Evidence map›Paper›PMID 35197311›Full record

ReviewDrug metabolism and disposition: the biological fate of chemicals2022

Mathematical Models to Characterize the Absorption, Distribution, Metabolism, and Excretion of Protein Therapeutics.

Shufang Liu, Dhaval K Shah

Abstract readReview
In one paragraph

Review in Drug metabolism and disposition: the biological fate of chemicals, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. 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

2 authors.

Shufang LiuDepartment of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, The State University of New York at Buffalo, Buffalo, New York.
Dhaval K ShahDepartment of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, The State University of New York at Buffalo, Buffalo, New York dshah4@buffalo.edu.

Funding

Enhancement of ADC selectivity by inverse targeting: Mechanistic studies and optimizationR01CA256928 · NCI · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI BALTHASAR, JOSEPH P · 2021 to 2025
$1.8M
Pharmacokinetic strategies to increase monoclonal antibody uptake, distribution, and efficacy for treatment of solid tumorsR01CA246785 · NCI · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI BALTHASAR, JOSEPH P, SHAH, DHAVAL K · 2020 to 2024
$1.8M
Translational Systems Pharmacokinetic Models of Novel Anticancer BiologicsR01GM114179 · NIGMS · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI SHAH, DHAVAL K · 2015 to 2019
$1.7M
Modulation of antigen pharmacokinetics with pH dependent antibodyR21AI138195 · NIAID · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI PARK, SHELDON · 2018 to 2019
$425k
NCI NIH HHS R01 CA246785NCI NIH HHS R01 CA256928NIAID NIH HHS R21 AI138195NIGMS NIH HHS R01 GM114179
6 · The paper itself

Abstract

Therapeutic proteins (TPs) have ranked among the most important and fastest-growing classes of drugs in the clinic, yet the development of successful TPs is often limited by unsatisfactory efficacy. Understanding pharmacokinetic (PK) characteristics of TPs is key to achieving sufficient and prolonged exposure at the site of action, which is a prerequisite for eliciting desired pharmacological effects. PK modeling represents a powerful tool to investigate factors governing in vivo disposition of TPs. In this mini-review, we discuss many state-of-the-art models that recapitulate critical processes in each of the absorption, distribution, metabolism/catabolism, and excretion pathways of TPs, which can be integrated into the physiologically-based pharmacokinetic framework. Additionally, we provide our perspectives on current opportunities and challenges for evolving the PK models to accelerate the discovery and development of safe and efficacious TPs. SIGNIFICANCE STATEMENT: This minireview provides an overview of mechanistic pharmacokinetic (PK) models developed to characterize absorption, distribution, metabolism, and elimination (ADME) properties of therapeutic proteins (TPs), which can support model-informed discovery and development of TPs. As the next-generation of TPs with diverse physicochemical properties and mechanism-of-action are being developed rapidly, there is an urgent need to better understand the determinants for the ADME of TPs and evolve existing platform PK models to facilitate successful bench-to-bedside translation of these promising drug molecules.

Indexed as

Models, Biological

Identifiers

PMID35197311
PMCPMC11022906

What Socratic 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 Socratic graph.