inConversation with Dr Padmanabha RV Reddy
In an era where pharmaceutical innovation is increasingly defined not just by molecular breakthroughs but by the ability to translate them into reliable, scalable, and cost-efficient therapies, the conversation around manufacturability has moved decisively upstream. No longer a late-stage checkpoint, it is now a strategic determinant embedded at the very inception of development.
In this context, we engage with Dr. Padmanabha RV Reddy, whose global leadership across organizations such as Novartis, Abbott, Johnson & Johnson, and Dr. Reddy’s offers a rare vantage point at the intersection of science, engineering, and commercial execution. Drawing from deep cross-functional experience, he articulates how early design decisions, digital enablement, and Quality by Design frameworks are reshaping the way organizations think about developing medicines that are not only clinically effective, but also consistently manufacturable at scale.
In many development programs, manufacturability considerations enter the discussion only during late-stage scale-up. From your experience, what specific scientific and engineering parameters should development teams evaluate during early formulation and process design to ensure that a molecule can transition smoothly from laboratory feasibility to commercial-scale manufacturing?
In the past, manufacturability was often considered late during scale‑up, when risks were already locked in. Today, leading pharmaceutical organizations have moved away from that mindset. Manufacturability is now designed in from day one, (either candidate be a NCE/NBE or Generic/biosimilar development) starting at the business case and Target Product / Quality Target Product Profile (TPP/QTPP) stage, because it directly determines patient safety, speed to market, cost of goods, and long‑term commercial success.
Early development teams evaluate scientific and engineering parameters together, not in silos. For small molecules; this includes (molecule stability, Solubility and Bioavailability, dose strength, solid‑state behaviour(pH, temp. light, moisture, stress oxd/redn. Compatibility), material variability, flow and content uniformity, especially critical for ultra or very low‑dose products. For biologics;, early focus is on process sensitivity, raw‑material variability, scale effects on CQAs, and robustness of upstream and downstream operations (batch or continuous mfg). In advanced drug‑delivery platforms, manufacturability hinges on early technology selection such as continuous processing, novel encapsulation, device–formulation integration, or aseptic fill‑finish—because these decisions drive CAPEX, scalability, and supply reliability.
The industry trend is clear: technology selection is a strategic decision, not a late technical one. Teams now align formulation, process, quality, regulatory and operations (Supply, CoGs) early to identify CQAs and link them to CMAs and CPPs using a risk‑based QbD framework. This enables realistic assessment of development effort, timelines, and investment needed to achieve right‑first‑time manufacturing.
What has accelerated this shift is the practical use of AI and in‑silico tools. Development teams increasingly use predictive models that mine prior internal process data and platform knowledge to forecast manufacturability risks early such as content uniformity failure in low‑dose solids, scale sensitivity in biologics, or yield loss in complex delivery systems, supply reliability and comparability. These tools guide smarter DoE design, faster technology down‑selection, and early mitigation strategies reducing rework, late surprises, and costly scale‑up failures.
In short, modern manufacturability is about making the right technology and design choices early, supported by data, predictive analytics, and cross‑functional alignment—so medicines can be delivered safely, reliably, consistently, and affordably, while protecting both patients and the business.
Early design decisions often lock in constraints that later affect yield, process robustness, and cost of goods. Could you share examples where seemingly small formulation or process assumptions during the discovery or preclinical stage later created significant manufacturing challenges—and what structured frameworks teams can adopt to anticipate such issues earlier?
For decades, discovery teams were rightly judged on getting a safe and effective molecule into humans as fast as possible. The hard lesson many organizations learned later is that manufacturability can’t be “fixed” at the end—early formulation and process choices quietly “lock in” constraints that show up much later as poor yield, fragile processes, high cost of goods, and quality failures that can trigger regulatory observations (e.g., FDA Form 483 inspectional observations are documented when investigators see objectionable conditions that may violate requirements).
Modern development trends now push teams toward Quality by Design (QbD): building quality into the product and process from the start, supported by quality risk management and lifecycle thinking. This aligns with the ICH Q8/Q9/Q10 family and related regulatory guidance emphasizing predefined objectives, process understanding, risk management, and control strategy.
Likewise, FDA’s lifecycle process validation approach explicitly begins with Stage 1: Process Design (i.e., you must design and understand the process before expecting consistent commercial performance).
Structure approach is that Teams can anticipate manufacturability risks early by adopting a Quality by Design–based structured framework that starts with a clear Quality Target Product Profile, identifies critical quality attributes, and systematically links them to material attributes and process parameters using formal risk‑management tools such as FMEA. Embedding stage‑gate manufacturability reviews, supported by early stability, scale‑relevant experiments, and digital modeling or data analytics, ensures that assumptions are tested before they harden into constraints. This lifecycle, risk‑based approach helps teams design products that are not only safe and effective, but also robust, scalable, and inspection‑ready from the outset.
Examples: “small” early assumptions that later create major commercial headaches
I) Solid form choice (salt / polymorph) assumed “equivalent”
At discovery scale, one crystalline form may look fine. At plant scale, the same form can behave very differently—changing filtration speed, drying time, flowability, compressibility, or stability. Result: scale‑up delays, batch variability, and re‑development of the drug substance route or downstream operations.
II) Particle size and powder flow assumed “manageable later” (oral solids)
A lab blend may be uniform, but at scale the same powder may segregate, stick to equipment, or flow inconsistently. That can cause content uniformity failures, weight variability, poor dissolution, and repeated deviations/investigations. If the root cause traces back to early material‑attribute choices, it becomes slow and expensive to correct.
III) Excipient compatibility assumed without enough stress testing
A seemingly minor formulation shortcut (e.g., “this excipient is common”) can later reveal incompatibilities—moisture‑driven degradation, impurity growth, color change, potency drift, or container/closure interactions. These issues often appear as stability failures and can cascade into supply disruptions and regulatory scrutiny.
IV) “We’ll concentrate it later” (biologics and injectables)
Early programs often use dilute solutions. Later, commercial presentations may require higher concentration for patient convenience. That shift can raise viscosity, aggregation risk, and filtration challenges—creating fill‑finish failures, longer cycle times, or added unit operations.
V) Sterile/lyophilized product cycle assumed “like the platform”
Platform assumptions help speed, but small differences (heat transfer, vial/stopper interactions, cake structure) can create long lyophilization cycles, variable residual moisture, reconstitution failures, or container closure integrity concerns.
As pharmaceutical processes become more complex—incorporating enabling formulations, continuous manufacturing concepts, and advanced delivery systems—how should development teams balance molecular performance with manufacturability considerations such as equipment compatibility, process scalability, and supply chain resilience?
As pharmaceutical technologies grow more complex, development teams should balance molecular performance and manufacturability by applying Quality by Design principles that evaluate formulation, process, equipment fit, and supply chain risks in parallel rather than sequentially. This means selecting molecules and delivery technologies that meet clinical goals within a well‑understood design space, while stress‑testing scalability, equipment compatibility, and sourcing robustness early using risk management, platform knowledge, and data‑driven tools. By integrating continuous manufacturing concepts, process analytical technologies, and digital modeling modelling into development, teams can optimize performance without over‑engineering solutions that later prove fragile or supply‑constrained, enabling products that are both clinically differentiated and reliably manufacturable at commercial scale
In your view, how should organizations practically integrate the principles of Quality by Design (QbD) into early-stage development so that critical quality attributes (CQAs) and critical process parameters (CPPs) are not only defined but also meaningfully linked to scalable manufacturing strategies?
As processes become more complex, development teams should balance molecular performance and manufacturability by co‑designing the molecule, formulation, and process from the outset using Quality by Design principles. This approach requires testing clinical performance goals in parallel with equipment compatibility, scalability, and sourcing risks, rather than deferring these questions to late development. By leveraging platform technologies, risk‑based decision making, and—where appropriate—continuous manufacturing and advanced process analytics, teams can select solutions that deliver the required therapeutic benefit while remaining robust, scalable, and resilient across the commercial supply chain
Many development programs ultimately transition from small-scale experimental setups to large-scale commercial equipment, often in brownfield manufacturing environments. What are the most common scale-up disconnects you observe between development laboratories and production facilities, and how can organizations structure cross-functional collaboration to reduce these risks?
Development teams should treat molecular performance and manufacturability as equal design inputs, not sequential trade‑offs. By applying Quality by Design from the outset—testing clinical performance alongside equipment fit, scale‑up behavior behaviour, and supply chain risks—teams can select formulations and processes that deliver therapeutic benefit and are robust at commercial scale. Early use of platform processes, risk‑based decision making, and advanced analytics or continuous manufacturing concepts ensures innovation reaches patients reliably, without late‑stage redesigns or supply disruptions.
From a regulatory and GMP perspective, how does early attention to manufacturability influence validation strategy, control strategy development, and inspection readiness—particularly when regulators increasingly expect stronger scientific justification for process design and lifecycle management?
From a regulatory and GMP perspective, early focus on manufacturability directly strengthens validation, control strategy, and inspection readiness by anchoring them in sound process design rather than late‑stage testing. Regulators now expect validation to start with scientifically justified process understanding (Stage 1 – Process Design) and continue through lifecycle monitoring, not as a one‑time exercise. When manufacturability is addressed early using QbD and risk‑based principles, control strategies are clearly linked to critical quality attributes, variability is proactively managed, and companies can confidently defend their process choices during inspections with data‑driven rationale—meeting growing expectations for lifecycle management and sustained state of control.
Looking ahead, digital tools such as process modelling, digital twins, PAT frameworks, and AI-driven development platforms are increasingly entering pharmaceutical R&D. How do you see these technologies reshaping the way manufacturability is evaluated during early development, and what capabilities organizations should begin building today to remain competitive in the next decade?
Early attention to manufacturability, benchmarked against current global pharma practices, enables companies to meet evolving regulatory expectations by designing validation and control strategies around real process understanding rather than retrospective fixes. Leading manufacturers now integrate QbD, PAT, and digital MES platforms with hybrid model (e.g., Mechanistic models – Mass balance, Mass transfer, heat transfer/kinetics – kinematic similarity + dynamic similarity via PAS‑X and with AI‑driven predictive analytics (AI tools machine learning layers such as Neural Networks NN’s NNs, Gaussian Processes, Random Forests) from development through commercial production, enabling real‑time control, continuous process verification, and proactive risk mitigation for every batch. This lifecycle, data‑driven approach allows firms to scientifically justify process design decisions, demonstrate a sustained state of control during inspections, and deliver consistent, high‑quality medicines at scale now considered the gold standard by regulators worldwide.
Disclaimer: The views and opinions expressed in this editorial are those of the interviewees and are based on their professional experience in pharmaceutical engineering and sterile manufacturing. They do not necessarily reflect the official views, policies, or positions of Hello Pharma, its management, or its affiliates. Hello Pharma does not endorse or take responsibility for any specific technical, commercial, or regulatory interpretations presented in this article. Readers are encouraged to independently evaluate the information shared, review applicable regulatory guidance, and rely on their own experience, expertise, and professional judgment before making decisions related to equipment selection, system design, validation strategy, or regulatory compliance.
