Best practices that enable Biopharma companies to better exchange work and data with their CRO/ CMO and other partners

Best practices that enable Biopharma companies to better exchange work/ data with their partners

This article explains the numerous challenges of Biopharma companies to effectively collaborate with external partners and offers information and technology best practices to address those.

Leading Biopharma companies transition to a new paradigm of drug development: They focus on creating a flexible environment that allows easy use and movement of internal and external resources (CROs, specialty labs, CMOs). Outsourcing use and work dependencies on partnerships are accelerating and broadening across discovery, pre-clinical, clinical and pre-launch phases. A recent survey indicated that over 70 percent of projects Biopharma projects involved two or more partners and almost 20 percent involved more than four partners.

Working with external parties poses numerous challenges:

Must exchange information with numerous participants. Data/ information needs not always specific. Must identify/ establish data owners, short/ long-term storage needs, IP/ sensitivity. Information exchange often happens on transaction basis. Majority of data transfer still occurs via email and file exchange. These manual processes does not support volume, complexity and scalability.

All players have their own processes & systems. High dependency of manual interaction. Each partner works with other organizations that have their own nomenclature, business practices, systems, processes and requirements.

Very difficult to build an IT structure to handle every possible relationship.

Solution:

1) Define objectives and high level-business requirements 2) Identify Information Needs & Access (who, what, when…) 3) Develop business Work & Information flows 4) Assess current Landscape & capabilities. 5) Establish a broad, well-conceived technical framework based on strategic needs. 6) Test framework in focused areas, ensure that it works and then scale it.

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Detailed level: 1) adopt an end-to-end view for each process and identify all workflows and transactions associated with it 2) identify all information input/ outputs along each process step. 3) Turn process steps into modules that describe specific work with data input/ outputs and specific information deliverables. 4) Use modules to plug in third parties/ partners as needed. 5) Manage the data: short-term use, long-term storage needs, IP sensitivity. 6) Assimilate data from different sources. 7) Transform data to standard vocabularies to facilitate unambiguous meaning and comparison. 8) Implement data governance: 9) Leverage technology to support and integrate machine-to-machine and human visible information exchanges (ex: Collaboration tools, data visualization, semantic web linked data concepts, traditional data ware houses portal technologies, cloud based solutions)

Example: R&D

There are numerous data sets in Pharma R&D that a specialized for a particular science or process. The data may be categorized as 1) Traditional scientific data based on studies and experiments. 2) Detailed result data including multi-parametric data, images, instrument data. 3) Summarized result data in tables and spreadsheets 4) Documents and reports covering earlier types 5) Operational data about programs, projects, or studies regarding status, tracking and reporting.

These data types are unfortunately often not well standardized. It difficult to communicate/ share data across the organization and even more difficult to do with external parties that are part of the product life cycle.

Read more on the how Artificial Intelligence can empower drug development

Harmonize data according good business practices and standards

It is critical to harmonize vocabularies, metadata, nomenclature and master data throughout the involved parties. It is good to use official standards such as SEND and SDTM from CDISC for non/clinical data. The CDISC Standard for the Exchange of Nonclinical Data (SEND) Implementation Guide provides the structures and implementation rules for the submission of data from single- and repeat-dose toxicity studies and carcinogenicity studies. CDISC SDTM is the name of the model (or framework) used for organizing data collected in human and animal clinical trials. The CDISC is the Clinical Data Interchange Standards Consortium, a standards development organization for dealing with medical research data.

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There a different ways for data harmonization:

1) Traditional ETL (extract, transform, load) models: Harmonization occurs within the company when external files are brought to the organization and loaded into the system. 2) BPM tools: Partner sends data through information exchange. BPM harmonizes vocabulary & meta data supported by the pharma vocabulary ontology and then processes the harmonized data for reports or storage. 3) Result data remains at source: External partner organization needs to harmonize the data first and then shares. 4) Third party tools conduct the harmonization and data exchange to the cloud. This data hub includes Data Virtualization (Federation), Semantic Linked Data Integration and Data warehouse.

Conclusion: At this time, there is no single tool solution that solves all integration needs. Minimizing the physical transfer of data minimizes backend work required to manage information and infrastructure to support it. Leverage a technology building block approach to develop a customized solution.

Conclusion

If you are interested in this or other Healthcare / Life Sciences topics, you can reach out to me directly at alexwsteinberg@hotmail.com or WeChat (ID: alexwsteinberg2 ).

About the author: Alex Steinberg comes out of a family of doctors, scientists and other health care professionals who have dedicated their lives to improve the health & well-being of people around the world. Alex drives digital transformation, innovation and intelligent automation efforts for the largest brand companies in China.

Special credits: This article leverages extensively text, content and graphics from ResultWorks, a professional services company offering strategy innovation, integrated business process analysis, information transformation, knowledge management, and change management consulting services for the life sciences industry. Special thanks to their extremely valuable business, medical and scientific contribution!

Legal disclaimer: This article represents my personal opinion and does not reflect that of my current/ previous employer(s) or clients. The article intends to increase awareness, understanding and dialog about Health Care and Life Science issues. It does not present any offer or advice in a legal sense. Markets and technology change quickly and information gets out-of-date. The reader is advised to always seek individual analysis & consultation.