Self-assembly of thermally responsive nanoparticles of a genetically encoded peptide polymer by drug conjugation - PubMed (original) (raw)

Self-assembly of thermally responsive nanoparticles of a genetically encoded peptide polymer by drug conjugation

Jonathan R McDaniel et al. Angew Chem Int Ed Engl. 2013.

Abstract

Chimeric polypeptides (CPs) that are derived from elastin-like polypeptides (ELPs) can self-assemble to form nanoparticles by site-specific covalent attachment of hydrophobic molecules to one end of the biopolymer backbone. Molecules with a distribution coefficient greater than 1.5 impart sufficient amphiphilicity to drive self-assembly into sub-100 nm nanoparticles.

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Figures

Figure 1

Figure 1. A) Sequence of the chimeric polypeptide

The 62 kDa ELP segment of the CP consists of 160 repeats of VPGXG with the guest residue X having the composition Val1Gly7Ala8. The 1.6 kDa cysteine-rich sequence at the C-terminus provides sites for covalent conjugation of maleimide derivatives of model compounds, shown in (B). B) Structure of the model compounds. The circle represents a visual map of the model compounds and their hydrophobicity as measured by the distribution coefficient at pH 7.4. The attachment of compounds with a Log(D) ≤ 1.5 (shown in blue) did not trigger self-assembly of the CP, whereas compounds with a Log(D) > 1.5 (shown in pink) triggered the CP to self-assemble into nanoparticles.

Figure 2

Figure 2. Physical properties of CP nanoparticles

(A) CP thermal characterization. Transition temperature (Tt) as a function of CP concentration of CPs conjugated to hydrophilic compounds (blue; unimer; compounds 1–5), and hydrophobic compounds (red; nanoparticle; compounds 6–10, 14) compared with an unconjugated control (black; unimer). The thermal behavior of all 6 CP-small molecule conjugates that formed nanoparticles was identical, and is hence plotted together as the mean of the Tt of each of the CP-small molecule conjugate, and the error bars are the standard deviation. The lines are linear fits to the data. (B) DLS results of the CP-conjugate of compound 8 with a Log(D) of 2.1, which shows the increase in Rh from ~6 nm corresponding to unimers prior to conjugation to the formation of nanoparticles with a Rh of ~33 nm after conjugation. (C) Relationship between the Tt (left Y-axis, data in red) and Rh (right Y-axis, data in black) as a function of Log(D) for all 14 conjugates. As the Log(D) increases to greater than 1.5, the particle Rh increases from the unimer size of 6 nm to nanoparticles with an Rh of 30 – 55 nm for different conjugates. The concentration dependence of the Tt (slope from A) decreases from an average value of −5.5 to −1.0 °C/Log(concentration). The curves in (C) are solely a guide to the eye.

Figure 3

Figure 3. Cryo-TEM

CP conjugates were imaged via cryo-TEM in phosphate buffered saline. A) N-methoxycarbonylmaleimide (Compound 1) did not form nanoparticles and is displayed as a negative control. The remaining conjugates spontaneously formed nanoparticles: B) n-benzylmaleimide (Compound 7); C) n-[4-(2-benzimidazolyl)phenyl]maleimide (Compound 9); D) 2-maleimido fluorene (Compound 12); E) n-(1-pyrenyl)maleimide (Compound 14); and F) paclitaxel. Scale bars represent 100 nm.

Figure 4

Figure 4

The apparent coordination number (#CPs per NP) versus the Log(D) of the small molecules conjugated to each CP. Above the threshold of Log(D) = ~1.5, the number of CPs per nanoparticle (#CPs/NP) increases with hydrophobicity of the conjugated small molecule [Log(D)]. The blue diamond, green square, and red triangle markers indicate gemcitabine, oxycodone, and paclitaxel, respectively. The lines are drawn solely as a guide to the eye.

Figure 5

Figure 5. Design of thermally sensitive CP nanoparticles

The transition temperature (Tt) as a function of CP1 and CP2 concentration. The CP1 data represents an average of conjugates 6–10 and 14, whereas the CP2 data represents an average of conjugates 6, 7, and 14. The black lines represent the targeted range of hyperthermia (38–42°C). The error bars are the standard deviation. The lines are linear fits to the data.

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