Lost in Translation (LiT): IUPHAR Review 6

Br J Pharmacol. 2014 May;171(9):2269-90. doi: 10.1111/bph.12580.

Abstract

Translational medicine is a roller coaster with occasional brilliant successes and a large majority of failures. Lost in Translation 1 ('LiT1'), beginning in the 1950s, was a golden era built upon earlier advances in experimental physiology, biochemistry and pharmacology, with a dash of serendipity, that led to the discovery of many new drugs for serious illnesses. LiT2 saw the large-scale industrialization of drug discovery using high-throughput screens and assays based on affinity for the target molecule. The links between drug development and university sciences and medicine weakened, but there were still some brilliant successes. In LiT3, the coverage of translational medicine expanded from molecular biology to drug budgets, with much greater emphasis on safety and official regulation. Compared with R&D expenditure, the number of breakthrough discoveries in LiT3 was disappointing, but monoclonal antibodies for immunity and inflammation brought in a new golden era and kinase inhibitors such as imatinib were breakthroughs in cancer. The pharmaceutical industry is trying to revive the LiT1 approach by using phenotypic assays and closer links with academia. LiT4 faces a data explosion generated by the genome project, GWAS, ENCODE and the 'omics' that is in danger of leaving LiT4 in a computerized cloud. Industrial laboratories are filled with masses of automated machinery while the scientists sit in a separate room viewing the results on their computers. Big Data will need Big Thinking in LiT4 but with so many unmet medical needs and so many new opportunities being revealed there are high hopes that the roller coaster will ride high again.

Keywords: Big Data; ENCODE; GWAS; adherence; breakthroughs; cost; golden years; monoclonal antibodies; patients; regulation; roller coaster; safety.

Publication types

  • Review

MeSH terms

  • Animals
  • Cardiovascular Diseases / diagnosis
  • Cardiovascular Diseases / therapy
  • Drug Discovery / methods
  • Drug Discovery / trends
  • Humans
  • Statistics as Topic / methods
  • Statistics as Topic / trends
  • Translational Medical Research / methods*
  • Translational Medical Research / trends*