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How It Helps Avoid Facility and Equipment Problems

A “Plan, Prevent, Prove” Approach

Chris Spivey is director of Industry Relations and Strategic Partnership, MJH Life Sciences.

Felicity Thomas is the former Associate Editorial Director of Pharmaceutical Technology, Pharmaceutical Technology Europe, BioPharm International.

Throughout many industries, continuous manufacturing has proven to be advantageous when compared with traditional batch processing, providing a faster, cheaper, and more flexible option. Within the pharmaceutical industry, there has been some adoption of continuous manufacturing processes; however, a fully end-to-end continuous pharmaceutical process is still challenging (1).

For CONTINUUS Pharmaceuticals—a spin out company from a multi-year collaboration between MIT and Novartis—producing drugs effectively and at a lower cost has been the primary mission. To learn more about the company and how it is enabling end-to-end continuous manufacturing, 

 spoke with the company’s senior marketing manager, Leonardo Vincenzi, at CPHI Barcelona 2023.

"We specialize in process development,” explained Vincenzi. “We have developed, over the years, a plethora of enabling technologies to allow the continuous manufacturing of pharmaceuticals.”

Generally dealing with large pharmaceutical companies, Vincenzi pointed out that CONTINUUS Pharmaceuticals provides process development technology that allows their customers to convert their batch manufacturing systems into continuous ones.

Recently, CONTINUUS Pharmaceuticals has worked with the United States government in response to the COVID-19 pandemic and to aid in the manufacture of some of the drugs that were in shortage, Vincenzi revealed.

“One of the class of drugs that were challenging for us and pushed us to develop new technologies were high-potency products and controlled substances,” Vincenzi said. “Therefore, we developed new technology and new labs for us to work on these kinds of products.”

For Vincenzi, the key differentiating factor for CONTINUUS Pharmaceuticals is the fact that they are offering an end-to-end solution, something that he hasn’t seen anywhere else. “We go from the raw material to your preferred drug product, all the way through your reaction, filtration, drying steps to the final tableting or vial filling,” he remarked.

1. Domokos, A.; Nagy, B; Szilágyi, B.; Marosi, G.; Nagy, Z.K. Integrated Continuous Pharmaceutical Technologies—A Review. 

CONTINUUS Pharmaceuticals was featured at the inaugural Start-Up Market on the show floor at CPHI Barcelona 2023. CPHI Barcelona ran from Oct. 24–26, 2023.

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Pharmaceutical Technology Europe® spoke with Leonardo Vincenzi from CONTINUUS Pharmaceuticals, a US company that specializes in process development, at the inaugural Start-Up Market on the show floor at CPHI Barcelona 2023.

Converting to Continuous Manufacturing with CONTINUUS Pharmaceuticals at CPHI Barcelona

Feliza Mirasol is the science editor for 

Hollie Barton, senior director, Chromatographic Services, for the PPD clinical research business of Thermo Fisher Scientific, spoke with 

®) at AAPS PharmSci 360 on the need for training the current and the new generation of analytical experts for the bio/pharma industry. With the emergence and growth of newer modalities of therapeutics, such as cell and gene therapies, messenger RNA-based and other nucleic acid-based therapeutics, and more advanced forms of antibodies, to name a few, 

the industry has been feeling the void of a lack in technical and analytic expertise

 (1). Barton highlights at least one approach toward filling that void and ensuring the future of analytical expertise.

“It used to be, when folks joined the BioA labs, they went through what was referred to as a new hire training, where they were just exposed to safety, GLP [good laboratory practice], the basics. What we're seeing now is an [addition] of trainers directly associated with functional teams they're supporting. These trainers are actual lab scientists who are coming in, and the first things that they're doing is exposing our folks to protocols, methodology, therapeutic areas, and helping them to understand exactly what they're working on,” Barton explains.

By having these in-house trainers available to staff, she continues, the staff are being advanced at a much quicker rate. “We're also seeing a lot of mentoring and one-on-one training that is being provided to get everybody where they need to be as quickly as possible,” Barton remarks.

AAPS PharmSci 360 ran Oct. 22–25 in Orlando, Fl.

analytical challenges in meeting turnaround times

1. Mirasol, F. Biologics Testing Highlights Need for Analytical Skills. 

Articles

Hollie Barton, senior director, Chromatographic Services, for the PPD clinical research business of Thermo Fisher Scientific, highlights one approach to ensuring the continuation of analytical expertise.

Ensuring the Next Generation of Analytical Experts (AAPS PharmSci 360)

Jennifer Markarian is a contributing editor for 

Drug target identification and lead optimization of hit compound candidates for orphan diseases | ©HYUNGKEUN - stock.adobe.com

Although biopharmaceutical production today generally takes place in centralized manufacturing facilities, industry and regulators are taking a close look at the benefits of decentralized or distributed manufacturing, which involves smaller and flexible-volume manufacturing operations in multiple locations closer to the site of use and even to point-of-care (POC) locations. POC manufacturing is seen as essential for efficiency in producing personalized medicines. In the near-term future, POC production is likely to be in a controlled environment such as a hospital, clinic, or pharmacy, while in the long term, POC production could extend to other locations. Such a model could enable quality drug production anywhere, from the battlefield to remote villages or even outer space, experts suggest.

The impetus for POC manufacturing comes in part from its potential to alleviate pressing problems, such as drug shortages, pandemic preparedness, and equitable availability of treatments. It is also driven by technological advances that promise to allow efficient and consistently high-quality production using new equipment, analytical tools, and quality control paradigms.

These technologies offer the benefit of making drugs much closer to where and when they are needed. “The advantages of making medicines on demand—to solve issues such as the difficulty of predicting demand and the complexity of the supply chain—are compelling,” states Govind Rao, professor at the University of Maryland, Baltimore County (UMBC) and director of UMBC’s Center for Advanced Sensor Technology.

FDA recognizes the need for flexible and agile manufacturing and sees the potential for portable, distributed manufacturing units to be used for POC manufacturing. In October 2022, the Center for Drug Evaluation and Research (CDER) published a discussion paper that highlighted areas to consider for drugs regulated by CDER as well as the Center for Biologics Evaluation and Research and called for public feedback (1). FDA and the Product Quality Research Institute (PQRI) also held a workshop in November 2022 to gather input from stakeholders. For advanced manufacturing technologies—particularly distributed manufacturing, POC manufacturing, artificial intelligence (AI), and end-to-end continuous manufacturing—seeking input is the first step in FDA’s new Framework for Regulatory Advanced Manufacturing Evaluation (FRAME) initiative, according to a presentation by Michael Kopcha, director of CDER’s Office of Pharmaceutical Quality (2).

The European Medicines Agency’s (EMA) Quality Innovation Group is focused on a similar list of advanced manufacturing technologies, which were discussed at a March 2023 focus group meeting (3). That decentralized manufacturing is being discussed is one reason for optimism regarding its uptake in Europe, suggests Celeste Lamm, director of Global Regulatory Affairs, CMC at Merck. In addition, she points to the European Commission’s proposed new directive (4) that includes a pathway for decentralized manufacturing within the European Union and provides an architecture for responsibility between a central site and decentralized sites. “However, the proposal limits decentralized manufacturing to applications where the central site is located within the EU, and it isn’t clear how the regulation would be applied if some of the decentralized sites were outside of the EU,” Lamm says.

Regulatory uncertainty is an ongoing challenge, with unanswered questions around how connected sites that are located in different regions will be regulated and a lack of global harmonization. Agencies in different regions are communicating with each other, and it is hoped that approaches will be similar. Because no final guidance has been released by any regulatory agency, any differences in requirements are still unknown, Lamm cautions.

Change will take time. Lamm sees similarities between the pace of adoption of distributed manufacturing and that of continuous manufacturing. “Both regulators and industry acknowledge the benefits, but adoption has been slow, because existing traditional manufacturing is frequently sufficient, and it can keep costs down to use existing facilities,” Lamm says. “Adoption of innovative manufacturing technology occurs gradually as there is opportunity to replace existing lines, where existing technology isn’t sufficient (for example, when local manufacturing is needed), and where there is enough clarity to calculate the long-term benefit to justify investment.”

A significant hurdle for distributed and POC manufacturing is how to ensure consistent quality of the drug product, but there is a growing availability of technologies that can meet this challenge. For example, prefabricated, portable cleanrooms and automated processes that fit in these spaces meet the need for standardization of equipment, process, and systems that is crucial to consistency. In addition, digital technology and cloud-based systems in use today make it easier to connect the data and quality systems of different locations. With these tools, distributed manufacturing can even reduce risk and contribute to consistency.

“Using enterprise quality systems across distributed sites is the natural extension of how we currently work in a global environment,” says Lamm. She says that regulators accept aspects of digitally connected quality system solutions, but they note that it is important to ensure that all personnel are similarly trained and are following the same practices.

“There are many flavors of distributed manufacturing for consideration when training personnel,” Lamm adds. Variations include the number of sites and the complexity of the manufacturing process. “The conversation regarding appropriate quality approaches for distributed manufacturing is ongoing, particularly for high volumes of sites or complex manufacturing processes. It is critical that we continue the dialogue between industry and regulators to address concerns.”

Quality assurance/quality control (QA/QC) methods for distributed manufacturing will need to be different from those currently used in centralized manufacturing, says Rao. His group at UMBC recently patented a method for using machine learning (ML) to ensure consistent product quality in UMBC’s Biological Medicines On-Demand (Bio-MOD) system, which uses a cell-free method for end-to-end continuous manufacturing of biologic drug substances (5).

“With cell-free manufacturing, one gene produces one protein in a process that is almost more like chemistry than biology in that it can be consistent,” says Rao. “Our ML algorithm can extract enough data from the process sensors and analytic measurements to get a high degree of confidence that everything about the process is consistent. If the process is the same, the product should be same. The algorithm can also identify any deviation from the expected, such as an air bubble or impurity. This automated function raises the safety level to a much higher level than the current method of testing a product after it is made.”

The Bio-MOD system is currently suitcase-sized, but could be scaled-out to larger volumes, says Rao. He adds that the ML approach can also be used in cell-based manufacturing.

Decentralized, POC manufacturing is particularly helpful for autologous cell and gene therapies (CGTs), because it solves some of the problems of long-distance, cold-chain shipping of patient-specific raw materials and finished products. Various technology providers have created closed, automated CGT manufacturing systems that aim to provide scalable throughput to meet the growing demand for CGTs in centralized or decentralized locations (6).

Sterile compounding is already occurring at point-of-care facilities, some of which are regulated by FDA as 503B outsourcing facilities under strict quality and safety guidelines. James Rorke, vice-president of North American Operations at Steriline, predicts that this rigor will, in the future, extend to some or all patient-specific 503A sterile compounding, which often takes place in pharmacies, hospitals, and institutional settings. Steriline’s Intelligent Compounding System (ICS) meets the need for aseptic patient-specific compounding in a compact footprint, Rorke says.

In the ICS, check-weighing provides the data to prove that every single container has been filled to the optimal volume, explains Rorke. “Incorporating 100% check weighing while filling allows us to target the primary fill volume to the lower end of the acceptable fill range, such that variances in the fill volume are more likely to fall in the nominal range or under filled. If under filled, we add the additional volume of product to meet the requirement. This function practically eliminates over-filled rejects, and it tightens the process window to eliminate waste.”

Biotech company Orgenesis has targeted POC CGT manufacturing with its POCare Network and its Octomera Mobile Production Units and Labs (OMPULs). In June 2023, the company and one of its network partners, the University of California (UC), Davis, announced that OMPULs will be installed and operated by Orgenesis at multiple UC medical and academic institutions (7).

Decentralized and standardized manufacturing is crucial for getting CGTs to patients at a reasonable cost, says Vered Caplan, CEO of Orgenesis. She says that the OMPULs are standardized units that are operated with standard procedures.

“We have defined everything—raw materials, disposables, all the process parameters, cleaning parameters, the environment in and around the unit, all the equipment, and procedures,” Caplan explains. “The unit is not just a mobile cleanroom. It is a closed, automated mini-production facility with standardized, validated isolation units inside. These units can then be duplicated and located at different sites, either in a hospital or nearby. Because of their agnostic nature, we can augment them to many modalities and even applications such as apheresis, if needed, to complete the full value chain.”

Comparability testing of products from the different units will demonstrate that standardization produces comparability; the extent of testing is decided using risk analysis. Caplan adds that although product quality is built-in to the standard process, the decentralized sites will also have quality assurance oversight through centralized audits. “Our work is driven by compliance to the most current guidelines, and our flexible nature allows us to be responsive as we start to see [new regulatory guidance] come to fruition,” she says.

Caplan suggests that hospitals are driving demand for decentralized manufacturing so they can meet their patients’ needs. She says standardized manufacturing platforms like the OMPUL will provide consistent quality at lower cost than each hospital creating their own platform. Orgenesis is also working with solution and equipment providers to ensure that the team is aware of the latest technologies and that they can easily be incorporated into the platform, Caplan says.

CDER. Distributed Manufacturing and Point-of-Care Manufacturing of Drugs, Discussion Paper. 

Kopcha, M. A Regulatory Perspective on Innovations in Pharmaceutical Manufacturing. Presentation at the FDA/PQRI Workshop of the Regulatory Framework for Distributed and Point of Care Pharmaceutical Manufacturing (Nov. 14, 2022).

Listen and Learn Focus Group Meeting Report

EC, Proposal for a Directive Of The European Parliament And Of The Council on the Union code Relating to Medicinal Products for Human Use, and Repealing Directive 2001/83/EC and Directive 2009/35/EC (April 26, 2023).

Rao, G. et al. US Patent 11,685,892 B2. “Methods to Incorporate Machine Learning Analytics for Optimizing Protein Purity, Potency, and Quality in an On-Demand Production System for Point-of-Care Delivery” (June 27, 2023).

Markarian, J. Automation Aids Cell and Gene Therapy Production. 

Orgenesis. Orgenesis and University of California, Davis Sign Partnership Agreement for Rollout of Cell and Gene Therapy Mobile Processing Units and Labs Throughout California. Press Release, June 7, 2023.

Jennifer Markarian is manufacturing reporter for 

Volume 47, No. 11
November 2023
Pages: 12–14

When referring to this article, please cite it as Markarian, J. Considering the Promises of Point-of-Care Manufacturing. 

Emergence of advanced manufacturing technology to ensure quality of biopharmaceutical drugs combined with efforts to identify a regulatory pathway indicate that a distributed manufacturing model is within reach. 

Considering the Promises of Point-of-Care Manufacturing

Prasanth Sambaraju is an independent researcher based in Hyderabad, India. 

Business data analysis and analytics of customers insights with charts. | Smart AI - Stock.adobe.com

Submitted: June 29, 2022
Accepted: July 5, 2023

Stability studies are carried out to study the impact on the quality of drug substance or drug product over a period of time under the combined influence of environmental factors like temperature, humidity, and exposure to light. One of the main purposes of stability testing is to establish shelf life for these drug products. The goal of this paper is to create an Excel spreadsheet, which can be used for statistical testing of more than three stability batches for poolability. The spreadsheet is designed to meet the poolability criteria as per International Council for Harmonisation Harmonised Tripartite guideline for evaluation of stability data, Q1E.

Stability studies in the pharmaceutical industry are intended to measure the degradation of drug product over time. These studies are conducted by taking random samples from a batch that is stored under specified conditions. By using data from these studies, degradation rate as a function of time is calculated. The shelf life is the point in time where the lower 95% confidence interval from the linear fitted regression line crosses the 90% labeled amount (1). The shelf-life estimation for a hypothetical single batch is shown in 

Figure 1. Shelf-life estimation for a single hypothetical batch. Where CI means confidence interval. (Figure courtesy of the author)

The International Council for Harmonisation (ICH) Tripartite Guideline, Q1E 

, describes the procedure for shelf-life estimation. The objective of statistical analysis outlined in the guideline is to determine variability among these batches and then estimate the shelf life accordingly (2).

When multiple batches are manufactured, data from these batches can be pooled for shelf-life calculation. According to ICH’s guideline (2), analysis of covariance (ANCOVA) is used to test statistical difference between both slopes and intercepts of regression lines generated from these multiple batches, using time as a covariate. A significance level of 0.25 is used as the criterion for pooling (3).

In a previous paper (4), the use of Microsoft Excel to test the equality of slopes and intercepts using ANCOVA for three batches was described. The goal of this paper is to test for equality of slope and intercepts using Microsoft Excel, when the number of test batches is greater than three. Two reported datasets each containing data from six batches will be used to demonstrate this approach.

Two stability data datasets each involving six batches from Ruberg and Stegeman’s research (1) were tested separately using the aforementioned approach. The procedure involved regression analysis of stability data using in-built Excel functions and then creating an ANCOVA table using these values. For multi-batch stability data

 shows the possible scenarios for drug product degradation. The common intercept separate slope (CISS) model is used only in special circumstances (5). The other models used to describe stability studies are listed below:

Figure 2. Different models to explain stability studies.

*CISS model is only used in special circumstances. (Figure courtesy of the author)

Common intercept common slope (CICS) model.

 This model indicates that all the batches have a common intercept and slope. The values for common slope and intercept for dataset 1 are obtained by using regression analysis in Excel.

Separate intercept separate slope (SISS) model. 

This model indicates that different batches have different slopes and different intercepts. These values can be obtained by regression analysis from individual batches separately. Residual sum of squares is obtained by adding squared differences between observed and expected value. The expected value is calculated using slope and intercept from individual batches.

Separate intercept common slope (SICS) model.

 This model indicates that different batches have common slope and separate intercept. These values can be obtained by regression analysis using dummy dependent variables containing ones and zeros.

ANCOVA calculations for comparing the intercepts and slopes of stability batches were calculated as described (4) and are shown in 

. These tests make a comparison between two models, simple (reduced) vs. complicated (full) model (5). The P-value obtained for row C in ANCOVA table (

) was less than 0.25, due to which null hypothesis was rejected (SICS model) and alternate hypothesis (SISS model) was used for shelf-life estimation.

Table I. Slope and intercept for different models. Where CICS means common intercept common slope; SISS means separate intercept separate slope; SICS means separate intercept common slope.

Table II. ANCOVA calculations for dataset 1. Where CICS means common intercept common slope; SISS means separate intercept separate slope; SICS means separate intercept common slope; SSE means sum of squared errors; SS is the difference between the sum of squared errors of null and alternate hypothesis and df is the difference between degrees of freedom of null and alternate hypothesis.

The calculations comparing intercepts and slopes of stability batches are shown in 

) obtained for dataset 2, the P-value for row C was greater than 0.25, thereby failed to reject the null hypothesis (SICS model). In row B, the P-value was less than 0.25, due to which null hypothesis was rejected (CICS model) and alternate hypothesis (SICS model) was used for shelf‑life estimation.

Table III. Slope and intercept for different models. Where CICS means common intercept common slope; SISS means separate intercept separate slope; SICS means separate intercept common slope.

Table IV. ANCOVA calculations for dataset 2. Where CICS means common intercept common slope; SISS means separate intercept separate slope; SICS means separate intercept common slope; SSE means sum of squared errors; SS is the difference between the sum of squared errors of null and alternate hypothesis and df is the difference between degrees of freedom of null and alternate hypothesis.

The stability model selected at the end of analysis for both datasets matches with the model used for shelf-life estimation (1). The advantage of this Excel spreadsheet approach is that it gives the users an easy and convenient way to test for poolability of stability data. The procedure doesn’t involve any programming and can be used when the number of batches is greater than three. The limitation of this method is that it can be used only to evaluate data from one factor, batches.

Ruberg, S.J.; Stegeman, J.W. Pooling Data for Stability Studies: Testing the Equality of Batch Degradation Slopes. 

Lee, H.-y.; Wu, P.-c.; Lee; Y.-j. stab: An R Package for Drug Stability Data Analysis. 

LeBlond, D. Hypothesis Testing: Examples in Pharmaceutical Process and Analytical Development. 

LeBlond, D.; Griffith, D.; Aubuchon, K. Linear Regression 102: Stability Shelf Life Estimation Using Analysis of Covariance. 

Prasanth Sambaraju is an independent researcher based in Hyderabad, India.

®
Vol. 47, No. 11
November 2023
Pages: 24-27

When referring to this article, please cite it as Sambaraju, P. Pooling of Batches for Stability Data Analysis. 

One of the main purposes of stability testing is to establish shelf life for these drug products. The goal of this paper is to create an Excel spreadsheet, which can be used for statistical testing of more than three stability batches for poolability. 

Pooling of Batches for Stability Data Analysis

Cynthia A. Challener is a contributing editor to Pharmaceutical Technology.

acetyldigitoxin molecular structure 3d, flat model, cardiac glycoside, structural chemical formula view from a microscope | ©Сергей Шиманович - stock.adobe.com

Poor water solubility continues to present a major challenge to formulators. Poor solubility (and permeability) generally translates to low bioavailability, which in turn results in reduced efficacy. Many approaches have been developed to enhance bioavailability, including the formation of solid amorphous dispersions and salts. Less common solutions include the use of coformers and the generation of cocrystals or complexes. One of the reasons these last three methods are employed less often is the greater challenges associated with finding the right molecule for a given API. Extensive experimental screening is often required. For this reason, predictive modeling capabilities could potentially accelerate the development of coformers, cocrystals, and complexes.

Coformers, cocrystals, and complexes of APIs have the potential to enhance bioavailability when carefully developed, but there are several challenges when traditional approaches are used, according to Jo Varshney, founder and CEO of VeriSIM Life.

“Exploration of the chemical combination space, given its vastness, is impractical,” Varshney states. For example, she points out that bioavailability may change in a non-linear fashion with different ratios of the same two coformers, or two very similar molecules may lead to a substantial change in the bioavailability of the API.

Thermodynamics of combination stability can also be a non-linear and/or non-monotonic function of the ratio of the two compounds, Varshney observes. “Traditional approaches can only explicitly investigate discrete combinations, and optimizing formulation stability using such sparse knowledge is often inaccurate,” she says.

Key to expanded use of predictive modeling in the development of specialized formulations aimed at increasing solubility enhancement is the advances being made in the fields of artificial intelligence (AI), machine learning (ML), and various other computational methods. Numerous technologies are now being applied across all aspects of drug discovery and development, including the formulation of APIs with poor solubility and bioavailability.

For the design and selection of coformers, cocrystals, and complexing agents in particular, the use of advanced neural network algorithms, ensemble-based methods, and knowledge-based models is making predictive modeling more relevant, according to Varshney.

They allow for more accurate interpolation between available experimental data. “This interpolation can be to different ratios of combinations or even to different types of combinations,” Varshney notes.

In addition, the inclusion of knowledge-based models in the framework of predictive modeling is having an impact. “When AI-based models are used in combination with knowledge-based models, more robust predictions can be generated even in limited-data scenarios, of which formulation science is definitely one,” contends Varshney. She adds that these knowledge-based models can accelerate the development of coformers, cocrystals, and complexing agents through an accurate consideration of the physiological, physicochemical, and structural aspects of molecules in the combinatorial space. VeriSIM Life uses such hybrid AI and knowledge-based models extensively in drug and formulation development.

The main benefit offered by predictive modeling is the possibility to save time and resources. Information gleaned from effective models can narrow down to a reasonable number the potential molecules that can serve as effective coformers or form attractive cocrystals or complexes. That in turn reduces the number of physical experiments that must be performed to identify the optimum solution.

Predictive modeling, says Varshney, allows for a rapid exploration of the combinatorial chemical space. She also emphasizes that when deployed in a hybrid-AI framework—especially those that emphasize the physicochemical and physiological aspects of the chemical moieties involved, predictive models can provide robust solutions for different compound combinations to enhance bioavailability of poorly soluble APIs.

Furthermore, Varshney observes that by extracting explainable insights from predictive modeling, it is possible to make observations about both existing and novel drug formulations to identify which features of drug formulations are most closely associated with improving bioavailability. “This increased understanding then guides novel formulation exploration in the most promising directions, further expediting the development of effective formulations,” she comments.

There are several advantages to using predictive modeling for identifying potential solutions for enhancing the bioavailability of poorly soluble APIs, agrees Sanjay Konagurthu, senior director, science and innovation, pharma services at Thermo Fisher Scientific. In addition to shortening development timelines, he emphasizes the ability to reduce risk in product development and remove barriers in each phase of development. Predictive modeling can also help reduce costs, avoid rework, reduce the amount of API required, and support sustainability, Konagurthu observes.

The greatest challenge to deploying predictive modeling solutions for coformer, cocrystal, and complex development is the need for high-quality data, according to both Varshney and Konagurthu.

“Predictive technologies/algorithms such as ML and AI are dependent on the quality and quantity of available data. Large data sets spanning the druggable formulation space are required,” Konagurthu explains. Unfortunately, that data can be hard to come by when it comes to coformer, cocrystal, and complex formulation.

Most predictive modeling technologies require a lot of high-quality data to make accurate predictions, the amount of which is essentially very limited for coformer, cocrystal, and complex formulations taking the vast space of possible compound combinations into consideration, Varshney agrees. She further notes that even that limited data are often not readily available for broad use because of the heterogeneity of the sources.

Adding to the challenges of limited data with limited access is the lack of data curation and accuracy standards, according to Varshney. “This issue leads to the propagation of errors from inaccurate data to predictions,” she explains.

There is also, Varshney believes, a lack of explainability with current modeling technologies. For instance, she says that if a complex or a combination is predicted to display low bioavailability, the underlying reason for it typically remains obscure. “This lack of explainability in terms of the physicochemical characteristics of the compounds in the combination precludes the design of combinations that could result in a higher bioavailability of the API,” Varshney explains.

There are some technical challenges as well. Konagurthu highlights the fact that predictive models require robust experimental validation, and models can be computationally intensive.

The best way to maximize the potential benefits of predictive modeling for the development of coformer, cocrystal, and complex formulations is to use a combination of approaches. The first, according to Varshney, is to use robust data mining algorithms to gather data from heterogeneous sources. The second is to employ enhanced data curation and accurate evaluation methods to reduce inaccuracies in the data used for predictive models. The third is to leverage knowledge-based models within the framework of predictive modeling to enable more robust, physicochemically accurate predictions in limited-data scenarios. Lastly, Varshney emphasizes the use of model explainability to help design the next generation of complexes, coformers, and cocrystals with higher API bioavailability.

Physical experimentation can then be employed to augment the available “prediction space” through the generation of high-quality data, Varshney observes. In addition, she notes that physical experimentation can help validate predictive models and increase trust in them, particularly when physical experimentation outcomes align with insights from model explainability.

As a separate comment, Varshney notes that VeriSIM Life’s BIOiSIM platform incorporates data gathering, data validation, hybrid AI, and model explainability aspects to de-risk and accelerate drug and formulation development.

Increasing importance for bioavailability enhancement

Despite the data, cost, and computational challenges, both Konagurthu and Varshney expect predictive modeling to be increasingly used to solve specific formulation development problems, including enhancement of bioavailability.

In the near term, Varshney expects efforts in predictive modeling for bioavailability enhancement during the drug formulation stage to focus on curating available data and incorporating novel algorithms, including hybrid AI methods, in order to identify and select appropriate coformers, cocrystals, etc. for certain poorly soluble APIs.

“In the longer term,” Varshney suggests, “the use of predictive modeling in formulation development and to improve bioavailability is going to be widely adopted by the pharma industry to reduce the burden of exhaustive experiments required to create appropriate cocrystals, coformers, etc.” She also expects it will help bring difficult-to-formulate drug compounds to market faster, thereby improving the lives of patients suffering with complex and rare diseases.

In the near term, Thermo Fisher Scientific anticipates significant advances in predictive modeling for formulation development and bioavailability enhancement, according to Konagurthu. “Increased integration of AI and ML techniques will enable the development of more accurate and versatile models,” he remarks.

In the longer term, the evolution of predictive modeling for formulation development and bioavailability enhancement will evolve in sophistication, Konagurthu contends. “Advances in quantum computing will allow for large and complex calculations and simulations, enabling the modeling of larger and more intricate molecular systems. In addition, with the incorporation of real-world data, such as patient outcomes and clinical-trial results, the precision and accuracy of these models will evolve even further,” he concludes.

Cynthia A. Challener, PhD, is a contributing editor to 


Vol. 47, No. 11
November 2023
Pages: 16-19

When referring to this article, please cite it as Challener, C.A. Predictive Modeling for Formulation Development: Coformers, Cocrystals, Complexes. 

Predictive modeling facilitates the identification of coformers, cocrystal components, and complexing agents.

Predictive Modeling for Formulation Development: Coformers, Cocrystals, Complexes

Hallie Forcinio is packing editor for Pharmaceutical Technology and Pharmaceutical Technology Europe, editorhal@sbcglobal.net.

The global pharmaceutical packaging market is projected to expand at a compound annual growth rate (CAGR) of 9.7% from 2023 to 2030, according to a study by Grand View Research (1). The growth in pharmaceutical packaging reflects the growth in the sector, due to expanding usage in developing nations and an aging population and reshoring in the United States, the world’s largest market for pharmaceuticals (2).

Analysts predict the strength of the US market will make the pharmaceutical sector one of the strongest performers in 2023–2024, even if a recession occurs, which many economists think is likely to happen especially if the Federal Reserve continues raising interest rates. A weaker economy would yield at least one positive; it would help retain personnel because employees are less likely to change jobs in uncertain times. It won’t, however, help hiring challenges, which are expected to continue for the remainder of the decade. As a result, automation is viewed as a good investment with growing interest in artificial intelligence (AI), digitalization, and robotics (2).

A report published by Connect in Pharma notes, that although not yet widely used, AI is no longer a future concept. Of the companies engaged in adopting AI, almost all recognize the important role digital tools are playing to improve data collection and reduce manufacturing downtime. AI and machine learning support continuous manufacturing practices, a key trend to reduce waste, improve efficiency, and increase line speeds. Digital tools also can reduce human error and improve precision fill/finish, workflow integration, and anticounterfeiting (3).

Furthermore, digital tools enhance the ability to record and share supply chain data with regulators and make it easier to comply with regulations like Annex 1, which sets requirements to minimize the risk of contamination of aseptically filled products. “Automation in fill/finish can help to address some challenges in meeting these requirements, while at the same time providing better efficacy in production,” said Andreas Raabe, CEO of Adragos Pharma, a Munich-based contract development and manufacturing organization. Despite the benefits of digitalization, multiple barriers exist including ensuring data integrity and security, identifying the right software, cost, and a lack of corporate leadership (3).

Usage of robots, which offer precision, efficiency, speed, compatibility with customized packaging, and the potential to minimize human interaction on sterile product lines, is expected to expand dramatically through 2032, according to a report from Global Market Insights. Adopting robots for picking and placing products conserves floor space, simplifies inspection, and supports tracking and tracing functions (4).

Stäubli Tec-Systems, for example, offers validated robotic systems designed to work in aseptic areas and withstand rigorous cleaning. Systems include the Stericlean range for aseptic environments and the new Stericlean+ models for use in isolators. The TX2-60 Stericlean+ robot with a hollow wrist option features an enhanced hygienic design, higher cleanability, a new FDA-compliant surface coating, and dynamic joint seals. Telemanipulation of the robot via joystick minimizes human interventions and offers the potential for a closed system (5).

Sectors poised for the strongest growth include inhalable biologics, long-acting injectable formulations, and connected devices. Exactly half of the Connect in Pharma survey respondents confirmed that their company is or will be developing, manufacturing, or packaging drugs that are inhaled (3).

“Nasal delivery is becoming increasingly prevalent across a variety of pharmaceutical niches,” said Deborah Smook, vice-president of Marketing & Business Development at TurboFil Packaging Machines, which introduced the Mini-Monoblock MDN-50 system to streamline packaging of multi-dose nasal products. The system fills, seals, and caps nasal spray containers at a rate of up to 50 pieces/min, occupies a compact footprint, and can be integrated with a checkweigher and wraparound labeler (6).

The likelihood of strong growth for inhalable drug delivery devices can be attributed to the potential for fast absorption and the high bioavailability of biologics delivered via the nasal passage or lungs. Dry powder inhalers and nasal delivery devices rank as the most popular primary packages. Interest is particularly strong in developing more sustainable reusable inhalers and inhalation devices for vaccine delivery (3).

Suppliers already are working on more sustainable designs. For example, Aptar Pharma has developed the APF (advanced preservative-free) Futurity metal-free nasal spray pump for nasal saline and other over-the-counter products. When used in combination with a high-density polyethylene (HDPE) or polypropylene container, a product with the APF Futurity pump can be conveniently recycled as one piece when empty, with no need to separate or detach any parts or materials before disposal. The result is a streamlined recycling process and higher quality recyclates. Class AA certification from 

 confirms the recyclability of the metal-free nasal spray pump (7).

Hurdles to more widespread adoption of inhalable biologics include patient safety, satisfactory bioavailability, the shorter-acting effects of inhalable drugs, ease of use, and an evolving regulatory environment. The new EU Medical Device Regulation, for example, creates significant regulatory challenges for drug-device combination product manufacturers that will delay the approval and launch process, including increased demands for manufacturers to document their manufacturing flow (3).

New delivery systems for injectables are another growth area. Half of the survey respondents reported that their company is or will be developing,
manufacturing, or packaging injectable formulations (3).

Another projected growth area, connected devices, opens doors to personalized care. Benefits include increased patient adherence and compliance, support for remote monitoring and data collection, improved patient outcomes, and the potential to reduce waste from expensive therapies. Hurdles to adoption of connected devices include who controls the data, how/where the data are shared, and whether devices should be single-use or reusable. Manufacturing costs, cybersecurity, patient hesitancy, and regulations also pose challenges to developers (3).

Faced with growing consumer and regulatory pressure to reduce its environmental impact, the pharmaceutical industry is taking steps to adopt more sustainable manufacturing and packaging practices. Although one-third of Connect in Pharma survey respondents have yet to embark on sustainability improvements, the industry is seeing increasing use of recycled materials in secondary packaging, the implementation of energy-efficient technologies, and the development of more eco-friendly drug delivery systems (3). Interest in compostable materials and polyethylene terephthalate (PET) containers made from renewable, plant-based materials also is growing (1).

In a study conducted by Aptar Pharma in 2022, 77% of respondents indicated that it was “important” or “very important” that the products they buy are recyclable (7). This often is accomplished with monomaterial designs like the Polyfoil MMB 545/645’ tube from Neopac, which is approved as fully compatible with the European rigid HDPE recycling stream. The tube structure replaces the typical ethylene vinyl alcohol barrier layer with a thin film barrier layer, which does not affect recyclability. The tests, performed by the Institut für Kunststofftechnologie und -recycling using the RecyClass Recyclability Evaluation Protocol for HDPE containers, show recycled tube material can be used in high-end or closed-loop applications at levels up to 25% (8).

Another desirable feature, recycled content, supports circularity and is likely to become more important in pharmaceutical packaging if the European Commission’s proposed amendment to its Packaging and Packaging Waste Directive is approved. The amendment would make the directive a full-fledged regulation and set elevated sustainability standards for packaging materials. Although it’s likely the pharmaceutical industry would be granted longer deadlines, this regulatory change would push the industry toward greater use of recycled materials to meet the more stringent sustainability rules (9). Post-consumer recycled content is found in glass containers, as well as some PET containers and secondary packaging (1).

However, increasing recycled content depends on improved collection, better sortation techniques, and greater capacity for end-of-life reprocessing, according to a report published by PMMI, The Association for Packaging and Processing Technologies, and AMERIPEN. Policy changes will be needed to meet these objectives and achieve a circular economy. According to the report, success will depend on the establishment of:

extended producer responsibility programs

universal access to recycling and composting services

standardized definitions to lessen consumer confusion and ensure stakeholders communicate a consistent message

alternatives to material bans so the optimum packaging option can be chosen for each application

federal investment into programs focused on more efficient packaging design and waste management

data collection to measure and benchmark the performance of a circular packaging economy

Reusables, particularly for secondary and distribution packaging, look like a promising element of a circular economy. However, infrastructure is lacking, and investment is needed to establish reverse logistics systems, which provide consumers with convenient, safe, and hygienic collection points. Production lines also must be equipped for cleaning and reuse of packaging. The costs to develop and scale an infrastructure for reusables pose a significant hurdle. A wide range of stakeholders will need to develop a collaborative strategy that incentivizes innovation (10).

Despite a strong interest in sustainability, the cost of sustainable materials and practices poses a major barrier. However, some survey respondents believe digitalization across the manufacturing line can support sustainability efforts. Fausto Artico, global R&D tech head and product director of Innovation and Data Science at GSK, explained, “As more processes become automated, it’s easier to make green optimizations. …when you use data science to monitor and log everything happening in systems, you finally have an idea of what the waste is, how much there is, and where it’s happening” (3).

Grand View Research. Pharmaceutical Packaging Market Size & Share Report, 2030, 

. Market Study (accessed Sept. 28, 2023).

PMMI, Business Intelligence, a division of PMMI The Association for Packaging and Processing Technologies. Guidance in an Uncertain Economy–Gauging the Risks Going Forward 2023-2024. Market Study. June 2023.

Connect in Pharma. The Future of Pharma Production, Manufacturing and Packaging Trend Report.

Global Market Insights. Pharmaceutical Robots Market Size, Share and Industry Analysis Report, Regional Outlook, Growth Potential, Competitive Market Share & Forecast, 2023–2032. Market Study. Pharmaceutical Robots Market Growth | Forecast, 2023-2032. 

Stäubli Tec-Systems GmbH Robotics. Robots with a Laser Focus on Pharma. Press Release. Sept. 18, 2023.

TurboFil Packaging Machines. TurboFil Introduces Inline Filling & Capping Machine for Multi-Dose Nasal Dispensers. Press Release. July 29, 2023.

Aptar Pharma. Aptar Pharma Launches First Metal-Free Highly Recyclable Nasal Spray Pump, Press Release. Jan. 20, 2023.

Neopac. Neopac’s ‘Polyfoil® MMB 545/645’ Tube Receives a RecyClass Recyclability Approval. Press Release. Feb. 23, 2023.

TekniPlex Healthcare. TekniPlex Healthcare to Lecture on Pharma-Grade Recycled Plastics at Pharma Packaging & Labeling Innovation Forum. Press Release. Sept. 7, 2023.

PMMI, The Association for Packaging and Processing Technologies, and AMERIPEN (the American Institute for Packaging and the Environment). 

2023 PACKAGING COMPASS: Evaluating Trends in U.S. Packaging Design over the Next Decade and Implications for the Future of a Circular Packaging System.


Vol. 47, Number 11
November 2023
Pages: 22-23, 31

When referring Forcinio, H. Market for Packaging Continues to Expand. 

Interest grows in AI, digitalization, robotics, and sustainability. 

Market for Packaging Continues to Expand

Scale up in gene therapy manufacturing currently revolves around viral vector scale up. Although some processes are similar to those in monoclonal antibody (mAb) production (e.g., cell culture to produce the viral vectors), the manufacture of viral vectors carries its own challenges, some of which may be addressed based on the history of mAb manufacture.

A look at the similarities between mAb manufacture and viral vector manufacture show that both products rely on a similar synthesis process: a biochemical reaction-based process and the use of cell culture vessels/bioreactors, according to Naveenganesh Muralidharan, senior manager, manufacturing science and technology group, AGC Biologics, and Suparna Sanyal, director of Commercial Development, Cell and Gene Therapy, Lonza.

With the exception of substrates used for their manufacture, viral vectors and mAbs share in common all the other physiological factors for production, such as temperature, pH, dissolved oxygen, and shear (either from mixing or from sparging gas into the bioreactor for oxygenation). In the production of both, the same process monitoring targets must also be addressed, such as minimizing toxic metabolite accumulation during the cell culture process and sensitivity to times when feed must be added as well as the specific rates at which feed must be added, Muralidharan emphasizes.

Other bioprocessing challenges include cell growth strategy and media optimization in addition to pH control, dissolved oxygen control, mixing, and shear stress, Sanyal adds.

However, one unique challenge that viral vector manufacture poses is that—in the case of lentivirus vectors—lentivirus allows for permanent integration of the vector into the host cell DNA, says Bruce Lahn, PhD, chief scientist, VectorBuilder, a US-headquartered biotech company specializing in gene delivery technologies. “This allows for theoretical ‘permanent’ expression of the transgene in the cells,” Lahn notes. Although other viral vectors, such as an adeno-associated virus (AAV) vector, can be more “transient” than lentivirus, meaning that, although AAV-mediated expression of transgenes, for example, has been sustainable for years, the AAV vector does not integrate into the host genome.

“AAV has a significantly lower immune reaction than lentivirus,” says Lahn, adding that “clinical trials with lentivirus were initially unsuccessful due to the significant patient immune response to the lentivirus itself.” Whereas, AAV clinical trials have demonstrated the safety of AAV vectors, which produce a much lower immune response than lentivirus, he explains.

Lahn furthermore notes that the differences between types of viral vectors (e.g., lentivirus vs. AAV) can be both advantageous and disadvantageous, depending on the end goal of the therapy in development. “If you are looking to target only a specific organ or tissue, then AAV would be your choice. For example, a lot of research has been done to target AAV specifically to retinal cells for the treatment of various vision disorders as well as target it to the brain to deliver therapeutic genes for neurological disorders,” he explains.

The decades of experience by the biopharma industry in the scale-up of mAb manufacturing can offer some lessons that have been applied to viral vector manufacturing. “The tools, technologies, know-how, and infrastructure for efficient cell expansion, seed train generation and scaling up production using single-use bioreactors has been well established in current good manufacturing practices (CGMP) processes for mAbs,” says Sanyal. Because of this, best practices such as cell banking, viability, safety and stability assessment of cell lines, cell growth strategies, metabolic analysis, and production parameter optimization can be easily leveraged for viral vector manufacturing, she emphasizes.

“Over the years of experience in mAb manufacturing processes, companies have templated scale-up activities in biologics. However, viral vector manufacturing processes are novel and highly variable from one manufacturer to other,” states Muralidharan. Therefore, what the industry has established thus far with regards to mAb manufacturing is an in-depth product knowledge for defining critical quality attributes and characterizing the critical process parameters of biomanufacturing processes. The industry also holds a detailed understanding of the variability introduced from raw materials and has well-established process operations for mAb process, says Muralidharan. However, this same depth of knowledge has yet to be established for new therapeutic technologies such as in-vitro gene therapies. As such, setting the release specifications for viral vector manufacturing based on the currently limited process and product knowledge of these molecules remains a challenging task, Muralidharan points out.

Other unique challenges associated with viral vector manufacture include the presence of impurities when produced. As Muralidharan explains, viral vectors manufactured from cell-substrate production systems can contain residual amounts of impurities such as host-cell DNA (hcDNA) and plasmid DNA (pDNA). In the final product, the presence of either impurity presents safety risks to treated patients. To address this risk, global regulatory agencies recommend tight, quantifiable limits for DNA impurities for protein therapeutics (10 ng/dose), according to Muralidharan. “However, because of the inherent nature of hcDNA and pDNA encapsidation in viral capsids, FDA permits viral vector manufacturers to minimize the amount and size of DNA impurities based on risk assessment, rather than requiring quantifiable DNA impurity limits,” he states.

Manufacturers, therefore, define their own hcDNA and pDNA specification limits for final products based on risk assessments to ensure patient safety. Such assessments are not necessarily straightforward, however, and require methods and materials that might not be readily available and that are yet to be established, Muralidharan adds.

“The production process for mAbs is largely standardized and cost-efficient with ease of scalability, using suspension bioreactors and stable cell lines,” says Sanyal. The processes used for viral vector manufacture, in comparison, are more diverse and bespoke. “The majority of viral vector production processes utilize transient transfection processes, which are difficult to scale up and require the use of costly plasmid DNAs,” she points out.

Sanyal also brings up the fact that the production of viral vectors for gene therapies is an emerging market and predominantly targets rare genetic disorders. As such, the target patient population is rather small and requires only small amounts of viral vector for the early phase clinical trials, or even in post-commercialization, she says. “For this reason, many therapeutic developers continue to utilize 2D adherent cell culture processes that are faster to implement but not scalable or cost-effective. As the gene therapy market matures, there is an increase in the recent approvals of viral-vector-based products and a concomitant shift towards the adoption of suspension bioreactor-based production processes to meet market demand,” she adds.

Monoclonal antibody and viral vector manufacture share similarities, but vector scale up faces unique challenges.

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