The FDA has released 76 new Product-Specific Guidances (PSGs), including 64 for drugs without approved generics and 29 for complex products, strengthening the pathway for high-quality generic drug development. These PSGs outline the FDA’s current expectations for bioequivalence, study design, and data requirements, helping manufacturers reduce development risk, accelerate ANDA approvals, and improve access to […]
Global manufacturers must treat the U.S. FDA’s Import Alert framework as a dynamic, operational risk-control mechanism, not just a customs nuisance. Import Alerts can trigger automatic “detention without physical examination” (DWPE), stop shipments at the border, and force expensive corrective paths. To stay resilient, manufacturers should understand how alerts are structured (DWPE, red/yellow/green lists), monitor
Continuous Supplier Monitoring (CSM) has become one of the most important capabilities for life sciences and pharmaceutical manufacturers today. Modern supply chains are global, complex, and heavily regulated. A single supplier failure, whether related to APIs, excipients, packaging, or cold-chain logistics, can disrupt production, delay batch release, impact regulatory inspections, or even put patient safety
The FDA’s expectations for SaMD (Software as a Medical Device) submissions have moved decisively toward a total product life cycle (TPLC) approach that emphasizes transparent device description, rigorous risk and performance characterization, comprehensive data management and bias mitigation plans, human factors evidence, and post-market monitoring and change control, especially for AI/ML-enabled SaMD. Sponsors must show
The FDA is increasing inspections of foreign contract manufacturers, often without notice. They focus on quality systems, data integrity, sterility controls, process validation, labs, supplier quality, and complaint handling. Many foreign sites face a higher risk because of inconsistent GMP practices and rising data-integrity issues. Pharma companies must strengthen oversight by improving quality agreements, running
CAPA effectiveness is central to product quality, patient safety, and regulatory compliance in life sciences. The right mix of metrics, including CAPA effectiveness rate, recurrence rate, average time to closure, on-time closure rate, backlog, root cause analysis quality, and cost of poor quality, provides leaders with actionable visibility. By utilizing clear KPIs, risk-based verification, integrated
In 2025, the principles of ALCOA+ (Attributable, Legible, Contemporaneous, Original, Accurate + Complete, Consistent, Enduring, Available, Traceable) are being reimagined in the age of AI and digital systems. For life-science and pharma manufacturing leaders, it’s no longer enough to treat data integrity as a checkbox; modern AI demands a new, more rigorous approach to how
FDA Form 483 observations and Warning Letters keep pointing to the same root causes, data integrity lapses, weak quality systems (including CAPA and quality unit failures), inadequate process/equipment qualification, poor contamination control and environmental monitoring, incomplete stability and supplier controls, and documentation gaps. FY2024 shows increased inspection activity and an uptick in warning letters, driven
The FDA’s 2025 draft guidances for AI/ML in drug and device development mark the transition from exploratory policy signals to concrete, operational expectations. Key new elements include a risk-based “credibility” framework for AI used to support regulatory decisions, lifecycle and marketing-submission recommendations for AI-enabled device software, explicit attention to foundation models/LLMs, stronger expectations for data
Pharma companies can automate regulatory submission workflows by combining robust submission platforms, data standards (eCTD and emerging v4.0 models), modular document management, rule-based and AI-assisted validation, and tight governance. Done right, automation reduces cycle time, cuts manual errors, improves traceability, and supports audit-readiness, but success requires clear process mapping, validated tools, vendor controls, data standards
