Solène Marie

Purpose: Cholesterol 24-hydroxylase (C24H) is central to brain cholesterol metabolism and is thought to be altered in many neurodegenerative diseases, including Alzheimer’s disease (AD). Only two fluorinated C24H-targeting PET radioligands, [¹⁸F]CHL-2205 and [¹⁸F]T008, have recently entered clinical use. Direct comparison is needed to guide radiotracer selection for future translational studies. Methods: Dynamic PET imaging was performed using either [¹⁸F]CHL-2205 or [¹⁸F]T008 in parallel conditions in cynomolgus macaques, healthy rats, the TgF344-AD rat model, and C24H-overexpressing mice. Arterial blood sampling enabled full kinetic modeling in healthy rats and nonhuman primates. An image-derived input function was validated in rats, and the specific binding was evaluated under C24H saturation using soticlestat. Results: [¹⁸F]CHL-2205 and [¹⁸F]T008 showed similar metabolism profiles in either macaques or rats. In macaques, brain uptake values ( V _T , Logan plot analysis) were 1.6-fold higher for [¹⁸F]CHL-2205 relative to [¹⁸F]T008. Healthy rats showed a similar pattern, with [¹⁸F]CHL-2205 exhibiting 1.5-fold higher brain V _T . In rats, soticlestat blockade revealed C24H-specific binding for both ligands, with an estimated binding potential (BPND) 1.3-fold higher for [¹⁸F]CHL-2205 compared to [¹⁸F]T008. In TgF344-AD rats, brain uptake increased by 1.9-fold with [¹⁸F]CHL-2205 versus 1.6-fold with [¹⁸F]T008 compared to wild-type controls. In C24H-overexpressing mice, [¹⁸F]CHL-2205 detected measurable increases in brain signal in several brain regions (up to 1.5-fold), while [¹⁸F]T008 did not in the same animals. Conclusion: Across species and models, both radioligands demonstrated appropriate characteristics for imaging C24H in vivo. However, [¹⁸F]CHL-2205 generally displayed higher specific binding and appeared more sensitive to detect moderate increases in C24H expression.

La distribution des médicaments du sang vers les tissus, où ils exercent leurs effets pharmacologiques et/ou toxiques, implique souvent des transporteurs qui régulent leur passage membranaire. Les transporteurs Organic Anion-Transporting Polypeptide (OATP), avec d’autres systèmes de transporteurs hépatocytaires, participent à l’élimination hépatique de nombreux médicaments. Certains OATPs ont été retrouvés dans d’autres organes où leurs répercussions pharmacocinétiques restent mal connues.L’objectif de ce travail a été de développer des approches d’imagerie originales permettant de mesurer sélectivement l’importance du transport OATP à l’interface sang-tissu. Tout d’abord, nous avons utilisé la [99mTc]mébrofénine, un radiopharmaceutique classiquement utilisé pour la scintigraphie hépatobiliaire. Chez le rat, nous avons validé un protocole d’inhibition pharmacologique ciblé, réalisable chez l’Homme, permettant de distinguer l’activité OATP sinusoïdale de l’activité d’excrétion biliaire, de manière non-invasive.Nous avons ensuite optimisé les méthodes d’analyse des images dynamiques TEP (tomographie par émission de positons) issues d’une nouvelle sonde substrat OATP, le [11C]glyburide, utilisée pour la première fois chez l’Homme. Une approche d’acquisition corps-entier dynamique nous a permis de quantifier l’activité OATP dans les tissus hépatiques et extra-hépatiques chez le primate puis chez l’Homme.L’imagerie translationnelle ouvre des perspectives d’études pharmacocinétiques originales, jusqu’alors inenvisageables chez l’Homme. Le développement de radiopharmaceutiques dédiés à la mesure de l’activité des transporteurs et l’optimisation de l’analyse des données d’imagerie qui en sont issues permettent d’en apprécier les répercussions fonctionnelles sur les étapes de distribution et d’élimination.

Background: Dynamic whole-body PET and total-body PET both supply large datasets that include multiple organs, opening the opportunity to study systems biology via appropriate analysis. Network analysis, commonly used with brain imaging, is applied here with whole-body PET to compare data from different tissues and subjects before and after precipitating a pharmacokinetic drug-drug interaction. This is done with [ ¹¹ C]glyburide PET, a radiotracer whose tissue distribution is mediated by organic anion-transporting polypeptides (OATP) transporter function. OATPs control the uptake of drugs, primarily into the liver. We examine three methods of network analysis to evaluate the effort, efficacy, and potential applications for further research use. This was performed with 22 dynamic [ ¹¹ C]glyburide whole-body PET scans of healthy humans. This includes 13 baseline scans, and 9 after infusion of rifampicin, a potent OATP inhibitor. All methods use Pearson correlation coefficient. The first method generates “ d- networks” using dynamic data from one region. The second method generates “ s- networks” using static data from multiple regions, but not all regions. The final method generates “Δ PCC networks” with static data from all regions. The first two methods compare subjects, whereas the third compares regions. Results: The d- network differentiates control and rifampicin subjects within the primary region of interest, the liver, but the differentiation is not complete, and there is still some correlation across subjects ( r>0.65, p . The s- network completely distinguishes trial groups but requires removing anomalous data from consideration ( r>0.65, p . Because d- and s- networks require selecting which regions are included, they are informed methods. In contrast, the Δ PCC networks are uninformed. This method utilises all data and still highlights significant regional variations between the two groups, such as in the liver, while remaining robust to confounding variation (| ΔPCC | >0.18, p . Conclusions: All three methods of network analysis with whole-body PET were successful at identifying key information in the dataset. Each method required different levels of processing and interpretation, making them applicable in different scenarios depending on whether the PET data is dynamic or static, if the kinetics in regions of interest are well understood, and whether the study is focused on comparing between treatment groups or regions.

Network analysis is a form of correlation analysis that is particularly useful in performing whole-body studies with positron emission tomography (PET), as it provides a way to quantify the similarities and differences in the data from a range of organs and patients. Network analysis was performed on dynamic whole-body PET with [11C]glyburide in humans, with and without infusion of rifampicin [1]. Glyburide is a substrate of several Organic Anion-Transporting Polypeptides (OATPs), which are proteins that control the uptake of drugs into the liver, although OATP expression in other organs has been reported. Rifampicin is a strong OATP inhibitor that has been shown to decrease the body’s ability to eliminate glyburide [1]. Time-activity curves (TACs) in kBq/mL from several organs of interest were used to perform network analysis with the Pearson correlation coefficient employed as the linear correlation measure. In the liver, network analysis was able to differentiate the control and rifampicin-treated groups (Fig. 1A). In this network, each node represents an average TAC from the liver of one patient (Fig. 1C), and the edges between nodes represent significant correlations. The edges are colour-coded to the strength of the Pearson value, showing stronger, more dense correlations within the control group. Additionally, network analysis was performed on static PET data (Fig. 1B), where each node represents a patient curve comprised of the average activity in each organ at equilibrium (Fig. 1D). With the static data, network analysis was able to separate the control and treatment groups into two components. This is highly dependent on the liver though and removing it from the curves leads to reduced differentiation. Overall, network analysis is sensitive to the differing uptake of [11C]glyburide throughout the body under normal and rifampicin-treated conditions. It provides confirmation that the control and treatment groups differ largely through the uptake in the liver, which is the predominant site of OATP function. Please click on the 'PDF' for the full abstract!

Tissue drug concentrations determine the efficacy and toxicity of drugs. When a drug is the substrate of transporters that are present at the blood:tissue barrier, the steady‐state unbound tissue drug concentrations cannot be predicted from their corresponding plasma concentrations. To accurately predict transporter‐modulated tissue drug concentrations, all clearances (CLs) mediating the drug's entry and exit (including metabolism) from the tissue must be accurately predicted. Because primary cells of most tissues are not available, we have proposed an alternative approach to predict such CLs, that is the use of transporter‐expressing cells/vesicles (TECs/TEVs) and relative expression factor (REF). The REF represents the abundance of the relevant transporters in the tissue vs. in the TECs/TEVs. Here, we determined the transporter‐based intrinsic CL of glyburide (GLB) and pitavastatin (PTV) in OATP1B1, OATP1B3, OATP2B1, and NTCP‐expressing cells and MRP3‐, BCRP‐, P‐gp‐, and MRP2‐expressing vesicles and scaled these CLs to in vivo using REF. These predictions fell within a priori set twofold range of the hepatobiliary CLs of GLB and PTV, estimated from their hepatic positron emission tomography imaging data: 272.3 and 607.8 mL/min for in vivo hepatic sinusoidal uptake CL, 47.8 and 17.4 mL/min for sinusoidal efflux CL, and 0 and 4.20 mL/min for biliary efflux CL, respectively. Moreover, their predicted hepatic concentrations (area under the hepatic concentration‐time curve (AUC) and maximum plasma concentration (C_max)), fell within twofold of their mean observed data. These data, together with our previous findings, confirm that the REF approach can successfully predict transporter‐based drug CLs and tissue concentrations to enhance success in drug development.