Response to "Pharmacometric Approach to Evaluate Drug Dosing Adherence".
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ABSTRACT:
SUBMITTER: Ding J
PROVIDER: S-EPMC8359226 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:During a pandemic caused by a novel pathogen (NP), drug repurposing offers the potential of a rapid treatment response via a repurposed drug (RD) while more targeted treatments are developed. Five steps of model-informed drug repurposing (MIDR) are discussed: (i) utilize RD product label and in vitro NP data to determine initial proof of potential, (ii) optimize potential posology using clinical pharmacokinetics (PK) considering both efficacy and safety, (iii) link events in the viral life cycle to RD PK, (iv) link RD PK to clinical and virologic outcomes, and optimize clinical trial design, and (v) assess RD treatment effects from trials using model-based meta-analysis. Activities which fall under these five steps are categorized into three stages: what can be accomplished prior to an NP emergence (preparatory stage), during the NP pandemic (responsive stage) and once the crisis has subsided (retrospective stage). MIDR allows for extraction of a greater amount of information from emerging data and integration of disparate data into actionable insight.
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Project description:Daptomycin is a candidate for therapeutic drug monitoring (TDM). The objectives of this work were to implement and compare two pharmacometric tools for daptomycin TDM and precision dosing. A nonparametric population PK model developed from patients with bone and joint infection was implemented into the BestDose software. A published parametric model was imported into Tucuxi. We compared the performance of the two models in a validation dataset based on mean error (ME) and mean absolute percent error (MAPE) of individual predictions, estimated exposure and predicted doses necessary to achieve daptomycin efficacy and safety PK/PD targets. The BestDose model described the data very well in the learning dataset. In the validation dataset (94 patients, 264 concentrations), 21.3% of patients were underexposed (AUC24h < 666 mg.h/L) and 31.9% of patients were overexposed (Cmin > 24.3 mg/L) on the first TDM occasion. The BestDose model performed slightly better than the model in Tucuxi (ME = -0.13 ± 5.16 vs. -1.90 ± 6.99 mg/L, p < 0.001), but overall results were in agreement between the two models. A significant proportion of patients exhibited underexposure or overexposure to daptomycin after the initial dosage, which supports TDM. The two models may be useful for model-informed precision dosing.
| S-EPMC8779485 | biostudies-literature
Project description:Moxidectin is a frontrunner drug candidate in the treatment of strongyloidiasis. A dose of 8 mg is recommended to treat this indication, which shows a reasonably good efficacy and tolerability profile. Yet, owing to the unique life cycle of Strongyloides stercoralis (S. stercoralis) that entails internal autoinfection, a curative treatment would be desirable. Population-based pharmacometric modeling that would help to identify an ideal dosing strategy are yet lacking. The aims of this study were to (i) explore the exposure-efficacy response relationship of moxidectin in treating S. stercoralis and (ii) evaluate whether moxidectin treatment outcomes in terms of cure rates at baseline as compared to post-treatment could be optimized. Our pharmacodynamic model suggests high predictive power (area under the concentration time curve-receiver operating characteristic [AUC-ROC] 0.817) in the probability of being cured by linking an exposure metric (i.e., AUC0-24 or maximum concentration [Cmax ]) to baseline infection intensity. Pharmacometric simulations indicate that with a minimum dose of 4 mg a maximum cure rate of ~ 95% is established in the low infection intensity group (larvae per gram [LPG] ≥0.4-1), whereas in the moderate-to-high intensity group (LPG >1) the cure rate plateaus at ~ 87%, following an 8 mg dose. To enhance efficacy further, studies using repeated dosing based on the duration of the autoinfection cycle, for example a two-dose regimen 3 weeks apart should be considered. Simulations revealed similar Cmax in both treatment courses of a two-dose regimen; hence safety should not be a concern. Collectively, our results provide evidence-based guidance for enhanced dosing strategies and should be considered when designing future treatment strategies.
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Project description:PurposeNumerous studies have investigated causes of warfarin dose variability in adults, whereas studies in children are limited both in numbers and size. Mechanism-based population modelling provides an opportunity to condense and propagate prior knowledge from one population to another. The main objectives with this study were to evaluate the predictive performance of a theoretically bridged adult warfarin model in children, and to compare accuracy in dose prediction relative to published warfarin algorithms for children.MethodAn adult population pharmacokinetic/pharmacodynamic (PK/PD) model for warfarin, with CYP2C9 and VKORC1 genotype, age and target international normalized ratio (INR) as dose predictors, was bridged to children using allometric scaling methods. Its predictive properties were evaluated in an external data set of children 0-18 years old, including comparison of dose prediction accuracy with three pharmacogenetics-based algorithms for children.ResultsOverall, the bridged model predicted INR response well in 64 warfarin-treated Swedish children (median age 4.3 years), but with a tendency to overpredict INR in children ≤2 years old. The bridged model predicted 20 of 49 children (41 %) within ± 20 % of actual maintenance dose (median age 7.2 years). In comparison, the published dosing algorithms predicted 33-41 % of the children within ±20 % of actual dose. Dose optimization with the bridged model based on up to three individual INR observations increased the proportion within ±20 % of actual dose to 70 %.ConclusionA mechanism-based population model developed on adult data provides a promising first step towards more individualized warfarin therapy in children.
| S-EPMC3651819 | biostudies-literature
Project description:The indicated dose of 4-factor prothrombin complex concentrate (4F-PCC) for urgent vitamin K antagonist (VKA) reversal in patients with an international normalized ratio (INR) of 2 to 4 is 25 IU/kg, but there is no indicated dose for INR <2. We explored 4F-PCC dosing strategies for baseline INR <2. Clinical trial data were used to develop pharmacometric models for Factor X (FX) and FII, accounting for covariates including baseline INR. FX and FII levels over time were simulated for mean baseline INR levels of the clinical trial participants plus baseline INRs 3.1, 1.9, and 1.6. For each INR, 200 virtual male patients were simulated to evaluate 4F-PCC doses of 35, 25, 20, 15, 12.5, and 10 IU/kg. Given an elevated bleeding risk with VKA therapy in Japanese vs Western populations, results were stratified by Japanese and non-Japanese patients. Target levels of FX and FII were ≥50% activity at 30 minutes after dosing in ≥80% of patients. FX- and FII-time models were developed with 1088 FX observations from 193 patients and 1074 FII observations from 192 patients. Model-based simulations indicated that at baseline INR 3.1, ≥80% of patients achieved ≥50% FX and FII activity with 25 IU/kg and 20 IU/kg 4F-PCC, respectively; at baseline INR 1.9, corresponding doses were 20 IU/kg and 15 IU/kg 4F-PCC, and at baseline INR 1.6, corresponding doses were 15 IU/kg, and 10 IU/kg 4F-PCC. Trends in Japanese and non-Japanese patients were similar. In conclusion, low 4F-PCC doses (15-20 IU/kg) may be sufficient to achieve hemostatic levels of FX and FII in Japanese and non-Japanese patients with baseline INR <2.
| S-EPMC7479946 | biostudies-literature
Project description:Food-drug interactions (FDIs) arise when nutritional dietary consumption regulates biochemical mechanisms involved in drug metabolism. This study proposes FDMine, a novel systematic framework that models the FDI problem as a homogenous graph. Our dataset consists of 788 unique approved small molecule drugs with metabolism-related drug-drug interactions and 320 unique food items, composed of 563 unique compounds. The potential number of interactions is 87,192 and 92,143 for disjoint and joint versions of the graph. We defined several similarity subnetworks comprising food-drug similarity, drug-drug similarity, and food-food similarity networks. A unique part of the graph involves encoding the food composition as a set of nodes and calculating a content contribution score. To predict new FDIs, we considered several link prediction algorithms and various performance metrics, including the precision@top (top 1%, 2%, and 5%) of the newly predicted links. The shortest path-based method has achieved a precision of 84%, 60% and 40% for the top 1%, 2% and 5% of FDIs identified, respectively. We validated the top FDIs predicted using FDMine to demonstrate its applicability, and we relate therapeutic anti-inflammatory effects of food items informed by FDIs. FDMine is publicly available to support clinicians and researchers.
| S-EPMC8776972 | biostudies-literature
Project description:PURPOSE:Clearance via renal replacement therapy (RRT) can significantly alter the pharmacokinetic profile of drugs. The aim of this study was (i) to improve the use of clinical trial data and (ii) to provide a model that allows quantification of all aspects of drug elimination via RRT including adsorption to dialysis membranes and/or degradation of the drug in the dialysate. METHODS:An integrated dialysis pharmacometric (IDP) model was developed to simultaneously incorporate all available RRT information. The sensitivity, accuracy and precision of the IDP model was compared to conventional approaches in clinical trial simulations and applied to clinical datasets of teicoplanin and doripenem. RESULTS:The IDP model was more accurate, precise and sensitive than conventional plasma-concentration-based approaches when estimating the clearanceRRT (relative bias <1%). In contrast to conventional approaches, adsorption and degradation were quantifiable using the IDP model (relative bias: -1.1% and?-?1.9%, respectively). Applied to clinical data, clearanceRRT, drug degradation (effluent-half-lifedoripenem: 13.5 h-1) and adsorption (polysulphone adsorption capacityteicoplanin: 31.2 mg) were assessed. CONCLUSION:The IDP model allows accurate, precise and sensitive characterization of clearanceRRT, adsorption and degradation. Successful quantification of all aspects of clearanceRRT in clinical data demonstrated the benefit of the IDP model as compared to conventional approaches.
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Project description:AimsThe aims of the study were to characterize the pharmacokinetics (PK) of alogliptin in healthy and type 2 diabetes mellitus (T2DM) subjects using a population PK approach and to assess the influence of various covariates on alogliptin exposure.MethodsPlasma concentration data collected from two phase 1 studies and one phase 3 study following administration of alogliptin (12.5-400 mg) were used for the PK model development. One- and two-compartment models were evaluated as base structural PK models. The impact of selected covariates was assessed using stepwise forward selection and backward elimination procedures. The predictability and robustness of the final model was evaluated using visual predictive check and bootstrap analyses. The final model was used to perform simulations and guide appropriate dose adjustments.ResultsA two-compartment model with first-order absorption and elimination best described the alogliptin concentration vs. time profiles. Creatinine clearance and weight had a statistically significant effect on the oral clearance (CL/F) of alogliptin. The model predicted a lower CL/F (17%, 35%, 80%) and a higher systemic exposure (56%, 89%, 339%) for subjects with mild, moderate and severe renal impairment, respectively, compared with healthy subjects. Effect of weight on CL/F was not considered clinically relevant. Simulations at different doses of alogliptin support the approved doses of 12.5 mg and 6.25 mg for patients with moderate and severe renal impairment, respectively.ConclusionsThe PK of alogliptin was well characterized by the model. The analysis suggested an alogliptin dose adjustment for subjects with moderate-to-severe renal impairment and no dose adjustments based on weight.
| S-EPMC4799927 | biostudies-literature
Project description:AimTo determine optimal sampling strategies to allow the calculation of clinical pharmacokinetic parameters for selected antipsychotic medicines using a pharmacometric approach.MethodsThis study utilized previous population pharmacokinetic parameters of the antipsychotic medicines aripiprazole, clozapine, olanzapine, perphenazine, quetiapine, risperidone (including 9-OH risperidone) and ziprasidone. d-optimality was utilized to identify time points which accurately predicted the pharmacokinetic parameters (and expected error) of each drug at steady-state. A standard two stage population approach (STS) with MAP-Bayesian estimation was used to compare area under the concentration-time curves (AUC) generated from sparse optimal time points and rich extensive data. Monte Carlo Simulation (MCS) was used to simulate 1000 patients with population variability in pharmacokinetic parameters. Forward stepwise regression analysis was used to determine the most predictive time points of the AUC for each drug at steady-state.ResultsThree optimal sampling times were identified for each antipsychotic medicine. For aripiprazole, clozapine, olanzapine, perphenazine, risperidone, 9-OH risperidone, quetiapine and ziprasidone the CV% of the apparent clearance using optimal sampling strategies were 19.5, 8.6, 9.5, 13.5, 12.9, 10.0, 16.0 and 10.7, respectively. Using the MCS and linear regression approach to predict AUC, the recommended sampling windows were 16.5-17.5 h, 10-11 h, 23-24 h, 19-20 h, 16.5-17.5 h, 22.5-23.5 h, 5-6 h and 5.5-6.5 h, respectively.ConclusionThis analysis provides important sampling information for future population pharmacokinetic studies and clinical studies investigating the pharmacokinetics of antipsychotic medicines.
| S-EPMC4239974 | biostudies-literature
Project description:IntroductionPangenotypic, all-oral direct-acting antivirals, such as glecaprevir/pibrentasvir (G/P), are recommended for treatment of hepatitis C virus (HCV) infection. Concerns exist about the impact on efficacy in patients with suboptimal adherence, particularly with shorter treatment durations. These post hoc analyses evaluated adherence (based on pill count) in patients prescribed 8- or 12-week G/P, the impact of nonadherence on sustained virologic response at post-treatment week 12 (SVR12), factors associated with nonadherence, and efficacy in patients interrupting G/P treatment.MethodsData were pooled from 10 phase 3 clinical trials of treatment-naive patients with HCV genotype 1-6 without cirrhosis/with compensated cirrhosis (treatment adherence analysis) and 13 phase 3 clinical trials of all patients with HCV (interruption analysis).ResultsAmong 2,149 patients included, overall mean adherence was 99.4%. Over the treatment duration, adherence decreased (weeks 0-4: 100%; weeks 5-8: 98.3%; and weeks 9-12: 97.1%) and the percentage of patients with ≥80% or ≥90% adherence declined. SVR12 rate in the intention-to-treat (ITT) population was 97.7% (modified ITT SVR12 99.3%) and remained high in nonadherent patients in the modified ITT population (<90%: 94.4%-100%; <80%: 83.3%-100%). Psychiatric disorders were associated with <80% adherence, and shorter treatment duration was associated with ≥80% adherence. Among 2,902 patients in the interruption analysis, 33 (1.1%) had a G/P treatment interruption of ≥1 day, with an SVR12 rate of 93.9% (31/33). No virologic failures occurred.DiscussionThese findings support the impact of treatment duration on adherence rates and further reinforce the concept of "treatment forgiveness" with direct-acting antivirals.
| S-EPMC8389353 | biostudies-literature