Regulatory Frameworks Grounded in Science

DoE, QbD and PAT 1. Introduction Evolution of pharmaceutical development: from empirical trial-and-error → risk-based scientific approaches. Regulatory drivers: ICH guidelines (Q8–Q14), FDA PAT initiative (2004). Importance of integrating design, knowledge, and real-time control. Positioning DoE, QbD, and PAT as a “triad” for robust, efficient, compliant development. 2. Historical Context and Regulatory Push Past reliance on end-product testing and its limitations. Shift to lifecycle management approaches. Role of FDA’s Critical Path Initiative. QbD introduced into regulatory lexicon in 2004; PAT guidance published. Global adoption: EMA, MHRA, WHO. 3. Understanding the Three Pillars 3.1 Quality by Design (QbD) – The Framework Definition & Philosophy: Proactive design vs reactive testing. Key Concepts: QTPP – Quality Target Product Profile. CQA – Critical Quality Attributes. CPP – Critical Process Parameters. CMA – Critical Material Attributes. Stages of Application: Early development → Technology transfer → Lifecycle management. Regulatory Basis: ICH Q8(R2), Q9, Q10, Q11, Q12, Q13, Q14. Tools: Risk assessments (FMEA, Ishikawa, Fault Tree Analysis), control strategy design. Case Study Example: QbD applied to controlled-release tablet development. 3.2 Design of Experiments (DoE) – The Optimizer Definition: Statistical framework for systematic factor–response exploration. Role in QbD: Tool to identify design space. Types of DoE: Screening designs (Plackett-Burman, Fractional Factorial). Optimization designs (Central Composite, Box-Behnken). Robustness studies. Benefits: Identifies interactions, reduces experiments, builds knowledge quantitatively. Case Example: Optimizing binder level, granulation time, and impeller speed. 3.3 Process Analytical Technology (PAT) – The Real-Time Guardian Definition: Real-time monitoring and control toolkit. Role: Ensures processes remain within validated design space. Techniques: NIR, Raman, FTIR, Particle size analyzers, Focused Beam Reflectance Measurement (FBRM). Applications: Blend uniformity. Moisture control. Coating thickness. Continuous manufacturing. Regulatory Context: FDA PAT Guidance (2004). Case Example: Inline NIR monitoring for RTRT (Real-Time Release Testing). 4. Interrelationship of the Three Pillars DoE as the engine of knowledge → defines design space. QbD as the overarching framework → integrates knowledge, risks, and control strategy. PAT as the execution safeguard → ensures adherence in manufacturing. Lifecycle integration (development → validation → continuous verification). 5. Benefits of Integrated Use Regulatory alignment & faster approvals. Cost savings through fewer failed batches. Increased robustness and reproducibility. Knowledge management & data-driven decision-making. Example: Continuous manufacturing systems where DoE defines design space, QbD integrates it, and PAT ensures execution.

FSSAI Introduces New Scientific Evidence Rule (Effective 1 Jan 2026) A turning point for food regulation in India India’s food regulatory framework is moving decisively from assumption-based safety to evidence-based decision making. From 1 January 2026, FSSAI will require robust scientific data for: Approval of new food products ➡️ Safety reviews of ingredients & formulations ➡️Revisions in existing food standards ➡️Risk assessments and exposure evaluations 🚫 “Trust us” claims are no longer acceptable Data will be the new currency of approval 🔬 What Scientific Evidence Will Be Required? Food businesses must submit standardised scientific dossiers, typically covering: 1️⃣ Nutritional Composition Macro & micro-nutrient profile Variability across batches Comparison with existing permitted foods 2️⃣ Intended Consumption Data Target population (adult / child / special groups) Serving size & frequency Cumulative exposure from multiple food sources 👉 This is critical to avoid over-exposure risks. 3️⃣ Toxicological Evidence NOAEL data Acute & chronic toxicity studies Margin of Safety (MoS) calculations 4️⃣ Allergen Risk Assessment Presence of known allergens Cross-contact risks Scientific justification for allergen labelling or exemptions 5️⃣ Safety Evidence from Studies Published peer-reviewed research In-house or third-party study reports International regulatory references (if scientifically justified) 🎯 Why This Rule Is a Big Deal ✔️ Moves India toward science-based regulation, not perception-based ✔️ Decisions aligned with Indian dietary patterns & exposure levels ✔️ Reduces ambiguity in approvals & objections ✔️ Builds long-term consumer trust in food safety This mirrors global regulatory thinking — but customized for Indian food habits, not blindly copied. 👩🔬 Impact on Food Businesses & Professionals Higher demand for: Food analysts Toxicology experts Accredited laboratories Regulatory & compliance consultants #FSSAI #FoodSafety #ScientificEvidence #FoodRegulation #RiskAssessment #FoodIndustryIndia #Compliance #QualityAssurance #FoodInnovation

Most people will read today's EMA announcement as a regulatory milestone. It is. But for some of us, it is also a timestamp on years of work that nobody was paying attention to. Today, EMA's CHMP issued its first draft qualification opinion for virtual control groups as a New Approach Methodology. The context of use is specific. Rat dose-range finding studies. Non-GLP. Replacing concurrent control groups with virtual comparators built from historical data. This is not a theoretical proposal. It is a regulatory mechanism. EMA is saying: we will accept evidence generated this way. I have been working on the foundations of this approach for years. Through the Pistoia Alliance Minimal Metadata Set Working Group, which I lead, we set out to answer a specific question: can standardized metadata make historical control data reusable across pharmaceutical companies? We built the MNMS framework, published in Lab Animal (Moresis, Gaburro et al., 2024), precisely to create the infrastructure that virtual control groups require. Without harmonized metadata, historical data cannot be pooled. Without pooling, there are no virtual controls. We then launched a cross-pharma proof of concept with five pharmaceutical companies, using 24/7 home cage monitoring data to test whether virtual control datasets could be generated from multi-site historical records. That work is ongoing. A manuscript is in preparation. Today's EMA opinion validates the exact principle we have been building toward. Two things matter here. First, this is the first EMA qualification of a NAM for toxicological assessment. That creates a regulatory blueprint. Not just for dose-range finding. For every study design where concurrent controls are required by convention rather than by scientific necessity. Second, the real bottleneck was never the algorithm. It was the metadata. Inconsistent annotation across institutions is what prevented historical control data from being reusable. Our Pistoia Alliance work addresses exactly that gap. When metadata standards are enforced at acquisition, historical data become scientific assets. When they are not, they remain institutional waste. The consultation runs until May 12, 2026. I encourage every preclinical scientist, toxicologist, and regulatory affairs professional to read the draft opinion and submit comments. This is not the end of concurrent controls. It is the beginning of a framework where every control animal that does not need to exist, does not. That is not aspiration. That is now regulatory architecture. Gaburro S, Moresis A et al. Lab Animal 2024;53:67-78. Steger-Hartmann T et al. ALTEX 2020;37(3):343-349. Golden E et al. ALTEX 2024;41(2):303-318. I lead the Pistoia Alliance MNMS Working Group. If you are building historical control databases or working on metadata harmonization, reach out. The infrastructure matters more than the models. #VirtualControlGroups #NAMs #PistoiaAlliance #3Rs #EMA #PreclinicalResearch #Reduction

The FDA has released its draft guidance on the use of New Approach Methodologies (NAMs), marking a significant step toward transforming how we evaluate drug safety and efficacy, and reinforcing that alternatives to animal testing are the future. It is time for us to embrace them (see our recent Nature Magazine Commentary, https://lnkd.in/gmWENZ7x) For decades, animal testing has been the default foundation of preclinical research. Today, we are witnessing a clear regulatory shift toward human-relevant, data-driven, and mechanistically informed models. This new guidance outlines a structured validation framework built on four key principles: ·       Context of Use: Clear definition of NAMs’ intended regulatory purpose ·       Human Biological Relevance: Demonstration of how NAMs can assess toxicity ·       Technical Characterization: Establishment of scientific confidence through robust, reliable, and reproducible methods ·       Fit-for-Purpose: Assurance that NAMs help in regulatory decision-making (e.g., drug review and potential approval) Importantly, for industry, the FDA emphasizes that NAMs do not always need to be fully “validated” to be impactful, fit-for-purpose approaches, supported by weight-of-evidence, can already contribute to regulatory submissions. This represents a meaningful shift, opening the door to more flexible and human-centric drug development strategies. This direction strongly aligns with where the field is heading, and where we are actively building. At Greenstone Biosciences (https://greenstonebio.com/), we are developing next-generation, human-centric drug discovery platforms grounded in NAMs. Our work integrates human iPSC-derived systems, 3D organoids and microphysiological systems (MPS), and AI-driven in silico approaches to better capture human biology and improve predictive accuracy. By combining these modalities, we aim to reduce reliance on animal testing, uncover disease-relevant mechanisms, and accelerate the development of safer and more effective therapies, spanning areas from cardiovascular disease to rare disorders. NAMs are no longer optional, they are becoming foundational to the future of drug development. 🔗 FDA draft guidance: https://lnkd.in/gh25PDxx #NAMs #DrugDevelopment #FDA #GreenstoneBiosciences #Organoids #AI #PrecisionMedicine #TranslationalScience

Advancing the Future of Drug Discovery & Regulation with AI Proud to see Northeastern University spotlight two critical advances at the intersection of AI, life sciences, and regulatory science—areas I’ve been deeply committed to accelerating. In one story, teams explore how AI-driven discovery is transforming how quickly and precisely new therapeutics can be identified. In the companion article, we confront the equally important challenge: How do we regulate AI-enabled drug discovery responsibly, without slowing innovation? These are not separate conversations—they are two sides of the same coin. Innovative science requires equally innovative regulatory frameworks. That’s why much of my work at Northeastern focuses on building the systems, guidance, and workforce training needed to ensure AI tools in drug development are trustworthy, transparent, and aligned with global regulatory expectations. From shaping international regulatory-science initiatives to developing next-generation training programs, our goal is clear: Accelerate safe, effective biomedical innovation—without compromising scientific or ethical standards. Grateful to collaborate with outstanding colleagues across Northeastern’s Institute for Experiential AI, the College of Science, CPS, and our global partners who are helping to build a smarter, more agile ecosystem for the future of medicine. Read more: AI for Drug Discovery: https://lnkd.in/e3dj8M3K AI Regulation & Standards: https://lnkd.in/e4Zg2r9s Excited for what’s ahead. The future of drug development will be shaped by AI—and we’re helping lead the way. #AIDrugDiscovery #AIBiotech #LifeSciencesInnovation #DrugDevelopment #DigitalBiology #AIRegulation #RegulatoryScience #QualityByDesign #ResponsibleAI #Bioethics #NortheasternUniversity #ExperientialAI #FutureOfWork #WorkforceDevelopment #STEMEducation #Innovation #TechForGood #Biotechnology #HealthcareInnovation #ResearchImpact Rominder (Romi) Singh

Today, the FDA unveiled a game-changing framework that could accelerate how we bring gene therapies to patients. In a recent paper in the New England Journal of Medicine, Marty Makary M.D., M.P.H. (FDA Commissioner) and Vinay Prasad, MD MPH (Director of CBER) articulate a new regulatory pathway dubbed the “plausible mechanism pathway.” Under this approach, manufacturers could leverage platform data built from earlier therapies and existing safety/technology platforms to streamline review of subsequent, closely-aligned gene therapies. Historically each highly tailored therapy (e.g., designed for a unique mutation) faced a full regulatory submission from scratch. The new guidance proposes that once a therapy platform has demonstrated consistent outcomes in “several consecutive patients with different bespoke therapies,” subsequent uses can reuse prior data and technology. Bottom line for pharma and biotech leaders: This is a signal to evaluate how your gene-therapy and platform strategies align with a regulatory horizon that increasingly favors reuse of validated platforms rather than reinventing the wheel for each new variant. This regulatory evolution marks a pivotal moment for advanced therapies. If the medical-innovation ecosystem can rise to the challenge, we may see the promise of individualized gene therapies move from isolated “one-patient” use cases into sustainable, reimbursement for many. #GeneTherapy #AdvancedTherapies #RegulatoryScience #RareDisease #PrecisionMedicine #Biotech #Pharma #HealthcareInnovation https://lnkd.in/gxUCK_sS