Predicting real world outcomes

How our unique methodology enables the seamless integration of curated scientific knowledge with real world patient data

The Effect Model Law

The Effect Model law, discovered by our co-founder and Chief Scientific Officer Pr. Jean-Pierre Boissel, states that a functional relationship exists for each individual between the frequency (observation) or the probability (prediction) of a morbid event without any treatment () and the frequency or probability of the same event with a treatment ().

This relationship applies to a single individual, individuals within a population, or groups. This law enables the prediction of the Absolute Benefit () of a treatment for a given patient. It has wide-reaching implications in R&D for new pharmaceutical products as well as personalized medicine.

A proof of the intrinsic nature of the Effect Model is that it emerges from the combination of a disease model and a virtual population without being explicitly accounted for in the modeling process.

The law has been demonstrated theoretically and confirmed empirically. It is gradually emerging as a standard for regulatory decision-making in Europe to perform relative effectiveness analyses, as evidenced by the EUnetHTA guideline published in February 2013.

Applying the Effect Model requires the combination of a Formal Model of a disease with a Virtual Population of patients. The virtual population consists in a collection of individuals whose descriptors are drawn partly from real world available data (epidemiological studies, census data, etc.) and for the remainder are parameters from the disease model.

While the disease model is deterministic, genotypic and phenotypic variability is accounted for at the virtual population level.

Computing the Effect Model

Applying the Effect Model law is a two-step process. First, the Formal Model of the disease of interest is applied to the selected Virtual Population to determine the distribution of the base risk of a clinical event without a treatment (x-axis on the chart). Second, the Global Therapeutic Model (i.e. the disease model combined with the submodel of the drug under investigation) is applied to the same Virtual Population to determine the distribution of the clinical event risk modified by the treatment.

This operation yields the Absolute Benefit () derived from the drug or drug candidate for each patient of the Virtual Population. The Number of Prevented Events is simply the sum of all ABs over the Virtual Population.

Bridging the efficacy-effectiveness gap

It is widely acknowledged that the efficacy-effectiveness gap frequently observed in clinical practice needs to be bridged. This gap stems from the differences between satisfactory efficacy data from clinical trials and the actual health outcomes observed in real life once the drug enters the market.

In this context, the NPE serves as a powerful metric to predict real world outcomes.

Comparing NPEs between competing treatments or drug candidates enables the establishment of the proof of commercial relevance of a given drug product.

Serve as a decision-support metric to drive resources allocation across a pipeline of pharmaceutical products, conduct comparative effectiveness analyses versus standard of care and cost-effectiveness studies.

Value proposition

In this new paradigm, resources allocation decisions are based on a quantified prediction of outcomes on real patients from the first phases of research, as early as the target identification phase.

Throughout R&D, the application of the Effect Model law:

  • Reduces attrition costs by accelerating the establishment of proof of commercial relevance
  • Focuses spending and speeds up development by exploring scenarios in silico ahead of in-vitro/in-vivo spending
  • Improves pricing and reimbursement outcomes by demonstrating a product’s value

Each step of the way, the standardized metric is the Number of Prevented Events computed from the Effect Model.