Molecular Modeling in Formulation Development

3d rendering molecule on a displayed on a medical interface | Image Credit: ©Production Perig- stock.adobe.com

The potential benefits afforded by molecular modeling when applied to formulation development are, in fact, numerous. Molecular modeling tools, while initially applied to enhance drug-discovery efforts, are increasingly used to inform other aspects of drug development. Formulation development, which traditionally has been performed using physical experimentation in a time-consuming trial-and-error approach, is one area that can particularly benefit from in silico analyses.

Evolving role

“While the use of molecular modeling tools has historically been used extensively in the drug discovery setting, application to formulation development has been slowly evolving,” says Sanjay Konagurthu, senior director of science and innovation in the pharma services business of Thermo Fisher Scientific. Molecular modeling, he observes, can accelerate many aspects of formulation development.

“Compared with trial-and-error approaches, the application and intentional use of the scientific method is a superior approach to formulation development,” notes Nathan Bennette, director and scientific advisor, pharmaceutical development at Catalent. “In this context,” he contends, “molecular modeling tools are extremely useful to increase the efficiency of the scientific process, from problem-statement definition to experiment design and through the development of a formulation strategy.”

For instance, to tackle the challenge of poorly soluble compounds for oral drug delivery, Thermo Fisher Scientific has developed artificial intelligence/machine learning (AI/ML) models specifically to identify the most effective combinations of solubility-enhancement technologies and formulations in early drug development, according to Konagurthu.

Mechanistic insights and more

Molecular modeling can play a crucial role in various aspects of formulation development through its ability to offer valuable insights on molecular behaviors and its predictive capabilities. “One of the primary applications is the visualization and understanding of physical and molecular processes which yields deeper intuition of the underlying mechanisms,” says Bennette. “This mechanistic understanding allows for the generation of rational hypotheses and more informed decision-making,” he adds.

Molecular modeling tools also enable formulators to explore and evaluate various formulation options, according to Bennette. “Such models allow scientists to simulate scenarios, analyze the performance of different formulations, and predict outcomes, which aids in the selection and optimization of formulations against the target product profile and critical performance attributes,” he explains.

Many modeling tools

Several different modeling tools can be applied during formulation development, from molecular mechanics to advanced quantum mechanical (QM) calculations. Examples highlighted by Konagurthu include density functional theory (DFT), molecular dynamics (MD) simulations, quantitative structure activity relationship (QSAR) models, and statistical techniques. These tools are leveraged within Thermo Fisher Scientific’s AI/ML models to aid in formulation development, including the company’s proprietary Quadrant 2 platform for addressing formulation challenges associated with solubility and bioavailability.

Bennette points to physiologically- based biopharmaceutics modeling (PBBM) for understanding absorption and the Flory-Huggins and newer, more sophisticated, perturbed chain statistical associating fluid (PC-SAFT) models for estimating the miscibility of solid solutions and phase boundaries in dynamic systems.

Once the rate limiting step for absorption of a drug substance for an oral formulation is understood using PBBM modeling, these parameters can be used to estimate the impacts of different formulation approaches, such as the use of bioavailability-enhancing technology, before any laboratory testing is needed, says Bennette. “If, for example, the rate limiting step is identified as low solubility, then an amorphous solid dispersion (ASD) might be selected as an enabling technology to improve bioavailability,” he notes.

The Flory-Huggins or PC-SAFT models can then be incorporated to identify formulations with suitable physical and chemical stability and provide insight into manufacturing conditions, according to Bennette. Understanding the miscibility of solid solutions is key to successful development of stable and metastable ASD formulations. For formulations in which the composition and thermal history change throughout the manufacturing process, the PC-SAFT model is of particular use.

Once a formulation is selected, Bennette comments that modeling tools such as FreeThink’s ASAPprime software can be used to predict the shelf-life of the product, including the impact of packaging, through its application of fundamental Arrhenius rate laws.

“Such a comprehensive modeling approach to formulation development ensures a higher probability of clinical and eventual commercial product success,” Bennette states.

Predicting molecular interactions

Gaining a fundamental understanding of drug–drug and drug–excipient interactions is critical to developing stable formulations that can perform and do not pose manufacturing challenges. Modeling tools are beginning to be used to understand chemical stability and excipient compatibility, according to Konagurthu. “The use of these tools for both small and large molecules has been evolving and increasing with the advent of high-performance computing,” he notes.

A key current challenge to using modeling for predicting drug–drug and drug–excipient interactions is the need to ensure the models are validated using real-world, experimental data. Often, however, only limited and sparse data sets are available given the relatively new use of modeling in this application, observes Konagurthu. “Lack of access to sufficient, high-quality data can hinder the prediction capacity of such models,” he says.

Using modeling to predict molecular interactions is an evolving and growing field, though. “With advances in software, hardware, and AI/ML techniques, we anticipate this technology will continue to be an exciting and expanding area that could result in better outcomes and accessibility of life-saving therapies for the patients who need them the most,” Konagurthu states. “It is important that scientists embrace these new technologies that can help them overcome long-standing challenges in the pursuit of more effective treatments. We believe that these molecular modeling tools hold great potential for the pharmaceutical industry and patients around the world,” he continues.

Thermo Fisher Scientific, for example, has developed some of these tools for tackling formulation challenges with ASDs.

Enabling time, cost, and materials savings

The potential benefits afforded by molecular modeling when applied to formulation development are, indeed, numerous. “Mechanistic models enable visualization of molecular processes, formation of rational hypotheses, and interpretation of data against the backdrop of fundamental kinetic and thermodynamic principles,” comments Bennette. “Predictive models—both physics- and statistics-based—are useful for defining specific experimental conditions, rationally selecting formulation candidates, and informing key risk factors for formulations and processes,” he says.

“Modeling tools thus make the whole process of formulation development more efficient by enabling formulators to use a rational, mechanistic approach to defining the key problem statements, developing hypotheses, and interpreting data,” Bennette remarks. “Within the framework of these models, developers can ‘work smarter, not harder’ and avoid time- and resource-intensive trial-and-error approaches,” he emphasizes. “Indeed, by leveraging predictive molecular modeling tools that enable in silico formulation development, significant savings in time, costs, and materials can be saved, especially for expensive active pharmaceutical ingredients,” Konagurthu concludes.

About the author

Cynthia A. Challener, PhD, is a contributing editor to Pharmaceutical Technology®.

Article details

Pharmaceutical Technology®
Vol. 48, No. 9
September 2024
Pages: 15–16

Citation

When referring to this article, please cite it as Challener, C.A. Molecular Modeling in Formulation Development. Pharmaceutical Technology® 2024 48 (9).