Evidence map›Paper›PMID 36759492›Full record

ReviewAAPS PharmSciTech2023

Best Practices for Integration of Dissolution Data into Physiologically Based Biopharmaceutics Models (PBBM): A Biopharmaceutics Modeling Scientist Perspective.

Sivacharan Kollipara, Adithya Karthik Bhattiprolu, Rajkumar Boddu, Tausif Ahmed, Siddharth Chachad

Abstract readReview
PubMed Publisher
In one paragraph

Review in AAPS PharmSciTech, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Review
  10. Review
  11. Article
  12. Article
  13. Article
  14. Review
  15. Review
  16. Review
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

5 authors.

Sivacharan KolliparaBiopharmaceutics Group, Global Clinical Management, Integrated Product Development Organization (IPDO), Dr. Reddy's Laboratories Ltd, Bachupally, Medchal Malkajgiri District, Hyderabad, 500 090, Telangana, India.ORCID http://orcid.org/0000-0002-7332-9021
Adithya Karthik BhattiproluBiopharmaceutics Group, Global Clinical Management, Integrated Product Development Organization (IPDO), Dr. Reddy's Laboratories Ltd, Bachupally, Medchal Malkajgiri District, Hyderabad, 500 090, Telangana, India.ORCID http://orcid.org/0000-0003-4618-6835
Rajkumar BodduBiopharmaceutics Group, Global Clinical Management, Integrated Product Development Organization (IPDO), Dr. Reddy's Laboratories Ltd, Bachupally, Medchal Malkajgiri District, Hyderabad, 500 090, Telangana, India.ORCID http://orcid.org/0000-0002-8666-3643
Tausif AhmedBiopharmaceutics Group, Global Clinical Management, Integrated Product Development Organization (IPDO), Dr. Reddy's Laboratories Ltd, Bachupally, Medchal Malkajgiri District, Hyderabad, 500 090, Telangana, India. tausifahmed@drreddys.com.ORCID http://orcid.org/0000-0001-6866-6691
Siddharth ChachadBiopharmaceutics Group, Global Clinical Management, Integrated Product Development Organization (IPDO), Dr. Reddy's Laboratories Ltd, Bachupally, Medchal Malkajgiri District, Hyderabad, 500 090, Telangana, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dissolution is considered as a critical input into physiologically based biopharmaceutics models (PBBM) as it governs in vivo exposure. Despite many workshops, initiatives by academia, industry, and regulatory, wider practices are followed for dissolution data input into PBBM models. Due to variety of options available for dissolution data input into PBBM models, it is important to understand pros, cons, and best practices while using specific dissolution model. This present article attempts to summarize current understanding of various dissolution models and data inputs in PBBM software's and aims to discuss practical challenges and ways to overcome such scenarios. Different approaches to incorporate dissolution data for immediate, modified, and delayed release formulations are discussed in detail. Common challenges faced during fitting of z-factor are discussed along with novel approach of dissolution data incorporation using P-PSD model. Ways to incorporate dissolution data for MR formulations using Weibull and IVIVR approaches were portrayed with examples. Strategies to incorporate dissolution data for DR formulations was depicted along with practical aspects. Approaches to generate virtual dissolution profiles, using Weibull function, DDDPlus, and time scaling for defining dissolution safe space, and strategies to generate virtual dissolution profiles for justifying single and multiple dissolution specifications were discussed. Finally, novel ways to integrate dissolution data for complex products such as liposomes, data from complex dissolution systems, importance of precipitation, and bio-predictive ability of QC media for evaluation of CBA's impact were discussed. Overall, this article aims to provide an easy guide for biopharmaceutics modeling scientist to integrate dissolution data effectively into PBBM models.

Indexed as

BiopharmaceuticsModels, BiologicalAdministration, OralDrug CompoundingLiposomesSolubilityLiposomesDissolutionPBBMP-PSDSafe spaceSpecificationsWeibullZ-factor

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.