ANSYS Case Studies Leveraging IoT for Efficient Drug Scale-Up in Pharmaceuticals: A Case Study of Dr. Reddy’s

	Leveraging IoT for Efficient Drug Scale-Up in Pharmaceuticals: A Case Study of Dr. Reddy’s ANSYS

Leveraging IoT for Efficient Drug Scale-Up in Pharmaceuticals: A Case Study of Dr. Reddy’s

ANSYS

Applicable Industries	Life Sciences Pharmaceuticals
Applicable Functions	Logistics & Transportation Product Research & Development
Use Cases	Last Mile Delivery
Challenge	The pharmaceutical industry is fraught with numerous challenges, from drug delivery to equipment design optimization and scale-up problems. Increasing raw material costs and the unavailability of the right raw materials at the right time pose significant issues in meeting stringent product delivery deadlines. Dr. Reddy’s, a global pharmaceutical company, faced these challenges and sought to explore engineering simulations to address them effectively. The company engaged with ANSYS to leverage their expertise in this field, aiming to develop accurate scale-up conditions by performing steady-state and transient simulations at each scale. They sought to study parameters like velocity distributions, mixing times, and species concentrations from one scale to the other. Read More
About Customer	Dr. Reddy’s is a NYSE listed company that manufactures and markets APIs, Finished Dosages, and Biologics in over 100 countries worldwide. As a vertically integrated global pharmaceutical company, Dr. Reddy’s has proven research capabilities, including a promising drug discovery pipeline and presence across the pharmaceutical value chain. The company has expertise in scaling up complex products from lab to the plant scale with an array of modeling tools. Process experts in the company help in reducing the scale-up risks from lab to the plant. Read More
Solution	ANSYS consultants used simulations to help Dr. Reddy’s understand the differences in micro-, meso-, and macro-mixing times from lab scale to plant scale. They also identified the risks involved in the scale-up at each particular rpm level and the formation of dead zones in plant scale simulations. Furthermore, they studied the evolution of individual species concentration and mixing performance using transient simulations. This consultation with ANSYS on engineering simulations provided valuable insights into the physics of scale-up and identified the risks involved. It guided Dr. Reddy’s in lowering the risk of scale-up batches and helped make better-informed decisions where minimal experimental data existed or where experimental data was difficult to obtain. Read More Log in to view content
Contents

Applicable Industries

Life Sciences

Pharmaceuticals

Applicable Functions

Logistics & Transportation

Product Research & Development

Use Cases

Last Mile Delivery

Challenge

The pharmaceutical industry is fraught with numerous challenges, from drug delivery to equipment design optimization and scale-up problems. Increasing raw material costs and the unavailability of the right raw materials at the right time pose significant issues in meeting stringent product delivery deadlines. Dr. Reddy’s, a global pharmaceutical company, faced these challenges and sought to explore engineering simulations to address them effectively. The company engaged with ANSYS to leverage their expertise in this field, aiming to develop accurate scale-up conditions by performing steady-state and transient simulations at each scale. They sought to study parameters like velocity distributions, mixing times, and species concentrations from one scale to the other.

About Customer

Dr. Reddy’s is a NYSE listed company that manufactures and markets APIs, Finished Dosages, and Biologics in over 100 countries worldwide. As a vertically integrated global pharmaceutical company, Dr. Reddy’s has proven research capabilities, including a promising drug discovery pipeline and presence across the pharmaceutical value chain. The company has expertise in scaling up complex products from lab to the plant scale with an array of modeling tools. Process experts in the company help in reducing the scale-up risks from lab to the plant.

Solution

ANSYS consultants used simulations to help Dr. Reddy’s understand the differences in micro-, meso-, and macro-mixing times from lab scale to plant scale. They also identified the risks involved in the scale-up at each particular rpm level and the formation of dead zones in plant scale simulations. Furthermore, they studied the evolution of individual species concentration and mixing performance using transient simulations. This consultation with ANSYS on engineering simulations provided valuable insights into the physics of scale-up and identified the risks involved. It guided Dr. Reddy’s in lowering the risk of scale-up batches and helped make better-informed decisions where minimal experimental data existed or where experimental data was difficult to obtain.

Impact #1	The collaboration with ANSYS and the use of their simulations provided Dr. Reddy’s with a deeper understanding of the scale-up process and the risks involved. It allowed the company to make better-informed decisions, even in situations where experimental data was minimal or hard to obtain. This not only reduced the risk of scale-up batches but also facilitated the buy-in for engineering-based decision making from the non-engineers within the company. The use of these simulations translated into a more efficient and effective scale-up process, ultimately leading to improved product delivery.

Impact #1

The collaboration with ANSYS and the use of their simulations provided Dr. Reddy’s with a deeper understanding of the scale-up process and the risks involved. It allowed the company to make better-informed decisions, even in situations where experimental data was minimal or hard to obtain. This not only reduced the risk of scale-up batches but also facilitated the buy-in for engineering-based decision making from the non-engineers within the company. The use of these simulations translated into a more efficient and effective scale-up process, ultimately leading to improved product delivery.

Benefit #1	50 percent reduction in uncertainty in identifying the right process window even before the raw material for experimentation was available.

Benefit #1

50 percent reduction in uncertainty in identifying the right process window even before the raw material for experimentation was available.

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Overview

Leveraging IoT for Efficient Drug Scale-Up in Pharmaceuticals: A Case Study of Dr. Reddy’s

Operational Impact

Quantitative Benefit