Evidence map›Paper›PMID 35840856›Full record

ReviewPharmaceutical research2023

Physiologically Based Pharmacokinetics Modeling in Biopharmaceutics: Case Studies for Establishing the Bioequivalence Safe Space for Innovator and Generic Drugs.

Di Wu, Maitri Sanghavi, Sivacharan Kollipara, Tausif Ahmed, Anuj K Saini, Tycho Heimbach

Abstract readReview
PubMed Publisher
In one paragraph

Review in Pharmaceutical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
–field-weighted citation impact
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

27 citing papers in PubMed.

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4 · The record

Corrections and comments

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

6 authors.

Di WuPharmaceutical Sciences, MRL, Merck & Co., Inc., Rahway, New Jersey, 07065, USA.
Maitri SanghaviPharmacokinetics & Biopharmaceutics Group, Pharmaceutical Technology Center (PTC), Zydus Lifesciences Ltd., NH-8A, Sarkhej-Bavla Highway, Moraiya Ahmedabad, Gujarat, 382210, India.ORCID http://orcid.org/0000-0002-7724-6151
Sivacharan KolliparaBiopharmaceutics Group, Global Clinical Management, Dr. Reddy's Laboratories Ltd., Integrated Product Development Organization (IPDO), Bachupally, Medchal Malkajgiri District, Hyderabad, Telangana, 500 090, India.ORCID http://orcid.org/0000-0002-7332-9021
Tausif AhmedBiopharmaceutics Group, Global Clinical Management, Dr. Reddy's Laboratories Ltd., Integrated Product Development Organization (IPDO), Bachupally, Medchal Malkajgiri District, Hyderabad, Telangana, 500 090, India.ORCID http://orcid.org/0000-0001-6866-6691
Anuj K SainiPharmacokinetics & Biopharmaceutics Group, Pharmaceutical Technology Center (PTC), Zydus Lifesciences Ltd., NH-8A, Sarkhej-Bavla Highway, Moraiya Ahmedabad, Gujarat, 382210, India.ORCID http://orcid.org/0000-0001-7131-4267
Tycho HeimbachPharmaceutical Sciences, MRL, Merck & Co., Inc., Rahway, New Jersey, 07065, USA. Tycho.Heimbach@Merck.com.ORCID http://orcid.org/0000-0003-1224-0629

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

For successful oral drug development, defining a bioequivalence (BE) safe space is critical for the identification of newer bioequivalent formulations or for setting of clinically relevant in vitro specifications to ensure drug product quality. By definition, the safe space delineates the dissolution profile boundaries or other drug product quality attributes, within which the drug product variants are anticipated to be bioequivalent. Defining a BE safe space with physiologically based biopharmaceutics model (PBBM) allows the establishment of mechanistic in vitro and in vivo relationships (IVIVR) to better understand absorption mechanism and critical bioavailability attributes (CBA). Detailed case studies on how to use PBBM to establish a BE safe space for both innovator and generic drugs are described. New case studies and literature examples demonstrate BE safe space applications such as how to set in vitro dissolution/particle size distribution (PSD) specifications, widen dissolution specification to supersede f2 tests, or application toward a scale-up and post-approval changes (SUPAC) biowaiver. A workflow for detailed PBBM set-up and common clinical study data requirements to establish the safe space and knowledge space are discussed. Approaches to model in vitro dissolution profiles i.e. the diffusion layer model (DLM), Takano and Johnson models or the fitted PSD and Weibull function are described with a decision tree. The conduct of parameter sensitivity analyses on kinetic dissolution parameters for safe space and virtual bioequivalence (VBE) modeling for innovator and generic drugs are shared. The necessity for biopredictive dissolution method development and challenges with PBBM development and acceptance criteria are described.

Indexed as

BiopharmaceuticsDrugs, GenericDrug LiberationModels, BiologicalSolubilityTherapeutic EquivalencyDrugs, Genericbioequivalence safe spacef2 testin vitro dissolutionphysiologically based biopharmaceutics modeling (PBBM)physiologically based pharmacokinetic modeling (PBPK)

Identifiers

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Registered trials

None linked

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.