ReviewMolecules (Basel, Switzerland)2021
Molecular Simulation and Statistical Learning Methods toward Predicting Drug-Polymer Amorphous Solid Dispersion Miscibility, Stability, and Formulation Design.
Review in Molecules (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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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.
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Who cites it
24 citing papers in PubMed.
- Identifying Optimal Drug Loading in Stable Amorphous Solid Dispersion Formulations: A Rheological Approach.AAPS PharmSciTech · 2026Article
- Hot-Melt Processed Glibenclamide Glassy Solutions: A Novel Oral Delivery Platform for Enhanced Bioavailability in Diabetes.Pharmaceutics · 2026Article
- Amorphous solid dispersions as a strategy to enhance the bioavailability and stability of formulations containing plant active ingredients: An integrative review.Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2026Review
- Synergistic Enhancement of Diosgenin Dissolution and Bioavailability via Ternary Solid Dispersions and Self-Assembled Polymeric Micelles.Drug design, development and therapy · 2026Article
- In Silico Predictions Driving the Development of 3D-Printed Drug Delivery Systems.Pharmaceutics · 2025Article
- Physics-Based Solubility Prediction for Organic Molecules.Chemical reviews · 2025Review
- Investigating the Formation and Molecular Solubilization Mechanism of Emodin Solid Dispersions by Molecular Dynamics Simulation.Molecules (Basel, Switzerland) · 2025Article
- Computer-driven formulation development of Ginsenoside Rh2 ternary solid dispersion.Drug delivery and translational research · 2025Article
- Quantitative Analysis of Physical Stability Mechanisms of Amorphous Solid Dispersions by Molecular Dynamic Simulation.The AAPS journal · 2024Article
- Computational Support to Explore Ternary Solid Dispersions of Challenging Drugs Using Coformer and Hydroxypropyl Cellulose.Molecular pharmaceutics · 2024Article
- Predicting the Release Mechanism of Amorphous Solid Dispersions: A Combination of Thermodynamic Modeling and In Silico Molecular Simulation.Pharmaceutics · 2024Article
- Data-Driven Design of Novel Polymer Excipients for Pharmaceutical Amorphous Solid Dispersions.Bioconjugate chemistry · 2024Article
- Accelerating Formulation Design via Machine Learning: Generating a High-throughput Shampoo Formulations Dataset.Scientific data · 2024Article
- Multiscale modelling of transport in polymer-based reverse-osmosis/nanofiltration membranes: present and future.Discover nano · 2024Review
- Status of Polymer Fused Deposition Modeling (FDM)-Based Three-Dimensional Printing (3DP) in the Pharmaceutical Industry.Polymers · 2024Review
- Composition Dependency of the Flory-Huggins Interaction Parameter in Drug-Polymer Phase Behavior.Pharmaceutics · 2023Article
- An AI Approach to Generating MIDD Assets Across the Drug Development Continuum.The AAPS journal · 2023Article
- Molecular Interactions between APIs and Enteric Polymeric Excipients in Solid Dispersion: Insights from Molecular Simulations and Experiments.Pharmaceutics · 2023Article
- Stabilizing Mechanisms of β-Lactoglobulin in Amorphous Solid Dispersions of Indomethacin.Molecular pharmaceutics · 2022Article
- Recent Advances in Amorphous Solid Dispersions: Preformulation, Formulation Strategies, Technological Advancements and Characterization.Pharmaceutics · 2022Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Amorphous solid dispersions (ASDs) have emerged as widespread formulations for drug delivery of poorly soluble active pharmaceutical ingredients (APIs). Predicting the API solubility with various carriers in the API-carrier mixture and the principal API-carrier non-bonding interactions are critical factors for rational drug development and formulation decisions. Experimental determination of these interactions, solubility, and dissolution mechanisms is time-consuming, costly, and reliant on trial and error. To that end, molecular modeling has been applied to simulate ASD properties and mechanisms. Quantum mechanical methods elucidate the strength of API-carrier non-bonding interactions, while molecular dynamics simulations model and predict ASD physical stability, solubility, and dissolution mechanisms. Statistical learning models have been recently applied to the prediction of a variety of drug formulation properties and show immense potential for continued application in the understanding and prediction of ASD solubility. Continued theoretical progress and computational applications will accelerate lead compound development before clinical trials. This article reviews in silico research for the rational formulation design of low-solubility drugs. Pertinent theoretical groundwork is presented, modeling applications and limitations are discussed, and the prospective clinical benefits of accelerated ASD formulation are envisioned.
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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.