Latest Projects
Our projects transform R&D innovation through pre-competitive collaboration. We bring together the key constituents to identify the root causes that lead to R&D inefficiencies. We develop best practices and technology pilots to overcome common obstacles.
Project one: Microbiome
This project aims to develop a cross-industry microbiome expert group committed to addressing challenges around the influence of the microbiome on drugs and dietary supplements.
The same drug or dietary supplement can behave differently in different people. This is in part due to the difference in the genome between individuals and it is also increasingly evident that this is caused, in part, by the different microbiome within individuals.
If we are to achieve the health care objective of precision medicine then a greater understanding of this interaction is required.
- The microbiome has a profound impact on drug behavior in an individual
- This project allows for data pooling from different organizations an elegant and difficult pre-competitive activity
- A better understanding of the microbiome will lower barriers to R&D innovation
This project aims to develop a cross-industry microbiome expert group committed to addressing challenges around the influence of the microbiome on drugs and dietary supplements.
1) Establish a systematic, global and standardized map of the microbiome-derived metabolism and develop a database to include all the generated data so far on the bi-directional interaction between microbiome and drugs/dietary supplements.
2) Such data could be used as the foundation for developing various machine-learning algorithms that could help in predicting the influence of the microbiome and identify key enzymes which contribute to drug metabolism
3) create a common methodological framework for determining the bi-directional interaction between microbiome and drugs/dietary supplements, and for accurate data generation. This common framework could include validated methods and protocols, software, and computing infrastructure.
This project is relevant for pharma, nutraceutical organizations, and academic organizations looking at the effects of the microbiome.
Scientists involved in Microbiome Research, ADME/Tox Scientists, Bioinformaticians, Data Scientists, and AI/ML interested parties are welcome to participate. We are currently meeting monthly to continue to develop the project. Click here to express your interest.
Project two: Data Visualization
This project aims to establish best practices for data visualization supporting life science research and identify opportunities for communicating and implementing these best practices.
Technology breeds technology and especially so in the Life Sciences. As such, we have more tools and data in our armory than ever before. However, with this data deluge comes complex challenges in maximizing the value of that data.
Data visualization is the graphical representation of quantitative and qualitative information to reinforce human cognition. The interpretation and understanding emanating from the analysis of scientific data can be optimized by incorporating best practices, strategies, and principles.
R&D life sciences might establish a data-visualization practice to put scientists at the center of the data visualization experience to optimize the cognitive ability of scientists in interpreting data.
The goal of this project will be to create a data visualization community of interest that will address the needs for data visualization to support life science research and identify opportunities for communicating and implementing these best practices.
- Forge a project team to enable sharing of best practices
- Raise awareness of the value of Data Viz in the life sciences
The project will support organizations to become more aware of the field of data visualization and its benefits to enable scientists to better understand and gain insights from their experimental data.
- The team meets twice a month to share best practices.
- Developing themes of importance to the community.
- We are running education workshops amongst interested members through the UXLS 2021 conference.
Project three: Methods Hub
The Methods Hub project seeks to build a bridge for analytical methods to transition from text-based information to fully digitized, machine-readable instruction sets. In this new paradigm, human interpretation and transcription go away. Data integrity, method reproducibility, and interoperability increase providing value to many in the Pharma industry, including manufacturers, CRO/CMOs, and regulators.
Analytical Methods are often authored and published in free-text formats that lack both structural and semantic consistency.
Given the widespread adoption of software and systems (LIMS, ELNs, LES, etc. systems) that must accurately represent methods in ways that machines can store, export, import, and compare what was specified in the method against executed results, the current paradigm presents problems for Data Entry, Reproducibility and Results Analysis.
Methods that are authored in free text are often entered into electronic systems in 2 ways:
- Humans (e.g. analytical chemists) transcribing the method into electronic systems
- Software programs using NLP, of various degrees of sophistication and quality, “screen scraping” the content to avoid the need for manual transcription
Both of these approaches introduce the possibility for error as well as interpretation of the method, depending on the subject matter expert or even the Natural Language Processing algorithm’s interpretation. This can impact reproducibility as well as introduce unintended errors.
The key objective of the Methods Hub project is to simplify the transferring of method information between two parties. This will involve the creation of a cloud-based database that will allow downloading and sharing of analytical methods in machine-readable formats. This will:
- Increase reproducibility of methods as deviations are reduced
- Reduce variance in the execution of the method
- Reduce time for method transfer
- Automate quality standards live within workflows
- Increase consistency of information between different sources on a Method Marketplace
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- Scientific Literature, Publishers
- Pharmacopeia USP
- Commercial Method Information sources
Methods Hub could lead to commercially available repositories of methods where digital as well as text-based method information with appropriate metadata, in a machine-readable format, would be shared and exchanged. The platform would allow for both free and paid downloads of monographs or manuscripts, the interchange between CRO/CMO and Pharma, and also free access of methods that are currently open source.
To date, Pistoia Alliance has, in collaboration with Allotrope Foundation, developed a Methods database based on Zontal Space with a public API specification and human-readable representation of a limited number of chromatography instruction sets. This will provide a good entry point for the Methods Hub Project.
All available sources for Analytical Method information will be evaluated in terms of their information structure from free-text, via semi-structured to fully digital information e.g. based on the Allotrope Foundation Ontology (AFO) and other available ontology providers.
For free-text and semi-structured methods, the extraction of key metadata would be considered.
Natural Language processing tools could be used to pre-populate a standard format for a fully digital representation of the method. Alternatively, well-implemented methods can be exported from a Chromatography Data System (CDS) and just validated by comparison to the text-based description.
