Lost in Translation (LiT): IUPHAR Review 6

Authors

  • Colin T Dollery

    Corresponding author
    1. R&D Management, GlaxoSmithKline, Stevenage, Essex, UK
    • Correspondence

      Professor Colin Terence Dollery, R&D Management, GlaxoSmithKline, Medicines Research Centre, Gunnels Wood Rd, Stevenage, Herts SG1 2NY, UK. E-mail: colin.dollery@gsk.com

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  • Sir Colin Dollery is a member of the Clinical Translational Pharmacology Group of the Nomenclature Committee of the International Union of Basic and Clinical Pharmacology (NC-IUPHAR). The role of this group is to provide advice on the translational aspects of receptor/target pharmacology, and to translate activity at drug target sites to clinical efficacy. Further information can be found on the IUPHAR/BPS Guide to PHARMACOLOGY website (http://www.guidetopharmacology.org/). This has recently been updated to include curated information on all data-supported targets of approved drugs, their interacting ligands and expert comments on clinical efficacy.

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.

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