Pharmaceutical Technology Is Now PharmTech: A Sharper Name for a Smarter Industry

Navigating FDA Shakeups and Obesity Drug Wars

Behind the Headlines Episode 28

Ask the Expert

Outsourcing Partnerships Fuel Global Biopharma Discovery and Scale-Up

Drug Digest February 2026 

How It Helps Avoid Facility and Equipment Problems

A “Plan, Prevent, Prove” Approach

Ryan Kelly is the Interim Chief Executive Officer and Senior Director of Supply Chain Security & Brand Protection at Rx-360, an international, member-driven pharmaceutical supply chain consortium focused on patient safety, quality, and supply chain integrity. In this role, he leads cross-industry initiatives spanning joint audits, supply chain security, and quality oversight, and works closely with regulators, manufacturers, suppliers, and logistics partners to strengthen risk management across global supply networks.

Ryan Kelly, Rx360, explains how third-party audits and integrated quality-supply chain strategies protect pharma supply chains and patient safety.

Ryan Kelly on Smarter Pharma Supply Chain Audits

Ryan Kelly, interim CEO and senior director of supply chain security and brand protection at Rx360, discusses how the pharmaceutical industry is rethinking the relationship between quality and supply chain management in Part 1 of a two-part interview with 

A key part Rx360’s work involves third-party auditing, which Kelly argues offers a distinct advantage over internal audits. As he explains, "Sometimes if it's your own internal organization, it may be a little challenging to call a baby ugly." By coming in as an outside party, Rx360 can surface issues that might otherwise go unaddressed, remediate them through CAPA, and ultimately reduce how often audits need to happen. 

Kelly also spoke to a broader cultural shift underway in the industry. Traditionally, quality and supply chain have operated as separate functions, with quality focused on manufacturing and batch release and supply chain focused on logistics and execution. Rx360 is working to break down that divide. As Kelly states, "The way that I move product through the supply chain, maintaining temperature, maintaining visibility, maintaining control of that product all through the supply chain, that's gonna impact quality."

On the audit side, Kelly flagged a common blind spot: companies that believe they have supplier redundancy but actually don't. If a primary and secondary supplier are drawing from the same source, a single disruption can expose the entire supply chain. His advice is to map your supply chain thoroughly, understand every tier of your supplier base, and assess geographic, regional, financial, and logistical risks. Quality, he emphasizes, can't be added after the fact, it has to be designed in from the start.

Videos

Portfolio Manager
Intensified Downstream Systems
Sartorius

Global Technical Product Specialist
Thermo Fisher Scientific

At INTERPHEX 2026, David Chau and Stuart Tindal discuss automation, single-use durability challenges, and the shift to predictive control in continuous bioprocessing.

6393806285112

, David Chau of Thermo Fisher and Dr. Stuart Tindal of Sartorius, both members of the Bioprocess Systems Alliance (BPSA), discuss the intersection of automation, single-use technologies, and continuous bioprocessing, as well as the challenges manufacturers face as the industry evolves.

Chau and Tindal agree that automation has moved from a nice-to-have to an essential requirement in modern bioprocessing. As operations become increasingly data-driven and interconnected, robust automation is indispensable for maintaining process control and consistency. Chau emphasizes that successful implementation begins with deep process understanding—knowing the inputs, outputs, and parameters that need to be controlled—before attempting to automate, whether building a continuous process from scratch or retrofitting an existing one.

A central tension the speakers address is the durability of single-use technologies in continuous manufacturing contexts. While single-use systems offer speed and flexibility that are well-suited to R&D and pilot-scale work, those same characteristics become liabilities when processes must run for weeks or months at commercial scale. The industry, vendors, and end users alike must confront the material and mechanical limitations of these technologies and innovate accordingly.

On process control, both Tindal and Chau advocate moving beyond conventional PID controllers toward predictive and model-based control strategies. For complex or labile molecules with tight critical quality attributes, advanced control loops that are fast, adaptive, and predictive are essential. Tindal frames says rather than simply reacting to set points, manufacturers need to actively steer processes along a continuous trajectory, keeping within an ever-narrowing window of acceptable parameters from start to finish.

How Predictive Control Is Reshaping Continuous Bioprocessing

PharmTech Weekly Roundup-April 24, 2026

This week’s stories reflect the push to modernize facilities and manufacturing processes, the mounting impact of trade policy on the supply chain, and the persistent challenge of getting the right drugs to patients reliably.

ISPE recognized six facilities across the year’s 

, with winners spanning synthetic DNA manufacturing, plasma fractionation, and cell therapy—recognizing the global standard for what pharmaceutical facilities can achieve.

key first-year goals under its 2025 Roadmap to Reducing Animal Testing,

 advancing new approach methodologies—including computational modeling and organ-on-a-chip systems—as more scientifically accurate alternatives to animal studies.

The US Customs and Border Protection launched its 

on April 20, 2026, providing a way for pharmaceutical importers to reclaim funds following a February Supreme Court rebuke of executive tariff authority.

Asahi Kasei has completed its acquisition of German biotech Aicuris,

 adding three antiviral assets targeting severe infections in immunocompromised patients—a move that deepens its specialty pharma platform in transplantation and infectious disease.

Our editors were at INTERPHEX in New York this week, attending conference sessions and interviewing experts. One of the sessions discussed how the next 

must balance automation, digital integration, and sustainability while meeting regulatory expectations—requiring manufacturers to rethink both design and operational strategies.

Todd Vaughn and Christa Myers of CRB Group

 about how contamination control requirements and revised GMP Annex 1 standards are driving robotics adoption in aseptic manufacturing, with fully automated, gloveless isolator systems moving from emerging technology to operational expectation.

Experts from Merck, AstraZeneca, J&J, Regeneron, and Basetwo AI expressed that, despite broad interest in AI across drug discovery and manufacturing, there are 

—with data fragmentation, regulatory uncertainty, and workforce skills gaps remaining the primary barriers to scale.

Optimized CDMO facility design is increasingly defined by flexibility—modular layouts, multi-product capability, and digital infrastructure—allowing contract manufacturers to onboard new programs faster while maintaining GMP compliance, according to 

, who spoke with PharmTech ahead of his INTERPHEX quick fire session.

Rohan Kumar, laboratory analyst at CS Analytical 

highlighted that while interest in sustainable pharmaceutical packaging is growing, significant gaps remain between industry ambitions and commercially viable, regulatory-compliant solutions.

episode discusses how combination products present unique complaint investigation challenges because responsibility often spans multiple components and regulatory frameworks—making cross-functional coordination and clear root-cause methodology essential for compliance.

The industry is under pressure to modernize faster, build supply chains that can absorb disruption, and close the gap between technological ambition and operational reality. Stay current with industry news and trends at PharmTech.com.

The industry is feeling the push to modernize facilities and manufacturing processes. 

Director of Digital Solutions
Endress+Hauser

Industry Manager - Life Sciences
Endress+Hauser

Director 
Life Sciences Global Accounts
Rockwell Automation

At INTERPHEX 2026, Bethany Silva, Jason Pennington, and Mel Radford discuss how smart sensors, connectivity, and advanced analytics are enabling predictive, insight-driven pharma manufacturing.

Smart Instrumentation's Role in Pharma Manufacturing

, Jason Pennington, Director of Digital Solutions, Endress+Hauser; Bethany Silva, Industry Manager - Life Sciences, Endress+Hauser; and Mel Radford, Director - Life Sciences Global Accounts, Rockwell Automation, discuss with 

 how smart instrumentation, connectivity, and advanced analytics are transforming pharmaceutical manufacturing operations.

The panel emphasizes that while data from sensors is abundant, its value depends entirely on context. Simply collecting data is insufficient; manufacturers need a unified data model and a unified namespace that captures not just what an instrument measured, but when, how, and where, so that information can be meaningfully integrated across systems to drive real process insights.

Connectivity is identified as a foundational prerequisite, enabling data trapped in devices to flow into broader systems. However, the speakers stress that technology alone is not enough and that successful adoption requires change management and people-first thinking, since even the best technology fails without organizational buy-in.

On the instrumentation side, the conversation moves beyond traditional measurements like flow, pressure, and temperature toward advanced PAT tools such as Raman spectroscopy, which can provide molecular-level insight into cell growth and biological processes. This richer analytical data is essential for feeding and optimizing AI models and digital twins, reducing the number of design-of-experiment cycles needed in process development.

Perhaps most significantly, the panel describes a shift in manufacturing philosophy away from simple alarm thresholds toward predictive, trend-based process control. Rather than reacting when a parameter goes out of range, manufacturers can now anticipate deviations and intervene proactively, moving toward outcome-driven, policy-based process management.

Strategic Advsor and Independent Board Director

Bioprocessing veteran Bryan Poltilove discusses how biopharma manufacturing is shifting toward distributed, point-of-care, just-in-time models without compromising quality standards.

 Bryan Poltilove on Evolving Bioprocessing Manufacturing Paradigms

Bryan Poltilove, a strategic advisor and independent board director with over two decades of experience in bioprocessing and advanced therapy manufacturing, shares his perspective on how the industry is shifting in Part 1 of a two-part interview with 

Poltilove emphasizes that while GMP manufacturing and validation remain non-negotiable table stakes, the broader manufacturing landscape is undergoing a significant transformation. He draws a parallel to the automotive industry's shift toward just-in-time production in the 1980s, arguing that biopharma is undergoing a similar paradigm shift, moving away from large, centralized inventory models toward more flexible, distributed approaches.

Key to this evolution is the growth of point-of-care and place-of-care manufacturing, particularly in cell therapy, where modular clean rooms and specialized cell processing technologies are enabling production at hospital settings. Poltilove stresses that this shift must not come at the expense of reproducibility, quality standards, or regulatory compliance. The challenge lies in achieving greater logistical flexibility while preserving those fundamentals.

As a concrete example of innovation in this space, Poltilove highlights Nucleus Biologics, whose approach to cell culture media manufacturing he describes as a "Nespresso/Keurig-type model,” enabling on-demand, point-of-care reagent production rather than reliance on large pre-made lots. Having spent nearly 20 years in the cell culture media space, including leading portfolios of Thermo Fisher’s Gibco products for more than a decade, Poltilove called the Nucleus Biologics technology one of the most potentially revolutionary he has encountered throughout his career.

Avi has 20 years of industry experience in gene therapy, viral vectors, and vaccines. He is currently the President of SK pharmteco Cell and Gene North America managing the business unit. Previously he worked at Novartis Gene Therapies where he supported a pipeline of 15+ gene therapy products in development from preclinical to Phase III development and led the development and implementation of 4 gene therapy manufacturing processes across 4 CGMP and 3 laboratory sites. Prior to his role at SK pharmteco, Avi was the Chief Technical Officer at the Center for Breakthrough Medicines. His prior positions include Head of Technical Development at Novartis Gene Therapies, Director of Manufacturing Science & Technology at Avexis, Inc., and Product lead at Seqirus. Avi holds an MS in Biology from Adelphi University and an MBA from the University of North Carolina Kenan-Flagler Business School.

Avi Nandi, SK Pharmteco, explains how CDMOs simplify cell & gene therapy supply chains by managing lead times, consistency, and material expiry.

Avi Nandi on How CDMOs Are Simplifying Cell & Gene Supply Chains

Avi Nandi, SK Pharmteco discusses the company's work as a CDMO and the challenges of managing media and buffer supply chains for cell and gene therapy manufacturers in Part 1 of a two-part interview with 

Nandi spoke at a product showcase focused on bringing media and buffer manufacturing technology on-site to simplify supply chain logistics for both manufacturers and service providers.

Nandi outlines 3 key supply chain concerns his team navigates: procurement lead times, lot-to-lot consistency, and material expiry. Most of SK Pharmteco's clients are in the early to late-stage clinical phase, "From the time of identification to the time of use tends to be short, just because the demand for clinical supply is the priority for these clients." This compressed timeline makes it difficult to give suppliers adequate lead time and leaves little room for materials to expire before use.

Consistency across batches is equally critical. "Ensuring that one lot looks the same, in a reproducible manner, is also critical, particularly as your product scales through clinical and then ultimately commercial launch,” Nandi says.

Nandi also addresses how CDMOs like SK Pharmteco are positioned to help de-risk supply chains. Because they serve a broad and diverse client base, CDMOs have stronger visibility into demand patterns than individual innovators and can manage inventory more strategically. However, he notes that the investment burden is shared. Earlier stage products, with their unique materials and speed-first priorities, present more risk, while commercially staged products open the door to more robust inventory and supplier redundancy strategies.

Avi Nandi, SK Pharmteco, explains at INTERPHEX 2026 how CDMOs simplify cell & gene therapy supply chains by managing lead times, consistency, and material expiry.

At INTERPHEX 2026, MasterControl CEO Dave Edwards details how AI tools are boosting efficiency and compliance in pharma quality and manufacturing without replacing human oversight.

Dave Edwards on Why AI Readiness in Pharma Starts With Data and a Mindset Shift

Dave Edwards, CEO, MasterControl speaks with 

about how AI is reshaping quality and manufacturing operations in regulated pharmaceutical environments. Edwards highlights that disconnected systems remain a core industry challenge, creating data silos that prevent companies from gaining a complete picture of quality and manufacturing performance, and sometimes driving workers back to paper-based processes.

He emphasizes that AI readiness begins with having data in digital systems, since AI cannot function without accessible, structured information. Beyond infrastructure, he stresses that an innovative mindset is equally essential, particularly given the caution warranted in regulated environments. MasterControl's approach keeps customer data contained within its own technology stack, preventing it from being shared across customers or used to train external models.

Edwards outlines several AI tools, including the Quality Event Summarizer, which provides operators with an at-a-glance overview of a quality event directly within their workflow screen, helping them work more efficiently without removing human judgment from the compliance process. A regulatory chat feature allows users to run operating procedures through an AI tool to identify potential compliance gaps. The company is also building a tool to convert paper manufacturing templates into digital formats operators can follow in real time, accelerating the paper-to-digital transition.

Edwards frames AI as an enabler and enhancer rather than a replacement for human workers, arguing that regulated environments will continue to require people but that AI can help them work smarter and solve problems more effectively.

Maria Batalha is part of the Consultancy Services in Product Life Cycle Management at ValGenesis as a Data Science Specialist. She joined ValGenesis in 2021 and was involved in the design of digital solutions for industry 4.0 that included advanced MVDA techniques, prototyping activities with R and Shiny framework and machine learning modelling in python. As part of ValGenesis Consulting Services team, Maria has focused her work into the Continued Process Verification framework and implementation, tech transfer activities and process intensification. Maria has a master's in Bioengineering by Universidade Técnica de Lisboa, a Post Graduation in Enterprise Data Science and Analytics by Universidade NOVA IMS Lisboa and 6 years of experience in industry projects mainly in the field of medical devices and consultancy

Maria Batalha Discusses how her team at Valgenesis assited a comapny in centralizing drug substance data, standardize processes, and unlock powerful AI-driven insights.

Maria Batalha on How Digital Tools Transform a Drug Substance Lifecycle


Maria Batalha, Senior Project Lead, Delivery, Valgenesis, shares her perspective on her "Digitally-Enabled Drug Substance Lifecycle: A Case Study in Industry 4.0" panel in Part 1 of a two-part interview with 

Batalha presents a real-world example of how digital transformation can modernize pharmaceutical manufacturing. The panel explores how centralizing critical process information, from R&D through commercial manufacturing, into a unified, FAIR-compliant digital repository can address the longstanding challenge of fragmented data spread across standalone spreadsheets and siloed teams. By leveraging AI-enabled analytics and visualization tools, the approach supports deeper process understanding, regulatory compliance, and future technology transfers, all in alignment with Industry 4.0 principles.

Batalha describes a consulting engagement in which her team at Valgenesis worked closely alongside a pharmaceutical client to guide them through digitizing their drug substance lifecycle data. Crucially, the client retained full ownership of their documents throughout, Maria's team served as advisors rather than data handlers. As she explains, "We were just guiding them throughout the process on what they needed to do, what type of things they needed to be aware of while they are handling these documents."

One significant challenge the team encountered was inconsistent data terminology across different internal groups. Batalha’s team worked to help the client establish standardized naming conventions, ensuring that variables remained traceable and unambiguous across the entire product lifecycle.

The project ultimately delivered a meaningful payoff. When the client was finally able to explore their structured data within the Valgenesis platform, they uncovered previously unknown causes behind rejected batches from their engineering process. Batalha notes that the moment was particularly impactful,  "Just by looking at the data, several clicks, they were able to do some kind of type of analysis, it was very powerful for them at that point."

Batalha also acknowledges the common human challenge of digital adoption, noting that initial resistance gave way to genuine enthusiasm once the client saw the platform's capabilities in action, a dynamic she describes as typical whenever organizations transition to new digital solutions.

Maria Batalha, Valgenesis, discusses how her team assited a comapny in centralizing drug substance data, standardize processes, and unlock powerful AI-driven insights.

Pramote Cholayudth previously was the managing director of the Professional Conference Center, Ltd. He currently is a validation consultant to Biolab Co., Ltd., in Thailand. He is the founder and manager of PM consult, cpramote2000@yahoo.com.

Worker operating pharma capsule filling machine | Image Credit: ©Kadmy - stock.adobe.com

Submitted: January 8, 2026
Accepted: March 4, 2026

Net fill weight in capsule in-process control (IPC) is often calculated by subtracting a fixed average shell weight from gross weight. Because individual shell weights vary, this computation introduces bias and artificial variance. This work justifies using T- and F-tests to evaluate whether the computed (biased) net fill weight remains acceptable for IPC decision-making during capsule filling operations.

A simulation-based approach was applied to generate empirical acceptance limits for T (mean difference) and F (variance ratio) statistics by modeling fill-weight and shell-weight distributions. These limits replace conventional critical values and reflect practical process variability. The method enables determination of equivalence between idealized and computed net fill weights in terms of mean and variance and provides a GMP-aligned statistical justification for continued IPC use of computed net weight measurements.

In capsule filling operations, in-process control (IPC) of net fill weight is a critical element for ensuring dosage accuracy and maintaining compliance with good manufacturing practice (GMP) requirements. In routine manufacturing, net fill weight is commonly computed by subtracting a fixed average empty-capsule shell weight from the measured gross capsule weight. While operationally convenient, this approach assumes uniform shell weight and does not account for inherent shell-to-shell variability.

In practice, individual capsule shells exhibit measurable weight variation. When a fixed shell-weight average is applied to all capsules, this variability introduces bias and artificial dispersion into IPC net fill-weight data. As illustrated in 

, shell-weight variability alone can widen the observed net fill-weight distribution and reduce calculated process capability indices (CpK), even when the true fill-weight process remains unchanged. This effect complicates interpretation of IPC trends and may lead to conservative or misleading conclusions regarding process control.

From a statistical perspective, the impact of shell-weight variability raises a fundamental question: can the computed (biased) net fill weight still be considered equivalent to the idealized net fill weight for IPC purposes? In pharmaceutical process validation (PV) and continued process verification (CPV), equivalence is often assessed in terms of central tendency (mean) and dispersion (variance) rather than point-by-point agreement. Classical statistical tools for this purpose are the T-test, which evaluates equality of means, and the F-test, which evaluates equality of variances. These tests have been widely applied in pharmaceutical validation contexts to justify interchangeability of analytical or process-related datasets, provided that appropriate acceptance criteria are established.

However, direct application of theoretical critical values for T- and F-tests may not adequately reflect realistic manufacturing variability, sampling uncertainty, or combined decision risk when both tests are applied simultaneously. Prior work by the author has demonstrated that simulation-based approaches can be used to derive empirical acceptance limits for T- and F-statistics that better represent practical process behavior and joint confidence requirements in PV and CPV applications.

Accordingly, this study focuses on justifying the use of T- and F-test–based equivalence evaluation for biased net fill-weight data used in IPC during capsule filling operations. A simulation-based framework is employed to establish empirical acceptance limits for both statistics, enabling equivalence decisions to be made on the basis of realistic process and measurement variability rather than solely theoretical assumptions.

The objective of this article is to provide such a statistically defensible and GMP-aligned justification for continued IPC reliance on computed net fill weight when equivalence in mean and variance can be demonstrated. In practice, samples evaluated need to be collected separately during PV and/or CPV activities but not taken from routine IPC trending data. For each sample set (eg, n = 30 during PV or n = 10-20 during CPV), gross capsule weight was measured and the net fill weight was computed by subtracting a fixed average empty capsule shell weight. The computed net fill weights were then compared with reference net fill weights obtained from direct weighing of the capsule contents for the same units.

Effect of capsule shell-weight variability on observed net fill-weight distributions. Figure 1

 summarizes the effect of empty capsule shell-weight variability on the observed distribution of net fill weights under identical mean fill conditions. In all cases, the mean net fill weight is centered at 100% of target, indicating that shell-weight variability does not introduce a systematic shift in central tendency. However, the presence of shell-weight variability results in a clear widening of the observed net fill-weight distribution.

Figure 1. Normal Distributions of Idealized and Computed Net Fill Weights Illustrating Variance Inflation Caused by Empty Capsule Shell-Weight Variability Under Identical Mean Fill Conditions. Figure courtesy of the author.

 presents Monte Carlo–simulated normal distributions for idealized and computed net fill weights. When shell-weight variability is absent (idealized condition), the simulated net fill-weight distribution exhibits a standard deviation (SD) of 2.38% of target. Introduction of shell-weight variability through computation using a fixed average shell weight increases the SD to 2.56% of target. This variance inflation occurs despite identical mean fill weight and identical underlying fill-weight process assumptions.

 illustrates the same variance inflation effect using idealized normal distributions. In this conceptual comparison, the only difference between distributions is the magnitude of the standard deviation, which increases from 2.38% to 2.56% of target when shell-weight variability is incorporated. This idealized illustration confirms that the observed widening of the distribution arises solely from shell-weight variability and not from changes in the fill-weight process itself. This variance inflation is accompanied by a reduction in calculated process capability, with CpK decreasing from 1.05 under idealized conditions to 0.98 under computed conditions.

 demonstrates that shell-weight variability alone can inflate observed net fill-weight variance and adversely affect IPC-based capability metrics, even when the true fill-weight process remains unchanged.

Supplementary Background for Figures 1a and 1b

The shell-weight variability range observed in 

 arises from the outcome of Monte Carlo simulation (N = 2.4 million). Quality unit data indicated that capsule size #2 (used for a nominal fill weight of 315 mg per capsule) has a nominal empty-shell weight of approximately 62 mg with a SD of 2.99 mg. After normalization, a simulation with mean zero and SD of 2.99 mg was performed. The resulting minimum and maximum simulated deviations were −15.13 mg and +16.76 mg, corresponding to −4.80% and +5.32% of target fill weight, respectively, as reported in 

Using the Extreme Value (EV) approximation method,

 expected extrema of a normally distributed sample were estimated as:

where ( n = 2.4x10^6). With m = 0 and s = 2.99 mg, this yields expected extreme deviations of approximately ±16.21 mg, corresponding to ±5.15% of target fill weight. The close agreement between the observed simulation outcomes and the theoretical approximation supports the robustness of the simulated shell-weight variability range.

Methodology for establishing F- and T- test limits

 summarize the definitions, statistical formulae, and evaluation conditions (eg, sample sizes, paired datasets, and acceptance limits) for the F- and T-tests applied therein. In the present study, a Monte Carlo simulation framework (n = 30) was employed to generate biased net fill-weight data by incorporating a controlled variability component, with the target fill weight normalized to 100% of nominal.

Table 1: Statistical Definitions and Hypotheses for F- and T-tests

Table 2: F and T Formulae and Limits: Condition: n

 demonstrate that theoretical one-sided critical limits for F- and T-tests closely match empirically observed limits obtained by Monte Carlo simulation, thereby validating the use of joint-confidence–adjusted theoretical thresholds for PV and CPV equivalence assessments. 

 demonstrates the critical F and T values for various sample sizes.

Figure 2: F distributions (Figure 2a – left) and T Distributions (Figure 2b – right). Figure courtesy of the author.

Simulation Study for Justification of 95% Joint Confidence Interval (95% JCI)

The justification for applying a 95% joint confidence interval (95% JCI) rather than a conventional 95% confidence interval (95% CI) was evaluated using a Monte Carlo simulation study. A custom simulation program implemented in Microsoft Excel (Visual Basic for Applications, VBA) was used to generate paired datasets (n₁ = n₂ = 30) drawn simultaneously from normal populations with identical mean (μ = 100) and standard deviation (σ = 5). These values were selected for illustrative purposes; alternative parameter values lead to equivalent conclusions when appropriately justified.

For each Monte Carlo iteration, both F- and T-statistics were computed from the paired datasets. A total of 2.4 million iterations were generated to characterize the empirical distributions of the F- and T-statistics. The simulated distributions were compared with their corresponding theoretical distributions, as illustrated in 

, using acceptance limits initially defined according to the conventional 95% confidence interval.

Under independent application of conventional 95% CI limits to both F- and T-tests, a separate simulation study showed that the probability of simultaneous acceptance of both tests is approximately 90.24% (mean of 90.23%, 90.22%, and 90.28% across three independent simulations of 150,000 iterations each), in close agreement with the theoretical joint probability of 90.25%. This result demonstrates that independent 95% CI application does not preserve an overall 95% confidence level when multiple statistical criteria are applied concurrently.

To restore an overall joint confidence level of 95%, adjusted one-sided critical limits for the F- and T-tests were derived, as summarized in 

. When these joint-confidence–adjusted limits were applied, the simulated probability of simultaneous acceptance increased to 94.98% (mean of 94.94%, 95.06%, and 94.95% across three independent simulations of 150,000 iterations each), confirming close agreement between empirical simulation results and theoretical expectations.

 illustrates simulated F- and T-statistic values generated under conditions of n₁ = n₂ = 20, with target fill weight μ = 315 mg and process standard deviation σ = 3.5 mg, representing a worst-case scenario relative to the σ = 2.99 mg condition discussed above. The simulated F and T values correspond to 20 hypothetical routine batches evaluated under CPV-like sampling conditions. One simulated lot exhibits an F value (3.35) exceeding the joint-confidence–adjusted limit of 2.89, representing an isolated variance excursion in the absence of a corresponding mean shift. 

 demonstrates that occasional variance-based limit exceedances are statistically expected under CPV sampling conditions and should be interpreted within a joint mean–variance framework rather than as isolated failures.

Figure 3: F and T Charts (Simulated Data): n1 = n2 = 20. Figure courtesy of the author.

Implications for IPC Interpretation and Process Capability Assessment

When net fill weight is computed using a fixed average capsule shell weight, inherent shell-to-shell variability is propagated into IPC data, inflating the observed variance without altering the underlying fill-weight process. This variance inflation can reduce calculated CpK values and complicate interpretation of control charts and trends, as illustrated in 

. Importantly, this effect represents a measurement-induced artifact rather than a loss of filling accuracy and, if unrecognized, may lead to unnecessary investigations or overly conservative process decisions.

Justification for Simulation-based Statistical Evaluation

The results demonstrate that direct application of theoretical critical values for T- and F-tests may not adequately reflect IPC data derived from computed net fill weights. Shell-weight variability can inflate variance-related statistics without affecting the mean (

), such that reliance on theoretical F-test limits alone may result in over-rejection of otherwise acceptable processes. Likewise, interpretation of T-test outcomes may be incomplete when mean and variance are evaluated jointly. A simulation-based framework enables empirical acceptance limits for T- and F-statistics to be established under realistic process and measurement assumptions, supporting statistically justified equivalence decisions in PV and CPV.

This analysis focuses on capsule shell-weight variability as a primary source of variance inflation; other measurement-related factors—including balance resolution, environmental influences, and sampling strategy—may similarly affect IPC data and warrant further investigation. The proposed approach does not replace regulatory acceptance criteria but provides a statistically justified framework for interpreting IPC data within existing GMP requirements. Integration of simulation-based acceptance limits with established control strategies may enhance robustness while maintaining regulatory compliance.

Interpretation of Critical F- and T-Values Across Sample Sizes

 summarizes theoretical critical values for the F-test and T-test across sample sizes commonly encountered in IPC applications. For each sample size (n), degrees of freedom are defined as df₁ = df₂ = n − 1 for the F-test and df(T) = 2n − 2 for the two-sample T-test. The reported limits correspond to nominal 95% joint confidence requirements under classical assumptions.

As expected, both F- and T-critical values decrease with increasing sample size, reflecting reduced sampling uncertainty at larger n. For smaller sample sizes (eg, n = 10 – 20), relatively high F-critical values indicate greater tolerance for variance differences, whereas T-critical values remain comparatively stable. At larger sample sizes (eg, n ≥ 50), F-critical values approach unity, increasing sensitivity to variance inflation effects.

Importantly, these theoretical critical values are derived independently for each statistic and do not account for joint decision risk when T- and F-tests are applied simultaneously. As demonstrated in this study, shell-weight variability can disproportionately affect variance-related statistics—particularly at larger sample sizes—thereby increasing the likelihood of F-test rejection even when mean equivalence is maintained. This observation reinforces the need for simulation-based derivation of empirical acceptance limits that reflect realistic process and measurement variability, especially in PV and CPV contexts.

Notably, the statistical evaluation is based on dedicated PV and CPV sample sets designed for paired comparison of computed and reference net fill weights, rather than routine IPC trending data, reinforcing the relevance and validity of the observed variance effects under controlled manufacturing conditions.

Capsule filling operations frequently rely on computed net fill weights, obtained by subtracting a fixed average empty-capsule shell weight from the measured gross capsule weight. The present study demonstrates that this computational approach can introduce a measurable bias and propagate inherent shell-weight variability directly into the observed net fill-weight distribution. As a result, the variance of the biased dataset is artificially inflated, even when the underlying fill-weight process remains accurate, stable, and unchanged.

The analysis confirms that such variance inflation can reduce calculated process capability indices and complicate interpretation of in-process control data if not properly recognized. Monte Carlo simulation and idealized distribution comparisons show that the observed dispersion effect arises from the measurement and calculation model itself, rather than from process instability or loss of dosage accuracy.

Accordingly, this article provides explicit justification for using T- and F-tests as structured evaluation tools for biased net fill-weight data when dedicated PV and CPV samples are employed. When both tests are applied simultaneously, equivalence in mean and variance can be objectively assessed, supporting the statistical interchangeability of idealized and biased net fill-weight datasets. A simulation-based statistical framework offers a rational basis for establishing empirical acceptance limits under realistic manufacturing conditions, thereby enabling GMP-compliant process performance monitoring and reliable IPC-based decision-making during routine capsule filling operations.

Cholayudth P. Full tolerance coverage method for assessing uniformity of dosage units with large sample sizes. 

https://www.pharmtech.com/view/full-tolerance-coverage-method-for-assessing-uniformity-of-dosage-units-with-large-sample-sizes

 Columbia University Press, New York, 1958.

Sec. 1.3.5.9: F-test for equality of two variances, www.itl.nist.gov.

Sec. 1.3.5.3: Two-sample T-test for equal means, www.itl.nist.gov.

Pramote Cholayudth, cpramote2000@yahoo.com, is the founder and manager of PM Consult. He is an industrial pharmacist with more than 40 years of experience. He is a guest speaker on process validation to industrial pharmaceutical scientists organized by Thailand’s FDA.

Articles

Computed capsule net fill weight derived by subtracting a fixed average shell weight from gross weight can distort IPC datasets because shell-to-shell weight variability propagates into the calculation. This inflates observed variance without shifting the mean, depressing CpK and potentially triggering unnecessary investigations. A Monte Carlo framework (up to 2.4 million iterations) supports using paired T- and F-tests to assess equivalence of computed versus reference net weights, but with empirically derived, joint-confidence–adjusted acceptance limits. The approach provides a GMP-aligned justification for continued IPC use during PV/CPV.

This article provides a statistically defensible and GMP-aligned justification for continued IPC reliance on computed net fill weight when equivalence in mean and variance can be demonstrated. 

Justification of T- and F-Test Evaluation for Biased Net Fill Weight as Part of In-Process Control Data During the Capsule Filling Operation

Susan J. Schniepp is a fellow at Regulatory Compliance Associates, Inc. and a member of Pharmaceutical Technology's Editorial Advisory Board.

Siegfried Schmitt, PhD, is Vice President Technical at Parexel International, Siegfried.Schmitt@parexel.com.

Ask the Expert: Investigating Patient Complaints About Combination Products

Welcome to the Ask the Expert video series. This series is an extension of the PharmTech 

Group’s long-running print column in which industry experts answer common quality and regulatory questions from the industry. Have a question you would like answered? Send it to 

, and it may appear in a future episode or print column.

In this episode of Ask the Expert, Susan J. Schniepp, distinguished fellow at Nelson Labs, and Siegfried Schmitt, PhD, vice president, Technical at Parexel, address the complexities of investigating patient complaints regarding pre-filled syringe (PFS) and combination products. Schniepp and Schmitt make it clear; for manufacturers who purchase commercial syringes for their drug products, the marketing authorization holder (MAH) remains ultimately responsible for product performance, regardless of their level of control over the component manufacturer.

Investigating these types of events comes with complications, such as the lack of physical evidence (eg, returned devices, photos, or videos) when products are self-administered at home. While the rise of home-administered medications like GLP-1s has tempted some firms to cite “human error” as a root cause, manufacturers must ensure devices are “fit for purpose” across diverse populations, including the elderly and those with disabilities. If real-world evidence reveals a performance gap, it may indicate that initial usability testing may not have been sufficiently varied or robust.

There are also limitations with traditional corrective and preventive actions and recall procedures for these types of complaints. Identifying specific patients for recalls is often impossible once a product is dispensed, and privacy laws, such as HIPAA in the United States, can hinder a manufacturer’s ability to gather critical patient-specific investigation data. Furthermore, responding to patient complaints requires different language than communicating with healthcare professionals to ensure the investigation's logic is understood.

To manage these risks, the experts stress the importance of highly granular quality agreements. These agreements must move beyond simple checklists to explicitly define which party takes the lead in investigations, preventing a “blame culture” between drug manufacturers and component suppliers. Finally, companies must regularly revisit complaint procedures to adapt to evolving business environments, such as direct-to-patient shipping, to ensure they can effectively defend their “effectiveness checks” during regulatory inspections.

Watch the video to find out how Sue and Siegfried answer the question, “My company got a complaint from a patient that their prefilled syringe failed to inject. We make the drug product and purchase the syringe. How do we investigate a problem with the commercial syringe component that we used?”

Susan J. Schniepp is distinguished fellow at Nelson Labs and a member of 

Siegfried Schmitt, PhD, is vice president, Technical at Parexel and a member of 

Susan J. Schniepp, distinguished fellow at Nelson Labs, and Siegfried Schmitt, PhD, vice president, Technical at Parexel, discuss how companies can investigate patient complaints involving prefilled syringes and combination products.

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