ReviewCurrent drug metabolism2012
Physiologically based pharmacokinetic models: integration of in silico approaches with micro cell culture analogues.
Review in Current drug metabolism, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
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.
Who cites it
9 citing papers in PubMed.
- Review
- Old Drug, New Science: Metformin and the Future of Pharmaceutics.Pharmaceutics · 2026Review
- Formulation development and evaluation, in silico PBPK modeling and in vivo pharmacodynamic studies of clozapine matrix type transdermal patches.Scientific reports · 2025Article
- Advancements in Virtual Bioequivalence: A Systematic Review of Computational Methods and Regulatory Perspectives in the Pharmaceutical Industry.Pharmaceutics · 2024Review
- In-Depth Analysis of Physiologically Based Pharmacokinetic (PBPK) Modeling Utilization in Different Application Fields Using Text Mining Tools.Pharmaceutics · 2022Review
- Use of PBPK Modeling To Evaluate the Performance of Dissolv It, a Biorelevant Dissolution Assay for Orally Inhaled Drug Products.Molecular pharmaceutics · 2019Article
- Physiologically-based pharmacokinetic models: approaches for enabling personalized medicine.Journal of pharmacokinetics and pharmacodynamics · 2016Review
- Microengineered cell and tissue systems for drug screening and toxicology applications: Evolution ofTechnology · 2015Article
- Role of MALDI-MSI in combination with 3D tissue models for early stage efficacy and safety testing of drugs and toxicants.Expert review of proteomicsReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
There is a large emphasis within the pharmaceutical industry to provide tools that will allow early research and development groups to better predict dose ranges for and metabolic responses of candidate molecules in a high throughput manner, prior to entering clinical trials. These tools incorporate approaches ranging from PBPK, QSAR, and molecular dynamics simulations in the in silico realm, to micro cell culture analogue (CCAs)s in the in vitro realm. This paper will serve to review these areas of high throughput predictive research, and highlight hurdles and potential solutions. In particular we will focus on CCAs, as their incorporation with PBPK modeling has the potential to replace animal testing, with a more predictive assay that can combine multiple organ analogs on one microfluidic platform in physiologically correct volume ratios. While several advantages arise from the current embodiments of CCAS in a microfluidic format that can be exploited for realistic simulations of drug absorption, metabolism and action, we explore some of the concerns with these systems, and provide a potential path forward to realizing animal-free solutions. Furthermore we envision that, together with theoretical modeling, CCAs may produce reliable predictions of the efficacy of newly developed drugs.
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What OpenQuestion holds
Registered trials
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.