For decades, formulation scientists have operated under a high-stakes paradigm of "try, test, and make again." The process of developing drug delivery systems—particularly for complex formulations like long-acting injectables and implants—is notoriously slow, often requiring months of dissolution testing per iteration. A single delay in this cycle can cost companies millions in lost R&D time and postponed regulatory filings.
However, a shift is underway. As the pharmaceutical industry prepares for the PharmSci 360 conference in New Orleans this October, the spotlight is turning toward a new frontier: microstructure intelligence. By leveraging advanced imaging and generative artificial intelligence, companies like DigiM Solution are moving beyond bulk measurements, enabling scientists to "see inside the dose" and predict drug-release behavior with unprecedented precision before a single batch is manufactured.
The Hidden Variable: Why Conventional Testing Fails
Traditional dissolution testing measures how quickly and completely a drug releases its active ingredient. While essential, this method is often a "black box." It tells scientists that a drug is failing or succeeding, but it rarely explains why.
Shawn Zhang, Ph.D., co-founder and CEO of DigiM Solution, argues that the industry’s reliance on bulk measurements often masks the critical internal attributes that dictate performance. "From the outside, two products can look identical," Zhang explains. "But once you open them up, you see the primary particles are deposited differently because of different process conditions."
Conventional techniques like laser diffraction or standard scanning electron microscopy (SEM) often fail to capture the spatial distribution of the active pharmaceutical ingredient (API), excipients, and pores. In a joint study with the University of Connecticut and the FDA, DigiM demonstrated that even minor variations in stir rates or silicone oil viscosity during manufacturing could lead to vastly different internal architectures—one uniform, one "patchy"—despite the finished microspheres appearing identical under standard screening.
Chronology of an Innovation: From Imaging to Simulation
The evolution of DigiM’s approach reflects a broader transition in pharmaceutical engineering—from descriptive science to predictive digital modeling.
2014: Establishing the Foundation
DigiM began its journey by focusing on the "digital transformation" of physical samples. The goal was to bridge the gap between raw imaging data and actionable, quantitative metrics. By 2014, the company was already building the infrastructure necessary to handle complex 2D and 3D data, emphasizing data integrity and reproducibility.
2021: The Pivot to Generative AI
Recognizing that simply visualizing a dose was insufficient for modern development, the team began developing generative AI capabilities. The objective was to move beyond observing existing structures to predicting how variations in manufacturing would alter performance.
2024: Validation via Nature Communications
The company’s methodology achieved significant industry validation with a Nature Communications paper co-authored by researchers from Genentech and Merck. The study successfully used generative AI to predict the "percolation threshold" of excipients in tablets—the point at which ingredients connect to form a continuous network. This study confirmed that digital models could accurately mirror physical realities, such as mercury intrusion and permeability.
2025–2026: Regulatory Recognition
The FDA has begun integrating these methodologies into its bioequivalence frameworks. Recent draft guidances for generic products—including minocycline hydrochloride dental powder and dexamethasone ophthalmic inserts—specifically cite the use of advanced microstructure imaging as a viable alternative to traditional clinical endpoints.
The Mechanics of Microstructure Intelligence
To convert a "pretty picture" into a roadmap for drug release, DigiM utilizes a multi-stage process that combines high-resolution imaging with machine learning.
1. Advanced Data Acquisition
DigiM operates a network of over 50 laboratories and synchrotron facilities. By utilizing focused ion beam scanning electron microscopy (FIB-SEM) and high-resolution computed tomography (CT), the company can image particles at three orders of magnitude higher resolution than hospital-grade scanners. This allows for the capture of internal architectures—pores, API clusters, and excipients—that were previously invisible.
2. Semantic and Instance Segmentation
The core of the software, digiM I2S, employs supervised machine learning and convolutional neural networks. A domain expert manually labels a small sample of the image, teaching the AI to distinguish between material phases. Once trained, the software can segment every pixel or voxel in a 3D scan, effectively creating a digital twin of the pharmaceutical dose.
3. Predictive Dissolution Modeling
The final step is the simulation. Using the ivisLab platform, the system models the physical and chemical evolution of the drug. Unlike traditional dissolution models, which require prior experimental data for calibration, this approach uses the physical microstructure as an initial condition. It calculates mass-transport properties—such as effective diffusivity—based on the actual spatial arrangement of the ingredients.
Implications: The "Chocolate Chip Cookie" Effect
Zhang frequently uses the analogy of a chocolate chip cookie to explain the importance of connectivity. In a drug dose, the API acts as the "chips."
"If the API is connected into a network, it can find its way out through the dissolved API," Zhang explains. "If the chips are all dispersed, the API is trapped in isolated islands."
By understanding this connectivity, developers can optimize their formulas digitally. If a simulation shows that the API is not properly percolating, researchers can tweak the formulation or manufacturing parameters in the digital model before ever stepping into the lab. This is particularly transformative for long-acting injectables, where a single dissolution test can take 90 days. Reducing these "wet lab" cycles not only lowers the cost—since many early-stage drug substances are, in Zhang’s words, "more expensive than gold"—but it also dramatically accelerates the path to clinical trials.
Regulatory and Industry Impact
The implications for the regulatory landscape are profound. The FDA’s recent draft guidances represent a shift toward "model-informed drug development" (MIDD). By accepting microstructure characterization as a proxy for physical performance, the agency is signaling a willingness to reduce the burden of repetitive, time-consuming clinical testing for generic versions of complex products.
For the pharmaceutical industry, this represents a move toward a more deterministic future. Rather than relying on trial and error, companies can now utilize a "totality of evidence" approach where digital, mechanistic, and empirical data converge.
Looking Ahead
As Shawn Zhang prepares to discuss these findings at PharmSci 360, the message to the industry is clear: the future of drug development lies in the marriage of high-resolution physics and pragmatic AI. By moving away from the "try, test, and make again" cycle, companies can reduce the inherent risks of formulation development.
The ability to predict, simulate, and optimize the internal architecture of a drug dose is no longer a futuristic concept—it is a functional reality. As these tools become more accessible, the pharmaceutical industry stands to enter an era of increased efficiency, lower costs, and more reliable drug delivery, ultimately ensuring that patients receive the treatments they need faster and with greater confidence in their performance.
For the scientist in the lab, this means fewer failed experiments. For the company, it means faster market entry. And for the patient, it means the potential for more effective, precisely engineered medications.
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