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 .

No abstract

The current gold-standard of estimating adverse events of a drug are clinical trials. However, these trials fail to represent real-life practice where patients have additional diseases (comorbidities) and commonly use numerous drugs when entering the clinic. Therefore, there is a rise of interest in combinational therapies, also because effective combinations are expected to prevent therapy resistance. At this moment it is not feasible to predict the adverse events of new combination therapies due to lack of available information both from a dimensionality (i.e., number of adverse events recorded per patient) as well as from a patient-number perspective. When available, this information allows to choose combinational therapies with acceptable adverse events. In this study we developed a preliminary method to predict adverse events of drug combinations in order to select combinations with a mild adverse event profile. We used the FAERS, an FDA post-marketing adverse events registry as data source containing 15 million adverse-event records. First, we developed a method to visualize the adverse events profiles of monotherapy and combination therapy using dimension reduction to accurately represent the relation between adverse events over many patients. These adverse-event profiles are then fed to a convolutional neural network (CNN) to generate an explainable prediction-model for adverse events occurring in combination therapies. The CNN trained on monotherapy is able to learn from the data and recognize adverse event patterns. The learned pattern information, as stored in the so-called latent space, can be converted back onto the original adverse event profiles. This showed a high similarity to the original data, also for unseen combination therapy effects. Furthermore, a t-SNE analysis on the latent space of the CNN is able to separate additive and synergistic adverse event patterns in combination therapy. Our CNN model can successfully learn complex adverse-event patterns for single drugs and their combinations, which are all encoded in the latent space. The developed method is therefore applicable to determine the combinatorial effects of highly complex adverse event profiles. Citation Format: Asli Kucukosmanoglu, Silvia Scoarta, Thomas Wijnands, George Kanev, Bart Westerman, Bert Kiewit, David Noske, Tom Wurdinger. The adverse events atlas, towards a strategy to predict synergistic adverse events of combination therapies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6312.