Editorial: Pharmacokinetic Evaluation and Modeling of Clinically Significant Drug Metabolites
Bibliographic record
Abstract
Pharmacokinetic Evaluation and Modeling of Clinically Significant Drug MetabolitesPharmacokinetic evaluations and modeling in order to correlate in vitro drug dissolution kinetics with their in vivo release and absorption kinetics, estimated from the deconvolution of their pharmacokinetic data, is known as IVIVC.IVIVC is a main tool in the development of new drug formulations, and is required by regulators in the case of extended-release drugs, following wellestablished procedures that have subsequently been tried in applications for immediate-release medicinal products (Emami, 2010;Cardot and Davit, 2012).Additionally, successful IVIVCs allow subsequent waivers of in vivo studies for bioequivalence (Karalis et al., 2010).In most cases, applications of such correlations for immediate release lipophilic drugs encounter difficulties due to slow and incomplete dissolution from dosage forms, extensive metabolism, distribution in deep compartments, and enterohepatic circulation (Chrenova et al., 2010).Recently, an alternative IVIVC method for such drugs has been proposed: the correlation between the dissolution kinetics of parent drugs in vitro and the plasma kinetics of their metabolites (Mircioiu et al., 2019), mainly due to the fact that metabolites have a simpler pharmacokinetic model (Marchidanu et al., 2013).On the other hand, the performance of IVIC is dependent on the results of in vitro dissolution methods.It is necessary to model the release kinetics and establish the most biorelevant method (Cardot and Davit, 2012;Preda et al., 2012;Paolino et al., 2019).The use of metabolites PK in the evaluation of bioequivalence is also a challenge.There are critical situations that cannot be solved without an evaluation of the metabolites if: 1) the parent drug levels in plasma are too low to allow accurate analytical measurements, 2) the parent drug is unstable in the biological matrix, 3) the parent drug is an inactive prodrug, 4) the formation of the metabolite occurs rapidly, or 5) the metabolite significantly contributes to the overall net activity.In the frame of this research topic, contributions have been received concerning:-Development of biorelevant in vitro dissolution tests -Modeling of the PK of metabolites.-Use of PK of metabolites in the evaluation of bioequivalence-time scaling for IVIVC.-Drug-drug pharmacokinetic interactions.Li et al. studied the influence of danhong injection, a mixture of Salvia miltiorrhiza and Carthamus tinctorius, on the PK of acetylsalicylic acid (ASA), using an analysis based on 5,183 clinical cases by determining the PK and disposition profile of salicylic acid (SA), the primary
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.018 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".