advances in Cardiovascular PKPD Modeling and Simulation.
Bloomingdale P et al. Boolean network modeling in systems pharmacology. JOPA 45; 159-180; 2018.
Modeling complex systems using a series of ordinary differential equations is often difficult because of the need for many model parameters, some of which may be undefined. Boolean network models represent a new class of qualitative models that are capable of describing complex systems that do not require knowledge of every model parameter in the model. Bloomingdale et al. present a review of Boolean networks and present examples of such networks. This article was from a Themed Issue on Mathematical Pharmacology.
Dosne A-G et al., An automated sampling importance resampling procedure for estimating parameter uncertainty. JOPA 44; 509-520, 2017.
There are different methods to assess parameter uncertainity in nonlinear mixed effect models, each of which may lead to different estimates. The adequacy of these different methods are rarely assessed. Building on an earlier publication in JPKD (Dosne et al., JOPA 43; 597-608; 2017), Dosne et al. present an automated method to assess parameter uncertainty, which they call the sampling importance resampling method, and illustrate its use with 25 real-life datasets. SIR was about 10-times faster than bootstrapping with similar confidence interval width. This manuscript recently won the International Society of Pharmacometrics (ISoP) 2018 Technical Manuscript Award.
He H and Cao Y. Chemotherapeutic dosing implicated by pharmacodynamic modeling of in vitro cytotoxic data: a case study of paclitaxel. JOPA 44; 491-501, 2017.
Recently, oncologists are proposing that alternate high and low doses of chemotherapy may improve the risk-benefit profile of these toxic drugs. Using in vitro data, He and Cao modeled tumor cells as susceptible to either concentration- or time-dependent killing. Their model provided a high degree of modeling flexibility and they demonstrated its value using paclitaxel as the probe drug. They showed that there are three types of cancers that pulsed high and low dosing may be useful and that it was the ratio of steady-state concentration (Css) to cytotoxicy sensitivity (KC50) that was the critical factor in deciding whether a maximum tolerated dose or pulsed dose should be used. If Css/KC50 was > 1, then pulsed dosing was preferred.
Sadiq MW et al. A whole-body physiologically-based pharmacokinetic model (WB-PBPK) of ciprofloxacin: a step towards predicting bacterial kiling at sites of infection. JOPA 44; 69-79, 2017.
A nonlinear mixed effect PBPK model for ciprofloxacin was developed in 102 patients in an intensive care unit for different infections. Frequentist priors were used to facilitate convergence. Attached to this was a pharmacodynamic model for bacterial killing by the immune system, which included bacterial killing by neutrophils. Based on the model, unbound drug concentrations could be estimated in different tissues, of which kidney and lung were the highest, which is in line with ciprofloxacin’s use in the treatment of pneumonia and urinary tract infections. Their model could easily be extended to other drugs and point to indications most likely to have clinical benefit.