How we do itWe apply the in silico trials standard methodology with the most robust and versatile technology platform in our industry
#EffectModel #diseasemodels #virtualpatients #WISE #GitHealth #SimWork #Haskell
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
The Effect Model enables us -according to the indicators that you have, either clinical, genetic, imaging or biomarkers, – to go down to the patient and to go much further in an individual prescription.
Our work is to really get to the bottom of these mechanisms. To identify step-by-step the sequences that lead to the disease and to the treatment that can be given.
In itself, the virtual population, irrespective of its important role in the use of models, has other qualities. The first quality is to be able to collect data that are today stored in various dispersed databases…
In this European project called SysClad, Novadiscovery was instructed to explore in-silico the
efficacy of a partial blocking of the signaling pathway mTor for the prevention of lung transplant rejection.