Evidence map›Paper›PMID 27647273›Full record

ReviewJournal of pharmacokinetics and pharmacodynamics2016

Physiologically-based pharmacokinetic models: approaches for enabling personalized medicine.

Clara Hartmanshenn, Megerle Scherholz, Ioannis P Androulakis

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of pharmacokinetics and pharmacodynamics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
64citing papers in PubMed, 1 pooled it
–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

64 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Re-Thinking Pharmacokinetics in Ovarian Cancer: What Do Organoids Add?International journal of molecular sciences · 2026
    Review
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4 more citing papers are in PubMed but not listed here.

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

3 authors.

Clara HartmanshennDepartment of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, 98 Brett Road, Piscataway, NJ, 08854, USA.
Megerle ScherholzDepartment of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, 98 Brett Road, Piscataway, NJ, 08854, USA.
Ioannis P AndroulakisDepartment of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, 98 Brett Road, Piscataway, NJ, 08854, USA. yannis@rci.rutgers.edu.

Funding

Corticosteroid Pharmacokinetics and PharmacodynamicsR37GM024211 · NIGMS · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI JUSKO, WILLIAM J. · 2005 to 2014
$6.4M
CORTICOSTEROID PHARMACOKINETICS/DYNAMICSR01GM024211 · NIGMS · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI JUSKO, WILLIAM J. · 1985 to 2018
$4.7M
NIGMS NIH HHS R01 GM024211NIGMS NIH HHS R37 GM024211
6 · The paper itself

Abstract

Personalized medicine strives to deliver the 'right drug at the right dose' by considering inter-person variability, one of the causes for therapeutic failure in specialized populations of patients. Physiologically-based pharmacokinetic (PBPK) modeling is a key tool in the advancement of personalized medicine to evaluate complex clinical scenarios, making use of physiological information as well as physicochemical data to simulate various physiological states to predict the distribution of pharmacokinetic responses. The increased dependency on PBPK models to address regulatory questions is aligned with the ability of PBPK models to minimize ethical and technical difficulties associated with pharmacokinetic and toxicology experiments for special patient populations. Subpopulation modeling can be achieved through an iterative and integrative approach using an adopt, adapt, develop, assess, amend, and deliver methodology. PBPK modeling has two valuable applications in personalized medicine: (1) determining the importance of certain subpopulations within a distribution of pharmacokinetic responses for a given drug formulation and (2) establishing the formulation design space needed to attain a targeted drug plasma concentration profile. This review article focuses on model development for physiological differences associated with sex (male vs. female), age (pediatric vs. young adults vs. elderly), disease state (healthy vs. unhealthy), and temporal variation (influence of biological rhythms), connecting them to drug product formulation development within the quality by design framework. Although PBPK modeling has come a long way, there is still a lengthy road before it can be fully accepted by pharmacologists, clinicians, and the broader industry.

Indexed as

Circadian RhythmModels, BiologicalPharmacokineticsSex CharacteristicsAge FactorsFemaleHumansMalePharmaceutical PreparationsPrecision MedicinePharmaceutical PreparationsAgeCircadianGenderInter-patient variabilityPBPK modelingPersonalized medicineSexSpecial populations

Identifiers

PMID27647273
PMCPMC5204363

What OpenQuestion holds

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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.