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- Targeting trypanothione synthetase and Trypanothione reductase: development of common inhibitors to tackle Trypanosomatid diseasePublication . Augusto, André; Costa, Inês; Conceição, Jaime; Cristiano, Maria de LurdesNeglected Tropical Diseases (NTDs) encompass a range of disorders, including infectious diseases caused by viruses, bacteria, parasites, fungi, and toxins, mainly affecting underprivileged individuals in developing countries. Among the NTDs, those caused by parasites belonging to the Trypanosomatidae family are particularly impacting and require attention, since the lack of financial incentives has led to constraints on the development of novel drugs to tackle them effectively. To circumvent the minor advances in drug discovery in this area, academic research emerges as a crucial player, namely through the identification and validation of new drug targets, thereby contributing to the development of more efficient, safe, and less expensive therapies against Trypanosomatidae infections. Noteworthy, this is a matter of utmost urgency since these diseases are endemic in countries with low socioeconomic standards. This review provides a comprehensive understanding of the current paradigm of NTDs caused by parasites belonging to the Trypanosomatidae family, addressing the ongoing limitations and challenges associated to the current chemotherapy solutions for these diseases and discussing the opportunities unravelled by recent research that led to the identification of new biomolecular targets that are common to Trypanosomatidae parasites. Among these, the unique properties of Trypanothione Synthetase (TryS) and Trypanothione Reductase (TryR), two key protozoan enzymes that are essential for the survival of Trypanosoma and Leishmania parasites, will be emphasised. In addition to a critical analysis of the latest advances in the discovery of novel molecules capable of inhibiting TryS and TryR, the possibility of dual targeting through a combination of TryS and TryR inhibitors will be addressed.
- Artificial intelligence in pharmacovigilance: a narrative review and practical experience with an expert-defined bayesian network toolPublication . Caixinha Algarvio, Rogério; Conceição, Jaime; Rodrigues, Pedro Pereira; Ribeiro, Inês; Silva, Renato Ferreira daBackground Pharmacovigilance is vital for monitoring adverse drug reactions (ADRs) and ensuring drug safety. Traditional methods are slow and inconsistent, but artificial intelligence (AI), through automation and advanced analytics, improves efficiency and accuracy in managing increasing data complexity. Aim To explore AI’s practical applications in pharmacovigilance, focusing on efficiency, process acceleration, and task automation. It also examines the use of an expert-defined Bayesian network for causality assessment in a Pharmacovigilance Centre, demonstrating its impact on decision-making. Method A comprehensive literature narrative review was conducted in MEDLINE (via PubMed), Scopus, and Web of Science using a set of targeted keywords, including but not limited to “pharmacovigilance”, “artificial intelligence”, “adverse drug reactions” and “drug safety”. Relevant studies were analysed without restrictions on publication year or language. The search was carried out in January 2025. Results AI has greatly improved pharmacovigilance by streamlining signal detection, surveillance, and ADR reporting automation. Techniques like data mining and automated signal detection have expedited safety signal identification, while duplicate detection has enhanced data precision in safety evaluations. AI has also refined real-world evidence analysis, deepening drug safety and efficacy insights. Predictive models now anticipate ADRs and drug-drug interactions, enabling proactive patient care. At a regional pharmacovigilance center, the implementation of an expert-defined Bayesian network has optimized causality assessment, reducing processing times from days to hours, minimizing subjectivity, and improving the reliability of drug safety evaluations. Conclusion AI holds significant promise for enhancing pharmacovigilance practices, yet its practical application remains primarily confined to academic research, with integration hindered by data quality issues, regulatory barriers, and the need for more transparent algorithms.