Organic anion transporting polypeptide 2B1 (OATP2B1) is a widely expressed membrane transporter with diverse substrate specificity. In vitro and clinical studies suggest a role for intestinal OATP2B1 in the oral absorption of medications. Moreover, OATP2B1 is highly expressed in hepatocytes where it is thought to promote liver drug clearance. However, until now, a shortcoming of studies implicating OATP2B1 in drug disposition has been a lack of in vivo models. Here, we report the development of a knockout (KO) mouse model with targeted, global disruption of the Slco2b1 gene to examine the disposition of two confirmed mOATP2B1 substrates, namely, fexofenadine and rosuvastatin. The plasma pharmacokinetics of intravenously administered fexofenadine was not different between KO and wildtype (WT) mice. However, after oral fexofenadine administration, KO mice had 70% and 41% lower maximal plasma concentration (C max) and area under the plasma concentration-time curve (AUC 0-last) than WT mice, respectively. In WT mice, coadministration of fexofenadine with grapefruit juice (GFJ) or apple juice (AJ) was associated with reduced C max by 80% and 88%, respectively, while the AUC 0-last values were lower by 35% and 70%, respectively. In KO mice, AJ coadministration reduced oral fexofenadine C max and AUC 0-last values by 67% and 59%, respectively, while GFJ had no effects. Intravenous and oral rosuvastatin pharmacokinetics were similar among WT and KO mice. We conclude that intestinal OATP2B1 is a determinant of oral fexofenadine absorption, as well as a target for fruit juice interactions. OATP2B1 does not significantly influence rosuvastatin disposition in mice. SIGNIFICANCE STATEMENT A novel mouse model with targeted disruption of the Slco2b1 gene revealed that OATP2B1 is a determinant of oral absorption but not systemic disposition of fexofenadine, as well as a target of fruit juice interactions. Rosuvastatin oral and intravenous pharmacokinetics were not dependent on OATP2B1. These findings support the utility of the Slco2b1 KO mouse model for defining mechanisms of drug disposition at the intersection of in vitro and clinical pharmacology.

Like neocortical structures, the archicortical hippocampus differs in its folding patterns across individuals. Here, we present an automated and robust BIDS-App, HippUnfold, for defining and indexing individual-specific hippocampal folding in MRI, analogous to popular tools used in neocortical reconstruction. Such tailoring is critical for inter-individual alignment, with topology serving as the basis for homology. This topological framework enables qualitatively new analyses of morphological and laminar structure in the hippocampus or its subfields. It is critical for refining current neuroimaging analyses at a meso- as well as micro-scale. HippUnfold uses state-of-the-art deep learning combined with previously developed topological constraints to generate uniquely folded surfaces to fit a given subject's hippocampal conformation. It is designed to work with commonly employed sub-millimetric MRI acquisitions, with possible extension to microscopic resolution. In this paper we describe the power of HippUnfold in feature extraction, and highlight its unique value compared to several extant hippocampal subfield analysis methods.

The archicortical hippocampus differs, like the neocortex, in its folding patterns between individuals. Here, we present an automated and robust BIDS-App, HippUnfold, for defining and indexing subject-specific hippocampal folding in MRI, analogous to popular tools used in neocortical reconstruction. This is critical for inter-individual alignment, with topology as the basis for homology. This topological framework enables qualitatively new analyses of morphological and laminar structure in the hippocampus or hippocampal subfields, and is critical for the advancement of neuroimaging analyses at a meso- or micro-scale. HippUnfold uses state-of-the-art deep learning combined with previously developed topological constraints on hippocampal tissue. It is designed to work with commonly employed sub-millimetric MRI acquisitions, with extensibility to microscopic resolutions as well. In this paper we illustrate the power of HippUnfold in feature extraction, and its construct validity compared to several extant hippocampal subfield analysis methods.

We present a comprehensive study on the non-invasive measurement of hippocampal perfusion. Using high-resolution 7 Tesla arterial spin labelling data, we generated robust perfusion maps and observed significant variations in perfusion among hippocampal subfields, with CA1 exhibiting the lowest perfusion levels. Notably, these perfusion differences were robust and detectable even within five minutes and just fifty perfusion-weighted images per subject. To understand the underlying factors, we examined the influence of image quality metrics, various tissue microstructure and morphometry properties, macrovasculature and cytoarchitecture. We observed higher perfusion in regions located closer to arteries, demonstrating the influence of vascular proximity on hippocampal perfusion. Moreover,ex vivocytoarchitectonic features based on neuronal density differences appeared to correlate stronger with hippocampal perfusion than morphometric measures like gray matter thickness. These findings emphasize the interplay between microvasculature, macrovasculature, and metabolic demand in shaping hippocampal perfusion. Our study expands the current understanding of hippocampal physiology and its relevance to neurological disorders. By providingin vivoevidence of perfusion differences between hippocampal subfields, our findings have implications for diagnosis and potential therapeutic interventions. In conclusion, our study provides a valuable resource for extensively characterising hippocampal perfusion.

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