The IBM z14 is designed for cognitive and analytics processing to support business use cases. With the explosive growth in the amount and richness of data and data-driven business processes, being able to process, analyze, and apply machine learning in a timely and efficient manner is often a business necessity. Improvements to the single-instruction-multiple-data (SIMD) facility, as well as improvements to Java garbage collection, provide improved support for different software packages. Software such as Apache Spark has been enabled for both z/OS and Linux on z to provide the software infrastructure required for cognitive and analytics processing while new software such as IBM Machine Learning for z/OS provides additional assistance for data scientists. End-to-end cross-platform solutions that require data that is stored on the z14 can also be improved using these new software and hardware capabilities. This paper explores the various hardware capabilities of z14, as well as the enabling software and end-to-end solutions.

Measles virus (MV) infects 30 million children every year, resulting in more than half a million deaths. Vitamin A (retinol) treatment of acute measles can reduce measles-associated mortality by 50–80%. We sought to determine whether or not retinoids can act directly to limit MV output from infected cells. Physiologic concentrations of retinol were found to inhibit MV output in PBMC and a range of cell lines of epithelial and endothelial origin (40–50%). Near complete inhibition of viral output was achieved in some cells/lines treated with all-trans retinoic acid (ATRA) and 9-cis RA (9cRA). Important attenuation of the anti-MV effect of retinoids in R4 cells, a subclone of a retinoid-responsive cell line (NB4) deficient in RAR signaling, demonstrates that this effect is mediated at least in part by nuclear retinoid receptor signaling pathways. Inhibition of MV replication could not be fully explained as a result of retinoid effects on cell differentiation, proliferation or viability, particularly at low retinoid concentrations (1–10nM). These data provide the first evidence that retinoids can directly inhibit MV in vitro, and raise the possibility that retinoids may have similar actions in vivo.

Biomarkers are indicators of normal biological processes, pathogenic processes, or pharmacological responses to a therapeutic intervention. The biopharmaceutical industry is building significant molecular-imaging capabilities and in this context is incorporating biomarker concepts throughout its research. In this paper, we discuss and propose information technology (IT) standards and architectures that support incorporation of imaging biomarkers into the drug discovery and clinical development process. In particular, we cover various uses of emerging imaging technologies in biopharmaceutical research and development, examples of imaging biomarkers in therapeutic areas, IT requirements related to the use of imaging technologies, challenges related to the integration of imaging biomarker data with clinical and genotypic data, and the need to integrate external public data sources. We discuss IT standards and architectures associated with the inclusion of biomarker-related data in the submission of new drug applications, with emphasis on imaging technologies. We suggest extensions to the Study Data Tabulation Model of the Clinical Data Interchange Standards Consortium and the JANUS Data Model of the Food and Drug Administration with data elements based on imaging biomarkers.

Biopharmaceutical industry is incorporating biomarker concepts throughout the R&D processes, including the inclusion of biomarker-related data in the submission of new drug applications. In this paper we discuss the role of IT and information systems that support incorporation of surrogate biomarkers in the clinical development process, with emphasis on new molecular imaging technologies and associated IT requirements. In addition, we discuss how recently introduced FDA standards regarding submission data and FDA guidance documents related to genomic and imaging data can be accommodated in a solution architecture for (surrogate) biomarker-based clinical development.: