Evaluation of in vitro absorption, distribution, metabolism, and excretion and assessment of drug-drug interaction of rucaparib, an orally potent poly(ADP-ribose) polymerase inhibitor.

1. The absorption, distribution, metabolism, elimination, and drug-drug interaction (DDI) potential of the poly(ADP-ribose) polymerase (PARP) inhibitor rucaparib was characterised in vitro.2. Rucaparib showed moderate cellular permeability, moderate human plasma protein binding (70.2%), and slow metabolism in human liver microsomes (HLMs). In HLMs, cytochrome P450 (CYP) 1A2 and CYP3A contributed to the metabolism of rucaparib to its major metabolite M324 with estimated fractions of metabolism catalysed by CYP (fm,CYP) of 0.27 and 0.64, respectively. Rucaparib reversibly inhibited CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3As (IC50, 3.55, 12.9, 5.42, 41.6, and 17.2-22.9 µM [2 substrates], respectively), but not CYP2B6 or CYP2C8 (>190 µM). No time-dependent inhibition of any CYP was observed. In cultured human hepatocytes, rucaparib showed concentration-dependent induction of CYP1A2 mRNA and downregulation of CYP3A4 and CYP2B6 mRNA. In transfected cells expressing drug transporters, rucaparib was a substrate for P-gp and BCRP, but not for OATP1B1, OATP1B3, OAT1, OAT3, or OCT2. Rucaparib inhibited P-gp and BCRP (IC50, 169 and 55 µM, respectively) and slightly inhibited OATP1B1, OATP1B3, OAT1, and OAT3 (66%, 58%, 58%, and 42% inhibition, respectively) at 300 µM. Rucaparib inhibited OCT1, OCT2, MATE1, and MATE2-K (IC50, 4.3, 31, 0.63, and 0.19 µM, respectively).3. DDI risk assessment using static models suggested potential CYP-related DDIs, with rucaparib as a perpetrator. Caution is advised when co-administering rucaparib with sensitive substrates of MATEs, OCT1, and OCT2.

The Evans' Index revisited: New cut-off levels for use in radiological assessment of ventricular enlargement in the elderly.

BACKGROUND AND PURPOSE: Assessment of ventricular enlargement is subjective and based on the radiologist's experience. Linear indices, such as the Evans Index (EI), have been proposed as markers of ventricular volume with an EI≥0.3 indicating pathologic ventricular enlargement in any subject. However, normal range for EI measured on magnetic resonance imaging (MRI) scans are lacking in healthy elderly according to age and sex. We propose new age and sex specific cut-off values for ventricular enlargement in the elderly population. MATERIALS AND METHODS: 534 participants (53% women) aged 65-84 years; 226 patients with Alzheimer's disease (AD), and 308 healthy elderly controls (CTR) from the AddNeuroMed and ADNI studies were included. The cut-off for pathological ventricular enlargement was estimated from healthy elderly categorized into age groups of 5 years range and defined as EI 97,5 percentile (mean+2SD). Cut-off values were tested on patients with Alzheimer's disease and a small sample of patients with probable idiopathic normal pressure hydrocephalus (iNPH) to assess the sensitivity. RESULTS: The range of the EI in healthy elderly is wide and 29% of the CTR had an EI of 0.3 or greater. The EI increases with age in both CTR and AD, and the overall EI for women were lower than for men (p<0.001). New EI cut off values for male/female: 65-69 years 0.34/0.32, 70-74 years 0.36/0.33, 75-79 years 0.37/0.34 and 80-84 years 0.37/0.36. When applying the proposed cut-offs for EI in men and women aged 65-84, they differentiated between iNPH and CTR with a sensitivity of 80% and for different age and sex categories of AD and CTR with a sensitivity and specificity of 0-27% and 91-98%, respectively. CONCLUSION: The range of the EI measurements in healthy elderly is wide, and a cut-off value of 0.3 cannot be used to differentiate between normal and enlarged ventricles in individual cases. The proposed EI thresholds from the present study show good sensitivity for the iNPH diagnosis.

Preclinical Efficacy and Safety Assessment of an Antibody-Drug Conjugate Targeting the c-RET Proto-Oncogene for Breast Carcinoma.

PURPOSE: The RET proto-oncogene has been implicated in breast cancer, and the studies herein describe the preclinical and safety assessment of an anti-RET antibody-drug conjugate (ADC) being developed for the treatment of breast cancer. EXPERIMENTAL DESIGN: RET protein expression was analyzed in breast tumor samples using tissue microarrays. The fully human anti-RET antibody (Y078) was conjugated to the DM1 and DM4 derivatives of the potent cytotoxic agent maytansine using thioether and disulfide linkers, respectively. The resulting compounds, designated Y078-DM1 and Y078-DM4, were evaluated for antitumor activity using human breast cancer cell lines and established tumor xenograft models. A single-dose, 28-day, safety study of Y078-DM1 was performed in cynomolgus monkeys. RESULTS: By immunohistochemistry, RET expression was detected in 57% of tumors (1,596 of 2,800 tumor sections) and was most common in HER2-positive and basal breast cancer subtypes. Potent in vitro cytotoxicity was achieved in human breast cancer cell lines that have expression levels comparable with those observed in breast cancer tissue samples. Dose-response studies in xenograft models demonstrated antitumor activity with both weekly and every-3-weeks dosing regimens. In cynomolgus monkeys, a single injection of Y078-DM1 demonstrated dose-dependent, reversible drug-mediated alterations in blood chemistry with evidence of on-target neuropathy. CONCLUSIONS: RET is broadly expressed in breast cancer specimens and thus represents a potential therapeutic target; Y078-DM1 and Y078-DM4 demonstrated antitumor activity in preclinical models. Optimization of the dosing schedule or an alternate cytotoxic agent with a different mechanism of action may reduce the potential risk of neuropathy. Clin Cancer Res; 21(24); 5552-62. ©2015 AACR.

Two-tensor model-based bootstrapping on classified tensor morphologies: estimation of uncertainty in fiber orientation and probabilistic tractography.

In this manuscript, fast and clinically feasible model-based bootstrapping algorithms using a geometrically constrained two-tensor diffusion model are employed for estimating uncertainty in fiber orientation. A Monte-Carlo-based tensor morphology voxel classification algorithm is initially applied using single-tensor bootstrap samples before the use of a two-tensor model-based bootstrapping algorithm. Classification of tensor morphologies allows the tensor morphology to be considered when selecting the most appropriate bootstrap procedure. A constrained two-tensor model approach can greatly reduce data acquisition and computational times for whole bootstrap data volume generation compared to other multifiber model techniques, facilitating widespread clinical use. For comparison, we propose a new repetition-bootstrap algorithm based on classified voxels and the constrained two-tensor model. Tractography with these bootstrapping algorithms is also developed to estimate the connection probabilities between brain regions, especially regions with complex fiber configurations. Experimental results on synthetic data, a hardware phantom and human brain data demonstrate the superior performance of our algorithms compared to conventional approaches.

Quantitative and qualitative assessment of structural magnetic resonance imaging data in a two-center study.

BACKGROUND: Multi-center magnetic resonance imaging (MRI) studies present an opportunity to advance research by pooling data. However, brain measurements derived from MR-images are susceptible to differences in MR-sequence parameters. It is therefore necessary to determine whether there is an interaction between the sequence parameters and the effect of interest, and to minimise any such interaction by careful choice of acquisition parameters. As an exemplar of the issues involved in multi-center studies, we present data from a study in which we aimed to optimize a set of volumetric MRI-protocols to define a protocol giving data that are consistent and reproducible across two centers and over time. METHODS: Optimization was achieved based on data quality and quantitative measures, in our case using FreeSurfer and Voxel Based Morphometry approaches. Our approach consisted of a series of five comparisons. Firstly, a single-center dataset was collected, using a range of candidate pulse-sequences and parameters chosen on the basis of previous literature. Based on initial results, a number of minor changes were implemented to optimize the pulse-sequences, and a second single-center dataset was collected. FreeSurfer data quality measures were compared between datasets in order to determine the best performing sequence(s), which were taken forward to the next stage of testing. We subsequently acquired short-term and long-term two-center reproducibility data, and quantitative measures were again assessed to determine the protocol with the highest reproducibility across centers. Effects of a scanner software and hardware upgrade on the reproducibility of the protocols at one of the centers were also evaluated. RESULTS: Assessing the quality measures from the first two datasets allowed us to define artefact-free protocols, all with high image quality as assessed by FreeSurfer. Comparing the quantitative test and retest measures, we found high within-center reproducibility for all protocols, but lower between-center reproducibility for some protocols than others. The upgrade showed no important effects. CONCLUSIONS: We were able to determine (for the scanners used in this study) an optimised protocol, which gave the highest within- and between-center reproducibility of those assessed, and give details of this protocol here. More generally, we discuss some of the issues raised by multi-center studies and describe a methodical approach to take towards optimization and standardization, and recommend performing this kind of procedure to other investigators.

Brain network analysis: separating cost from topology using cost-integration.

A statistically principled way of conducting brain network analysis is still lacking. Comparison of different populations of brain networks is hard because topology is inherently dependent on wiring cost, where cost is defined as the number of edges in an unweighted graph. In this paper, we evaluate the benefits and limitations associated with using cost-integrated topological metrics. Our focus is on comparing populations of weighted undirected graphs that differ in mean association weight, using global efficiency. Our key result shows that integrating over cost is equivalent to controlling for any monotonic transformation of the weight set of a weighted graph. That is, when integrating over cost, we eliminate the differences in topology that may be due to a monotonic transformation of the weight set. Our result holds for any unweighted topological measure, and for any choice of distribution over cost levels. Cost-integration is therefore helpful in disentangling differences in cost from differences in topology. By contrast, we show that the use of the weighted version of a topological metric is generally not a valid approach to this problem. Indeed, we prove that, under weak conditions, the use of the weighted version of global efficiency is equivalent to simply comparing weighted costs. Thus, we recommend the reporting of (i) differences in weighted costs and (ii) differences in cost-integrated topological measures with respect to different distributions over the cost domain. We demonstrate the application of these techniques in a re-analysis of an fMRI working memory task. We also provide a Monte Carlo method for approximating cost-integrated topological measures. Finally, we discuss the limitations of integrating topology over cost, which may pose problems when some weights are zero, when multiplicities exist in the ranks of the weights, and when one expects subtle cost-dependent topological differences, which could be masked by cost-integration.

The AddNeuroMed framework for multi-centre MRI assessment of Alzheimer's disease: experience from the first 24 months.

OBJECTIVE: To describe the AddNeuroMed imaging framework for multi-centre magnetic resonance imaging (MRI) assessment of longitudinal changes in Alzheimer's disease and report on early results from the first 24 months of the project. METHODS: A multi-centre study similarly to a faux clinical trial has been established to assess longitudinal MRI changes in Alzheimer disease (AD), mild cognitive impairment (MCI) and healthy control subjects using an image acquisition protocol compatible with Alzheimer disease neuroimaging initiative (ADNI). Comprehensive quality control (QC) measures have been established throughout the study. An intelligent web-accessible database holds details on both the raw images and data processed using a sophisticated image analysis pipeline. RESULTS: A total of 378 subjects have been recruited (130 AD, 131 MCI, 117 healthy controls) of which a high percentage (97.3%) of the T1-weighted volumes passed the QC criteria. Measurements of normalized whole brain volume and whole brain cortical thickness showed significant differences between AD and controls, AD and MCI and MCI and controls. CONCLUSIONS: A framework for multi-centre MRI studies of Alzheimer's disease has been established consisting of a harmonized MRI acquisition protocol across centres, rigorous QC at both the sites and central data analysis hub and an automated image analysis pipeline. Early results demonstrate the high quality of the images acquired and the applicability of the automated image analysis techniques employed.