Evidence map›Paper›PMID 38575716›Full record

ArticleThe AAPS journal2024

Mechanistic Modeling of In Vitro Biopharmaceutic Data for a Weak Acid Drug: A Pathway Towards Deriving Fundamental Parameters for Physiologically Based Biopharmaceutic Modeling.

Venkata Krishna Kowthavarapu, Nitin Bharat Charbe, Churni Gupta, Tatiana Iakovleva, Cordula Stillhart, Neil John Parrott, Stephan Schmidt, Rodrigo Cristofoletti

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Article in The AAPS journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
–field-weighted citation impact, top 97% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 2 countries.

Venkata Krishna KowthavarapuCenter for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics Lake Nona (Orlando), College of Pharmacy, University of Florida, 6550 Sanger Road, Office 467, Orlando, Florida, 32827, USA.
Nitin Bharat CharbeCenter for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics Lake Nona (Orlando), College of Pharmacy, University of Florida, 6550 Sanger Road, Office 467, Orlando, Florida, 32827, USA.
Churni GuptaCenter for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics Lake Nona (Orlando), College of Pharmacy, University of Florida, 6550 Sanger Road, Office 467, Orlando, Florida, 32827, USA.
Tatiana IakovlevaCenter for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics Lake Nona (Orlando), College of Pharmacy, University of Florida, 6550 Sanger Road, Office 467, Orlando, Florida, 32827, USA.
Cordula StillhartPharmaceutical Research & Development, Formulation & Process Development, F. Hoffmann-La Roche Ltd., 4070, Basel, Switzerland.
Neil John ParrottPharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070, Basel, Switzerland.
Stephan SchmidtCenter for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics Lake Nona (Orlando), College of Pharmacy, University of Florida, 6550 Sanger Road, Office 467, Orlando, Florida, 32827, USA.
Rodrigo CristofolettiCenter for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics Lake Nona (Orlando), College of Pharmacy, University of Florida, 6550 Sanger Road, Office 467, Orlando, Florida, 32827, USA. rcristofoletti@ufl.edu.ORCID http://orcid.org/0000-0003-2619-0343
University of Florida · USRoche (Switzerland) · CHUniversity of Central Florida · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mechanistic modeling of in vitro experiments using metabolic enzyme systems enables the extrapolation of metabolic clearance for in vitro-in vivo predictions. This is particularly important for successful clearance predictions using physiologically based pharmacokinetic (PBPK) modeling. The concept of mechanistic modeling can also be extended to biopharmaceutics, where in vitro data is used to predict the in vivo pharmacokinetic profile of the drug. This approach further allows for the identification of parameters that are critical for oral drug absorption in vivo. However, the routine use of this analysis approach has been hindered by the lack of an integrated analysis workflow. The objective of this tutorial is to (1) review processes and parameters contributing to oral drug absorption in increasing levels of complexity, (2) outline a general physiologically based biopharmaceutic modeling workflow for weak acids, and (3) illustrate the outlined concepts via an ibuprofen (i.e., a weak, poorly soluble acid) case example in order to provide practical guidance on how to integrate biopharmaceutic and physiological data to better understand oral drug absorption. In the future, we plan to explore the usefulness of this tutorial/roadmap to inform the development of PBPK models for BCS 2 weak bases, by expanding the stepwise modeling approach to accommodate more intricate scenarios, including the presence of diprotic basic compounds and acidifying agents within the formulation.

Indexed as

BiopharmaceuticsModels, BiologicalAdministration, OralComputer SimulationIbuprofenIntestinal AbsorptionSolubilityIbuprofenabsorptionbiopharmaceutic modelingdissolutionintraluminal concentrationssolubility

Identifiers

PMID38575716
OpenAlexW4393951881

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.