Pharmaceutical Industry Articles & Reports - Page 5

A 2026 pharma AI procurement protocol defining seven approval stages, security and GxP evidence, pilot outcomes, rework coding, and a critical-path worksheet without invented medians.

A 2026 implementation guide to EMA Write PMS API integration, covering GET $everything, POST $merge, version control, asynchronous responses, resource graphs, and validation.

A 2026 EHDS readiness blueprint for pharma, biotech, medtech and CRO teams, covering 2029 and 2031 data phases, permits, secure environments, HealthData@EU and IP controls.

A 2026 FDA AEMS E2B(R3) cutover runbook covering the October 1 deadline, applicability, R2 transformation risks, ESG NextGen testing, acknowledgements, validation, and hypercare.

A 2026 implementation blueprint for FDA PreCheck Type V DMFs, mapping four POR elements to facility data ownership, traceability, change control, and a 90-day launch plan.

A 2026 evidence index for 12 GxP SaaS products, comparing release notice, validation packs, test evidence, API policies, gated access, and customer regression burden.

A 2026 guide to Claude text watermarks, the detection API, C2PA credentials, EU AI Act Article 50, and evidence controls for pharma content.

A 2026 entry guide to the Anthropic Adaptyv protein design competition, comparing three tracks, five weekly windows, wet-lab testing, publication, and IP terms.

A 2026 decision guide to Anthropic MHS for pharma labs, covering preview status, device architecture, SiLA 2 boundaries, GxP controls, pilot design, and verification evidence.

Compare DNAnexus, Seven Bridges, and Seqera in 2026 across workflow languages, deployment topology, data residency, integrations, validation evidence, and cost modeling.

A 2026 implementation guide to EMA ePI FHIR v1.0, covering the resource graph, PLM versus external generation, identifiers, procedure timing, validation, and rollout readiness.

A 2026 decision guide to Anthropic LSVP requirements, Standard versus High-risk grants, 30-day retention, PHI separation, product surfaces, and pharma governance readiness.

A 2026 post-deadline guide to EUDAMED registration, four-module scope, UDI hierarchy, legacy-device deadlines, and manual, bulk XML, or M2M integration choices.

A dated calendar of drug patent, exclusivity, and settlement-based market-entry events for 2027-2030, with a reproducible method and September 2026 sourcing.

How virtual and external control arms are built, validated, and regulated in 2026: FDA and EMA guidance, exchangeability, bias, sensitivity analysis, and documented case studies.

A 2026 analyst guide to FDA's 510(k), De Novo, and PMA pathways for AI-enabled medical devices, with a reproducible tabulation of FDA's own device list, PCCP adoption data, and peer-reviewed clinical evidence audits.

A 2026 evidence review of enterprise AI deployment failures and outcomes: MIT, RAND, S&P Global, and Gartner failure-rate studies, McKinsey, IBM, Deloitte, and BCG ROI benchmarks, and a transparent case-selection method.

2026 data report on life sciences industry employment trends and salary data, covering BLS occupational wages, regional pay premiums, seniority benchmarks, and 2024-2026 hiring and layoff dynamics.

A 2026 reference on MCP server permissions architecture: the OAuth 2.1 authorization layer, documented vulnerability classes like tool poisoning, and a least-privilege connector design method.

A 2026 data report classifying the specific AI skills, roles, and training pathways found in current pharma job postings, using BLS, O*NET, Lightcast, and a direct sample from six drugmakers' career sites.

A 2026 methodology guide to evaluating biology foundation models covering benchmark design, sequence-homology data leakage in genomic and protein datasets, and the progression from held-out tests to experimental validation.

A 2026 evidence review of AI radiology accuracy from prospective and randomized studies, covering FDA authorization counts, false positive rates, workflow efficiency, and patient outcomes.

A 2026 data-driven analysis of AI and entry-level hiring, covering the Stanford ADP payroll study, NACE, Indeed Hiring Lab, ILO and OECD statistics, exposure methodology, and competing causal explanations.

A 2026 guide to reading AI benchmark scores: detecting data contamination, evaluation harness and prompt-format effects, statistical significance, and leaderboard reliability, with a reproducible comparison checklist.