Biology subjects

Parisi, D.

Publications and source records attributed to Parisi, D..

3 recordsLinked to original sources

bioRxiv2025

Catalyzing computational biology research at an academic institute through an interest network

Biology has been transformed by the rapid development of computing and the concurrent rise of data-rich approaches such as -omics or high-resolution imaging. However, there is a persistent computational skills gap in the biomedical research workforce. Inherent limitations of classroom teaching and institutional core support highlight the need for accessible ways for researchers to explore developments in computational biology. An analysis of the Scripps Research Genomics Core revealed an increasingly diverse set of experiments: the share of experiments other than bulk RNA- or DNA-seq increased from 34% to 60% within 10 years, requiring more tailored computational analyses. These challenges were tackled by forming a volunteer-led affinity group of over 300 academic biomedical researchers interested in computational biology referred to as the Computational Biology and Bioinformatics (CBB) affinity group. This adaptive group has provided continuing education and networking opportunities through seminars, workshops and coding sessions while evolving along with the needs of its members. A survey of CBBs impact confirmed the groups events increased the members exposure to computational biology educational and research events (79% respondents) and networking opportunities (61% respondents). Thus, volunteer-led affinity groups may be a viable complement to traditional institutional resources for enhancing the application of computing in biomedical research.

bioinformatics↗

bioRxiv2019

Drug repositoning or target repositioning: a structural perspective of drug-target-indication relationship for available repurposed drugs.

Drug repositioning aims to find new indications for existing drugs, in order to reduce drug development cost and time. Currently, numerous successful stories of drug repositioning have been reported and many drugs are already available on the market. Although many of those cases are products of serendipitous findings, repositioning opportunities can be uncovered systematically by following either a disease-centric approach, as a result of a close relation between an old and new indication, a target-centric one, which links a known target and its established drug to a new indication, or a drug-centric approach, which connects a known drug to a new target and its associated indication. The three approaches differ in their complexity, potential, and limits, and most important the necessary starting information and computational power. Which one is predominant in current drug repositioning and what does this imply for future developments? To address these questions, we systematically evaluated over 100 drugs, 200 targets structures and over 300 indications from the Drug Repositioning Database. Each of the analysed cases has been classified based on one of the three repositioning approaches, showing that the majority, more than 60%, falls within the disease-centric definition, around 30% within the target-centric, and only less than 10% within the drug-centric. We therefore concluded that so far repositioning has mainly been disease and target repositioning and not, as drug repositioning, as expected by definition. We discuss the reasons and suggest directions to exploit the full potential of techniques useful for drug-centric in order to sustain future rationale repositioning pipelines.

bioinformatics↗

bioRxiv2019

Structure-based drug repositioning reveals ibrutinib as VEGFR2 inhibitor

Many drugs are promiscuous and bind to multiple targets. On the one hand, these targets may be linked to unwanted side effects, but on the other, they may achieve a combined desired effect (polypharmacology) or represent multiple diseases (drug repositioning). With the growth of 3D structures of drug-target complexes, it is today possible to study drug promiscuity at the structural level and to screen vast amounts of drug-target interactions to predict side effects, polypharmacological potential, and repositioning opportunities. Here, we pursue such an approach to identify drugs inactivating B-cells, whose dysregulation can function as a driver of autoimmune diseases. Screening over 500 kinases, we identified 22, whose knock out impeded the activation of B-cells. Among these 22 is the gene KDR, whose gene product VEGFR2 is a prominent cancer target with anti-VEGFR2 drugs on the market for over a decade. The main result of this paper is that structure-based drug repositioning for the identified kinase targets identified the cancer drug ibrutinib as micromolar VEGFR2 inhibitor with a very high therapeutic index in B-cell inactivation. These findings prove that ibrutinib is not only acting on the Brutons tyrosine kinase BTK, against which it was designed. Instead, it may be a polypharmacological drug, which additionally targets angiogenesis via inhibition of VEGFR2. Therefore ibrutinib carries potential to treat other VEGFR2 associated disease. Structure-based drug repositioning explains ibrutinibs anti VEGFR2 action through a specific pattern of interactions shared between BTK and VEGFR2. Overall, structure-based drug repositioning was able to predict these findings at a fraction of the time and cost of a conventional screen.

bioinformatics↗