Experiential learning in Industrial Pharmacy
This course introduces the student to the manufacturing of various pharmaceutical dosage forms at an industrial scale. The student will learn about good manufacturing practices, plant requirements, effluent treatments. The course will introduce the student to quality control, quality assurance procedures and packaging of bulk raw materials and finished products. The student will get a hands on training on the process of analytical method development and formulation development methods of herbal products and cosmetics. The course also covers the various aspects of regulatory and marketing procedures of pharmaceuticals.
Dates: 3rd and 4th week of July 2025 (Two credit program)
Learning Outcomes
On completion, students will be able to:
- Understand the setup of pharmaceutical manufacturing plant, environmental controls for production of various dosage forms
- Understand the formulation and development of herbal and cosmetic products
- Understand the manufacturing process, packaging and quality control of various dosage forms
- Understand the basic aspects of quality assurance procedures and analysis of drugs
- Understand the regulatory and marketing procedures of pharmaceuticals
Fee Details
The Program fee is EUR 1000 (Appx).
Included in the Fee:
- Accommodation at University Guest House (on a twin sharing basis) for the duration
- Breakfast and Lunch on all working days
- Internal Transport
- Medicare and Wi-Fi
Not Included in the Fee:
- Flight Tickets
- Travel other than that specified in Program
- Essential linear algebra for deep learning
- Fundamentals of linear classification: weights, bias, scores, and loss functions
- Calculus for the gradient descent algorithm
- Forward and backward propagation with regularization
- Batch processing for large datasets
- Linear to nonlinear classification via activation functions
- Computational setup of a shallow neural network
- Tuning neural network performance
- Pre-processing data and batch normalization
- Cross-validation for validating model performance
- Extending the computational setup from a shallow to a deep neural network
- Introduction to the TensorFlow library
- Application projects: implementing shallow and deep neural network models using TensorFlow; implementing machine learning models on edge devices using Edge Impulse.
Name of the Coordinators
Coordinator Details
- Department: Department of Pharmaceutics, Manipal College of Pharmaceutical Sciences, MAHE, Manipal
- Official email ID: hebbar.srinivas@manipal.edu
- Contact number: +91 9743490607
Program Highlights
- In-depth exposure to the principles and application of Industrial pharmacy
- Hands-on experience in developing and validating analytical methods for testing raw materials and finished formulations.
- Practical training in the formulation techniques and followed by Understanding of national and international regulatory frameworks governing drug approval, licensing, and compliance.
Offered by
Manipal College of Pharmaceutical Sciences, MAHE