Biology subjects

Drygin, D.

Publications and source records attributed to Drygin, D..

2 recordsLinked to original sources

bioRxiv2025

The novel RNA polymerase I transcription inhibitor PMR-116 exploits a critical therapeutic vulnerability in a broad-spectrum of high MYC malignancies.

Ribosome biogenesis (RiBi) is a key determinant of cell growth and proliferation and is highly elevated in cancer due to the activation by oncogenes such as MYC. First-generation RiBi inhibitor CX-5461, while demonstrating clinical potential for cancer treatment, also induces DNA damage through off-target inhibition of TOP2 and potentially other mechanisms, bringing into question RiBi as a target for cancer therapy. In this study, we test second-generation RiBi inhibitor, PMR-116. PMR-116 exhibits improved drug-like properties compared to first-generation RiBi inhibitors and has robust anti-tumour activity in the absence of global DNA damage signalling in a broad range of pre-clinical models of haematologic and solid cancers, particularly in malignancies where MYC is either the driver of disease or is elevated. Thus, our work demonstrates that RiBi is a genuine target for cancer therapy and highlights the potential to exploit a critical therapeutic vulnerability in high-MYC human cancers with dismal therapeutic outcomes. Statement of significanceDespite the development of new cancer therapies, most advanced malignancies remain incurable. We demonstrate that PMR-116, a second-generation RiBi inhibitor, has robust therapeutic efficacy in preclinical models of cancer, offering great promise to treat a broad spectrum of human solid and haematologic malignancies, especially where MYC is a driver.

cancer biology↗

bioRxiv2024

Deep Kernel Inversion: Rapid and Accurate Molecular Interaction Prediction for Drug Design

Computational drug design offers the opportunity to dramatically accelerate novel therapeutics for untreated diseases. Designing compounds with optimal efficacy and specificity, however, requires understanding and optimizing immense numbers of molecular interactions. While advances in predicting one-to-one molecular interactions continue, there has been limited progress in scaling one-to-many or many-to-many molecular interaction models. In this paper, we introduce a deep learning framework that embeds molecules into a high-dimensional vector space, which we have named Deep Kernel Inversion. In this framework, the dot product between vectors accurately predicts molecular interactions. This approach reduces the complexity of predicting an entire molecular interaction network from O(n2) to O(n), enabling new molecular design tasks previously inaccessible to computational approaches. In the case of human protein-protein interactions (PPI), we demonstrate a 100,000 fold decrease in the computation required to map the full human PPI network. We also demonstrate best-in-class performance across multiple molecular interaction tasks with this approach. This work offers a new way forward in scaling accurate molecular interaction predictions with applications in mapping biological pathways, target discovery, drug design, and therapeutic development.

systems biology↗