“When the Pharma Giants Met the Tech Giants”
“When the Pharma Giants Met the Tech Giants”
Yin and YangThe pharmaceutical industry is an example of “Yin and Yang” or “Dark and Bright” Duality. In fact, pharma is a paradox of conscience and corruption. The pharmaceutical industry has contributed more to the well-being of humanity than any other industry. But lately, the business model of research-based pharmaceutical companies is under significant pressure.
Their return on R&D investment has dropped to its lowest levels in decades, and their public reputation in U.S. and around the world (anti vaccine movement in Europe) is worse than ever. This worrisome mix of little growth potential and low reputation is the main reason why investors are increasingly worried.
Current Business Model and Reputation of PharmaIt’s a fact that the current business model of pharma companies is not working efficiently. For each $1 billion spent on R&D, the number of new medicines approved has halved roughly every nine years since 1950. The estimated return on these (fewer) products has itself declined substantially since 2010, from 10.1% to 3.7%. On average, the thirty large and small pharmaceutical and biotech companies got only 11% of their 2017 revenue from drugs developed within the past five years. Every year almost $10 trillions are spent on healthcare industry (healthcare equipment and services, pharma, biotechnology, and life sciences) and only a fraction ($140 billion per year) is spent on pharma R&D resulting in only 30 to 40 new drug approvals per year.
An August 2016 Gallup Poll found that no industry is held in lower esteem by U.S. citizens than pharmaceuticals (the sector’s worst showing in 16 years). The bottom five industries in this poll are all reviewed more negatively than positively, with three — the federal government, pharmaceuticals and healthcare — receiving negative ratings from more than half of the public.
A new analysis (2018), found that public opinion, trust and reputation of pharmaceutical companies appear to be eroding. There was also a significant decline in the public’s perception of the transparency, openness and authenticity of drug makers.
Despite the negative public opinion, pharmaceutical companies do play a crucial positive role in society. And since there are two sides to every story, the truth is always somewhere in the middle. Pharma has always attracted idealistic passionate had working people keen to work on ways of solving health problems, but in the same time, the realities of the pharmaceuticals’ market have made the temptation for corporate crime unusually acute.
So despite corruption and lack of transparency, remains the fact that the main problem of pharma is its conservative nature while dealing with a process — drug development — that simply doesn’t work anymore due to the lack of innovation, amid digital disruption, rapid technological advances and other issues such as lack of data reproducibility.
In fact, regarding the lack of data reproducibility, there’s an unspoken rule in the pharmaceutical industry: “Half of all biomedical research produced by academics — and that it was supposed to be innovative lead-generators for pharma — will ultimately prove false”. For that reason in 2011 a group of researchers at Bayer decided to test this rule.
So, looking at sixty-seven recent drug discovery projects they have found that in more than 75% of cases the published data did not match up with their in-house attempts to replicate. These were not studies published in fly-by-night oncology journals, but blockbuster research featured in Science, Nature, Cell, and the like.
In fact, the medical literature is considered by its own practitioners to be the least reliable. Interestingly, chemists, physics and engineers, are among the most confident in the literature of their own field.
Of course, there is also an unspoken rule in the academia, regarding specific academic literature — used by doctors to guide decisions — that can be ghost managed, behind the scenes, to an undeclared agenda.
In reality, some academic articles are often written by a commercial writer (ghostwriter) employed by pharma, with an academic’s name placed at the top to give imprimatur of independence and scientific rigor. Often these academics have had little or no involvement in collecting the data or drafting the paper.
So, while academia and pharma are involved in this “the chicken or the egg” debate regarding data reproducibility, the reality is that technological innovation in pharma is somehow left behind.
Therefore, admitting that the biology of a single cell turned out to be far more complicated than expected and that we are going to need Supercomputers, AI and Blockchain to understand the complexity of Life Sciences, is probably the best thing to do for promoting innovation in drug development.
Drug Development: Status QuoDrug development is the process of bringing new pharmaceutical drugs to the market once a lead compound has been identified through drug discovery. For every 10000 compounds screened in discovery, approximately 250 compounds will pass through to more rigorous preclinical studies. Eventually, 5 compounds of these 250 will pass to highly regulated clinical trials (Phase1–3). Excluding cancer drugs from the results — which made up fully 31% of all drug programs studied and have overall success rates 5.1% —the overall success rate of all other drugs that enter Phase 1 clinical trials and ultimately reaching FDA approval is 11,9%. The entire process of drug development takes 10 to 15 years and $2.6 billion to bring a drug to market.
Throughout the entire drug development, each company generates terabytes of data, kept hidden behind a firewall (big data unpublished). Partly, this is due to strict regulatory and compliance standards and partly due to an extremely competitive environment in which these companies operate. Most of this messy data sit in silos and for a long time companies did not actually consider this suitable for retrospective analysis. Management often doesn’t see sufficient value in something that requires such a major investment.
Try now to imagine drug development as a set of 3 boxes: the discovery, the preclinical and the clinical box. Each of these boxes is siloed from another box, with no linearity and communication throughout the entire process of drug development (due to mutual distrust). Apparently, published data from all three boxes very often (50–70% times) lack of reproducibility (false negative and false positive results) while big data are left unpublished. The discovery box is left without regulations, the preclinical box is somehow regulated and the clinical box is completely regulated. Even thought discovery (and innovation) usually arrive from the absence of strictly enforced rules and regulations — in fact, antibiotics were discovered by accident — there is a limit to the complete absence of rules. Usually, once you have surpassed that limit “anarchy” might start.
In the preclinical box candidate drugs are tested on small animals in cages in some basement and in an aseptic environment, while we all know that humans have a normal life in a normal environment the so-called “exposome,” (air particles, pollutants, viruses, and everything we come into contact with each day).
In the clinical box, candidate drugs are tested on humans while well-designed experiments are carried out in laboratories. Imagine now each of these clinical boxes full of a finite number of tiny smaller boxes. Each smaller box represents a patient or a lab experiment (more or less). For each of these smaller boxes, pharmas have built around them huge protective shields (regulations) in order to monitor every parameter that can affect — and they don’t want that — the millions of variables inside each smaller box.
That is, the internal stability of each smaller box is “controlled” by monitoring and stabilizing XYZ parameters external to the smaller box. In other words, we don’t have real time (real life) monitoring of the ABC parameters inside each smaller box. Hopefully, this problem is going to be effectively resolved by using the smart healthcare wearables (internal sensors) for patients, while real-time logistically smart sensors — monitoring internal parameters while doing experiments in the lab — I am pretty sure are patented somewhere on the earth right now.
Conclusion, between the scientific process and the regulatory environment the current drug development process needs a big dose of innovation.
Drug Innovation: Tech InnovationLuckily, we have a reality in front of us now to truly start building business models where data can be protected but shared at the same time, and this reality is called Blockchain.
Blockchain in pharma can be used in: Supply chain of large molecules, Drug safety (how drugs are manufactured), Public safety and consumer awareness, Recall management, Clinical trial management and The manufacturing supply chain (a logistics nightmare). Exochain (a blockchain pharma startup) manages secure storage of patient health information on the blockchain, allowing individuals to control how clinical trial researchers may interact with their medical data. A blockchain startup Qad.re could also help pharma deal with the problem of fake pharmaceutical drugs in the supply chain (10% of market share and an estimated 1 million deaths).
Moreover, it is not a surprise that Apple, Intel and Google have all recently made large investments buying up AI startups. And while none of them have experience in drug development they are all aggressively positioning themselves to enter the healthcare market space. Silicon Valley is accustomed to rapidly innovating and embracing new technologies and could very well surpass traditional pharma to disrupt the whole industry. Microsoft announced its plans to ‘solve’ cancer. A team of computer scientists, programmers, and engineers will test A.I. programs to perform a variety of tasks like creating comprehensive models of cells to understand how they communicate with each other or how a cancer patient’s system could react to different drugs.
Benevolent AI uses the predictive power of its AI algorithm to design new molecules, extracting a new hypothesis based on a knowledge graph composed of over a billion relationships between genes, targets, diseases, proteins and drugs.
London-based AI company DeepMind, which is owned by Google’s parent company Alphabet, is training its software to fold proteins for drug discovery. The company is planning to apply an algorithm that is based on one of its core technologies called AlphaGo — software that beat top human players in a strategy game called Go — to ultimately build drugs.
Boston-based biopharma company Berg is using AI to research and develop diagnostics and therapeutic treatments in multiple areas, including oncology, by applying an algorithm and probability-based AI to analyse large numbers of patients’ genotype, phenotype and other characteristics.
Baidu research announced it has created an algorithm that is better than human pathologists at identifying tumor cells in breast tissue. Early results also show AI has become more accurate than people in the detection of skin cancer.
XtalPi, integrates quantum physics and AI in drug research and development, to develop a hybrid physics and AI powered software platform for accurate molecular modeling of drug-like small molecules.
In this very detailed list (updated June 2020), you can find most of the 220 startups using AI in drug discovery, grouped roughly by research phase: preclinical, clinical etc.
Members of the Google Brain team announced that they have crafted computer vision for the identification of protein crystallization, claiming accuracy rates around 94 percent. Protein crystallization determines the shape of cells and can play a role in discovery of drugs to treat various illnesses.
IBM Watson Health launched a genomic sequencing service in partnership with Quest Diagnostics, which aims to make advances in precision medicine by integrating cognitive computing and genomic tumour sequencing to quickly uncover highly personalised cancer treatment.
Of course the list of these innovative companies is endless. I am convinced that in ten years from now we will see fundamental changes in the traditional models of drug development. This will not be only because of AI, but rather a combination of Big Data — supported by sensors and electronic medical record (EMR) integration — improved telecommunication/ telemedicine and AI.
EpilogueSince 2000, digital disruption has demolished 52% of the Fortune 500, this means pharma must fire up and fuel co-innovation like never before across all levels, geographies and partners. The battle for the giants has just begun but often, the most novel solutions come from outside the industry silo.
Thank you for reading 👓💙
Marina
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