Acute coronary syndromes (ACS) remain life-threatening disorders that are associated with high morbidity and mortality. Dual-antiplatelet therapy with aspirin and clopidogrel has shown to reduce cardiovascular events in patients with ACS. However, there is substantial inter-individual variability in response to clopidogrel treatment in addition to prolonged recovery of platelet reactivity as a result of irreversible binding to P2Y12 receptors. This high inter-individual variability in treatment response has primarily been associated with genetic polymorphisms in the genes encoding for cytochrome (CYP) 2C19 that affect clopidogrel’s pharmacokinetics. While FDA has issued a boxed warning for CYP2C19 poor metabolizers due to a potentially reduced efficacy in these patients, results from multivariate analyses suggest that additional factors, including age, sex, obesity, concurrent diseases and drug-drug interactions, may all contribute to the overall between-subject variability in treatment response. However, the extent to which each of these factors contributes to the overall variability and how they are interrelated is currently unclear. The objective of this review article is to provide a comprehensive update on the different factors that influence clopidogrel’s pharmacokinetics and pharmacodynamics and how they mechanistically contribute to inter-individual differences in response to clopidogrel treatment.

AlignRT3C can be used as a nonionizing IGSPS with accuracy comparable to current image/marker-based systems. IGSPS and CBCT can be combined for high-precision positioning without the need for patient-attached localization devices.

The advent of readily available temporal imaging or time series volumetric (4D) imaging has become an indispensable component of treatment planning and adaptive radiotherapy (ART) at many radiotherapy centers. Deformable image registration (DIR) is also used in other areas of medical imaging, including motion corrected image reconstruction. Due to long computation time, clinical applications of DIR in radiation therapy and elsewhere have been limited and consequently relegated to offline analysis. With the recent advances in hardware and software, graphics processing unit (GPU) based computing is an emerging technology for general purpose computation, including DIR, and is suitable for highly parallelized computing. However, traditional general purpose computation on the GPU is limited because the constraints of the available programming platforms. As well, compared to CPU programming, the GPU currently has reduced dedicated processor memory, which can limit the useful working data set for parallelized processing. We present an implementation of the demons algorithm using the NVIDIA 8800 GTX GPU and the new CUDA programming language. The GPU performance will be compared with single threading and multithreading CPU implementations on an Intel dual core 2.4 GHz CPU using the C programming language. CUDA provides a C-like language programming interface, and allows for direct access to the highly parallel compute units in the GPU. Comparisons for volumetric clinical lung images acquired using 4DCT were carried out. Computation time for 100 iterations in the range of 1.8-13.5 s was observed for the GPU with image size ranging from 2.0 x 10(6) to 14.2 x 10(6) pixels. The GPU registration was 55-61 times faster than the CPU for the single threading implementation, and 34-39 times faster for the multithreading implementation. For CPU based computing, the computational time generally has a linear dependence on image size for medical imaging data. Computational efficiency is characterized in terms of time per megapixels per iteration (TPMI) with units of seconds per megapixels per iteration (or spmi). For the demons algorithm, our CPU implementation yielded largely invariant values of TPMI. The mean TPMIs were 0.527 spmi and 0.335 spmi for the single threading and multithreading cases, respectively, with <2% variation over the considered image data range. For GPU computing, we achieved TPMI =0.00916 spmi with 3.7% variation, indicating optimized memory handling under CUDA. The paradigm of GPU based real-time DIR opens up a host of clinical applications for medical imaging.

In the medical imaging field, we need fast deformable registration methods especially in intra-operative settings characterized by their time-critical applications. Image registration studies which are based on Graphics Processing Units (GPUs) provide fast implementations. However, only a small number of these GPU-based studies concentrate on deformable registration. We implemented Demons, a widely used deformable image registration algorithm, on NVIDIA's Quadro FX 5600 GPU with the Compute Unified Device Architecture (CUDA) programming environment. Using our code, we registered 3D CT lung images of patients. Our results show that we achieved the fastest runtime among the available GPU-based Demons implementations. Additionally, regardless of the given dataset size, we provided a factor of 55 speedup over an optimized CPU-based implementation. Hence, this study addresses the need for on-line deformable registration methods in intra-operative settings by providing the fastest and most scalable Demons implementation available to date. In addition, it provides an implementation of a deformable registration algorithm on a GPU, an understudied type of registration in the general-purpose computation on graphics processors (GPGPU) community.

Clopidogrel (Plavix®), is a widely used antiplatelet agent, which shows high inter-individual variability in treatment response in patients following the standard dosing regimen. In this study, a physiology-directed population pharmacokinetic/pharmacodynamic (PK/PD) model was developed based on clopidogrel and clopidogrel active metabolite (clop-AM) data from the PAPI and the PGXB2B studies using a step-wise approach in NONMEM (version 7.2). The developed model characterized the in vivo disposition of clopidogrel, its bioactivation into clop-AM in the liver and subsequent platelet aggregation inhibition in the systemic circulation reasonably well. It further allowed the identification of covariates that significantly impact clopidogrel’s dose–concentration–response relationship. In particular, CYP2C19 intermediate and poor metabolizers converted 26.2% and 39.5% less clopidogrel to clop-AM, respectively, compared to extensive metabolizers. In addition, CES1 G143E mutation carriers have a reduced CES1 activity (82.9%) compared to wild-type subjects, which results in a significant increase in clop-AM formation. An increase in BMI was found to significantly decrease clopidogrel’s bioactivation, whereas increased age was associated with increased platelet reactivity. Our PK/PD model analysis suggests that, in order to optimize clopidogrel dosing on a patient-by-patient basis, all of these factors have to be considered simultaneously, e.g. by using quantitative clinical pharmacology tools.