Imagine trying to find a specific plant species in a dense forest. Now imagine trying to detect tiny aggregates in the human brain without invasive methods. This is the challenge researchers face when it comes to detecting α-synuclein aggregates, which are associated with neurodegenerative diseases. However, a recent study has made progress by developing novel radiotracers that can potentially visualize α-synuclein in the living brain. These radiotracers are designed with a special methylenedioxy group that improves their binding affinity to α-synuclein. Using computational modeling and machine learning, researchers predicted the binding sites and analyzed the structure-properties of these radiotracers. As a result, they identified four radiotracers with high binding affinity to α-synuclein. These radiotracers were labeled with fluorine-18 ([18F]) and successfully evaluated for their selectivity and binding affinity. In biodistribution experiments conducted with mice, two of these radiotracers showed promising brain pharmacokinetics and stability. With further validation, one particular radiotracer ([18F]d8) may become a powerful tool for visualizing α-synuclein in preclinical and clinical settings. These findings bring us closer to understanding and diagnosing neurodegenerative diseases associated with α-synuclein aggregates. For more details on this exciting research, check out the full article!

The accumulation of α-synuclein aggregates (α-syn) in the human brain is an occurrence common to all α-synucleinopathies. Non-invasive detection of these aggregates in a living brain with a target-specific radiotracer is not yet possible. We have recently discovered that the inclusion of a methylenedioxy group in the structure of diarylbisthiazole (DABTA)-based tracers improves binding affinity and selectivity to α-syn. Subsequently, complementary in silico modeling and machine learning (ML) of tracer–protein interactions were employed to predict surface sites and structure–property relations for the binding of the ligands. Based on this observation, we developed a small focused library of DABTAs from which 4-(benzo[d][1,3]dioxol-5-yl)-4′-(3-[18F]fluoro-4-methoxyphenyl)-2,2′-bithiazole [18F]d2, 6-(4′-(3-[18F]fluoro-4-methoxyphenyl)-[2,2′-bithiazol]-4-yl)-[1,3]dioxolo[4,5-b]pyridine [18F]d4, 4-(benzo [d][1,3]dioxol-5-yl)-4′-(6-[18F]fluoropyridin-3-yl)-2,2′-bithiazole [18F]d6, and 6-(4′-(6-[18F]fluoropyridin-3-yl)-[2,2′-bithiazol]-4-yl)-[1,3]dioxolo[4,5-b]pyridine [18F]d8 were selected based on their high binding affinity to α-syn and were further evaluated. Binding assay experiments carried out with the non-radioactive versions of the above tracers d2, d4, d6, and d8 showed high binding affinity of the ligands to α-syn: 1.22, 0.66, 1.21, and 0.10 nM, respectively, as well as excellent selectivity over β-amyloid plaques (Aβ) and microtubular tau aggregates (>200-fold selectivity). To obtain the tracers, their precursors were radiolabeled either via an innovative ruthenium-mediated (SNAr) reaction ([18F]d2 and [18F]d4) or typical SNAr reaction ([18F]d6 and [18F]d8) with moderate-to-high radiochemical yields (13% – 40%), and high molar activity > 60 GBq/μmol. Biodistribution experiments carried out with the tracers in healthy mice revealed that [18F]d2 and [18F]d4 showed suboptimal brain pharmacokinetics: 1.58 and 4.63 %ID/g at 5 min post-injection (p.i.), and 1.93 and 3.86 %ID/g at 60 min p.i., respectively. However, [18F]d6 and [18F]d8 showed improved brain pharmacokinetics: 5.79 and 5.13 %ID/g at 5 min p.i.; 1.75 and 1.07 %ID/g at 60 min p.i.; and 1.04 and 0.58 %ID/g at 120 min p.i., respectively. The brain uptake kinetics of [18F]d6 and [18F]d8 were confirmed in a dynamic PET study. Both tracers also showed no brain radiometabolites at 20 min p.i. in initial in vivo stability experiments carried out in healthy mice. [18F]d8 seems very promising based on its binding properties and in vivo stability, thus encouraging further validation of its usefulness as a radiotracer for the in vivo visualization of α-syn in preclinical and clinical settings. Additionally, in silico and ML-predicted values correlated with the experimental binding affinity of the ligands.

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