Browsing by Author "Alam, Parvez"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    A same day α-synuclein RT-QuIC seed amplification assay for synucleinopathy biospecimens
    (Springer Nature, 2025) Parveen, Sabiha; Alam, Parvez; Orrù, Christina D.; Vascellari, Sarah; Hughson, Andrew G.; Zou, Wen-Quan; Beach, Thomas G.; Serrano, Geidy E.; Goldstein, David S.; Ghetti, Bernardino; Cossu, Giovanni; Pisano, Giada; Pinna, Beatrice; Caughey, Byron; Pathology and Laboratory Medicine, School of Medicine
    Parkinson’s disease (PD), dementia with Lewy bodies (DLB), and other synucleinopathies are characterized by the accumulation of abnormal, self-propagating aggregates of α-synuclein. RT-QuIC or seed amplification assays are currently showing unprecedented diagnostic sensitivities and specificities for synucleinopathies even in prodromal phases years in advance of the onset of Parkinsonian signs or dementia. However, commonly used α-synuclein seed amplification assays take ≥48 h to perform as applied to patients’ diagnostic biospecimens. Here, we report the development of a faster α-synuclein RT-QuIC assay that is as analytically sensitive as prior assays of this type, but can be completed in ≤12 h for brain, skin, and intestinal mucosa, with positive signals often arising in <5 h. CSF assays took a few hours longer. Our same-day α-synuclein RT-QuIC (sdRT-QuIC) assay should increase the practicality, cost-effectiveness, and throughput of measurements of pathological forms of α-synuclein for fundamental research, clinical diagnosis, and therapeutics development.
  • Thumbnail Image
    Item
    Discovery of potent inhibitors of α-synuclein aggregation using structure-based iterative learning
    (Springer Nature, 2024) Horne, Robert I.; Andrzejewska, Ewa A.; Alam, Parvez; Brotzakis, Z. Faidon; Srivastava, Ankit; Aubert, Alice; Nowinska, Magdalena; Gregory, Rebecca C.; Staats, Roxine; Possenti, Andrea; Chia, Sean; Sormanni, Pietro; Ghetti, Bernardino; Caughey, Byron; Knowles, Tuomas P. J.; Vendruscolo, Michele; Pathology and Laboratory Medicine, School of Medicine
    Machine learning methods hold the promise to reduce the costs and the failure rates of conventional drug discovery pipelines. This issue is especially pressing for neurodegenerative diseases, where the development of disease-modifying drugs has been particularly challenging. To address this problem, we describe here a machine learning approach to identify small molecule inhibitors of α-synuclein aggregation, a process implicated in Parkinson's disease and other synucleinopathies. Because the proliferation of α-synuclein aggregates takes place through autocatalytic secondary nucleation, we aim to identify compounds that bind the catalytic sites on the surface of the aggregates. To achieve this goal, we use structure-based machine learning in an iterative manner to first identify and then progressively optimize secondary nucleation inhibitors. Our results demonstrate that this approach leads to the facile identification of compounds two orders of magnitude more potent than previously reported ones.