Disk Diffusion Breakpoint Determination Using a Bayesian Nonparametric Variation of the Errors-in-Variables Model

Drug dilution (MIC) and disk diffusion (DIA) are the two most common antimicrobial susceptibility tests used by hospitals and clinics to determine an unknown pathogen\u27s susceptibility to various antibiotics. Both tests use breakpoints to classify the pathogen as either susceptible, indeterminant, or resistant to each drug under consideration. While the determination of these drug-specific MIC classification breakpoints is straightforward, determination of comparable DIA breakpoints is not. It is this issue that motivates this research. Traditionally, the error-rate bounded (ERB) method has been used to calibrate the two tests. This procedure involves determining DIA breakpoints which minimize the observed discrepancies between results generated from both tests over a wide range of pathogen strains (or isolates). While simple and intuitive, this approach is very sample dependent and lacks precision. Model-based approaches were first proposed in 2000. These approaches model the underlying true relationship between the two tests and thus focuses on calibrating the probabilities of classification rather than the observed test results. Both a Bayesian parametric (2000) and a frequentist nonparametric (2008) procedure have been proposed. However, due to various computational difficulties and an absence of easy to use software for clinicians, neither approach has been adopted for use. In this thesis, we present a novel Bayesian nonparametric model that combines the strengths of the previous two model-based approaches. The resulting approach provides the flexibility of a nonparametric model to describe the true DIA/MIC relationship within a Bayesian framework in order to extract as much information as possible from the observed data. We demonstrate the strength of this approach via a series of simulation studies comparing it to the ERB and previous model-based approaches using breakpoint determination accuracy and model fit statistics as the comparison criteria. We conclude with applications to several real data sets and a discussion regarding software implementation and future work

Clustering MIC data through Bayesian mixture models: an application to detect M. Tuberculosis resistance mutations

Antimicrobial resistance is becoming a major threat to public health throughout the world. Researchers are attempting to contrast it by developing both new antibiotics and patient-specific treatments. In the second case, whole-genome sequencing has had a huge impact in two ways: first, it is becoming cheaper and faster to perform whole-genome sequencing, and this makes it competitive with respect to standard phenotypic tests; second, it is possible to statistically associate the phenotypic patterns of resistance to specific mutations in the genome. Therefore, it is now possible to develop catalogues of genomic variants associated with resistance to specific antibiotics, in order to improve prediction of resistance and suggest treatments. It is essential to have robust methods for identifying mutations associated to resistance and continuously updating the available catalogues. This work proposes a general method to study minimal inhibitory concentration (MIC) distributions and to identify clusters of strains showing different levels of resistance to antimicrobials. Once the clusters are identified and strains allocated to each of them, it is possible to perform regression method to identify with high statistical power the mutations associated with resistance. The method is applied to a new 96-well microtiter plate used for testing M. Tuberculosis

Predicting Efavirenz Concentrations in the Brain Tissue of HIV-Infected Individuals and Exploring their Relationship to Neurocognitive Impairment

Sparse data exist on the penetration of antiretrovirals into brain tissue. In this work, we present a framework to use efavirenz (EFV) pharmacokinetic (PK) data in plasma, cerebrospinal fluid (CSF), and brain tissue of eight rhesus macaques to predict brain tissue concentrations in HIV-infected individuals. We then perform exposure-response analysis with the model-predicted EFV area under the concentration-time curve (AUC) and neurocognitive scores collected from a group of 24 HIV-infected participants. Adult rhesus macaques were dosed daily with 200 mg EFV (as part of a four-drug regimen) for 10 days. Plasma was collected at 8 time points over 10 days and at necropsy, whereas CSF and brain tissue were collected at necropsy. In the clinical study, data were obtained from one paired plasma and CSF sample of participants prescribed EFV, and neuropsychological test evaluations were administered across 15 domains. PK modeling was performed using ADAPT version 5.0 Biomedical Simulation Resource, Los Angeles, CA) with the iterative two-stage estimation method. An eight-compartment model best described EFV distribution across the plasma, CSF, and brain tissue of rhesus macaques and humans. Model-predicted median brain tissue concentrations in humans were 31 and 8,000 ng/mL, respectively. Model-predicted brain tissue AUC was highly correlated with plasma AUC (γ = 0.99, P 0.05). This analysis provides an approach to estimate PK the brain tissue in order to perform PK/pharmacodynamic analyses at the target site. © 2019 The Authors. Clinical and Translational Science published by Wiley Periodicals, Inc. on behalf of the American Society for Clinical Pharmacology and Therapeutics

Herd-level Risk Factors Associated with Antimicrobial Susceptibility Patterns and Distributions in Fecal Bacteria of Porcine Origin.

The purpose of this dissertation is threefold: to determine the differences in apparent prevalence and the antimicrobial susceptibility of Campylobacter spp. between antimicrobial-free and conventional swine farms; secondly, to introduce an appropriate statistical model to compare the minimum inhibitory concentration distributions of Escherichia coli and Campylobacter spp. isolated from both farm types; and thirdly, to examine the potential herd level risk factors that may be associated with antimicrobial resistance of Campylobacter spp. and E. coli isolates from finishers on antimicrobial-free and conventional farming systems. In addition, a critical review of studies that have compared the levels and patterns of antimicrobial resistance among animals from antimicrobial-free and conventional farming practices was performed. Fecal samples from 15 pigs were collected from each of 35 antimicrobial-free and 60 conventional farms in the Midwestern U.S. Campylobacter spp. was isolated from 464 of 1,422 fecal samples, and each isolate was tested for susceptibility to 6 antimicrobials. The apparent prevalence of Campylobacter spp. isolates was approximately 33 percent on both conventional and antimicrobial-free farms. The proportion of antimicrobial resistance among Campylobacter was higher for three antimicrobials within conventional compared to antimicrobial-free farms. The susceptibilities of populations of bacteria to antimicrobial drugs were summarized as minimum inhibitory concentration (MIC) frequency distributions. The use of MIC values removed the subjectivity associated with the choice of breakpoints which define an isolate as susceptible or resistant. A discrete-time survival analysis model was introduced as the recommended statistical model when MICs are the outcome. A questionnaire was completed by each farm manager on biosecurity, preventive medication, vaccines, disease history, and production management. Multivariable population-averaged statistical models were used to determine the relationships among antimicrobial susceptibility patterns and potential herd-level risk factors. Controlling for herd type (antimicrobial-free versus conventional), each antimicrobial-bacterial species combination yielded unique combinations of risk factors; however, housing type, history of rhinitis, farm ventilation, and history of swine flu were significant in more than one model. A variety of herd-level practices were associated with the prevalence of antimicrobial resistance on swine farms. Further studies are encouraged when considering interventions for antimicrobial resistance on both antimicrobial-free and conventional farms