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Therapeutics discovery relies on multiple, complementary analytical techniques to best position new drug candidates for commercial success.

Various assay technologies are utilized in the wet lab for their respective strengths. Combining two or more methodologies with different selectivity (an orthogonal approach or orthogonal method which uses fundamentally different principles of detection or quantification to measure a common value or trait) is a key confirmational step in drug discovery to maximize your understanding of a lead candidate’s properties.

In lead identification, an orthogonal assay approach eliminates false positives or confirms the activity identified during the primary assay.

This approach is popular among regulators. The FDA, MHRA and EMA have all indicated in guidance that orthogonal methods should be used to strengthen the underlying analytical data.

What are some common orthogonal approaches?

It is wholly dependent on the chosen primary technique. For example, infra-red spectroscopy is considered a suitable counterpart to mass spectrometry. Orthogonal approaches are an FDA-approved method of confirmation, and each technique performed should complement the secondary technique chosen as a secondary, or confirmational method.

Challenges With Orthogonal Assay Data

Data wrangling is a challenge given the breadth of instrumentation and methodologies available for orthogonal confirmation.

Scientists frequently reference the difficulties of pulling individual results together for in-depth analysis across an entire project. Their tools and data sources can vary; for example, low throughput through ultra-high throughput enzymatic or immuno screening (HTS), High Content Screening (HCS), in vivo studies, Surface Plasmon Resonance (SPR), and more. The most important prerequisite for lab data management and analysis is an ability to work with all modalities and data types, allowing users to search and combine results across all assay data from a single platform.

Another key data management challenge facing researchers is the ability to collaborate effectively across different scientific undertakings. Sharing data among assay developers and scientists performing HTS or secondary screening depends on the ability to seamlessly transfer the results of such studies.

Scientists need to combine the individual results of these assays to decide the next steps based on the data – and orthogonal experiments play an important role in the decision making. If the orthogonal methods yield results in agreement with the same conclusion, the data can be trusted, and subsequent decisions can be based on it.

Use Cases of Orthogonal Assay Development

Revvity Signals has worked extensively with partner companies to develop orthogonal assays. On a project with Carterra, researchers worked to improve discovery using HT-SPR to complement AlphaLISA. As a primary method, the AlphaLISA FcRn binding assay is a robust high-throughput immunoassay used to measure relative affinities of therapeutic antibodies to FcRn, to predict the half-life in vivo. The researchers then used HT-SPR as an orthogonal approach to reinforce the findings from AlphaLISA.

Revvity Signals partner companies such as Pelago are also using orthogonal approaches to complement and confirm their own systems. At Revvity Signals, we have been promoting orthogonal approaches for our respective markets for years.

Revvity Signals Research Suite

Signals Research Suite was designed to support researchers from experiment planning through data collection to combined analysis for entire teams.

Revvity Signals Research Suite is comprised of three key tools which work seamlessly together to collect, combine and interactively analyze results from experiments:

• Signals Notebook is an electronic lab notebook (ELN). This is where information about lab experiments is captured and collected.
• Signals VitroVivo transforms the raw data into actionable results.
• Signals Inventa collects the various individual results from Signals VitroVivo, combines and analyzes the data, and generates interactive reports for SAR analysis.

Since High Content Screening can also be a possible orthogonal approach, many Research Suite customers are using Revvity Signals Image Artist in conjunction with Signals VitroVivo. While Image Artist is not officially a part of the Research Suite, customers access, store, analyze and share image data from HCS and cellular imaging systems, then use results of the primary analysis with Signals VitroVivo for profiling image data and hit selection.
 

Data Capture with Signals Research Suite

For assay development, the ability to collect and analyze every assay result type while delivering flexible centralized data and calculation management across assay techniques is essential. All instrument data should be made available through guided workflows with easy configurations – eliminating IT dependency. The entire process from instrument data import to reporting should be processed in minutes.

Signals Research Suite builds on these essential features with cross-study analytics and the integration of in vitro & In vivo data to generate novel interactive candidate nomination visualizations and reports across therapeutic areas. Scientists need complete control over workflows for every technique, modality, and data type. Our objective was to create a platform flexible enough for scientists to use for both one-off assay development and more sophisticated assays, with support for a wide range of techniques and the ability to scale to ultra-high data volumes.

For orthogonal assay development, unified data management combined with rich interactive analytics can prove critical to accelerating and improving drug discovery.

Learn how Signals Research Suite can help your lab solve the unique challenges of assay development during drug discovery.

R&D leaders are deploying next-generation digital capabilities to accelerate scientific discovery and bolster returns on investment.

The pharmaceutical and chemicals industries are no strangers to digital technology, with decades of experimentation using data and statistical techniques to improve productivity and innovation. But the results were historically disappointing relative to the promise.

Over the past two or three years, the pace of digital transformation is increasing thanks to the improved performance, power, and adaptability of tools, and investments in cloud computing, data architecture, and visualization technologies. There are also an increasing number of use cases for machine learning and, in future, quantum computing, which will accelerate the development of molecules and formulations.

The broad digital transformation taking place in R&D is allowing researchers to automate time-consuming manual processes and opening new research horizons in thorny problems that have failed to elicit breakthroughs. This new report, based on interviews with R&D executives at companies including Novartis, Roche, Merck, Syngenta, and BASF, explores the use cases, best practices, and roadmaps for digitalizing science.

Exploring patterns in complex datasets

Rich, accessible, and shareable data are the fuel on which today’s breakthrough analytics and computing tools rely. To ensure that datasets are usable for scientific purposes, leading companies are focusing on FAIR data principles (findable, accessible, interoperable, and reusable), developing robust metadata and governance protocols, and using advanced analytics and data visualization tools.

Digital transformation is opening up R&D horizons in areas such as genomics that could lead to breakthroughs in precision medicine. It is also creating opportunities for decentralized clinical trials, unleashing future innovations in digi-ceuticals and healthcare wearables.

Reaching the right study faster

Experiments and clinical trials carry a huge cost for both industries, both financially and in terms of human and scientific resources. Advanced simulation, modelling, AI-based analytics, and quantum computing are helping identify the strongest candidate for new therapies, materials, or products, allowing only the most promising to proceed to the costly experimental phase.

Organizational overhaul

R&D leaders foster bottom-up innovation by giving research teams freedom to experiment with new technologies and techniques. They also drive top-down strategic initiatives for sharing ideas, harmonizing systems, and channeling digital transformation budgets. As in any industry, AI and automation are changing ways of working in scientific research. Rather than being seen as a threat to research careers, leading organizations in pharma and chemicals are demonstrating that digital provides new opportunities for collaboration and the breaking down of silos. They celebrate wins, encourage feedback, and nurture open discussions about culture shifts in the workplace.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial

Faced with rising costs and lower returns, pharma and chemical companies are accelerating digital transformation to develop new formulations, faster.

Creating data is easy. The rapid analysis and use of that data, however, is orders of magnitude more difficult.

Biopharma companies are under pressure from all sides to leverage the power of data analytics. Financial stakeholders expect a timely return on their investment. Regulators are closely scrutinizing the biopharma industry as it explores new therapeutic platforms. Patients and their families are awaiting much-needed cures and life-enhancing treatments.

Getting answers means analyzing oceans of data. The challenge with clinical data is its ever-increasing scope of variety and complexity. The emergence of new clinical trial designs (for example, adaptive design or decentralized trials) has led to more data sources, requiring new data collection solutions that support collection outside of the clinic (e.g., patient-centric systems that don’t require a patient to travel to the hospital in order to report results). The result has been a broad variety of data sources and a profusion of complex data.

Clinical study teams need fast, flexible analytics which provide a holistic view highlighting data relationships as well as a centralized method of collaboration.

The two most sought-after qualities among analytics platform users are speed and flexibility. Can the platform help minimize user time, maximize speed-to-result and accelerate decision-making? Is it also flexible enough to enable innovation, allowing users to discover and visualize hidden insights?

How Biopharma Harnesses Clinical Data

How, specifically, is Spotfire® being used in the management of clinical trials? Companies report using it for in-trial clinical data review, risk-based monitoring, drug safety review, and clinical site performance monitoring – among other roles. At our recent Nexus user conference, clinical analytics leaders from Pharma discussed their use of Spotfire® to harness data, streamline workflows and improve efficiencies.

Our Clinical analytics practice built Spotfire® Solutions to rapidly adapt to different clinical use cases, roles, data sources, therapies, and protocols. So while the individual applications and uses discussed by those companies varied considerably, all of them experienced two primary benefits – speed (the fast!) and flexibility (the flexible!).

The Fast…and the Flexible

Speed-of-results drives the bio/pharma industry at almost every stage of discovery, development, and commercialization. Faster results condense clinical trial data review and improve the timeliness of decision-making. It can also shorten time-to-failure or impact patient outcomes in a healthcare setting.

When it comes to highly varied and complex datasets employed in a clinical trial, the flexibility and extensibility of an analytics and visualization platform has a direct bearing on its ability to handle almost limitless clinical use cases. Dynamic, adaptable platforms can empower medical and clinical data review, clinical operations and pharmacovigilance, Risk Based Quality Management (RBQM), biomarker-based patient/cohort selection, and much more.

• Bristol-Myers Squibb uses Spotfire® to improve their clinical data review time. They experienced a 70% reduction in time for review of standard safety data, improving from hours to mere minutes. They’ve also seen an estimated 50-75% time savings per subject. Spotfire® provided Bristol Myers Squibb’s Translational Bioinformatics Data Science clinical review process with a better understanding of safety, efficacy, biomarker, and response data for scientific clinical review across studies in every phase. It also made it easier to gain effortless insights and dive deep into the data.

They created a dashboard with an integrated 360-degree view of the data in both aggregated and individual levels, improving the ability of reviewers to make informed decisions across the study cycle. The ability to nimbly wrangle the data via the data canvas was also important, allowing BMS to take into account new or future requirements.

• Gilead Sciences turned to Spotfire® to power clinical trial analytics and explore the emerging use of Natural Language Processing to assess drug safety. One of the key challenges confronting Gilead is common to others in the industry – the difficulties posed by huge unstructured data sets. Gilead employs automated natural language processing (NLP) to improve adverse event data collection without compromising safety. Working with a massive dataset, this nascent field of NLP could automate, simplify and shorten drug safety reviews, gather data more efficiently and improve drug safety.
• For Bayer, Spotfire® delivered information in near-real-time, allowing at-a-glance assessments of site performance by clinical research associates. They are using data-driven Site Risk Leveling Indicators for RBQM in combination with their clinical data warehouse.

Dashboards in Spotfire® are built once and then used across different study data for risk-based monitoring, source data verification (SDV) and Source Data Review (SDR). Bayer has more than 750 dashboards used by nearly 600 users across clinical development. With an Ongoing Site Risk Level (OsRL) indicator, they’ve gained effortless insight into adverse events incidence, data entry timeliness, protocol deviations volume, principal investigator oversight, changes due to Source Data Verification (SDV) and much more – at the study, country & site level.

• Clinical departments across Johnson & Johnson’s therapeutic areas use Spotfire® with custom signal detection extensions for medical review. They have deployed a highly customized Spotfire^® environment for review tracking and communications, allowing for faster identification and management of clinical data discrepancies. With visualization, they can immediately spot data outliers – and data can be visualized in multiple ways to serve the preferences of different users.

Fast Speed-to-Result and Fast Implementation.

Speed has two meanings. There’s the notion we discussed above – time-to-result. And there is also the speed of deployment, how quickly the Spotfire^® platform can be implemented and running in your lab.

Revvity Signals Clinical Practice has developed modular solutions for Spotfire^® that can be deployed in as little as six weeks. The line listing review capability can be deployed in 2 weeks, offering the immediate benefit of having an audit trail of your review history.

“Work quickly and adapt alongside me.” What does your dream analytics platform look like?

The bio/pharma industry relies on data analytics across the entire drug lifecycle – from drug discovery and process development to manufacturing, clinical trials and post-market review. Platforms focused on fast and flexible analytics will minimize user time, maximize speed-to-result and accelerate decision-making while enabling both innovation and collaboration.

The Big Pharma companies discussed above built their own clinical solutions in Spotfire®, but there are countless examples of how small and mid-sized biopharma also leverage Spotfire® to improve across the drug discovery, development and commercialization timeline.

When analyzing data there is often a competition between getting an analysis set up just the way you want versus implementing a consistent method across your data and organization.

Often with flexible tools, it is easy to adjust the analysis, but difficult to use it repeatedly at scale. Whereas with scalable tools, once a system is setup, the difficulty of changing it often precludes the adjustment. You live with the less-than-ideal solution or do many one-off operations.

The Calculations Explorer App allows a user to easily build an analysis template using transformations, visualizations, business rules, and curve fits. A user can use one of the 70+ included out of the box templates, modify one to suit, or start from scratch. All analysis steps are captured into a single template file to be shared or deployed. This template can then be further incorporated into a larger workflow using other modular Apps within Signals VitroVivo allowing a complete solution from raw data to results. Workflows can be shared easily with other users. Finally, the next time an adjustment is needed to the analysis, the workflow can be easily updated.

Flexible and Self-Service combined with Scalable Data Management

Watch the video to see in action, how we help our customers solve these challenges.

Signals VitroVivo unites assay development, low throughput to ultra-high throughput production assays, High Content Screening, and in vivo studies so users can search across all assay and screening data in a single platform. Learn more here.

We recently launched Signals Image Artist™ the fast, efficient image analysis and data management solution for High Content Screening and cellular image data that integrates with Revvity Signals Signals™ VitroVivo for secondary data analysis.

High Content Screening is a mainstream technology for drug research. More and more experiments are run on more and modern instruments either directly in the labs of pharma companies or outsourced by CROs. These experiments produce very detailed results very quickly. The turnaround time from experiment to experiment is quicker than ever before. Data handling and analysis needs to be kept up by automated high-performance processes. Labs and screening groups are confronted with an ever-increasing amount of image data to analyze and interpret. Signals Image Artist™ is a robust, powerful, and dependable solution for scientific image data management and analysis, which scales with your labs’ evolving needs.

Revvity Signals is the only company on the market today, which offers everything you need to plan, run and analyze High Content Screening experiments. From automated instruments, e.g. Opera Phenix Plus to PhenoVue reagents for Cell Painting, microplates, e.g. PhenoPlate and the management, analysis and interpretation of the experiment results with our Signals Image Artist and Signals VitroVivo software.

Signals Image Artist is out of the box ready to import all your data from the instrument while preserving all meta data. And since the original file formats are kept untouched during the import, you can use Signals Image Artist as your reference database for all your HCS data and primary analysis. It comes with an object store and a high-performance compute cluster built-in no matter how many nodes you have for the analysis of your data. The software is completely containerized and can easily be installed in the cloud or on-premise. We have made sure the analysis of your experiment data and the collaboration with your colleagues is happening inside one web interface, always guaranteeing your high security standards. Our building block approach to HCS analysis helps scientists to get started very quickly and be consistent in their analysis over time and across groups. So there is no programming necessary to analyze even the most challenging result sets coming from 3D cell cultures, cell painting or fast kinetic assays. All of your images, meta data and analysis results are stored in one single platform allowing it to be accessed together to keep it in context in the system as well as through other software directly via REST API with no need to export and re-import images and manage it alongside data outside of Signals Image Artist. This makes Signals Image Artist the most powerful and complete solution to handle and analyze High Content Screening data on the market today. And it is directly integrated for downstream analysis of the data with Signals VitroVivo.

Signals VitroVivo unites assay development, low throughput to ultra-high throughput assays, High Content Screening and many other assay types, and even in vivo studies so you can search and analyze across all assay and screening data in a single platform. Coupled with Signals Image Artist, scientists are empowered to quickly process, analyze, share, and store* phenotypic screening, cell painting, 3D and fast kinetic data like never before. They now have access to reliable data faster, more efficiently, and with more flexibility than before. From a science perspective, cell painting, 3D image analysis, and kinetic data are especially hot topics now, and we offer scientists the flexible and powerful means to explore these areas without the need to do any programming. From an IT perspective, Signals Image Artist uses high performance computing and an industry standard object store to provide a scalable, multi-user solution for image analysis and management that can expand with your labs evolving needs.

To learn more about Signals Image Artist™, and how we support a range of R&D customers, read more here.

With the recent emergence of the Omicron variant over the Thanksgiving holiday weekend, scientists are under tremendous pressure to understand a number of questions. Some key ones include:

1. Do current vaccines & boosters need to be tweaked?

By analyzing the genomic sequences of Omicron, alongside taking antibodies from a vaccinated person or animal, scientists are racing to find out if the current vaccines and boosters can neutralize the Omicron variant. If the variant is resistant or if it requires a higher titer of antibodies to neutralize the virus, then the vaccines or boosters may need to be tweaked.

2. What is the transmissibility of the Omicron variant, and how does it compare to the Delta variant?

If Omicron is partially resistant to current vaccines & boosters and if the variants transmission rate is low, it may not be as problematic and only stays in certain regions, therefore avoiding becoming drivers of new waves like Delta.

In our webinar¹ last year, Emerging Trends and Data about SARS-CoV-2 Epidemiology and Genetics from a Data Analytics Perspective, at around 12:01, Revvity Signals’ Dan Weaver PhD, Director Product Portfolio, acknowledged the efforts of the international community to gather together genomic sequencing data from all of the relevant sources and begin annotating them. Months following this webinar, we now see again, how critically important it is to have international scientific collaboration + robust genomic and data analytics tools like Signals Inventa² + a commitment to the global scientific and data analytics communities sharing data insights, so public health officials can continue to guide the public and save lives.

We need these informatics tools more than ever to enable scientists to gather and analyze available data, to provide important insights to the international scientific community. Let’s keep up our scientific community’s commitment to global scientific collaboration, in terms of sharing genetic surveillance and scientific and data analytics insights about these variants. We are thrilled that our scientific software has the power to help this effort at this critical time in the pandemic, to help the international science community continue to create vaccines and anti-viral treatments to save lives.

To learn more about Signals Inventa² and how we support a range of small molecule R&D and biologic R&D customers in drug discovery and materials science, read more here. To learn more about how Revvity Signals is the exclusive provider of Spotfire® for scientific and clinical R&D applications, read here. 
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¹ This webinar is available in the COVID-19 Analytics Resource Center
² Formerly known as Signals Lead Discovery

In a recent September blog post, we mentioned the increasingly urgent drive to discover and develop novel biological therapeutics in areas such as oncology, and the need for researchers to be equipped with the best possible tools to capture, manage and exploit all the available data.

For our customers who work in drug discovery or materials science, understanding structure-activity relationships (SAR) can have a huge impact on lead optimization and give them a competitive edge.

With the growing adoption of multidisciplinary drug discovery teams, and the abundance of molecular property information and biological and chemical assay data, research scientists are faced with an ever-increasing array of parameters. One way to analyze this data is by studying activity cliffs. Activity Cliffs are changes in activity or potency caused by changes to a chemical scaffold.

In the new videos below, we see a glimpse of this powerful set of capabilities that allow scientists to see how a small change in a molecule’s chemical scaffold can have a significant change in its potency or other effect. This is more proof of Lead Discovery Premium’s powerful SAR capabilities in supporting faster insights and better science in the search for new therapies. We are confident that these exceptional and flexible capabilities will continue to help our wide range of research scientists, advance their lead optimization effort, in the search of the next much-needed therapy, vaccine, and/or materials science product.

To learn more about Lead Discovery Premium, and how we support a range of small molecule R&D and biologic R&D scientists in drug discovery and materials science, read more here. You can learn more about the extensive list of differentiating capabilities in our Lead Discovery data sheet, in addition to the reference videos below:

Lead Discovery Premium | Activity Cliffs
Core Decomposition | Matched Molecular Pair Analysis | Neighbor Properties

Biotechnology is at the forefront of the world’s mind as new therapies and tools are developed to combat the COVID-19 pandemic.

Behind the COVID crisis, Revvity Signals’ solutions are applicable to multiple industries. For example, our customers are engineering microbial strains to develop new fragrances, improving crop performance through genome engineering, and creating new enzyme catalysts for greener chemistry. The foundation of biotechnology is molecular sequence design, often starting with plasmid vectors or protein sequences. With an October release we’ve brought the language of molecular biology into Signals^TM Notebook, the modern, cloud-native electronic lab notebook (ELN).

Because many competitive informatics software doesn’t support sequence files in a first-class fashion, scientists have developed workarounds like shared folders with their designs and a separate spreadsheet (or sheets!) that describes key features of plasmids or protein designs. Now documenting the design process that begins biologics development couldn’t be simpler with Revvity Signals Notebook.

Our new Biological Sequence Element supports a wide array of DNA and protein files. It’s simple to drop a file into an experiment and instantly get an annotated map of the underlying sequence. Open the Element into full-size mode and dynamically explore deeper features of the molecular design. Integration with SnapGene, a popular tool for molecular design, enables design and update of sequences housed in the Signals cloud.

The Biological Sequence Element also unlocks scientific collaboration. I’m reminded of a group engineering cell lines to support biomarker validation and assays. The team was spread across London, Chicago, and San Francisco for a global company. These colleagues collaborated by emailing files back and forth with notes on designs and experimental advice. A very inefficient and siloed process.

Now the designs and experimental context can be shared directly in Signals Notebook. And, commenting enables all the incredibly valuable scientific discussion to be shared outside of a one-to-one email message.

We’re excited to bring these new capabilities to our partners. I’m looking forward to hearing your response to this new feature and excited to share our continuing plans to fully support biologics workflows across our Signals Research Suite.