Design principles
The Well-Architected Framework identifies a set of general design principles to facilitate good design in the cloud. In addition, the following design principles should also be considered for designing and operating life sciences workloads.
The development of drug therapies is highly regulated for safety, efficacy, and security of drug therapies. In the United States, the Federal Drug Administration (FDA) mandates a series of Good Practices (GxP) which covers practices related to laboratories, clinical development, and manufacturing. This requires careful management of data integrity throughout the scenarios. Drugs are represented by data including the related research, biological data, genomic data, data gathered through clinical trials, data related to its manufacture, and real world data (RWD) collected after they are used in real world clinical settings. Data collaboration is also a common design principle, as there are several organizations involved including research institutions, governmental regulators, contract research organizations (CROs), and healthcare payors and providers.
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Implement strong security foundations: Follow industry best practices as detailed in the AWS Well-Architected Security Pillar whitepaper. Address GxP requirements that affect IT both as a source of requirements for workloads and as a set of controls over how those workloads should be built and operated.
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Adhere to regulatory frameworks: Become familiar with regulatory frameworks based on geography and industry best practices such as ISO 27001 and HIPAA. Create a controlled infrastructure with a prescribed account vending process, incorporating a layered model emphasizing infrastructure qualification and tooling verification. Shift from document-based to data-driven processes, prioritizing data integrity to align with FDA requirements (21 CFR Part 11 and ALCOA+). Implement continuous adhrerence through automated audit controls using defined configurations, compliance packs, and regular auditing to create a comprehensive, trustworthy environment that can integrate with a data lake.
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Develop robust quality management systems: Effective quality management (QM) and quality risk managment (QRM) is mandatory in the highly-regulated pharmaceutical, biotechnology, and medical device industries. Integrate effective QM and QRM practices into information technology systems to improve product quality, patient safety, and regulatory adherence throughout the product lifecycle.
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Design comprehensive data management strategies: Begin by reviewing the Data Analytics Lens. Address complexity based on the type of data created, collected, and stored, and consider the complete lifecycle of that data in relation to project phases. For geographically distributed operations such as multi-site clinical trials, decentralized research, or global manufacturing, implement distributed file systems that enable edge compute capabilities while maintaining centralized governance. Design architectures that process and validate data locally at clinical sites, manufacturing facilities, or research labs to optimize performance and reduce latency, while synchronizing to central repositories for regulatory adherence, audit, and collaboration. Balance local autonomy with centralized security controls, verifying data integrity and regulatory adherence across edge locations through encrypted data transfer, access controls, and comprehensive audit trails.
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Enable secure cross-organizational collaboration: Implement robust governance frameworks, audit trails, access controls, and long-term archival plans for managing data throughout its entire lifecycle. Adopt standardized formats (like CDISC, FHIR, and ISA-95) to promote collaboration between teams and systems. Consider each phase of the project from design through closeout while verifying that data can be securely shared with research institutions, regulators, and contract research organizations (CROs) while adhering to regulations. For more detail, see Data lifecycle.
