Successfully translating anti-cancer nanomedicines from pre-clinical proof of concept to demonstration of therapeutic value in the clinic is challenging. Having made significant advances with drug delivery technologies, we must learn from other areas of oncology drug development, where patient stratification and target-driven design have improved patient outcomes. We should evolve our nanomedicine development strategies to build the patient and disease into the line of sight from the outset. The success of small molecule targeted therapies has been significantly improved by employing a specific decision-making framework, such as AstraZeneca's 5R principle: right target/efficacy, right tissue/exposure, right safety, right patient, and right commercial potential. With appropriate investment and collaboration to generate a platform of evidence supporting the end clinical application, a similar framework can be established for enhancing nanomedicine translation and performance. Building informative data packages to answer these questions requires the following: (I) an improved understanding of the heterogeneity of clinical cancers and of the biological factors influencing the behaviour of nanomedicines in patient tumours; (II) a transition from formulation-driven research to disease-driven development; (III) the implementation of more relevant animal models and testing protocols; and (IV) the pre-selection of the patients most likely to respond to nanomedicine therapies. These challenges must be overcome to improve (the cost-effectiveness of) nanomedicine development and translation, and they are key to establishing superior therapies for patients.
Nanomedicine; EPR effect; Clinical translation; Pre-clinical models; Industry; Companion diagnostics; Patient pre-selection.
Citation: Jennifer I. Harea, Twan Lammers, Marianne B. Ashforde, Sanyogitta Puri, Gert Storm, Simon T. Barry Challenges And Strategies In Anti-cancer Nanomedicine Development: An Industry Perspective doi:10.1016/j.addr.2016.04.025
Received: 24 February 2016, Revised: 20 April 2016, Accepted: 21 April 2016, Available online: 29 April 2016
Copyright: © 2016 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
This work was financially supported by AstraZeneca. The authors kindly acknowledge Dr. Colin Howes for helpful comments and discussions. The European Research Council is gratefully acknowledged for financial support (ERC Starting Grant 309495 (NeoNaNo) and proof-of-concept grant 680882 (CONQUEST); both to TL).