Search Results (4)

Safety signal detection is an integral component of Pharmacovigilance (PhV), which is defined by the World Health Organization as “science and activities relating to the detection, assessment, understanding, and prevention of adverse effects or any other possible drug related problems”. The purpose of safety signal detection is to identify new or known adverse events (AEs) resulting from the use of pharmacotherapeutic products. While post-marketing spontaneous reports from different sources are commonly utilized as a data source for detecting these signals, there are underlying challenges arising from data complexity. This paper investigates the implementation of the Apriori algorithm, a popular method in association rule mining, to identify frequently co-occurring drugs and AEs within safety data. We discuss previous applications of the Apriori algorithm for safety signal detection and conduct a detailed study of an improved method specifically tailored for this purpose. This enhanced approach refines the classical Apriori method to effectively reveal potential associations between drugs/vaccines and AEs from post-marketing safety monitoring datasets, especially when AEs are rare. Detailed comparative simulation studies across varied settings coupled with the application of the method to vaccine safety data from the Vaccine Adverse Event Reporting System (VAERS) demonstrate the efficacy of the improved approach. In conclusion, the improved Apriori algorithm is shown to be a useful screening tool for detecting rarely occurring potential safety signals from the use of drugs/vaccines using post-marketing safety data. Full article

The urgent requirement for swift diagnostic methods in pathogen identification and antimicrobial susceptibility testing is emphasized by rising bacterial resistance and limited treatment options, which are particularly critical in sepsis management. The shift from traditional phenotype-based methods to rapid molecular and mass spectrometry techniques has significantly reduced result turnaround times, enhancing patient outcomes. In this systematic review with meta-analysis, the aspects of correct empirical antimicrobial therapy are evaluated to determine their impact on mortality. We performed a systematic review and meta-analysis on EMBASE, the Cochrane Library, Web of Science, and MEDLINE. Studies evaluating mortality associated with empirical adequate and inadequate therapy in different sites of infection were included. Outcomes included clinical cures in microbiologically evaluable patients. Among the sites of infection, the most studied were bloodstream infections (n = 9), followed by respiratory tract infections (n = 5), intra-abdominal infections (n = 5), and urinary tract infections (evaluated by 3 studies). Inadequate therapy was associated with an increase in mortality between 11 and 68%. Technologies to speed up pathogen identification are extremely necessary to reduce mortality. Full article

Today, drug abuse has developed into a social problem and begun to demand specific measures from different social sectors and government agencies all over the world. Despite significant efforts made toward relevant mechanistic targets, such as the dopamine transporter (DAT), the development of pharmacotherapeutic treatments of psychostimulant abuse has remained a challenge so far. Using a set of 49 2-[(diphenylmethyl)sulfanyl]ethanamines described as DAT inhibitors, 2D-QSAR/PLS studies were performed using two different approaches of variable selection: Ordered predictors selection (OPS) and genetic algorithm (GA). All structures were optimized at the B3LYP/6-311G++(d,p) level of theory. The molecular descriptors were obtained in the Dragon 6 program (topological, geometric, molecular, and constitutional) and GaussView 03 (electronic). Both models were formed by two latent variables. Model 1 (OPS) was constructed with four molecular descriptors (GATS3m, Mor15p, SpMin3_Bh(s), and HOMO-1), while six (Mor13m, CATS2D_09_LL, RDF110u, RDF085m, Mor24s, and RDF010s) were required to obtain model 2 (GA). The models can be considered reasonably different: In model 1, electronic features predominate, whereas in model 2, steric and geometric effects do. The overall test indicated that models 1 and 2 have equivalent predictive ability (Average r²_m Overall = 0.730 versus 0.710 and Delta r²_m Overall = 0.122 versus 0.151). However, model 1 is simpler (it has only four descriptors, which facilitates its interpretation), presents more relevant information used in the construction of its two latent variables (75.99% versus 64.07%), and its calibration is more significant than that of model 2 (F_n,n−p−1 = 115.814 versus 80.888, for the same tabled F value, where n = 36, and n − p − 1 = 3.256, with alfa = 0.05). Considering these results, although model 2 may also be considered a good result, model 1, obtained using the OPS approach for variable selection, may be considered more reliable for prediction purposes. This result is in agreement with good results previously obtained using the OPS methodology. Full article

The lack of commercially-available pediatric drug products and dosage forms is well-known. A group of clinicians and scientists with a common interest in pediatric drug development and medicines-use systems developed a practical framework for identifying a list of active pharmaceutical ingredients (APIs) with the greatest market potential for development to use in pediatric patients. Reliable and reproducible evidence-based drug formulations designed for use in pediatric patients are needed vitally, otherwise safe and consistent clinical practices and outcomes assessments will continue to be difficult to ascertain. Identification of a prioritized list of candidate APIs for oral formulation using the described algorithm provides a broader integrated clinical, scientific, regulatory, and market basis to allow for more reliable dosage forms and safer, effective medicines use in children of all ages. Group members derived a list of candidate API molecules by factoring in a number of pharmacotherapeutic, scientific, manufacturing, and regulatory variables into the selection algorithm that were absent in other rubrics. These additions will assist in identifying and categorizing prime API candidates suitable for oral formulation development. Moreover, the developed algorithm aids in prioritizing useful APIs with finished oral liquid dosage forms available from other countries with direct importation opportunities to North America and beyond. Full article