Screening


References 1 Swinney, DC and Anthony, J. How were new medicines discovered? Nat Rev Drug Discov, 2011. 10(7): p. 507-19. 2 Swinney, DC. Opportunities for phenotypic screening in drug discovery. Drug Discovery World, 2014. Fall: p. 33-42. 3 Scannell, JW and Bosley, J. When Quality Beats Quantity: Decision Theory, Drug Discovery, and the Reproducibility Crisis. PLoS One. 2016. 11(2): p. e0147215. 4Vincent, F, Loria, P, Pregel, M, Stanton, R, Kitching, L, Nocka, K et al. Developing predictive assays: the phenotypic screening ‘‘rule of 3’’. Sci Transl Med 2015. 7(293). 293ps215. 5 Horvath, P, Aulner, N, Bickle, M, Davies, AM, Nery, ED, Ebner, D et al. Screening out irrelevant cell-based models of disease. Nat Rev Drug Discov 2016. 15: p.751–69. 6 Hockemeyer, D and Jaenisch, R. Induced Pluripotent Stem Cells Meet Genome Editing. Cell Stem Cell. 2016. 5;18(5): p. 573-86. 7 Avior, Y, Levy, G, Zimerman, M, Kitsberg, D, Schwartz, R, Sadeh, R, Moussaieff, A, Cohen, M, Itskovitz-Eldor, J and Nahmias, Y. Microbial-derived lithocholic acid and vitamin K2 drive the metabolic maturation of pluripotent stem cells- derived and fetal hepatocytes. Hepatology. 2015. 62: p. 265-78. 8 Berg, E, Hsu, YC and Lee, JA. Consideration of the cellular microenvironment: physiologically relevant co- culture systems in drug discovery. Adv Drug Deliv Rev. 2014. 69-70: p. 190-204. 9 Moffat, JG, Vincent, F, Lee, JA, Eder, J and Prunotto M. Opportunities and challenges in phenotypic drug discovery: an industry perspective. Nat Rev Drug Discov. 2017. 16(8): p. 531-543. 10 Ingber, DE. Reverse Engineering Human Pathophysiology with Organs- on-Chips. Cell. 2016. 10;164(6): p. 1105-9.


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networks, has built our knowledge of intracellular signalling mechanisms, but has not led to compa- rable advances in our understanding of disease biology. This is particularly true at higher levels of organisation, including how information is com- municated between cells and tissues to effect human outcomes. The regulatory mechanisms at these higher levels of organisation are closer to clinical outcomes and therefore are more tightly associated with and predictive of therapeutic effi- cacy. Indeed, it is at this level that the differences between animal models and humans is the greatest. Small differences at the molecular or pathway level, can become amplified at the next higher level of organisation, a consequence of modular, hierar- chical complex system architectures. The focus on subcellular pathway regulation may explain why we have not been more successful in target valida- tion. In highly networked systems, feedback mech- anisms exist to keep the system balanced and func- tioning in the face of external stimuli and stress. Understanding the feedback and communication mechanisms that operate at these higher levels of organisation will not only help us identify pheno- typic screening assays with higher predictive valid- ity, but also validated drug targets.


Effective integration of disease biology into drug discovery requires a multi-pronged approach that combines both phenotypic and target-based meth- ods (Figure 1). This involves taking a holistic approach to the disease, mapping out the known biology, and then exploring multiple phenotypic as well as target-based screens for their applicability to the disease. Discovery efforts in a particular disease should begin with a theoretical framework of the disease on to which existing knowledge of cell types, cellular interactions, biological processes, known mechanisms and clinical biomarkers are mapped. Given the limitations of animal models for human translation, it is best to develop these theo- retical frameworks primarily from human clinical information, such as genetic associations, drug effects and biomarker studies. Then, as phenotypic assays representing various cell responses and inter- actions within the framework are developed and piloted, and the effects of genetic and compound perturbations on potential biomarkers are mea- sured, the resulting information can be used to refine the model, fill in knowledge gaps and build support for particular therapeutic hypotheses. Advanced cellular models, such as organs-on-a-chip or microphysiological systems and bioprinted tis- sues have an important role to play here as well, to test hypotheses and evaluate novel targets or bio- logical processes in a more complex setting, as they


provide a useful bridge from simpler, higher- throughput assays to the clinical situation. At this point, potential targets may emerge and decisions to initiate target-based screening can be made. The information generated is also used to select the most suitable phenotypic assays for high through- put screening. Although this process can be time- consuming, the phenotypic assays used for primary screening to discover hits with novel mechanisms, can also be applied to support the validation of compounds identified from target-based screens. For a single disease, an organisation will typically be prosecuting multiple assays and mechanisms. While this takes greater resources, it also increases the odds of success, and builds the organisation’s internal expertise in phenotypic assays and disease biology – a competitive edge.


There are additional advantages of taking this integrated approach, despite the larger initial investment required. As phenotypic assays are prosecuted over time, the data from these assays can be combined to build large chemical biology datasets that can yield valuable insights into chem- ical structure-function relationships and also address the problem of drug safety. Pharmaceutical discovery groups are familiar with the value that has been gained from mining target-based screen- ing data to support in silico-based computer-aided drug design which permits the prediction of target activity from chemical structure14. In this way, chemical space can be sampled more efficiently without having to synthesise every possible chemi- cal analogue. Large phenotypic assay data sets can be mined too for valuable insights, not only on chemical structure-function relationships but also for the discovery of novel efficacy or toxicity mech- anisms. These discoveries are enabled by annota- tion of tested compounds for clinical indication or adverse effects, from known effects of the drugs or tool compounds evaluated during assay character- isation; or for target interactions from information that is available from compounds in the screening deck that have been run in historical target-based screening campaigns.


The potential of this approach to address the problem of clinical failures due to unexpected tox- icities represents a strategic advantage and can benefit an organisation across all therapeutic pro- grammes. An estimated 30% of programmes fail in clinical testing due to safety issues15. The mecha- nisms of most toxicities are not known and animal testing has shown limited predictivity. The ability to mine large chemical biology data sets for corre- lations to known clinical activities, including adverse effects, as well as target mechanisms, helps


Drug Discovery World Fall 2017


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