These cannot reach common man unless it translates to a major movement.
Science/Engineering/Medical students and professionals need to make this movement involving societies/communities.
– the globalisation of the markets, as demand for medicine rises in the developing world
– the globalisation of R&D, as a growing share of R&D migrates to Asia
– the globalisation of the regulations governing the development of new medicines, as national and federal agencies collaborate
– the globalisation of information, as healthcare payers share data on the clinical and financial performance of medicines
Science & Research talent:
– We cannot train a new generation of research scientists unless there are scientists to train.
– Nor can we make the medicines people need without society’s support – and we are dishonest if we pretend otherwise.
– We cannot expect charities and individual philanthropists to fund the research that is required to develop new therapies.
Several relatively small changes would make a considerable difference. <Source PwC Report: Pharma 2020>
– Investing in school science labs and specialist teachers, and giving science a more prominent place on the school curriculum, would encourage more students to study the sciences at university, thus creating a larger pool of researchers on whom the industry could call.
– Altering the patent laws to recognise the value of long-term research
– Rewarding the development of vaccines and cures more generously
– Demonstrating a genuine commitment to the prevention of disease
All these would likewise help to put the industry on a firm footing in its efforts to decode the molecular basis of disease – surely one of the biggest and most worthwhile intellectual challenges the world faces.
For technologists: Together with EMRs, “smart cards” containing details of patients’ individual health records (much as store cards track their shopping habits) and semantic technologies to link different kinds of data, pervasive healthcare will create a day-to-day environment that equates with the controlled environment in which clinical trials are conducted today.
– Semantic Web (scientists can move seamlessly from one database to another, analyse disparate forms of data, connect genomic/proteomic/metabolomic data with clinical data)
– Autonomous Experimentation using Robot Scientists (Machine Learning, Artificial Intelligence to carry out the entire cycle of scientific experimentation, including the origination of hypotheses to explain observations, the devising of experiments to test these hypotheses and the physical implementation of the experiments using laboratory robots)
– Mathematical models (to better understand disease, develop biomarkers & medicines)
– Pervasive Healthcare (the use of remote devices to monitor patients on a real-time basis wherever they are)
Systems Biology: Short Overview by the Systems Biology Research Group, UCSD (http://sbrg.ucsd.edu) (YouTube link)