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Volume 18 Issue 6, June 2019

Volume 18 Issue 6

Machine learning in drug discovery and development, inspired by the Review on p463.

Cover design: Susanne Harris. Original brain image credit: CallMeTak/iStock/Getty Images Plus.


  • Comment |

    It is increasingly important that regulatory agencies catalyse the translation of scientific and technological innovation into improved patient-centred health care. Here, we highlight the strategic goals of the European Medicines Agency to advance regulatory science and optimize the opportunities arising in areas such as cell and gene therapies, big data and artificial intelligence.

    • Philip A. Hines
    • Richard H. Guy
    • Marisa Papaluca-Amati

News & Analysis

  • News |

    Inhibitors of the innate immune system’s NLRP3 inflammasome promise potential in Parkinson disease, Alzheimer disease, non-alcoholic steatohepatitis, gout and much more, catching the eye of Novartis, Genentech and others.

    • Asher Mullard
  • News in Brief

  • Biobusiness Briefs

  • An Audience With

  • An Audience With |

    As a child, John Tsai used to take apart his older brother’s toys to see how they were made. This curiosity propelled him into a career first as an electrical engineer, and then on to medical school and into drug development. Last year he joined Novartis as head of global drug development and Chief Medical Officer, after nearly 20 years at Pfizer, Bristol-Myers Squibb and Amgen. At Novartis he is now overseeing, amongst other things, the company’s embrace of advanced therapies — including its gene therapy Zolgensma for spinal muscular atrophy. He spoke with Asher Mullard about applying an engineering mindset to drug development, the opportunities for operational efficiencies in clinical science and the future of advanced therapies at Novartis.

    • Asher Mullard
  • From the Analyst's Couch

Research Highlights


  • Review Article |

    The recent approval of the first RNA interference (RNAi)-based therapy has generated considerable excitement in the field. Here, Rossi and colleagues discuss key advances in the design and development of RNAi drugs leading up to this landmark achievement, assess the current clinical pipeline and highlight future opportunities and challenges for RNAi-based therapeutics.

    • Ryan L. Setten
    • John J. Rossi
    • Si-ping Han
  • Review Article |

    Advances in the design of vectors based on retroviruses, such as lentiviruses and gammaretroviruses, have led to improvements in the safety and stability of gene therapies directed at haematopoietic stem and progenitor cells. In this Review, Cavazzana and colleagues discuss the results from recent clinical trials of retroviral vectors for the treatment of genetic disorders, including severe combined immunodeficiencies and β-haemoglobinopathies (β-thalassaemia and sickle cell disease). They highlight the progress made and the remaining challenges in applying gene therapies more broadly.

    • Marina Cavazzana
    • Frederic D. Bushman
    • Emmanuelle Six
  • Review Article |

    Machine learning has been applied to numerous stages in the drug discovery pipeline. Here, Vamathevan and colleagues discuss the most useful techniques and how machine learning can promote data-driven decision making in drug discovery and development. They highlight major hurdles in the field, such as the required data characteristics for applying machine learning, which will need to be solved as machine learning matures.

    • Jessica Vamathevan
    • Dominic Clark
    • Shanrong Zhao


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