Old, slow and expensive: that’s how Pharma feels.
By: Flavio Aliberti
Tags: Life, Big Data, Digital, Pharmaceutical, Science

By PIRO4D from Pixabay

Can big, fast and cheap data help discovery new drugs?

“Discover new ways to improve and extend people’s lives” (Novartis) “ to improve people’s lives whenever and wherever, needed” (Pfizer). “to improve the quality of human life by enabling people to do more, feel better, live longer (GSK)

Reading these mission statements, the largest pharma organizations all agree they want to improve our lives, although odds are low that they will do it by discovering new drugs…unless something “big” around data will change the equation.

Making drugs was a great business in the ’50s. Back then, for every 1 billion USD spent on R&D, Pharma organizations could bring to the market 30 drugs. Huge returns, great political support and an expanding need of care have created pharma giants we all know.

After 60 years, nothing remains the same: today, it would cost 3 billion USD to bring 1 single drug to the market while politicians’ support is fading away as the healthcare increasing costs are one of the hottest potatoes in every developed country elections.

So, no matter how much progress we have made in all fields, drug discovery has become very slow and expensive. This observation is so fascinating to deserve a name: Eroom’s law, which is Moore’s law spelled backward.

Regulatory and organizational barriers, bottom-up science approach, generics competition drying out investments, are some of the reasons identified during the years, although countermeasures have not yet inverted this trend.

Photo by National Cancer Institute on Unsplash

Can Big Data help?

Big Data in Healthcare is expected to grow exponentially in the next years reaching USD 34.27 billion by 2022. Considered a promising land for drugs discovery and development, Pharma organizations, who own most of the data in the sectors, are investing into it accumulating large reserves of data.

Sharing the same view large cloud service Companies (Amazon, Google, and Microsoft) are putting their stake into it by developing services in full compliance.

So far, no much has happened thought.

There is today the potential to center Clinical Trial Architecture on fast and cheap electronic manipulation of large volume of data provided that we consider and include three key considerations:

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Higher model complexity: to give an example the “genome human program” targeted for 13 years the sequencing of human genes under the assumption that sequencing would be key for understanding human biology and for applications to other fields. In reality, the program demonstrated that sequencing all variants in a human genome is not enough for that and the problem statement shifted to investigate their relationship to other layer of information, like transcript levels, protein levels, metabolites or functional traits.

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2. Stronger biases: The way we statistically use data will need to change: despite the advantages of big studies, large sample size will magnify the bias associated with error resulting from sampling or study design. Based on the genome example, scaling up the sample size requires to consider the structure not only in terms of molecular data, but also including and managing all type of available data (clinical, bio-samples, etc) .

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3. Scarcity of new skills: clinical data (demographics, death/survival data, questionnaires, etc.), imaging data (MR, Ultra-Sound, PET, CT), bio-sample data (values from blood, urine, etc.), molecular data (genomics, proteomics, etc.), data from wearables (blood pressure, heart rate, insulin level, etc.), are data type currently available in more or less standard formats. Integrating and managing all of them will require a mix of information technology, statistical and medical field knowledge. In this area, AI and ML will augment but will not replace human intelligence.

Conclusions

Big Data will be a great opportunity for organizations that take very seriously new challenges that this innovation will bring. Researching new models to overcome the increasing complexity, defining new tools and processes to scale up with sense, developing new skills to identify new patterns are the active principles to improve discovery and development of new drugs.

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Disclaimer: Views or opinions represented in this article are personal and belong solely to the article writer and do not represent those of people, institutions or organizations that the writer may or may not be associated with in professional or personal capacity, unless explicitly stated.

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