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References 1 Jespersen, H, Lindberg, MF, Donia, M, Söderberg, EMV, Andersen, R, Keller, U, Ny, L, Svane, IM, Nilsson, LM, Nilsson, JA. Clinical Responses to Adoptive T-Cell Transfer Can Be Modeled in an Autologous Immune-Humanized Mouse Model. Nat. Commun. 2017, 8 (1), 707. 2Volden, P. New PBMC- humanized Mice Support Efficient NK-cell Engraftment https://www.taconic.com/tacon ic-insights/oncology-immuno- oncology/humanized-mice-nk- cell-engraftment.html (accessed Mar 11, 2019). 3 Nishimura, Y, Tomita, Y, Yuno, A, Yoshitake, Y, Shinohara, M. Cancer Immunotherapy Using Novel Tumor-Associated Antigenic Peptides Identified by Genome-Wide CDNA Microarray Analyses. Cancer Sci. 2015, 106 (5), 505-511. 4 Inovio Reports 2nd Patient Achieving Full Remission from HPV-Related Head & Neck Cancer after Treatment with Synthetic DNA Vaccine and a PD-1 Checkpoint Inhibitor. Inovio Pharmaceuticals, Inc. January 24, 2019. 5Wang, Y, Su, L, Morin, MD, Jones, BT, Mifune, Y, Shi, H, Wang, KW, Zhan, X, Liu, A, Wang, J et al. Adjuvant Effect of the Novel TLR1/TLR2 Agonist Diprovocim Synergizes with Anti-PD-L1 to Eliminate Melanoma in Mice. Proc. Natl. Acad. Sci. U. S. A. 2018, 115 (37), E8698-E8706. 6 Pushalkar, S, Hundeyin, M, Daley, D, Zambirinis, CP, Kurz, E, Mishra, A, Mohan, N, Aykut, B, Usyk, M, Torres, LE et al. The Pancreatic Cancer Microbiome Promotes Oncogenesis by Induction of Innate and Adaptive Immune Suppression. Cancer Discov. 2018, 8 (4), 403-416. 7 Matson, V, Fessler, J, Bao, R, Chongsuwat, T, Zha, Y, Alegre, ML, Luke, JJ, Gajewski, TF. The Commensal Microbiome Is Associated with Anti-PD-1 Efficacy in Metastatic Melanoma Patients. Science (80-. ). 2018, 359 (6371), 104- 108.


Three studies in humans, published in Science,


found variations in the gut microbiome composi- tions of patients who did or did not respond to checkpoint inhibitor therapy. In one of the studies, conducted at the University of Chicago, patients with metastatic melanoma who responded better to a PD-L1 inhibitor were found to have a higher number of eight bacteria. When the same PD-L1 blocker was administered to mice, it was only effective in those that received fecal microbiota transplants from responder patients, with five of the eight bacterial strains associated with anti-PD- L1 in the patients also found in the mice7. For most microbiome-related research, germ-


free animal models serve as the requisite host. However, other tools and services are emerging to more fully support the exploding interest in study- ing how the microbiome may impact cancer pro- gression and treatment. For instance, it is possible to take any genetically-engineered strain of a mouse model and rederive it as a germ-free model, combining a germ-free host environment with the unique genetics the investigator wishes to study. Performing custom microbiota associations with a genetically-engineered model is also becoming more common, providing a personalised approach to therapeutic investigation.


Where AI might lead As the drug discovery world strives to improve study reproducibility and translatability, the field of artificial intelligence (AI) is highly likely to play a role in achieving these goals. The increasingly massive data sets involved in preclinical and clini- cal research – including data on the human genome, tumour genetics, the microbiome and therapeutic responses, among others – will necessi- tate more effective and efficient means of analysis, which AI can offer. Whether it is designing and developing more predictive and targeted animal models, determining a drug target with greater pre- cision, or identifying new indications for existing therapies, the ability to model the necessary data can accelerate insights, better inform decision- making, and ultimately yield improved outcomes. Precisely how AI will be applied to pharmaceuti-


cal research in general, and specifically oncology research, is still taking shape, with many potential avenues under discussion. Given that cancer is a multi-factor disease that rarely involves just a sin- gle genetic mutation, and the fact that combination cancer therapies are increasingly common, oncolo- gy may be a prime target for the application of AI. Over time AI could enhance investigators’ abilities to consider the many factors that can impact a can-


14


cer’s development and progression – including age, gender, geography and more – and how various therapies in combination might yield the best out- comes. Collaboration will continue to be a hallmark of


oncology drug discovery, with the potential to accelerate the pace of innovation, reduce time to market and improve study translatability. From immunotherapy, to vaccines, to manipulation of the microbiome, an improved understanding of cancer biology and treatment efficacy and safety will require combining technologies, capabilities and expertise in unique ways that will ultimately transform cancer treatment.


DDW


Dr Michael Seiler is Vice-President, Commercial Products at Taconic Biosciences. Dr Seiler has a PhD in interdisciplinary cell and molecular biology from the Baylor College of Medicine, completed postdoctoral research in immune cell development at the University of Chicago and has a master’s in business administration from Boston University’s Questrom School of Business. He has authored 15 articles on gene and cell therapies and immune cell developmental biology.


Drug Discovery World Spring 2019


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