Application of the Multistate Tuberculosis Pharmacometric Model in Patients With Rifampicin‐Treated Pulmonary Tuberculosis

Svensson, Robin; Simonsson, Ulrika S. H.

doi:10.1002/psp4.12079

Cited by 28 publications

(45 citation statements)

References 34 publications

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“…4 As such, the estimation and prediction of drug effects on this phenotypic resistant subpopulation is crucial in order to develop and predict a successful treatment regimen. The MTP model was developed using in vitro information from classical time-kill experiments and has been successful in describing the effects after exposure to rifampicin, not only for in vitro in monotherapy but also for assessing efficacy of drug combinations in vitro together with the General Pharmacodynamic Interaction model, 5,6 in vivo monotherapy, 7 in vivo assessment of drug combinations, 8 and clinical settings 9 suggesting its value for describing drug effects as well as for translational applications.…”

mentioning

confidence: 99%

Forecasting Clinical Dose–Response From Preclinical Studies in Tuberculosis Research: Translational Predictions With Rifampicin

Wicha

Clewe

Svensson

et al. 2018

Clin Pharma and Therapeutics

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A crucial step for accelerating tuberculosis drug development is bridging the gap between preclinical and clinical trials. In this study, we developed a preclinical model‐informed translational approach to predict drug effects across preclinical systems and early clinical trials using the in vitro‐based Multistate Tuberculosis Pharmacometric (MTP) model using rifampicin as an example. The MTP model predicted rifampicin biomarker response observed in 1) a hollow‐fiber infection model, 2) a murine study to determine pharmacokinetic/pharmacodynamic indices, and 3) several clinical phase IIa early bactericidal activity (EBA) studies. In addition, we predicted rifampicin biomarker response at high doses of up to 50 mg/kg, leading to an increased median EBA0‐2 days (90% prediction interval) of 0.513 log CFU/mL/day (0.310; 0.701) compared to the standard dose of 10 mg/kg of 0.181 log/CFU/mL/day (0.076; 0.483). These results suggest that the translational approach could assist in the selection of drugs and doses in early‐phase clinical tuberculosis trials.

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mentioning

confidence: 99%

Forecasting Clinical Dose–Response From Preclinical Studies in Tuberculosis Research: Translational Predictions With Rifampicin

Wicha

Clewe

Svensson

et al. 2018

Clin Pharma and Therapeutics

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“…For example, recently a multistate tuberculosis pharmacometric model describing different bacterial states of Mycobacterium tuberculosis was developed based on in vitro data [4]. For clinical implementation of this model [46], most of the parameters pertaining to the natural bacterial growth were fixed to the in vitro estimates; however the exposure-response parameters related to drug effect had to be estimated from clinical data and were different from the in vitro drug effect parameters. More research efforts are needed in this area to better facilitate the quantitative translation of mechanistic PK/PD models to clinical situations.…”

Section: Bench To Bedside Translation Of Pk/pd Models For Anti-infectmentioning

confidence: 99%

Translational PK/PD of anti-infective therapeutics

Rathi

Lee

2016

Drug Discovery Today: Technologies

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Translational PK/PD modeling has emerged as a critical technique for quantitative analysis of the relationship between dose, exposure and response of antibiotics. By combining model components for pharmacokinetics, bacterial growth kinetics and concentration-dependent drug effects, these models are able to quantitatively capture and simulate the complex interplay between antibiotic, bacterium and host organism. Fine-tuning of these basic model structures allows to further account for complicating factors such as resistance development, combination therapy, or host responses. With this tool set at hand, mechanism-based PK/PD modeling and simulation allows to develop optimal dosing regimens for novel and established antibiotics for maximum efficacy and minimal resistance development.

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“…The Multistate Tuberculosis Pharmacometric (MTP) model, which predicts the change in bacterial number for fast-(F), slow-(S), and non-multiplying (N) bacteria, with and without drug effects, is a semi-mechanistic PK-PD model for studying exposure-response relationships for anti-tubercular drugs and was first developed using in vitro data (5). The MTP model has successfully been implemented by Chen et al to estimate drug efficacy in a murine model (6,7), to quantify human early bacterial activity with clinical trial simulations (8) and for predicting early bacterial activity in humans using only in vitro information (9). Chunli Chen and Sebastian G. Wicha contributed equally to this work.…”

Section: Introductionmentioning

confidence: 99%

Comparisons of Analysis Methods for Assessment of Pharmacodynamic Interactions Including Design Recommendations

Chen

Wicha

Nordgren

et al. 2018

AAPS J

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Quantitative evaluation of potential pharmacodynamic (PD) interactions is important in tuberculosis drug development in order to optimize Phase 2b drug selection and ultimately to define clinical combination regimens. In this work, we used simulations to (1) evaluate different analysis methods for detecting PD interactions between two hypothetical anti-tubercular drugs in in vitro time-kill experiments, and (2) provide design recommendations for evaluation of PD interactions. The model used for all simulations was the Multistate Tuberculosis Pharmacometric (MTP) model linked to the General Pharmacodynamic Interaction (GPDI) model. Simulated data were re-estimated using the MTP-GPDI model implemented in Bliss Independence or Loewe Additivity, or using a conventional model such as an Empirical Bliss Independence-based model or the Greco model based on Loewe Additivity. The GPDI model correctly characterized different PD interactions (antagonism, synergism, or asymmetric interaction), regardless of the underlying additivity criterion. The commonly used conventional models were not able to characterize asymmetric PD interactions, i.e., concentration-dependent synergism and antagonism. An optimized experimental design was developed that correctly identified interactions in ≥ 94% of the evaluated scenarios using the MTP-GPDI model approach. The MTP-GPDI model approach was proved to provide advantages to other conventional models for assessing PD interactions of anti-tubercular drugs and provides key information for selection of drug combinations for Phase 2b evaluation.

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Application of the Multistate Tuberculosis Pharmacometric Model in Patients With Rifampicin‐Treated Pulmonary Tuberculosis

Cited by 28 publications

References 34 publications

Forecasting Clinical Dose–Response From Preclinical Studies in Tuberculosis Research: Translational Predictions With Rifampicin

Forecasting Clinical Dose–Response From Preclinical Studies in Tuberculosis Research: Translational Predictions With Rifampicin

Translational PK/PD of anti-infective therapeutics

Comparisons of Analysis Methods for Assessment of Pharmacodynamic Interactions Including Design Recommendations

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