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

Cynthia A. Challener

A scientist in sterile coverall gown using a daily checklist check the cleanroom condition status in laboratory. Cleanroom facility. | Image Credit: ©warut – Stock.Adobe.com

As the number of biologic drugs has grown in the pharmaceutical pipeline and market, aseptic manufacturing capabilities have steadily grown in importance. Assuring sterility across many different modalities and product formats/sizes/volumes (e.g., vials, cassettes, prefilled syringes/pens, microdosing systems, intranasal delivery systems, on-body devices, bags, etc.), many of which are increasingly potent, has also become more challenging. Innovation in processing technologies has been essential to address the evolving needs of manufacturers. Key areas of development have included digitalization and automation solutions.

Repeatability and speed with improved quality and yield

Digitalization and automation provide numerous benefits to aseptic processing operations. Two of the most important, according to Alex Strauss, senior principal process equipment engineer with Curia, are greater speed combined with improved repeatability. “The key to automation is having a recipe-driven approach that is qualified. When the operator selects the recipe, the process is then repeatable. In addition, processes such as visual inspection can be completed much more rapidly than is possible by human operators and with incredible accuracy,” he explains.

New filling systems, adds Tom Clemens, senior director of engineering and facilities at Lifecore Biomedical, perform non-destructive weight checks of individual products immediately after filling, improving quality and increasing batch yields. Many also include automated camera verification systems to check vial crimping and stopper placement. “When an issue is detected, the system can automatically stop the line so the problem can be investigated immediately, avoiding costly material losses and potentially much longer production delays,” he says.

Many newer biologic products, including antibody-drug conjugates, radio-immunoconjugates, and cell and gene therapies, require high containment solutions to ensure operator safety. The diversity of products with high activity produced in multiproduct facilities has also created the need for effective solutions that reduce cross-contamination risks.

Regulations such as Annex 1 requirements in the European Union (1) have also been an important driver toward greater adoption of isolator technology to increase product safety by minimizing potential human impacts on sterile drug products, according to Clemens. Many manufacturers have therefore been shifting to the use of isolators for aseptic processing.

“Due to the nature of these closed, isolator-based filling systems, automation and robotics have been incorporated to perform necessary processing steps within the sterile environment,” Clemens observes. For instance, he notes that while older systems may have required an operator to remove a lid from a ready-to-use (RTU) tub of vials or syringes, many new isolator fillers use automated systems that heat the lid adhesive and then remove the lid from the tub with a variety of rollers. “This approach,” Clemens states, “reduces human contact and potential particle generation.”

Visual inspection operations are another area that has benefited significantly from advances in automation and digitalization technologies. These automated visual inspection systems use high-speed cameras and advanced software to automatically check for particulates and defects and can operate at fairly high speeds, according to Strauss. The Syntegon system deployed at Curia’s Albuquerque, NM facility, for instance, can inspect 400 vials per minute, matching the filling system’s filling speed.

As importantly, newer automated visual inspection systems are now available that can reliably evaluate highly viscous formulations (in excess of 100,000 centipoise) in vials and syringes, which they were previously not able to do. “Traditional automated visual inspection systems spin a vial/syringe and rely upon a camera to enable evaluation of the internal movement of the product to identify contaminants, which is not a suitable approach for viscous formulations because they do not flow in the same manner as lower viscosity solutions,” Clemens explains. Newer systems, he comments, incorporate additional cameras and advanced virtual image processing to identify particulates and defects and can even improve performance over time by incorporating machine learning algorithms.

Robotic systems do not just support the incorporation of aseptic filling processes within isolators and visual inspection activities. They have been widely implemented in sterile drug manufacturing processes to automate many different repetitive tasks, including vial filling, sealing, and labeling. By automating these activities, human interaction with sterile drug products is minimized, reducing contamination risks. Furthermore, robotic systems minimize product losses and enable the accurate filling of products that require only very small volumes.

Robotic systems can also eliminate some of the steps required when human operators are more involved in the aseptic filling process. For instance, the VarioSys Flex Line system at its Albuquerque, NM facility uses robotics to remove vials from a pre-sterilized tub, eliminating the need for a vial washer and sterilizing tunnel, according to Eric Schneider, director of global equipment engineering at Curia.

“In manually operated systems, it is not possible to aseptically remove primary containers without obstructing the first air (uninterrupted unidirectional filtered airflow used to provide a sterile environment) and/or subjecting the vials to scratches, etc., thus creating the need for additional steps,” Schneider says. With the VarioSys Flex Line, operator interventions are minimized, simplifying the process while also affording improved quality and safety outcomes.

Schneider does caution, however, that even the best robotics and automation systems still rely on human operators. “Training programs for operators are essential,” he emphasizes. To address this need, Curia operates Sterile University, a program designed to provide both training for new employees and continuing education for all manufacturing and quality staff through a simulated manufacturing environment that replicates the facilities, equipment, operations, and protocols found at the company.

Improvements in automated and digitized analytics

Automation and digitalization of analytics that support aseptic processing has several benefits as well. One of them, according to Jon Kallay, senior scientific portfolio specialist for microbial solutions with Charles River Laboratories, has been the facilitation of the adoption of rapid testing techniques that enable faster product release while still ensuring product quality.

One challenge with complex biologics highlighted by Kallay is the tendency for these products to appear turbid during routine sterility testing due to their physicochemical properties. Identification of such turbidity during manual visual inspection operations typically prompts a lengthy evaluation process to confirm the turbidity is not due to contamination.

Rapid test methods that evaluate multiple attributes, such as detection of adenosine triphosphate, respiration, and other microbial indicators, when combined with automated analysis of test results, provide accurate indicators and eliminate the subjective aspect of test results, Kallay contends. “Analysts no longer need to shake a bottle and determine if it fits their understanding of ‘turbid.’ Results are automatically analyzed and determined to be positive or negative based on the standards established during the validation of the technology,” he says. “Objective results eliminate unnecessary procedures while allowing quick release of product to patients,” Kallay concludes. In addition, more rapid access to test results allows users to respond quickly to any contamination events that could impact the facility and other product lots.

Digitizing the test process also improves data integrity. Documentation errors are not uncommon when test results are managed manually, according to Kallay. With digital test technologies, however, test materials and results are immediately documented and automatically transcribed to the laboratory information management system (LIMS) data storage system, avoiding opportunities for operator error.

The ability to collect real-time manufacturing data during aseptic processing has been, according to Clemens, a real game-changer, as it has made it possible to monitor quality in a proactive rather than a reactive manner. Lifecore Biomedical, for instance, uses a dashboard approach displayed on monitors throughout the facility to keep a watchful eye on manufacturing operations real-time and quickly identify and resolve line stoppages. “Trending and identification of variances, such as between shifts, also support identification of specific groups of operators that could benefit from additional training or opportunities for improving the mix of operator experience levels within shifts,” Clemens notes.

A specific example of an important development in real-time monitoring, according to Laura Choteau, quality control manager at Catalent Biologics, has been the introduction of real-time monitoring for visible particles. “Today, automation allows us to read culture plates from the start of incubation through to the end, with full traceability of the plate’s location and condition throughout. As a consequence, we can detect contamination risks much earlier and respond immediately. That reduces the likelihood of recurring contamination and helps us get much closer to identifying the root cause,” she comments.

Other notable examples of process analytical technology tools improving aseptic processing operations, according to Jean-François Boé, senior director of drug product development for Catalent Biologics, include onboard mass spectrometry and Raman spectroscopy systems embedded into compounding or fill/finish units. “The data generated from these instruments give us real-time visibility into critical process parameters. In the long term, this level of control could reduce the need for traditional final release testing, shifting more quality assurance upstream and closer to the point of manufacture,” he says.

Like Schneider, Boé emphasizes the role still played by human operators in many monitoring activities. For instance, air monitoring systems in Grade A areas still often require operators to insert culture plates, typically using glove ports. This need does carry a small but real risk of contamination, as well as the potential for plate insertion to be accidentally skipped, which would create a gap in monitoring and raises questions during batch review. These issues could be avoided, Boé says, with the use of robotics to install the plates, which would “add a layer of reliability to aseptic operations and increases confidence in the process.”

New data management software equally impactful

Automation in analytics has been around for some time and continues to improve with advances in technologies. What has driven a real shift, according to Choteau, has been how the generated data are managed and used. “Data management plays a central role in helping us act faster and make smarter decisions in aseptic environments. Modern LIMS platforms now allow us to automate calculations and perform trend analysis continuously, which provides more frequent and reliable insights, helps ensure accuracy, and allows quality teams to concentrate on investigations when abnormal trends appear,” she explains.

Off-the-shelf, user-friendly visualization and reporting software, meanwhile, has enabled engineering teams to use data in a way that would have required significant involvement of information technology personnel in the past, Clemens notes. For example, Lifecore Biomedical tracks all line interventions digitally. “Having operators identify reasons for interventions in a consistent manner from shift to shift enables our engineering teams to identify trends and take corrective actions before real issues arise,” he says.

Furthermore, both process efficiency and control are also increased. “Automation brings greater control and traceability. It minimizes sampling errors, confirms that all required samples have been taken, and provides a clear record for each action. This helps maintain compliance and strengthens confidence in our processes,” Choteau states.

Staff training, like with other aspects of digitalization and automation of aseptic processing, remains an essential component, however. Operator readiness to respond in the event of system failures must be maintained, and staff must be able to manage operations manually when needed, according to Choteau. Data security, she adds, is equally important. “Protecting data from cyber threats and ensuring its integrity remains a top priority as more systems become connected and automated,” Choteau observes.

Digital twins support effective process simulation

Modeling is an important aspect of digitalization that is benefiting many process development and manufacturing activities, including sterile production operations. Digital twin technology, for instance, is becoming increasingly valuable in drug substance manufacturing and beginning to show potential in aseptic fill/finish operations, according to Boé.

Digital twins in the pharmaceutical manufacturing context are virtual models of processes that are continuously updated with real-time data, allowing assessment of performance. “By creating a digital replica of equipment and processes, digital twins help us simulate scenarios, predict outcomes, and optimize parameters before moving to physical implementation,” he says. Boé finds this approach especially useful during process transfer, because it allows teams to anticipate risks, reduce variability, and avoid costly delays.

Artificial intelligence has growing value

Digital twins are not the only example of how automation and digitalization are being combined with artificial intelligence (AI), machine learning (ML), and other advanced computing capabilities to further improve aseptic processing. When leveraged together, these myriad technologies are accelerating timelines and enhancing process control, according to Boé.

As an example, Boé points to the benefits AI-driven models can provide for freeze-drying (lyophilization) processes. In particular, he notes they can be used to optimize cycle development, significantly reducing the time required to reach a robust process and positively impacting project timeliness and resource efficiency.

In fact, Boé expects AI and ML to play an even larger role and provide growing value as complexity—product, process, and equipment-related—continues to increase. “AI and ML help with not only data analysis and trending, failure prediction, and workflow optimization; they also support more informed decision making. All of these benefits ultimately lead to greater efficiency and reliability of aseptic manufacturing operations,” he concludes.

1. EC. Annex 1. Manufacture of Sterile Products. 

EudraLex–Volume 4–Good Manufacturing Practice (GMP) Guidelines


Vol. 49, No. 8
October 2025
Pages: 18–20

When referring to this article, please cite Challener, C.A. Advances in Digitalization and Automation of Aseptic Processing. 

Articles

The increasing prevalence of biologic drugs has heightened the importance of aseptic manufacturing, necessitating innovations in processing technologies. Digitalization and automation have enhanced aseptic processing by improving speed, repeatability, and quality. The adoption of isolator technology and robotics has minimized human contact, reducing contamination risks. Automated visual inspection systems and real-time monitoring have further improved quality control. Data management advancements have facilitated faster decision-making and process optimization. Digital twins and AI are increasingly valuable for simulating processes and enhancing control, ultimately improving efficiency and reliability in aseptic manufacturing.

The increasing diversity and complexity of injectable drug products is driving innovation.

Advances in Digitalization and Automation of Aseptic Processing

pillInterface | Image Credit: © Jackie Niam -stock.adobe.com

Advanced drug discovery tools are leading the identification of increasingly complex small molecules with physicochemical properties that lend promising biologic and pharmacologic activity while simultaneously creating tremendous challenges in developing formulations capable of delivering the drug to its intended site of action. Many different molecular properties acting alone and interdependently with each other and different formulation excipients and buffers influence the pharmacokinetics and pharmacodynamics of APIs. Using traditional empirical methods for investigating and identifying potential formulations for these complex molecules requires excessive time and resources at a time when reducing cost and accelerating development are essential to success. Formulation scientists are turning to predictive modeling approaches and platform formulation processes that leverage artificial intelligence (AI) and machine learning (ML) to more rapidly and comprehensively evaluate complicated formulation spaces including complex molecules and advanced drug delivery technologies (1–4).

Moving from academia to industrial application

The use of AI/ML for industrial pharmaceutical formulation development is at the nascent stage. “Much of the progress so far has come from academic research, which often seeds future industry innovation, as well as from emerging startups and the steady improvement of open-source models,” says Pauric Bannigan, chief science officer and cofounder of Intrepid Labs.

“Interest is growing rapidly, fueled by increasing recognition of AI/ML’s potential to navigate high-dimensional formulation design spaces, optimize excipient selection, and predict stability or release performance before extensive lab work,” Bannigan observes. Given these developments and the rapid advances in lab automation currently being made, he believes it is only a matter time before AI and ML become widely applied to formulation development.

How are growing formulation challenges overcome?

Small-molecule drug developers face several challenges, and many of them are well-suited to being addressed using AI/ML approaches, according to Bannigan.

Top of the list, says Daniel Joseph Price, head of the Excipients Business and working within Process Solutions in the Life Science business of Merck KGaA, Darmstadt, Germany, is the poor solubility of drug substances, with approximately 70–90% of drugs currently in the pipeline suffering from this problem (5). “This challenge is pronounced for newer drug candidates that often surpass Lipinski’s Rule of Five (6), which sets foundational criteria for oral bioavailability. In this context, overcoming solubility issues to ensure effective oral drug delivery and hence enhancing patient acceptance remains paramount,” Price emphasizes.

Other small-molecule formulation challenges highlighted by Bannigan include achieving precise release kinetics for controlled- and sustained-release systems, identifying robustness early for scale-up and manufacturability, and minimizing API waste during screening. “Each of these challenges is a multidimensional problem where dozens of formulation components and process parameters interact in nonlinear ways,” he says.

Formulations that address these key issues often involve multiple excipients. Different combinations of excipients must thus be investigated along with different formulation concentrations and processing parameters (e.g., mixing speed and time, temperature control, compression force, drying method and rate, etc.) to identify the optimum formulation design. Even with tight constraints, the exploration space can be enormous, according to Bannigan.

Pharmaceutical formulation, Bannigan adds, is a high-dimensional, sparse-data challenge where dozens of excipients and process parameters interact in nonlinear ways. As an example, he notes that with three excipients, five possible concentrations, and five process parameters at three settings each, the total number of possible unique formulations would be 3,645,000. And that would be a conservative case. “In reality, formulations often use more excipients in varying ratios with additional process parameters, pushing the possibilities into the tens or hundreds of millions,” he says. Exploring them all is simply not possible using conventional approaches, including predictive modeling systems leveraging conventional statistical methods.

Without AI/ML, in fact, Bannigan observes that countless breakthrough formulations, and more importantly the medicines they could enable, will remain buried in the noise, invisible to traditional trial-and-error approaches. “AI/ML can cut through this complexity by modeling these interactions, learning from each experimental result, and steering exploration toward the most informative and promising regions of the design space,” he contends.

Specifically, Bannigan notes, AI/ML can map complex, multi-objective relationships between components and performance; identify promising solutions that human intuition might overlook; and adapt in real time as new experimental data arrives through active learning. “By efficiently navigating vast combinatorial spaces, AI/ML not only accelerates the discovery of robust, scalable formulations, it reduces API waste, increases the probability of hitting target product profiles, and brings hidden, high-potential medicines into view,” he observes.

The application of AI/ML in formulation development offers a transformative approach to current formulation challenges, agrees Price. “By using AI/ML, scientists can streamline the formulation process, effectively narrowing down the design space. This allows them to concentrate on formulations and excipients that are likely to have a significant impact, rather than getting lost in exhaustive trial-and-error testing.”

“Furthermore, AI/ML enables researchers to explore a wider array of options, facilitating innovative solutions without the extensive time investment typically required for traditional formulation trials,” Price states. For instance, he anticipates companies harnessing AI/ML technologies to investigate broader ranges of chemicals and solutions by trying out things that go beyond the obvious, fostering greater innovation in drug formulation. “This strategic shift could ultimately lead to more effective therapies and improved patient outcomes,” he says.

Improving predictive formulation modeling

Formulation development has traditionally relied on mechanistic or simplified models to guide decision-making and optimize outcomes, according to Price. Examples include the use of the Arrhenius equation to predict the stability of drug substances under accelerated conditions, or Lipinski’s Rule of Five, which provides heuristic understanding for assessing the drug-likeness and oral bioavailability of small molecules. These models, which are rooted in physical chemistry and empirical rules, have delivered significant value—often requiring limited data to produce accurate and interpretable results for well-understood systems, Price notes.

The greater complexity of current drug candidates combined with the increased volume and variety of available formulation data is revealing the limitations of mechanistic models to solve some formulation challenges, Price says. Many new drug molecules fall outside the “rule-of-five” chemical space and exhibit behaviors that are not easily captured by conventional equations.

“In this evolving landscape, AI/ML models offer distinct advantages. ML algorithms can process and learn from large, multidimensional datasets, capturing subtle, nonlinear relationships between formulation variables and outcomes such as solubility, stability, and bioavailability,” Price comments. Unlike mechanistic models, he adds, ML approaches are not constrained by predefined assumptions and can uncover patterns that may be invisible to human experts, enabling formulation scientists to explore a far broader design space, accelerate optimization cycles, and address formulation challenges that would otherwise be intractable using traditional methods. Thus, while mechanistic models remain foundational tools in formulation science, Price believes AI/ML models provide powerful complementary capabilities, especially for novel drug development scenarios.

It is also advantageous, according to Bannigan, that AI/ML algorithms can be slotted directly into existing predictive modeling approaches by turning what is usually a static experimental plan into a living, adaptive process. “Instead of running a fixed set of tests designed at the start using design-of-experiment approaches, AI/ML begins with a small number of experiments, learns from the results, and instantly updates its recommendations for the next round. This cycle repeats, zeroing in on the most promising parts of the formulation space while skipping unproductive paths,” Bannigan explains. The advantages over traditional approaches include speed, efficiency, and reach. “Predictive models leveraging AI/ML enable identification of high-potential formulations faster using less API, and often find solutions that conventional methods never get close to,” he concludes.

Enhancing platform formulation strategies

AI/ML can also be used to improve platform processes for formulation development. One example, says Bannigan, is the use of these technologies as decision-making engines within self-driving labs and automated formulation platforms. “In this case, the AI/ML system analyzes incoming experimental data in real time, recommends the next most informative experiments, and identifies multiple viable formulation paths rather than prematurely converging on a single lead,” he explains. The system also standardizes workflows so that every project benefits from cumulative platform learning.

When integrated into automated laboratory platforms, AI/ML technologies can, adds Price, not only help reduce the manual effort required and variability associated with human operation, but enable the generation of large volumes of high-quality, consistent experimental data that can be leveraged to train and refine machine learning models, enabling the discovery of complex patterns and relationships that might otherwise go unnoticed.

Price also notes that the integration of ‘active learning’ strategies such as Bayesian Optimization, which works in tandem with ML models to iteratively select the most informative experiments that will most efficiently reduce model uncertainty and accelerate the search for optimal formulations, enhances this process.

“This synergy between automation, AI/ML, and active learning allows researchers to maximize the impact of each experiment, accelerate formulation optimization, and tackle challenging formulation development problems,” Price says. He adds that “these advances enable drug developers to innovate faster, reduce costs, and make more confident, evidence-based decisions throughout the formulation lifecycle.”

“The tightly integrated, data-rich, closed-loop cycle created by combining automation, AI/ML, and active learning not only accelerates the design–build–learn process and boosts experimental efficiency, it also reduces API consumption and increases the likelihood of finding scalable, high-performance formulations,” Bannigan contends. This intelligent, iterative process, he believes, will help companies identify novel formulation approaches and, ultimately, new life-altering medicines that would otherwise go undiscovered.

Increasing the likelihood of candidate success

Accelerated formulation development and identifying new formulation strategies are not the only advantages of leveraging AI/ML technologies when exploring formulation spaces. By rapidly focusing development on formulations with the highest predicted likelihood of meeting target product profiles, AI/ML also cuts down both early attrition and late-stage failures from chemistry, manufacturing, and controls-related issues, according to Bannigan.

 correlation from the start, ensures scalable formulations are identified sooner, and conserves API so teams can explore backup candidates in parallel. The result is a higher probability that promising small-molecule drugs will make it through development and into the hands of patients, essentially turning potential therapies into real, market-ready medicines faster and with less risk,” Bannigan contends.

One industrially relevant example is a predictive model developed by Merck KGaA, Darmstadt, Germany for accurately and reliably predicting cocrystal coformers for a range of APIs without the need for any prior experiments. Optimum cocrystals, according to Price, often exhibit significantly higher solubilities than the original API, but identifying them can be challenging. The AI-based model was trained using a carefully curated, large dataset of test drugs and coformers covering a large chemical space and supplemented with targeted experimental data and has been shown to dramatically improve the likelihood of finding the optimum coformer (by a factor of three compared to the trial-and-error approach) (2).

While hindrances to adoption of AI/ML in formulation development do exist, such as fragmented, inconsistent, and proprietary historical data; cultural and regulatory caution that demands interpretability; and the lack of integrated automation infrastructure to operationalize models effectively, Bannigan anticipates AI/ML becoming a standard part of formulation workflows within the next one to three years. He sees such systems becoming integrated with high-throughput platforms, driving faster design–build–learn cycles, and using active learning to adapt in real time with model design and experiment selection guided by large language models (LLMs) capable of rapidly synthesizing literature, historical data, and domain expertise.

In the longer term (3–10 years), Bannigan predicts foundation models, federated learning, and AI-driven excipient design will transform formulation development from trial-and-error to precision engineering, with LLMs evolving into specialized scientific copilots. “In this future, more therapies will advance successfully from development to the patients who need them,” he concludes.

Huanbutta, K.; et al. Artificial Intelligence-Driven Pharmaceutical Industry: A Paradigm Shift in Drug Discovery, Formulation Development, Manufacturing, Quality Control, and Post-Market Surveillance. European Journal of Pharmaceutical Sciences, 2024 203, 106938. DOI: 10.1016/j.ejps.2024.106938

Merck KgaA, Harnessing AI To Speed Up Drug Formulation.EMD Group Science Space Blog, emdgroup.com.

Bannigan, P.; et al. Machine Learning Directed Drug Formulation Development. Advanced Drug Delivery Reviews, 2021 175, 113806. DOI: 10.1016/j.addr.2021.05.016

Konagurthu, S. Revolutionizing Drug Development: AI-driven Solutions for Poor solubility and Bioavailability. Patheon Blog, Patheon.com, April 15, 2024.

Ting, J.M.; et al., Advances in Polymer Design for Enhancing Oral Drug Solubility and Delivery. Bioconjugate Chemistry, 2018 29 (4), 939-952. DOI: 10.1021/acs.bioconjchem.7b00646

Halford, B. Wrestling with Lipinski’s Rule of 5. C & E News, 2023 101 (8). https://cen.acs.org/pharmaceuticals/drug-discovery/Wrestling-Lipinski-rule-5/101/i8

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


Vol. 49, No. 8
October 2025
Pages: 14-17

When referring to this article, Challener, C. A. Predictive Modeling for Small-Molecule Formulation Development Using Advanced Algorithms. 

Advanced drug discovery tools are identifying complex small molecules with promising biologic and pharmacologic activity, posing challenges in formulation development. Traditional empirical methods are time-consuming and resource-intensive, prompting a shift towards AI/ML-driven predictive modeling and platform formulation processes. These technologies optimize excipient selection, predict stability, and enhance formulation efficiency. AI/ML can navigate complex formulation spaces, reduce API waste, and improve the likelihood of successful drug development. Despite challenges, AI/ML is expected to become integral to formulation workflows, transforming drug development from trial-and-error to precision engineering.

Artificial intelligence and machine learning are anticipated to boost success rates.

Predictive Modeling for Small-Molecule Formulation Development Using Advanced Algorithms 

Pink-white and blue-white capsule pills with concept of precision medicine with nanotechnology, proteomics, and advanced artificial intelligence to enhance diagnostics and patient-specific solutions. | Image Credit: ©Artinun - stock.adobe.com

Patient adherence to medication is a challenge. Patients prefer the use of oral medicines over injectable ones for obvious reasons. Therefore, there is an ongoing need to develop personalized and patient-centric delivery solutions, including devices, for the administration of oral medicines that fit patient lifestyles and optimize the safe and effective use of oral medicines. Key attributes include improved taste and ease of swallowing, patient-friendly administration, flexible dosing, and reduced dosing frequencies.

More sophisticated and tailored solutions are emerging, including modified-, sustained-, extended, and controlled-release formulations; smart pills incorporating digital technologies for sensing and reporting; transdermal patches; and subcutaneous implants. New manufacturing methods are also being explored to enable more personalized and patient-centric dosage forms, such as 3D printing of oral solid dosage (OSD) drugs.

Personalized OSD forms aim to improve outcomes and adherence by tailoring medicines to each patient’s needs. “Standard tablets are easy to mass-produce but do not account for differences in age, weight, genetics, or disease progression,” says Bing Xun Tan, pharmaceutical application laboratory manager, Roquette Health & Pharma Solutions. “Customizing dose strength or form maximizes therapeutic efficacy while minimizing side effects, which is crucial for pediatric and geriatric patients,” he notes.

Personalized formulations support drug titration—such as loading and maintenance dosing—as well as body surface area- or weight-based adjustments, which are particularly important in oncology and pediatric care, according to Thierry Jomini, CEO and founder of AbbatiaLabs. In addition, he observes that personalized dosing facilitates safe medication tapering, giving patients a better chance to discontinue treatment optimally, and helps combat issues like antibiotic resistance and drug habituation. Finally, comments Jomini, solid oral forms offer superior stability compared to liquids, enabling global accessibility even in regions without refrigeration, thus ensuring reliable treatment delivery across diverse environments.

Advances like 3D printing, multiparticulate systems, and improved excipients such as pregelatinized starch or mannitol now enable bespoke formulations, according to Tan. Regulators, he adds, are also starting to play a role. FDA’s pediatric exclusivity provision (1), for instance, provides a financial incentive for pharmaceutical manufacturers to conduct and submit pediatric studies supporting the development of age-appropriate dosage forms that could also allow for dose titration. Similarly, in Europe, the Pediatric Use Marketing Authorization (PUMA) (2) incentivizes medicines developed exclusively for use in the pediatric population with data and market protection for product authorization.

“These drivers support the shift from ‘one-size-fits-all’ medicines towards precision approaches that make drug therapy safer, more effective, and better adapted to the patient,” Tan concludes.

Enhancing treatment efficacy through better adherence

The drivers for patient-centric formulation development are similar to those for personalized OSD drugs because both approaches focus on enhancing treatment efficacy and patient adherence by emphasizing individual preferences and needs, says Lydia Marinou, technical sales manager with Adragos Pharma.

As examples, Marinou points out that flexible dosing schedules and dosage forms that align with patients' lifestyles can significantly improve adherence rates, and the use of mobile health applications that gather patient feedback further supports this trajectory. “Successful case studies, such as patient-centric inhalers collecting real-time data, illustrate how understanding patient profiles and medication adherence can lead to more effective and satisfactory treatment outcomes,” she adds.

John van den Anker, chief scientific officer of AbbatiaLabs, agrees that the primary motivation for both personalized and patient-centric products is improved adherence to therapy. “Medications that are easier to administer—whether by patients themselves or by caregivers—significantly increase the likelihood of consistent treatment,” he notes. “Moreover,” he continues, “patient-centric products help reduce dosing errors, which are a leading cause of medication-related issues, particularly in settings requiring precise, bodyweight-based dosing such as pediatric care.”

Patient-centric design, according to Tan, focuses on making medicines easier to use—like easier-to-swallow tablets or chewables, taste-masked forms for children or older adults, or fast-melt options including orally disintegrating tablets (ODTs) for those with dysphagia. “By reducing behavioral and sensory barriers, these designs encourage correct use. Commercially, they appeal to patients, caregivers, and providers seeking better compliance,” he says.

In addition, Tan comments that as lifestyle medicines (like those for weight loss) grow in popularity, there is also a market-driven demand for more lifestyle-friendly, personalized formats. This trend, he observes, mirrors similar trends in nutraceuticals and cosmetics, where personalization is already a significant consumer demand.

Significant overlap between personalized and patient-centric OSD drugs

Consequently, there is a clear overlap between personalized and patient-centric oral solid dosage drugs, as both concepts prioritize ease of use and accuracy in drug administration, according to Jomini.

Tan, meanwhile, views personalized OSD drugs with doses and/or drug combinations customized for individuals as, effectively, a subset of the broader patient-centric approach. “While personalization focuses on pharmacological customization (right drug, right dose), patient-centric design ensures that the dosage form itself is acceptable, convenient, and adapted to the patient’s needs and preferences,” he explains. In practice, Tan notes that advances like 3D printing enable both at once: for example, a chewable, flavored tablet designed for a child that also delivers a precise weight-based dose.

Van den Anker contends that flexible dosing devices—such as POWDOSE from AbbatiaLabs—exemplify this convergence by enabling patients or caregivers to easily and reliably select the precise dose needed, typically in just a couple of simple steps. “These innovations support both individualized treatment regimens and user-friendly administration, making them essential tools for delivering truly tailored and accessible therapies,” van den Anker believes.

Overall, concludes Tan, “patient-centric design provides the usability framework, while personalization ensures pharmacological precision. Together, they represent a shift from viewing the patient as a passive recipient of standardized treatment to an active participant whose unique needs and circumstances shape the final product.”

Recent technology advances of note for van den Anker include flexible and user-friendly dosing solutions that allow precise, individualized dosing. He also highlights 3D printing as an emerging and powerful tool for creating customized drug formulations tailored to each patient’s needs; long-acting formulations, which contribute by reducing dosing frequency and improving adherence; and easy-to-swallow formats—such as multiparticulates and dissolvable tablets—that enhance patient comfort and usability, particularly for children and the elderly. “Together, these innovations are transforming the landscape of oral drug delivery toward more tailored and accessible care,” he states.

Manufacturing, scalability, and regulatory complexity challenges

Developing patient-centric and personalized OSD drugs is not a simple endeavor, however. There are several challenges along the way. The biggest hurdles, according to Tan, lie in manufacturing, scalability, and regulatory complexity.

One major barrier highlighted by Jomini is the need for the pharmaceutical industry to invest in new infrastructure, such as equipment for 3D printing individualized doses or systems for filling flexible dosing devices, which also require compatible multiparticulate formulations.

Technologies like 3D printing, adds Tan, are well suited to one-off or small-batch production, but because each tablet is printed layer-by-layer, this method is too slow and costly for large-scale deployment. In addition, he notes that supply chains must also adapt, particularly for excipients. “The sourcing of ready-to-use blends and functional excipients that streamline small-batch production has never been more important,” Tan states.

Furthermore, Tan emphasizes that highly individualized production demands new distribution models, specialized training, and flexible quality systems capable of ensuring consistency and safety for varied products. Regulations need updating as well. “Regulatory frameworks, while evolving, still generally assume mass production of standardized forms. Clear guidance on quality controls, validation, and data protection (especially when handling patient-specific data) is still developing,” Tan says.

Another challenge for van den Anker is the need for development of robust clinical evidence clearly demonstrating that precise dosing leads to better health outcomes. In addition, he observes that for many personalized and patient-centric OSD drugs, “payers must be willing to support potentially higher costs for these advanced therapies in recognition of their benefits in adherence and therapeutic effectiveness.”

Overall, according to Tan, “the crux is how to scale up personalization without compromising cost-effectiveness, safety, or regulatory compliance—challenges that will require not only technological solutions but also reimagined business and regulatory models.”

Regulatory considerations significantly impact the development of personalized and patient-centric OSD drugs, and agencies are adapting their frameworks to better accommodate innovations, Marinou says. She points to incorporation of quality-by-design principles to ensure safety and efficacy as an example.

Regulatory frameworks must continue to evolve, however, to address novel challenges while maintaining safety and quality, according to Tan. “New production methods require robust validation (especially for on-demand and small-scale manufacturing environments), as well as solid quality controls and assured compliance with good manufacturing practices. Handling patient-specific data also demands stringent privacy protections,” he explains.

The approval of patient-customized 3D-printed medicines and devices exemplifies the evolving landscape for Marinou (3–5). She emphasizes, though, that addressing compliance complexities and establishing robust pharmacovigilance systems remain critical for companies aiming to introduce personalized treatments that align with regulatory standards. “Navigating the complexities of compliance is essential,” she says, “particularly as the inclusion of real-world evidence, which allows companies to leverage patient data for adaptive regulatory pathways, becomes increasingly important.”

If delivery is achieved using specialized devices, there are additional regulatory considerations. Products that include a device component—such as a flexible dosing system—may be classified as combination products, which can introduce added regulatory complexity, especially for developers more accustomed to small-molecule drugs, says Jomini. “While this pathway is more common in biologics and injectables, it poses a challenge in the OSD space. Moreover, any medical claims, such as improved outcomes, must be substantiated by robust clinical evidence,” he observes. Despite these hurdles, Jomini does note that specialized regulatory expertise is available to support developers through this process.

Also on the positive side, Tan notes that many pharmaceutical regulatory agencies are already encouraging patient-focused approaches. In addition to the aforementioned pediatric development incentives, he highlights FDA’s Patient-Focused Drug Development initiative (6), which emphasizes incorporating patient perspectives and needs. “Together, these regulatory drivers support safer, more tailored therapies that meet the multifaceted needs of a diverse, global patient body,” Tan says.

Despite the many challenges, developers of OSD drugs are pursuing innovative solutions that support the creation of personalized and patient-centric products designed to increase medication adherence and provide better outcomes.

Tan notes that the ability of 3D printing to enable on-demand, patient-specific tablets with tailored doses and flavors has been demonstrated for pediatric use in hospital trials (7). “Hospital pharmacies are beginning to adopt 3D printers and precise dosing devices to create tailored oral forms at the point of care—such as child-friendly, flavored tablets with exact doses,” he notes.

Fast-melt tablets and dissolvable oral films improve administration for those with swallowing difficulties, while multiparticulate systems, such as pellets and minitablets, enable flexible drug-dose combinations that can reduce pill burden, according to Tan. He points to the OraFID (Sensidose, part of Navamedic ASA) prefilled dose dispenser as simplifying minitablet administration by allowing patients and caregivers to accurately dispense the required dose. “Minitablets and multiparticulates promise superior accuracy and dosing flexibility, allowing complex regimens to be delivered in single capsules or even sprinkles with specific release profiles,” he says.

Other advances highlighted by Tan include improvements to excipient offerings such as plant-based mannitol and other starch derivatives, which ultimately support better taste-masking, compressibility, and customized release profiles. “The industry’s growing understanding of the importance of these factors is driving the development of excipients designed specifically for patient-centric forms and advanced manufacturing, laying the groundwork for truly personalized, user-friendly oral medicines,” he says.

Innovative drug delivery systems, meanwhile, are transforming personalized medicine, according to Marinou. “Nanoscale carriers, such as dendrimers and liposomes, enhance drug solubility and targeting, allowing for treatments such as personalized cancer therapies that deliver chemotherapeutics directly to tumor cells. Layered tablets provide controlled release of multiple drugs, which is particularly useful for the management of chronic conditions, as seen in combination therapies for diabetes. Additionally, smart delivery systems, such as implantable devices that can adjust dosages based on real-time physiological data, represent the future of medication adherence and effectiveness, allowing for tailored treatments that adapt to individual patient dynamics,” she comments.

In the near future, van den Anker believes the development of oral formulations for traditionally injectable therapies, such as oral insulins and GLP-1 drugs for obesity and type 2 diabetes, will have significant potential to advance personalized and patient-centric OSD drugs. “These innovations could greatly improve patient comfort and adherence,” he states.

Longer-term, Tan believes artificial intelligence (AI)-driven formulation design and fully decentralized manufacturing will revolutionize production of personalized and patient-centric OSD drugs. “AI models can help predict optimal doses and formulations based on individual patient data, while advanced manufacturing units in pharmacies or clinics produce these custom forms on demand,” he observes. Tan also expects smart ingestible devices and biosensors will eventually provide the added benefit of real-time monitoring and responsive drug release, enabling dynamic personalization (8). “Once the lingering technological and regulatory barriers have been addressed, these developments could create an interconnected, on-demand systems delivering personalized oral therapies at scale within the next decade.”

FDA. The Pediatric Exclusivity Provision: Status Report to Congress, January 2001. 

EMA. Paediatric-use Marketing Authorisations. Ema.europa.eu. Updated July 31, 2025. 

https://www.ema.europa.eu/en/human-regulatory-overview/marketing-authorisation/paediatric-medicines-marketing-authorisation/paediatric-use-marketing-authorisations

Alqahtani; et al. 3D Printed Pharmaceutical Systems for Personalized Treatment in Metabolic Syndrome. 

Aprecia. FDA Approves the First 3D Printed Product. Press Release. Aug. 3, 2015, 

https://aprecia.com/resources/press/fda-approves-the-first-3d-printed-drug-product/

Triastek. Triastek's 3D-Printed Gastric Retention Product T20G Receives FDA IND Clearance. Press Release. March 17, 2025. 

FDA. CDER Patient-Focused Drug Development. 

https://www.fda.gov/drugs/development-approval-process-drugs/cder-patient-focused-drug-development

Stoops; et al., Clinical Implementation of a Paediatric 3D-printed Combination of Sulfamethoxazole and Trimethoprim. 

Chai; et al., Utilizing an Ingestible Biosensor to Assess Real-Time Medication Adherence. 

 Dec;11(4):439-44. DOI: 10.1007/s13181-015-0494-8

Cynthia A. Challener, PhD is a contributing editor to

The development of personalized and patient-centric oral solid dosage (OSD) drugs aims to improve medication adherence and therapeutic outcomes by tailoring treatments to individual patient needs. Innovations such as 3D printing, smart pills, and flexible dosing devices are transforming drug delivery, making it more precise and user-friendly. However, challenges in manufacturing, scalability, and regulatory compliance persist. Regulatory frameworks are evolving to accommodate these innovations, emphasizing patient-focused approaches. Future advancements may include AI-driven formulation design and real-time monitoring systems, further enhancing personalized medicine.

New manufacturing methods are being explored, but challenges are still present in personalized medicine development.

Personalized and Patient-Centric Administration of Oral Medicines

Pharmacokinetic (PK)/pharmacodynamic (PD) modeling of small-molecule drug candidates provides information essential for selecting and progressing optimal drug candidates. Effective PK/PD modeling, however, requires specialized expertise, time, and resources that are not always readily available.

Use of advanced artificial intelligence (AI) and machine learning (ML) algorithms can ameliorate resource limitations while also enabling the identification of patterns not readily apparent to human analysts, thus streamlining development while providing more robust predictions of 

PK/PD modeling plays important role in drug development

Understanding the absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties of small-molecule drug candidates can inform the evaluation of small-molecule drug candidates through discovery and development efforts. Detailed information about these attributes can help weed out undesirable molecules and move forward candidates with a higher likelihood of success. In vitro and in vivo PK/PD screening studies are time-consuming and costly, however, with ADMET assessments often becoming a bottleneck in drug development programs (1).

In silico approaches for determination of ADMET properties have, therefore, become crucial to accelerating drug discovery and development efforts, allowing rapid evaluation of large numbers of compounds. “PK/PD modeling provides a quantitative framework that is essential for effective, data-driven drug development,” states Pauline Traynard, product manager for MonolixSuite at SimulationsPlus. “By integrating diverse data from laboratory experiments and clinical studies, these models allow us to understand the complex interplay between a drug’s concentration in the body and its therapeutic effect. This mechanistic insight is critical for optimizing dosing regimens, designing more efficient and informative clinical trials, and making crucial go/no-go decisions,” she explains.

Ultimately, Traynard observes, PK/PD modeling helps reduce late-stage attrition rates and supports a more streamlined, cost-effective, and successful path to regulatory approval and clinical use.

Increasing complexity creating challenges

Greater understanding of disease mechanisms and advances in rapid synthesis of compound libraries based on increasingly complex molecules, many which have limited solubility (and therefore bioavailability) under physiological conditions, has created several challenges to achieving effective PK/PD modeling.

“Emerging trends such as highly potent molecules with narrow therapeutic indices and complex drug delivery technologies (e.g., long-acting injectables or lipid nanoparticles) pose significant modeling challenges,” says Traynard. She also notes that many advanced drug candidates exhibit nonlinear kinetics, require specialized absorption models, or interact with biological barriers in non-intuitive ways.

An additional significant challenge, Traynard adds, arises during clinical development. “Understanding and predicting the high degree of variability in how different patients respond to a drug is difficult to decipher from the limited samples per patient typically collected during clinical trials,” she comments.

AI/ML algorithms overcome many modeling challenges

AI/ML algorithms are instrumental in overcoming many of these modern PK/PD challenges, according to Traynard, due to their superior ability to identify complex patterns in high-dimensional data where mechanistic understanding is still incomplete.

The most impactful applications for AI/ML-based PK/PD modeling are, Traynard observes, in early-stage discovery and the enhancement of mechanistic models. “In discovery, AI/ML-driven prediction of ADME and toxicity is invaluable for rapidly screening vast compound libraries to de-risk projects before significant investment is made,” she explains.

For instance, Traynard notes that AI/ML models can rapidly and accurately predict a full suite of ADME properties directly from a chemical structure, allowing scientists to screen and optimize thousands of candidates virtually and focus resources on the most promising ones. In clinical development, meanwhile, ML-based models can in early development be employed to analyze sparse patient data for efficient identification of factors contributing to variability in drug response, leading to more robust population PK models and better-informed dosing strategies, according to Traynard.

AI/ML techniques are particularly valuable for classes of APIs where traditional modeling methods face limitations due to underlying biological or chemical complexity. Traynard highlights compounds with poor solubility and permeability (Biopharmaceutics Classification System Class II/IV), where AI/ML can effectively model the intricate relationship between a drug’s formulation and its in vivo absorption to predict bioavailability. “It is also used for APIs with complex safety profiles or non-linear pharmacokinetics, as AI/ML is adept at integrating diverse biological data to identify safety signals, such as the potential for organ injury, that are difficult to predict with simpler models,” she says.

Significant benefits can also be obtained when using ML models in conjunction with in vitro-in vivo extrapolation and physiologically based pharmacokinetic (PBPK) modeling because integration of these approaches provides a more comprehensive picture of a drug’s ADMET profile (2). Augmentation of established PBPK and population PK/PD models with model structure learning and parameter estimation is, says Traynard, a second key application. As an example, such modified models are useful for identifying influential covariates or nonlinear relationships within clinical data.

A third important application of advance algorithms in PK/PD modeling is the automation of model development workflows, which Traynard believes can reduce time-intensive manual steps by implementing ML-guided model selection, fit optimization, and diagnostics.

The biggest challenge to enabling the widespread use of AI/ML algorithms to improve PK/PD modeling is the same issue facing the development of AI/ML models for all other applications in the biopharmaceutical industry: the need for large quantities of high-quality, reliable data. “Limited access to large, high-quality datasets needed to train reliable AI models is a major challenge to broader AI/ML adoption in PK/PD modeling, largely due to restrictions and experimental variability,” observes Traynard.

Another important concern noted by Traynard is the inherent “black box” nature of some algorithms, which may lack the transparency needed for regulatory or internal scientific confidence.

Hybrid approaches combining established mechanistic models like population PK/PD or PBPK with interpretable AI components are therefore gaining traction. “This strategy grounds the AI’s powerful pattern-recognition abilities in the context of known biology, making the results more interpretable, scientifically plausible, and trustworthy for both scientists and regulators,” Traynard comments.

Regulatory acceptance is crucial, of course, and there is growing interest from regulators in AI/ML’s potential for population simulation, dose selection, and decision support, according to Traynard, particularly when integrated into PBPK or exposure-response models used in regulatory filings.

“The core regulatory concerns,” observes Traynard, “are model transparency, validation, and managing bias to ensure that any AI/ML tool is fit-for-purpose.” As a result, she says, there is a strong emphasis on establishing “good machine learning practice” and developing explainable AI that allows regulators to understand the basis of a model’s predictions.

Going forward, Traynard believes the evolution of AI/ML in PK/PD modeling is likely to center on a deeper integration with mechanistic science, creating powerful hybrid systems that are both more predictive and explainable. “As these predictive models become more robust, we anticipate advances in automated clinical trial simulation and design, where AI could be used to forecast trial outcomes under different scenarios and optimize protocol parameters in silico before a single patient is enrolled,” she adds.

Looking further out, Traynard notes that leveraging AI to deliver truly personalized medicine represents an important frontier. “The ambition,” she explains, “is to use sophisticated algorithms to integrate vast, patient-specific datasets to enable more accurate personalized predictions of an individual’s unique response to a drug, paving the way for individual treatment optimization.”

Myung, Y.; de Sá. A.G.C.; and Ascher, D.B. Deep-PK: Deep Learning for Small Molecule Pharmacokinetic and Toxicity Prediction Open Access. 

 52 (W1), pp. W469–W475. DOI: 10.1093/nar/gkae254

Bassani, D.; Parrott, N. J.; Manevski, N.; and Zhang, J. D. Another String to your Bow: Machine Learning Prediction of the Pharmacokinetic Properties of Small Molecules.

 19(6), 683–698. DOI:10.1080/17460441.2024.2348157

Cynthia A. Challener, PhD, has been a freelance technical writer for more than 25 years and is a contributing editor to 

Pharmacokinetic/pharmacodynamic (PK/PD) modeling is crucial for drug development, providing insights into drug absorption, distribution, metabolism, elimination, and toxicity (ADMET). However, traditional PK/PD modeling faces challenges due to complex drug properties and patient variability. Advanced AI and ML algorithms offer solutions by identifying patterns in high-dimensional data, enhancing early-stage discovery, and improving mechanistic models. Despite their potential, AI/ML models require large, high-quality datasets and face transparency issues. Hybrid models combining AI with mechanistic approaches are gaining traction, promising more predictive and explainable outcomes.

Artificial intelligence and machine learning can help identify complex patterns.

Overcoming PK/PD Modeling Challenges

medicine and vision concept - woman closeup and eye diagram, future technologies | Image Credit: ©Julia - stock.adobe.com

Aging of the global population and the rise of chronic diseases, particularly diabetes, have contributed to an increase in the occurrence of eye diseases, such as glaucoma, keratitis, age-related macular degeneration (AMD), dry eye disorder (DED), and diabetic retinopathy (DR). Pharmaceutical companies have responded with growing investment in the research and development of novel eye treatments and drug delivery systems designed to address key challenges in ocular therapy. Current treatments range from traditional small-molecule and antibody-based drugs to cell and gene therapies and nanoparticle delivery solutions, along with various types of ocular implants and the use of drug-eluting contact lenses (1,2).

One area of focus has been the development of solutions for the sustained delivery of treatments, particularly to the posterior part of the eye. Many ocular therapies are formulated as eye drops that must be administered frequently to overcome the many physical and mechanistic barriers in the eye and ensure sufficient dosing. Sustained and targeted delivery products are helping reduce the dosing frequency by overcoming ocular barriers and increasing bioavailability.

Delivery of therapeutics to the anterior, or front of the eye, is generally simpler than delivery to the posterior part, because topical eye drops are an option. There are limitations to their effectiveness, however. “Drops do not maintain good bioavailability and are relatively quickly flushed out by nasolacrimal drainage,” explains Jeffrey S. Heier, chief scientific officer with Ocular Therapeutix. “While they are a convenient mode of delivery,” he adds, “they present challenges in terms of not only bioavailability, but limited duration of therapeutic effect, both of which contribute to inconsistent patient compliance and suboptimal outcomes.”

Treatments for glaucoma (high eye pressure), as well as infections and inflammation of the eye, are good examples. Glaucoma eye drops typically need to be administered at least once daily, and in many cases, multiple times a day. For some conditions, Heier observes, drops must be delivered on an hourly basis. “Such a dosing frequency is only sustainable for a short period of time. In fact, it is readily accepted that even with easy-to-administer options like eye drops that must be applied just once daily, adherence remains a major issue due to the frequency of this regimen,” he says.

Another option for treatment of the anterior segment of the eye is straightforward intracameral injections, according to Heier. This method of administration delivers ocular therapies directly into the anterior chamber of the eye, allowing for targeted delivery and resulting in enhanced bioavailability (3).

While treatments for infection, inflammation, high eye pressure, and neovascularization have been shown to be effective, this method of delivery is considered “off-label,” as FDA has not approved products specifically for this use, except two intracameral implants (see below). In addition, many patients are hesitant to receive eye injections.

Targeting the back of the eye presents unique challenges, not only in achieving therapeutic durability but also in navigating complex regulatory expectations, Heier comments. Indeed, delivery to the posterior segment is anatomically more complicated, especially when the durability of treatment is a primary objective.

“Although advances in intravitreal medicines have shown incremental increases in durability compared to prior standard of care, current retinal therapies rely on frequent intravitreal injections to maintain their effect,” observes Peter Jarrett, chief technical officer for Ocular Therapeutix.

For many patients, even therapies that require administration every three to four months can be challenging, as this type of regimen places a significant burden on patients and care partners, according to Jarrett. “To treat the full spectrum of patients with retinal disease or other conditions affecting the posterior segment, there is still a need for more durable treatments, and the development of longer-lasting treatments is such a critical focus,” he says.

New sustained-delivery therapies, however, must be proven to be as safe and effective as existing treatments that may require much more frequent administration. In addition, Jarrett notes that targeted delivery to the retina and posterior segment of the eye necessitates overcoming several blood-ocular barriers, including the blood-aqueous barrier and the blood-retina barrier.

“Historically,” Jarrett explains, “overcoming these physiological defenses has required delivering drugs at high concentrations. However, this approach often results in increased systemic exposure, which in turn can lead to a higher incidence of adverse events and toxicities. Striking the right balance between effective ocular delivery and systemic safety remains a focus in the development of retinal therapies.”

Potential for better treatments with greater convenience

While small steps taken to enhance ocular delivery have had positive impacts, there is still real potential for innovative solutions, particularly those leveraging sustained-release delivery technologies, to have dramatic effects on patient outcomes.

“While incremental improvements can be achieved through sophisticated formulations, such as enhancing solubility, prolonging ocular surface retention, or improving penetration through ocular barriers, these refinements often reach a ceiling in terms of clinical impact,” Heier remarks. “In contrast, transformative advances in treating chronic eye diseases are more directly realized through sustained-release delivery technologies, which bypass many of the pharmacokinetic limitations of topical or injectable drugs by providing continuous, controlled delivery of therapeutics directly to targeted intraocular tissues over extended periods,” he contends.

In addition, Jarrett emphasizes how important it is for developers to realize that results obtained in clinical trials often do not translate to the real world for treatments with dosing regimens patients have difficulty consistently maintaining as part of their daily lives. “A durable therapy that reduces treatment burden could offer not just convenience, but potentially better long-term outcomes for patients who would otherwise fall through the cracks of an intensive treatment schedule. Recognizing this balance between ideal clinical trial conditions and the real-world experience of patients is key to advancing more durable treatment options,” he states.

Nanoscale drug-delivery systems are recognized to achieve enhanced bioavailability and targeted and sustained delivery of many types of therapeutics addressing a wide variety of conditions, including eye diseases (2). Examples of different types of nanoparticles used for ocular drug delivery include nanomicelles, liposomes, polymeric nanospheres, and peptide-based nanoparticles (2).

Nanomicelles in preclinical testing have been shown to experience good uptake by corneal cells and when administered topically to achieve sustained delivery of drugs to treat DED, neovascular AMD, inflammation, and other eye disorders (1). Biocompatible lipid nanoparticles can deliver drugs with both hydrophobic and hydrophilic properties for extended periods. In one animal model of glaucoma, large unilamellar liposomes achieved sustained delivery of latanoprost, a common drug used to reduce eye pressure, for up to five days (4).

Peptide-based nanoscale delivery systems are particularly attractive because they are not only biocompatible, but can be used to target specific cell receptors, disrupt endosomal membranes, and carry drug payloads to the nucleus (1). Of the two main types of systems (elastin-like polypeptides and silk fibroin polymers), the former have been investigated for sustained release of drug substances from contact lenses due to their ability to remain adhered to their surfaces.

Researchers continue to investigate the potential of nanoscale materials for the formulation of extended-release ocular therapeutics, with efforts focused on increased drug-loading capacity, ocular residence time, and release periods while reducing potential toxicities (1).

Drug-eluting contact lenses and canalicular plugs

Given the limitations and issues associated with the use of eye drops and intracameral injections for treatment of conditions in the anterior segment of the eye, other options for delivery are looking to leverage the accessibility of this portion of the eye, according to Heier. Examples include drug-eluting contact lenses and canalicular plugs.

As indicated previously, sustained, topical delivery of ocular therapies to the anterior of the eye is possible using contact lenses loaded with active drug substances, such as peptide-based nanoparticles carrying drug payloads. It is even possible to improve drug loading by incorporating non-covalent binding sites into the lenses when they are manufactured (1). Delivering drugs using contact lenses is attractive because this approach is non-invasive and can potentially support increased drug uptake compared to topical applications (eye drops, creams, ointments). ACUVUE Theravision contact lenses (Johnson & Johnson Vision Care), which provide sustained delivery of ketotifen for the treatment of itchy eyes, are the first FDA-approved drug-eluting contact lens product (5).

Canalicular plugs belong to a class of treatments known as punctal plugs, which are inserts designed to slow or block the outflow of tears from the eye (6). Punctal plugs can be temporary, semi-permanent (resorbable), or permanent (synthetic, usually silicone), and some are able to provide sustained release of drug substances. They are most widely used to treat DED and have been shown over three decades to be generally safe and effective; although, they can fall out, become partially displaced, or lead to watery eyes.

Recent developments have helped address many of these issues. One example is a canalicular gel device (Lacrifill, Nordic Pharma) introduced in June 2024 (7). The chemically crosslinked particulate hyaluronic acid (HA) hydrogel device is designed to completely fill the canaliculus regardless of its size in different patients, preventing dislodgement and the buildup of stagnant tears and mucus that can result in infection. It is meant to be used for up to six months and can be easily removed by applying a sterile solution.

The aging global population and rise in chronic diseases like diabetes have led to increased eye diseases, prompting pharmaceutical companies to invest in novel ocular treatments and drug delivery systems. These include small-molecule drugs, cell and gene therapies, and nanoparticle delivery solutions. Sustained delivery systems are crucial for reducing dosing frequency and improving bioavailability, especially for posterior eye treatments. Innovations like drug-eluting contact lenses, canalicular plugs, and ocular implants are being developed to enhance treatment efficacy and patient compliance, with nanoscale drug-delivery systems showing promise for targeted, sustained delivery.

Hydrogels, drug-eluting contact lenses, and other implant technologies show real promise.