Mu Sigma

Pharma organizations are undergoing changes where the focus is more on transformative medicines catering to unmet need and on smaller agile R&D cycles. Tackling a complex ecosystem of patients, providers, payers, and regulators among other entities requires organizations to strengthen their predictive modeling capabilities. Traditional predictive modeling can be complemented further through application of System Dynamics, Networking Modeling, and Simulation. These models enable future insights while utilizing historical behavior, provide the ability to incorporate real-world feedback instead of reliance only on linear cause-effect relationships, and account for a more exhaustive set of parameters that capture customer interactions.

Various industries are utilizing these System Dynamics and Network Modeling techniques to enhance models for product launch, market mix, and ROI optimization, among others. Pharma companies can benefit from these methods to improve their commercialization effectiveness across similar problem areas. One such instance is where we helped a pharmaceutical company leverage system dynamics to model the effect of a biosimilar launch on the market share. Being able to simulate the impact of a biosimilar launch made the organization more proactive and nimble in modifying strategies. In this webinar, we will look to answer two key questions:

• How have network modeling and system dynamics been applied by pharma and organizations across other industries to drive business value?
• How can these techniques help pharma commercialization teams tackle the uncertain and complex ecosystem?

Presenters:

• Sridhar Turaga, Head of Technology, Mu Sigma Inc.

The pharmaceutical industry today is evolving to develop patient experience as a core dimension when bringing new drugs to market. Shifting patient expectations combined with innovative technologies will have a dramatic impact on drugs and healthcare in the coming years. To cater to shifting trends, pharma companies are now turning towards patient data to power their decision making.

Real world data (RWD) accounts for 95% of the patient data, as opposed to the meagre 5% covered by clinical trials. Pharma companies are spending close to 20 Million USD annually on generating RWD-based insights. However, data fragmentation and non-standardized formats across RWD sources – coupled with incomplete and/or inaccurate data capture – raise concerns on the quality of RWD. In once such instance, the challenge was with low coverage of a key biomarker in one data source (<10%) while the coverage was better in another (>50%). We improved the coverage by experimenting with techniques such as Random Forest and Neural Networks to predict the values of the biomarker in the low-coverage dataset.

Parallelly, there is a boom in machine learning (ML) being used for data quality processes, which can aide stakeholders in overcoming the obstacles faced in the consumption of RWD. Various ML/DL algorithms can be implemented for the imputation of missing data, prediction of variables completely absent in a data source, and detect anomalies, thereby improving the completeness and accuracy of data. Effectiveness of the methods is measured through a combination of accuracy parameters, benchmarking against results from industry standard publications, and improvement in the number of potential studies. Through this webinar, we’ll be exploring:

• What are the challenges in using Real World Data for product commercialization?
• How can ML algorithms be leveraged to improve the quality of RWD sources?
• What are the RWD elements (such as biomarkers) that could enrich a study based on patient data?

Presenters:

• Bingcao Wu, M.S, Associate Director, Real-World Market Access Analytics, Janssen Scientific Affairs
• Siddhant Deshmukh, Engagement Manager, Mu Sigma