Tucuxi

Dubovitskaya, Alevtina; Buclin, Thierry; Schumacher, Michael; Aberer, Karl; Thoma, Yann

doi:10.1145/3107411.3107439

Cited by 14 publications

(5 citation statements)

References 16 publications

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“…Clinical pharmacokinetics is a field dedicated to optimizing the dosing regimens of specific medications, aiming to enhance their effectiveness while minimizing undesirable effects in clinical practice [ 1 , 2 ]. In practical terms, therapeutic drug monitoring (TDM) serves as the application of this discipline in a clinical setting, involving individualized dosing based on tracking drug concentrations.…”

Section: Introductionmentioning

confidence: 99%

“…Primary tools employed for TDM include pharmacokinetic (PK) nomograms, equations, and software. Unfortunately, therapeutic failures and toxicities remain prevalent, particularly in medications and populations exhibiting substantial inter- and intraindividual PK variability [ 1 , 2 ].…”

Section: Introductionmentioning

confidence: 99%

“…To address this issue, computer programs have been developed to assist professionals in predicting the optimal dosage regimen for individual patients [ 1 , 2 ]. Model-informed precision dosing (MIPD) represents an advanced discipline within TDM, providing dose individualization primarily based on TDM measurements, and secondarily on PK population models that account for individual characteristics and intra/inter-patient variabilities.…”

Section: Introductionmentioning

confidence: 99%

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Model-Informed Precision Dosing Software Tools for Dosage Regimen Individualization: A Scoping Review

Del Valle-Moreno,

Suarez-Casillas,

Mejías-Trueba

et al. 2023

Pharmaceutics

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Background: Pharmacokinetic nomograms, equations, and software are considered the main tools available for Therapeutic Drug Monitoring (TDM). Model-informed precision dosing (MIPD) is an advanced discipline of TDM that allows dose individualization, and requires a software for knowledge integration and statistical calculations. Due to its precision and extensive applicability, the use of these software is widespread in clinical practice. However, the currently available evidence on these tools remains scarce. Objectives: To review and summarize the available evidence on MIPD software tools to facilitate its identification, evaluation, and selection by users. Methods: An electronic literature search was conducted in MEDLINE, EMBASE, OpenAIRE, and BASE before July 2022. The PRISMA-ScR was applied. The main inclusion criteria were studies focused on developing software for use in clinical practice, research, or modelling. Results: Twenty-eight software were classified as MIPD software. Ten are currently unavailable. The remaining 18 software were described in depth. It is noteworthy that all MIPD software used Bayesian statistical methods to estimate drug exposure and all provided a population model by default, except NONMEN. Conclusions: Pharmacokinetic software have become relevant tools for TDM. MIPD software have been compared, facilitating its selection for use in clinical practice. However, it would be interesting to standardize the quality and validate the software tools.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Model-Informed Precision Dosing Software Tools for Dosage Regimen Individualization: A Scoping Review

Del Valle-Moreno,

Suarez-Casillas,

Mejías-Trueba

et al. 2023

Pharmaceutics

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“…An automated CDSS software requires an embedded population pharmacokinetic (popPK) model for rifampicin to predict drug concentrations and propose meaningful adjustments correctly. The software will be based on the core computing engine of Tucuxi [10, 11], a model-informed precision dosing software for Bayesian forecasting already developed that helps clinicians adapt medical drug dosages. This software computations are based on the popPK models implemented within the software and the model-identified influential patient covariates, potential drug concentration measurements, and specific drug PK targets validated by TDM experts for dose optimization.…”

Section: Methods and Analysismentioning

confidence: 99%

The TuberXpert project protocol: Towards a Clinical Decision Support System for therapeutic anti-tuberculosis medical drugs monitoring in Tanzania

Thoma,

Cathignol,

Pétermann

et al. 2023

Preprint

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IntroductionThe End Tuberculosis (TB) Strategy requires a novel patient treatment approach contrary to the “one-size fits all” model. It is well known that each patient’s physiology is different and leads to various rates of drug elimination. Therapeutic Drug Monitoring (TDM) offers a way to manage drug dosage adaptation but requires trained pharmacologists, which is scarce in resource-limited settings. We, therefore, aim to create an unprecedented Clinical Decision Support System (CDSS) that will offer a printable report containing advice for the field clinicians to guide the adaptation of TB treatment depending on the patient.Methods and analysisA population pharmacokinetic model for rifampicin will be developed and thoroughly validated, before being implemented into Tucuxi, an existing Model Informed Precision Dosing software. A cross-sectional study will be conducted to define the best way to display information to clinicians. In addition, a pragmatic prospective study will focus on a decision tree that will be implemented as a CDSS. Expert pharmacologists will validate the CDSS, and, finally, field implementation in Tanzania will occur coupled with a prospective study to assess clinicians’ adherence to the CDSS recommendations.Ethics and disseminationThis is a game-changing transdisciplinary project combining technology and pharmacometrics to enable appropriate dosages of anti-TB drugs in TB patients at various levels of the healthcare delivery system in TB-endemic settings. The project is part of the Adaptive Diseases control Expert Programme in Tanzania, which has been approved at the local health research committee serving Kibong’oto Infectious Diseases Hospital (KIDH) and National Health Research Committee with reference numbers KNCHREC003 and NIMR/HQ/R.8a/Vol.IX/2988, respectively. Furthermore, the Ministries of Health and Regional Administrative & Local Government Authority have endorsed the implementation of this protocol. Dissemination will be done through scientific publications, conferences, and local press in Tanzania. Social media will also be used to gain more visibility.Strengths and limitations of this study-To our knowledge, this is the first study to investigate the application of CDSS technology at varying healthcare delivery systems levels to guide TDM in TB patients in TB-endemic settings.-Routine implementation of TDM-CDSS, particularly for rifampicin, a backbone for TB treatment, is expected to transform TB’s clinical management in resource-limited settings.-Anti-TB dosage optimization will improve treatment outcomes of patients who would otherwise succumb or develop drug-resistant TB because of sub-optimal drug exposure. This will considerably contribute to the End TB strategy, particularly with arduous forms of TB with either Human Immunodeficiency Virus (HIV) co-infection or coexistent Diabetes Mellitus (DM) or malnutrition.-Clinicians could also use the CDSS decision tree without access to IT infrastructure.-A lack of computer infrastructure in health facilities may prevent implementing a centralized system in resource-limited countries.

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“…We simulated the potential effect of different sparse-sampling strategies on pharmacokinetic (PK) parameter estimates and AUC 24 estimates using the Tucuxi Bayesian forecasting software (www.tucuxi.ch), using a validated two-compartment tobramycin population pharmacokinetic model developed for patients with and without CF by Hennig et al 12–14 Model implementation was verified in NONMEM on a set of test cases and demonstrated identical outputs. The Hennig et al model is based on 5605 concentration–time measurements from 732 patients (465 with CF and 267 without CF).…”

Section: Methodsmentioning

confidence: 99%

Bayesian Forecasting for Intravenous Tobramycin Dosing in Adults With Cystic Fibrosis Using One Versus Two Serum Concentrations in a Dosing Interval

Drennan

Thoma

Barry

et al. 2021

Therapeutic Drug Monitoring

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Background: Intravenous tobramycin treatment requires therapeutic drug monitoring (TDM) to ensure safety and efficacy when used for prolonged treatment, as in infective exacerbations of cystic fibrosis. The 24-hour area under the concentration–time curve (AUC24) is widely used to guide dosing; however, there remains variability in practice around methods for its estimation. The objective of this study was to determine the potential for a sparse-sampling strategy using a single postinfusion tobramycin concentration and Bayesian forecasting to assess the AUC24 in routine practice. Methods: Adults with cystic fibrosis receiving once-daily tobramycin had paired concentrations measured 2 hours (c1) and 6 hours (c2) after the end of infusion as routine monitoring. AUC24 exposures were estimated using Tucuxi, a Bayesian forecasting application that incorporates a validated population pharmacokinetic model. Simulations were performed to estimate AUC24 using the full data set using c1 and c2, compared with estimates using depleted data sets (c1 or c2 only), with and without concentration data from earlier in the course. The agreement between each simulation condition and the reference was assessed graphically and numerically using the median difference (∆) AUC24 and (relative) root mean square error (rRMSE) as measures of bias and accuracy, respectively. Results: A total of 55 patients contributed 512 concentrations from 95 tobramycin courses and 256 TDM episodes. Single concentration methods performed well, with median ∆AUC24 <2 mg·h·L−1 and rRMSE of <15% for sequential c1 and c2 conditions. Conclusions: Bayesian forecasting implemented in Tucuxi, using single postinfusion concentrations taken 2–6 hours after tobramycin administration, yield similar exposure estimates to more intensive (two-sample) methods and are suitable for routine TDM practice.

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Tucuxi

Cited by 14 publications

References 16 publications

Model-Informed Precision Dosing Software Tools for Dosage Regimen Individualization: A Scoping Review

Model-Informed Precision Dosing Software Tools for Dosage Regimen Individualization: A Scoping Review

The TuberXpert project protocol: Towards a Clinical Decision Support System for therapeutic anti-tuberculosis medical drugs monitoring in Tanzania

Bayesian Forecasting for Intravenous Tobramycin Dosing in Adults With Cystic Fibrosis Using One Versus Two Serum Concentrations in a Dosing Interval

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