to the right patients
In the face of intensifying pressure by regulators and payers to demonstrate a drug’s real-life effectiveness, Novadiscovery teams up with Cegedim Strategic Data to help pharmaceutical companies navigate the pre- and post-market approval cycles.MORE The BRI Global Solutions consortium
The traditional biopharmaceutical R&D paradigm needs a radical rethink
The biomedical knowledge capital is doubling every ten years since the fifties. But this continuous inflow of scientific findings fails to be translated into improved therapeutic innovation and patient care capabilities.
We believe mathematical formalism is the only available tool to structure and leverage this ever increasing stock of knowledge.
Beyond the sheer quantity of knowledge readily available in the scientific literature and its heterogeneous nature - drawing on multiple domains of expertise, living organisms are complex systems characterized by retroactions and redundancies. Furthermore, these systems are based on quantitative relationships between their constituent biological entities.
The mathematical representation of living systems can help manage this complexity and open up new avenues for breakthrough treatments.
The traditional R&D paradigm is characterized by serendipity, trial-and-error and costly back-dated failures. The only available gold standard to evaluate a compound's efficacy is the Phase III of clinical trials, at the very end of the R&D process.
Predictive analytics can expand the scope of research for breakthrough drugs and turn R&D into a structured process where compound efficacy is measured long before clinical trials. Failures can thus be identified upstream to reduce attrition costs.
A drug's efficacy is not a digital outcome: efficacious or inefficacious. It is a quantity, measured in terms of clinical event risk reduction, which varies from one patient to another.
Furthermore, idealized Phase III clinical trials results give limited guidance as to how the compound will fare on real patients in a given population or group (American, female caucasian, etc.).
What is needed is a methodology to predict, for each patient, the quantity of efficacy derived from available treatments for a given condition. This will pave the way for the implementation of personalized medicine in day-to-day practice.
It is also imperative to translate Phase III clinical trials results into real-life outcomes to establish a compound's efficacy relative to competitor products already available on the market.
Novadiscovery is leading the shift from a R&D model that relies heavily on serendipity to one driven by knowledge-based mathematical modeling and predictive analytics
The entirety of drugs produced since the birth of the pharmaceutical industry in the late 19th century were discovered on the back of c.500 therapeutic targets. But the human body contains tens of thousands of potential drug targets which remain unexplored to this day.
A mathematical model of a disease process enables the structured exploration of a large number of such potential targets and their combinations, which are beyond the reach of current exploratory approaches.
Mathematical models and predictive analytics can be used as a sandbox to explore multiple assumptions cheaper and faster than with traditional approaches.
In particular, Novadiscovery can help funnel down the most promising scenarios to be explored in-vitro and in-vivo. For e.g., optimal dose-effect relationships can be determined in silico to reduce the number of necessary real life trials.
In silico simulation is not meant to replace traditional in-vitro and in-vivo trials. Rather, it should be used ex ante to optimize these trials and making them cheaper and faster to complete.
The payer-driven market is shifting from buying products to buying outcomes on real patients. Drug pricing and reimbursement decisions are increasingly based on evidence of effectiveness relative to competitor products.
The Effect Model law, discovered by Novadiscovery’s co-founder and Chief Scientific Officer, enables the prediction of real patient outcomes long before clinical trials. The resulting estimation of a drug candidate’s value can be benchmarked against competitors to establish proof of commercial relevance upstream.
The same analytical framework can be applied in daily medical practice. Given a condition, optimal treatment selection should be based on a prediction of the Absolute Benefit (i.e. risk reduction) specific to each patient for each available treatment option in order to determine the best drug for the right patient.
Novadiscovery applies its unique technology from the early stages of pharmaceutical research all the way to day-to-day patient care
When applied to a given R&D program, the Effect Model law offers the ability to explore a virtually limitless range of scenarios and measure their consequences in terms of outcomes on real patients.
Our unique suite of analytical solutions enables informed R&D decision-making to optimize resources allocations.
The Benefit Risk Impact (BRI) Global Solutions consortium brings together a unique set of expertise to help pharmaceutical companies navigate the regulatory environment in pre- and post-market approval cycles.
Novadiscovery partners with Cegedim Strategic Data and a number of expert academic research units to offer integrated consulting solutions structured around a unique access to large patient databases, analytics and modeling, epidemiology, regulatory affairs and public health expertises.
Our mathematical disease models and virtual populations can be leveraged in daily medical practice. For a given patient and a given condition, each available treatment will be ranked according to its predicted efficacy.
Novadiscovery offers physicians and clinicians access to web applications in order to optimize treatment selection decisions.
Our success ultimately depends on the quality of our human capital. We look for people with a passion for excellence, a belief in the power of the team, integrity and leadership.
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