Machine learning predicts liposome uniformity across 306 formulations

Machine learning can estimate how uniformly microscopic liposomes are produced, offering manufacturers a way to screen formulations before committing to costly laboratory campaigns. A study of 306 microfluidic liposome formulations found that a gradient-boosting model could predict the polydispersity index, or PDI, with higher validation performance than a previously reported mixture-of-experts benchmark. The result may help developers focus experiments on combinations of lipid composition and flow settings more likely to produce consistent particles, although predicting average particle size remained substantially more difficult.

Liposomes are lipid vesicles used to carry medicines and other therapeutic payloads. Their size, morphology, composition and stability can affect how they interact with cells, circulate in the bloodstream, release their cargo and withstand environmental stresses. These properties are therefore treated as critical quality attributes in the manufacture of liposomal drugs. Microfluidic systems, which guide liquids through tiny channels, have become an attractive production platform because researchers can adjust total flow rate, the ratio between the two liquid streams, solvent conditions and channel geometry to influence lipid self-assembly in a controlled environment.

Not all liposomes are the same size, and that variation matters. PDI is typically derived from dynamic light-scattering measurements and describes the breadth of a particle-size distribution rather than its average. A low PDI indicates a comparatively uniform population, whereas a higher value indicates a broader range of vesicle sizes. Broad distributions can make it harder to correlate dose with biological response and can contribute to problems with encapsulation, payload release, stability and sensitivity to temperature changes, residual solvent or purification. The variables that determine liposome dispersion are interconnected: fluid dynamics, diffusion-limited solvent exchange, lipid phase behavior and downstream processing can act together, making conventional one-factor-at-a-time experiments incomplete.

The researchers addressed that challenge by building a data set linking formulation and operating variables to two measurable outcomes: particle size, expressed in nanometers, and PDI. The original inputs included total flow rate, the flow-rate ratio between the two liquid streams, the concentrations of egg sphingomyelin, hydrogenated soy phosphatidylcholine, cholesterol and PEG-lipid, the identity of the aqueous phase, either ultrapure water or phosphate-buffered saline, and the type of microfluidic chip. The data were divided into 214 training samples, 46 validation samples and 46 test samples. Twelve additional wet-laboratory samples were reserved as an external validation set, although the reported results do not state how the models performed on that set.

To give the algorithms more useful information, the team expanded the original nine predictors to 19 engineered variables. These included the fractions of cholesterol, PEG-lipid, sphingomyelin and the principal membrane-forming lipid; the absolute concentration of the main lipid; interaction terms such as cholesterol multiplied by PEG and total flow rate multiplied by flow-rate ratio; and squared terms intended to capture nonlinear effects. The transformations were calculated directly from the measured inputs, allowing the resulting relationships to remain linked to recognizable aspects of liposome self-assembly rather than turning the analysis into an opaque numerical exercise.

Eleven predictive algorithms were evaluated, including ridge, lasso and elastic-net regression; radial-basis support vector regression; random forest, gradient boosting, XGBoost, LightGBM and CatBoost; and shallow and deep multilayer perceptrons. Hyperparameters were selected through grid search with fivefold cross-validation, while neural networks used the Adam optimizer and early stopping based on validation loss. Performance was assessed with the coefficient of determination, or R², root mean squared error and mean absolute error. The models were also examined with permutation importance and SHAP, a model-agnostic method that decomposes individual predictions into the contributions of different features and can provide global feature rankings as well as local explanations.

PDI proved to be the more predictable target. Gradient boosting achieved a validation R² of 0.7650, an RMSE of 0.1058 and an MAE of 0.0589. Its predictions followed the ideal diagonal across PDI values from 0.05 to 0.80 with little systematic bias. Random forest ranked second for PDI with a validation R² of 0.6653, followed by XGBoost at 0.6192. The leading gradient-boosting result also exceeded one previously reported mixture-of-experts benchmark with an R² of 0.7307. This performance is consistent with the ability of tree ensembles to capture nonlinear relationships and interactions in modest-size tabular data without requiring the very large samples often needed by deep neural networks. XGBoost’s very high reported training fit, with a training R² of 0.99, also suggested that it could be at risk of overfitting given the limited data set.

By contrast, the best model for particle size was a deep multilayer perceptron, but its validation R² reached only 0.4598, with an RMSE of 368.42 nanometers and an MAE of 272.83 nanometers. The size data were highly skewed, with a reported skewness of 5.70, and 9.2% of samples were classified as outliers. Predictions became especially variable above 500 nanometers. The authors noted that size can change sharply when formulations move between operating regimes, including when they cross a lipid-concentration threshold, undergo a phase transition or experience changes in solvent-composition kinetics. The size distribution contained a few large-size formulations and a longer tail of smaller-size formulations, a pattern that is difficult to capture reliably with a limited amount of data.

The interpretability analysis nevertheless produced useful clues. The primary lipid proportion ranked first in feature importance, followed by the PEG-lipid ratio, sphingomyelin fraction, flow-rate ratio and cholesterol fraction. SHAP analysis indicated that higher values of the main-lipid ratio were generally associated with lower PDI. PEG-lipid was also important, but at high concentrations its relationship with uniformity became detrimental. The results indicate that composition ratios and flow dynamics are not interchangeable controls: developers may need to adjust the balance among membrane constituents while simultaneously setting the rate at which the streams mix to achieve a narrow distribution. These are model-derived associations, not evidence that any single factor has been proven to cause a particular biological or manufacturing outcome.

The immediate application is model-assisted screening rather than fully autonomous formulation. The strongest PDI predictor could first identify regions of the formulation space that merit laboratory attention, after which targeted experiments could refine both size and dispersion. The authors also point to logarithmic size targets, robust loss functions, quantile or interval predictions, multi-task learning and explicit uncertainty estimates as possible ways to improve the weaker size model. More data are especially needed for large-particle and underrepresented operating regimes, while reporting performance on the 12-sample external validation would help establish whether predictions transfer across batches, operators, chips and measurement conditions. The study therefore does not remove the need for experimental testing, but it offers a way to make experiments more informed and to identify candidate conditions for narrower liposome populations without yet establishing improved clinical performance or product outcomes.

Subject of Research: Explainable machine learning for predicting liposome polydispersity in microfluidic manufacturing

Article Title: Data-driven prediction of polydispersity in microfluidic liposome production for pharmaceutical formulation optimization

Article References: Olamat, A., Abu Owida, H., Arabiat, A., & Ozel, P. (2026). Data-driven prediction of polydispersity in microfluidic liposome production for pharmaceutical formulation optimization. Pharmacia, 73, Article e200497. https://doi.org/10.3897/pharmacia.73.e200497

Image Credits: AI Generated

DOI: 10.3897/pharmacia.73.e200497

Keywords: liposomes, microfluidics, machine learning, polydispersity index, pharmaceutical formulation, gradient boosting, particle size, process optimization, SHAP analysis, drug delivery, Data-driven, prediction