ArticleInternational journal of molecular sciences2019
In Silico Prediction of PAMPA Effective Permeability Using a Two-QSAR Approach.
Article in International journal of molecular sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed.
- Artificial Intelligence in Selected Domains of Drug Discovery: A Critical Narrative Review.Drug design, development and therapy · 2026Review
- Cracking the Skin Barrier: Models and Methods Driving Dermal Drug Delivery.Pharmaceutics · 2025Review
- Development of an Innovative Nanosystem Based on Functionalized Albumin and Oxidized Gellan for the Synergistic Delivery of Curcumin and Temozolomide in the Treatment of Brain Cancer.Gels (Basel, Switzerland) · 2025Article
- The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery.Pharmaceutics · 2023Review
- β Boswellic Acid Blocks Articular Innate Immune Responses: An In Silico and In Vitro Approach to Traditional Medicine.Antioxidants (Basel, Switzerland) · 2023Article
- Development of artificial neural network models to predict the PAMPA effective permeability of new, orally administered drugs active against the coronavirus SARS-CoV-2.Network modeling and analysis in health informatics and bioinformatics · 2023Article
- Application of multi-objective optimization in the study of anti-breast cancer candidate drugs.Scientific reports · 2022Article
- In Silico Prediction of Skin Permeability Using a Two-QSAR Approach.Pharmaceutics · 2022Article
- Using in vitro ADME data for lead compound selection: An emphasis on PAMPA pH 5 permeability and oral bioavailability.Bioorganic & medicinal chemistry · 2022Review
- Investigation of the Uptake and Transport of Aspirin Eugenol Ester in the Caco-2 Cell Model.Frontiers in pharmacology · 2022Article
- Artificial intelligence to deep learning: machine intelligence approach for drug discovery.Molecular diversity · 2021Review
- Fluoroquinolone-Transition Metal Complexes: A Strategy to Overcome Bacterial Resistance.Microorganisms · 2021Review
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
- Predicting Pharmacokinetic Properties of Potential Anticancer Agents via Their Chromatographic Behavior on Different Reversed Phase Materials.International journal of molecular sciences · 2021Article
- Assessment of Finite and Infinite Dose In Vitro Experiments in Transdermal Drug Delivery.Pharmaceutics · 2021Article
- Development of a Hierarchical Support Vector Regression-Based In Silico Model for Caco-2 Permeability.Pharmaceutics · 2021Article
- In Silico Prediction of Intestinal Permeability by Hierarchical Support Vector Regression.International journal of molecular sciences · 2020Article
- Partial Least Square Model (PLS) as a Tool to Predict the Diffusion of Steroids Across Artificial Membranes.Molecules (Basel, Switzerland) · 2020Article
- Alternatives to Biological Skin in Permeation Studies: Current Trends and Possibilities.Pharmaceutics · 2020Review
- Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches.Biotechnology and bioprocess engineering : BBE · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Oral administration is the preferred and predominant route of choice for medication. As such, drug absorption is one of critical drug metabolism and pharmacokinetics (DM/PK) parameters that should be taken into consideration in the process of drug discovery and development. The cell-free in vitro parallel artificial membrane permeability assay (PAMPA) has been adopted as the primary screening to assess the passive diffusion of compounds in the practical applications. A classical quantitative structure-activity relationship (QSAR) model and a machine learning (ML)-based QSAR model were derived using the partial least square (PLS) scheme and hierarchical support vector regression (HSVR) scheme to elucidate the underlying passive diffusion mechanism and to predict the PAMPA effective permeability, respectively, in this study. It was observed that HSVR executed better than PLS as manifested by the predictions of the samples in the training set, test set, and outlier set as well as various statistical assessments. When applied to the mock test, which was designated to mimic real challenges, HSVR also showed better predictive performance. PLS, conversely, cannot cover some mechanistically interpretable relationships between descriptors and permeability. Accordingly, the synergy of predictive HSVR and interpretable PLS models can be greatly useful in facilitating drug discovery and development by predicting passive diffusion.
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.