AIMC Topic: Solubility

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Machine learning estimation and optimization for evaluation of pharmaceutical solubility in supercritical carbon dioxide for improvement of drug efficacy.

Scientific reports
This study focuses on predicting the solubility of paracetamol and density of solvent using temperature (T) and pressure (P) as inputs. The process for production of the drug is supercritical technique in which the focus was on theoretical investigat...

A mechanistic framework for predicting tablet disintegration: Integrating the Representative Capillary Evolution Model (RCEM) and the Dynamic Void Fraction Evolution Model (DVFEM).

International journal of pharmaceutics
The disintegration behaviour of pharmaceutical tablets is a critical quality attribute influencing drug release, yet predicting it from formulation and processing parameters remains challenging due to complex underlying mechanisms. This work presents...

Utilization of sequential model-based optimizer integrated machine learning models in correlation of famotidine solubility in supercritical carbon dioxide.

Scientific reports
We investigated solubility variations of a medication in supercritical carbon dioxide with an insight into preparation of nanomedicines with improved aqueous solubility. As the case study, the solubility of famotidine (FAM) medicine in sc-CO (supercr...

Advances in Pharmaceutical Cocrystals and Nano-Cocrystals: Strategies for Enhancing Solubility and Translating to Clinical Use.

AAPS PharmSciTech
Poor oral bioavailability in most modern pharmaceuticals is primarily caused by poor aqueous solubility. Most NCEs (New Chemical Entities) and nearly 40% of drugs on the market fall into either Biopharmaceutical Classification System (BCS) class II o...

Predictive analysis of solubility data with pressure and temperature in assessing nanomedicine preparation via supercritical carbon dioxide.

Scientific reports
This work presents a comprehensive study on the prediction of phenytoin solubility at supercritical state using advanced techniques including machine learning analysis. The solubility of small-molecule pharmaceutical was analyzed and calculated to en...

Integrating artificial intelligence and physiologically based pharmacokinetic modeling to predict in vitro and in vivo fate of amorphous solid dispersions.

Journal of controlled release : official journal of the Controlled Release Society
Amorphous solid dispersions (ASDs) have emerged as a pivotal strategy in enhancing the dissolution profiles of poorly water-soluble drugs. Although the apparent dissolution rate (both molecular and colloidal drugs) within ASDs has been determined in ...

Ever-Increasing Role of Computational Tools in Solid-State Pharmaceutics: Advancing Drug Development with Enhanced Molecular Understanding and Risk Assessment.

Molecular pharmaceutics
The field of solid-state pharmaceutics comprises a broad range of investigations into various structural aspects of pharmaceutical solids, establishing a rational structure-property correlation. These solid systems allow the tunability of the physico...

Advancing Aqueous Solubility Prediction: A Machine Learning Approach for Organic Compounds Using a Curated Data Set.

Journal of chemical information and modeling
Aqueous solubility is one key property of a chemical compound that determines its possible use in different applications, from drug development to materials sciences. In this work, we present a model for the prediction of aqueous solubility that leve...