The "Drug Discovery and Healthcare Big Data Bubble" Will Explode! Discover the reasons in the english translation of Manuel Gea Interview!

Due to many requests from english speaking people, we translated the Interview in english with the necessary links to better understand our agumentation and to get access to the sources.

Please find the english translation of the “Wide Angle” interview of Manuel Gea, co-founder and CEO of Bio-Modeling Systems, published in the 28/11/2016 edition of “Biotech et Finances” and written by Viviane de Laveleye, journalist at Biotech & Finances, the reference newsletter of the health & biotech community.

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TITLE: THE DRUG DISCOVERY AND HEALTHCARE BIG DATA BUBBLE WILL EXPLODE!

With a 90% failure-rate and payers ever more reluctant, the drug discovery system is in dire straits. This is the alarm Manuel Gea, CEO de Bio-Modeling Systems, has been raising for several years. Be it fallacious publications or misuse of the digital revolution, the failing of the current model are numerous. BMSystems endeavors to by-pass them by offering its partners an alternative approach which spares them financing programs doomed to failure.

BIOTECH ET FINANCES: What are your observations regarding the current drugs discovery model?

MANUEL GEA: the current system is extremely risky. A new drug is sought in terms of a new target, sometimes exploitable in several indications, which promotes unwanted effects. As long as clinical trial’s success-rate remained favorable, for a while this was around 20%, financing was no problem and the system was sustainable. However, today, the failure-rate has reached 90% while payers are ever more demanding and increasingly reluctant to pay premium prices. The model has therefore reached the limits of its possibilities. It is no longer sustainable, even for the pharmaceutical industry. All current results demonstrate we are heading for the wall. In 2016, the number of FDA approvals dropped from 51 (2015) to 22 for lack of candidates!

BF: What are the principal reasons for such a failure-rate?

MG: It isn’t the development aspects which are problematic; most programs fail because the concepts on which they are based are erroneous. These rely upon affirmations expounded by so-called, “opinion leaders “who spend their time trying to prove they are right. The concepts of A beta and Tau as causal in Alzheimer’s disease has been prevalent for at least the past ten years. Yet, they merely are non-specific consequences. Such hypotheses generate 99% failures. Perhaps is it high time to revisit the paradigm. Moreover, the research process is structured much more as a function of regulatory rather than disease-associated aspects. Finally, published scientific research, on which drugs development is largely based, is of dismally low reliability. First, researchers are pressured to publish ever more. As a result, the temptation to exaggerate the import, and sometimes even invent experimental results so as to get published increases constantly. According to Stanford University, 85% of research efforts are wasted because consisting of exaggerated or fallacious results. Some clinical studies turn out to be useless because, in an effort to obtain an NMA , they were so biased that they do not reflect actual clinical reality anymore. It is to redress this state of affairs that Stanford created the METRICS Institute (Meta-Research Innovation Center at Stanford). Furthermore, many published research reports turn out to be irreproducible (1). Both Amgen and Bayer attempted to reproduce the published results of studies in oncology. Amgen was unable to reproduce the results of 90% of a total of 53 studies while Bayer declared the results of 79% of 67 projects to be irreproducible. In addition, published research does not necessarily represent actual current knowledge. In 2008, a study in the Journal of Medicine addressed the published results in 74 clinical trials for psychotropic drug candidates. Of these, 38 were positive and 36 negative. While 37 of the 38 positives were published, only 3 of the 36 negatives were also published. Solely what is positive end-up being published, not only because no one wishes the competition to become aware of deleterious pathways, but also because journals do not like publishing negative results. The net outcome is a highly biased vision of reality leading to a waste of resources: what could have prevented launching programs doomed to failure was not published.

BF: You also advocate a major shift in the manner to carry out scientific research.

MG: In the past, the lead was in the hands of clinicians, physiologists and biologist who had a holistic vision of the patient. They were looking for solutions that could be adapted to the nature of the problem. In the 70’s, the advent of molecular biology gave rise to a profound shift. Scientists decided that biology was akin to chemistry and, consequently, a Cartesian science where everything could be addressed in terms of components and thus placed in the hand of hyper-specialists. This conception leads nowhere because life is a “complex” phenomenon (as explained by Dr. Baby to Dr. Google and Watson), which, at the opposite of a “complicated” system, cannot begin to be described through the ensemble of its components. The fall in success-rate started then. Screes of data where produced to be eventually used for building mathematical models which would explain life. All that is not readily understandable is deemed useless and therefore of no interest. This effectively shackles knowledge gathering. For example, the non-coding DNA (most of the human genome) has long been considered “junk DNA” when it actually plays very major structural and regulatory roles.

BF: in this context, what is the contribution of the digital revolution?

MG: The future will be digital and biology but who will lead! The fundamental basis for algorithms in the universe of the web is characteristic of a “Cuddly Bear” world. The data producers are honest. If I look for a product on Amazon or if I enter a key word on Google, it’s probably because it’s of interest to me. The algorithms which analyze my behavior on the web will then use this data to generate correlations and target ads. If the analysis is faulty, the ads will be poorly addressed. But there are no deleterious consequences. If we now move to the realm of life sciences, the situation becomes radically different. First of all, we are now dealing with a complex system. Secondly, in opposition to consumers on the web, scientists aren’t necessarily trustworthy. The usefulness of algorithms thus gets buckled beyond redress. Thirdly, in medicine, it is causes and effects correlations that are of real interest and are absolutely required. Finally, in the internet world, there are men and women specialized in the art of addressing client’s experiences. They critically scrutinize the results they get and are capable of challenging them. In the healthcare world, there are no or very few biologists capable of mastering these algorithms which largely remain in the hands of computer scientists. Most generally, these data scientists or bio-informatics specialists, who have had no or little training in physiology and biology, are convinced that the models they are developing depict biological reality as such and are not mere approximations. Some even have the pretention of explaining the brain by modeling neurons electronically. Yet, the brain is much more than just neurons. It is by addressing this complexity that we understood the mechanisms leading to and sustaining Creutzfeldt-Jakob’s disease. Thus, today, we find ourselves in the grotesque situation where fortunes are being spent to produce so-called “discoveries” on the basis of models that do not even begin to represent biological reality.

BF: You thus preconize a change in the discovery paradigm. What does it consist of?

MG: Drug-discovery is akin to a police enquiry where several affairs are intertwined, incriminating evidence has been lost, witnesses are unreliable, etc. To resolve the case, hypotheses must be generated and demonstrated heuristically. To this end, one must accept that complex systems cannot be approached from a Cartesian standpoint. All that concerns data treatment is very well managed using computers. But integrative clinicians, biologists and physiologists, endowed with very wide competences and capable of challenging and mastering these data, are absolutely required. Focus has been placed on hyper-specialized data scientists, forgetting that it is excellent generalists, capable of apprehending complex systems and understanding an individual as a whole that are actually needed. Unfortunately, explaining to an executive that “big-data will save the world” is a lot sexier than pointing out the necessity to train generalists capable of mastering the processes.

BF: How does BMSystems fits within this paradigm?

MG: We anticipated these problems and conceived, in 2002, the CADI (Computer Assisted Deductive Integration) modeling platform which functions on the basis of heuristic and holistic processes. We recruited widely knowledgeable integrative biologists and, together with computer scientists, developed the tools enabling them to work. We applied the principle whereby while it isn’t always possible to prove that something is true, it is always possible to prove it false. Our integrative biologists thus generate hypothesis that they then endeavor to destroy, thus eliminating all that is impossible to retain only what remains likely. When attempting to further restrict the possibilities become unproductive, we launch the necessary experimentation. We do not search for the optimal solution but for an acceptable solution allowing the least risky development program to be launched. We thus have the ability to eliminate projects unlikely to succeed, thereby providing our clients with considerable savings. In 2002, we were regarded as extra-terrestrials. We nevertheless created the company in 2004, signed our first contract with an industrial pharma and launched an exceptionally productive collaboration with the CEA who invested considerable funds to carry out the in-vivo experimental verifications of the Creutzfeldt-Jakob pathogenesis and clinical progression model we had developed using our platform. We are profitable since 2006 and, although we have repeatedly proven that a change in paradigm is actually possible and fruitful, the real problem remains. Today, the majority of the people prefers to crash with everyone else rather than take the risk of succeeding. It is for this very reason that we launched conferences sponsored by Centrale Santé and ESSEC Santé. Our message is that the digital revolution could really be an accelerator provided its development and implementation can be mastered. However, with respect to complex systems, digital approaches, just like electricity during the last century; do not constitute an end in themselves. Those who made their fortune on the internet believe that what they have developed can be applied successfully to life sciences and that informatics can be a substitute for intelligence. The claim that androids will replace humans amounts to delirium. We are currently witnessing a colossal bubble starting to explode, the Theranos scandal being a prime tell-tale. A 9 M$ valorization upon an omnipotent drop of blood was a swindle. Even Google, with its moonshots is ”going back to earth”. Our industry can generate dreams and people have a short memory. Targeted cancer therapy is largely ineffective in the long term. The tumor initially does regress, but it all too often relapses with fatal consequences and the gain in life expectancy remains largely negligible. When only a large proportion of tumoral cells are being targeted, those escaping mass-extinction are now free to expand and diversify. Immunotherapy is all too often presented as a near oncological panacea, yet it proves effective in only 20-25% of cases and produces substantial side effects. But this negative reality is consistently ignored by “dominant thinking”.

BF: Can you give concrete examples of professionals who believed in your approach?

MG: In the context of our internal research activities, we have had a long academic collaboration with the Max Planck Institute for Psychiatry in Munich. Following a talk given in 2008 by my associate Fran?ois Iris at a conference in San Francisco, Prof. Christoph Wilhelm Türck, head of the proteomics unit, was struck by our approach. Our subsequent collaboration produced considerable progress in our understanding of the mechanisms behind chronic anxiety. By exploring the probable causes of psychiatric disorders, and not solely their symptomatology, we discovered a form of mechanistic continuum between chronic anxiety, schizophrenia, bipolar disorders and major depression. Through our partnership with the CEA, we challenged the traditional approaches to Creutzfeldt-Jakob’s disease which only considered the prion’s effects upon neurons but could not explain why healthy neurons died in the brain and not in the spinal cord. Yet, neurons represent only 50% of brain cells populations, the other 50% consisting of astrocytes, microglial and endothelial cells. By analyzing the system as a whole, we discovered that astrocytes were responsible for the massive demise of healthy neurons together with the mechanisms implemented. We then asked whether these very mechanisms could be harnessed for the treatment of psychiatric and neurological disorders, what was confirmed. We are thus entirely revisiting the drug-discovery process. We implement a mechanism-based medicine approach which, as opposed to evidence-based medicine, focuses on causal aspects to understand the mechanisms of pathogenesis. But care should be taken not to confuse mechanism-based medicine with “drug’s mechanisms of action” or with “functions of identified targets”, as is all too often the case. We endeavor to find combined interventions which act on those targets closest to the key physiological mechanisms so as to diminish the risks of unwanted effects. Another research program led, in 2006, to the creation of Pherecydes-Pharma which also has products in phase II.

BF: Are you optimistic regarding changes in attitudes?

MG: The current model has been regarded as perfectly normal for so long that challenging its acceptability already constitutes quite a revolution. The manner in which programs are financed must be reinvented. It really is possible to produce effective therapeutic approaches at reasonable costs. We aren’t the only ones who dare think out of the box. We belong to an exploding minority who has engaged a battle against so-called opinion leaders and dominant thinking. Given the 90% failure-rate they generate, we have good reasons to be optimistic.

Do not hesitate to contact us to let us know your comments or questions.

Manuel GEA

Co-founder & CEO

BMSYSTEMS www.bmsystems.net

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