JMIR Publications

ABSTRACT

Background:

Tanzania has a high burden of Tuberculosis (TB). In 2018, the country estimated 142’000 new TB cases. The country is also facing the challenges posed by TB/HIV coinfection and an emerging TB/diabetes mellitus (DM) epidemic. Treatment of TB/HIV and TB/DM patients is complicated, with a considerable proportion of cases ending with unfavorable outcomes including treatment failure, relapse, and death. Unfavorable treatment outcomes are largely driven by both incomplete observance and pharmacokinetic variability of first line antitubercular (anti-TB) drugs, subsequently leading to insufficient circulating drug exposure and development of drug resistant TB, or to excessive exposure and toxicity leading to treatment interruption. The global community, through the END TB strategy [1], has declared its willingness to end TB by 2035, and a central component of the arsenal for this includes resorting to the correct use of anti-TB drugs, in particular the first-line agents: isoniazid, rifampicin, ethambutol and pyrazinamide. Precision dosage of medicines based on drug concentration monitoring is an important patient-centered approach for optimizing observance and efficacy and preventing adverse effects. Such a process can take advantage of software tools like Tucuxi [2] [3] – a software already developed by HEIG-VD and CHUV – that will be used as the core computing component for the interpretation of drug concentration results and consequential dosage optimization. Aims and

Objective:

We will develop an automated Clinical Decision Support System (CDSS) to help practitioners with the dosage adaptation of rifampicin, one of the essential medical drugs targeting TB, known for large pharmacokinetic variability and frequent suboptimal blood exposure. Such an advanced system will encourage the spread of a dosage-individualization culture, including among practitioners not specialized in pharmacology. We will give particular attention to the design of the measurement and interpretation report, so to fit the final users’ needs. The software, in order to predict correctly drug concentrations and to propose meaningful adjustments, requires an embedded pharmacokinetic model for rifampicin. Thus, the objectives of this project are to: (1) in a first step, develop the appropriate population pharmacokinetic (popPK) model for rifampicin for Tanzanian patients and implement it within Tucuxi; (2) optimize the reporting of relevant information to practitioners for drug dosage adjustment; (3) automate the delivery of the report in line with the measurement of drug concentration [4]; (4) validate and implement the final system in the field.

Methods:

The three teams will combine their efforts to deliver the first automated Therapeutic Drug Monitoring (TDM) CDSS for TB. A study, led by Kibong’oto Infectious Diseases Hospital (KIDH), involving final users in Tanzania, will help organizing the information required as well as devising the best way to represent it. It will consist of interviews where mock reports will be shown to participants to identify the most required items. In parallel, a rifampicin popPK model will be developed taking advantages of (a) the published literature, complemented with (b) data provided by a study already planned in Tanzania and (c) samples collected within this project. This model will then be implemented within the Tucuxi framework by CHUV and HEIG-VD. The automated report generation will be developed by HEIG-VD and validated through selected case studies by CHUV and KIDH. Finally, the KIDH team will validate the generated report by confronting the dosage adjustments with decisions of clinicians in the field, within a prospective study comparing the adjusted utilization of rifampicin with its traditional prescription during the control period. Expected

Results:

At the end of the TuberXpert project, Tanzania will possess a new tool to help the practitioners with the adaptation of drug dosage targeting complicated TB cases (TB/HIV, TB/DM, TB/malnutrition). This automated system will be validated and used in the field, and the system will be proposed to other countries affected by endemic TB. In addition, this approach will serve as proof of concept regarding the feasibility and suitability of TDM for further anti-TB drugs, in particular second-line treatments considered important to monitor. It will also be fairly straightforward to extend the software's capabilities to other drugs, which will help improve the management of neglected diseases by making the most of TDM.

Conclusions:

The results of this 3-year project will pave the way of automated TDM, not only for anti-TB drugs, but also for all drugs that can be monitored.