Pharmaceutical Drug Development Firm Enhanced Disease Taxonomy & Clinical Trial Outcomes with Real World Evidence Analytics

Jun 23, 2020

Engagement Summary

Pharmaceutical drugs have the potential to eliminate diseases and increase the life expectancy of mankind. New drugs provide evidence of effectiveness and perceived value. Mankind is struggling with the rising cost of pharmaceutical products and delays associated with pharmaceutical drug development. The client, a prominent US-based pharmaceutical company, was looking forward to leveraging real-world analytics to design an effective approach to enhance the drug development process. Our real-world data analytics experts helped the client to improve the success rate of clinical trials and redesign the drug development process.

The Client’s Challenge

The client is a prominent player in the US pharmaceutical industry. With more than a hundred patent drugs under their brand, the client wanted to improve the success rate of clinical trials. Most of the major drug development firms today are implementing real-world data analytics in drug discovery and development. Research findings have revealed real-world data has numerous benefits, for example, and early access to medication schemes is an important factor that can help in improving drug development and discovery. Thus, global regulatory bodies are becoming more transparent and flexible.

Quantzig’s real-world data analytics solutions are specifically curated to help organizations evaluate drug treatment efficacy and reduce costs related to pharmaceutical drug development. Request a free demo to know more.

The key challenges included-

Problem Statement 1

Unstructured and unorganized information: Pharmaceutical drug development and drug discovery is mostly an unorganized part of the drug development branch. Real-world data analytics can help pharma drug development firms to access unstructured information from various sources into a unified source. Thus, the client wanted to deploy artificial intelligence and real-world data analytics to analyze all the unstructured and unorganized data sources into a unified one.

Problem Statement 2

Lengthy clinical trials: Currently this client is facing challenges with lengthy clinical trials. They wanted to deploy real-world data analytics to shorten the time span of clinical data.

Problem Statement 3

Improve disease taxonomy: This US pharmaceutical industry player was facing problems with disease taxonomy and drug development related to diseases. Real-world data analytics can improve the complete disease taxonomy by incorporating molecular findings to enhance disease classification. 

Request a free proposal to gain customized insights on improving pharmaceutical drug development with the help of real-world data.

How we Helped the Client?

Quantzig’s analytics experts combined predictive analytics with real-world data analytics to understand the client’s challenges. A deep dive into the challenges faced by this US pharmaceutical industry player helped our experts to understand the priority of deploying advanced data analytics in their drug development process. Our experts also highlighted the factors that were leading to the wrong selection of patients for clinical trials. Drug development is a complex and costly affair accompanied by a high degree of uncertainty, our real-world data analytics helped pharmaceutical drug development companies to reduce drug discovery and development time. Furthermore, this collaboration helped the drug development firm to decrease the duration of clinical trials.

Our real-world evidence analytics experts help pharmaceutical drug development companies to curtail the complexities related to drug discovery. To know more speak to our analytics experts today.

Business Outcome

Pharmaceutical drug development isn’t an easy process. Medical researchers deal with several ordeals on the way to discover a new drug. It includes risks related to drug discovery and violating regulatory norms. Quantzig’s real-world data analytics solutions helped the client to:

  1. Organize unstructured information into a unified source
  2. Reduce the time of clinical trials and improve outcomes
  3. Improve drug discovery with automated alarms on regulatory norms
  4. Enhance disease taxonomy
  5.  Gain an understanding of the real-world use and effectiveness of current treatments

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