Publications

The composition of the lung microbiome contributes to both health and disease, including obstructive lung disease. Because it has been estimated that over 70% of the bacterial species on body surfaces cannot be cultured by currently available techniques, traditional culture techniques are no longer the gold standard for microbial investigation. Advanced techniques that identify bacterial sequences, including the 16S ribosomal RNA gene, have provided new insights into the depth and breadth of microbiota present both in the diseased and normal lung. In asthma, the composition of the microbiome of the lung and gut during early childhood development may play a key role in the development of asthma, while specific airway microbiota are associated with chronic asthma in adults. Early bacterial stimulation appears to reduce asthma susceptibility by helping the immune system develop lifelong tolerance to innocuous antigens. By contrast, perturbations in the microbiome from antibiotic use may increase the risk for asthma development. In chronic obstructive pulmonary disease, bacterial colonisation has been associated with a chronic bronchitic phenotype, increased risk of exacerbations, and accelerated loss of lung function. In cystic fibrosis, studies utilising culture-independent methods have identified associations between decreased bacterial community diversity and reduced lung function; colonisation with Pseudomonas aeruginosa has been associated with the presence of certain CFTR mutations. Genomic analysis of the lung microbiome is a young field, but has the potential to define the relationship between lung microbiome composition and disease course. Whether we can manipulate bacterial communities to improve clinical outcomes remains to be seen.

BACKGROUND

A proportion of patients with tuberculosis diagnosed by sputum culture during screening before antiretroviral therapy (ART) have false-negative Xpert MTB/RIF assay results (Xpert-negative tuberculosis). We determined the characteristics and early ART program outcomes of such patients.

METHODS

Adult patients who enrolled in a South African township ART service were systematically screened for pulmonary tuberculosis regardless of symptoms by testing paired sputum samples with Xpert MTB/RIF and liquid culture. The ART service provided follow-up for all patients, and early (90-day) programmatic outcomes were determined.

RESULTS

Among 602 patients screened, 523 had ≥1 Xpert and culture result, yielding 89 culture-positive tuberculosis diagnoses. Of these, 37 (42%) of the patients with tuberculosis were Xpert-negative when a single sputum sample was tested, compared with 25 (28%) when 2 samples were tested. Compared with patients with Xpert-positive tuberculosis, those with Xpert-negative tuberculosis (using either definition) had substantially higher CD4 cell counts, lower plasma viral loads, higher hemoglobin concentrations, and higher body mass index. Their tuberculosis was also less advanced, with a lower frequency of prolonged cough (≥2 weeks), less extensive radiographic abnormalities, and a lower frequency of detectable lipoarabinomannan antigenuria and mycobacteriuria. Xpert-negative cases were all sputum smear negative with prolonged time to culture positivity (median, 21 days). Despite greater delays in starting tuberculosis treatment, Xpert-negative patients were less likely to die during follow-up.

CONCLUSIONS

Compared to patients with Xpert-positive tuberculosis diagnosed during pre-ART screening, Xpert-negative cases had less advanced immunosuppression and less advanced tuberculosis and did not have adverse outcomes despite substantial delays in starting tuberculosis treatment.

The Biopharmaceutics Drug Disposition Classification System (BDDCS) was successfully employed for predicting drug-drug interactions (DDIs) with respect to drug metabolizing enzymes (DMEs), drug transporters and their interplay. The major assumption of BDDCS is that the extent of metabolism (EoM) predicts high versus low intestinal permeability rate, and vice versa, at least when uptake transporters or paracellular transport is not involved. We recently published a collection of over 900 marketed drugs classified for BDDCS. We suggest that a reliable model for predicting BDDCS class, integrated with in vitro assays, could anticipate disposition and potential DDIs of new molecular entities (NMEs). Here we describe a computational procedure for predicting BDDCS class from molecular structures. The model was trained on a set of 300 oral drugs, and validated on an external set of 379 oral drugs, using 17 descriptors calculated or derived from the VolSurf+ software. For each molecule, a probability of BDDCS class membership was given, based on predicted EoM, FDA solubility (FDAS) and their confidence scores. The accuracy in predicting FDAS was 78% in training and 77% in validation, while for EoM prediction the accuracy was 82% in training and 79% in external validation. The actual BDDCS class corresponded to the highest ranked calculated class for 55% of the validation molecules, and it was within the top two ranked more than 92% of the time. The unbalanced stratification of the data set did not affect the prediction, which showed highest accuracy in predicting classes 2 and 3 with respect to the most populated class 1. For class 4 drugs a general lack of predictability was observed. A linear discriminant analysis (LDA) confirming the degree of accuracy for the prediction of the different BDDCS classes is tied to the structure of the data set. This model could routinely be used in early drug discovery to prioritize in vitro tests for NMEs (e.g., affinity to transporters, intestinal metabolism, intestinal absorption and plasma protein binding). We further applied the BDDCS prediction model on a large set of medicinal chemistry compounds (over 30,000 chemicals). Based on this application, we suggest that solubility, and not permeability, is the major difference between NMEs and drugs. We anticipate that the forecast of BDDCS categories in early drug discovery may lead to a significant R&D cost reduction.