Scientists and engineers in the biotech and pharmaceutical industries use MATLAB®, software developed by MathWorks, for multidisciplinary data analysis, predictive modeling for drug development and clinical scenarios, quality and process control followed by yield optimization, and implementation of hybrid approaches as end-to-end workflows. With the help of a multifaceted platform that facilitates early and easy modeling and prototyping, engineering teams can streamline their workflow from research to design and implementation in a single environment.

In this video, we are going to use case studies to explain a wide range of mathematical methods and applications for the entire drug development process, including:

• Confirming or rejecting drug target candidates with PK/PD and Quantitative Systems Pharmacology (QSP)
• Determining the bioequivalence of two drug formulations
• Estimating the proportion of at-risk patients from a population sample
• Batch process optimization
• Automating visual detection of defects in a pill production process using AI

You will learn about:

• Pharmacokinetics/Pharmacodynamics (PK/PD) modeling
• Distribution fitting, validation, and analysis
• Monte Carlo simulation for estimating unknown probabilities
• Applied optimization techniques
• Automated anomaly detection
• Development of Web Applications to share algorithms
• Automatic C++/CUDA code generation for embedded systems

Watch this video to

learn how MATLAB supports end-to-end data analytics and modeling workflows in the biotech and pharmaceutical industries.