|Articles|April 8, 2008

India Converts Failing Vaccine Plants to Testing Labs

The Indian government has reacted to the enforced closure of three of its oldest vaccine manufacturing plants by launching a plan to convert them to drug testing laboratories.

INDIA-April 8. 2008-Nick Taylor reports for LabTechnologist.com that the BCG Vaccine Laboratory, Pasteur Institute of India and the Central Research Institute were shut down in January after failing to comply with current good manufacturing practices (cGMP). However, they will now receive a new lease of life testing the safety and efficacy of drugs and vaccines produced and sold within the country or exported. In addition to this they will be responsible for detecting counterfeit and spurious batches of drugs. To read more, click here: www.labtechnologist.com//news/ng.asp?n=84492&c=8YMEJy20NE7MmUMHbx1tWw%3D%3D.

Stay current in clinical research with Applied Clinical Trials, providing expert insights, regulatory updates, and practical strategies for successful clinical trial design and execution.

Related to this article

October 2nd, 2026

Hierarchical Endpoints in Cardiology: How Much Treatment Benefit, and From Where

Hierarchical composite endpoints analyzed through pairwise comparisons more accurately reflect multifaceted treatment benefit than time-to-first-event composites, but transparent outcome prioritization, patient involvement in ranking, and reporting of Net Treatment Benefit remain underutilized despite their importance to interpretation.

October 2nd, 2026

What ClinOps Professionals Just Starting With AI Should Keep Top of Mind

In this video interview, Claire Riches, VP of clinical solutions at Citeline, offers practical guidance for clinical operations teams beginning their AI journey—making the case that the tools are more accessible than many assume and that waiting to start is a competitive risk in itself.

October 2nd, 2026

ACT Brief: Proactive Trial Risk Management, Federal Infrastructure Modernization, Pediatric Heart Disease Treatment

In today's ACT Brief, we examine how AI enables sponsors to pressure-test protocols before enrollment, HHS's multi-initiative effort to accelerate trial design and execution, and FDA approval expanding heart disease therapy to adolescents.

© Tada Images - © Tada Images - stock.adobe.com

October 1st, 2026

HHS Launches SURPASS Program and Three Complementary Initiatives to Transform Clinical Trial Design and Infrastructure

The new effort combines adaptive platform trial design, AI-enabled site activation, nationwide data infrastructure, and patient data contribution tools to reduce timelines, costs, and patient burden across clinical development.

October 1st, 2026

What an AI-Informed Approach Means for How Sponsors Think About Risk

In this video interview, Claire Riches, VP of clinical solutions at Citeline, explains how AI is shifting trial risk management from reactive to proactive—enabling sponsors to pressure-test protocols and anticipate pivots before a single patient is enrolled.

October 1st, 2026

ACT Brief: AI as Design Advisor, Autonomous Agent Governance, Oral Weight-Loss Therapy Switch

In today's ACT Brief, we examine AI's role as trial design advisor with human leadership, how to govern autonomous agents in regulated operations, and real-world outcomes from switching to oral weight-loss therapy.

September 30th, 2026

The Control Plane for Agentic Clinical Operations: Governing Autonomous Digital Actors in Regulated Trials

AI agents in clinical operations acquire broader autonomous capability through expanded permissions, tools, memory, and delegated authority, requiring governance focused on whether effective capability has shifted outside approved boundaries rather than whether software has changed.

September 30th, 2026

Where AI Leads in Trial Design—and Where Humans Still Need To

In this video interview, Claire Riches, VP of clinical solutions at Citeline, makes the case for AI as a sophisticated strategic advisor in trial design while arguing that humans must remain in the lead—especially when factors the model can't fully account for are at stake.