David Z. D’Argenio

Chikungunya Virus: In Vitro Response to Combination Therapy With Ribavirin and Interferon Alfa 2a.

INTRODUCTION We evaluated the antiviral activities of ribavirin (RBV) and interferon (IFN) alfa as monotherapy and combination therapy against chikungunya virus (CHIKV). METHODS Vero cells were infected with CHIKV in the presence of RBV and/or IFN alfa, and viral production was quantified by plaque assay. A mathematical model was fit to the data to identify drug interactions for effect. We ran simulations using the best-fit model parameters to predict the antiviral activity associated with clinically relevant regimens of RBV and IFN alfa as combination therapy. The model predictions were validated using the hollow fiber infection model (HFIM) system. RESULTS RBV and IFN alfa were effective against CHIKV as monotherapy at supraphysiological concentrations. However, RBV and IFN alfa were highly synergistic for antiviral effect when administered as combination therapy. Simulations with our mathematical model predicted that a standard clinical regimen of RBV plus IFN alfa would inhibit CHIKV burden by 2.5 log10 following 24 hours of treatment. In the HFIM system, RBV plus IFN alfa at clinical exposures resulted in a 2.1-log10 decrease in the CHIKV burden following 24 hours of therapy. These findings validate the prediction made by the mathematical model. CONCLUSIONS These studies illustrate the promise of RBV plus IFN alfa as a potential therapeutic strategy for the treatment of CHIKV infections.

Editorial: Special Issue on Multiscale Modeling and Analysis in Computational Biology and Medicine—Part-1

The 22 papers in this special issue demonstrate some of the most exciting developments in multiscale modeling and analysis in computational biology and medicine across all levels of time, scale, and organ systems.

A local effect of CYP24 inhibition on lung tumor xenograft exposure to 1,25-dihydroxyvitamin D3 is revealed using a novel LC–MS/MS assay

The vitamin D(3) catabolizing enzyme, CYP24, is frequently over-expressed in tumors, where it may support proliferation by eliminating the growth suppressive effects of 1,25-dihydroxyvitamin D(3) (1,25(OH)(2)D(3)). However, the impact of CYP24 expression in tumors or consequence of CYP24 inhibition on tumor levels of 1,25(OH)(2)D(3)in vivo has not been studied due to the lack of a suitable quantitative method. To address this need, an LC-MS/MS assay that permits absolute quantitation of 1,25(OH)(2)D(3) in plasma and tumor was developed. We applied this assay to the H292 lung tumor xenograft model: H292 cells eliminate 1,25(OH)(2)D(3) by a CYP24-dependent process in vitro, and 1,25(OH)(2)D(3) rapidly induces CYP24 expression in H292 cells in vivo. In tumor-bearing mice, plasma and tumor concentrations of 1,25(OH)(2)D(3) reached a maximum of 21.6 and 1.70ng/mL, respectively, following intraperitoneal dosing (20μg/kg 1,25(OH)(2)D(3)). When co-administered with the CYP24 selective inhibitor CTA091 (250μg/kg), 1,25(OH)(2)D(3) plasma levels increased 1.6-fold, and tumor levels increased 2.6-fold. The tumor/plasma ratio of 1,25(OH)(2)D(3) AUC was increased 1.7-fold by CTA091, suggesting that the inhibitor increased the tumor concentrations of 1,25(OH)(2)D(3) independent of its effects on plasma disposition. Compartmental modeling of 1,25(OH)(2)D(3) concentration versus time data confirmed that: 1,25(OH)(2)D(3) was eliminated from plasma and tumor; CTA091 reduced the elimination from both compartments; and that the effect of CTA091 on tumor exposure was greater than its effect on plasma. These results provide evidence that CYP24-expressing lung tumors eliminate 1,25(OH)(2)D(3) by a CYP24-dependent process in vivo and that CTA091 administration represents a feasible approach to increase tumor exposure to 1,25(OH)(2)D(3).

Feedback control indirect response models.

A general framework is introduced for modeling pharmacodynamic processes that are subject to autoregulation, which combines the indirect response (IDR) model approach with methods from classical feedback control of engineered systems. The canonical IDR models are modified to incorporate linear combinations of feedback control terms related to the time course of the difference (the error signal) between the pharmacodynamic response and its basal value. Following the well-established approach of traditional engineering control theory, the proposed feedback control indirect response models incorporate terms proportional to the error signal itself, the integral of the error signal, the derivative of the error signal or combinations thereof. Simulations are presented to illustrate the types of responses produced by the proposed feedback control indirect response model framework, and to illustrate comparisons with other PK/PD modeling approaches incorporating feedback. In addition, four examples from literature are used to illustrate the implementation and applicability of the proposed feedback control framework. The examples reflect each of the four mechanisms of drug action as modeled by each of the four canonical IDR models and include: selective serotonin reuptake inhibitors and extracellular serotonin; histamine H2-receptor antagonists and gastric acid; growth hormone secretagogues and circulating growth hormone; β2-selective adrenergic agonists and potassium. The proposed feedback control indirect response approach may serve as an exploratory modeling tool and may provide a bridge for development of more mechanistic systems pharmacology models.

Intracellular-signaling tumor-regression modeling of the pro-apoptotic receptor agonists dulanermin and conatumumab

Dulanermin (rhApo2L/TRAIL) and conatumumab bind to transmembrane death receptors and trigger the extrinsic cellular apoptotic pathway through a caspase-signaling cascade resulting in cell death. Tumor size time series data from rodent tumor xenograft (COLO205) studies following administration of either of these two pro-apoptotic receptor agonists (PARAs) were combined to develop a intracellular-signaling tumor-regression model that includes two levels of signaling: upstream signals unique to each compound (representing initiator caspases), and a common downstream apoptosis signal (representing executioner caspases) shared by the two agents. Pharmacokinetic (PK) models for each drug were developed based on plasma concentration data following intravenous and/or intraperitoneal administration of the compounds and were used in the subsequent intracellular-signaling tumor-regression modeling. A model relating the PK of the two PARAs to their respective and common downstream signals, and to the resulting tumor burden was developed using mouse xenograft tumor size measurements from 448 experiments that included a wide range of dose sizes and dosing schedules. Incorporation of a pro-survival signal—consistent with the hypothesis that PARAs may also result in the upregulation of pro-survival factors that can lead to a reduction in effectiveness of PARAs with treatment—resulted in improved predictions of tumor volume data, especially for data from the long-term dosing experiments.

FLT3 and CDK4/6 inhibitors: Signaling mechanisms and tumor burden in subcutaneous and orthotopic mouse models of acute myeloid leukemia

FLT3ITD subtype acute myeloid leukemia (AML) has a poor prognosis with currently available therapies. A number of small molecule inhibitors of FLT3 and/or CDK4/6 are currently under development. A more complete and quantitative understanding of the mechanisms of action of FLT3 and CDK4/6 inhibitors may better inform the development of current and future compounds that act on one or both of the molecular targets, and thus may lead to improved treatments for AML. In this study, we investigated in both subcutaneous and orthotopic AML mouse models, the mechanisms of action of three FLT3 and/or CDK4/6 inhibitors: AMG925 (Amgen), sorafenib (Bayer and Onyx), and quizartinib (Ambit Biosciences). A composite model was developed to integrate the plasma pharmacokinetics of these three compounds on their respective molecular targets, the coupling between the target pathways, as well as the resulting effects on tumor burden reduction in the subcutaneous xenograft model. A sequential modeling approach was used, wherein model structures and estimated parameters from upstream processes (e.g. PK, cellular signaling) were fixed for modeling subsequent downstream processes (cellular signaling, tumor burden). Pooled data analysis was employed for the plasma PK and cellular signaling modeling, while population modeling was applied to the tumor burden modeling. The resulting model allows the decomposition of the relative contributions of FLT3ITD and CDK4/6 inhibition on downstream signaling and tumor burden. In addition, the action of AMG925 on cellular signaling and tumor burden was further studied in an orthotopic tumor mouse model more closely representing the physiologically relevant environment for AML.

Editorial: TBME Letters Special Issue on Multiscale Modeling and Analysis in Computational Biology and Medicine—Part-2

Editorial of the second part of the TBME Letters Special Issue on Multiscale Modeling and Analysis in Computational Biology and Medicine

Affiliation between the American Society of Pharmacometrics and the Journal of Pharmacokinetics and Pharmacodynamics

In 1982, the Journal of Pharmacokinetics and Pharmacodynamics first gave voice to the discipline of pharmacometrics [1] by providing a forum for the scholarly contributions that were seminal to the early development of this new field of study. In the intervening years, pharmacometrics has expanded far beyond its initial focus on the analysis of measurements (metrics) in pharmacokinetics and biopharmaceutics to become a quantitative science that encompasses a spectrum of activities from basic research into disease and mechanisms of drug action through the rational use of medicines in patient care [2]. During this time, the Journal has continued to serve as the intellectual home for new advances in pharmacokinetics and pharmacodynamics, as well as in pharmacometrics. Because of this history, the American Society of Pharmacometrics (ASoP) and the Journal of Pharmacokinetics and Pharmacodynamics have inaugurated an affiliation designating the Journal as an official publication of ASoP. This strategic partnership with the Journal of Pharmacokinetics and Pharmacodynamics is representative of ASoP’s mission to promote the central role of pharmacometrics in advancing the discovery, development, and utilization of new and existing medicines for the treatment and prevention of disease. This role is characterized by three concepts: (i) quantitative integration of multi-source data and knowledge through the application of clinical, biomedical, biological, engineering, statistical, and mathematical concepts, resulting in (ii) continuous methodological and technological innovation, supporting new scientific understanding and knowledge, which in turn (iii) impacts research, discovery, development, decision making, approval, and utilization of medicines [2]. Together with the Editor-in-Chief of the Journal, Professor William Jusko, we are happy to announce that Dr. Peter Bonate will be ASoP’s designated Associate Editor to the Journal of Pharmacokinetics and Pharmacodynamics. Peter’s own scholarly contributions to the field of pharmacometrics together with his extensive industry experience provide him with a unique perspective on the innovations needed to advance the methodologies and impact of pharmacometrics. Under Peter’s collaborative leadership, the affiliation between ASoP and the Journal of Pharmacokinetics and Pharmacodynamics will accelerate the Society’s efforts to serve its membership and advance its vision for the discipline of pharmacometrics.

Pharmacokinetic Parameter Estimation with Stochastic Dynamic Models

The pharmacokinetic parameter estimation problem is reexamined within the framework of stochastic dynamic systems. Using this formalism, two sources of uncertainty are incorporated into the parameter estimation procedure: measurement error and process or model error. Consideration is given to linear dynamic models, with both model and measurement error terms modeled as Gaussian random processes. The maximum likelihood estimate of the parameters is obtained by using the Kaiman filter formulation of the model to compute the likelihood function which is then maximized by direct nonlinear optimization. This approach to maximum likelihood estimation, given process or model error as well as output error, is evaluated using several simulated pharmacokinetic parameter estimation problems.

Glucagon increases insulin levels by stimulating insulin secretion without effect on insulin clearance in mice

HighlightsGlucagon effects on insulin kinetics in mice were assessed using population modeling analysis approach.Glucagon was shown to enhance prehepatic insulin secretion, but there was no effect on insulin clearance in mice.Our findings therefore show that the increase in circulating insulin achieved by glucagon is due to increased insulin secretion without any further influence on insulin clearance. Abstract Circulating insulin is dependent on a balance between insulin appearance through secretion and insulin clearance. However, to what extent changes in insulin clearance contribute to the increased insulin levels after glucagon administration is not known. This study therefore assessed and quantified any potential effect of glucagon on insulin kinetics in mice. Prehepatic insulin secretion in mice was first estimated following glucose (0.35 g/kg i.v.) and following glucose plus glucagon (10 &mgr;g/kg i.v.) using deconvolution of plasma C‐peptide concentrations. Plasma concentrations of glucose, insulin, and glucagon were then measured simultaneously in individual mice following glucose alone or glucose plus glucagon (pre dose and at 1, 5, 10, 20 min post). Using the previously determined insulin secretion profiles and the insulin concentration‐time measurements, a population modeling analysis was applied to estimate the one‐compartment kinetics of insulin disposition with and without glucagon. Glucagon with glucose significantly enhanced prehepatic insulin secretion (Cmax and AUC0–20) compared to that with glucose alone (p < 0.0001). From the modeling analysis, the population mean and between‐animal SD of insulin clearance was 6.4 ± 0.34 mL/min for glucose alone and 5.8 ± 1.5 mL/min for glucagon plus glucose, with no significant effect of glucagon on mean insulin clearance. Therefore, we conclude that the enhancement of circulating insulin after glucagon administration is solely due to stimulated insulin secretion.