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jerryking : drug_development   6

Opinion: Why economics must go digital - The Globe and Mail
DIANE COYLE
CAMBRIDGE
CONTRIBUTED TO THE GLOBE AND MAIL
PUBLISHED JUNE 9, 2019

But economists’ benchmark mental world – particularly their instinctive framework for thinking about public policy questions – is one where competition is static, preferences are fixed and individual, rival goods are the norm, and so on.

Starting from there leads inexorably to presuming the “free market” paradigm. As any applied economist knows, this paradigm is named for a mythical entity. But this knowledge somehow does not give rise to an alternative presumption, say, that governments should supply certain products.......Having led a review of the spread of anti-microbial resistance – which will kill millions of people if new drugs are not discovered – O’Neill is dismayed by the lack of progress made by private pharmaceutical companies.

Drug discovery is an information industry, and information is a non-rival public good which the private sector, not surprisingly, is under-supplying. That conclusion is not remotely outlandish in terms of economic analysis. And yet, the idea of nationalizing part of the pharmaceutical industry is outlandish from the perspective of the prevailing economic-policy paradigm......Or consider the issue of data, which has lately greatly exercised policymakers. Should data collection by digital firms be further regulated? Should individuals be paid for providing personal data? And if a sensor in a smart-city environment records that I walk past it, is that my data, too? The standard economic framework of individual choices made independently of one another, with no externalities, and monetary exchange for the transfer of private property
Big_Tech  digital_economy  drug_development  economics  increasing_returns_to_scale  market_power  network_effects  personal_data  pharmaceutical_industry  platforms 
june 2019 by jerryking
Novartis’s new chief sets sights on ‘productivity revolution’
SEPTEMBER 25, 2017 | Financial Times | Sarah Neville and Ralph Atkins.

The incoming chief executive of Novartis, Vas Narasimhan, has vowed to slash drug development costs, eyeing savings of up to 25 per cent on multibillion-dollar clinical trials as part of a “productivity revolution” at the Swiss drugmaker.

The time and cost of taking a medicine from discovery to market has long been seen as the biggest drag on the pharmaceutical industry’s performance, with the process typically taking up to 14 years and costing at least $2.5bn.

In his first interview as CEO-designate, Dr Narasimhan says analysts have estimated between 10 and 25 per cent could be cut from the cost of trials if digital technology were used to carry them out more efficiently. The company has 200 drug development projects under way and is running 500 trials, so “that will have a big effect if we can do it at scale”.......Dr Narasimhan plans to partner with, or acquire, artificial intelligence and data analytics companies, to supplement Novartis’s strong but “scattered” data science capability.....“I really think of our future as a medicines and data science company, centred on innovation and access.”

He must now decide where Novartis has the capability “to really create unique value . . . and where is the adjacency too far?”.....Does he need the cash pile that would be generated by selling off these parts of the business to realise his big data vision? He says: “Right now, on data science, I feel like it’s much more about building a culture and a talent base . . . ...Novartis has “a huge database of prior clinical trials and we know exactly where we have been successful in terms of centres around the world recruiting certain types of patients, and we’re able to now use advanced analytics to help us better predict where to go . . . to find specific types of patients.

“We’re finding that we’re able to significantly reduce the amount of time that it takes to execute a clinical trial and that’s huge . . . You could take huge cost out.”...Dr Narasimhan cites one inspiration as a visit to Disney World with his young children where he saw how efficiently people were moved around the park, constantly monitored by “an army of [Massachusetts Institute of Technology-]trained data scientists”.
He has now harnessed similar technology to overhaul the way Novartis conducts its global drug trials. His clinical operations teams no longer rely on Excel spreadsheets and PowerPoint slides, but instead “bring up a screen that has a predictive algorithm that in real time is recalculating what is the likelihood our trials enrol, what is the quality of our clinical trials”.

“For our industry I think this is pretty far ahead,” he adds.

More broadly, he is realistic about the likely attrition rate. “We will fail at many of these experiments, but if we hit on a couple of big ones that are transformative, I think you can see a step change in productivity.”
adjacencies  algorithms  analytics  artificial_intelligence  attrition_rates  CEOs  data_driven  data_scientists  drug_development  failure  Indian-Americans  kill_rates  massive_data_sets  multiple_targets  Novartis  pharmaceutical_industry  predictive_analytics  productivity  productivity_payoffs  product_development  real-time  scaling  spreadsheets  Vas_Narasimhan 
november 2017 by jerryking
Sponsor Generated Content: 4 Industries Most in Need of Data Scientists
June 16, 2014 12:00 am ET
4 Industries Most in Need of Data Scientists
NARRATIVESby WSJ. Custom Studios for SAS

Agriculture
Relying on sensors in farm machinery, in soil and on planes flown over fields, precision agriculture is an emerging practice in which growing crops is directed by data covering everything from soil conditions to weather patterns to commodity pricing. “Precision agriculture helps you optimize yield and avoid major mistakes,” says Daniel Castro, director of the Center for Data Innovation, a think tank in Washington, D.C. For example, farmers traditionally have planted a crop, then applied fertilizer uniformly across entire fields. Data models allow them to instead customize the spread of fertilizer, seed, water and pesticide across different areas of their farms—even if the land rolls on for 50,000 acres.

Finance
Big data promises to discover better models to gauge risk, which could minimize the likelihood of scenarios such as the subprime mortgage meltdown. Data scientists, though, also are charged with many less obvious tasks in the financial industry, says Bill Rand, director of the Center for Complexity in Business at the University of Maryland. He points to one experiment that analyzed keywords in financial documents to identify competitors in different niches, helping pinpoint investment opportunities.

Government
Government organizations have huge stockpiles of data that can be applied against all sorts of problems, from food safety to terrorism. Joshua Sullivan, a data scientist who led the development of Booz Allen Hamilton’s The Field Guide to Data Science, cites one surprising use of analytics concerning government subsidies. “They created an amazing visualization that helped you see the disconnect between the locations of food distribution sites and the populations they served,” Sullivan says. “That's the type of thing that isn't easy to see in a pile of static reports; you need the imagination of a data scientist to depict the story in the data.”

Pharma
Developing a new drug can take more than a decade and cost billions. Data tools can help take some of the sting out, pinpointing the best drug candidates by scanning across pools of information, such as marketing data and adverse patient reactions. “We can model data and prioritize which experiments we take [forward],” Sullivan says. “Big data can help sort out the most promising drugs even before you do experiments on mice. Just three years ago that would have been impossible. But that's what data scientists do—they tee up the right question to ask.”
drug_development  precision_agriculture  farming  data_scientists  agriculture  massive_data_sets  data  finance  government  pharmaceutical_industry  product_development  non-obvious  storytelling  data_journalism  stockpiles 
june 2014 by jerryking
In search of genomic incentives - The Globe and Mail
JONATHAN KIMMELMAN

The Globe and Mail

Last updated Wednesday, Dec. 19 2012

how drug development is failing science. Medical innovation involves a peculiar mix of seemingly contradictory motivations. Scientists and sponsors are driven by the pursuit of knowledge and a desire to relieve human suffering. But they also seek fame and fortune. Medical journals want to foster progress as well, but they sell more subscriptions when they report breakthroughs.

With the right balance of incentives, these often parochial motivations can work together and propel the best science toward the clinic. But countless failures in drug development – and their burdens for patients and health-care systems – should prompt a hard look at whether we’re striking that balance properly.

Consider the tensions between: (a) truth and compassion; (b) Truth and fortune...Physicians, patients, payers and public health programs depend on the research enterprise to supply a steady stream of medical evidence. The process of creating this social good, however, is driven by a mix of parochial interests. Personalized medicine – and other ways policy-makers are trying to prime medical innovation – will only deliver on its full potential if policies bring these motives into alignment with the goal of generating reliable and relevant medical evidence.
drug_development  genomics  innovation  medical  personalization  personalized_medicine  aligned_interests 
december 2012 by jerryking
The Rise of Backyard Biotech - Magazine - The Atlantic
JUNE 2011 ATLANTIC MAGAZINE
The Rise of Backyard Biotech
Powered by social networking, file sharing, and e-mail, a new cottage industry is bringing niche drugs to market.

By QUINN NORTON
innovation  biotech  home_based  DIY  medical  pharmaceutical_industry  cottage_industries  drug_development 
may 2011 by jerryking

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