Drug Discovery
industry collaborations were then initiated during the last decade, allowing partners to share expertise and knowledge on disease mechanisms, novel drug targets and assay technologies. While academic col- laborators can profit from industrial know-how in development and specialised resources including medicinal chemistry, HTS and preclinical study set- ups, industrial partners can access new discoveries and ideas from academia to enhance their innova- tion potential. Substantiating the early benefits of academic-industry collaborations and to further facilitate the flow of respective knowledge and competences, several pharmaceutical companies decided to relocate their R&D sites to be near world-renowned academic hubs6. Additionally, translational medicine contributes to improve the drug discovery paradigm. With the aim of bringing new drugs to the market more rapidly and safely, translational medicine relies on a workflow involv- ing multiple feedbacks from clinicians, including pathologists, at the different stages of drug develop- ment (Figure 2). Quantitative pathology, otherwise known as digital pathology, emerged as a new dis- cipline playing a pivotal role in translational medicine-based drug discovery.
Cutting-edge technologies to the rescue In the early times of modern drug discovery, the main technological arsenal available to scientists was comprised of microscopes, pipettes, test tubes and primitive immunoassays, such as radioim- munoassays (RIAs). With the emergence of indus- trialised target-based drug discovery, microscopes were relegated to a second-string role while pipettes, test tubes and immunoassays underwent significant improvements leading to automated liq- uid handlers, microplates, ultrasensitive non-radio- metric immunoassays and associated multilabel detectors. Ironically, scientists were replaced by robots during large-scale assay execution (HTS). The needs for changing the drug discovery work- flow and the consequent resurgence of phenotypic assays necessitated substantial adjustments, includ- ing improvement of time-proven/relegated tech- nologies such as microscopy.
Advances in microscopy Novel microscopy-based technologies such as high- content screening (HCS) and high-content image analysis (HCIA) have proven to be very valuable in the most recent drug discovery efforts. Not only does generating high quality images allow for detec- tion of a specific target signal, it enables recording of holistic phenotypic changes happening in the whole
Drug Discovery World Fall 2018
cell, organoid or small organism analysed as well. However, literature shows that most of the high con- tent data published so far only relied on a few image-based features measured from all samples tested, limiting access to more complete valuable phenotypic information available8. The lack of advanced technologies allowing for the multipara- metric analysis of all collectable data was originally hypothesised as being one of the major impediments limiting the potential of HCS and HCIA. Artificial intelligence including machine learning is used to alleviate these limitations. Supervised machine learning (SML) software helps to perform automatic phenotypic classification. Such software is consid- ered essential for high content data analysis – even if it comes at a cost – as expert pre-identified refer- ences are required to set biologically-relevant predic- tive models. To overcome this bottleneck, develop- ment of enabling methods to decipher high-content screening results, unbiased from existing control phenotypes, is currently under way. These methods are based on modelling data issued from unsuper- vised multiparametric analysis that create self- organising maps (SOMs), which eventually helps grouping treatments that generate similar phenotyp- ical responses9. That approach, referred to as active learning, has the potential to identify novel chemo- types and cellular phenotypes while confirming expected hits on already identified targets. Results obtained to date show that active learning signifi- cantly reduces the time and costs to reveal the same phenotypic targets identified using SML10. HCS and HCIA are both based on classical opti-
cal microscopy with a resolution limited by diffrac- tion to approximately 200nm. Stimulated emission depletion (STED) microscopy technique, whose inventors received the 2014 Nobel prize in chem- istry, overcomes the diffraction-limited resolution barrier by using a pair of lasers to control the exci- tation state of fluorescent molecules in a targeted manner allowing resolution of 50nm or less1. STED is primarily a point-scanning technique where the fluorescence spot produced by a first laser is sharpened by stimulated emission induced by the second laser. STED provides much sharper images compared to classical microscopy allowing visualisation of individually-labelled biomolecules even in a complex environment. For instance, nanoscale STED imaging of green fluorescent pro- tein-labelled neurons was demonstrated in living brain slices12.
Advances in cellular models Improvements in cell imaging technologies discussed above are complemented by the development of
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