Medicine’s transistor moment

In line with NOVADISCOVERY’s transition from startup to scale-up, the life sciences industry has recently gone through its transistor moment, in the words of Bill Maris (CEO of GV, the venture capital investment arm of Alphabet)

Biology’s inherent complexity is driving the convergence between medicine and engineering sciences

Complexity, a signature of life, can be defined as a mix of redundancy and feedbacks1. Even when all of a biological system's constituent parts are known, failure to account for their quantitative interactions results in the impossibility to predict its behavior. Disease mechanisms exhibit complexity at each layer (genes, RNAs, proteins, cells, tissues, organs, patients and populations) and in all the interactions between these layers. 

This inherent complexity explains the failure of the traditional therapeutic R&D paradigm. The discovery and development of a new drug is a risky and time-consuming trial-and-error process which brings the candidate from ideation to market through a series of in vitro and in vivo trials of increasing complexity and cost. 

The past decade has seen the emergence of a new field in biomedical research: the application of mathematical modeling and computer simulation to manage biology’s complexity and build predictive analytics to de-risk the R&D process.

Oncology illustrates the limitations of the old R&D paradigm

Inefficiency of target discovery

Serendipity rather than a focus on downstream clinical impact still drives therapeutic innovation, resulting in costly back-loaded failures. Phase 2 and 3 attrition rates in cancer trials are estimated at 79% and 65%, resp. (source: Nature Review Drug Discovery, Vol 9, March 2010).

Increasing complexity of clinical research in oncology

In light of the growing number of products in development, it has become impossible to explore the entirety of potential drug combinations with conventional clinical trials. According to research by Leerink, there are 6 090 potential combinations trials for cancer immunotherapies (source: “Lifting the curve and rising the bar”, Leerink, 2015).

Furthermore, the “fragmentation” of cancer in several molecular subtypes (“as many cancers as patients”) makes recruiting for and running statistically-significant trials hopeless.

Mounting payer pressure to identify biomarkers and demonstrate value

The cost of new cancer drugs is pushing payers to impose smaller and well-defined target populations as well as performance-based contracts with drug developers. Commercial risk has risen significantly in response to these developments. Since 2006, NICE (UK) has issued negative recommendations for 41% of the cancer drugs reviewed (source: Ernst & Young 2015).

In silico clinical trials are triggering a shift towards an outcomes-driven R&D paradigm

A recent paper by Scannel and Bosley2 has shown that an increase of 1% of the predictive value of screening and disease models would increase gains in R&D outputs ten- to 100-fold.

NOVADISCOVERY’s vision for the future of R&D is a paradigm where each decision is based on the prediction of clinical outcomes, from the outset of research (when selecting a new target) down to market (when demonstrating a drug’s value in terms of comparative effectiveness).

The in silico R&D paradigm
Fig 1: The in silico R&D paradigm

The market for in silico is poised for significant growth over the next decade

While early entrants in the market for in silico services started their journey a decade ago competing against non-consumption, the technology is now considered a must-have to secure a decisive competitive advantage in the hottest corners of the life sciences market, such as immuno-oncology R&D, where the ability to characterize optimal responders is the primary driver of commercial success. 

The technology’s path to mainstream adoption is now charted: regulators such as the FDA and the EMA are actively looking into “model-informed drug development” to support new drug approvals. The European Commission has earmarked research grant funding for 2017 with a dedicated Horizon 2020 call “In silico trials for developing and assessing biomedical products”. Finally, the Avicenna Alliance, the association for predictive medicine, is actively engaging stakeholders to identify policy and regulatory obstacles to the enhancement of in silico medicine. A roadmap for in silico clinical trials was published in January 2016 (Avicenna Roadmap - PDF).

Biotech companies, which typically have a concentrated exposure on a limited number of R&D programs, use in silico to inform their bets and build a convincing business case with a view to partner their drug candidates with large pharmaceutical companies.

Contract research organizations are also investigating the technology to improve their value proposition beyond their traditional offering.

  1. Bridging systems medicine and patient needs | Boissel JP, Auffray C, Denis Noble, Hood L, Boissel FH | CPT Pharmacometrics Syst. Pharmacol 2015; 4, e26 (doi: 10.1002/psp4.26)
  2. Scannell JW, Bosley J (2016) When Quality Beats Quantity: Decision Theory, Drug Discovery, and the Reproducibility Crisis. PLoS ONE 11(2): e0147215. (doi: 10.1371/journal.pone.0147215)

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 

Bernard MUNOS
Innothink Center for Biomedical Simulation