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

Cynthia A. Challener

Autonomous solubility tester. Imitation dissolution of the tablet in the human stomach. Scientific research of pharmaceutical laboratory. Laboratory research in pharmacy. | Image Credit: ©neznamov1984 -stock.adobe.com

Formulators challenged by promising investigational drug candidates with high bioactivity but limited solubility increasingly find amorphous solid dispersions (ASDs) an attractive solution for enhancing bioavailability. The FDA approved nearly 50 drug products formulated as ASDs between 2012 and 2023.

 However, identifying the optimum method of ASD preparation (eg, spray drying [SD] or hot-melt extrusion [HME]) and choosing the polymeric carrier, surfactant, and drug loading level can be challenging.

Traditionally, extensive physical experimentation using a trial-and-error approach has been required, which is both costly and time-consuming. As computational power and mechanistic understanding of ASD stability and solubility behavior have expanded, predictive modeling and simulation using quantum mechanics (QM), molecular mechanics (MM), molecular dynamics (MD), and physiologically based pharmacokinetic approaches have helped accelerate formulation development by providing information on thermodynamic properties and potential drug-excipient interactions, and miscibility, among other attributes that impact ASD performance during storage and in vivo.

Predictive Modeling in ASD Formulation Development

For many poorly soluble molecules, dozens of potential ASD formulations are possible. Ongoing research is leading to the development of new ASD forms. Similarly, the choices of polymer carriers, surfactants, and other excipients suitable for generating optimal ASDs are continually expanding. Physical screening of possible formulations consumes expensive API in addition to consuming time and human resources.

Predictive tools help narrow the field from dozens of formulations to a targeted set of 2 to 6, reducing API consumption, effort, and time in the experimentation process, according to Josh Marsh, BAE and PBBM lead scientist with Lonza. “By reducing experimental burden early in development, they support more efficient process development and scale-up activities,” he says.

Wider Use of Predictive Modeling and Simulation in ASD Formulation

Several trends have contributed to greater use of predictive modeling and simulation approaches in ASD formulation development. Advances in workflow automation and scalability have made broader simulation-based screening practical, including evaluation of excipients in the context of the intended manufacturing route, according to Shiva Sekharan, global portfolio leader for formulations and CSP software with Schrödinger. “Customized multistage MD workflows and advances in machine learning help better leverage historical experimental data, allowing prioritization of formulations and processing conditions for testing,” he says.

Advances in computational resources are also reducing barriers, says Craig Sather, associate director CMC and principal investigator of bioavailability enhancement at Lonza. “The computing power available today enables faster iteration and larger-scale simulation studies,” he says.

Using Predictive Modeling/Simulation to Identify the Best Enabling ASD Technology

Extensive knowledge of various molecule attributes is essential to selecting the optimum ASD technology. “Successful ASD development begins with a thorough understanding of the molecule's characteristics and behavior,” says Aimee Spenceley, pre-form team leader with Codis.

Selecting among SD, HME, or alternatives such as coprecipitation and freeze-drying depends on both API properties and excipient choice, Sekharan adds.

According to Spenceley, solubility limitations, thermal properties, crystallization tendency, chemical stability, and interactions with potential excipients all guide selection of the most appropriate enabling technology—whether SD, HME, or alternative approaches—and processing conditions. “The objective is to develop a formulation that delivers enhanced bioavailability while maintaining a suitable stability profile and sufficient downstream processability to create a commercially viable drug product,” she says.

SD requires the API and polymer to be compatible with a common, process-appropriate solvent system, whereas HME requires a thermally stable, thermoplastic polymer with suitable melt viscosity at a temperature the API can tolerate, Sekharan explains. “Some polymers can be used with either route, so the choice ultimately depends on drug-polymer miscibility, target drug loading, thermal stability, solvent constraints, processability, scale, downstream processing, and the intended dosage form,” he says.

Because every molecule presents a unique set of challenges, Marsh emphasizes the importance of starting with understanding the specific problem statement and physicochemical properties of the compound and letting the data lead to the right formulation.

Data gathered using microscale screening approaches that allow rapid assessment of polymer compatibility, process suitability, and formulation performance while minimizing API consumption can be used to model and extrapolate formulation and process behavior, establishing operating windows that support scalability and technology transfer, Spenceley observes. “This strategy enables early identification of the most promising development pathway, reducing technical risk and accelerating progression toward a robust commercial process,” she says.

Physical Experimentation Informs Predictive ASD Modeling

ASD development is best supported by an integrated modeling and experimental strategy. “Structure-property assessments, thermodynamic and physical stability models, spray-drying process models, dissolution models, and physiologically based biopharmaceutics models can be used together to assess API-polymer compatibility, drug loading limits, crystallization risk, manufacturability, and the translation of in vitro performance to in vivo exposure,” Sather says. He adds that these models are most useful when they generate hypotheses that can be tested using targeted, scale-relevant experiments.

Sather highlights scale as being particularly important, as changes in droplet size, drying kinetics, secondary solvent removal, and thermal history can affect ASD phase behavior even when conventional bulk characterization and dissolution results appear comparable. This issue came to light in a recent development program in which surface-sensitive analysis identified crystallization in solvent-wet ASD produced at larger SD scale that was not detected initially by bulk solid-state methods. “This finding led to a more integrated evaluation of formulation composition, process thermodynamics, drying kinetics, and wet-product handling,” he says.

Specific experimental methods used at Codis to screen excipients, including thermal miscibility studies, film-casting assessments, and liquid-liquid phase separation investigations, provide an early understanding of ASD prototype formulation behavior and help define compositions that can deliver enhanced solubility while maintaining physical stability, according to Spenceley. Microdissolution testing in biorelevant media is used to benchmark solubility and supersaturation behavior and provide an indication of likely in vivo performance, whereas microflux studies can be used to assess permeation enhancement potential. “These tools generate data that can be modeled and extrapolated to support formulation optimization, reduce development risk, and improve confidence in progression toward scalable ASD products,” she says.

Modeling/Simulation Techniques for Formulation Development

Several techniques are being used for predictive modeling and simulation applied to ASD formulation. Two models widely used to predict the physical stability of ASDs, according to Marsh, include the perturbed-chain statistical associating fluid theory (PC-SAFT) and the conductor-like screening model for real solvents (COSMO-RS), which are used to predict favorable or unfavorable interactions between APIs and common polymers used in ASD formulations. However, Marsh notes that these models are limited because their applicability for assessment of long-term stability of ASDs is not universal.

PC-SAFT is a molecular thermodynamics model, whereas COSMO-RS is a quantum chemistry–based equilibrium thermodynamics method. They are used to predict attributes such as density, thermal expansion coefficient, glass transition temperature, and heat capacity, as well as to calculate phase equilibria and predict chemical reactions within ASDs.

Some accelerated stability modeling techniques are also used, including the Accelerated Stability Assessment Program (ASAP) and advanced kinetic modeling (AKM).

 These models are particularly useful for ASD formulations that tend to crystallize or undergo chemical degradation.

The integration of thermodynamic modeling, multiscale molecular simulation, and targeted experiments has also been shown to be effective, according to Sekharan. In one study, PC-SAFT indicated hydration-induced liquid-liquid phase separation, whereas coarse-grained simulations showed increasing drug-rich aggregation with drug loading; microscopy confirmed that, at 40 wt%, a drug-rich interfacial layer impeded further dissolution.

 “This study demonstrates how modeling can identify formulation-specific release mechanisms and guide experimental design rather than simply rank candidates,” Sekharan says.

What Predictive Modeling and Simulation Tools are Useful in ASD Formulation Development?

Numerous tools leveraging various techniques provide ASD formulators the ability to model and simulate important aspects of ASD formulation performance. Accelerated stability modeling tools, such as FreeThink ASAP, are increasingly used to predict long-term stability risk and support formulation selection, according to Spenceley.

Marsh highlights Simulations Plus’ ADMET Predictor as a tool for predicting physicochemical properties and informing experimental design before moving to in vitro screening. ADMET Predictor, together with Simulations Plus’ GastroPlus, can also support the prediction of in vivo performance.

Lonza also constructs physiologically based biopharmaceutics models that combine in vitro data with in silico predictions to determine which formulations perform best in various simulated animal models and physiological conditions. “These models can be used to evaluate food effects, proton pump inhibitor effects, and performance across patient populations, helping us better understand formulation risks and identify strategies to mitigate them before clinical development,” Marsh says. This approach is critical, he adds, because relying on in vitro testing alone can overlook the interplay of formulation and physiology and rule out potentially viable formulation options.

Sekharan notes that Schrödinger believes a multiscale approach for predictive modeling of formulations is essential because ASD performance is inherently a multiscale problem. He explains that QM and atomistic MD characterize API-polymer energetics, specific interactions, and compatibility, whereas coarse-grained MD extends the analysis to phase separation, aggregation, and dissolution mechanisms over larger lengths and time scales.

FEP+, a proprietary physics-based free energy perturbation technology for computationally predicting protein-ligand binding, can be used to calculate relative free energy differences associated with molecular transfer between solvent environments, providing an additional quantitative approach to questions of solvation and partitioning. Glass-transition and penetrant-loading calculations assess molecular mobility and water uptake.

In addition, these capabilities can be integrated with established thermodynamic methods such as PC-SAFT and Flory–Huggins, as well as with dosage-form, biopharmaceutic, and pharmacokinetic models, to connect molecular interactions with bulk-phase behavior, tablet performance, and in vivo exposure and guide experimental follow-up, Sekharan observes. He adds that formulation machine learning (ML) further supports screening by mapping ingredient structure and composition to target properties and prioritizing candidates for testing.

The Role of AI and ML in ASD Formulation Development

There is growing interest across the industry in how artificial intelligence (AI) and ML approaches can be applied to drug development and formulation science, including for ASDs, particularly as these approaches apply to chemical structure and formulation data rather than traditional language-based data sets.

ML algorithms such as transfer learning, one-shot and zero-shot learning, and Bayesian-based optimization, for instance, help formulators uncover nonlinear relationships between various formulation parameters and target quality attributes that human researchers cannot detect.

 In one study, researchers trained multiple ML models over 3 to 6 months using stability data for more than 600 ASDs to accurately predict ASD stability and other properties.

 PharmSD, meanwhile, is a freely available, web-based, AI-driven computational platform for predicting the physical stability, dissolution type, and dissolution rate of ASDs.

Mechanistic models, Sather says, remain particularly valuable because they are grounded in established physical and thermodynamic principles and can be applied even when large training data sets are unavailable. He believes the best results with ML models are obtained when they are paired with historical ASD expertise, high-quality experimental data, and mechanistic understanding. “The value of these models ultimately depends on the quality of underlying data and scientific rigor used to develop and validate them. Successful implementation requires expertise in both data science and formulation development to ensure model outputs are relevant, reliable, and actionable,” he says.

Lonza, for instance, has demonstrated the use of advanced ML algorithms for particle size prediction for SD dispersions to support manufacturability assessment across scales. “Our success has shown that AI and ML can expand the existing experimental and predictive modeling tool kit,” Sather says. “Their role, however,” he continues, “is not to replace experimentation or scientific judgment, but to focus development on the highest-value experiments, reduce API consumption, and improve confidence in performance, manufacturability, and scalability across the product life cycle.”

Future Use of Predictive Modeling/Simulation for ASD Formulation Development

As understanding of ASD kinetics and behavior mechanisms grows, modeling and simulation techniques advance, and computational speed increases, predictions of general ASD formulation properties, long-term stability, and in vivo performance will improve and become a more valued and notable component of ASD formulation development.

Marsh expects the focus to be on improving prediction of long-term ASD formulation stability. “That will require generation of a significant amount of high-quality data and thus significant investment, but the resulting models may support higher active loading in ASD intermediates and thus optimized active loading in final solid dosage forms, reducing pill burden and improving the patient experience,” he says.

Sather anticipates better integration of modeling tools and data streams to allow teams to evaluate complex formulation challenges more efficiently and build on prior knowledge. “Ultimately, predictive modeling is most powerful when combined with experimental data, scientific expertise, and a clear understanding of the development pathway. By integrating these capabilities early on, drug developers can make more informed formulation decisions, reduce development risk, and build confidence in formulation performance, manufacturability, and scalability through to commercialization,” Sather says.

Regardless of future advances in predictive modeling/simulation for ASD formulations, Marsh emphasizes that human expertise remains the gold standard when it comes to interpreting in vitro data, understanding model inputs and outputs, and informing formulation and development strategies for drug developers. Access to sufficient quantities of high-quality experimental data is also essential and a major challenge, according to Sather.

It is important to remember that models and simulations will always provide answers; informed interpretation is necessary to ensure those outputs are meaningful.

 Models/simulations used to aid ASD formulation development should be selected and applied with careful thought and consideration. In addition, more research is needed to overcome current limitations and improve their performance.

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

Articles

Amorphous solid dispersions are increasingly deployed to address poor solubility, yet selecting the optimal manufacturing route, excipient set, and drug loading remains complex. Predictive modeling across thermodynamics, molecular simulation, and biopharmaceutics narrows formulation space, reduces API use, and accelerates scale-up by anticipating miscibility, crystallization risk, and in vivo exposure. Workflow automation, improved computing, and multiscale methods enable practical screening, while targeted experiments remain essential for validation, particularly to uncover scale-dependent phase behavior. AI/ML augments—not replaces—mechanistic models and expert interpretation.

Effective modeling and simulation can reduce physical experimental requirements, ASD development risks, and time to market.

Application of Predictive Modeling in the Development of Amorphous Solid Dispersions 

Creative image of embryonic stem cells, cellular therapy, 3d illustration | Image Credit: ©pinkeyes - stock.adobe.com

Control of process and product quality is essential to ensuring the development and manufacture of safe and effective medicines. For autologous adoptive cell therapies, establishing control can be challenging due to significant variability in raw materials. The quality of patient cells, media formulations containing serum, and viral vectors used for gene delivery depends on numerous factors, not all of which can be easily controlled. Understanding the potential variabilities and how they may impact the cell therapy process and product quality is therefore essential.

Adopting a risk-based, phase‑appropriate control strategy that identifies critical raw materials, qualifies suppliers, defines functional specs and testing (including safety), and predefines adaptive controls is essential to minimize negative impacts on product identity, purity, potency, and safety, according to Chy-Anh Tran, head of science and technology with FUJIFILM Biotechnologies in California. Establishing, to the extent possible, standardized manufacturing practices that are still sufficiently flexible to accommodate raw material variability is equally important.

Starting material variability arises from several factors. The most consequential source, observes Cellares COO Ossama Eissa, is the starting material itself. “Patient apheresis quality is shaped by disease state, prior treatments, and collection method, and you simply cannot standardize it away,” he states.

Prior systemic therapies to which patients have been exposed significantly impact T cell fitness and functionality, adds Chantale Bernatchez, senior director of cell therapy process development at CTMC. “This consideration applies equally to hematologic malignancies and solid tumors, therefore influencing the performance of multiple T cell–based therapeutic modalities, including chimeric antigen receptor (CAR)-T, T-cell receptor (TCR)-engineered T cells, and tumor-infiltrating lymphocyte (TIL) therapies,” she says.

The collection method, encompassing the device, protocol settings, anticoagulant type, operator technique, timing relative to the last treatment, and the use of mobilization agents, can also introduce variability, Tran says. Handling conditions for patient-derived cellular raw materials, including transport temperature and time, fresh vs cryopreserved workflows, and storage duration, can compound the variabilities, according to Tran.

Variability in viral vectors and media components, especially if media are uncharacterized or not chemically defined, further compounds the challenge, notes Eissa. Downstream manipulations can also magnify raw material variabilities.

 “All these factors combined make a flexible, data-rich manufacturing system essential,” he concludes.

Creating Numerous Process Development and Manufacturing Challenges

The consequences of failing to effectively manage raw material variability in autologous adoptive cell therapies can be severe. Failure to incorporate clinically representative starting material during process development introduces substantial risk to manufacturing robustness. “This gap frequently manifests as unanticipated variability or elevated batch failure rates during clinical production, ultimately necessitating reactive process redesign and delaying program progression,” says Bernatchez. In the worst-case scenario, failure of a production batch means the patient, who has been waiting for perhaps several weeks, does not receive what is likely a last-line treatment.

 The drugmaker also experiences lost revenues.

During process development, differences in raw materials make it hard to assess true process effects, so assay results can be highly variable, Tran notes. “For instance, healthy donor data may not always predict patient cells. As a result, teams have to adjust on the fly, which makes runs harder to compare and adds time, effort, and cost,” she comments.

For good manufacturing practice (GMP) compliance, Tran notes that variability drives unreliable yields and cycle times, elevating out-of-specification rates and deviations. “Here again, operators must make more real-time adjustments, straining scheduling, capacity, and logistics,” she says.

Eissa agrees: “Compounding the inherent, unavoidable variability of patient cells during early development stages with avoidable variability from manufacturing processes makes it nearly impossible to distinguish true process signals from noise, leading to parameters that don't hold in GMP and ultimately driving batch failures.”

In addition, supplier lot differences require prequalification/bridging and widen critical quality attribute (CQA) spread, challenging release and regulatory justification, according to Tran.

It is therefore imperative, emphasizes Eissa, to eliminate avoidable variability through chemically defined media and well-characterized processes, so that the unavoidable biological variability from patient material can be isolated and managed on its own terms. “Equally important,” he observes, “is to systematically and progressively characterize patient material variability through GMP manufacturing and across clinical phases, building the knowledge needed to predict its impact at commercial scale and design a process robust enough to succeed across most or the full spectrum of patients it will serve.”

Use a Multipronged Approach to Enhance Control of Cellular Variability

To accommodate the variability inherent to patient-derived raw materials, it is necessary to enhance overall process robustness. That can be achieved by using patient-derived material during process development instead of donor cells, according to Bernatchez. “This approach enables early characterization of variability across key process parameters under conditions that reflect the intended clinical starting material,” she explains.

In addition, because not all unit operations are equally sensitive to variability in starting materials, Bernatchez notes that it is also important to systematically identify those most affected by patient-to-patient heterogeneity so that optimization efforts can be targeted. She highlights optimization of ex vivo T-cell expansion processes because it can partially mitigate deficits in T-cell function that otherwise limit viral transduction efficiency and proliferative capacity.

In this context, Bernatchez says longitudinal data trending from development studies through clinical manufacturing are critical, particularly in cases where development activities relied predominantly on healthy donor material. “Continuity in data analysis enables the early detection of out-of-trend performance and supports timely implementation of corrective process adjustments,” she observes.

More specific steps noted by Eissa that can be taken include the following:

Use of automated manufacturing and quality control testing, chemically defined media, standardized apheresis handling (collection, transport, timing), and well-characterized unit operations to eliminate avoidable variability and remove confounding factors so patient biology can be studied clearly.

Profile incoming apheresis material and collate the information as a database progressively across clinical phases, spanning diverse patient populations, disease states, and treatment histories to thoroughly characterize patient material and identify which starting material attributes predict process performance and product quality.

Integrate electronic batch records and laboratory information management systems for real-time data capture and analysis to further connect process data to starting material attributes and product outcomes.

Apply multivariate modeling and statistical process control to predict batch performance from incoming material profiles and use a design-of-experiment approach during development to separate patient-driven signals from process noise.

Collaborate with clinical sites and apheresis centers to create a structured apheresis checklist that captures all relevant starting material attributes at the point of collection and educates clinical staff on what matters and why, flags patients whose material may not meet ideal criteria before manufacturing begins, and provides critical context when process or testing issues arise downstream.

On the latter point, Eissa observes that “even when collection proceeds outside ideal parameters, the checklist creates an early indicator of where the process may struggle, turning what would otherwise be an unexpected deviation into an anticipated and manageable variable.”

With respect to patient material characterization, Eissa believes it should be treated as a core development deliverable. “The knowledge built through clinical phases should make the commercial process predictably robust across the full patient population, not just ideal donors,” he notes.

Minimize Variability in Media and Viral-Vector Raw Materials

Although variability in patient-derived cellular starting materials often dominates the discussion of raw material variability in the development of autologous cell therapies, use of serum-containing media formulations and, for adoptive cell therapies that require genetic modification, batch-to-batch variability of viral vectors can exacerbate the challenges posed by the variability of cellular starting materials. Using well-characterized and defined reagents can help minimize these issues.

For media formulations, variability can, according to Bernatchez, be reduced through the use of chemically defined media supplemented with serum replacement components. “The increasing availability of GMP-grade, chemically defined media may enable greater control and consistency in cell therapy manufacturing processes,” she comments.

When it comes to viral vectors, a key issue can be variable potency due to different concentrations of functional infectious particles from batch to batch. A key objective of process development is, therefore, to define the optimal ratio of viral particles to T cells required to achieve a target transduction efficiency, according to Bernatchez. “Once established using a reference viral-vector lot, it is generally assumed that maintaining a constant particle-to-cell ratio across subsequent lots of the same construct will yield comparable T-cell engineering outcomes,” she explains.

This assumption does not consistently hold, however, because variability introduced during viral vector production and purification can significantly affect particle infectivity and functional potency. “As such,” says Bernatchez, “reliance solely on particle counts or nominal multiplicity of infection may be insufficient to ensure process consistency.”

The development and implementation of advanced analytical methods to more accurately characterize viral vector quality and infectivity are therefore essential. “These approaches enable more robust definition of CQAs and support improved standardization of viral-vector manufacturing processes, ultimately ensuring consistent production of T-cell therapy products that meet predefined specifications,” she concludes.

Decentralized Manufacturing Can Make a Difference

A big change with significant potential to help reduce and manage raw material variability for autologous adoptive cell therapies is to switch from a centralized to a decentralized manufacturing strategy. “Decentralized and point-of-care manufacturing approaches offer the possibility to mitigate risks associated with logistical delays during the transport of patient-derived materials, including starting material (eg, apheresis products or tumor tissue) and final drug product, that have the potential to adversely affect cell viability and functional quality,” notes Bernatchez.

Cold-chain and logistics variability are real and underappreciated sources of risk, agrees Eissa. However, he emphasizes that decentralized manufacturing only reduces these risks at scale if it involves deployment of a standardized, automated platform consistently across sites. “It is essential to achieve decentralized manufacturing without sacrificing the process consistency that patient outcomes depend on,” he states.

Collaboration With Vendors Is Essential to Success

Vendors have an important role to play in helping to minimize raw material variability. Developers and manufacturers of autologous, adoptive cell therapies therefore have the best chance of managing raw material variability by collaborating closely with vendors, including media suppliers, viral-vector producers, and hospitals and apheresis centers.

In fact, Eissa believes the latter group should be considered as key participants in the first step of the manufacturing process. That means engaging clinical sites early and working to help them understand how apheresis quality impacts manufacturing success and patient outcomes; codeveloping the apheresis checklist mentioned above; providing guidance on patient selection and preapheresis conditioning, especially timing relative to prior treatment; and qualifying and auditing apheresis centers as supply chain partners, with periodic retraining and performance trending across sites.

“The earlier clinical sites [and suppliers of media and viral vectors] are engaged as partners with shared accountability, the more predictable and manageable starting material variability becomes,” Eissa concludes.

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Life Science Connect Editorial Staff. How to promote batch-to-batch consistency in CAR-T therapies. Cell & Gene. March 18, 2024. Accessed July 7, 2026. 

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Challener CA. Managing raw material variability for autologous cell therapies. 

https://www.biopharminternational.com/view/managing-raw-material-variability-for-autologous-cell-therapies

Raw material variability is a central constraint in autologous adoptive cell therapy manufacturing, driven primarily by heterogeneous patient apheresis shaped by disease, prior therapies, and collection/handling logistics. Additional variability from serum-containing media and viral vectors can obscure true process effects, elevate deviations, and increase batch failure risk with direct patient and commercial consequences. A risk-based, phase-appropriate control strategy emphasizes supplier qualification, functional specifications, standardized yet flexible practices, and data-rich manufacturing to separate unavoidable biology from avoidable process noise and improve robustness.

It is essential to understand how raw material variability can create challenges in process control for autologous adoptive cell therapies. 

Overcoming Raw Material Variability Challenges for Autologous Adoptive Cell Therapies 

Scientist comparing 2 vials in a laboratory research setting. | Image Credit: ©Phimwilai -stock.adobe.com


Despite the inherent variability of recombinant proteins and antibodies, biosimilars must be comparable to their corresponding branded counterparts with respect to safety, efficacy, and pharmacokinetic and stability profiles.

 Trends in the sector include an increasing focus on patient centricity—such as citrate-free buffer systems—and a move to high-concentration formulations that allow subcutaneous (SC) administration. Together, these pose growing challenges for biosimilar formulators seeking to demonstrate comparability through analytical and functional characterization, particularly amid complex intellectual property issues.

What Are the Main Biosimilar Formulation Development Challenges?

Biosimilar formulation development requires navigating scientific, regulatory, and legal challenges, according to Sung Keun Chang, drug product team leader for Samsung Bioepis. One of the main challenges he highlights is the thicket of patents for reference products that are designed to extend patent protection.

Such patents not only include the core molecule, but also excipients or a class of excipients in the drug formulation, manufacturing processes, and delivery devices, Chang notes. “Due to such protection, buffers, stabilizers, or surfactants that are included in biosimilar formulations need to be re-optimized or replaced with alternatives. In extreme cases, excipients such as buffers cannot be utilized at all if there is patent protection for the class of excipients,” he says.

The industry shift toward high-concentration protein formulations (HCPF), primarily designed to enhance patient convenience through SC delivery, is making biosimilar formulation optimization even more difficult. Maintaining the stability of high-concentration formulations and managing their higher viscosities during manufacturing and administration can be challenging.

“A key issue is the constraints placed on formulators of SC formulations by proprietary-based excipient systems, which create bottlenecks to demonstrated comparability with reference products,” observes Venkat Reddy Sunkara, global head of Regulatory CMC at ICON.

Recent regulatory trends toward waiving comparative efficacy studies (CES) in biosimilar development offer additional opportunities to develop biosimilars. However, with analytical comparability replacing clinical proof of efficacy because biologic products are process-defined, not composition-defined, it is even more essential to define uncertainties analytically in a manner that is aligned with regulatory requirements, according to Kimberly Salgado, head of ICON’s Center for Biosimilar Drug Development. Chang agrees that the new regulatory flexibility poses challenges for biosimilar formulators in meeting the rigorous, comprehensive requirements for analytical characterization. Demonstrating the comparability of post–translational modifications can be particularly challenging.

What Are the Implications for Biosimilar Commercialization?

Surmounting these scientific, regulatory, and legal hurdles is a prerequisite for approval and commercialization of a biosimilar product. “In many cases, formulation-related patents can evolve into a family of patents that minimize the possible design space for biosimilar formulations during the life cycle of the product. As a result, biosimilar companies need to navigate around a moving and evolving target when developing biosimilar formulations,” Chang comments.

In addition, for biosimilars being designed for improved patient convenience (eg, SC delivery), it is necessary to demonstrate not only therapeutic equivalency, but improved performance attributes, such as lower local irritation and better tolerability, according to Salgado.

How Can Biosimilar Developers Demonstrate Analytical Comparability?

The first step, says Salgado, is to identify tiered, risk-based critical quality attributes (CQAs) likely to impact clinical safety and efficacy, and link them to the product’s mechanism of action (MoA) and clinical relevance.

Equally important is the need to account for the inherent lot-to-lot variability of reference products and regional sourcing differences using high-resolution sensitive techniques that can detect subtle differences, according to Sunkara.

Demonstrating analytical similarity is therefore often achieved by analyzing a large number of batches of the reference product over an extended period to determine the acceptable variability range for the biosimilar product, Chang observes. “Using an insufficient sample size may result in a similarity range that is too narrow to be met,” he adds.

Finally, both Sunkara and Chang emphasize the importance of using complementary, orthogonal analytical methods based on different scientific principles to ensure that target CQAs are robustly met.

Overcoming Challenges in High-Concentration Formulations

High-concentration formulations can pose multiple challenges, Chang observes, including an increased propensity for aggregation and particle formation due to dense protein concentrations, as well as difficulties in manufacturing and drug delivery because of higher viscosities.

In addition, Sunkara notes that most of these formulations use proprietary excipient systems for which establishing analytical comparability can be difficult.

Some of these challenges can be overcome by optimizing pH, ionic strength, or excipients during formulation development, according to Chang. Indeed, using innovative excipients may be necessary to achieve stable formulations that can be administered using “patient-friendly” delivery mechanisms.

“For example, altering the pH away from the isoelectric point can reduce electrostatic-driven interaction,” Chang comments. “Certain amino acids can also effectively mask the hydrophobic patches on protein surfaces, thereby preventing protein-to-protein interaction. In addition, viscosity can be further controlled by minimizing or avoiding the use of certain stabilizers that increase viscosity, or by adding excipients that disrupt self-association,” he says.

Biosimilar formulations must also meet product shelf-life expectations and support device functionality, and properties such as syringeability, injector performance, and patient usability must also be considered, says Salgado. The latter issues can be managed by fine-tuning formulations for pH and osmolality and using advanced filtration techniques to reduce particle seeding. Container closure systems must also be optimized to ensure minimal silicone oil use.

Underlying all these solutions to overcoming challenges in the formulation of high-concentration biosimilars, Sunkara contends, is the use of advanced characterization techniques to offset any risks.

Are There Best Practices for Overcoming Regulatory Hurdles for More Patient-Friendly Biosimilars?

Biosimilars benefit patients and health care systems through affordability, expanded patient access, and innovation. One of their key benefits, emphasized by Chang, is to enhance the usability of the reference product by adding innovative features. He highlights the removal of citrate from biosimilar formulations, as it is now known to be a potential cause of injection site pain (ISP) during subcutaneous delivery. Biosimilar developers also often provide a wider choice of drug delivery devices than available for the reference product, such as autoinjectors or other advanced drug delivery devices, in addition to existing vials and prefilled syringes.

During formulation development, Sunkara emphasizes following International Council for Harmonisation’s (ICH) Q5E principles for comparability strategies and using orthogonal analytical and functional assays that are relevant to the MoA to mitigate potential risks.

 Equally important, he notes, is conducting device and drug product formulation development in parallel and employing human factors/usability studies to demonstrate dose accuracy, implementing controls for extractables and leachables, and assuring container closure integrity.

Developers of more patient-friendly biosimilar formulations may also need to establish a robust bridging strategy to demonstrate that the improvements do not affect the product’s quality, safety, or efficacy, according to Chang. “As part of the bridging strategy, it is important to build a robust data package along with a comprehensive risk assessment to justify such changes,” he adds. He also highly recommends engaging with regulators early in the development stage to determine whether proposed analytical and clinical bridging strategies will be sufficient for justification.

Overcoming Challenges Created by Shrinking Timelines

Biosimilar developers are under intense pressure to shorten development timelines while still meeting ever‑higher regulatory and scientific expectations. “To meet these objectives,” contends Salgado, “it is necessary to adopt a combination of scientific, regulatory, operational, and strategic solutions to compress timelines without compromising approvability or commercial viability.”

Salgade highlighted the following examples:

Early and iterative regulatory engagement to avoid rework triggered by post–submission questions;

Reference product procurement strategies;

Platform-based approaches to process and analytical method development that are backed by accumulated knowledge;

Parallelization of chemistry, manufacturing, and controls (CMC), and nonclinical and clinical activities;

Leveraging advanced analytics to de-risk clinical programs;

Ensuring access to ready-to-use manufacturing capacities/capabilities.

Formulation development can also be accelerated using high-throughput screening technologies that enable parallel testing of a wide range of buffers, stabilizers, and surfactants in a short timeframe, adds Chang. He also points to the use of automated equipment for sample preparation and analytical testing as a means for further shortening development timelines.

All of these efforts must be pursued while maintaining adherence to regulatory expectations. Salgado notes that biosimilar developers should focus on the totality of evidence of similarity derived from their analytical data, followed by a more abbreviated clinical trial to confirm this similarity if needed. She adds that using final formulations in pharmacokinetic similarity studies can reduce risk and ensure a stronger demonstration of comparability, which is important because if similarity can be demonstrated through a strong CMC and nonclinical package, CES can potentially be avoided.

Impact of FDA’s Proposed Changes for Demonstrating Biosimilarity

The FDA in October 2025 introduced several proposed changes to its requirements for establishing the comparability of biosimilars to reference products.

 The draft guidance eliminates the need for comparative human clinical studies if biosimilar developers can provide sufficient analytical testing data to demonstrate similarity.

 In a separate initiative, the agency has elected to stop recommending that “switching studies” be conducted to show interchangeability between different biosimilars.

These regulatory updates from the FDA, as well as streamlined pharmacokinetic requirements and the expanded acceptance of non-US-licensed comparator products, are expected to significantly reduce biosimilar development costs by up to 25% while shortening timelines, according to a Celltrion official. “In this evolving environment, biosimilar developers are increasingly focused on enhancing efficiency across the entire development process while maintaining robust analytical comparability and regulatory compliance,” the spokesperson remarks. “These changes are meaningful and are expected to accelerate pipeline expansion, including into small and mid-sized markets that were previously less accessible due to high development costs.”

Are There Any Emerging Challenges to Biosimilar Formulation Development?

With innovator companies increasingly launching line extensions (eg, high‑concentration, citrate‑free, device‑enabled, longer shelf life, room‑temperature stable), biosimilar developers often still chase original reference products. However, observes Salgado, “Future commercial relevance and clinical acceptance will depend on matching (or at least not falling behind) the latest reference product attributes.” Successful biosimilar developers will need to find a way to eliminate these gaps.

Using novel excipients is one way to do so, but there is currently no separate regulatory pathway for approving such excipients. Consequently, novel excipients are considered high-risk and often are avoided in biosimilar formulations (especially in SC products), particularly where switching/substitution is expected, according to Sunkara. He also notes that regulatory expectations are increasing for particle analysis, with data now typically required not only on particle counts but also on particle morphology, composition, and immunological relevance.

Newer technical challenges with increasing relevance for biosimilar developers highlighted by Sunkara include the inability of current screening tools to fully predict nonlinear behaviors such as self‑association, shear sensitivity, and viscosity jumps, of high-concentration formulations; the increasing role of autoinjectors and other advanced delivery systems in dictating formulation limits, including maximum viscosities, injection forces, and dwell times; and the rising ability to detect minor formulation‑related differences previously invisible to older, less-sensitive analytical methods.

What Will Matter Most for Future Biosimilar Formulation Development?

Given the current trend in biologics development to more patient-friendly, convenient products, biosimilar developers will need to balance innovative formulation approaches with regulatory compliance, quality assurance, and cost management to ensure commercial success.

 Selecting optimal formulations will require input from cross-functional teams of experts, incorporation of patient-centric formulation innovations, comprehensive characterization and establishment of comparability, and the use of quality-by-design approaches, process analytical technologies, and artificial intelligence tools. This strategy will ensure cost-effective development, product performance (quality, safety, efficacy), regulatory compliance, and avoidance of patent-based legal restrictions.

In this vein, companies such as Celltrion are strengthening competitiveness through integrated capabilities spanning development, manufacturing, and direct sales, while driving formulation innovation that reflects the needs in clinical practice, according to the Celltrion spokesperson. “The launch of differentiated formulations—such as liquid intravenous and SC options—demonstrates how biosimilar developers can improve patient convenience and operational efficiency in real-world settings,” the Celltrion official concludes.

Bas TG. Innovative formulation strategies for biosimilars: trends focused on buffer-free systems, safety, regulatory alignment, and intellectual property challenges. 

Leukocare Editorial Team. Biosimilar formulation development: navigating the delicate balance. Accessed May 6, 2026. 

https://www.leukocare.com/blog/biosimilar-formulation-development

ICH. Q5E Comparability of biotechnological/biological products subject to changes in their manufacturing process,

tep 4 version. November 18, 2004. Accessed May 6, 2026. 

https://database.ich.org/sites/default/files/Q5E_Guideline.pdf

FDA. FDA moves to accelerate biosimilar development and lower drug costs. October 29, 2025. Accessed May 6, 2026. 

https://www.fda.gov/news-events/press-announcements/fda-moves-accelerate-biosimilar-development-and-lower-drug-costs

FDA. Scientific considerations in demonstrating biosimilarity to a reference product: updated recommendations for assessing the need for comparative efficacy studies. Draft guidance for industry. October 29, 2025. Accessed May 6, 2026. 

https://www.fda.gov/regulatory-information/search-fda-guidance-documents/scientific-considerations-demonstrating-biosimilarity-reference-product-updated-recommendations

High-concentration formulations and patient centricity are challenges to the formulation of biosimilars, which already face patent protection barriers.

Overcoming Formulation Challenges for Biosimilars

A wide range of advanced therapies for various oncology indications are under development today, including next-generation antibody-based therapies, genetically engineered cell therapies, RNA-based medicines, radioligand therapies, personalized cancer vaccines, and immunotherapies addressing novel biomarkers, among others.

 Many of these therapies leverage novel delivery systems to achieve more targeted solutions and address multiple targets simultaneously for increased efficacy.

To learn more about the new targets and modalities being developed to improve cancer treatment, 

 spoke with Tim Heffernan,PhD,vice president and head of the Therapeutics Discovery Division at The University of Texas MD Anderson Cancer Center. Heffernan shared his thoughts on new approaches to fighting solid and liquid tumors, challenges developers face when translating emerging technologies from research into the clinic and commercialization, and a few promising preclinical and clinical candidates.

What newly identified targets are eliciting the greatest interest?

: There has been a clear shift toward more complex, precision-driven strategies that exploit oncology targets through multiple complementary therapeutic approaches.

Tumor-specific antigens are increasingly leveraged as molecular “beacons” to anchor therapies that deliver cytotoxic payloads, such as antibody-drug conjugates (ADCs); redirect or amplify antitumor immunity via immune cell engagers or engineered cellular therapies (eg, chimeric antigen receptor [CAR]-T and CAR-NK [natural killer] cells); and localize high-energy, short-range radiation through radioligand therapies (RLTs).

As pharmaceutical R&D capabilities have advanced, the portfolio of high-priority tumor-selective targets has expanded beyond established antigens such as CD19, human epidermal growth factor receptor 2 (HER2), and trophoblast cell surface antigen 2 (TROP2) to include B-cell maturation antigen (BCMA), B7-H3, delta-like protein 3, prostate-specific membrane antigen (PSMA), 6 transmembrane epithelial antigen of the prostate 1 (STEAP1), and others. The growing diversity of targets in development underscores major advances in protein engineering and tumor-targeting technologies.

In parallel, a deeper understanding of protein structure and conformational dynamics has enabled small-molecule discovery to move beyond traditional catalytic inhibition toward allosteric, covalent, and induced proximity-based mechanisms of action. These innovations have fundamentally reshaped strategies for targeting tumor dependencies and have provided a blueprint for pharmacologically addressing historically “undruggable” targets, including Kirsten rat sarcoma virus oncogene homologue (

Which new solutions are overcoming challenges presented by the tumor microenvironment?

: Advances in protein engineering are creating new opportunities to enhance spatial control and tumor selectivity while incorporating mechanisms to counter the immunosuppressive tumor microenvironment. Key areas of innovation include:

Immune cell engagers(eg, CD3-, 4-1BB-, and CD16a-based formats) that localize immune activation within tumors, increasing antitumor potency while minimizing systemic toxicity.

Bispecific and multispecific antibodies armored with or without costimulatory signaling (eg, CD28) that combine tumor targeting, immune cell engagement, and T-cell activation within a single construct, enabling deeper and more durable immune responses. Most notable is the development of advanced programmed death-ligand 1 [PD-L1] x vascular endothelial growth factor (VEGF) bispecific antibodies.

Engineered cytokines and targeted delivery strategies (eg, interleukin [IL]-12–based approaches) designed to harness potent immunostimulatory activity while mitigating dose-limiting systemic toxicities through improved localization and control.

Are there any notable advances/step changes in treatment approaches for liquid tumors?

: Several notable advances have reshaped treatment paradigms in liquid tumors. One major area of progress involves strategies to redirect the immune system. T-cell engager (TCE) antibodies have generated significant interest due to their ability to complement CAR-T cell therapy. In B-cell lymphomas, CD20 × CD3 bispecific antibodies have emerged as effective therapies, with multiple TCEs now approved. Similarly, BCMA × CD3 and G-protein-coupled receptor 5D (GPRC5D) × CD3 TCEs have been approved for the treatment of multiple myeloma.

Advanced oncology R&D is increasingly organized around precision targeting and modality diversification, using tumor antigens as anchors for ADCs, immune cell engagers, engineered cell therapies, and radioligand approaches. Expanding target repertoires and protein engineering advances are enabling multispecific constructs, engineered cytokines, and improved tumor selectivity to better navigate immunosuppressive microenvironments. In hematologic malignancies, CD3 bispecifics, menin inhibitors for defined genomic subsets, and noncovalent BTK inhibition are reshaping care. Key translation bottlenecks remain tumor heterogeneity, adaptive resistance, and narrow therapeutic index.

The industry is shifting toward increasingly complex, precision-driven modalities for the treatment of cancer.