A simple method to monitor serum concentrations of fluoxetine and its major metabolite for pharmacokinetic studies

Biomedical Chromatography

Emm

Yeleswaram

2011

A rapid, sensitive and selective bioanalytical method was developed for the simultaneous determination of fluoxetine and its primary metabolite norfluoxetine in human plasma. Sample preparation was based on supported liquid extraction (SLE) using methyl tert-butyl ether to extract the analytes from human plasma. Chromatography was performed on a Synergi 4 μ polar-RP column using a fast gradient. The ionization was optimized using ESI (+) and selectivity was achieved by tandem mass spectrometric analysis using MRM functions, m/z 310 → 44 for fluoxetine, m/z 296 → 134 for norfluoxetine and m/z 315 → 44 for fluoxetine-d5 (internal standard). The method is linear over the range of 0.05-20 ng/mL (using a human plasma sample volume of 0.1 mL) with a coefficient determination of greater than 0.999. The method is accurate and precise with intra-batch and inter-batch accuracy (%bias) of < ± 15% and precision (%CV) of <15% for both analytes. A run time of 4 min means a high throughput of samples can be achieved. To our knowledge, this method appears to be the most sensitive one reported so far for the quantitation of fluoxetine and norfluoxetine and can be used for routine therapeutic drug monitoring or pharmacokinetic studies.

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simultaneous determination of fluoxetine and its major active metabolite norfluoxetine in human plasma by LC‐MS/MS using supported liquid extraction

Biomedical Chromatography

Emm

Yeleswaram

2011

“…As a racemate, we adjusted some of its ADME and PK parameters according to the literature to make the predicted curve much better tting the experimental data. [16][17][18] The key ADME parameters predicted by ADME Predictor for the 18 drugs were all listed in the Table S1, including the detail for the adjusted parameters of Fluoxetine. The observed drug concentration data of each template drug was extracted from published concentration-time (C-T) curves using WebPlotDigitizer (https://automeris.io/WebPlotDigitizer/).…”

Section: Methodsmentioning

confidence: 99%

A New Approach for In Silico Prediction of Oral Drug Concentration Profiles in Human for Drug Candidates Lack Experimental Pharmacokinetic Data

Zhai

et al. 2021

Preprint

The purpose of this study is to develop a novel protocol to preliminarily predict the concentration profiles of a target drug based on the PBPK model of a structurally similar template drug by combining two software for PBPK modeling, the SimCYP simulator and ADMET Predictor. The method was evaluated by utilizing 13 drug pairs which come from 18 drugs in the built-in database of the SimCYP software. All drug pairs have their Tanimoto scores no less than 0.5. As each drug in a drug pair can serve both the target and template roles, in total, there are 26 sets studied in this work. Three versions (V1, V2 and V3) of models for the target drug were constructed by replacing the corresponding parameters of the template drug step by step with those predicted by ADME Predictor for the target drug. Normalized RMSE (NRMSE) were introduced for the evaluation of the model performance. We found that the performance of the three versions of models depends on structure similarity of the drug pairs. For Group I drug pairs (TS ≤ 0.7), V2 and V3 perform better than V1 in term of NRMSE; for Group II drug pairs (0.7 < TS ≤ 0.9), 8 out of 10 V3 models have NRMSE lower than 0.2, the cutoff we applied to judge if the simulated C-T curve is satisfactory or not. Obviously, V3 outperforms the V1 and V2 versions. For the two drug pairs belong to Group III (TS > 0.9), V2 outperforms V1 and V3, suggesting more unnecessary replacement can lower the performance of PBPK models. We also investigated how the prediction accuracy of ADMET Predictor as well as its collaboration with SimCYP influence the quality of PBPK models constructed using SimCYP. In conclusion, we generated a practical guidance on applying two mainstream software packages, ADMET Predictor and SimCYP, to construct PBPK models for drugs or drug candidates which lack of ADME parameters in model construction.

“…As a racemate, we adjusted some of its ADME and PK parameters according to the literature to make the predicted curve much better fitting the experimental data. [11][12][13] The key ADME parameters predicted by ADME Predictor for the 18 drugs were all listed in the Table S1 , including the detail for the adjusted parameters of Fluoxetine. The observed drug concentration data of each template drug was extracted from published concentrationtime (C-T) curves using WebPlotDigitizer (https://automeris.io/WebPlotDigitizer/).…”

Section: Validation Of Pbpk Models For Drug Templatesmentioning

confidence: 99%

A Promising New Approach for In Silico Prediction of Drug Concentration Profiles for Drug Candidates Lack of Experimental Pharmacokinetic Data

Zhai

et al. 2020

Preprint

Aim: The purpose of this study is to develop a novel protocol to predict the concentration profiles of a target drug based on the PBPK model of a structurally similar template drug by combining two software for PBPK modeling, the SimCYP simulator and ADMET Predictor. Methods: The method was evaluated by utilizing 13 drug pairs which come from 18 drugs in the built-in database of the SimCYP software. All drug pairs have their Tanimoto scores no less than 0.5. Three versions (V1, V2 and V3) of models for the target drug were constructed by gradually replacing the corresponding parameters of the template drug with those predicted by ADME Predictor for the target drug. Normalized RMSE and Wilcoxon rank-sum test were introduced for the evaluation of the model performance. Results: Overall, V3 models demonstrated better performance than the V1 and V2 models did. The relationship between the model performance and structural similarity of drug pairs was also explored. Three protocols have come out as guidance on how to build PBPK models for target drugs: (1) V1 models are recommended when the structural similarity is very high; (2) V2 models are recommended when the similarity is below 0.65 or high than 0.85; (3) V3 models are recommended when the similarity is below 0.85. Conclusion: By leveraging the prediction accuracy and application practicality, this novel approach has a great promise in predicting the preliminary PK profiles for novel drugs, propelling the drug discovery process by suggesting drug candidates with promising PK profiles.