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1.
Clin Cancer Res ; 30(8): 1685-1695, 2024 Apr 15.
Artigo em Inglês | MEDLINE | ID: mdl-38597991

RESUMO

PURPOSE: Combination therapies are a promising approach for improving cancer treatment, but it is challenging to predict their resulting adverse events in a real-world setting. EXPERIMENTAL DESIGN: We provide here a proof-of-concept study using 15 million patient records from the FDA Adverse Event Reporting System (FAERS). Complex adverse event frequencies of drugs or their combinations were visualized as heat maps onto a two-dimensional grid. Adverse event frequencies were shown as colors to assess the ratio between individual and combined drug effects. To capture these patterns, we trained a convolutional neural network (CNN) autoencoder using 7,300 single-drug heat maps. In addition, statistical synergy analyses were performed on the basis of BLISS independence or χ2 testing. RESULTS: The trained CNN model was able to decode patterns, showing that adverse events occur in global rather than isolated and unique patterns. Patterns were not likely to be attributed to disease symptoms given their relatively limited contribution to drug-associated adverse events. Pattern recognition was validated using trial data from ClinicalTrials.gov and drug combination data. We examined the adverse event interactions of 140 drug combinations known to be avoided in the clinic and found that near all of them showed additive rather than synergistic interactions, also when assessed statistically. CONCLUSIONS: Our study provides a framework for analyzing adverse events and suggests that adverse drug interactions commonly result in additive effects with a high level of overlap of adverse event patterns. These real-world insights may advance the implementation of new combination therapies in clinical practice.


Assuntos
Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos , Humanos , Interações Medicamentosas , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/diagnóstico , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/epidemiologia , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/etiologia
2.
Sci Rep ; 9(1): 3165, 2019 02 28.
Artigo em Inglês | MEDLINE | ID: mdl-30816338

RESUMO

Leprosy is an infectious disease caused by Mycobacterium leprae affecting the skin and nerves. Despite decades of availability of adequate treatment, transmission is unabated and transmission routes are not completely understood. Despite the general assumption that untreated M. leprae infected humans represent the major source of transmission, scarce reports indicate that environmental sources could also play a role as a reservoir. We investigated whether M. leprae DNA is present in soil of regions where leprosy is endemic or areas with possible animal reservoirs (armadillos and red squirrels). Soil samples (n = 73) were collected in Bangladesh, Suriname and the British Isles. Presence of M. leprae DNA was determined by RLEP PCR and genotypes were further identified by Sanger sequencing. M. leprae DNA was identified in 16.0% of soil from houses of leprosy patients (Bangladesh), in 10.7% from armadillos' holes (Suriname) and in 5% from the habitat of lepromatous red squirrels (British Isles). Genotype 1 was found in Bangladesh whilst in Suriname the genotype was 1 or 2. M. leprae DNA can be detected in soil near human and animal sources, suggesting that environmental sources represent (temporary) reservoirs for M. leprae.


Assuntos
Hanseníase/genética , Mycobacterium leprae/isolamento & purificação , Microbiologia do Solo , Animais , Bangladesh/epidemiologia , Ecossistema , Genótipo , Humanos , Hanseníase/epidemiologia , Hanseníase/microbiologia , Hanseníase/transmissão , Mycobacterium leprae/genética , Mycobacterium leprae/patogenicidade , RNA Ribossômico 16S/genética , Suriname/epidemiologia
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