ReviewInternational journal of environmental research and public health2023
The Role of "Physiologically Based Pharmacokinetic Model (PBPK)" New Approach Methodology (NAM) in Pharmaceuticals and Environmental Chemical Risk Assessment.
Review in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 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
36 citing papers in PubMed.
- Physiologically Based Pharmacokinetic (PBPK) Modeling of Antidepressants: A Scoping Review of Existing Models, Applications, and Software Tools.Clinical pharmacokinetics · 2026Article
- A Physiologically Based Pharmacokinetic Model to Predict Potential Drug-Drug Interactions of TPN171, a Novel Phosphodiesterase Type 5 Inhibitor.CPT: pharmacometrics & systems pharmacology · 2026Article
- Pulmonary Drug Delivery in the Era of Nanomedicine: From Biological Barriers to Artificial Intelligence-Driven Optimization.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Interactions between long-acting antiretrovirals and opioids: a call for clinical awareness.Clinical microbiology reviews · 2026Review
- Prediction variability in physiologically based pharmacokinetic modeling of tissue disposition under deep uncertainty.NPJ systems biology and applications · 2026Article
- Artificial intelligence for drug delivery: Yesterday, today and tomorrow.Acta pharmaceutica Sinica. B · 2026Review
- Integration of Machine Learning With PBPK and QSAR Modeling Approaches to Facilitate Drug Discovery and Development.CPT: pharmacometrics & systems pharmacology · 2026Review
- The Placenta as a Target Organ for Poly- and Perfluoroalkyl Substances (PFASs): Molecular Mechanisms of Toxicity.International journal of molecular sciences · 2026Review
- Review on Predictive Models and Integration Strategies for Holistic Impact Assessment of Chemicals and Materials.Environmental science & technology · 2026Review
- From Population-Based PBPK to Individualized Virtual Twins: Clinical Validation and Applications in Medicine.Journal of clinical medicine · 2026Review
- The Impact of Maternal BMI on the Efficacy and Safety of Oral Misoprostol for Labor Induction.Pharmaceuticals (Basel, Switzerland) · 2025Article
- A drug-drug interaction study to evaluate the effects of strong CYP3A4 inducer on the pharmacokinetics of segesterone acetate and ethinyl estradiol in a contraceptive vaginal system.Contraception and reproductive medicine · 2025Article
- Cross-Species Extrapolation of Neonatal Fc Receptor (FcRn) Binding Affinity to Predict Monoclonal Antibody Pharmacokinetics in Humans Using Physiologically Based Pharmacokinetic Modeling (PBPK): Are We There Yet?ACS pharmacology & translational science · 2025Article
- VitroBert: modeling DILI by pretraining BERT on in vitro data.Journal of cheminformatics · 2025Article
- In Silico ADME Methods Used in the Evaluation of Natural Products.Pharmaceutics · 2025Review
- Breathing hazards: Perspective on the adverse health effects of organophosphate flame retardants.Journal of hazardous materials advances · 2025Article
- Formulation Strategy of BCS-II Drugs by Coupling Mechanistic In-Vitro and Nonclinical In-Vivo Data with PBPK: Fundamentals of Absorption-Dissolution to Parameterization of Modelling and Simulation.AAPS PharmSciTech · 2025Review
- Nuclear hormone receptors control fundamental processes of human fetal neurodevelopment: Basis for endocrine disruption assessment.Environment international · 2025Article
- Accuracy and precision analyses of single-time-point dosimetry utilising physiologically-based pharmacokinetic modelling and non-linear mixed-effects modelling.EJNMMI physics · 2025Article
- Assessing the Application of Physiologically Based Pharmacokinetic Models in Acute Chemical Incidents.Journal of xenobiotics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Physiologically Based Pharmacokinetic (PBPK) models are mechanistic tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognized by regulatory authorities for predicting organ concentration-time profiles, pharmacokinetics and daily intake dose of xenobiotics. The extension of PBPK models to capture sensitive populations such as pediatric, geriatric, pregnant females, fetus, etc., and diseased populations such as those with renal impairment, liver cirrhosis, etc., is a must. However, the current modelling practices and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology and calculation of biochemical parameters for integrating knowledge and refining existing PBPK models. Specific PBPK covering compartments such as cerebrospinal fluid and the hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints such as developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in silico models where experimental data are unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building of qAOPs and use of machine learning for improving existing models, along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.
Indexed as
Identifiers
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.