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Related Concept Videos

Drug Dissolution: Requirements and Profile Comparison01:14

Drug Dissolution: Requirements and Profile Comparison

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The acceptance criteria for dissolution profile data are anchored in Q values, representing the percentage of drug dissolved within a specified period. This assessment unfolds in three stages:First Stage: The test passes if all six drug dosage units are equal to or greater than Q plus 5%; otherwise, the sample proceeds to the second stage.Second Stage: The average of twelve units must be equal to or greater than Q, with no unit falling below Q - 15% to pass; if not, it progresses to the final...
242
Factors Affecting Dissolution: Drug Permeability, Stability and Stereochemistry01:20

Factors Affecting Dissolution: Drug Permeability, Stability and Stereochemistry

500
Orally administered drugs primarily enter the systemic circulation via passive diffusion through the intestinal membranes. The drug's absorption is influenced by drug stability in the gastrointestinal GI tract, membrane permeability, the surface area available for absorption, luminal drug concentration, and residence time in the lumen. Drug permeability can be enhanced by adjusting the lipophilicity, polarity, or molecular size of the drug, promoting its passive transport across intestinal...
500
In Vitro Drug Dissolution: Alternative Methods01:17

In Vitro Drug Dissolution: Alternative Methods

194
Alternative drug dissolution methods include the rotating bottle, intrinsic dissolution test, peristalsis, and the Franz diffusion cell method. The rotating bottle method involves meticulously rotating tightly capped controlled-release beads in a temperature-controlled bath. Periodic decanting of samples allows for residue assay, followed by refilling with fresh medium and testing at various pH levels to emulate the gastrointestinal tract conditions.In contrast, the intrinsic dissolution test...
194
Factors Influencing Drug Absorption: Pharmaceutical Parameters01:28

Factors Influencing Drug Absorption: Pharmaceutical Parameters

402
Solid dosage forms such as tablets and capsules undergo rigorous manufacturing processes to ensure stability and effectiveness. Their dissolution and absorption properties are influenced significantly by the choice of excipients (inactive ingredients that serve various roles in the formulation), and the methodology applied during production. The manufacturing parameters, such as compression force and granulation techniques, significantly affect dissolution rates. Elevated compression forces...
402
Bioavailability Enhancement: Drug Solubility Enhancement01:16

Bioavailability Enhancement: Drug Solubility Enhancement

221
Body:Bioavailability is a critical factor in determining a drug's effectiveness. It refers to the proportion of a drug that enters the circulation when introduced into the body and is, as a result, able to have an active effect. Enhancing bioavailability is essential for drugs with poor solubility, as it can significantly impact their therapeutic efficacy. Various methods are employed to increase the solubility of drugs, thereby enhancing their bioavailability.Micronization and nanonization are...
221
In Vitro Drug Dissolution: Compendial Testing Models II01:09

In Vitro Drug Dissolution: Compendial Testing Models II

245
Various dissolution methods are utilized to assess a drug’s dissolution rate, including the flow-through cell, paddle-over-disk, cylinder, and reciprocating disk methods.The flow-through cell apparatus (USP (United States Pharmacopeia) method 4) comprises a reservoir for the dissolution medium and a pump that propels the medium through the cell containing the test sample. This method is crucial for assessing modified-release dosage forms with minimally soluble active ingredients,...
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Related Experiment Video

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DeepSeek-LLM with Adaptive RAG for Pharmaceutical Dissolution Prediction.

Leqi Lin1, Xingyu Zhou1, Kaiyuan Yang1

  • 1State Key Laboratory of Synergistic Chem-Bio Synthesis, Department of Chemical Engineering, School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China.

Pharmaceutical Research
|October 8, 2025
PubMed
Summary

This study uses advanced AI, including Large Language Models (LLMs) and diffusion models, to predict drug dissolution profiles. This approach accelerates drug development by reducing the need for lengthy experiments and connecting physical properties with microstructure.

Keywords:
LLMdeepseekdiffusiondissolutionpharmaceutical engineeringsolid dosage

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Area of Science:

  • Pharmaceutical Sciences
  • Computational Chemistry
  • Artificial Intelligence in Drug Development

Background:

  • Traditional drug dissolution prediction relies heavily on time-consuming and expensive empirical experiments.
  • Integrating advanced computational methods can significantly accelerate the drug development pipeline.
  • Generative AI offers novel approaches to model complex pharmaceutical processes.

Purpose of the Study:

  • To accelerate and enhance pharmaceutical drug dissolution prediction using Large Language Models (LLMs) and AI-diffusion models.
  • To reduce reliance on costly and time-intensive experimental methods in drug development.
  • To establish a framework for the broader adoption of generative AI in pharmaceutical research.

Main Methods:

  • A DeepSeek-based LLM framework was developed, incorporating prompt engineering techniques (zero-shot, few-shot, chain-of-thought) and adaptive weighted retrieval-augmented generation (RAG).
  • A diffusion model was employed to synthesize morphological parameters from Scanning Electron Microscopy (SEM) images, bypassing multi-instrument characterization errors.
  • These synthesized parameters were integrated into the RAG database to ground LLM predictions in structure-performance relationships.

Main Results:

  • The LLM, particularly using few-shot chain-of-thought with RAG, demonstrated good agreement between predicted and experimental dissolution profiles.
  • Sensitivity analysis quantified the reliability and stability of the prompt engineering strategies.
  • The integration of diffusion-generated structural data with LLM predictions successfully linked macro-scale physical properties to microstructural characteristics, showing a close trend with acceptable error metrics (RMSE and PCC).

Conclusions:

  • The DeepSeek-based LLM framework shows significant potential for describing drug powder dissolution behavior.
  • Few-shot chain-of-thought with RAG emerged as the most effective prompt strategy for dissolution profile prediction.
  • The combination of diffusion models and LLMs effectively bridges AI-driven predictions with physical and structural drug properties, paving the way for enhanced drug development.