"Virtual man" may ease drug R&D woes: report
PARIS (Reuters) - New computing technologies and the evolution of a "virtual man" to predict the effects of new drugs before they enter clinical trials could transform the fortunes of pharmaceutical research, a report said on Friday.
By 2020, the drug research and development process may be shortened by two thirds, clinical trial costs slashed and productivity increased dramatically, said the report from consultancy PricewaterhouseCoopers (PwC).
"Pharma needs a faster, more predictive way of testing molecules before they go into humans," said Steve Arlington of PwC. In particular, the concept of a "virtual man" could evolve from linking emerging technologies, he said.
A marked slowdown in the rate of new drugs reaching the market -- despite record rates of R&D investment -- is arguably the biggest single challenge facing the global drugs industry.
Big Pharma profits are in jeopardy since patents on many drugs launched in the 1990s will expire in the next few years and only four of the world's top 10 companies have enough new products to fill the looming sales gap, PwC said.
Improving innovation and boosting productivity is an issue taking centre stage at this week's annual meeting of the European Federation of Pharmaceutical Industries and Associations.
Executives meeting in Paris fear the drug industry's ability to innovate is being hampered by soaring R&D costs, government cost-cutting measures and higher regulatory hurdles.
Many of those pressures are here to stay but PwC's Arlington thinks companies should be able to improve returns on investment by harnessing smart technology.
Computer-generated virtual models of the heart, other organs and cell systems are already being developed to simulate the physiological effects of drugs, with impressive results.
PwC said some companies using virtual technology had reduced clinical trial times by 40 percent and cut the number of patients required -- a major cost -- by two thirds.
New drug candidates developed from such computer modeling will still have to be tested on patients before they are approved, but by building up a much more complete picture beforehand companies should save both time and money.
(Editing by David Holmes)
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