| |August 20189far off when 'the capability' will exist that informs scientists about what problems are worth solving as well as an integrated path forward!Clinical -There are several technology solu-tions in the Clinical domain and these continue to evolve. From the days of paper records for patient enrolment, this space has come a long way with technologies such as Internet of Things (IoT) beginning to play an important role. When it comes to patients, things to con-sider include and are not limited to assessment of treatment options, decisions around specific drug treatment regimen, data needed for decision-making during treat-ment, sample collection, data gen-eration and analysis. An area that offers a plethora of opportunities is the area of im-aging. Tumor samples are an ob-vious area for technology applica-tion. Imaging of sample sections, potential 3D reconstruction using imaging as a means of non-inva-sive diagnosis, analysis for bio-markers are some of the technol-ogy-enabled examples. With this comes the requirement for image analysis tools, metadata gathering in the context of other sample data sets, image management, samples management and workflow man-agement. A lot of these activities are performed manually today and there is tremendous potential for the utilization of AI and machine learning in this space. Wearable de-vices is another area of tremendous opportunity, but that's a separate topic by itself.Thanks to technology, valuable data feedback opportunities from this domain to the world of discov-ery and pre-clinical research are possible enabling researchers to refine their treatment target spac-es appropriately using real-world data. Unthinkable, even a few years ago!Manufacturing -Gone are the days when manu-facturing in Pharmaceuticals was significantly labor-intensive. With improvements in engineer-ing technology, it is possible today to automate significant portions of the manufacturing processes both in the drug substance as well as drug product areas. At a large enough scale, there is potential to replicate processes with speed and relatively minimal effort as well as error reductions using in-line, at-line or even predictive technolo-gies. The ability that tools provide to model large scale processes at small scale can provide a phenom-enal advantage. Quality-by-de-sign requires predictive analy-ses related to safe, reproducible and scalable operating spaces, all part of the veritable Holy Grail in this space.Continuous Flow technology is an example of improvements in this space and comes with advan-tages of increased safety as well as quality improvements compared to batch processing. A key component of making this happen are develop-ments in the world of sensors and the data collection abilities that they bring to the table. This ups the ante quite a bit when it comes to de-riving patterns about parameters such as efficiency and quality in real time. Conclusion -As may have become clear by now, technology has become not just a supplement to various activities in pharmaceuticals but in some cases it makes possible applications us-ing information that were just not possible even as recently as a de-cade ago. While still nascent in terms of impact, application of tools and techniques for pattern analysis for decision-making, learning and predictive analysis are already here and will continue to drive evolution in this industry. GONE ARE THE DAYS WHEN MANUFACTURING IN PHARMACEUTICALS WAS SIGNIFICANTLY LABOR-INTENSIVESrivatsan Krishnan
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