Model-based Drug Development

Whole body physiologically-based pharmacokinetic models: their use in clinical drug development

BACKGROUND

Whole-body physiologically-based pharmacokinetic (WB-PBPK) models mathematically describe an organism as a closed circulatory system consisting of compartments that represent the organs important for compound absorption, distribution, metabolism and elimination.

OBJECTIVES

To review the current state of WB-PBPK model use in the clinical phases of drug development.

METHODS

A qualitative description of the WB-PBPK model structure is included along with a review of the varying methods available for input parameterisation. Current and potential WB-PBPK model application in clinical development is discussed.

CONCLUSIONS

This modelling tool is at present used for small and large molecule drug development primarily as a means to scale pharmacokinetics from animals to humans based on physiology. The pharmaceutical industry is active in employing these models to clinical drug development although the applications in use now are narrow in comparison to the potential. Expanded integration of WB-PBPK models into the drug development process will only be achieved with staff training, managerial will, success stories and regulatory agency openness.

Permeability testing of biomaterial membranes

The permeability characteristics of biomaterials are critical parameters for a variety of implants. To analyse the permeability of membranes made from crosslinked ultrathin gelatin membranes and the transmigration of cells across the membranes, we combined three technical approaches: (1) a two-chamber-based permeability assay, (2) cell culturing with cytochemical analysis and (3) biochemical enzyme electrophoresis (zymography). Based on the diffusion of a coloured marker molecule in conjunction with photometric quantification, permeability data for a gelatin membrane were determined in the presence or absence of gelatin degrading fibroblasts. Cytochemical evaluation after cryosectioning of the membranes was used to ascertain whether fibroblasts had infiltrated the membrane inside. Zymography was used to investigate the potential release of proteases from fibroblasts, which are known to degrade collagen derivatives such as gelatin. Our data show that the diffusion equilibrium of a low molecular weight dye across the selected gelatin membrane is approached after about 6-8 h. Fibroblasts increase the permeability due to cavity formation in the membrane inside without penetrating the membrane for an extended time period (>21 days in vitro). Zymography indicates that cavity formation is most likely due to the secretion of matrix metalloproteinases. In summary, the combination of the depicted methods promises to facilitate a more rational development of biomaterials, because it provides a rapid means of determining permeability characteristics and bridges the gap between descriptive methodology and the mechanistic understanding of permeability alterations due to biological degradation.

Use of quantitative pharmacology in the development of HAE1, a high-affinity anti-IgE monoclonal antibody

HAE1, a high-affinity anti-IgE monoclonal antibody, is discussed here as a case study in the use of quantitative pharmacology in the development of a second-generation molecule. In vitro, preclinical, and clinical data from the first-generation molecule, omalizumab, were heavily leveraged in the HAE1 program. A preliminary mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model for HAE1 was developed using an existing model for omalizumab, together with in vitro binding data for HAE1 and omalizumab. When phase I data were available, the model was refined by simultaneously modeling PK/PD data from omalizumab studies with the available HAE1 phase I data. The HAE1 clinical program was based on knowledge of the quantitative relationship between a pharmacodynamic biomarker, suppression of free IgE, and clinical response (e.g., lower exacerbation rates) obtained in pivotal studies with omalizumab. A clinical trial simulation platform was developed to predict free IgE levels and clinical responses following attainment of a target free IgE level (</=10 IU/ml). The simulation platform enabled selection of four doses for the phase II dose-ranging trial by two independent methods: dose-response non-linear fitting and linear mixed modeling. Agreement between the two methods provided confidence in the doses selected. Modeling and simulation played a large role in supporting acceleration of the HAE1 program by enabling data-driven decision-making, often based on confirmation of projections and/or learning from incoming new data.

Modeling corticosteroid effects in a rat model of rheumatoid arthritis I: mechanistic disease progression model for the time course of collagen-induced arthritis in Lewis rats

A mechanism-based model was developed to describe the time course of arthritis progression in the rat. Arthritis was induced in male Lewis rats with type II porcine collagen into the base of the tail. Disease progression was monitored by paw swelling, bone mineral density (BMD), body weights, plasma corticosterone (CST) concentrations, and tumor necrosis factor (TNF)-alpha, interleukin (IL)-1beta, IL-6, and glucocorticoid receptor (GR) mRNA expression in paw tissue. Bone mineral density was determined by PIXImus II dual energy X-ray densitometry. Plasma CST was assayed by high-performance liquid chromatography. Cytokine and GR mRNA were determined by quantitative real-time polymerase chain reaction. Disease progression models were constructed from transduction and indirect response models and applied using S-ADAPT software. A delay in the onset of increased paw TNF-alpha and IL-6 mRNA concentrations was successfully characterized by simple transduction. This rise was closely followed by an up-regulation of GR mRNA and CST concentrations. Paw swelling and body weight responses peaked approximately 21 days after induction, whereas bone mineral density changes were greatest at 23 days after induction. After peak response, the time course in IL-1beta, IL-6 mRNA, and paw edema slowly declined toward a disease steady state. Model parameters indicate TNF-alpha and IL-1beta mRNA most significantly induce paw edema, whereas IL-6 mRNA exerted the most influence on BMD. The model for bone mineral density captures rates of turnover of cancellous and cortical bone and the fraction of each in the different regions analyzed. This small systems model integrates and quantitates multiple factors contributing to arthritis in rats.

Leveraging prior quantitative knowledge to guide drug development decisions and regulatory science recommendations: impact of FDA pharmacometrics during 2004-2006

The End-of-Phase 2A meetings are proposed to identify opportunities to make innovative medical products available sooner and to increase the quality of drug applications through early meetings between sponsors and the FDA. This article summarizes the overall experience across 11 pilot End-of-Phase 2A meetings since 2004. Four case studies are presented in more detail to demonstrate the various issues and methods encountered at these meetings. Overall, industry and FDA scientists ranked these meetings to be "very helpful" (average score of 4 on a scale of 1 to 5). In almost all the instances the sponsors changed their drug development plans subsequent to these extensive quantitative analyses-based meetings. A draft Guidance is being developed to be issued in 2008, and we hope this initiative will be resourced by then.

Pharmacokinetic/pharmacodynamic modeling of renin biomarkers in subjects treated with the renin inhibitor aliskiren

A semimechanistic pharmacokinetic/pharmacodynamic (PK/PD) model was developed to evaluate the effects of aliskiren on the renin-angiotensin system (RAS) in humans. Mean data were extracted from a three-way crossover, placebo-controlled study. Outcome measures included the time-course of plasma renin activity (PRA) and plasma concentrations of aliskiren, active renin (AR), angiotensin I (ANG I), and angiotensin II (ANG II). The disposition of aliskiren may be best described as a two-compartment model with nonlinear elimination and distribution. The four biomarkers of RAS inhibition were co-modeled, and the AR showed a dose-dependent increase after the administration of aliskiren. This effect was described in terms of an indirect stimulatory response model in conjunction with an empirical submodel of functional adaptation. The estimated concentration of aliskiren necessary for producing 50% inhibition of PRA is 0.66 ng/ml, which is similar to in vitro estimates (0.33 ng/ml) after correction for plasma protein binding. The final and reduced models test the current hypothesis that RAS is inhibited by direct renin antagonism, and also provide suitable platforms for future clinical study design and analysis.

Extensions to the visual predictive check to facilitate model performance evaluation

The Visual Predictive Check (VPC) is a valuable and supportive instrument for evaluating model performance. However in its most commonly applied form, the method largely depends on a subjective comparison of the distribution of the simulated data with the observed data, without explicitly quantifying and relating the information in both. In recent adaptations to the VPC this drawback is taken into consideration by presenting the observed and predicted data as percentiles. In addition, in some of these adaptations the uncertainty in the predictions is represented visually. However, it is not assessed whether the expected random distribution of the observations around the predicted median trend is realised in relation to the number of observations. Moreover the influence of and the information residing in missing data at each time point is not taken into consideration. Therefore, in this investigation the VPC is extended with two methods to support a less subjective and thereby more adequate evaluation of model performance: (i) the Quantified Visual Predictive Check (QVPC) and (ii) the Bootstrap Visual Predictive Check (BVPC). The QVPC presents the distribution of the observations as a percentage, thus regardless the density of the data, above and below the predicted median at each time point, while also visualising the percentage of unavailable data. The BVPC weighs the predicted median against the 5th, 50th and 95th percentiles resulting from a bootstrap of the observed data median at each time point, while accounting for the number and the theoretical position of unavailable data. The proposed extensions to the VPC are illustrated by a pharmacokinetic simulation example and applied to a pharmacodynamic disease progression example.