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Accelerating pre-formulation investigations in early drug product life cycles using predictive methodologies and
Harsh S Shah1, Kaushalendra Chaturvedi1, Shanming Kuang1
1J-Star Research Inc., 6 Cedarbrook Drive, Cranbury, NJ 08512, USA.
Therapeutic Delivery
|November 18, 2021
Summary
Computational methodologies accelerate drug development by predicting molecular properties, making the drug product lifecycle more efficient and reliable. Case studies show these predictive tools shorten research timelines and improve drug quality.
Area of Science:
- Pharmaceutical Sciences
- Computational Chemistry
- Drug Development
Background:
- The pharmaceutical industry faces increasing pressure for faster and more cost-effective drug discovery.
- A deep molecular-level understanding is crucial for optimizing the drug product lifecycle.
- Traditional methods can be time-consuming and costly.
Purpose of the Study:
- To summarize unique predictive methodologies for pharmaceutical scientists.
- To demonstrate how computational tools can accelerate research and development.
- To highlight the role of predictive tools in ensuring drug product quality.
Main Methods:
- Review of computational methodologies including simulations, virtual screening, and mathematical modeling.
- Analysis of case studies showcasing the application of predictive tools.
- Integration of predictive tools with analytical techniques.
Main Results:
- Predictive tools, alone or combined with analytical techniques, accurately predict physicochemical properties of drug substances and products.
- Successful application of these methodologies has been demonstrated across various case studies.
- Reduced research and development timelines were observed.
Conclusions:
- Precisely developed computational methodologies enhance the efficiency, cost-effectiveness, and reliability of the drug product lifecycle.
- Predictive tools are vital for achieving time-sensitive research goals and mitigating risks to drug quality.
- The adoption of these advanced computational approaches is recommended for pharmaceutical development.
Keywords:
cocrystalscomputational studiescrystal structure predictionmathematical modelingpolymorphspreformulationsaltssolid state characterizationMore Related Videos
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