Browsing by Author "Dooley, Kelly E"

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    Pharmacokinetics, SAfety/tolerability, and EFficacy of high-dose RIFampicin in tuberculosis-HIV co-infected patients on efavirenz- or dolutegravir-based antiretroviral therapy: study protocol for an open-label, phase II clinical trial (SAEFRIF)
    (2020-02-13) Nabisere, Ruth; Musaazi, Joseph; Denti, Paolo; Aber, Florence; Lamorde, Mohammed; Dooley, Kelly E; Aarnoutse, Rob; Sloan, Derek J; Sekaggya-Wiltshire, Christine
    Abstract Background Tuberculosis (TB) is a significant public health problem that causes substantial morbidity and mortality. Current first-line anti-TB chemotherapy, although very effective, has limitations including long-treatment duration with a possibility of non-adherence, drug interactions, and toxicities. Dose escalation of rifampicin, an important drug within the regimen, has been proposed as a potential route to higher treatment efficacy with shorter duration and some studies have suggested that dose escalation is safe; however, these have almost entirely been conducted among human immunodeficiency (HIV)-negative TB patients. TB-HIV co-infected patients on antiretroviral therapy (ART) are at increased risk of drug-drug interactions and drug-related toxicities. This study aims to determine the safety of higher doses of rifampicin and its effect on the pharmacokinetics of efavirenz (EFV) and dolutegravir (DTG) in TB-HIV co-infected patients. Methods This study is a randomized, open-label, phase IIb clinical trial among TB-HIV infected adult outpatients attending an HIV clinic in Kampala, Uganda. Patients newly diagnosed with TB will be randomized to either standard-dose or high-dose rifampicin (35 mg/kg) alongside standard TB treatment. ART-naïve patients will be randomly assigned to first-line ART regimens (DTG or EFV). Those who are already on ART (DTG or EFV) at enrollment will be continued on the same ART regimen but with dose adjustment of DTG to twice daily dosing. Participants will be followed every 2 weeks with assessment for toxicities at each visit and measurement of drug concentrations at week 6. At the end of intensive-phase therapy (8 weeks), all participants will be initiated on continuation-phase treatment using standard-dose rifampicin and isoniazid. Discussion This study should avail us with evidence about the effect of higher doses of rifampicin on the pharmacokinetics of EFV and DTG among TB-HIV co-infected patients. The trial should also help us to understand safety concerns of high-dose rifampicin among this vulnerable cohort. Trial registration ClinicalTrials.gov, ID: NCT03982277. Registered retrospectively on 11 June 2019.
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    A treatment recommender clinical decision support system for personalized medicine: method development and proof-of-concept for drug resistant tuberculosis
    (2022-03-02) Verboven, Lennert; Calders, Toon; Callens, Steven; Black, John; Maartens, Gary; Dooley, Kelly E; Potgieter, Samantha; Warren, Robin M; Laukens, Kris; Van Rie, Annelies
    Background Personalized medicine tailors care based on the patient’s or pathogen’s genotypic and phenotypic characteristics. An automated Clinical Decision Support System (CDSS) could help translate the genotypic and phenotypic characteristics into optimal treatment and thus facilitate implementation of individualized treatment by less experienced physicians. Methods We developed a hybrid knowledge- and data-driven treatment recommender CDSS. Stakeholders and experts first define the knowledge base by identifying and quantifying drug and regimen features for the prototype model input. In an iterative manner, feedback from experts is harvested to generate model training datasets, machine learning methods are applied to identify complex relations and patterns in the data, and model performance is assessed by estimating the precision at one, mean reciprocal rank and mean average precision. Once the model performance no longer iteratively increases, a validation dataset is used to assess model overfitting. Results We applied the novel methodology to develop a treatment recommender CDSS for individualized treatment of drug resistant tuberculosis as a proof of concept. Using input from stakeholders and three rounds of expert feedback on a dataset of 355 patients with 129 unique drug resistance profiles, the model had a 95% precision at 1 indicating that the highest ranked treatment regimen was considered appropriate by the experts in 95% of cases. Use of a validation data set however suggested substantial model overfitting, with a reduction in precision at 1 to 78%. Conclusion Our novel and flexible hybrid knowledge- and data-driven treatment recommender CDSS is a first step towards the automation of individualized treatment for personalized medicine. Further research should assess its value in fields other than drug resistant tuberculosis, develop solid statistical approaches to assess model performance, and evaluate their accuracy in real-life clinical settings.