Revolutionizing pharmacokinetics: the dawn of AI-powered analysis.

This editorial explores how artificial intelligence (AI) is revolutionizing the science of pharmacokinetics (PK). It discusses the challenges of conventional PK analysis and how AI has transformed this area. It highlights the promise of artificial intelligence (AI) in predicting pharmacokinetic profiles from chemical structures and its application in several aspects of pharmacology, including dosage customization and drug interactions. Additionally, it emphasizes how important ethical issues and openness are to AI applications, especially when it comes to pharmacokinetic prediction and dataset adaptation. Future directions for AI in PK are discussed, with the creation of all-inclusive AI pharmacokinetics/pharmacometrics software being envisioned. Drug discovery and patient care could be transformed toward more individualized and effective healthcare solutions with the help of this software, which could handle tasks such as data cleaning, model selection, and regulatory report preparation. The editorial highlights the importance of AI in improving pharmaceutical sciences while urging caution and teamwork in navigating its possible uses in pharmacokinetics.

Robust automated constellation-based landmark detection in human brain imaging.

A robust fully automated algorithm for identifying an arbitrary number of landmark points in the human brain is described and validated. The proposed method combines statistical shape models with trained brain morphometric measures to estimate midbrain landmark positions reliably and accurately. Gross morphometric constraints provided by automatically identified eye centers and the center of the head mass are shown to provide robust initialization in the presence of large rotations in the initial head orientation. Detection of primary midbrain landmarks are used as the foundation from which extended detection of an arbitrary set of secondary landmarks in different brain regions by applying a linear model estimation and principle component analysis. This estimation model sequentially uses the knowledge of each additional detected landmark as an improved foundation for improved prediction of the next landmark location. The accuracy and robustness of the presented method was evaluated by comparing the automatically generated results to two manual raters on 30 identified landmark points extracted from each of 30 T1-weighted magnetic resonance images. For the landmarks with unambiguous anatomical definitions, the average discrepancy between the algorithm results and each human observer differed by less than 1 mm from the average inter-observer variability when the algorithm was evaluated on imaging data collected from the same site as the model building data. Similar results were obtained when the same model was applied to a set of heterogeneous image volumes from seven different collection sites representing 3 scanner manufacturers. This method is reliable for general application in large-scale multi-site studies that consist of a variety of imaging data with different orientations, spacings, origins, and field strengths.

Longitudinal diffusion changes in prodromal and early HD: Evidence of white-matter tract deterioration.

INTRODUCTION: Huntington's disease (HD) is a genetic neurodegenerative disorder that primarily affects striatal neurons. Striatal volume loss is present years before clinical diagnosis; however, white matter degradation may also occur prior to diagnosis. Diffusion-weighted imaging (DWI) can measure microstructural changes associated with degeneration that precede macrostructural changes. DWI derived measures enhance understanding of degeneration in prodromal HD (pre-HD). METHODS: As part of the PREDICT-HD study, N = 191 pre-HD individuals and 70 healthy controls underwent two or more (baseline and 1-5 year follow-up) DWI, with n = 649 total sessions. Images were processed using cutting-edge DWI analysis methods for large multicenter studies. Diffusion tensor imaging (DTI) metrics were computed in selected tracts connecting the primary motor, primary somato-sensory, and premotor areas of the cortex with the subcortical caudate and putamen. Pre-HD participants were divided into three CAG-Age Product (CAP) score groups reflecting clinical diagnosis probability (low, medium, or high probabilities). Baseline and longitudinal group differences were examined using linear mixed models. RESULTS: Cross-sectional and longitudinal differences in DTI measures were present in all three CAP groups compared with controls. The high CAP group was most affected. CONCLUSIONS: This is the largest longitudinal DWI study of pre-HD to date. Findings showed DTI differences, consistent with white matter degeneration, were present up to a decade before predicted HD diagnosis. Our findings indicate a unique role for disrupted connectivity between the premotor area and the putamen, which may be closely tied to the onset of motor symptoms in HD. Hum Brain Mapp 38:1460-1477, 2017. © 2017 Wiley Periodicals, Inc.

124I-Iodo-DPA-713 Positron Emission Tomography in a Hamster Model of SARS-CoV-2 Infection.

PURPOSE: Molecular imaging has provided unparalleled opportunities to monitor disease processes, although tools for evaluating infection remain limited. Coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is mediated by lung injury that we sought to model. Activated macrophages/phagocytes have an important role in lung injury, which is responsible for subsequent respiratory failure and death. We performed pulmonary PET/CT with 124I-iodo-DPA-713, a low-molecular-weight pyrazolopyrimidine ligand selectively trapped by activated macrophages cells, to evaluate the local immune response in a hamster model of SARS-CoV-2 infection. PROCEDURES: Pulmonary 124I-iodo-DPA-713 PET/CT was performed in SARS-CoV-2-infected golden Syrian hamsters. CT images were quantified using a custom-built lung segmentation tool. Studies with DPA-713-IRDye680LT and a fluorescent analog of DPA-713 as well as histopathology and flow cytometry were performed on post-mortem tissues. RESULTS: Infected hamsters were imaged at the peak of inflammatory lung disease (7 days post-infection). Quantitative CT analysis was successful for all scans and demonstrated worse pulmonary disease in male versus female animals (P < 0.01). Increased 124I-iodo-DPA-713 PET activity co-localized with the pneumonic lesions. Additionally, higher pulmonary 124I-iodo-DPA-713 PET activity was noted in male versus female hamsters (P = 0.02). DPA-713-IRDye680LT also localized to the pneumonic lesions. Flow cytometry demonstrated a higher percentage of myeloid and CD11b + cells (macrophages, phagocytes) in male versus female lung tissues (P = 0.02). CONCLUSION: 124I-Iodo-DPA-713 accumulates within pneumonic lesions in a hamster model of SARS-CoV-2 infection. As a novel molecular imaging tool, 124I-Iodo-DPA-713 PET could serve as a noninvasive, clinically translatable approach to monitor SARS-CoV-2-associated pulmonary inflammation and expedite the development of novel therapeutics for COVID-19.

Functional deficit in hippocampal activity during fear extinction recall in the single prolonged-stress model of PTSD in male rats.

To interrogate whether altered function of the hippocampal-mPFC circuit underlies the deficit in fear extinction recall in rats subjected to single-prolonged stress (SPS), changes in brain region-specific metabolic rate were measured in male rats (control and SPS treated). Brain region metabolic rates were quantified using uptake of 14C-2-deoxyglucose (14C-2DG) during fear memory formation, fear memory extinction and extinction recall. Control and SPS rats had similar regional brain activities at baseline. During extinction recall, 14C-2DG uptake decreased in hippocampal regions in control rats, but not in SPS rats. SPS rats also exhibited a significant deficiency in fear extinction recall, replicating a previously reported finding. Reduced hippocampal activity during fear extinction recall in control animals may reflect reduction in fear overgeneralization, thereby enabling discrimination between distinct contexts. In contrast, persistent levels of hippocampal activity in SPS-exposed male animals during fear extinction recall may reflect the dysfunctional persistence of fear overgeneralization. Future studies in females can test gender-specificity of these effects, with appropriate attention to luteal dependent effects on extinction of fear learning. Detailed knowledge of regional brain activities underlying stress-induced deficits in extinction recall may help identify therapeutic targets in PTSD.