Modelos baseados em redes neurais artificiais para o diagnóstico em triagem de tuberculose resistente e multirresistente no Brasil

Drug-Resistant TB (DR-TB) implicates in more complex treatments and leads to higher deceased and morbidity numbers. In this context, we propose the use of screening tests that early identi es patients with higher probability of having DR-TB and prioritize them. Arti cial Neural Networks and Classi cation And Regression Tree models are generated, and a boosting algorithm is applied, considering as input the patient's symptoms and social-demographic variables. Speci c scores by each State are produced and the results are compared to a national-wide approach. Models with di erent complexity levels were developed in order to t the available resources in each site, being guided by variable relevance and data quality. Models developed by each State achieved an average sensitivity higher than 85% when screening RJ patients considering DR-TB from non DR-TB, against 82.7% using the national approach, indicating that the local clinical scores can better capture operational di erences present in the health system.A tuberculose (TB) resistente a drogas (TB-DR) implica em tratamentos mais complexos, acarretando em taxas de mortalidade e morbidade mais elevadas. Neste contexto, propomos o uso de testes de triagem que identifiquem precocemente os pacientes com maior probabilidade de TB-DR, para serem priorizados. Modelos baseados em Redes Neurais Artificiais e Árvores de Classificação e Regressão foram desenvolvidos, incluindo algoritmos de boosting. Consideram-se sintomas e variáveis sócio demográficas e clínicas dos pacientes. Foram produzidos escores a nível estadual e os resultados comparados aos de uma abordagem nacional. Modelos de diferentes complexidades foram desenvolvidos para se adequarem aos recursos disponíveis nos locais de aplicação, sendo guiados pela relevância das variáveis e a qualidade dos dados. Observou-se que os modelos estaduais obtiveram uma sensibilidade, em média, maior que 85% na triagem de pacientes para o diagnóstico de TB-DR, contra 82,7% da abordagem nacional, indicando que os escores clínicos locais tendem a capturar melhor as desigualdades operacionais do sistema de saúde

A pharmacometric approach to optimal use of second line drugs for multidrug-resistant tuberculosis

Until the recent introduction of short course regimens, treatment regimens for multidrug resistant TB (MDR-TB) were long and toxic. Consequently, only approximately half of MDRTB patients completed their treatment. TB dosing guidelines have historically been unrefined with little consideration for pharmacokinetic/pharmacodynamic relationships. Large knowledge gaps therefore exist in the understanding of pharmacokinetic/pharmacodynamic relationships for both efficacy and toxicity in MDR-TB. My PhD used clinical pharmacology approaches to improve the understanding of drug exposures, toxicity, and exposure-toxicity relationships during the first 12 weeks of MDR-TB therapy. Aims and methods 1. Using non-compartmental analyses, describe the pharmacokinetics of cycloserine and, using regression modelling, explore the association of covariates with cycloserine exposure. 2. Using validated screening tools, describe the incidence of neuropsychiatric toxicity in MDR-TB patients, and explore associations with cycloserine pharmacokinetics. 3. Using a validated pain-rating scale in a crossover study design, investigate whether the addition of a local anaesthetic reduces kanamycin-related injection pain, and explore effects on kanamycin pharmacokinetics. 4. Using geometric mean ratios, compare the exposures of crushed versus whole formulations of pyrazinamide, moxifloxacin, ethionamide, ethambutol, cycloserine, and isoniazid. Results and conclusions We found no measurable terizidone in plasma supporting the hypothesis that terizidone is hydrolysed pre-systemically to cycloserine. The cycloserine time-concentration profile supports once daily dosing of terizidone. We describe a high incidence of peripheral neuropathy in MDR-TB patients with both cycloserine clearance and high-dose pyridoxine significantly associated with neuropathy on multivariate analysis. The addition of a local anaesthetic reduced the pain experienced by MDR-TB patients in the first 15 minutes post intramuscular administration of kanamycin, which could improve adherence to MDR-TB treatment. We also found the bioavailability of crushed isoniazid to be approximately 42% less than the whole tablet formulation, and therefore recommend that the crushing of isoniazid be avoided. Although some recent treatment advances have improved MDR-TB outcomes, enhancing the understanding of drugs used to treat MDR-TB, which continues to have an unacceptably high mortality and treatment-related morbidity, is a public health priority. This thesis comprises four peer-reviewed publications, all of which made a pragmatic contribution to the fight against MDR-TB

Genomics: a Swiss army knife to fight leprosy

Leprosy, a highly disabling and stigmatizing infectious disease, is caused by Mycobacterium leprae and the newly discovered agent, Mycobacterium lepromatosis. Though treatable with antibiotics, leprosy has still not been eradicated, and around 200,000 new cases are reported every year worldwide, mainly in India, Brazil, and Indonesia. Tipping the balance towards leprosy elimination begins with improving our understanding of the pathogenesis and the transmission of the disease, which remains poorly understood. Current research on leprosy is markedly hindered by our incapacity to cultivate the leprosy bacilli on artificial media, as well as by the variation of the clinical forms of the disease. The rise of genomics in the 2000s has helped to get around these problems by opening new ways of studying organisms. Tools were developed to recover enough genetic material for downstream genomic applications; however, none of them is yet suitable for high throughput purposes. In this thesis, we describe an optimized DNA extraction method from skin tissue that allows direct whole-genome sequencing, and enabled us to obtain ~ 250 genome sequences of M. leprae from different geographical locations throughout the world. Firstly, this dataset deepened our insight into the phylogeny of M. leprae, and points to the ancestral strain originating in East Asia and/or Europe. In addition, analysis of more than twenty drug-resistant strains revealed mutations in candidate genes potentially associated with new biological mechanisms such as drug resistance. Moreover, we analysed isolates from restricted geographic areas and from recurrent cases, and show that the distinction between relapse and reinfection with a closely related strain can be made but this remains challenging. The whole genome sequencing of M. lepromatosis was achieved in 2015, and the discovery and use of new specific molecular detection methods allowed us to identify M. lepromatosis in the red squirrel population in the British Isles. In parallel, M. leprae was also discovered in red squirrels on Brownsea Island in the south of England. Though the risk of transmission from animals to humans is not yet clear, the discovery of a new animal reservoir for leprosy bacilli in a non-endemic country raises the question about the existence of other such reservoirs, especially in endemic countries, which could contribute to ongoing transmission. Reliable and sensitive methods for detection of leprosy bacilli are crucial for early diagnosis and monitoring the disease. We show that efficient cell lysis during extraction increases the yield of genetic material recovered from leprosy bacilli and significantly improves the sensitivity of diagnosis by PCR for all leprosy forms. Overall, our results highlight the impact and efficiency of genomics and whole genome sequencing for uncovering new biological mechanisms in unculturable bacteria such as the leprosy bacilli. Our results generated new hypotheses that await testing, and underline the massive potential of omics and bioinformatics for better understanding and fighting the disease