Oral Cinnarizine for the treatment of Covid19-associated Chilblain-Like Lesions: an old drug for a new disease?

Advancing drug-target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining

BACKGROUND

The pharmaceutical field faces a significant challenge in validating drug target interactions (DTIs) due to the time and cost involved, leading to only a fraction being experimentally verified. To expedite drug discovery, accurate computational methods are essential for predicting potential interactions. Recently, machine learning techniques, particularly graph-based methods, have gained prominence. These methods utilize networks of drugs and targets, employing knowledge graph embedding (KGE) to represent structured information from knowledge graphs in a continuous vector space. This phenomenon highlights the growing inclination to utilize graph topologies as a means to improve the precision of predicting DTIs, hence addressing the pressing requirement for effective computational methodologies in the field of drug discovery.

RESULTS

The present study presents a novel approach called DTIOG for the prediction of DTIs. The methodology employed in this study involves the utilization of a KGE strategy, together with the incorporation of contextual information obtained from protein sequences. More specifically, the study makes use of Protein Bidirectional Encoder Representations from Transformers (ProtBERT) for this purpose. DTIOG utilizes a two-step process to compute embedding vectors using KGE techniques. Additionally, it employs ProtBERT to determine target-target similarity. Different similarity measures, such as Cosine similarity or Euclidean distance, are utilized in the prediction procedure. In addition to the contextual embedding, the proposed unique approach incorporates local representations obtained from the Simplified Molecular Input Line Entry Specification (SMILES) of drugs and the amino acid sequences of protein targets.

CONCLUSIONS

The effectiveness of the proposed approach was assessed through extensive experimentation on datasets pertaining to Enzymes, Ion Channels, and G-protein-coupled Receptors. The remarkable efficacy of DTIOG was showcased through the utilization of diverse similarity measures in order to calculate the similarities between drugs and targets. The combination of these factors, along with the incorporation of various classifiers, enabled the model to outperform existing algorithms in its ability to predict DTIs. The consistent observation of this advantage across all datasets underlines the robustness and accuracy of DTIOG in the domain of DTIs. Additionally, our case study suggests that the DTIOG can serve as a valuable tool for discovering new DTIs.

Chilblain-like lesions onset during SARS-CoV-2 infection in a COVID-19-vaccinated adolescent: case report and review of literature

Background

COVID toes or chilblain-like skin lesions have been widely reported during COVID-19 pandemic. Most cases were described in patients with negative microbiological tests for SARS-CoV-2, therefore the possible relationship with SARS-CoV-2 infection, as well as with the nowadays broadly available mRNA-based vaccination, has not been fully elucidated. 

Case presentation

We here describe the case of a 14-year-old male who developed chilblain-like skin eruptions during SARS-CoV-2 infection despite two mRNA-based vaccine doses and review the clinical and epidemiological characteristics of chilblain-like lesions as a cutaneous presentation of COVID-19 in children.

Conclusions

Most children and adolescent with COVID toes have a mild or asymptomatic SARS-CoV-2 infection. Our report aims to highlight the possible onset of these skin lesions in vaccinated children, if infection has occurred, and the potential use of systemic corticosteroids as a first line treatment. Additional evidence is required to better understand SARS-CoV-2 infection and cutaneous manifestations in children and determine the relationship between chilblain-like lesions and COVID-19 vaccination.