In silico formulation optimization and particle engineering of pharmaceutical products using a generative artificial intelligence structure synthesis method

Pharmaceutical drug dosage forms are critical for ensuring the effective and safe delivery of active pharmaceutical ingredients to patients. However, traditional formulation development often relies on extensive lab and animal experimentation, which can be time-consuming and costly. This manuscript presents a generative artificial intelligence method that creates digital versions of drug products from images of exemplar products. This approach employs an image generator guided by critical quality attributes, such as particle size and drug loading, to create realistic digital product variations that can be analyzed and optimized digitally. This paper shows how this method was validated through two case studies: one for the determination of the amount of material that will create a percolating network in an oral tablet product and another for the optimization of drug distribution in a long-acting HIV inhibitor implant. The results demonstrate that the generative AI method accurately predicts a percolation threshold of 4.2% weight of microcrystalline cellulose and generates implant formulations with controlled drug loading and particle size distributions. Comparisons with real samples reveal that the synthesized structures exhibit comparable particle size distributions and transport properties in release media.

Introduction

A structure is an arrangement of matter. Typical pharmaceutical dosage forms have three design aspects: (1). Qualitative (referred to as Q1 hereafter), i.e., the choice of substances such as an active pharmaceutical ingredient (API) or a type of excipient; (2). Quantitative (referred to as Q2 hereafter), i.e., the amount of substance such as a loading of API; and (3). Structural (referred to as Q3 hereafter), i.e., the arrangement of the chosen substances in the chosen amount such as the particle size of the API and whether the API is spatially uniform throughout the dosage form. In pharmaceutical development, the interplay of Q1, Q2 and Q3 dictates the performance and quality of a drug product. For example, in the context of a polymer encapsulated drug product, the swell rate of a chosen polymer excipient (Q1), the amount of this polymer (Q2), and how the polymer is spatially arranged (Q3) will all impact the rate of API release from the drug product.

When designing a formulation to achieve desirable performance, iterative testing is often needed. Each iteration may include developing or modifying a manufacturing process, producing a product with a hope that its structures will meet the desired quality attributes, and determining the structure’s performance through experiments such as in vitro or in vivo dissolution testing. If the design process is based on trial and error, it can require an arbitrarily large number of iterations to achieve a structure with the correct properties to sustain therapeutic performance. For long-acting drug products where release profiles are measured in months, even a small number of iterations can be time and cost prohibitive. The later the development stage, the more costly each iteration can become. For example, a formulation change to support phase III clinical trials will potentially require costly bioequivalence studies and major updates to the manufacturing processes and analytical methods. Suboptimal formulation definition in early development can have a huge negative impact on later stage development. Conversely, formulation and dosage form choices are often considered non-critical in earlier development stages due to constraints in time and material availability. Pharmaceutical scientists are thus often stuck with large formulation uncertainties that proliferate the risk downstream due to the lack of cost and time effective tools. Therefore, minimizing the amount of physical manufacturing and experimentation can accelerate the development of the right structure in the early stages, and ultimately bring a product to the market in both a time and cost-efficient manner.

Structure imaging has already proven effective at reducing physical experimentation requirements, with in silico processing and simulations evaluating critical quality attributes (CQAs) in a fraction of the time and often with higher accuracy¹. However, collecting structure images from physical samples still requires time and expense on top of manufacturing of the samples to be imaged. Thus, the structure images of physical samples should be collected purposefully and reused thoroughly. Furthermore, a structure image generation method should be capable of generating structures with novel attributes while maintaining essential characteristics of the exemplar structures from existing images. For example, if the exemplar structure consists of a porous material compacted from crystalline particles, a generated structure with a higher porosity should consist of particles exhibiting intra-crystalline morphology and spatially distributed in a manner similar to the exemplar structure, rather than having a simplified geometry such as spheres dispersed randomly matching porosity and particle size.

While recent advances in artificial intelligence (AI) have impacted drug discovery and clinical data mining, tremendous opportunity also exists in drug development. This paper introduces a generative AI method that synthesizes formulations with structural features in silico from exemplar images of an existing, sub-optimal formulation. This method uses an image generator steered by input attributes typical to formulation development such as particle size and drug loading, to generate structural features of synthetic images with the specified input attributes. The generated attributes can either accurately match the exemplar attributes or vary according to user control, without being limited to attribute combinations that are represented in the exemplar data. This work demonstrates the potential of the generative AI method for extrapolation from existing formulations to new formulations that can be synthesized, analyzed, and optimized in silico. This in turn can potentially cut the costs for manufacturing or testing these new formulations, shorten their development cycle, and improve both environmental and social welfare.

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Following excipients are mentioned in the study besides other: DCP Dicalcium phosphate dihydrate, MCC microcrystalline cellulose

Hornick, T., Mao, C., Koynov, A. et al. In silico formulation optimization and particle engineering of pharmaceutical products using a generative artificial intelligence structure synthesis method. Nat Commun 15, 9622 (2024). https://doi.org/10.1038/s41467-024-54011-9

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