Drug Discovery 2019

Whether you are performing killing assays, immunophenotyping of TILs, characterizing the tumor microenvironment, or investigating immunogenicity, primary cells are essential for the discovery of new therapeutics. Conclusive and relevant assay results depend on reliable sample processing, including cell separation and tumor dissociation. We have developed flexible automated solutions for primary cell isolation from various starting materials to ensure the best fit for every drug discovery program. Join us to learn about the best combination of cell isolation instruments and reagents for your research.

Learning Objectives:

1. Efficient and reproducible automated isolation of primary cells 
2. Use of primary cells in drug discovery assays

In-vitro epithelial barrier models that are more representative of in-vivo tissues are urgently needed. Here we present extracellular matrix-supported intestinal tubules in a perfused microfluidic platform. These intestinal tubules exhibiting tissue (polarization) markers ErbB1, ErBb2, Ezrin, Glut-2, MRP2 and ZO-1, and crypt-like morphology. Drug-induced toxicity was assessed by apical exposure of the intestinal barriers to model compounds. This microfluidic platform is readily implemented in routine laboratories and allows for recording of response to pharmacological stimuli in real-time using automated imaging techniques. The co-culture capabilities of the platform can be explored to create complex tissue configurations, for example by incorporating mesenchymal and immune cells in the ECM adjacent to the epithelial tubes. In conclusion, intestinal tubules cultured in the OrganoPlate® are suitable for high-throughput toxicity screening, trans-epithelial transport studies, and complex co-culture models to recreate an in vivo-like microenvironment.

Learning Objectives: 

1. Identify challenges in standard in-vitro models 
2. Be able describe the method of seeding, growing and investigating intestinal tubular structures on the OrganoPlate® format 
3. Be able to translate this knowledge in generating high-throughput studies for drug development and discovery

Isolated hepatocytes or their enzymes comprise the basis of most in vitro DMPK and toxicity assays used to predict human hepatic outcomes. Because hepato-specific functions are typically lost in the first hours or days of culture in many in vitro hepatic models, such models are of limited use in instances where meaningfully predictive simulation of a compound’s properties require that the in vitro model maintain such function stably over longer time periods, such as where compounds clear slowly, where metabolites are generated over extended periods of time, where membrane polarization is required for biliary canalicular formation, or where cellular conditions may cumulate after repeated dosing, such as in cholestatic toxicities. To address this limitation, more advanced liver models have been developed and extensively characterized.  These long-term models have been shown to more accurately predict intrinsic clearance, metabolite generation, and toxicity profiles.

Learning Objectives:

1.  Learn about advanced liver models for improved in vitro prediction.  
2.  Learn about the next wave of organ-on-chip with microfluidic advancements.  

Cellular research is optimal when using physiologically-relevant cell phenotypes and genotypes of human origin. This assertion has accelerated the adoption of primary cells, stem cells, and increasingly, patient-specific cells in drug discovery. Over the last five years, technological improvements in three-dimensional (3D) cell culture technology, to better mimic in vivo physiology, have also accelerated, not only in the areas of cancer and neurological research, but also in the assessment of clinical candidates for metabolic and toxicological liabilities. In this presentation, existing, and future, developments in 3D cell culture technology, (including the use of phenotypically-relevant human iPS cells) on fundamental research and drug discovery will be addressed. 

Learning Objectives: 

1. Understanding current status of 3D technologies  
2. Understanding applications to disease modeling and drug discovery

Surgery is the first line of treatment for Stage III melanoma. Often adjuvant therapy is administered post-surgery, which can include weeks of radiation, chemotherapy, targeted drug therapy, or immunotherapy. Previous preclinical studies suggest that treatment with neoadjuvant immune checkpoint blockade may lead to increased survival compared to adjuvant treatment1. This webinar will cover recent data from parallel but independent clinical trials explored the efficacy of anti-PD-1 and/or anti-CLTA4 combination immunotherapy plus surgery in patients with early stage melanoma2,3. 

In each study, safety of the treatment regimen and evidence of immune activity in response to treatment was evaluated. In addition to standard H&E and immunohistochemistry (IHC) staining before and after treatment each study also employed NanoString’s GeoMx™ Digital Spatial Profiler to perform high-plex proteomic analysis with spatial resolution. GeoMx DSP enabled the discovery of markers of antigen presentation (β2M) and T cells (CD3) present on pretreatment tumor biopsies that correlated with clinical efficacy in each study. Additionally, the responses to therapy in each study were strong enough to merit larger trials and continued investigation of the most effective immunotherapy strategy.

FOR RESEARCH USE ONLY. Not for use in diagnostic procedures.

Learning Objectives: 

1. Gain an appreciation for how multiplexed spatial profiling via GeoMx can aid in tumor characterization
2. Understand how GeoMx profiling was used in the 2 studies presented to identify biomarkers of response to immunotherapy and complement other molecular profiling strategies

Advanced Cell Diagnostics’ RNAscope products and assay services are utilized widely for tissue-based gene expression analysis in early target validation, preclinical animal efficacy and safety studies and in biomarker development.  RNAscope is clinically validated and performs reliably on routine FFPE archival human tissues and clinical biopsies and any animal model tissues.  Detection limits are at single molecule level, enabling quantitative, cell-specific expression analysis within tissue microenvironments.  There are over 15,000 RNAscope probes in use and over 1,800 publications – more than 2-per-day.  Continuing advances in ACD RNA ISH technology are enable a wide range of applications, including:

-    Multiplexed RNA ISH
-    Dual ISH/IHC for target analysis combined with cell marker staining
-    Isoform-specific assays with exon junction probes
-    Tissue-based gene edit detection
-    Viral RNA and DNA genome detection
-    AAV gene therapy biodistribution analysis
-    CAR-TCR T cell detection and tumor infiltration assays with lentiviral probes

RNAscope technologies are enabling faster and more rigorous tissue-based target and biomarker research with cell-specific, single-molecule sensitive RNA detection in the context of complex disease microenvironments.  The technology, methods and its applications will be illustrated.

Learning Objectives:

1. How RNAscope® in situ hybridization is utilized for tissue-based, cell-specific expression analysis
2. How RNAscope® is enabling gene therapy and cell therapy tissue biodistribution analysis

Ageing is a complex process that has been observed in all biological systems at every level of organisation. Some anti-ageing interventions have demonstrated life-extending effects in model organisms. However, the translation of these interventions in human clinical practice remains limited due to the absence of comprehensive ageing biomarkers. Recent studies suggest that a set of biomarkers, rather than any individual biomarker, constitute the most effective means of assessing the health status. The presentation will cover the development of comprehensive and robust biomarkers of ageing using deep learning and blood biochemistry, transcriptomics, and even imaging data, to be able to track the effectiveness of the various interventions we are developing.

Learning Objectives: 

1. Different AI Approaches to aging biomarker development
2. How AI can revolutionise aging research and how aging research can accelerate AI research

The last two decades have seen an explosion in the volume of oncology data generated using next-generation sequencing (NGS) and multi-omics techniques. As a result, there is a growing need for computational tools that are powerful enough to generate meaningful hypotheses about the biological mechanisms underlying cancer. In this presentation, we demonstrate how two of QIAGEN’s bioinformatics solutions – Ingenuity Pathway Analysis (IPA) software and the OmicSoft OncoLand database – can be combined to generate testable hypotheses in an immuno-oncology case study (GSE67501). This study sought to understand the mechanism underlying the failure of anti-PD-1 therapy in advanced renal cell carcinoma patients. The study data, along with tens of thousands of other public-domain datasets (including TCGA, SRA, GEO, GTEx and others), have been reprocessed using OmicSoft’s bioinformatics pipeline, and are readily available in OmicSoft’s OncoLand platform. Analysis of these data in OncoLand revealed that UGT1 family members and immune genes were differentially regulated in responder vs non-responder populations, similar to as described in the original publication. These data were then interpreted in IPA, which has millions of findings from peer-reviewed publications. Immunologic, cell cycle, metabolite transport and solute related pathways and functions were found to be enriched. Using IPA’s analytic and predictive capabilities, along with the public data curated from TCGA and GEO and other sources present in OncoLand, we demonstrate how these QIAGEN solutions can be used to generate hypotheses for mechanism of action, and to discover potential therapeutic targets and biomarkers.

Learning Objectives: 

1. Introduction to databases backing Ingenuity Pathway Analysis and Oncoland
2. Studying potential biomarkers and targets through Oncoland’s data mining and comparison tools
3. Generation of peer-reviewed literature backed hypotheses through Ingenuity Pathway Analysis

AI is transforming drug discovery and development (3D) including clinical trials by increasing efficiencies, decreasing the gap between data and our ability to utilize data and most importantly making this journey patient centric. Non-invasive and non-intrusive animal and patient monitoring technologies lead and continue to lead development of novel digital biomarkers. Incorporation of digital endpoints and computational technologies into nonclinical and clinical trials leads to earlier and objective detection of physiological and behavioral changes, including off-target effects that lead to earlier decisions thereby reducing animal and patient burden. Leveraging recent advances in high-throughput technologies and machine learning provides an opportunity for more precise and more efficient target identification process. This talk will discuss these technologies and present their impact through case studies within clinical and nonclinical phases of 3D.

The Chain of Custody in our labs is the unbroken link between the objects used in your research: your people, the animal models, the cell lines, reagents, compounds; the lab instruments and devices; the data records your store your data in; the link between the analytics and the data collected. In the Smart Lab – there is no place for uncertainty. SMART requires your research and data to be Specific, measurable, auditable, repeatable BUT, preclinical research is a long way from being SMART.

Learning Objectives: 

1. Manual recording of experimental data, manual input of results data – they leave the process open to human error, transcription issues and  do not provide audit trails and lack transparency. In the late 80,  early 90s there was an often quoted saying in the early days of data science: Garbage in; Garbage out. 
2. There is significant excitement, rightfully so, about Machine Learning and AI. But if you are training your algorithms on poor quality data – really how good are your algorithms? The key strategic advantage of a SmartLab based on digital ID and automated data collection is the data authenticity, accuracy and reliability.  And what you will see is the higher quality of data will drive insights faster. You can even start to move toward near real time discovery of  exceptions, of errors, of trends.