Directional preference for glioblastoma cancer cell membrane encapsulated nanoparticle population: A probabilistic approach for cancer therapeutics

Abstract

Background: Selective cancer cell recognition is the most challenging objective in the targeted delivery of anti-cancer agents. Extruded specific cancer cell membrane coated nanoparticles, exploiting the potential of homotypic binding along with certain protein-receptor interactions, have recently proven to be the method of choice for targeted delivery of anti-cancer drugs. Prediction of the selective targeting efficiency of the cancer cell membrane encapsulated nanoparticles (CCMEN) is the most critical aspect in selecting this strategy as a method of delivery.

Materials and methods: A probabilistic model based on binding scores and differential expression levels of Glioblastoma cancer cells (GCC) membrane proteins (factors and receptors) was implemented on python 3.9.1. Conditional binding efficiency (CBE) was derived for each combination of protein involved in the interactions. Selective propensities and Odds ratios in favour of cancer cells interactions were determined for all the possible combination of surface proteins for 'k' degree of interaction. The model was experimentally validated by two types of Test cultures.

Results: Several Glioblastoma cell surface antigens were identified from literature and databases. Those were screened based on the relevance, availability of expression levels and crystal structure in public databases. High priority eleven surface antigens were selected for probabilistic modelling. A new term, Break-even point (BEP) was defined as a characteristic of the typical cancer cell membrane encapsulated delivery agents. The model predictions lie within ±7% of the experimentally observed values for both experimental test culture types.

Conclusion: The implemented probabilistic model efficiently predicted the directional preference of the exposed nanoparticle coated with cancer cell membrane (in this case GCC membrane). This model, however, is developed and validated for glioblastoma, can be easily tailored for any type of cancer involving CCMEN as delivery agents for potential cancer immunotherapy. This probabilistic model would help in the development of future cancer immunotherapeutic with greater specificity.

Keywords: cancer cell membrane; encapsulated nanoparticle; glioblastoma; homotypic binding; human serum albumin nanoparticles; probabilistic model.

Figure 1

Probabilistic Model.

Figure 2

Probability distribution @k interactions; P:Probability, CP: conditional probability.

Figure 3

Individual Protein vs Degree of interaction quiver plots; 3D S_p plot for Individual surface proteins towards (A) GCC, (B) NHC and (C) F; 3D S_p plot @k interactions towards (D) GCC, (E) NHC and (F) F.

Figure 4

Distribution of $S_{p,GCC}^{k,k_F}$ Vs $S_{p,F}^{k,k_F}$ (A–D). $S_{p,GCC}^{k,k_F}$ (E–H). $S_{p,F}^{k,k_F}$ . (A) zero Factors; (B) 1 Factor; (C) 2 Factors; (D) 3 Factors; (E) zero Factors; (F) 1 Factor; (G) 2 Factors; (H) 3 Factors.

Figure 5

(A) Network plot of class wise fractional distribution of $S_{p,GCC}^{k,k_F}$ and corresponding odds ratio: node label = odds ratio of the fractional population in favour GCC interaction for the specific class ‘k’ and subclass ‘k_F ’/fraction of population directed towards GCC for the specific class ‘k’ and subclass ‘k_F ’. (B) Network plot of class wise fractional distribution of $S_{p,GCC}^{k,k_F}$ and corresponding odds ratio: node label = odds ratio of the fractional population in favour of F interaction for the specific class ‘k’ and subclass ‘k_F ’/fraction of population directed towards F for the specific class ‘k’ and subclass ‘k_F ’.

Figure 6

Probabilistic Expected Directional preference. (A) 3D S_p plot @k interactions towards (B) Resultant E(x)_k of S_p,k (C) comparison of E(x)_k of S_p,k for GCC, NHC and F.