Refining the rheological characteristics of high drug loading ointment via SDS and machine learning

Abstract

This paper presents an optimized preparation process for external ointment using the Definitive Screening Design (DSD) method. The ointment is a Traditional Chinese Medicine (TCM) formula developed by Professor WYH, a renowned TCM practitioner in Jiangsu Province, China, known for its proven clinical efficacy. In this study, a stepwise regression model was employed to analyze the relationship between key process factors (such as mixing speed and time) and rheological parameters. Machine learning techniques, including Monte Carlo simulation, decision tree analysis, and Gaussian process, were used for parameter optimization. Through rigorous experimentation and verification, we have successfully identified the optimal preparation process for WYH ointment. The optimized parameters included drug ratio of 24.5%, mixing time of 8 min, mixing speed of 1175 rpm, petroleum dosage of 79 g, liquid paraffin dosage of 6.7 g. The final ointment formulation was prepared using method B. This research not only contributes to the optimization of the WYH ointment preparation process but also provides valuable insights and practical guidance for designing the preparation processes of other TCM ointments. This advanced DSD method enhances the screening approach for identifying the best preparation process, thereby improving the scientific rigor and quality of TCM ointment preparation processes.

Fig 1. The rheological properties of the ointment.

(A) Determine the yield stress starting from the change in storage modulus (DoE 4); (B) Box plot of yield stress for 14 batches of ointment samples; (C) Change in storage modulus under temperature scanning (DoE 4); (D) Box plot of storage modulus at 35°C (DoE 4); (E) Flow Curve—Pseudoplasticity (DoE 4); (F) Paste viscosity at low, medium, and high shear rates (DoE 4).

Fig 2. Sorted parameter estimates based on t-Ratio for various formulation and process parameters.

(A)Yield stress; (B)Storage modulus; (C)High shear viscosity; (D) Low shear viscosity. *p<0.05, **p<0.01, ***p<0.001.

Fig 3. Summary of forecast results.

(A) The graphical representation of the methodology on a cell plot; (B) Optimization method; (C) The projected outcomes of the factor prediction.

Fig 4. Design optimization.

(A) ROC curve based on decision tree model; (B) The residual between the predicted value generated by the Gaussian process model and the actual value; (C) Contour map of predicted results.

Fig 5. Split results based on decision tree.

Fig 6. Microscopic analysis of active pharmaceutical ingredients.

(A) Radix Sanguisorbae Carbonisata calcium oxalate cluster; (B) Radix Sanguisorbae Carbonisata calcium oxalate cluster (Crossed polars); (C) Radix Sanguisorbae Carbonisata reticum reticulate vessel; (D) Radix Sanguisorbae Carbonisata reticum reticulate vessel (Crossed polars); (E) Radix Sanguisorbae Carbonisata cork cell; (F) Plant soot charcoal particles; (G) Corydalis Rhizoma parenchyma cell; (H) Corydalis Rhizoma parenchyma cell (Crossed polars); (I) Corydalis Rhizoma spiral vessel; (J) Corydalis Rhizoma spiral vessel (Crossed polars); (K) Corydalis Rhizoma sclerenchyma cell;(L)Corydalis Rhizoma sclerenchyma cell(Crossed polars); (M) Natrii Sulfas; (N) Natrii Sulfas (Crossed polars); (O) Calcine Alunite;(P) Calcine Alunite (Crossed polars). (The scale of F, M and O is 100 μm, and the rest are 20 μm).

Fig 7. Microscopic analysis of ointment.

(A) Ointment base (Crossed polars); (B) The ointment shows microscopic characteristics; (C) Microscopic characteristics of ointment (Crossed polars); (D) Particle size analysis. (The scale is 100 μm).