Leveraging Mathematical Modeling to Quantify Pharmacokinetic and Pharmacodynamic Pathways: Equivalent Dose Metric

Treatment response assays are often summarized by sigmoidal functions comparing cell survival at a single timepoint to applied drug concentration. This approach has a limited biophysical basis, thereby reducing the biological insight gained from such analysis. In particular, drug pharmacokinetic and pharmacodynamic (PK/PD) properties are overlooked in developing treatment response assays, and the accompanying summary statistics conflate these processes. Here, we utilize mathematical modeling to decouple and quantify PK/PD pathways. We experimentally modulate specific pathways with small molecule inhibitors and filter the results with mechanistic mathematical models to obtain quantitative measures of those pathways. Specifically, we investigate the response of cells to time-varying doxorubicin treatments, modulating doxorubicin pharmacology with small molecules that inhibit doxorubicin efflux from cells and DNA repair pathways. We highlight the practical utility of this approach through proposal of the “equivalent dose metric.” This metric, derived from a mechanistic PK/PD model, provides a biophysically-based measure of drug effect. We define equivalent dose as the functional concentration of drug that is bound to the nucleus following therapy. This metric can be used to quantify drivers of treatment response and potentially guide dosing of combination therapies. We leverage the equivalent dose metric to quantify the specific intracellular effects of these small molecule inhibitors using population-scale measurements, and to compare treatment response in cell lines differing in expression of drug efflux pumps. More generally, this approach can be leveraged to quantify the effects of various pharmaceutical and biologic perturbations on treatment response

Emerging Techniques in Breast MRI

As indicated throughout this chapter, there is a constant effort to move to more sensitive, specific, and quantitative methods for characterizing breast tissue via magnetic resonance imaging (MRI). In the present chapter, we focus on six emerging techniques that seek to quantitatively interrogate the physiological and biochemical properties of the breast. At the physiological scale, we present an overview of ultrafast dynamic contrast-enhanced MRI and magnetic resonance elastography which provide remarkable insights into the vascular and mechanical properties of tissue, respectively. Moving to the biochemical scale, magnetization transfer, chemical exchange saturation transfer, and spectroscopy (both “conventional” and hyperpolarized) methods all provide unique, noninvasive, insights into tumor metabolism. Given the breadth and depth of information that can be obtained in a single MRI session, methods of data synthesis and interpretation must also be developed. Thus, we conclude the chapter with an introduction to two very different, though complementary, methods of data analysis: (1) radiomics and habitat imaging, and (2) mechanism-based mathematical modeling

On the Effect of DCE MRI Slice Thickness and Noise on Estimated Pharmacokinetic Biomarkers – A Simulation Study

Simulation of a dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) multiple sclerosis brain dataset is described. The simulated images in the implemented version have 1×1×1mm3 voxel resolution and arbitrary temporal resolution. Addition of noise and simulation of thick-slice imaging is also possible. Contrast agent (Gd-DTPA) passage through tissues is modelled using the extended Tofts-Kety model. Image intensities are calculated using signal equations of the spoiled gradient echo sequence that is typically used for DCE imaging. We then use the simulated DCE images to study the impact of slice thickness and noise on the estimation of both semi- and fully-quantitative pharmacokinetic features. We show that high spatial resolution images allow significantly more accurate modelling than interpolated low resolution DCE images.acceptedVersio

Evaluation of Microbubbles as Contrast Agents for Ultrasonography and Magnetic Resonance Imaging

Background: Microbubbles (MBs) can serve as an ultrasound contrast agent, and has the potential for magnetic resonance imaging (MRI). Due to the relatively low effect of MBs on MRI, it is necessary to develop new MBs that are more suitable for MRI. In this study, we evaluate the properties of SonoVueH and custom-made Fe 3O 4-nanoparticle-embedded microbubbles (Fe3O4-MBs) in terms of contrast agents for ultrsonography (US) and MRI. Methodology/Principal Findings: A total of 20 HepG2 subcutaneous-tumor-bearing nude mice were randomly assigned to 2 groups (i.e., n = 10 mice each group), one for US test and the other for MRI test. Within each group, two tests were performed for each mouse. The contrast agent for the first test is SonoVueH, and the second is Fe 3O 4-MBs. US was performed using a Technos MPX US system (Esaote, Italy) with a contrast-tuned imaging (CnTI TM) mode. MRI was performed using a 7.0T Micro-MRI (PharmaScan, Bruker Biospin GmbH, Germany) with an EPI-T2 * sequence. The data of signal-to-noise ratio (SNR) from the region-of-interest of each US and MR image was calculated by ImageJ (National Institute of Health, USA). In group 1, enhancement of SonoVueH was significantly higher than Fe 3O 4-MBs on US (P,0.001). In group 2, negative enhancement of Fe3O4-MBs was significantly higher than SonoVueH on MRI (P,0.001). The time to peak showed no significant differences between US and MRI, both of which used the same MBs (P.0.05). The SNR analysis of the enhancement process reveals a strong negative correlation in both cases (i.e., SonoVueH r=20.733, Fe 3O 4-MBs r = 20.903