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Harnessing the predictive power of preclinical models for oncology drug development

Nature Reviews Drug Discovery volume 21, pages 99–114 (2022)Cite this article

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Abstract

Recent progress in understanding the molecular basis of cellular processes, identification of promising therapeutic targets and evolution of the regulatory landscape makes this an exciting and unprecedented time to be in the field of oncology drug development. However, high costs, long development timelines and steep rates of attrition continue to afflict the drug development process. Lack of predictive preclinical models is considered one of the key reasons for the high rate of attrition in oncology. Generating meaningful and predictive results preclinically requires a firm grasp of the relevant biological questions and alignment of the model systems that mirror the patient context. In doing so, the ability to conduct both forward translation, the process of implementing basic research discoveries into practice, as well as reverse translation, the process of elucidating the mechanistic basis of clinical observations, greatly enhances our ability to develop effective anticancer treatments. In this Review, we outline issues in preclinical-to-clinical translatability of molecularly targeted cancer therapies, present concepts and examples of successful reverse translation, and highlight the need to better align tumour biology in patients with preclinical model systems including tracking of strengths and weaknesses of preclinical models throughout programme development.

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Fig. 1: Reverse translation is an iterative process.
Fig. 2: Varying model features affect reproducibility and fidelity to patient context.
Fig. 3: Forward and reverse translation are complementary strategies.

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Acknowledgements

The authors dedicate this work to the memory of Dr Georgia Hatzivassiliou. They also express gratitude to clinical trial patients and their families for their selfless contributions to research that is crucial for translation research and paves the way for improved patient treatments.

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Authors and Affiliations

  1. Department of Cell Development & Cancer Biology, Oregon Health & Science University, Portland, OR, USA

    Alexander Honkala & Sanjay V. Malhotra

  2. Center for Experimental Therapeutics, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA

    Sanjay V. Malhotra & Shivaani Kummar

  3. Division of Hematology & Medical Oncology, Oregon Health & Science University, Portland, OR, USA

    Shivaani Kummar

  4. ORIC Pharmaceuticals, South San Francisco, CA, USA

    Melissa R. Junttila

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All authors researched data, wrote, contributed to discussions of the content and edited and reviewed the article before submission.

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Correspondence to Shivaani Kummar or Melissa R. Junttila.

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Competing interests

S.K. is a consultant and member of the advisory boards of Boehringer Ingelheim, Springworks Therapeutics, Bayer, Genome & Company, HarbourBiomed, Seattle Genetics, Mundibiopharma and Gilead. She is also cofounder of PathomIQ, and her spouse is a member of the advisory board of Cadila Pharmaceuticals and cofounder of Arxeon. A.H. is a founder and shareholder of Arxeon. S.V.M. is founder and shareholder of Arxeon and scientific adviser for Cadila Pharmaceutical. M.R.J. is an employee of and owns stocks from ORIC Pharmaceuticals.

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Nature Reviews Drug Discovery thanks Uri Ben-David, Sara Colombetti and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

Cancer Therapeutics Response Portal: A CTD2 Network Resource for Mining Candidate Cancer Dependencies: https://ocg.cancer.gov/e-newsletter-issue/issue-11/cancer-therapeutics-response-portal-ctd%C2%B2-network

DepMap: The Cancer Dependency Map Project at Broad Institute: https://depmap.org/portal/ccle/

Office of Cancer Genomics: https://ocg.cancer.gov/node/300

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Honkala, A., Malhotra, S.V., Kummar, S. et al. Harnessing the predictive power of preclinical models for oncology drug development. Nat Rev Drug Discov 21, 99–114 (2022). https://doi.org/10.1038/s41573-021-00301-6

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