Biology subjects

Zuberi, S.

Publications and source records attributed to Zuberi, S..

4 recordsLinked to original sources

bioRxiv2024

PRECLINICAL DEVELOPMENT OF A ROMIDEPSIN NANOPARTICLE DEMONSTRATES SUPERIOR TOLERABILITY AND EFFICACY IN MODELS OF HUMAN T-CELL LYMPHOMA AND LARGE GRANULAR LYMPHOCYTE LEUKEMIA

Histone deacetylase (HDAC) inhibitors are a widely recognized and valued treatment option for patients with relapsed or refractory peripheral T cell lymphomas (PTCL). Romidepsin is a relatively selective Class I HDAC inhibitor originally approved for patients with relapsed or refractory (R/R) cutaneous T cell lymphoma (CTCL) and subsequently R/R PTCL. Unfortunately, the FDA approval of romidepsin for R/R PTCL was withdrawn due to a negative Phase 4 post-marketing requirement (PMR), diminishing further the treatment options for patients with PTCL. Herein we describe the development of a first-in-class polymer nanoparticle of romidepsin (Nanoromidepsin) using an innovative amphiphilic di-block copolymer-based nanochemistry platform. Nanoromidepsin exhibited superior pharmacologic disposition, with improved tolerability and safety in murine models of T-cell lymphoma. Nanoromidepsin also exhibited superior anti-tumor efficacy in multiple models including in vitro T cell lymphoma (TCL) cell lines, ex vivo LGL leukemia primary patient samples, and murine TCL xenografts. Nanoromidepsin demonstrated greater accumulation in tumors and a statistically significant improvement in overall survival (OS) compared to romidepsin in murine xenograft models. These findings collectively justify the clinical development of Nanoromidepsin in patients with T-cell malignancies.

cancer biology↗

bioRxiv2023

Drug repurposing for Leishmaniasis with Hyperbolic Graph Neural Networks

Leishmanisis, a neglected tropical disease caused by protozoan parasites of the genus Leishmania, affects millions of individuals living in poverty across the world and is second to malaria in parasitic causes of death. Although current drugs for treating leishmaniasis exist, these are either highly toxic, ineffective, or expensive. For this reason, there is an urgent need to identify affordable, safer, and more effective treatments. Drug repurposing is a promising method for identifying existing molecules with the potential to treat leishmaniasis. Here, we present a deep learning model for drug repurposing based on hyperbolic graph neural networks. We leverage experimentally validated protein-drug interactions and molecular descriptors across three different parasites to train and validate our model. The final network model shows significant gains over the best baseline model, with an 11.6% increase in precision of the top scoring 0.5% protein-drug pairs. Finally, our model identified two experimental drugs that could target three L. major proteins involved in drug resistance and cell cycle regulation, which play an essential role in ensuring the parasites survival inside the host. Author summaryLeishmanisis is a deadly and neglected tropical disease. Current treatments are ineffective, highly toxic, and unaffordable for the majority of affected individuals who live in extreme poverty. To accelerate the discovery and development of novel treatments against this disease, we develop a model that proposes existing drugs with the potential to treat leishmaniasis. Our methodology is based on hyperbolic graph neural networks, a class of deep learning models that can incorporate protein-drug interaction information from similar but well-studied parasites, in addition to utilizing chemical and molecular features. Empirically, this additional information leads to improved predictions of drug interactions with L. major proteins. Overall, the protein-drug pairs predicted by our model suggests two existing drugs that could target essential pathways for growth and survival of Leishmania parasites, which could be exploited either as a booster to increase the therapeutic effect of an existing anti-leishmaniasis drug, or as a novel chemotherapeutic treatment against the disease.

bioinformatics↗

bioRxiv2022

Molecular basis of FAAH-OUT-associated human pain insensitivity

Chronic pain affects millions of people worldwide. Studying pain insensitive individuals helps to identify novel analgesic strategies. Here we report how the recently discovered FAAH-OUT lncRNA-encoding gene, which was found from studying a pain insensitive patient with reduced anxiety and fast wound healing, regulates the adjacent key endocannabinoid system gene FAAH, which encodes the anandamide-degrading fatty acid amide hydrolase enzyme. We demonstrate that the disruption in FAAH-OUT lncRNA transcription leads to DNMT1-dependent DNA methylation within the FAAH promoter. In addition, FAAH-OUT contains a conserved regulatory element, FAAH-AMP, that acts as an enhancer for FAAH expression. Furthermore, using transcriptomic analyses we have uncovered a network of genes that are dysregulated from disruption of the FAAH-FAAH-OUT axis, thus providing a coherent mechanistic basis to understand the human phenotype observed and a platform for development of future gene and small molecule therapies.

neuroscience↗

bioRxiv2022

Conserved patterns across ion channels correlate with variant pathogenicity and clinical phenotypes

Clinically identified genetic variants in ion channels can be benign or cause disease by increasing or decreasing the protein function. Consequently, therapeutic decision-making is challenging without molecular testing of each variant. Our biophysical knowledge of ion channel structures and function is just emerging, and it is currently not well understood which amino acid residues cause disease when mutated. We sought to systematically identify biological properties associated with variant pathogenicity across all major voltage and ligand-gated ion channel families. We collected and curated 3,049 pathogenic variants from hundreds of neurodevelopmental and other disorders and 12,546 population variants for 30 ion channel or channel subunits for which a high-quality protein structure was available. Using a wide range of bioinformatics approaches, we computed 163 structural features and tested them for pathogenic variant enrichment. We developed a novel 3D spatial distance scoring approach that enables comparisons of pathogenic and population variant distribution across protein structures. We discovered and independently replicated that several pore residue properties and proximity to the pore axis were most significantly enriched for pathogenic variants compared to population variants. Using our novel 3D scoring approach, we showed that the strongest pathogenic variant enrichment was observed for pore-lining residues and alpha-helix residues within 5[A] distance from the pore axis center and not involved in gating. Within the subset of residues located at the pore, the hydrophobicity of the pore was the feature most strongly associated with variant pathogenicity. We also found an association between the identified properties and both clinical phenotypes and fucntional in vitro assays for voltage-gated sodium channels (SCN1A, SCN2A, SCN8A) and N-methyl-D-aspartate (NMDA) receptor (GRIN1, GRIN2A, GRIN2B) encoding genes. In an independent expert-curated dataset of 1,422 neurodevelopmental disorder pathogenic patient variants, and 679 electrophysiological experiments that pore axis distance is associated with seizure age of onset and cognitive performance as well as differential gain vs. loss-of-channel function. In summary, we identified biological properties associated with ion-channel malfunction and show that these are correlated with in vitro functional read-outs and clinical phenotypes in patients with neurodevelopmental disorders. Our results suggest that clinical decision support algorithms that predict variant pathogenicity and function are feasible in the future.

genetics↗