The 128 US$ billion (US only) Arthritis Conundrum:

Arthritis is the major cause for debilitation with a disease burden of 128B annually in the US in 2003 alone according to the CDC. Within this difficult to diagnose condition the disease formerly named Rheumatoid Arthritis (RA) constitutes a specific conundrum; RA and related conditions affect 1% of the world population of all ages with annual cost of app. 48billion$ in the US. While there is possibly progress to differentiate early stage osteoarthritis form RA with a new, algorithm-guided biomarker test, those solutions that would have a significant impact on the quality of life as well as socioeconomic disease burden are still urgently awaited for; i.e. Arthritis needs PRECISION MEDICINE.

The problem:
Most drugs for arthritis in general, but in particular RA only work for varying subpopulations with no patterns known to distinguish specific subgroups responsive to a given drug a priori. This evidence is highly suggestive for the conclusion that “RA” is a heterogeneous syndrome of look-a-likes affecting joints as a common denominator; i.e. the disease formerly named Rheumatoid Arthritis (RA).
Furthermore, we currently miss the “window of opportunity” in RA (first treatment to be initiated during the first 6 weeks or 3-4 months after first symptoms occur) to drive disease in remission or to low disease activity, “simply” because the uncertainty on any drug’s efficacy in a given patient, which leads to an “algorithm” for treatment starting with the least expensive drugs first, while costly biologicals may be more effective in inducing remission or a low disease activity. This is a socioeconomic disaster neglecting the major impact on the quality of life and loss of productivity of patients for subsequent decades. From a patient's prospective the contemporary RA treament strategy may much seem like a "trial and error" based on his physician's intuition (compare shift to Precision Medicine).

A most recent publication (Editorial in the BMJ 3/2015) adds to the uncertainties and further increases the pressure on the expensive biologicals in as much as aggressive combination treatment with cheaper drugs may not be inferior than anti-TNF treatment for patients with established (1,5 -12years) RA over a period of 12months; i.e. identification of optimal responders is a key for future reimbursement for any biological; see also Rheumatology: An Evolutionary force in Biologics (Curr Pharm Des. 2015 Mar 13. in press).

Of note, most post hoc analysis, even for new drug target validation in clinical trials and/or biomarkers to stratify patients and monitor disease activity are confounded by the fact, that current diagnosis of RA is "flawed", which almost per definition must result in equally not precise approaches to post hoc analysis of any treatment and monitoring approach (biomarkers, drug/patient stratification). First, it is most likely that chronic disease reflects different stages of these progressing pathologies with anticipated varying responsiveness to established and most likely new interventions. Second, patients will also receive varying drugs during the course of disease and each drug imprints an additional specific and generic “fingerprint” in the course of the pathology resulting in an uncontrollable bias for any analytics approach.

In consequence we need “hypothesis free” or better unbiased longitudinal multi-omics data harvesting approaches to differentiate the respective subgroups on a molecular level starting with the early, first time diagnosed patients to start with to avoid future bias.

Besides the scientific questions, there here are many open challenges, which have recently been addressed by the US Scientific Strategy 2015-2020 - Arthritis Foundation
Of note, in light of most recent research patients with osteoarthritis may equally benefit from further insight in “rheumatic” conditions.

Who should decipher the arthritis puzzle: Industry versus Academia?
The following quotes from an editorial published by the National?Biomarker?Development?Alliance?(NBDA) 09/2014 in the related paper documents a major problem residing in academic approaches:

“The systematic failure in biomarker R&D is further illustrated by the fact that the US FDA has approved less than one protein biomarker per year since the mid 1990s” (in contrast with more than 100.000 manuscripts claiming a biomarker candidate)
…
“Most biomarker discovery still takes place in government-funded university laboratories that are ill equipped to undertake the myriad procedures required for stringent biomarker profiling on the scale required to achieve the evidence needed to attract larger investment in clinical trials.”
…
“While acknowledging that most flawed biomarker studies may involve multiple, cascading errors, the penalty for mistakes in early discovery, especially bias in sample collection or lack of standards for collection and storage is manifest in late stage failure.”

Not to say that academia acted not with its best effort, but is it really academia<#s responsibility to generate the standards required for making research comparable at larger scale? Does academia have the resources and capabilities aligned in a standardized manner to deliver cutting-edge explorative and translational R&D at industry scale?

Who is going to pay for optimized patient stratification, i.e. Precision Medicine in arthritic conditions?
For example, in Germany payers have little incentives to tackle this problem, since they do not care about accelerated loss of productivity; cynically they benefit the sooner a patient retires due to disability, the better for them.

Must pharma invest to develop a comprehensive patient stratification ecosystem versus current approaches to identify solely responder groups to a company’s own drug?
From my end they should, not least since such an ecosystem will eventually result in the optimized treatment of any patient with “his” drug. Consequently this would secure a company its customer’s base, optimally starting with treatment already once disease is diagnosed first time.
Provided the BMJ review quoted above, I feel the industry actually has no choice; i.e. demonstrate superior efficacy or vanish! The evolutionary pressure will only allow those players to survive, that not only deliver the best drug for a given patient, but actually those engaging in the identification of “their” responders early in the discovery process and establish value in a sense that an AI-based systems enables the MD to find the right therapeutic approach for his patient ab initio versus the current "trail and error" approach. Having said this, it is self-evident that a priori patient stratification into molecular subgroups will ultimately result in new therapeutic avenues, again creating a strategic advantage for early adopters to Precision Medicine.

“The one that has the best data & algorithms will win”
…free adoption from Andrew Ng

Summary and vision:
As long as the arthritis conundrum is not solved industry, providers and payers inadequately address patients and society’s needs while billions are wasted annually, simply because no player so far identified the right incentive for himself to take a bold and ambitions approach. Having said this I feel we are at an inflection point since newer research and regulation as well as increasing pressure from payers and patients mandate novel analytics approaches; these will include longitudinal, multi-omics approaches to harvest Big DATA alongside the Precision Medicine paradigm shift. At the same time the technological capabilities have emerged from the convergence of technologies (Big DATA/AI with molecular diagnostics) and will eventually solve not only the arthritis conundrum, but serve many more chronic conditions.
Nevertheless, given “natural” diverting interests one of the most pressing challenges to all stakeholders will be to establish lean, fast growing flagship project like recently suggested in Science Translational Medicine Precision  medicine: Beyond the inflection point, project, which however require substantial funding to create value fast; for patients, payer and investors.

Further reading:
Reinventing Biomedical/-pharmaceutical R&D
Will Google disrupt Medicine, Health Care
Are we expecting Big DATA to create the Elixir of Life?  or How to avoid "garbage in - garbage out"
SYMBIOTIC INNOVATION: A paradigm shift in R&D

Disclosure:
InnVentis, a multi-omics/machine learning company was developed alongside the “Symbiotic Innovation” paradigm with the support of deep innovation GmbH, which is gratefully acknowledged. InnVentis goal is to build a vertically integrated B2B and B2C solution for precise diagnostics and therapeutic decision making & disease monitoring in real-time (supported by artificial intelligence) for major chronic inflammatory diseases; i.e. enable Precision Medicine.

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