Review Topical Sections

Colloidal stability of liposomes

  • In recent years, the development of novel approaches for the formation of lipid nano-formulations for efficient transport of drug molecules in living systems offers a wide range of biotechnology applications. However, despite the remarkable progress in recent methodologies of synthesis that provide a wide variety of solutions concerning the liposome surface functionalization and grafting with synthetic targeting ligands, the action of most liposomes is associated with a number of unwanted side effects diminishing their efficient use in nanomedicine and biotechnology. The major limitation in the use of such versatile and smart drug delivery systems is connected with their limited colloidal stability arising from the interaction with the complex environment and multiform interactions established within the specific biological media. Herein, we review the main interactions involved in liposomes used in drug delivery processes. We also analyze relevant strategies that aim at offering possible perspectives for the development of next-generation of liposomes nanocarriers that are able to overcome the critical issues during their action in complex biological media.

    Citation: Domenico Lombardo, Pietro Calandra, Maria Teresa Caccamo, Salvatore Magazù, Mikhail Alekseyevich Kiselev. Colloidal stability of liposomes[J]. AIMS Materials Science, 2019, 6(2): 200-213. doi: 10.3934/matersci.2019.2.200

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  • In recent years, the development of novel approaches for the formation of lipid nano-formulations for efficient transport of drug molecules in living systems offers a wide range of biotechnology applications. However, despite the remarkable progress in recent methodologies of synthesis that provide a wide variety of solutions concerning the liposome surface functionalization and grafting with synthetic targeting ligands, the action of most liposomes is associated with a number of unwanted side effects diminishing their efficient use in nanomedicine and biotechnology. The major limitation in the use of such versatile and smart drug delivery systems is connected with their limited colloidal stability arising from the interaction with the complex environment and multiform interactions established within the specific biological media. Herein, we review the main interactions involved in liposomes used in drug delivery processes. We also analyze relevant strategies that aim at offering possible perspectives for the development of next-generation of liposomes nanocarriers that are able to overcome the critical issues during their action in complex biological media.


    The historical Lane-Emden model was introduced first time by astrophysicist Jonathan Homer Lane and Robert Emden [1,2] working on the thermal performance of a spherical cloud of gas and classical law linked to thermodynamics [3]. The singular models have several applications in broad field of applied science and engineering such as catalytic diffusion reactions along with error estimate problems [4], density profile of gaseous star [5], stellar configuration [6], spherical annulus [7], isotropic continuous media [8], the theory of electromagnetic [9] and morphogenesis [10]. It is always not easy to solve the system of singular equations-based models due to their complex nature and singular points. To mention a few schemes that have been applied to solve such models include Legendre wavelets spectral technique [11], Bernoulli collocation scheme [12], variational iteration technique [13], Haar wavelet quasilinearization method [14], spectral collocation scheme [15], differential transformation approach [16] and Adomian decomposition technique [17].

    All these above cited approaches have their precise merits and imperfections, however stochastic solver has not been extensively implemented to solve multi-singular third kind of nonlinear system (MS-TKNS) using the artificial neural networks (ANNs) together with particle swarm optimization (PSO) and interior-point algorithm (IPA), i.e., ANN-PSO-IPA. The stochastic computing solvers have been widely applied to resolve numerous applications [18,19,20,21,22]. Recently, the stochastic solvers presented the solution of models for financial market forecasting [23], prey-predator nonlinear system [24], nonlinear singular functional differential model [25,26], SITR nonlinear system [27,28], singular delay differential system [29], nonlinear periodic boundary value problems [30], HIV nonlinear system [31], SIR nonlinear system of dengue fever [32] and alternative approach based on fuzzy-neuro methods to solve linear and nonlinear optimization problems [33,34]. These submissions enhance the worth of the stochastic solvers to authenticate the convergence and precision of the suggested ANN-PSO-IPA. The general form of the MS-TKNS is written as [35]:

    {d3Udχ3+2α1χd2Udχ2+P(P1)χ2dUdχ+H1(χ)F1(U,V)=G1(χ),d3Vdχ3+2α2χd2Vdχ2+Q(Q1)χ2dVdχ+H2(χ)F2(U,V)=G2(χ),U(0)=A,dU(0)dχ=d2U(0)dχ2=0,V(0)=B,dV(0)dχ=d2V(0)dχ2=0. (1)

    Where F1 and F2 are the nonlinear functions, P and Q are positive constants, G1 and G2 are indicated as a source functions.

    The purpose of this study is to present the solution of the model (1) via intelligent computing ANN-PSO-IPA. The contributions of the paper are as follows:

    ●  A novel neuro-swarm computing intelligent heuristics ANN-PSO-IPA is accessible for multi-singular nonlinear third order EF-SDEs.

    ●  The overlapping outcomes of the proposed ANN-PSO-IPA with the exact outcomes for three examples of MS-TKNS enhance the exactness, consistency and convergence.

    ●  Authorization of the precise performance is validated via statistical remark using the ANN-PSO-IPA based on Theil's Inequality Coefficient (TIC), Root Mean Square Error (RMSE), Variance Account For (VAF), Semi Interquartile (SI) Range.

    ●  Beside practically precise continuous outcomes on whole input training intermission, an easy implementable process, simplicity in perception, stability and robustness are other well-intentioned announcements for the designed neuro-swarm intelligent computing approach.

    The remaining structure of the current study is given as: Section 2 indicates the design methodology through PSO-IPA. The mathematical form of performance measures can be found in section 3. Section 4 shows the numerical results of the designed ANN-PSO-IPA. In the final section, final submissions and future guidance are provided.

    The design of ANN-PSO-IPA for MS-TKNS is presented in two steps, given as:

    Step 1: An error based objective function is accessible by using the mean square error sense.

    Step 2: The learning procedure of the structures is presented using the hybrid of PSO-IPA.

    The ANNs are famous to solve the various applications in different domain of applied science and engineering. The proposed outcomes are denoted by U(χ) and V(χ), while dnUdχn and dnVdχn show the nth derivative, mathematically given as:

    [ˆU(χ),ˆV(χ)]=[mi=1aU,iZ(wU,iχ+ϕU,i),mi=1aV,iZ(wV,iχ+ϕV,i)],[dnˆUdχn,dnˆVdχn]=[mi=1aU,idndχnZ(wU,iχ+ϕU,i),mi=1aV,idndχnZ(wV,iχ+ϕV,i)]. (2)

    Where m shows the neurons and n is the derivative order. The unknown weight vectors are a,w and ϕ. W=[WU,WV], for WU=[αU,wU,ϕU] and WV=[αV,wV,ϕV]. The components of the weight vector are given as:

    aU=[aU,1,aU,2,aU,3,...,aU,m],aV=[aV,1,aV,2,aV,3,...,aV,m],wU=[wU,1,wU,2,wU,3,...,wU,m],wV=[wV,1,wV,2,wV,3,...,wV,m],ϕU=[ϕU,1,ϕU,2,ϕU,3,...,ϕU,m],ϕV=[ϕV,1,ϕV,2,ϕV,3,...,ϕV,m].

    The log-sigmoid Z(χ)=1(1+eχ) is used as an activation function. The updated form of the system (2) using the approximate results of ˆU(χ) and ˆV(χ) are written as:

    [ˆU(χ),ˆV(χ)]=[mi=1aU,i1+e(wU,ix+ϕU,i),mi=1aV,i1+e(wV,ix+ϕV,i)],[dˆUdχ,dˆVdχ]=[mi=1aU,iwU,ie(wU,ix+ϕU,i)(1+e(wU,ix+ϕU,i))2,mi=1aV,iwV,ie(wV,ix+ϕV,i)(1+e(wV,ix+ϕV,i))2],[d2ˆUdχ2,d2ˆVdχ2]=[mi=1aU,iw2U,i{2e2(wU,ix+ϕU,i)(1+e(wU,ix+ϕU,i))3e(wU,ix+ϕU,i)(1+e(wU,ix+ϕU,i))2},mi=1aV,iw2V,i{2e2(wV,ix+ϕV,i)(1+e(wV,ix+ϕV,i))3e(wV,ix+ϕV,i)(1+e(wV,ix+ϕV,i))2}],[d3ˆUdχ3,d3ˆVdχ3]=[mi=1aU,iw3U,i{6e3(wU,ix+ϕU,i)(1+e(wU,ix+ϕU,i))46e2(wU,ix+ϕU,i)(1+e(wU,ix+ϕU,i))3+e(wU,ix+ϕU,i)(1+e(wU,ix+ϕU,i))2},mi=1aV,iw3V,i{6e3(wV,ix+ϕV,i)(1+e(wV,ix+ϕV,i))46e2(wV,ix+ϕV,i)(1+e(wV,ix+ϕV,i))3+e(wV,ix+ϕV,i)(1+e(wV,ix+ϕV,i))2}] (3)

    The error based objective formulation is written as:

    E=E1+E2+E3, (4)
    E1=1NNm=1(χ2md3ˆUdχ3+2α1χmd2ˆUdχ2+P(P1)dˆUdχ+χ2mH1F1(ˆU,ˆV)χ2mG1)2, (5)
    E2=1NNm=1(χ2md3ˆVdχ3+2α2χmd2ˆVdχ2+Q(Q1)dˆVdχ+χ2mH2F2(ˆU,ˆV)χ2mG2)2, (6)
    E3=16((ˆUA)2+(ˆU1)2+(ˆU1)2+(ˆVB)2+(ˆV)2+(ˆV)2) at χ=0. (7)

    Where N=1h,xm=mh. The Objective functions E1 and E2 are associated with the system of differential equations and E3 is the corresponding initial conditions.

    The optimization is performed to solve the MS-TKNS using the hybrid framework of PSO- IPA.

    PSO is an effective research method that has widely used as an alternative optimization of genetic algorithms that were discovered by Kennedy and Eberhart [36]. In the theory of search space, a single candidate result of decision variables in the optimization procedure is called a particle and set of these particle formulated a swarms. For the refinement of optimization variables in standard PSO utilized iterative process of optimizing based on local Pρ1LB and global Pρ1GB best position of the particle in the swarm. The mathematical relations of position Xi along with the velocity Vi in PSO are given, respectively, as follows:

    Xρi=Xρ1i+Vρ1i, (8)
    Vρi=ωVρ1i+η1(Pρ1LBXρ1i)r1+η2(Pρ1GBXρ1i)r2 (9)

    where ρrepresent the current flight index, the inertia vector is denoted by ω varying between 0 and 1, η1 and η2 indicate the cognitive and social accelerations, respectively, while, r1 and r2 are vectors form with pseudo real number between 0 and 1. Further information regrading PSO can be seen in [37], while few recent applications address by PSO include parameter estimation [38], nonlinear electric circuits [39], optimize performance of induction generator [40], optimization of permanent magnets synchronous motor [41] and systems of equations based physical models [42].

    The quickly converges performance of PSO is attained by the process of hybridization with the appropriate local search approach by taking the PSO best values as an initial weight. Consequently, in the presented study, an effective local search scheme based on interior-point (IPA) is exploited for rapid fine-tuning of the results by the PSO algorithm. The hybrid of PSO-IPA train the ANNs as well as fundamental parameter setting for both PSO and IPA are tabulated in Table 1. Recently, IPA is used to power flow optimization incorporating security constraints [43], multistage nonlinear nonconvex problems [44], image processing [45] and multi-fractional order doubly singular model [46]. The hybrid of PSO-IPA train the decision variables of ANNs as per procedure and settings tabulated in Table 1.

    Table 1.  Detailed pseudo code of PSO-IPA to solve the nonlinear third order EF-SDEs.
    Start of PSO
    Step-1: Initialization: Generate the primary swarm randomlyand amend the parameters of {PSO} and {optimoptions} routine.
    Step-2: Fitness Evaluation: Scrutinize the {fitness value} for each particle in Eq (4).
    Step-3: Ranking: Rank to each particle of the least standards of the {fitness function}
    Step-4: Stopping Standards: Stop, if any of the below form achieved
    ● Selected flights
    ● Fitness level
    When accomplished the above values, then go to Step-5
    Step-5: Renewal: The Eqs (8) and (9) are used for the position and velocity.
    Step-6: Improvement: Repeat the above steps 02–06, until the entire flights are attained.
    Step-7: Storage: The attained best fitness values is stored and elect as the global best particle.
    End of PSO
    Start the process of PSO-IPA
    Inputs: Use the global best values
    Output: WPSO-IPS are the PSO-IPA's best values
    Initialize: Take {global best values} as a {start point}
    Termination: Terminate the process, when {Fitness = E = 10−18}, {TolX = 10−20}, {TolCon = TolFun = 10−21}, {MaxFunEvals = 260000} and {Generation = 1500}.
    While: [Stop]
    Fitness Evaluation: The Eq (4) is used for the fitness value E
    Adjustments: Invoke the routine {fmincon} for the IP algorithm to adjust the values of the weight vector.
    Store to fitness values using the basic form of the weight vector
    Store: WPSO-IPS values, final adaptive weight values, function count, fitness, time and generations for the present run.
    End of the PSO-IPA

     | Show Table
    DownLoad: CSV

    The current study is associated to present the statistical measures for solving the MS-TKNS. Therefore, three performances based on Theil's inequality coefficient (TIC) mean absolute deviation (MAD) and Variance Account For (VAF) and their global variables are Global TIC (G.TIC), Global MAD (G.MAD) and Global EVAF (G.EVAF) are applied. The mathematical descriptions of these statistical operators are provided as:

    [TICU, TICV]=[1nni=1(U(χi)ˆU(χi))2(1nni=1U2(χi)+1nni=1ˆU2(χi)),1nni=1(V(χi)ˆV(χi))2(1nni=1V2(χi)+1nni=1ˆV2(χi))], (10)
    [RMSEU,RMSEV]=[1nni=1(UiˆUi)2,1nni=1(ViˆVi)2], (11)
    {[VAFU,VAFV]=[(1var(U(χi)ˆU(χi))var(U(χi)))100,(1var(V(χi)ˆV(χi))var(V(χi)))100][EVAFU,EVAFV]=[|100VAFU|,|100VAFV|]. (12)

    In this section, the detail discussion to solve three variants of the MS-TKNS is presented.

    Problem 1: Consider the MS-TKNS is:

    {d3Udχ3+2χd2Udχ2+3χdUdχU2V=17eχ3+9χeχ3+72χ3eχ3+27χ6eχ3,d3Vdχ3+2χd2Vdχ2+3χdVdχUV2=19eχ3+9χeχ3+72χ3eχ327χ6eχ3,U(0)=1,dU(0)dχ=d2U(0)dχ2=0,V(0)=1,dV(0)dχ=d2V(0)dχ2=0. (13)

    The exact/true solutions of the above Eq (13) are [eχ3,eχ3] and the fitness function becomes as:

    E=1NNm=0((χmd3ˆUdχ3+2d2ˆUdχ2+3dˆUdχχmˆU2ˆVχmG1)2+(χmd3ˆVdχ3+2d2ˆVdχ2+3dˆVdχχmˆUˆV2χmG2)2)+16((ˆU1)2+(dˆUdχ)2+(d2ˆUdχ2)2+(ˆV1)2+(dˆVdχ)2+(d2ˆVdχ2)2). (14)

    Problem 2: Consider the MS-TKNS is:

    {d3Udχ3+2χd2Udχ2+3χdUdχ+UV3=4+e3χ3+4e2χ3+6eχ3+19eχ3+9χeχ3+72χ3eχ3+27χ6eχ3,d3Vdχ3+2χd2Vdχ2+3χdVdχ+U3V=417eχ3+4e2χ3+6eχ3+3e3χ39χeχ3+72χ3eχ327χ6eχ3,U(0)=2,dU(0)dχ=d2U(0)dχ2=0,V(0)=2,dV(0)dχ=d2V(0)dχ2=0. (15)

    The true solutions of the above equation are [1+eχ3,1+eχ3] and the objective function becomes as:

    E=1NNm=0((χmd3ˆUdχ3+2d2ˆUdχ2+3dˆUdχ+χmˆUˆV3χmG1)2+(χmd3ˆVdχ3+2d2ˆVdχ2+3dˆVdχ+χmˆU3ˆVχmG2)2)+16((ˆU2)2+(dˆUdχ)2+(d2ˆUdχ2)2+(ˆV2)2+(dˆVdχ)2+(d2ˆVdχ2)2). (16)

    Problem 3: Consider the MS-TKNS is:

    {d3Udχ3+6χd2Udχ22χdUdχ+(1U)V=426χχ3+χ6,d3Vdχ3+6χd2Vdχ22χdVdχU(1V)=42+6χχ3χ6,U(0)=1,dU(0)dχ=d2U(0)dχ2=0,V(0)=1,dV(0)dχ=d2V(0)dχ2=0. (17)

    The true solutions of the Eq (17) are [1+χ3,1χ3] and the fitness function becomes as:

    E=1NNm=0((χmd3ˆUdχ3+6d2ˆUdχ22dˆUdχ+χm(1ˆU)ˆVχmG1)2+(χmd3ˆVdχ3+6d2ˆVdχ22dˆVdχ+χmˆU(1ˆV)χmG2)2)+16((ˆU1)2+(dˆUdχ)2+(d2ˆUdχ2)2+(ˆV1)2+(dˆVdχ)2+(d2ˆVdχ2)2). (18)

    In order to find the proposed solutions of the Problems 1, 2 and 3 based on the MS-TKNS by using the proposed solver ANN-PSO-IPA for 40 multiple trials to achieve the adaptable parameters. The plots of the weight sets are shown in Figure 1 for U(χ) and V(χ), respectively. These weights are the decision variables of ANNs as presented in equations 3 such that the fitness functions in (14), (16) and (18) for respective problems 1, 2 and 3 are optimized with PSO-IPA, i.e., initially for global search efficacy of PSO and fine tune with IPA for rapid local search. These sets of weights are applied in first equation of set (3) to find approximate solutions to the three problems and the mathematical form are given as:

    ˆUP1=6.7861+e(11.644χ+15.35)+4.5391+e(1.651χ+2.200)+0.5581+e(8.143χ+9.243)+...+4.3771+e(0.019χ+2.086)    , (19)
    ˆUP2=1.0081+e(1.230χ+0.015)+4.2091+e(12.45χ15.681)+0.3361+e(6.716χ+6.309)+...0.3291+e(1.124χ+3.324)    , (20)
    ˆUP3=0.0171+e(4.530χ3.801)+4.6821+e(2.529χ+2.999)+5.0981+e(0.715χ+0.913)+...+3.1591+e(2.099χ+8.587)    , (21)
    ˆVP1=5.0491+e(2.457χ+2.070)+0.0681+e(0.380χ+1.115)+0.0411+e(0.464χ+0.215)+...+1.7901+e(2.540χ1.143)    , (22)
    ˆVP2=1.5501+e(0.418χ1.586)0.6921+e(4.495χ+2.403)+1.5501+e(3.258χ+2.387)+...0.2531+e(0.32χ0.418)    , (23)
    ˆVP3=2.2371+e(0.454χ0.035)0.8251+e(1.564χ+0.541)0.2491+e(1.204χ0.301)+...0.7711+e(1,792χ+0.440)    , (24)
    Figure 1.  Best weight sets and result comparisons for Problems 1, 2 and 3.

    The optimization of the MS-TKNS is performed for the problems 1, 2 and 3 using the proposed solver ANN-PSO-IPA for 40 independent trials. Set of weights and results comparison are plotted graphically in Figure 1. It is specified that the exact and proposed solutions overlapped for both the indexes ˆU(χ) and ˆV(χ) of the problems 1, 2 and 3. This exact matches of the outcomes shows the correctness of the proposed methodology ANN-PSO-IPA. In order to calculate the comparison of the numerical results, the plots of the absolute error (AE) are drawn in Figure 2(a), (b) for ˆU(χ) and ˆV(χ). One can observe that most of the AE values of problems 1–3 for ˆU(χ) lie in the range of 10−6 to 10−7, 10−4 to 10−6 and 10−6 to 10−8, while, for ˆV(χ), these values lie around 10−5 to 10−6, 10−3 to 10−4 and 10−6 to 10−7. The plots of the performance measures through fitness, RMSE, TIC and EVAF are drawn in the Figure 2(c), (d) for ˆU(χ) and ˆV(χ). It is seen that the fitness values lie around to 10−06 to 10,−08 for problems 1 and 3, while the fitness values for Problem 2 are close to 10−08. The RMSE values of ˆU(χ) and ˆV(χ) for problem 1 and 3 are close to 10−6 to 10−8, while for Problem 2, the RMSE lie 10−4–10−6 for ˆU(χ) and 10−2–10–4 for ˆV(χ). The TIC performance lie around 10−6–10−8 for both indexes of all the Problems. The EVAF values for problem 1 and 3 lie 10−10–10−12 for both the indexes, while it lies around 10−8–10−10 for problem 2.

    Figure 2.  Absolute error and performance indices for Problems 1, 2 and 3.

    The convergence measures for the Problems 1–3 based on the MS-TKNS using the Fitness, histograms and boxplots for 10 numbers of neurons are provided in Figure 3. It is shown that most of the fitness values lie around 10−4–10−6 for Problem 1 and 3, while for Problem 3 these values lie around 10−6–10−8. The convergence of both the indexes of all the problems for RMSE, TIC and EVAF is provided in Figures 49. Most of the values for both the indexes of all the problems lie in good ranges.

    Figure 3.  Convergence procedures for the Problems 1, 2 and 3 based on the MS-TKNS using the Fitness, histograms and boxplots for 10 neurons.
    Figure 4.  Convergence investigations of ˆU(χ) for the Problems 1, 2 and 3 based on the MS-TKNS using the Fitness, histograms and boxplots for 10 neurons.
    Figure 5.  Convergence investigations of ˆV(χ) for the Problems 1, 2 and 3 MS-TKNS using the Fitness, histograms and boxplots for 10 neurons.
    Figure 6.  Convergence investigations of ˆU(χ) for the Problems 1, 2 and 3 based on the MS-TKNS for the TIC values.
    Figure 7.  Convergence investigations of ˆV(χ) for the Problems 1, 2 and 3 based on the MS-TKNS for the TIC values.
    Figure 8.  Convergence measures of ˆU(χ) for the Problems 1, 2 and 3 based on the MS-TKNS using the EVAF values.
    Figure 9.  Convergence measures of ˆV(χ) for the Problems 1, 2 and 3 based on the MS-TKNS for the EVAF, values.

    For more accuracy and precision, statistical indices are performed based on minimum (Min), standard deviation (SD), mean, SI range and Median. SI Range is one half of the difference of Q3 = 75% data, i.e., 3rd quartile and Q1 = 25% data, i.e., 1st quartile is calculated for 40 trials of ANN-PSO-IPA to solve three different problems of MS-TKNS. These statistical based outcomes for Problems 1–3 are tabulated in Tables 2 and 3 for the indexes ˆU(χ) and ˆV(χ). It is observed that most of the ˆU(χ) and ˆV(χ) values for Problems 1–3 lie in the best ranges. The global performance G.FIT, G.RMSE, G.TIC and G.EVAF of ˆU(χ) and ˆV(χ) for Problems 1, 2 and 3 are tabulated in Table 4. These performances of the global values for Problems 1, 2 and 3 for 40 independent trials are provided. The magnitude (Mag) and Median values for all Problems using the indexes ˆU(χ) and ˆV(χ) proved very good results based on the statistical global operators.

    Table 2.  The statistics results of ˆU(χ) for each problem of the MS-TKNS using the designed ANN-PSO-IPA.
    Mode
    The solution of ˆU(χ) for Problems 1–3
    0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
    P-1 Min 3×10−7 1×10−7 6×10−7 7×10−7 5×10−7 1×10−7 8×10−7 8×10−7 4×10−7 1×10−7 5×10−7
    Mean 2×10−5 2×10−5 2×10−5 3×10−5 3×10−5 4×10−5 5×10−5 5×10−5 6×10−5 6×10−5 6×10−5
    SD 4×10−5 4×10−5 4×10−5 4×10−5 4×10−5 5×10−5 5×10−5 6×10−5 6×10−5 6×10−5 6×10−5
    Median 7×10−6 6×10−6 9×10−6 1×10−5 1×10−5 2×10−5 2×10−5 3×10−5 3×10−5 3×10−5 3×10−5
    SIR 1×10−5 1×10−5 1×10−5 1×10−5 2×10−5 3×10−5 3×10−5 3×10−5 3×10−5 3×10−5 4×10−5
    P-2 Min 7×10−6 8×10−6 7×10−6 1×10−5 3×10−6 2×10−6 1×10−5 1×10−6 1×10−6 1×10−5 2×10−5
    Mean 3×10−3 3×10−3 3×10−3 3×10−3 4×10−3 6×10−3 1×10−2 1×10−2 2×10−2 4×10−2 5×10−2
    SD 1×10−2 1×10−2 1×10−2 1×10−2 1×10−2 2×10−2 5×10−2 9×10−2 1×10−1 2×10−1 3×10−1
    Median 3×10−4 3×10−4 3×10−4 2×10−4 2×10−4 2×10−4 2×10−4 2×10−4 2×10−4 2×10−4 3×10−4
    SIR 2×10−4 2×10−4 2×10−4 2×10−4 2×10−4 2×10−4 2×10−4 2×10−4 2×10−4 2×10−4 2×10−4
    P-3 Min 1×10−7 1×10−7 7×10−7 1×10−7 2×10−7 1×10−9 3×10−7 1×10−7 8×10−7 2×10−7 1×10−7
    Mean 8×10−6 9×10−6 1×10−5 1×10−5 1×10−5 1×10−5 2×10−5 2×10−5 2×10−5 3×10−5 3×10−5
    SD 1×10−5 1×10−5 1×10−5 2×10−5 2×10−5 3×10−5 3×10−5 4×10−5 4×10−5 5×10−5 6×10−5
    Median 3×10−6 3×10−6 3×10−6 5×10−6 6×10−6 7×10−6 8×10−6 1×10−5 1×10−5 1×10−5 1×10−5
    SIR 4×10−6 4×10−6 5×10−6 6×10−6 5×10−6 6×10−6 8×10−6 9×10−6 1×10−5 1×10−5 1×10−5

     | Show Table
    DownLoad: CSV
    Table 3.  The statistics results of ˆV(χ) for each problem of the MS-TKNS using the designed ANN-PSO-IPA.
    Mode
    The solution of ˆU(χ) for Problems 1–3
    0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
    P-1 Min 7×10−7 4×10−7 1×10−7 1×10−6 1×10−7 1×10−6 8×10−7 2×10−6 8×10−7 3×10−6 5×10−6
    Mean 3×10−5 3×10−5 3×10−5 4×10−5 5×10−5 5×10−5 6×10−5 7×10−5 7×10−5 8×10−5 8×10−5
    SD 5×10−5 4×10−5 5×10−5 5×10−5 6×10−5 7×10−5 8×10−5 9×10−5 9×10−5 1×10−4 1×10−4
    Median 1×10−5 1×10−5 1×10−5 2×10−5 2×10−5 3×10−5 4×10−5 4×10−5 5×10−5 5×10−5 5×10−5
    SIR 1×10−5 1×10−5 1×10−5 1×10−5 1×10−5 2×10−5 2×10−5 2×10−5 2×10−5 2×10−5 2×10−5
    P-2 Min 1×10−5 4×10−6 1×10−5 3×10−6 2×10−5 3×10−6 1×10−7 3×10−5 6×10−4 4×10−3 1×10−2
    Mean 1×10−3 5×10−3 1×10−2 2×10−2 3×10−2 4×10−2 4×10−2 5×10−2 5×10−2 6×10−2 9×10−2
    SD 3×10−3 2×10−2 9×10−2 1×10−1 2×10−1 2×10−1 2×10−1 3×10−1 3×10−1 3×10−1 3×10−1
    Median 2×10−4 1×10−4 2×10−4 2×10−4 2×10−4 2×10−4 2×10−4 3×10−4 2×10−3 9×10−3 3×10−2
    SIR 2×10−4 2×10−4 2×10−4 2×10−4 2×10−4 1×10−4 1×10−4 2×10−4 2×10−4 2×10−4 3×10−4
    P-3 Min 2×10−7 3×10−7 6×10−7 6×10−7 3×10−7 2×10−7 1×10−7 1×10−7 2×10−7 1×10−7 4×10−7
    Mean 9×10−6 8×10−6 9×10−6 1×10−5 1×10−5 1×10−5 1×10−5 1×10−5 2×10−5 2×10−5 2×10−5
    SD 1×10−5 1×10−5 1×10−5 1×10−5 1×10−5 2×10−5 2×10−5 3×10−5 3×10−5 4×10−5 5×10−5
    Median 3×10−6 2×10−6 3×10−6 4×10−6 5×10−6 6×10−6 8×10−6 8×10−6 9×10−6 1×10−5 1×10−5
    SIR 5×10−6 5×10−6 3×10−6 3×10−6 4×10−6 6×10−6 6×10−6 7×10−6 8×10−6 8×10−6 1×10−5

     | Show Table
    DownLoad: CSV
    Table 4.  Global performance of ˆU(χ) and ˆV(χ) for Problems 1, 2 and 3.
    Index
    Example
    G.FIT G.RMSE G.TIC G.EVAF
    Mag Median Mag Median Mag Median Mag Median
    ˆU(χ)
    1 6×10-6 3×10-6 4×10−5 2×10−5 7×10−6 4×10−6 2×10−9 5×10−10
    2 2×10−4 4×10−5 2×10−2 2×10−4 7×10−6 4×10−6 4×10−2 2×10−7
    3 1×10−6 3×10−7 2×10−5 9×10−6 1×10−5 6×10−6 4×10−9 1×10−10
    ˆV(χ)
    1 6×10−6 3×10−6 6×10−5 4×10−5 1×10−5 8×10−6 3×10−7 5×10−9
    2 2×10−4 4×10−5 5×10−2 1×10−2 1×10−5 7×10−6 3×10−1 2×10−3
    3 1×10−6 3×10−7 1×10−5 8×10−6 1×10−5 1×10−5 3×10−9 9×10−11

     | Show Table
    DownLoad: CSV

    In this research study, a stable, reliable and accurate numerical ANN-PSO-IPA is accessible to solve the multi-singular nonlinear third kind of Emden-Fowler system by using the ANN strength with continuous mapping. A fitness function of these networks is optimized for the global and local search capabilities of particle swarm optimization and interior-point algorithm, respectively. The proposed ANN-PSO-IPA is broadly applied to solve three different variants of the multi-singular nonlinear third kind of Emden-Fowler system. The precise and accurate performance is observed for ANN-PSO-IPA based on AE with consistent precision around 5 to 8 decimal places of precision for all three problems of the multi-singular nonlinear third kind of Emden-Fowler system. Statistical interpretations in terms of Min, Mean, SD, SI ranges and Median are performed to validate the convergence, robustness and accuracy of the proposed ANN-PSO-IPA for solving the multi-singular nonlinear third kind of Emden-Fowler system based Eqs 1–3.

    In the future, new stochastic solvers based on ANN optimized with evolutionary/swarming paradigm looks proficient to solve nonlinear biological systems [47,48,49,50], fluid dynamics models [51,52,53,54,55,56] and fractional models [57,58,59,60]. Additionally, the different ANNs structure exploiting variety of activation functions should be implemented to solve the MS-TKNS for improved performance.

    This paper has been partially supported by Ministerio de Ciencia, Innovacion y Universidades grant number PGC2018-0971-B-100 and Fundacion Seneca de la Region de Murcia grant number 20783/PI/18.

    All the authors of the manuscript declared that there are no potential conflicts of interest.



    [1] Chen G, Roy I, Yang C, et al. (2016) Nanochemistry and nanomedicine for nanoparticle-based dagnostics and therapy. Chem Rev 116: 2826–2885. doi: 10.1021/acs.chemrev.5b00148
    [2] Ali I, Lone MN, Suhail M, et al. (2016) Advances in nanocarriers for anticancer drugs delivery. Curr Med Chem 23: 2159–2187. doi: 10.2174/0929867323666160405111152
    [3] Pasqua L, Leggio A, Sisci D, et al. (2016) Mesoporous silica nanoparticles in cancer therapy: relevance of the targeting function. Mini Rev Med Chem 16: 743–753. doi: 10.2174/1389557516666160321113620
    [4] Chow EKH, Ho D (2013) Cancer nanomedicine: from drug delivery to imaging. Sci Transl Med 5: 216rv4.
    [5] Lee BK, Yun YH, Park K (2015) Smart nanoparticles for drug delivery: boundaries and opportunities. Chem Eng Sci 125: 158–164. doi: 10.1016/j.ces.2014.06.042
    [6] Bozzuto G, Molinari A (2015) Liposomes as nanomedical devices. Int J Nanomed 10: 975–999.
    [7] Allen TM, Cullis PR (2013) Liposomal drug delivery systems: from concept to clinical applications. Adv Drug Deliver Rev 65: 36–48. doi: 10.1016/j.addr.2012.09.037
    [8] Bobo D, Robinson KJ, Islam J, et al. (2016) Nanoparticle-based medicines: a review of FDA-approved materials and clinical trials to date. Pharm Res 33: 2373–2387. doi: 10.1007/s11095-016-1958-5
    [9] Wilhelm S, Tavares AJ, Dai Q, et al. (2016) Analysis of nanoparticle delivery to tumours. Nat Rev Mater 1: 16014. doi: 10.1038/natrevmats.2016.14
    [10] Iwamoto T (2013) Clinical application of drug delivery systems in cancer chemotherapy: review of the efficacy and side effects of approved drugs. Biol Pharm Bull 36: 715–718. doi: 10.1248/bpb.b12-01102
    [11] Brand W, Noorlander CW, Giannakou C, et al. (2017) Nanomedicinal products: a survey on specific toxicity and side effects. Int J Nanomed 12: 6107–6129. doi: 10.2147/IJN.S139687
    [12] Janssen Products Expert Committee, DOXIL (doxorubicin HCl liposome injection), 2018. Available from: https://www.doxil.com.
    [13] Ishida T, Harashima H, Kiwada H (2001) Interactions of liposomes with cells in vitro and in vivo: opsonins and receptors. Curr Drug Metab 2: 397–409. doi: 10.2174/1389200013338306
    [14] Ishida T, Harashima H, Kiwada H, et al. (2002) Liposome clearance. Bioscience Rep 22: 197–224. doi: 10.1023/A:1020134521778
    [15] Lombardo D, Calandra P, Barreca D, et al. (2016) Soft interaction in liposome nanocarriers for therapeutic drug delivery. Nanomaterials 6: E125. doi: 10.3390/nano6070125
    [16] Dai Y, Xu C, Sun X, et al. (2017) Nanoparticle design strategies for enhanced anticancer therapy by exploiting the tumour microenvironment. Chem Soc Rev 46: 3830–3852. doi: 10.1039/C6CS00592F
    [17] Sackmann E (1995) Physical basis of self-organization and function of membranes: physics of vesicles, In: Lipowsky R, Sackmann E, Handbook of Biological Physics, Elsevier, 213–303.
    [18] Israelachvili J, Wennerström H (1996) Role of hydration and water structure in biological and colloidal interactions. Nature 379: 219–225. doi: 10.1038/379219a0
    [19] Franks F (1972) Water-a comprehensive treatise, New York, NY, USA: Plenum.
    [20] Magazù S, Migliardo F, Telling MT (2007) Study of the dynamical properties of water in disaccharide solutions. Eur Biophys J 36: 163–171. doi: 10.1007/s00249-006-0108-0
    [21] Degiorgio V, Corti M (1985) Physics of amphiphiles: micelles, vesicles and microemulsions, Amsterdam: North-Holland.
    [22] Tanford C (1980) The hydrophobic effect: formation of micelles and biological membranes, 2 Eds., New York: Wiley.
    [23] Parsegian VA (2006) Van der Waals forces: a handbook for biologists, chemists, engineers, and physicists, Cambridge University Press.
    [24] Hunter RJ (1986) Foundations of Colloid Science, Oxford University Press.
    [25] Cevc G (1993) Electrostatic characterization of liposomes. Chem Phys Lipids 64: 163–186. doi: 10.1016/0009-3084(93)90064-A
    [26] Dan N (2002) Effect of liposome charge and PEG polymer layer thickness on cell-liposome electrostatic interactions. BBA-Biomembranes 1564: 343–348. doi: 10.1016/S0005-2736(02)00468-6
    [27] Lombardo D (2014) Modeling dendrimers charge interaction in solution: relevance in biosystems. Biochem Res Int 2014: 837651.
    [28] Akpinar B, Fielding LA, Cunningham VJ, et al. (2016) Determining the effective density and stabilizer layer thickness of sterically stabilized nanoparticles. Macromolecules 49: 5160–5171. doi: 10.1021/acs.macromol.6b00987
    [29] Wang Z, Zhu W, Qiu Y, et al. (2016) Biological and environmental interactions of emerging two-dimensional nanomaterials. Chem Soc Rev 45: 1750–1780. doi: 10.1039/C5CS00914F
    [30] Moore TL, Rodriguez-Lorenzo L, Hirsch V, et al. (2015) Nanoparticle colloidal stability in cell culture media and impact on cellular interactions. Chem Soc Rev 44: 6287–6305. doi: 10.1039/C4CS00487F
    [31] Plessis JD, Ramachandran C, Weiner N (1996) The influence of lipid composition and lamellarity of liposomes on the physical stability of liposomes upon storage. Int J Pharm 127: 273–278. doi: 10.1016/0378-5173(95)04281-4
    [32] Ceh B, Lasic DD (1995) A rigorous theory of remote loading of drugs into liposomes. Langmuir 11: 3356–3368. doi: 10.1021/la00009a016
    [33] Geng S, Yang B, Wang G, et al. (2014) Two cholesterol derivative-based PEGylated liposomes as drug delivery system, study on pharmacokinetics and drug delivery to retina. Nanotechnology 25: 275103. doi: 10.1088/0957-4484/25/27/275103
    [34] Kiselev MA, Janich M, Hildebrand A, et al. (2013) Structural transition in aqueous lipid/bile salt [DPPC/NaDC] supramolecular aggregates: SANS and DLS study. Chem Phys 424: 93–99. doi: 10.1016/j.chemphys.2013.05.014
    [35] Kiselev MA, Lombardo D, Lesieur P, et al. (2008) Membrane self assembly in mixed DMPC/NaC systems by SANS. Chem Phys 345: 173–180. doi: 10.1016/j.chemphys.2007.09.034
    [36] Hernández-Caselles T, Villalaín J, Gómez-Fernández JC (1993) Influence of liposome charge and composition on their interaction with human blood serum proteins. Mol Cell Biochem 120: 119–126. doi: 10.1007/BF00926084
    [37] Narenji M, Talae MR, Moghimi HR (2017) Effect of Charge on Separation of Liposomes upon Stagnation. Iran J Pharm Res 16: 423–431.
    [38] Krasnici S, Werner A, Eichhorn ME, et al. (2003) Effect of the surface charge of liposomes on their uptake by angiogenic tumor vessels. Int J Cancer 105: 561–567. doi: 10.1002/ijc.11108
    [39] Jain NK, Nahar M (2010) PEGylated nanocarriers for systemic delivery. Methods Mol Biol 624: 221–234. doi: 10.1007/978-1-60761-609-2_15
    [40] Dan N (2014) Nanostructured lipid carriers: effect of solid phase fraction and distribution on the release of encapsulated materials. Langmuir 30: 13809–13814. doi: 10.1021/la5030197
    [41] Bourgaux C, Couvreur P (2014) Interactions of anticancer drugs with biomembranes: what can we learn from model membranes? J Control Release 190: 127–138. doi: 10.1016/j.jconrel.2014.05.012
    [42] Lombardo D, Calandra P, Magazù S, et al. (2018) Soft nanoparticles charge expression within lipid membranes: The case of amino terminated dendrimers in bilayers vesicles. Colloid Surface B 170: 609–616. doi: 10.1016/j.colsurfb.2018.06.031
    [43] Dan N (2016) Membrane-induced interactions between curvature-generating protein domains: the role of area perturbation. AIMS Biophys 4: 107–120.
    [44] Lombardo D, Calandra P, Bellocco E, et al. (2016) Effect of anionic and cationic polyamidoamine (PAMAM) dendrimers on a model lipid membrane. BBA-Biomembranes 1858: 2769–2777. doi: 10.1016/j.bbamem.2016.08.001
    [45] Katsaras J, Gutberlet T (2000) Lipid bilayers: Structure and Interactions, Springer Science & Business Media.
    [46] Wanderlingh U, D'Angelo G, Branca C (2014) Multi-component modeling of quasielastic neutron scattering from phospholipid membranes. J Chem Phys 140: 05B602.
    [47] Kiselev MA, Lombardo D (2017) Structural characterization in mixed lipid membrane systems by neutron and X-ray scattering. BBA-Gen Subjects 1861: 3700–3717. doi: 10.1016/j.bbagen.2016.04.022
    [48] Kiselev MA, Lesieur P, Kisselev AM, et al. (2001) A sucrose solutions application to the study of model biological membranes. Nucl Instrum Meth A 470: 409–416. doi: 10.1016/S0168-9002(01)01087-7
    [49] Blanco E, Shen H, Ferrari M (2015) Principles of nanoparticle design for overcoming biological barriers to drug delivery. Nat Biotechnol 33: 941–951. doi: 10.1038/nbt.3330
    [50] Pirollo KF, Chang EH (2008) Does a targeting ligand influence nanoparticle tumor localization or uptake? Trends Biotechnol 26: 552–558. doi: 10.1016/j.tibtech.2008.06.007
    [51] Bae YK, Park K (2011) Targeted drug delivery to tumors: myths, reality and possibility. J Control Release 153: 198–205. doi: 10.1016/j.jconrel.2011.06.001
    [52] Mura S, Nicolas J, Couvreur P (2013) Stimuli-responsive nanocarriers for drug delivery. Nat Mater 12: 991–1003. doi: 10.1038/nmat3776
    [53] Xing H, Hwang K, Lu Y (2016) Recent developments of liposomes as nanocarriers for theranostic applications. Theranostics 6: 1336–1352. doi: 10.7150/thno.15464
    [54] Lombardo D, Kiselev AM, Caccamo MT (2019) Smart nanoparticles for drug delivery application: development of versatile nanocarrier platforms in biotechnology and nanomedicine. J Nanomater 2019: 3702518.
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