Evidence map›Paper›PMID 23408094›Full record

ReviewThe AAPS journal2013

Modeling, simulation, and translation framework for the preclinical development of monoclonal antibodies.

Kenneth T Luu, Eugenia Kraynov, Bing Kuang, Paolo Vicini, Wei-Zhu Zhong

Abstract readReview
In one paragraph

Review in The AAPS journal, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Drug Design and Discovery: Principles and Applications.Molecules (Basel, Switzerland) · 2017
    Article
  5. Review
  6. Article
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.

Kenneth T LuuDepartment of Clinical Pharmacology, Pfizer Global Research and Development, 10555 Science Center Drive, San Diego, CA 92121, USA. kenneth.luu@pfizer.com
Eugenia Kraynov
Bing Kuang
Paolo Vicini
Wei-Zhu Zhong

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The industry-wide biopharmaceutical (i.e., biologic, biotherapeutic) pipeline has been growing at an astonishing rate over the last decade with the proportion of approved new biological entities to new chemical entities on the rise. As biopharmaceuticals appear to be growing in complexity in terms of their structure and mechanism of action, so are interpretation, analysis, and prediction of their quantitative pharmacology. We present here a modeling and simulation (M&S) framework for the successful preclinical development of monoclonal antibodies (as an illustrative example of biopharmaceuticals) and discuss M&S strategies for its implementation. Critical activities during early discovery, lead optimization, and the selection of starting doses for the first-in-human study are discussed in the context of pharmacokinetic-pharmacodynamic (PKPD) and M&S. It was shown that these stages of preclinical development are and should be reliant on M&S activities including systems biology (SB), systems pharmacology (SP), and translational pharmacology (TP). SB, SP, and TP provide an integrated and rationalized framework for decision making during the preclinical development phase. In addition, they provide increased target and systems understanding, describe and interpret data generated in vitro and in vivo, predict human PKPD, and provide a rationalized approach to designing the first-in-human study.

Indexed as

Computer SimulationModels, BiologicalAnimalsAntibodies, MonoclonalDecision Support TechniquesDisease Models, AnimalDrug Dosage CalculationsDrug Evaluation, PreclinicalHumansNonlinear DynamicsSystems BiologyTranslational Research, BiomedicalAntibodies, Monoclonal

Identifiers

PMID23408094
PMCPMC3675753

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.