| 1. |
- Chen, Chunli, et al.
(författare)
-
Assessing Pharmacodynamic Interactions in Mice using the Multistate Tuberculosis Pharmacometric and General Pharmacodynamic Interaction Models
- 2017
-
Ingår i: CPT. - : John Wiley & Sons. - 2163-8306. ; 6:11, s. 787-797
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- The aim of this study was to investigate pharmacodynamic (PD) interactions in mice infected with Mycobacterium tuberculosis using population pharmacokinetics (PKs), the Multistate Tuberculosis Pharmacometric (MTP) model, and the General Pharmacodynamic Interaction (GPDI) model. Rifampicin, isoniazid, ethambutol, or pyrazinamide were administered in monotherapy for 4 weeks. Rifampicin and isoniazid showed effects in monotherapy, whereas the animals became moribund after 7 days with ethambutol or pyrazinamide alone. No PD interactions were observed against fast-multiplying bacteria. Interactions between rifampicin and isoniazid on killing slow and non-multiplying bacteria were identified, which led to an increase of 0.86 log(10) colony-forming unit (CFU)/lungs at 28 days after treatment compared to expected additivity (i.e., antagonism). An interaction between rifampicin and ethambutol on killing non-multiplying bacteria was quantified, which led to a decrease of 2.84 log(10) CFU/lungs at 28 days after treatment (i.e., synergism). These results show the value of pharmacometrics to quantitatively assess PD interactions in preclinical tuberculosis drug development.
|
|
| 2. |
- Chen, Chunli, et al.
(författare)
-
Population pharmacokinetics, optimised design and sample size determination for rifampicin, isoniazid, ethambutol and pyrazinamide in the mouse
- 2016
-
Ingår i: European Journal of Pharmaceutical Sciences. - : Elsevier BV. - 0928-0987 .- 1879-0720. ; 93, s. 319-333
-
Tidskriftsartikel (refereegranskat)abstract
- The current first-line therapy for drug-susceptible tuberculosis consists of rifampicin (RIF), isoniazid (INH), pyrazinamide (PZA) and ethambutol (EMB). In this study, we determined the population pharmacokinetics (PopPK) of RIF, INH, EMB and PZA using original experimental sampling designs for single-dose intravenous (IV) and single- and multiple-dose oral administration studies in the mouse model, and used these PopPK models to develop and evaluate new, more informative sampling designs with the aim of reducing the number of animals required for each drug. The RIF, INH, EMB and PZA blood concentrations after single oral and IV doses and multiple-dose oral administrations based on the original designs were used in the PopPK analysis using NONMEM software. The final PopPK models described the data well, Stochastic simulation and estimation were used to optimise the designs. The relative bias and relative imprecision of each pharmacokinetic parameter for each drug were derived and assessed to choose the final designs. The final single-dose IV and oral designs included up to eight samples per mouse with a total of 24 mice required for RIF and EMB and 33 mice for INH and PZA. In the new multiple-dose (zipper) oral designs, the mice were divided into two groups of three per dose, and four samples were taken from each mouse to cover all seven or eight sampling time points. The final number of mice required for the multiple-dose oral designs was 30 for RIF, INH and EMB, 36 for PZA. The number of mice required in the new designs for RIF, INH and EMB was decreased by up to 7-fold and the relative bias and relative imprecision in the parameter estimates were at least similar to those in the original designs.
|
|
| 3. |
- Chen, Chunli, et al.
(författare)
-
The multistate tuberculosis pharmacometric model : a semi-mechanistic pharmacokinetic-pharmacodynamic model for studying drug effects in an acute tuberculosis mouse model
- 2017
-
Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 44:2, s. 133-141
-
Tidskriftsartikel (refereegranskat)abstract
- The Multistate Tuberculosis Pharmacometric (MTP) model, a pharmacokinetic-pharmacodynamic disease model, has been used to describe the effects of rifampicin on Mycobacterium tuberculosis (M. tuberculosis) in vitro. The aim of this work was to investigate if the MTP model could be used to describe the rifampicin treatment response in an acute tuberculosis mouse model. Sixty C57BL/6 mice were intratracheally infected with M. tuberculosis H37Rv strain on Day 0. Fifteen mice received no treatment and were sacrificed on Days 1, 9 and 18 (5 each day). Twenty-five mice received oral rifampicin (1, 3, 9, 26 or 98 mg·kg-1·day-1; Days 1–8; 5 each dose level) and were sacrificed on Day 9. Twenty mice received oral rifampicin (30 mg·kg-1·day-1; up to 8 days) and were sacrificed on Days 2, 3, 4 and 9 (5 each day). The MTP model was linked to a rifampicin population pharmacokinetic model to describe the change in colony forming units (CFU) in the lungs over time. The transfer rates between the different bacterial states were fixed to estimates from in vitro data. The MTP model described well the change in CFU over time after different exposure levels of rifampicin in an acute tuberculosis mouse model. Rifampicin significantly inhibited the growth of fast-multiplying bacteria and stimulated the death of fast- and slow-multiplying bacteria. The data did not support an effect of rifampicin on non-multiplying bacteria possibly due to the short duration of the study. The pharmacometric modelling framework using the MTP model can be used to perform investigations and predictions of the efficacy of anti-tubercular drugs against different bacterial states.
|
|
| 4. |
- Smart, Sophie E., et al.
(författare)
-
Clinical predictors of antipsychotic treatment resistance: Development and internal validation of a prognostic prediction model by the STRATA-G consortium
- 2022
-
Ingår i: Schizophrenia Research. - : Elsevier. - 0920-9964 .- 1573-2509. ; 250
-
Tidskriftsartikel (refereegranskat)abstract
- IntroductionOur aim was to, firstly, identify characteristics at first-episode of psychosis that are associated with later antipsychotic treatment resistance (TR) and, secondly, to develop a parsimonious prediction model for TR.MethodsWe combined data from ten prospective, first-episode psychosis cohorts from across Europe and categorised patients as TR or non-treatment resistant (NTR) after a mean follow up of 4.18 years (s.d. = 3.20) for secondary data analysis. We identified a list of potential predictors from clinical and demographic data recorded at first-episode. These potential predictors were entered in two models: a multivariable logistic regression to identify which were independently associated with TR and a penalised logistic regression, which performed variable selection, to produce a parsimonious prediction model. This model was internally validated using a 5-fold, 50-repeat cross-validation optimism-correction.ResultsOur sample consisted of N = 2216 participants of which 385 (17 %) developed TR. Younger age of psychosis onset and fewer years in education were independently associated with increased odds of developing TR. The prediction model selected 7 out of 17 variables that, when combined, could quantify the risk of being TR better than chance. These included age of onset, years in education, gender, BMI, relationship status, alcohol use, and positive symptoms. The optimism-corrected area under the curve was 0.59 (accuracy = 64 %, sensitivity = 48 %, and specificity = 76 %).ImplicationsOur findings show that treatment resistance can be predicted, at first-episode of psychosis. Pending a model update and external validation, we demonstrate the potential value of prediction models for TR.
|
|
