Drug Discovery


References 1 DiMasi, JA, Grabowski, HG and Hansen, RW. Innovation in the pharmaceutical industry: New estimates of R&D costs. J Health Econ 47, 20- 33, doi:10.1016/j.jhealeco.2016.01.012 (2016). 2 Santos, R et al. A comprehensive map of molecular drug targets. Nat Rev Drug Discov 16, 19-34, doi:10.1038/nrd.2016.230 (2017). 3 Grignolo, A, Pretorius, S. Phase III Trial Failures: Costly, But Preventable. Applied Clinical Trials 25 (2016). 4 Scannell, JW, Blanckley, A, Boldon, H and Warrington, B. Diagnosing the decline in pharmaceutical R&D efficiency. Nat Rev Drug Discov 11, 191-200, doi:10.1038/nrd3681 (2012). 5 Chen, B and Butte, AJ. Leveraging big data to transform target selection and drug discovery. Clin Pharmacol Ther 99, 285-297, doi:10.1002/cpt.318 (2016). 6 International Human Genome Sequencing, C. Finishing the euchromatic sequence of the human genome. Nature 431, 931-945, doi:10.1038/nature03001 (2004). 7Venter, JC et al. The sequence of the human genome. Science 291, 1304-1351, doi:10.1126/science.1058040 (2001). 8 Kramer, R and Cohen, D. Functional genomics to new drug targets. Nat Rev Drug Discov 3, 965-972, doi:10.1038/nrd1552 (2004). 9 Lipardi, C and Paterson, BM. Retraction for Lipardi and Paterson, “Identification of an RNA- dependent RNA polymerase in Drosophila involved in RNAi and transposon suppression”. Proc Natl Acad Sci U S A 108, 15010, doi:10.1073/pnas.1111383108 (2011). 10 Crotty, S and Pipkin, ME. In vivo RNAi screens: concepts and applications. Trends Immunol 36, 315-322,


doi:10.1016/j.it.2015.03.007 (2015). 11 Livshits, G and Lowe, SW. Accelerating cancer modeling with RNAi and nongermline genetically engineered mouse models. Cold Spring Harb Protoc 2013, doi:10.1101/pdb.top069856 (2013). 12 Zamore, PD, Tuschl, T, Sharp, PA and Bartel, DP. RNAi: double-stranded RNA directs the ATP-dependent cleavage of mRNA at 21 to 23 nucleotide intervals. Cell 101, 25-33, doi:10.1016/S0092-8674(00)80620-0 (2000). 13 Fedorov, Y et al. Off-target effects by siRNA can induce toxic phenotype. RNA 12, 1188-1196, doi:10.1261/rna.28106 (2006). 14 Jackson, AL et al. Expression profiling reveals off-target gene regulation by RNAi. Nat Biotechnol 21, 635-637, doi:10.1038/nbt831 (2003). 15 Jackson, AL and Linsley, PS. Recognizing and avoiding siRNA off-target effects for target identification and therapeutic application. Nat Rev Drug Discov 9, 57-67, doi:10.1038/nrd3010 (2010).


16 Silva, JM et al. Second-generation shRNA libraries covering the mouse and human genomes. Nat Genet 37, 1281-1288, doi:10.1038/ng1650 (2005). 17 Fellmann, C et al. Functional identification of optimized RNAi triggers using a massively parallel sensor assay. Mol Cell 41, 733-746, doi:10.1016/j.molcel.2011.02.008 (2011). 18 Fellmann, C and Lowe, SW. Stable RNA interference rules for silencing. Nat Cell Biol 16, 10-18, doi:10.1038/ncb2895 (2014). 19 Fellmann, C et al. An optimized microRNA backbone for effective single-copy RNAi. Cell Rep 5, 1704-1713, doi:10.1016/j.celrep.2013.11. 020 (2013). 20Watanabe, C, Cuellar, TL and Haley, B. Quantitative evaluation of first, second, and third generation hairpin systems reveals the limit of mammalian vector-based RNAi. RNA Biol 13, 25-33,


doi:10.1080/15476286.2015.1128062 (2016). 21 Pelossof, R et al. Prediction of potent shRNAs with a sequential classification algorithm. Nat Biotechnol 35, 350-353, doi:10.1038/nbt.3807 (2017). 22 Gossen, M and Bujard, H. Tight control of gene expression in mammalian cells by tetracycline-responsive promoters. Proc Natl Acad Sci U S A 89, 5547-5551 (1992). 23 Premsrirut, PK et al. A rapid and scalable system for studying gene function in mice using conditional RNA interference. Cell 145, 145- 158, doi:10.1016/j.cell.2011.03. 012 (2011). 24 Zuber, J et al. RNAi screen identifies Brd4 as a therapeutic target in acute myeloid leukaemia. Nature 478, 524-528, doi:10.1038/nature10334 (2011). 25 Filippakopoulos, P et al. Selective inhibition of BET bromodomains. Nature 468, 1067-1073, doi:10.1038/ nature09504 (2010). 26 Bolden, JE et al. Inducible in vivo silencing of Brd4 identifies potential toxicities of sustained BET protein inhibition. Cell Rep 8, 1919-1929, doi:10.1016/j.celrep.2014.08.025 (2014). 27 Nakagawa, A et al. Selective and reversible suppression of intestinal stem cell differentiation by pharmacological inhibition of BET bromodomains. Sci Rep 6, 20390, doi:10.1038/srep20390 (2016). 28 Kim, YG, Cha, J and Chandrasegaran, S. Hybrid restriction enzymes: zinc finger fusions to Fok I cleavage domain. Proc Natl Acad Sci U S A 93, 1156-1160 (1996). 29 Miller, JC et al. A TALE nuclease architecture for efficient genome editing. Nat Biotechnol 29, 143-148, doi:10.1038/nbt.1755 (2011). 30 Cong, L et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819- 823, doi:10.1126/ science.1231143 (2013). 31 Jinek, M et al. A programmable dual-RNA- guided DNA endonuclease in adaptive bacterial immunity. Science 337, 816-821, doi:10.1126/science.1225829 (2012).


32 Mali, P et al. RNA-guided human genome engineering via Cas9. Science 339, 823-826, doi:10.1126/science.1232033 (2013). 33 Maruyama, T et al. Increasing the efficiency of precise genome editing with CRISPR-Cas9 by inhibition of nonhomologous end joining. Nat Biotechnol 33, 538-542, doi:10.1038/nbt.3190 (2015). 34 Li, D et al. Heritable gene targeting in the mouse and rat using a CRISPR-Cas system. Nat Biotechnol 31, 681-683, doi:10.1038/nbt.2661 (2013). 35Wang, H et al. One-step generation of mice carrying mutations in multiple genes by CRISPR/Cas-mediated genome engineering. Cell 153, 910-918, doi:10.1016/j.cell.2013.04.025 (2013). 36Yang, H et al. One-step generation of mice carrying reporter and conditional alleles by CRISPR/Cas-mediated genome engineering. Cell 154, 1370-1379, doi:10.1016/j.cell.2013.08.022 (2013). 37 Platt, RJ et al. CRISPR-Cas9 knockin mice for genome editing and cancer modeling. Cell 159, 440-455, doi:10.1016/j.cell.2014.09.014 (2014). 38 Dow, LE et al. Inducible in vivo genome editing with CRISPR-Cas9. Nat Biotechnol 33, 390-394, doi:10.1038/nbt.3155 (2015). 39 Chiou, SH et al. Pancreatic cancer modeling using retrograde viral vector delivery and in vivo CRISPR/Cas9-mediated somatic genome editing. Genes Dev 29, 1576-1585, doi:10. 1101/gad.264861.115 (2015).


Drug Discovery World Fall 2017


15


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68  |  Page 69  |  Page 70  |  Page 71  |  Page 72