Using Virtual Twin Technology to Predict Drug Exposure in Individual Patients
The “one-size-fits-all” approach to healthcare is becoming a thing of the past. The new paradigm of precision medicine aims at delivering the right treatment at the right time to the right patients. An integral part of precision medicine is administration of a precise dose, which is a critical step in the larger mission to deliver personalized healthcare. Precision dosing will provide patients with the most efficacious medications with minimum probability of adverse events.
Modeling and simulation techniques for drug development include both top-down (pharmacometrics) and bottom-up (PBPK models) approaches. While these approaches have had success in drug research and development, they have yet to become a regular tool for “point-of-care” clinical decisions. The transformation of health care from “one-size-fits-all” to a targeted approach utilizing information about an individual patient’s genetics and lifestyle continues to accelerate as the US FDA more regularly and rapidly approves new personalized medicines.
Virtual Twin™ technology will be an important step towards making this vision a reality. The idea is to match the characteristics of a real patient with his or her virtual twin to predict the optimal dose. This matching would happen at several levels:
- Age, weight, height, sex, and ethnicity
- Current drug dosage and co-medications
- Activity of metabolic enzymes and transporters
- Level of organ function
Realization of this technology would allow clinicians to first try different drug doses, schedules, and combinations in the virtual twin to determine an optimal dosing regimen for the patient.
In this webinar, Dr. Tom Polasek, a clinical pharmacologist at Certara Strategic Consulting, explained how he used the Simcyp Simulator to predict olanzapine exposure in individual patients. By watching this webinar you will learn how PBPK modeling and simulation technology can be re-purposed to support model-informed precision dosing.