Kristin Carlsson Petri: How pharmacometrics modelling can augment and replace clinical trials

Transformation in Trials

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This week we dive into pharmacometrics as a discipline and the potential for replacing and augmenting clinical trials with modelling. We speak to Kristin Carlsson Petri, who is a Director of Pharmacometrics.

Pharmacometrics modelling is a range of mathematical modelling techniques that can quantify our knowledge of drug biology and disease as well as trial information. The power of the methodology lies in the integration of knowledge across drug development and different compounds.

Even though pharmacometrics modelling is coming of age in recent years, it is not a new discipline. Its origins trace back to work conducted in the ‘50s and ‘60s, and one of the modelling programs most commonly used today were built in the 1970s in the Fortran language. The development of computers and computing power has allowed the discipline to grow, since the modelling is highly dependent technology.

Now the regulatory bodies request pharmacometrics analysis and the FDA has its own unit that conducts pharmacometric analysis on the data provided. Both the EMA and FDA encourage inclusion of pharmacometrics analysis especially for paediatrics studies.

In paediatrics studies pharmacometrics modelling is especially powerful. This is an area where data is usually scarce and fewer patients. But this population should still be offered evidence based medicine. By drawing on existing data from adult studies, some of the gaps can be closed without additional burden on the patients.

There are several examples of regulatory bodies accepting label expansion into paediatrics without the need to run clinical trials - relying solely on evidence from pharmacometric models. This is an opportunity - since it takes typically 10 years from a drug being developed for adults to it being approved for children. Closing this gap will increase access to evidence-based drugs for paediatric patients.

The area could grow even faster if data sharing across especially pharma and academia could be accelerated, and AI and machine learning could be applied to it. This is difficult, since the legal challenges of sharing data blok an efficient exchange.

We also speak about datasets collected continuously from wearables, and how they are challenging some of the techniques developed in decades past - and how these may need to be updated to the new technological possibilities.

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