Biology subjects

Katritsis, N. M.

Publications and source records attributed to Katritsis, N. M..

3 recordsLinked to original sources

bioRxiv2025

Multimodal gene and targeted drug therapy for chronic myelogenous leukemia: Computational target analysis and therapeutic validation

Developing an efficient and safe therapy necessitates a mechanistic understanding of the complex underlying pathology and manipulation of the multiple pathways at the molecular and genetic level. Network-based simulation of chronic myeloid leukemia (CML), a relatively well-understood cancer model, revealed the dynamics of simultaneously expressing pro-apoptotic BIM and silencing pro-survival MCL-1 in combination with the BCR-ABL-targeted tyrosine kinase inhibitor dasatinib. Viral/nonviral chimeric nanoparticles (ChNPs) composed of a BIM-expressing adeno-associated virus (AAV) core and a degradable polymeric shell that encapsulates MCL-1 siRNA (BIM/MCL-1 ChNPs) synergistically and selectively killed BCR-ABL+ CML cells in combination with dasatinib. In a mouse CML model, the BIM/MCL-1 ChNPs and dasatinib combination therapy suppressed proliferation of BCR-ABL+ hematopoietic cells and prevented leukemic infiltration of organs. The synergistic anti-leukemic effect was further pronounced in an acute phase model of the disease. This study investigated a strategy of developing a versatile and tunable multimodal therapy assisted by a computational toolset that analyzes the molecular foundation of a disease and predicts therapeutic response. The interdisciplinary approach developed and validated in this study can be used in discovering new therapies for cancer and other diseases. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=182 SRC="FIGDIR/small/656072v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@1e3632borg.highwire.dtl.DTLVardef@1839f12org.highwire.dtl.DTLVardef@4060c0org.highwire.dtl.DTLVardef@8ff0de_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO summarizing the study design and approach. A schematic representation of the integrated in silico and in vivo pipeline utilized in the study. The workflow begins with in silico simulations, including Boolean network modeling and protein-protein interaction (PPI) network analyses, leading to target discovery, optimized nanoparticle design, and validation in pathological contexts. This was followed by therapeutic efficacy assessments of BIM/MCL-1 ChNPs and their combination with dasatinib in BCR-ABL+ leukemia models in vitro and in vivo. C_FIG

cancer biology↗

bioRxiv2022

dialogi: Utilising NLP with chemical and disease similarities to drive the identification of Drug-Induced Liver Injury literature

Drug-Induced Liver Injury (DILI), despite its low occurrence rate, can cause severe side effects or even lead to death. Thus, it is one of the leading causes for terminating the development of new, and restricting the use of already-circulating, drugs. Moreover, its multifactorial nature, combined with a clinical presentation that often mimics other liver diseases, complicate the identification of DILI-related literature, which remains the main medium for sourcing results from the clinical practice and experimental studies. In this work- contributing to the Literature AI for DILI Challenge of the Critical Assessment of Massive Data Analysis (CAMDA) 2021- we present an automated pipeline for distinguishing between DILI-positive and negative papers. We used Natural Language Processing (NLP) to filter out the uninformative parts of a text, and identify and extract mentions of chemicals and diseases. We combined that information with small-molecule and disease embeddings, which are capable of capturing chemical and disease similarities, to improve classification performance. The former are directly sourced from the Chemical Checker (CC). For the latter, we collected data that encode different aspects of disease similarity from the National Library of Medicines (NLM) Medical Subject Headings (MeSH) thesaurus and the Comparative Toxicogenomics Database (CTD). Following a similar procedure as the one used in the CC, vector representations for diseases were learnt and evaluated. Two Neural Network (NN) classifiers were developed: one that only accepts texts as input (baseline model) and an augmented classifier that also utilises chemical and disease embeddings (extended model). We trained, validated, and tested the models through a Nested Cross-Validation (NCV) scheme with 10 outer and 5 inner folds. During this, the baseline and extended models performed virtually identically, with macro F1-scores of 95.04 {+/-} 0.61% and 94.80 {+/-} 0.41%, respectively. Upon validation on an external, withheld, dataset, representing imbalanced data, the extended model achieved an F1-score of 91.14 {+/-} 1.62%, outperforming its baseline counterpart, which got a lower score of 88.30 {+/-} 2.44%. We make further comparisons between the classifiers and discuss future improvements and directions, including utilising chemical and disease embeddings for visualisation and exploratory analysis of the DILI-positive literature.

bioinformatics↗

bioRxiv2020

Identification of SARS-CoV-2 induced pathways reveal drug repurposing strategies

The global outbreak of SARS-CoV-2 necessitates the rapid development of new therapies against COVID-19 infection. Here, we present the identification of 200 approved drugs, appropriate for repurposing against COVID-19. We constructed a SARS-CoV-2-induced protein (SIP) network, based on disease signatures defined by COVID-19 multi-omic datasets(Bojkova et al., 2020; Gordon et al., 2020), and cross-examined these pathways against approved drugs. This analysis identified 200 drugs predicted to target SARS-CoV-2-induced pathways, 40 of which are already in COVID-19 clinical trials(Clinicaltrials.gov, 2020) testifying to the validity of the approach. Using artificial neural network analysis we classified these 200 drugs into 9 distinct pathways, within two overarching mechanisms of action (MoAs): viral replication (130) and immune response (70). A subset of drugs implicated in viral replication were tested in cellular assays and two (proguanil and sulfasalazine) were shown to inhibit replication. This unbiased and validated analysis opens new avenues for the rapid repurposing of approved drugs into clinical trials.

pharmacology and toxicology↗