Download PDF
Leveraging IoT for Efficient Drug Scale-Up in Pharmaceuticals: A Case Study of Dr. Reddy’s
Applicable Industries
- Life Sciences
- Pharmaceuticals
Applicable Functions
- Logistics & Transportation
- Product Research & Development
Use Cases
- Last Mile Delivery
The 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 The 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.
The 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.
Operational Impact
Quantitative Benefit
Related Case Studies.
Case Study
Case Study: Pfizer
Pfizer’s high-performance computing software and systems for worldwide research and development support large-scale data analysis, research projects, clinical analytics, and modeling. Pfizer’s computing services are used across the spectrum of research and development efforts, from the deep biological understanding of disease to the design of safe, efficacious therapeutic agents.
Case Study
Fusion Middleware Integration on Cloud for Pharma Major
Customer wanted a real-time, seamless, cloud based integration between the existing on premise and cloud based application using SOA technology on Oracle Fusion Middleware Platform, a Contingent Worker Solution to collect, track, manage and report information for on-boarding, maintenance and off-boarding of contingent workers using a streamlined and Integrated business process, and streamlining of integration to the back-end systems and multiple SaaS applications.
Case Study
Process Control System Support
In many automated production facilities, changes are made to SIMATIC PCS 7 projects on a daily basis, with individual processes often optimised by multiple workers due to shift changes. Documentation is key here, as this keeps workers informed about why a change was made. Furthermore, SIMATIC PCS 7 installations are generally used in locations where documentation is required for audits and certification. The ability to track changes between two software projects is not only an invaluable aid during shift changes, but also when searching for errors or optimising a PCS 7 installation. Every change made to the system is labour-intensive and time-consuming. Moreover, there is also the risk that errors may occur. If a change is saved in the project, then the old version is lost unless a backup copy was created in advance. If no backup was created, it will no longer be possible to return to the previous state if and when programming errors occur. Each backup denotes a version used by the SIMATIC PCS 7 system to operate an installation. To correctly interpret a version, information is required on WHO changed WHAT, WHERE, WHEN and WHY: - Who created the version/who is responsible for the version? - Who released the version? - What was changed in the version i.e. in which block or module of the SIMATIC PCS 7 installation were the changes made? - When was the version created? Is this the latest version or is there a more recent version? - Why were the changes made to the version? If they are part of a regular maintenance cycle, then is the aim to fix an error or to improve production processes? - Is this particular version also the version currently being used in production? The fact that SIMATIC PCS 7 projects use extremely large quantities of data complicates the situation even further, and it can take a long time to load and save information as a result. Without a sustainable strategy for operating a SIMATIC PCS 7 installation, searching for the right software version can become extremely time-consuming and the installation may run inefficiently as a result.
Case Study
Drug Maker Takes the Right Prescription
China Pharm decided to build a cloud-based platform to support the requirements of IT planning for the next five to ten years which includes a dynamic and scalable mail resource pool platform. The platform needed to have the following functions: all nodes support redundancy, ensuring service continuity and good user experience, simple and easy-to-use user interfaces for end users and administrators and good compatibility and supports smooth capacity expansion.
Case Study
ELI LILLY ADOPTS MICROMEDIA’S ALERT NOTIFICATION SYSTEM
Pharmaceutical production is subject to a strict set of enforced rules that must be adhered to and compliance to these standards is critically necessary. Due to the efforts of WIN 911’s strategic partner Micromedia, Lilly was able to adopt an alarm notification infrastructure that integrated smoothly with their existing workflows and emergency hardware and protocols. These raw energy sources enable the industrial process to function: electricity, WIN-911 Software | 4020 South Industrial Drive, Suite 120 | Austin, TX 78744 USA industrial steam, iced water, air mixtures of varying quality. Refrigeration towers, boilers and wastewater are monitored by ALERT. Eli Lilly identified 15000 potential variables, but limitations compelled them to chisel the variable list down to 300. This allowed all major alarms to be covered including pressure, discharge, quantity of waste water discharged,temperature, carbon dioxide content, oxygen & sulphur content, and the water’s pH.
