Outcomes-driven innovation, development and value demonstration
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. Starting with this new methodology, NOVADISCOVERY has developed a full-scale analytics platform to predict downstream clinical outcomes from the outset of the R&D process in order to de-risk decision-making at critical transition points, optimize drug development and establish proof-of-commercial relevance.
Based on the EM law, the NOVADISCOVERY approach reconciles pharmaceutical medicine, systems biomedicine and clinical trial methodology. It provides a new methodological standard which 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?).
The standard for in silico clinical trials
Therapeutic efficacy is a complex concept. It is qualitative (the drug works or does not work), quantitative (a more or less high value of efficacy for a given patient or group of patients), subjective (efficacy is often perceived differently by physicians and patients) and temporal (is the drug effective for a long time?) - hence the need to determine the right paradigm of efficacy.
Thanks to the EM law we can now define for the very first time therapeutic efficacy as a quantity: the Absolute Benefit at the patient level and the corresponding Number of Prevented Events at the population level. This set of metrics operate as the guide to support outcomes-driven decision-making:
- Choosing the right target(s)
- Profiling optimal responders
- Optimizing in vitro and in vivo trial design
A large number of hypotheses can be tested in silico to generate reliable evidence cheaper and faster than with conventional trial and error approaches. The measure of efficacy is unbiased – each patient is his own control, and incorporates between and within phenotypic and genotypic variability.
The EM is emerging as the standard for in silico clinical trials in the same way the placebo-controlled randomized design established itself as the standard for in vivo trials at the end of the last century.
The standard for value-based pricing
The EM-based approach provides a cost-effective solution for industry and payer/HTA/provider in their joint quest for more value-focused pricing. It allows the reliable translation of clinical trial efficacy data onto broader, real-world patient populations that are relevant to payers. It does so taking into account the (necessarily) wider range of physiological characteristics and risk factors of that broader patient group. The EM’s unique strength is that it is grounded in the clinical data that remains at the heart of regulatory approval, and that remains central to many HTA processes. Yet at the same time it captures, quantifies and incorporates the inter-patient (and population-linked) variability that limits the power of randomized clinical trials to predict real world impact.
The EM provides an objective, quantified target outcome around which to build performance-based deal terms. It also opens up opportunities for biopharmaceutical companies to generate comparative outcomes data in order to gain a competitive edge – and for payers to determine in-class formulary coverage.
A full-scale technology platform
In order to address the dual challenge of the exponential growth in biomedical knowledge (c.15 million original articles scattered across 10 thousand journals according to PubMed) and the rate at which patient data is accumulating globally, NOVADISCOVERY has developed a best-of-breed technology platform.
GITHEALTH operates as a community-driven “knowledge engine” to curate and organize biomedical knowledge extracted from white and grey literature as well as patient data, with the ultimate objective to build and maintain state-of-the-art Knowledge Models of pathophysiological processes (e.g. apoptosis, tumor escape, metastasis, angiogenesis...).
Our SIMULATION FRAMEWORK is the second pillar of our technology platform. It is a modeling & simulation “action engine” designed to build, validate and apply Formal Models of pathophysiological processes to research questions. These Formal Models are the mathematical and computational translations of the Knowledge Models developed in the GITHEALTH module.
In this section:
In Europe, NOVADISCOVERY is helping lead the shift from a business model that relies heavily on serendipity to one driven by mathematics, analytics and computation
Innothink Center for Biomedical Simulation