How we do it

We apply the in silico trials standard methodology with the most robust and versatile technology platform in our industry

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The Effect Model

The result of decades of experience in clinical trials methodology, the Effect Model (EM) law has solved the main problem plaguing the development of in silico clinical trials: the paradigm of efficacy.

The Effect Model (EM) establishes the relationship between the course of the disease in untreated subjects and the course of disease in the same treated subjects by combining models of a disease and a drug candidate with virtual patients.

It therefore defines for each individual the predicted therapeutic benefit, or Absolute Benefit AB, resulting from the difference between two probabilities: Rc the risk of the clinical event of interest (tumor progression, side effects, death…) without treatment and Rt the same risk under treatment (AB = Rc – Rt).

With the EM, treatment efficacy becomes a quantified and predictable metric which can be benchmarked against competing therapies.

The Effect Model of everolimus (mTOR inhibitor) shows clinical benefit for a subset of patients:

As a result, this methodology yields the two metrics to identify optimal scenarios:

  • The Absolute Benefit of the treatment: a positive value (Rt<Rc) demonstrates the beneficial effect of the treatment, a zero value (Rc = Rt) the lack of benefit, and a negative value (Rt> Rc) signals the harmful nature of the treatment
  • The Number of Prevented Events (NPE): the total number of avoided clinical events thanks to a treatment for a given population of patients, which is simply the sum of all ABs over the population under investigation

The EM framework brings a number of substantial benefits over competing M&S approaches:

  • The key metric used to optimize our clients’ strategies is the clinical outcome, which is what matters to patients
  • It is unbiased as each virtual patient is her own control
  • It is standardized, i.e. a drug candidate’s Number of Prevented Events is used as a metric to support evidence-based decision-making (e.g. benchmarking competing therapies on the same population)
  • It bridges efficacy (i.e., what is the clinical benefit of a given drug candidate? how does it compare to alternative drugs?)and effectiveness (i.e., for a given amount of resources, how does the drug fare in terms of real-world patient outcomes?): if the virtual population is representative of a real-world population (e.g. the French melanoma patient population), real-world evidence and value demonstration can be embedded in clinical development decisions

How we do it