1,2-dipalmitoylphosphatidylglycerol

SLF consisted of the key components found in healthy human RTLF, the major soluble proteins, the abundant lipids and the antioxidants that were identified in a study by Bicer [23 ]. The proteins were albumin, IgG and transferrin, and the lipids were DPPC, DPPG and cholesterol. The preparation method was optimised into two stages, with the manufacture of a liposomal dispersion followed by addition of the proteins (Fig. 1). To prepare the liposomal component, 1.92 mL DPPC and 0.2 mL DPPG, from 25 mg/mL stock solutions in chloroform were combined in a bijou bottle, with 5 μL of cholesterol from a 200 mg/mL stock solution in chloroform also added. The mixture was stirred gently and the chloroform evaporated under a stream of nitrogen gas for 30 min (sufficient to ensure that the lipid film was solvent free) to produce a thin film of lipids. The proteins were added to the lipid film in aliquots of aqueous stock solutions: 4 mL of albumin (88 mg/mL), 4 mL of IgG (26 mg/mL) and 1 mL of transferrin (15 mg/mL). To represent lung antioxidant levels, 88.5 μL of the following antioxidant stock solutions were added: 10 mM ascorbate, 10 mM glutathione, and 5 mM urate in the HPLC-grade water. The mixture was vortexed for 5 min. Using an ultrasonicator/probe for 10 min at a pulse of 10 amplitude, the lipids were dispersed into the solution in the form of polydisperse multilamellar liposomes. Finally, 10 μL of 50 mg/mL gentamicin was added, followed by 775 μL of HBSS under gentle agitation.Fig. 1

A) Manufacturing steps of simulated lung fluid (SLF), B) Freeze-drying and reconstitution steps of SLF, C) Composition of SLF and D) Images of the original and the reconstituted SLF.

Fig. 1

In the following we give an overview of the protocol employed to simulate our systems. Full details of the protocol and analyses are given in the Supplementary Information.
For all the MDs, we employed the GROMOS force-field (version 54a756 (link) for the apoprotein and 53a657 (link) for the pigments and lipids), which treats all the atoms explicitly except for some of the non-polar hydrogens57 (link). See Supplementary Information for information on the development of the force-field parameters for LHCII cofactors. One monomer of LHCII from the crystal structure deposited by Liu et al. (Chain A, PDB 1RWT)6 (link) was embedded in a lipid bilayer composed of POPC58 (344 total lipids), mimicking native membrane conditions20 (link)22 , and solvated in more than 15k SPC-water molecules at neutral physiologic salt concentration (10 mM Na⁺Cl^–)59 (link). We produced six independent simulations each lasting ~1 μs (simulations A, B and C and A, B, C-N-term) including all the crystallographic cofactors bound to LHCII (pigments, interstitial water molecules, DPPG). See Fig. 1. A for a scheme of the simulation box. Water molecules found in the X-ray structure6 (link) were placed in the crystal although they were able to enter into the protein within 100 ns, as observed in an additional ~1 μs control simulation (MD No Water, Supplementary Figure S2.A-C and Video 1). Full set of simulations is described in detail in the Supplementary Information (Supplementary Table S1).
In all the simulations we first applied a careful multi-step equilibration (minimization, NVT and up to 140 ns NPT equilibration at 300 K) where position restraints (the position of selected atoms were restrained to the initial crystal coordinates) were gradually removed from the parts of the system we wanted to preserve from eventual distortions during the initial relaxation. These parts included chlorophyll rings, carotenoid chains, and the protein backbone (see full methods in SI). In three simulations (simulations A, B, C-N-term), just before the complete release of the position restraints, we removed constraints from the N-terminus, defined here as the first 39 residues (residues 14 to 536 (link)). We then allowed the N-terminus to equilibrate for 100 ns before removing all other position restraints. This test was used to obtain a more complete sampling of this highly disordered domain27 (link)29 (link) and to test the effect of different conformations on the nearby chlorophylls. For all of the simulations, we then ran unbiased NPT simulations over timescales on the order of a microsecond. Parameters for the simulations and analyses protocols are given in the SI.

A double-emulsion procedure (Li et al., 2019 (link)) was used to prepare MVLs containing ioversol and doxorubicin hydrochloride. Briefly, 1 mL of chloroform containing the lipids (41 mg HSPC:40.5 mg cholesterol:5 mg DPPG: 11.25 mg triolein) in 1 mL aqueous solution (the first aqueous solution) was emulsified by Scientz-IID Ultrasonic Homogenizer (Ningbo Scientz Biotechnology Co., Ltd. Ningbo, China) for 30 sec (30% power output, 25 °C) to produce a w/o emulsion. The first aqueous solution contains 1 mg of doxorubicin hydrochloride in 1 mL of 320 mg ioversol injection. This w/o emulsion (2 mL) was subsequently emulsified with 6 mL of the second aqueous solution containing 4% glucose (wt/vol) and 20 mM lysine at 2800 r/min by XHF-D mixer (Ningbo Scientz Biotechnology Co., Ltd. Ningbo, China) at 40 °C to prepare w/o/w emulsion. Then the w/o/w emulsion was diluted with 4 mL of second aqueous solution poured into 100-mL egg type flask. Chloroform was removed by flushing nitrogen over the surface of the double emulsion at 35–37 °C. The resultant MVLs were collected at 100g for 10 min, and resuspended in sterile saline solution after discarding the supernatant. BD Falcon™ cell strainers (BD Biosciences, USA) were used to isolate different size MVLs, improve the uniformity of MVLs. The ioversol and doxorubicin hydrochloride concentration in MVLs were determined by HPLC.

All measured signals are filtered to remove the wandering baseline and high frequency noise with the 4th-order Butterworth bandpass filter of the 0.5 Hz to 10 Hz bandwidth. To reduce differences of phase lag among different signals, an 8th-order all-pass filter was designed to equalize the group delay within the passband. Figure 3 shows the ECG (blue), PPG (red), differential PPG (DPPG, magenta), BCG (black), and IPG (green), and differential IPG (DIPG, purple). The PTT measured by BCG and IPG signals was defined as the interval between the J wave of BCG and the foot point of IPG for PTT1, and PTT2 is defined as the interval between the J wave of BCG and the main peak of DIPG. The Pan-Tompkins method was utilized to detect the R wave of ECG [48 (link)]. The first zero-crossing points of DIPG and DPPG after the R wave were defined as the foot-point times of IPG and PPG. The J wave of BCG is the first peak after the R wave. Then the first peaks of DIPG and DPPG are detected following their first zero-crossing points. In Figure 3, the R wave of ECG, J wave of BCG, and main peaks of DIPG and DPPG are marked by black dots, as are the foot points of IPG and PPG. Figure 4 shows the raw (top) and filtered (button) IPG signals.

Polynomial models were constructed for the optimisation process by employing a 29-run, 4-factor, 3-level Box–Behnken design using Design Expert software to develop and optimise the inhalable EGCG nano-liposome formulation. The Box–Behnken design was selected, as it demands fewer runs in the case of 3 or 4 independent variables compared with the centre composite design, and its avoids factor extremes, since the range of these factors was determined on the basis of the literature and our screening experiments to be the best acceptable range to achieve our formulation goal [41 ]. Four independent variables were evaluated, namely (A) the total lipid concentration (the total concentration of the two included lipids and cholesterol in the liposome solution, mg/mL), (B) the pH of the dispersion media (the rehydration solution), (C) the molar percentage of cholesterol, and (D) the D/L molar ratio, which is also known as “loading capacity” [42 (link)]. The 3 levels of each factor were represented as −1, 0, and +1, as depicted in Table 1.
The variables were selected on the basis of data compiled from a literature review [31 (link),41 ,43 (link)]. For example, it was reported that the total lipid concentration affected the encapsulation efficiency in some liposomal formulations [44 (link)], and that the pH of the dispersion media in the liposome formulations affected their sizes [45 (link)]. In addition, it was reported that the percentage of cholesterol in the liposome formulations affected the liposomes’ physical stability, including the PDI [43 (link),46 (link)]. The D/L molar ratio is considered to be a critical factor that expresses the actual capacity of the liposome to accommodate the drug. Maximising the D:L molar ratio can optimise a liposomal formulation [42 (link)].
In this research, the range of total lipid concentration (A) was 5–15 mg/mL, since the total concentration of lipids in the majority of liposome formulations in medicines falls within this range [47 (link)]. The range of pH of the dispersion media (B) was chosen to be between 3 and 6.5 because this range is suitable for the inhalation route [48 (link)]. The molar percentage of cholesterol (C) in the range of 0–20% was chosen to measure the impact of cholesterol on the stability of the formulation, the encapsulation efficiency, and other independent factors. The molar ratio of DPPG was kept the same (20%) for all the tested formulations in this design, as the presence of the negatively charged lipid, DPPG, ensured a sufficiently negative zeta potential and thus prevented agglomeration of the liposome [49 ,50 ]. When the molar ratio of cholesterol was 0, 10, and 20, the DPPC molar ratio used was 80, 70, and 60, respectively. The D/L molar ratio (D) within the 8–11 range was used, as a higher ratio was shown to formulate unstable liposomes. As was shown in our screening experiments, when the D/L molar ratio for this formulation was higher than 11, agglomeration of the liposomes occurred. The selected responses were as follows: R1, liposome size (z-average, nm); R2, polydispersity index (PDI); R3, encapsulation efficiency (%); R4, zeta potential; R5, PDI after 1 month. The centre point (CP) was run four times to measure the curvature and precision of the production process. All 29 formulations that were proposed by the Design Expert software, as shown in Table 2, were prepared to generate, evaluate, and analyse the model. Polynomial equations that described the correlation between the dependent and independent variables were obtained.