Pharmacoeconomic Simulation of Clinical Trials Using Predictive Analytics

Pharmacoeconomic Simulation of Clinical Trials Using Predictive Analytics

Problem:

Peripheral neuropathy is a condition caused by chronically high blood sugar and diabetes. It leads to weakness, numbness, and pain in hands, feet, and other body parts. About 60% of all people with diabetes eventually develop this disease. To make sustainable treatment decisions and provide personalized care strategies, scientists, doctors, and insurance companies use tools for in silico clinical trials. With these simulation-based tools, they can predict how a certain patient would respond to a drug and use this information to make personalized prescriptions.

Pfizer, one of the world's largest pharmaceutical companies, asked Fair Dynamics, in collaboration with Health Services Consulting Corporation, to develop a platform that would help the company’s researchers test a new drug for patients with painful diabetic peripheral neuropathy. The platform would be based on previous clinical studies and act as a decision support tool, which could assess a patient’s personal parameters, prescribe drug dosage, and predict possible outcomes. The platform also needed to be flexible and have a user-friendly interface to allow inexperienced users to work with it. To develop this platform, engineers applied AnyLogic simulation modeling.

Solution:

To create a predictive analytics platform, engineers needed to process raw data from different sources and categorize it. For this purpose, they integrated SAS data files and machine learning algorithm in an AnyLogic model. The algorithm grouped the data with patient profiles into six clusters with clustering variables, such as gender, age, disease duration, and others. These parameters were essential when completing patient treatment programs.

To include patients in the model, engineers used an AnyLogic agent-based modeling approach. It allowed users to set up patients with predefined parameters similar to those in the clusters. The patients would then fall into one of the identified clusters depending on these parameters.

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Following categorization, the treatment process of each patient was simulated in the model with several treatment scenarios. It was based on the data from the previously clustered patient profiles. To validate the model, the 4-6 weeks treatment for each patient was simulated.

Doctors were finally presented with the optimal treatment scenario and dosage for a patient. For each patient or cluster, users could export dynamically created reports.

AnyLogic capabilities for parallel computation also offered simulation of scenarios with multiple patients using the parameter variation experiment.

As the model was supposed to be used by inexperienced people, engineers used Java technologies, supported by Anylogic, to complete the convenient interface.

Outcome:

In this project, AnyLogic acted as a software tool for integrating various datasets, machine learning algorithm, and simulation capabilities. Altogether, they allowed the processing of diverse historical data and its regrouping into unique clusters. With AnyLogic agent-based modeling, engineers managed to complete an easily configurable predictive model and simulate personalized treatment processes with great precision. The model helped doctors make informed decisions on drug dosage for every patient and see how he or she would respond to the treatment. With Java-based design elements, the model’s interface became more intuitive and could be easily understood by new users.

Project presentation by Luigi Manca, Fair Dynamics

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