Machine learning-driven multifunctional peptide engineering for sustained ocular drug delivery

Abstract

Sustained drug delivery strategies have many potential benefits for treating a range of diseases, particularly chronic diseases that require treatment for years. For many chronic ocular diseases, patient adherence to eye drop dosing regimens and the need for frequent intraocular injections are significant barriers to effective disease management. Here, we utilize peptide engineering to impart melanin binding properties to peptide-drug conjugates to act as a sustained-release depot in the eye. We develop a super learning-based methodology to engineer multifunctional peptides that efficiently enter cells, bind to melanin, and have low cytotoxicity. When the lead multifunctional peptide (HR97) is conjugated to brimonidine, an intraocular pressure lowering drug that is prescribed for three times per day topical dosing, intraocular pressure reduction is observed for up to 18 days after a single intracameral injection in rabbits. Further, the cumulative intraocular pressure lowering effect increases ~17-fold compared to free brimonidine injection. Engineered multifunctional peptide-drug conjugates are a promising approach for providing sustained therapeutic delivery in the eye and beyond.

Fig. 1. Pilot 119 melanin binding peptide microarray screening with machine learning analysis.

a Schematic illustration of the first peptide microarray. Peptides were anchored to a microarray, and melanin nanoparticles (mNPs) with surface biotinylation (b-mNPs) were flowed over to characterize binding events. The fluorescence intensity of the biotin was detected using DyLight 680-conjugated streptavidin to quantify melanin binding for each peptide. An initial classification model was trained using the data generated. Random peptides were then classified by the model as melanin binding or non-melanin binding. Created with BioRender.com. b,c Plot showing the sizes (b) and ζ-potential (c) of mNPs (black dots, n = 6) and b-mNPs (gray squares, n = 6). Data are presented as mean ± SD. Group means were compared using Student’s t tests (two-tailed). d The optimal interaction profiling of b-mNPs against 16 positive control peptides (peptide numbers: 1–16) and 103 random peptides (peptide numbers: 17–119). e Permutation-based variable importance analysis of the melanin binding classification random forest. The x-axis indicates the mean decrease in prediction accuracy after variable permutation. The values are shown at the end of the bars. The top 20 important variables ranked by mean decrease in accuracy are shown. See Supplementary Data 8 for detailed variable descriptions.

Fig. 2. Schematic of the machine learning pipeline based on the super learner framework for the melanin binding data set.

a Scheme of a larger microarray, which includes 5499 peptides used to train a regression super learner. Random peptides were generated based on position-dependent amino acid frequencies calculated using the second peptide array data, and the melanin binding levels were predicted. Peptides with desired melanin binding levels were selected for further experimental validation. Created with BioRender.com. b Scheme of the super learner complexity reduction. Holdout predictions of peptides (shown as rows) were generated for each base model (shown as columns) with tenfold cross-validation (CV) on the input data set. A meta-learner (generalized linear model) was fitted on the holdout predictions with another tenfold cross-validation. The number of base models was reduced by applying an iterative reduction procedure (see Methods). The final super learner ensemble was trained on the input data set with the optimal combination of the selected base models. c Scheme of the machine learning pipeline for an unbiased model performance evaluation. The nested cross-validation includes an outer loop for model evaluation and an inner loop for model selection (cyan). The outer loop generated 10 sets of train-test splits using a Monte Carlo method, and the inner loop generated 10 sets of train-test splits using a modulo method. d Plot of the base models of the final melanin binding super learner. Coefficients of determination (R²) are denoted with color and conveyed as white text on the bars or gray text adjacent bars. Base model coefficients are indicated at the bar ends. There is one model having zero coefficient and not shown. See Methods and Supplementary Note 2 for information about model hyperparameter details and statistics of model performance.

Fig. 3. Experimental validations of final model predictions on melanin binding and cell-penetration.

a Schematic showing an in vitro melanin binding assay with melanin nanoparticles (mNPs) using a biotin quantification kit. The DyLight 494-tagged avidin emitted fluorescence when the biotinylated peptides displaced the weakly interacting 4′-hydroxyazobenzene-2-carboxylic acid (HABA or H). Created with BioRender.com. b Plot of the relationship between predicted melanin binding and binding measured experimentally in vitro. The x-axis indicates melanin binding predictions from the final super learner, and the y-axis indicates the experimental melanin binding values (n = 4 for each peptide). Dots represent the mean value for peptides. The black linear trend line conveys the Pearson correlation relationship (two-tailed), and the gray area indicates the 95% confidence interval. c, d Comparison of melanin binding and cell-penetration in melanin-induced human adult retinal pigment epithelial (ARPE-19) cells. Blue triangles denote predicted non-cell-penetrating peptides (non-CPP), and magenta dots represent predicted cell-penetrating peptides (CPP). The x-axes indicate melanin binding measured in vitro (n = 4 for each peptide), and the y-axes convey intracellular peptide concentration measured from the cell uptake assay (n = 3 for each peptide). Black linear trend lines indicate Pearson correlation relationships, with 95% confidence intervals shown as shaded areas. The correlation coefficients and p-values (two-tailed) are shown. e Summary of CPP (n = 113) and non-CPP (n = 14) intracellular concentrations. Box plot conveys median (middle line), 25th and 75th percentiles (box), and the 1.5 × interquartile range (whiskers). The p value was calculated using a Mann–Whitney U test (two-tailed).

Fig. 4. Melanin binding, cell-penetration model interpretation, and variable contributions to HR97 multifunctional peptide predictions.

Overall variable contributions to model predictions for (a) melanin binding and (b) cell-penetration. The top important variables analyzed using Shapley additive explanations (SHAP) are shown. Dots represent peptides from cross-validation test sets. The x-axes indicate SHAP values, indicative of variable contributions to model prediction ranging from 0 to 100. The variables were ranked based on the difference between the maximum and minimum SHAP values. The color gradient indicates the variable values normalized by percentile ranks. Higher variable values are indicated by darker magenta color and lower values by darker blue color. The minimum and maximum variable values are noted on the right of each subplot. c Scatter plot showing the in vitro melanin binding, in vitro cell-penetration, and predicted cytotoxicity values of the 127 candidate peptides. Dots represent peptides. HR97 (black dot) was selected based on the optimal multifunctional combination. d–f Variable contributions to HR97 multifunctional predictions for melanin binding, cell-penetration, and cytotoxicity. The top variables ranked by absolute SHAP values are shown. Magenta bars indicate positive contributions, and blue bars are negative contributions. The y-axis labels convey variable names and their values for HR97. E[f(X)] denotes the expected prediction value, and f(x) is the final prediction, calculated from the sum of all SHAP values plus E[f(X)]. See Supplementary Data 8 for detailed variable descriptions.

Fig. 5. Visualization of the peptide design space based on sequences and physiochemical properties.

a t-distributed stochastic neighbor embedding (t-SNE, used to visualize high-dimensional data) plots showing the peptide design space defined by the combination of one-hot encoded peptide sequences and variables used in melanin binding, cell-penetration, and cytotoxicity model training. Dots represent control peptides from Howell et al. (magenta) and Nosanchuk et al. (blue); peptides used in the pilot (purple) and second (gray and yellow) melanin binding peptide microarrays; and multifunctional peptide candidates (black and yellow) used in the validation experiments. HR97 and TAT are noted. b t-SNE plot of peptides colored by melanin binding prediction. Higher melanin binding values are colored by darker magenta and lower by darker blue. c t-SNE plot of peptides colored by cell-penetration prediction. Magenta dots represent predicted cell-penetrating peptides (CPP), and blue dots are predicted non-cell-penetrating peptides (non-CPP). d t-SNE plot of peptides colored by cytotoxicity prediction. Blue dots denote predicted toxic peptides, and magenta dots indicate non-toxic peptides.

Fig. 6. Characterization of HR97-brimonidine in vitro and in vivo.

a In vitro binding capacity and dissociation constant of HR97-biotin, HR97-brimonidine, and brimonidine characterized using a melanin nanoparticle (mNP) assay (red dots, n = 3–5). Values shown for comparison include those we previously measured for sunitinib and N-desethyl sunitinib, and literature values for other ophthalmic drugs^–. b In vitro stability of HR97-brimonidine conjugate in human aqueous humor for 28 days. The percent remaining was normalized to the starting concentration on day 0 (n = 3). Data are shown as mean ± SD. c Cathepsin cleavage assay of the HR97-brimonidine conjugate. HR97-brimonidine (n = 3) were incubated with human cathepsin cocktails or buffer only for 48 h at 37 °C (two-tailed t-test). Data are shown as mean ± SD. d Comparison of the intraocular pressure (IOP) change from baseline (ΔIOP) after a single ICM injection of HR97-brimonidine conjugate (white dots), brimonidine solution (black dots, 200 μg brimonidine equivalent), and a single drop of Alphagan P (gray dots, 0.15%) in normotensive Dutch Belted rabbits (n = 5 per group). The IOP was measured every 1–2 days until returning to the baseline. The red arrow highlights the further decrease in IOP provided by the HR97-brimonidine. Two-tailed t-test was used, *p < 0.05 (adjusted p values for days 2, 3, 4, 6, and 8 were 0.044, 0.007, 0.038, 0.007, 0.007, respectively). Data are presented as mean ± SEM. e Cumulative ΔIOP of brimonidine (black dots) and HR97-brimonidine (gray squares) after ICM injection. The cumulative ΔIOP was characterized by calculating the area under the curve over the 20-day measurement period (AUC_last, n = 5). Two-tailed t-test was used. Data are presented as mean ± SD. f Levels of brimonidine in the iris (black dots), aqueous (gray squares), and retina (white dots, n = 3–4) over time after ICM injection of HR97-brimonidine (200 μg brimonidine equivalent). The concentrations of brimonidine measured in the aqueous after a single drop of Alphagan P (0.15%) as part of a previous study at 2 h (maximal IOP lowering time point; dotted line) and 4 h (dashed line) after dosage are shown. Data are shown as mean ± SD.