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 Table of Contents  
REVIEW ARTICLE
Year : 2021  |  Volume : 12  |  Issue : 4  |  Page : 345-355  

Does skin permeation kinetics influence efficacy of topical dermal drug delivery system?: Assessment, prediction, utilization, and integration of chitosan biomacromolecule for augmenting topical dermal drug delivery in skin


1 Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research, Hyderabad, Telangana, India
2 Department of Biological Science, National Institute of Pharmaceutical Education and Research, Hyderabad, Telangana, India

Date of Submission04-Apr-2021
Date of Decision28-Jun-2021
Date of Acceptance19-Jul-2021
Date of Web Publication19-Oct-2021

Correspondence Address:
Dr. Jitender Madan
Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research, Hyderabad, Telangana
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/japtr.japtr_82_21

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  Abstract 


Skin permeation is an integral part of penetration of topical therapeutics. Zero order in addition to Higuchi permeation kinetic is usually preferred in topical drug delivery cargo. Penetration of therapeutic entities through epidermal barrier is a major challenge for scientific fraternity. Furthermore, penetration of therapeutic entities determines the transportation and ultimately therapeutic efficacy of topical dermal dosage forms. Apart from experimentation models, mathematical equations, in silico docking, molecular dynamics (MDs), and artificial neural network (Neural) techniques are being used to assess free energies and prediction of electrostatic attractions in order to predict the permeation phenomena of therapeutic entities. Therefore, in the present review, we have summarized the significance of kinetic equations, in silico docking, MDs, and ANN in assessing and predicting the penetration behavior of topical therapeutics through dermal dosage form. In addition, the role of chitosan biomacromolecule in modulating permeation of topical therapeutics in skin has also been illustrated using computational techniques.

Keywords: Artificial neural network, chitosan biomacromolecule, in silico docking, permeation, skin, topical delivery


How to cite this article:
Iyer A, Jyothi VG, Agrawal A, Khatri DK, Srivastava S, Singh SB, Madan J. Does skin permeation kinetics influence efficacy of topical dermal drug delivery system?: Assessment, prediction, utilization, and integration of chitosan biomacromolecule for augmenting topical dermal drug delivery in skin. J Adv Pharm Technol Res 2021;12:345-55

How to cite this URL:
Iyer A, Jyothi VG, Agrawal A, Khatri DK, Srivastava S, Singh SB, Madan J. Does skin permeation kinetics influence efficacy of topical dermal drug delivery system?: Assessment, prediction, utilization, and integration of chitosan biomacromolecule for augmenting topical dermal drug delivery in skin. J Adv Pharm Technol Res [serial online] 2021 [cited 2021 Nov 30];12:345-55. Available from: https://www.japtr.org/text.asp?2021/12/4/345/328635




  Introduction Top


Skin is the largest organ in human body accounting for approximately 15% of total body weight with a surface area of 1–2 m2. A plethora of skin disorders such as blisters, acne, hives, rosacea, actinic keratosis, carbuncle, psoriasis, eczema, cellulitis, in addition to basal and squamous cell carcinoma, melanoma, lupus, ringworm, vitiligo, and melasma have been documented in the literature [Figure 1].[1]
Figure 1: Schematic representation of classification of skin disorders

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Impetigo, a bacterial skin disorder, is superficial, crusting epidermal skin infection, further categorized as bullous and nonbullous impetigo.[2] On the other hand, fungal diseases are broadly classified into three categories, namely superficial, deep, and systemic infections based on depth of affected area. Causative agents for superficial infections include molds, yeasts, dermatophytes, and nondermatophytes.[3] Correspondingly, viral infection such as genital warts followed by chronic infection is caused by human papillomavirus that is intricate to treat.[4] Allergic skin infections such as atopic dermatitis, contact dermatitis, and pruritus have also been reported.[5] Dermatological diseases which are bound within primary category of illness need customized treatment modalities such as antimicrobials and vaccines [Figure 1].

Skin disorders are generally treated via systemic or topical route of administration. Systemic route has its own pros and cons such as desirable high bioavailability, nonselective biodistribution, and consequently deposition of subtherapeutic amount of drug entity at the site of target. Nevertheless, topical dermal drug delivery (TDDD) demonstrated upper-hand vis-à-vis systemic route for handling skin disorders.

Skin is prone to several physical and environmental stresses.[6] Topical formulation (ointments, gels, creams, lotions, solutions, suspensions, and shampoos) delivers drugs conveniently to the affected area.[7] However, only the active agent in the molecular state penetrates the skin. Generally, penetration and biodistribution depends on the barriers such as stratum corneum and the pathophysiological state. For instance, medicated ointment retains transepidermal water and facilitates drug transport by hydrating skin layers.[8] Thus, the thermodynamic activity and concentration gradient drives the transport of drug across the skin in a saturated vehicle than that from a dosage form with subsaturation.[9] Hence, topical dermal products designed for thermodynamics, chemical gradient, physical barrier, and pathophysiological state offer distinct release and permeation patterns.

Furthermore, advancements regarding permeation pattern were assessed by computational programs for predicting the drug permeation from TDDD systems. Moreover, mechanistic pathways and utility of chitosan biomacromolecule in augmenting TDDD were illuminated using computational techniques.


  Topical Dermal Drug Delivery: What we Should Know? Top


Skin: Organ of exposure and primary shield

Skin is the primary shield protecting all the vital organs from the external environment. It is a physical barrier that blocks the microorganism, pathogen, and allergen entry. It also offers metabolic, immunologic, and protection from ultraviolet rays. The physiological milieu in the skin is slightly acidic in nature owing to pH range of 4.7–5.7. Human skin comprises three main layers, specifically epidermis (50–150 μm thick), an outermost layer of skin without blood vessels, followed by 250-μm thick inner dermis layer below which resides a subcutaneous fat tissue [Figure 2]. Hence, nutrients have to circulate through epidermal-dermal intersection to preserve the vigor of the outermost layer.
Figure 2: Schematic representation of different layers of skin. Stratum corneum (15––20-μm) acts as the main barrier of the skin

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Epidermis layer is divided into five layers, the outermost of which is stratum corneum, stratum lucidum, stratum granulosum, stratum spinosum, and stratum germinativum being the deepest epidermal layer. Stratum corneum acts as a key barrier with a thickness of 15–20 μm and is composed of corneocytes which are implanted within a lamellar arrangement of rigid intercellular lipids. In this way, stratum corneum offers a strict barrier to molecules that are >500 Da.[10]

Skin contains two types of glands, namely eccrine and apocrine having 30–40 μm and 80–100 μm of average pore size, respectively. Moreover, epidermis also comprises melanocytes (production of melanin), keratinocytes, Langerhans cells (immunological response), and merkel cells (sensory perception). Including cellular components, pilosebaceous unit encompasses hair follicles which are associated with sebaceous glands. To preserve its optimal protective properties, renewal of the stratum corneum takes place depending on the anatomical site and age.[11] Skin houses enzymes such as alcohol dehydrogenase, flavin-dependent aldehyde dehydrogenase, monooxygenase, cytochrome P450, and carboxylesterase that participate in biotransformation of topically applied drugs and thereby determine the duration of action.[12]

Conventional topical dermal dosage forms: Limitations and applications

Topical dermal drug delivery systems (TDDDSs) have been used since ages for the treatment of skin diseases. Majority of conventional TDDDSs are designed for local action. Ointments, creams, gels, lotions, liniments, and oils are varying in their mode of application, physicochemical properties, compositions, and purpose of treatment. Ointment bases are majorly composed of petrolatum, mineral oil, waxes, fatty alcohols, or combination of these. The greasy nature owes to decreased patient compliance. Cream is an emulsion with the least stability due to high thermodynamic free energy resulting in cracking or phase separation. On the other hand, gels are comparatively more stable and nongreasy with high patient compliance. Upon application of TDDDS, a concentration gradient is established across the layers of skin, due to which rapid absorption occurs.[13]

Despite desirable features, still topical dermal dosage forms are associated with certain limitations.[14] Common drawback of TDDDS over other routes is that it requires a high therapeutic concentration of drug to maintain steady-state level at the site of action. Consequently, higher concentration promotes toxic reactions in dermal cells. Physical hitches include uncontrolled loss of active moiety due to evaporation or skin surface contacts along with unpleasant odor. Patient routine activities and hygiene of the skin also impact the dermal delivery of drug.

Penetration is the major challenge and penetration enhancers are utilized to increase the transportation of drugs in dermal layers by increasing the transfer rate through the epidermal layer and augment skin retention of active ingredient.[15],[16] Therefore, it is mandatory to optimize the application of penetration enhancers to maintain the therapeutic concentration of drug at the target area by integrating several assessment techniques such as permeation kinetic, in silico docking, molecular simulation techniques, artificial neural network (ANN), and nanoscaled TDDDS.


  Assessment of Skin Permeation: Experimental Models and Skin Permeation Mathematics Top


Experimental models used to measure skin permeation and retention

Drug transportation from TDDDS to the layers of skin initially depends on partitioning of drug between dosage form and stratum corneum. Subsequently, the diffusion of drug molecules across stratum corneum happens with the help of intercellular lipids. Following saturation of stratum corneum, drug transports from stratum corneum to dermis layer by crossing the viable epidermis cells. Subsequently, since dermis layer is perfused, diffused drug then enters systemic circulation via blood capillaries [Figure 3]. Therapeutic entity from topical dermal delivery cargo is usually absorbed via two pathways, namely transepidermal and transappendageal routes. Transepidermal is further subdivided into transfollicular and intercellular, whereas drug via transappendageal route diffuses either through intracellular space comprising hair follicles and sebaceous glands or through eccrine glands. However, all the transportation pathways destine in the dermis layer of skin.[17] A summary of vertical diffusion cell [Figure 4] and modified holding cell [Figure 4] in addition to other reported cells to assess skin permeation is presented in [Table 1].
Table 1: Experimental models to assess skin permeation and experimentation requirements

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Figure 3: Mechanisms of permeation of drug from skin through conventional and drug-loaded topical nanocarriers

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Figure 4: Schematic representation of diffusion cells employed to estimate release kinetics from topical dermal drug delivery systems

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Skin permeation: Mathematical model to predict skin concentration

Skin permeation is majorly determined by Fick's law, which states that flux (J) or absorption rate of any substance across a barrier is related to its diffusion which in turn is directly proportional to the concentration gradient.[24],[25],[26] For drugs administered topically, the concentration gradient depends on the difference observed between concentration of drug in the vehicle (Cv) and layer of skin[27] (Eq. 1).

J = KpCv (1)

Subsequently, the proportionality constant relating flux can be correlated as the permeability coefficient (Kp). Physicochemical properties of drugs, barriers, and interaction between drug and skin lipids affect the permeability coefficient. In other terms, partition coefficient (Km), diffusion coefficient (D), and length of the diffusion pathway (L) influence the penetration of the drug in skin. Hence, four factors control the skin permeation; however, Cv and Km are highly dependent on the vehicle which is of great practical importance (Eq. 2).[28]




  Assessment of Skin Permeation Kinetics: Mathematical Outlook Top


TDDDSs are designed in order to effectively deliver a therapeutic modality at the site of action; however, formulations offer distinct drug release and permeation patterns depending on the composition and/or cross-linking network. For instance, ointments due to the presence of lipid-soluble bases acquiesce lipidic nature and thus favor delivery of lipophilic molecules. In contrast, aqueous nature of gels promotes encapsulation of hydrophilic molecules. Hence, mechanism of drug release and permeation of molecules from the matrices are usually different owing to dissimilar compositions. This consequently displays diverse therapeutic behaviors of different semisolid dosage forms. Hence, permeation kinetic should be monitored carefully to predict the therapeutic efficacy of customized TDDDs.

To understand the concept behind the release kinetics and structuring the method of data analysis and interpretation, integration of drug delivery science and mathematical functions is performed to yield equations that can accurately predict the release kinetic and ultimately the therapeutic efficacy. Zero-order, first-order, Higuchi, Hixson-Crowell, Peppas, and Korsmeyer-Peppas [Figure 5] and [Table 2] equations are being employed to calculate the release kinetic of drug permeated from topical dermal dosage forms.[29]
Table 2: Mathematical models to assess skin permeation and experimentation requirements

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Figure 5: Release kinetic equations zero-order, Higuchi, first-order, Hixson-Crowell cube root law, and Korsmeyer-Peppas are generally employed to calculate skin permeation kinetics

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Considering the mathematical release kinetic equations, we noticed that zero-order release kinetic is superior to first order, Higuchi, Hixson-Crowell cube root law, and Korsmeyer-and Peppas model with regard to the continuous release of the drug at its action site. Further, subtypes of semisolid dosage forms such as ointment, cream, gel, and lotions could not be investigated under identical release kinetic equations due to distinct pharmaceutical features.[37] The zero-order release kinetic looks like a constant release of the drug over the entire time period. Zero-order release is modified into a first-order kinetic model. In order to surmount various physicochemical, biopharmaceutical, and physiological barriers, there is a need to modulate the release kinetic of therapeutic entity from semisolid dosage form for continuous supply at the target site.


  Measurement of Drug Permeation and Retention from Topical Dermal Dosage Forms Using Computational Techniques Top


Prediction of permeability using in silico docking techniques

Developing and assessing TDDDS entails the investment of time and money, thus, it is crucial to reinstate a few parameters, namely skin permeability of various topical therapeutic modalities, which are empirical such as porous pathway theories,[40] quantitative structure permeability relationships,[41] and setting up of rigorous structure-based models.[42] Decoding of stratum corneum structure allowed the development of a fitting virtual model[43] to precisely imitate its barrier properties. Therefore, a variety of computational techniques and their findings regarding drug permeation is summarized in [Table 3] and illustrated in [Figure 6], respectively.
Table 3: Computation techniques and their findings regarding drug permeation

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Figure 6: In silico analysis of chemical permeation enhancers with skin lipids for optimizing the permeation efficiency

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In vitro permeation analysis using artificial neural network

Artificial neural network (ANN)[58] was developed to forecast the release kinetic profile of drug in TDDDS. Polymer concentration, time, and carrageenan amount were the permeation governing factors and consequently cumulative amount of drug released and cumulative permeation of drug per unit surface area with respect to time were determined. Data were compared withFranz diffusion cells (FDC) mounted with excised rat skin. ANN accurately predicted the release kinetic profile of diclofenac sodium with variation in the range of 0.00–3.65 for cumulative drug release and 0.00–0.08 for the cumulative drug permeation. Moreover, ANN simultaneously demonstrated that release and diffusion mechanisms are influenced by the formulation parameters.[58] In another experiment, a predicting model for skin permeability represented as log Kp was established. A comparative evaluation was carried out between prediction and experimental results to obtain the relationship between Abraham descriptors and log Kp. Multiple linear regression model was computed that demonstrated n = 215 with determination coefficient and R2 = 0.699. In addition, the mean square error (MSE) was 0.243 along with F value of 493.556. Further, ANN model calculated n = 215 with MSE = 0.136 and R2 = 0.832 in addition to F = 1050.653. Comparative analysis suggested that ANN model displays a nonlinear relationship between Abraham descriptors and log Kp. Henceforth, Abraham descriptors are possibly employed to envisage skin permeability, but ANN model is profitable as it tenders advanced skin permeability calculations.[59]


  Utilization and Integration of Chitosan Biomacromolecule for Modulating Permeation Kinetic from Topical Drug Delivery Systems Top


Hydrophilic drugs prefer intracellular pathway to permeate drug molecules through water-filled openings. Transappendageal pathway refers to permeation of drug through the hair follicles [Figure 7]. Sebaceous gland and sweat ducts constitute a thrust pathway for infiltration of drug to bypass the stratum corneum. Superior density of hair follicles over the skin makes them a chief donor in this pathway [Figure 7].[60]
Figure 7: Mechanisms of penetration of vesicular drug delivery systems and particulate drug delivery system

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Biomaterials play a key role in tailoring the drug delivery vehicles for pharmaceuticals. Biodegradable and biocompatible polymers may be securely applied to the skin and are normally cost-effective. Biomaterials of natural origin (guar gum, Aloe vera gel, acacia gum, beeswax, wool fat, chitosan, alginic acid, pectin, phospholipid, cholesterol, etc.),[61] synthetic (polycaprolactone, poly-lactide-co-glycolide, polyvinylpyrrolidone, polyethylene glycol, etc.),[62] and semi-synthetic origin (thiolated chitosan, methyl cellulose, and hydroxypropyl methylcellulose)[63] are being used for customizing TDDDS for modulating release kinetic of therapeutic entities. However, none of them is individually effective to promote the permeation of drug in skin layers. Hence, two or more biomaterials are usually integrated.

Colloidal drug delivery systems (CDDSs) are continuously exploring for TDDDS. Further, CDDSs containing therapeutic modalities subsist in the colloidal shape and consist of small particles in the range of 10–400 nm. CDDSs can be subcategorized into vesicular drug delivery systems [Figure 7] and particulate drug delivery systems [Figure 7] and both can be customized with natural biomacromolecules. Molecular docking study predicted that neutral hydrophobic nanoparticles (2–5 nm) disrupted the lipid bilayer, and within ~ 200 ns, it penetrated into it, whereas the charged nanoparticles adsorbed on the bilayer head group. For neutral hydrophobic nanoparticles, the permeation barrier at the head group of the bilayer was very small which was revealed by the free energy calculation. For charged nanoparticles, minimum free energy was noticed. Permeation of neutral nanoparticles with 2-nm size was maximum and it was minimum for cationic nanoparticles of 3 nm size.[64]

Chitosan or deacetylated chitin, a linear polysaccharide composed of β-(1--4)-linked D-glucosamine and N-acetyl-D-glucosamine, was already approved by the Food and Drug Administration for external applications.[65] The permeability augmenting effects of chitosan and its derivatives have been studied in recent years [Table 4] which extensively offered desirable Higuchi type release pattern from TDDDS by both bioadhesion and a transient opening phenomena of the tight junction in the cell membrane.
Table 4: Chitosan-based delivery cargo assisted topical dermal drug delivery for skin disorders

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Positive charge on chitosan interacts with negatively charged tight junction of the dermal cells and opens the pores.[77] Moreover, chitosan expands the lipid monolayers such as fatty acids for instance unsaturated (oleic, linoleic, and R-linolenic acid) and saturated (stearic) acids and cholesterol at pH 4 upon reaching the saturated concentration. The order of expansion was linoleic acid > R-linolenic acid > cholesterol > stearic acid > oleic acid. As a consequence, the solid monolayers of cholesterol and stearic acid were loosened while liquid unsaturated acids were tightened. Hence, chitosan improves permeation through both hydrophobic and electrostatic lipid–chitosan interactions through hydrogen bond formation.[78] In another study, magnetic-adsorbent containing doped spinel ferrite (15%) was encapsulated in glutaraldehyde-cross-linked chitosan matrix. Adsorbent was used to get rid of acid orange 7 dye from aqueous solution. The mean free energy was calculated using Dubinin–Radushkevich isotherm that was in the range of 14.37–16.59 kJ/mol signifying the process of ion exchange. This phenomenon was further elucidated using ANN to compute the factors affecting the adsorption process. Pairing ANN and genetic algorithm presents the most favorable conditions for adsorption and removed 98.01% dye at pH 2.5 with sorbent dosage of 3.88 g/L.[79] Similarly, lysostaphin having positive potential due to Zn2+ ion interacted with chitosan polymeric gel with a positive binding energy of 10.1 kcal/ mol suggested its weak binding affinity. Chitosan gel formed hydrogen bond with amino acid residues; ASN 372, GLY 309, GLY 310, HIS 362, and THR 357 located at the lysostaphin active site.[80] MD simulations were also executed to acquire information regarding the effect of protonation state and degree of N-acetylation on chitosan molecular conformation and its capability to interact with xanthan gum. A considerable restriction in free rotation around the glycosidic bond was observed in protonated chitosan dimers independent to its degree of acetylation. Majorly electrostatic forces contribute toward the formation of complex between chitosan and xanthan gum. The most stable complex was produced when chitosan was at least half-protonated and the degree of N-acetylation was ≤50%. These calculations could be employed to fabricate the chitosan-based controlled release systems.[81] Therefore, several factors such as particle size, surface charge, bioadhesion, hydrogen bond formation, and degree of N-acetylation influence the release and permeation mechanism of drugs encapsulated in chitosan-based TDDDS.


  Conclusions Top


Dermatological illness is a massive domain that comprises diseases ranging from cuts, burns, and rashes to severe conditions such as psoriasis and impetigo along with oncological conditions such as basal cell carcinoma and melanoma. Drug release and permeation from a TDDDS depends on its physicochemical properties, skin condition, and carrier or dosage form design. Skin permeation kinetics can be evaluated using various methods among which FDC is most widely used. Mathematical models such as zero-order, first-order, Hixson-Crowell, Higuchi, and Korsmeyer-Peppas are used to calculate the drug release kinetics. Moreover, in silico docking, molecular modeling, and ANN for predicting skin permeation kinetics are also being used nowadays. Along these lines, key factors affecting release kinetic and permeation of a drug may be identified, assessed, and integrated with chitosan-based TDDDS for augmenting drug delivery to skin disorders.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Habif TP, Chapman MS, Dinulos JG, Zug KA. Skin Disease E-Book: Diagnosis and Treatment. UK: Elsevier Health Sciences; 2011.  Back to cited text no. 1
    
2.
Galindo E, Hebert AA. A comparative review of current topical antibiotics for impetigo. Expert Opin Drug Saf 2021;20:677-83.  Back to cited text no. 2
    
3.
Hay RJ, Ashbee RH. Fungal Infections. Rook's Textbook of Dermatology. 9th ed. UK: John Wiley & Sons; 2016. p. 1-110.  Back to cited text no. 3
    
4.
Karagounis TK, Pomeranz MK. Viral venereal diseases of the skin. Am J Clin Dermatol 2021;22:523-40.  Back to cited text no. 4
    
5.
Ahn J, Grinich EE, Choi Y, Guttman-Yassky E, Simpson EL. Emerging systemic therapeutic biologics and small molecules for atopic dermatitis: How to decide which treatment is right for your patients. J Allergy Clin Immunol Pract 2021;9:1449-60.  Back to cited text no. 5
    
6.
Jeng L, Mirchandani A. Skin health: What damages and ages skin? Evidence-based interventions to maintain healthy skin. In: A Prescription for Healthy Living. UK: Elsevier Sciences; 2021. p. 225-33.  Back to cited text no. 6
    
7.
Jyothi SL, Krishna KL, Shirin VA, Sankar R, Pramod K, Gangadharappa HV. Drug delivery systems for the treatment of psoriasis: Current status and prospects. J Drug Deliv Sci Technol 2021;62:102364.  Back to cited text no. 7
    
8.
Chen S, Hanning S, Falconer J, Locke M, Wen J. Recent advances in non-ionic surfactant vesicles (niosomes): Fabrication, characterization, pharmaceutical and cosmetic applications. Eur J Pharm Biopharm 2019;144:18-39.  Back to cited text no. 8
    
9.
Sugibayashi K. Skin permeation of chemicals. In: Skin Permeation and Disposition of Therapeutic and Cosmeceutical Compounds. Japan: Springer; 2017.  Back to cited text no. 9
    
10.
Puri A. Enhanced delivery of actives through skin from patches and formulations, and distribution within and across skin. Doctoral dissertation, Mercer University 2018.  Back to cited text no. 10
    
11.
Monteiro-Riviere NA. Comparative anatomy, physiology, and biochemistry of mammalian skin. Dermal ocular toxicology. Boca Raton: CRC press; 2020. p. 3-71.  Back to cited text no. 11
    
12.
Pyo SM, Maibach HI. Skin metabolism: Relevance of skin enzymes for rational drug design. Skin Pharmacol Physiol 2019;32:283-94.  Back to cited text no. 12
    
13.
Sahu SK, Raj R, Raj PM, Alpana R. Topical lipid based drug delivery systems for skin diseases: A review. Curr Drug Ther 2020;15:283-98.  Back to cited text no. 13
    
14.
Aarti N, Kalia YN, Guy RH. Transdermal drug delivery: Overcoming the skin's barrier function. Pharm Sci Technol Today 2000;3:318-26.  Back to cited text no. 14
    
15.
Singhvi G, Hejmady S, Rapalli VK, Dubey SK, Dubey S. Nanocarriers for topical delivery in psoriasis. In: Delivery of Drugs. UK: Elsevier; 2020. p. 75-96.  Back to cited text no. 15
    
16.
Nasr M, Al-Karaki R. Nanotechnological innovations enhancing the topical therapeutic efficacy of quercetin: A succinct review. Curr Drug Deliv 2020;17:270-8.  Back to cited text no. 16
    
17.
André Luís Morais R, Perissinato AG, Esselin de Sousa Lino M, Mudrik PS, Pereira GR. Evaluation of skin absorption of drugs from topical and transdermal formulations. Braz J Pharm Sci 2016;52:527-44.  Back to cited text no. 17
    
18.
Puig-Rigall J, Blanco-Prieto MJ, Aydillo C, Radulescu A, Molero-Vilchez D, Dreiss CA, et al. Poloxamine/D-α-Tocopheryl polyethylene glycol succinate (TPGS) mixed micelles and gels: Morphology, loading capacity and skin drug permeability. J Mol Liq 2021;324:114930.  Back to cited text no. 18
    
19.
Éva P, Paál TL, Erős I, Baki G, Csóka I. Drug release from semisolid dosage forms: A comparison of two testing methods. Pharm Dev Technol 2015;20:330-6.  Back to cited text no. 19
    
20.
Bao Q, Burgess DJ. Perspectives on physicochemical and in vitro profiling of ophthalmic ointments. Pharm Res 2018;35:234.  Back to cited text no. 20
    
21.
Sheshala R, Anuar NK, Abu Samah NH, Wong TW. In vitro drug dissolution/permeation testing of nanocarriers for skin application: A comprehensive review. AAPS PharmSciTech 2019;20:164.  Back to cited text no. 21
    
22.
Zsikó S, Cutcher K, Kovács A, Budai-Szűcs M, Gácsi A, Baki G, et al. Nanostructured lipid carrier gel for the dermal application of lidocaine: Comparison of skin penetration testing methods. Pharmaceutics 2019;11:E310.  Back to cited text no. 22
    
23.
Eman A, Yousef SA, Pastore MN, Telaprolu K, Mohammed YH, Namjoshi S, et al. Roberts skin models for the testing of transdermal drugs. Clin Pharmacol Adv Appl 2016;8:163.  Back to cited text no. 23
    
24.
Praça FS, Medina WS, Eloy JO, Petrilli R, Campos PM, Ascenso A, et al. Evaluation of critical parameters for in vitro skin permeation and penetration studies using animal skin models. Eur J Pharm Sci 2018;111:121-32.  Back to cited text no. 24
    
25.
Dinis M, Marto J, Trindade P, Gonçalves H, Salgado A, Machado P, et al. Improved morphine-loaded hydrogels for wound-related pain relief. Pharmaceutics 2019;11:76.  Back to cited text no. 25
    
26.
Almeida RN, Costa P, Pereira J, Cassel E, Rodrigues AE. Evaporation and permeation of fragrance applied to the skin. Ind Eng Chem Res 2019;58:9644-50.  Back to cited text no. 26
    
27.
Amarah AA, Petlin DG, Grice JE, Hadgraft J, Roberts MS, Anissimov YG. Compartmental modeling of skin transport. Eur J Pharm Biopharm 2018;130:336-44.  Back to cited text no. 27
    
28.
Cleary GW. Transdermal controlled release systems. In: Medical Applications of Controlled Release. Boca Raton: CRC Press; 2019. p. 203-52.  Back to cited text no. 28
    
29.
Unagolla JM, Jayasuriya AC. Drug transport mechanisms and in vitro release kinetics of vancomycin encapsulated chitosan-alginate polyelectrolyte microparticles as a controlled drug delivery system. Eur J Pharm Sci 2018;114:199-209.  Back to cited text no. 29
    
30.
Rong-Kun C, Raw A, Lionberger R, Yu L. Generic development of topical dermatologic products: Formulation development, process development, and testing of topical dermatologic products. AAPS J 2013;15:41-52.  Back to cited text no. 30
    
31.
Tawfeek HM, Abdellatif AA, Abdel-Aleem JA, Hassan YA, Fathalla D. Transfersomal gel nanocarriers for enhancement the permeation of lornoxicam. J Drug Deliv Sci Technol 2020;56:101540.  Back to cited text no. 31
    
32.
Shehata TM, Ibrahim MM, Elsewedy HS. Curcumin niosomes prepared from proniosomal gels: In vitro skin permeability, kinetic and in vivo studies. Polymers (Basel) 2021;13:791.  Back to cited text no. 32
    
33.
Morad H, Jahanshahi M, Akbari J, Saeedi M, Gill P, Enayatifard R. Novel topical and transdermal delivery of colchicine with chitosan based biocomposite nanofiberous system; formulation, optimization, characterization, ex vivo skin deposition/permeation, and anti-melanoma evaluation. Mater Chem Phys 2021;263:124381.  Back to cited text no. 33
    
34.
Mamatha J, Gadili S, Pallavi K. Formulation and evaluation of zidovudine transdermal patch using permeation enhancers. J Young Pharm 2020;12:s45.  Back to cited text no. 34
    
35.
Gustavo G, Es-Haghi SS, Nugay T, Cakmak M. Zero-order antibiotic release from multilayer contact lenses: Nonuniform drug and diffusivity distributions produce constant-rate drug delivery. Adv Healthc Mater 2017;6:1600775.  Back to cited text no. 35
    
36.
Wojcik-Pastuszka D, Krzak J, Macikowski B, Berkowski R, Osiński B, Musiał W. Evaluation of the release kinetics of a pharmacologically active substance from model intra-articular implants replacing the cruciate ligaments of the knee. Materials (Basel) 2019;12:E1202.  Back to cited text no. 36
    
37.
Pande VV, Patel VP, Zarekar NS, Pandit SR, Pote AK. Investigation of effects of shata dhauta ghrita on the skin permeation of fluconazole loaded topical antifungal nanolipogel. J Pharm Sci Res 2020;12:948-56.  Back to cited text no. 37
    
38.
Shakshuki A, Agu RU. Compounded topical gabapentin for neuropathic pain: Does choice of base affect efficacy? Int J Pharm Compd 2019;23:496-503.  Back to cited text no. 38
    
39.
Nikoo AM, Kadkhodaee R, Ghorani B, Razzaq H, Tucker N. Electrospray-assisted encapsulation of caffeine in alginate microhydrogels. Int J Biol Macromol 2018;116:208-16.  Back to cited text no. 39
    
40.
Ashrafi P, Sun Y, Davey N, Wilkinson SC, Moss GP. The influence of diffusion cell type and experimental temperature on machine learning models of skin permeability. J Pharm Pharmacol 2020;72:197-208.  Back to cited text no. 40
    
41.
Chen CP, Chen CC, Huang CW, Chang YC. Evaluating molecular properties involved in transport of small molecules in stratum corneum: A quantitative structure-activity relationship for skin permeability. Molecules 2018;23:E911.  Back to cited text no. 41
    
42.
Calcutt JJ, Roberts MS, Anissimov YG. Modeling drug transport within the viable skin – A review. Expert Opin Drug Metab Toxicol 2021;17:105-19.  Back to cited text no. 42
    
43.
Gajula K, Gupta R, Sridhar DB, Rai B. In-silico skin model: A multiscale simulation study of drug transport. J Chem Inf Model 2017;57:2027-34.  Back to cited text no. 43
    
44.
Baba H, Ueno Y, Hashida M, Yamashita F. Quantitative prediction of ionization effect on human skin permeability. Int J Pharm 2017;522:222-33.  Back to cited text no. 44
    
45.
Pecoraro B, Tutone M, Hoffman E, Hutter V, Almerico AM, Traynor M. Predicting skin permeability by means of computational approaches: Reliability and caveats in pharmaceutical studies. J Chem Inf Model 2019;59:1759-71.  Back to cited text no. 45
    
46.
Luechtefeld T, Rowlands C, Hartung T. Big-data and machine learning to revamp computational toxicology and its use in risk assessment. Toxicol Res (Camb) 2018;7:732-44.  Back to cited text no. 46
    
47.
Mojumdar EH, Pham QD, Topgaard D, Sparr E. Skin hydration: Interplay between molecular dynamics, structure and water uptake in the stratum corneum. Sci Rep 2017;7:1-13.  Back to cited text no. 47
    
48.
Nguyen HX, Puri A, Banga AK. Methods to simulate rubbing of topical formulation for in vitro skin permeation studies. Int J Pharm 2017;519:22-33.  Back to cited text no. 48
    
49.
Choi HK, Acharya G, Lee Y, Lee CH. A data-mining approach for the quantitative assessment of physicochemical properties of molecular compounds in the skin flux. AAPS PharmSciTech 2021;22:117.  Back to cited text no. 49
    
50.
Najib ON, Kirton SB, Martin GP, Botha MJ, Sallam AS, Murnane D. Multivariate analytical approaches to identify key molecular properties of vehicles, permeants and membranes that affect permeation through membranes. Pharmaceutics 2020;12:E958.  Back to cited text no. 50
    
51.
Wang H, Meng F. The permeability enhancing mechanism of menthol on skin lipids: A molecular dynamics simulation study. J Mol Model 2017;23:279.  Back to cited text no. 51
    
52.
Anderson SD, Tabassum A, Yeon JK, Sharma G, Santos P, Soong TH, et al. In silico prediction of ARB resistance: A first step in creating personalized ARB therapy. PLoS Comput Biol 2020;16:e1007719.  Back to cited text no. 52
    
53.
Ibrahim R. In-silico screening of selected apiaceae plants as melanogenesis inhibitors and their predicted skin permeability. Rec Pharm Biomed Sci 2020;4:51-61.  Back to cited text no. 53
    
54.
Prsakoeswa S, Rosita C, Purwanto DA, Endaryanto A. Molecular docking, pharmacokinetics, and toxicity prediction of epigallocatechin-3-gallate (EGCG) on IKK receptor in photoaging prevention. Indian J Forensic Med Toxicol 2020;14:1467-73.  Back to cited text no. 54
    
55.
Gupta S, Tewatia P, Misri J, Singh R. Molecular modeling of cloned Bacillus subtilis keratinase and its insinuation in psoriasis treatment using docking studies. Indian J Microbiol 2017;57:485-91.  Back to cited text no. 55
    
56.
Kildaci L, Budama-Kilinc Y, Kecel-Gunduz S, Altuntas E. Linseed oil nanoemulsions for treatment of atopic dermatitis disease: Formulation, characterization, in vitro and in silico evaluations. J Drug Deliv Sci Technol 2021;64:102652.  Back to cited text no. 56
    
57.
Hajare A, Dol H, Patil K. Design and development of terbinafine hydrochloride ethosomal gel for enhancement of transdermal delivery: In vitro, in vivo, molecular docking, and stability study. J Drug Deliv Sci Technol 2021;61:102280.  Back to cited text no. 57
    
58.
Lefnaoui S, Rebouh S, Bouhedda M, Yahoum MM. Artificial neural network for modeling formulation and drug permeation of topical patches containing diclofenac sodium. Drug Deliv Transl Res 2020;10:168-84.  Back to cited text no. 58
    
59.
Longjian C, Guoping L, Lujia H. Prediction of human skin permeability using artificial neural network (ANN) modeling 1. Acta Pharm Sin 2007;28:591-600.  Back to cited text no. 59
    
60.
Babar I, Ali J, Baboota S. Recent advances and development in epidermal and dermal drug deposition enhancement technology. Int J Dermatol 2018;57:646-60.  Back to cited text no. 60
    
61.
Santos LF, Correia IJ, Silva AS, Mano JF. Biomaterials for drug delivery patches. Eur J Pharm Sci 2018;118:49-66.  Back to cited text no. 61
    
62.
Barradas TN, Senna JP, Ricci E Júnior, Mansur CR. Polymer-based drug delivery systems applied to insects repellents devices: A review. Curr Drug Deliv 2016;13:221-35.  Back to cited text no. 62
    
63.
Kabir SM, Sikdar PP, Haque B, Rahman Bhuiyan MA, Ali A, Islam MN. Cellulose-based hydrogel materials: Chemistry, properties and their prospective applications. Prog Biomater 2018;7:153-74.  Back to cited text no. 63
    
64.
Gupta R, Rai B. Effect of size and surface charge of gold nanoparticles on their skin permeability: A molecular dynamics study. Sci Rep 2017;7:1-13.  Back to cited text no. 64
    
65.
Jyoti K, Pandey RS, Kush P, Kaushik D, Jain UK, Madan J. Inhalable bioresponsive chitosan microspheres of doxorubicin and soluble curcumin augmented drug delivery in lung cancer cells. Int J Biol Macromol 2017;98:50-8.  Back to cited text no. 65
    
66.
Md S, Kuldeep Singh JK, Waqas M, Pandey M, Choudhury H, Habib H, et al. Nanoencapsulation of betamethasone valerate using high pressure homogenization-solvent evaporation technique: Optimization of formulation and process parameters for efficient dermal targeting. Drug Dev Ind Pharm 2019;45:323-32.  Back to cited text no. 66
    
67.
Pandey M, Choudhury H, Gunasegaran TA, Nathan SS, Md S, Gorain B, et al. Hyaluronic acid-modified betamethasone encapsulated polymeric nanoparticles: Fabrication, characterisation, in vitro release kinetics, and dermal targeting. Drug Deliv Transl Res 2019;9:520-33.  Back to cited text no. 67
    
68.
Barradas TN, Senna JP, Cardoso SA, de Holanda E Silva KG, Elias Mansur CR. Formulation characterization and in vitro drug release of hydrogel-thickened nanoemulsions for topical delivery of 8-methoxypsoralen. Mater Sci Eng C Mater Biol Appl 2018;92:245-53.  Back to cited text no. 68
    
69.
Şenyiğit T, Sonvico F, Rossi A, Tekmen I, Santi P, Colombo P, et al. In vivo assessment of clobetasol propionate-loaded lecithin-chitosan nanoparticles for skin delivery. Int J Mol Sci 2016;18:E32.  Back to cited text no. 69
    
70.
Sahu P, Kashaw SK, Sau S, Kushwah V, Jain S, Agrawal RK, et al. pH Responsive 5-fluorouracil loaded biocompatible nanogels for topical chemotherapy of aggressive melanoma. Colloids Surf B Biointerfaces 2019;174:232-45.  Back to cited text no. 70
    
71.
Barone A, Mendes M, Cabral C, Mare R, Paolino D, Vitorino C. Hybrid nanostructured films for topical administration of simvastatin as coadjuvant treatment of melanoma. J Pharm Sci 2019;108:3396-407.  Back to cited text no. 71
    
72.
Martínez-Martínez M, Rodríguez-Berna G, Gonzalez-Alvarez I, Hernández MJ, Corma A, Bermejo M, et al. Ionic hydrogel based on chitosan cross-linked with 6-phosphogluconic trisodium salt as a drug delivery system. Biomacromolecules 2018;19:1294-304.  Back to cited text no. 72
    
73.
Sohrabi S, Haeri A, Mahboubi A, Mortazavi A, Dadashzadeh S. Chitosan gel-embedded moxifloxacin niosomes: An efficient antimicrobial hybrid system for burn infection. Int J Biol Macromol 2016;85:625-33.  Back to cited text no. 73
    
74.
El-Badry M, Fetih G, Shakeel F. Comparative topical delivery of antifungal drug croconazole using liposome and micro-emulsion-based gel formulations. Drug Deliv 2014;21:34-43.  Back to cited text no. 74
    
75.
George D, Maheswari PU, Begum KM. Synergic formulation of onion peel quercetin loaded chitosan-cellulose hydrogel with green zinc oxide nanoparticles towards controlled release, biocompatibility, antimicrobial and anticancer activity. Int J Biol Macromol 2019;132:784-94.  Back to cited text no. 75
    
76.
Ciro Y, Rojas J, Yarce CJ, Salamanca CH. Production and characterization of glutathione-chitosan conjugate films as systems for localized release of methotrexate. Polymers (Basel) 2019;11:E2032.  Back to cited text no. 76
    
77.
Mieremet A, Rietveld M, Absalah S, van Smeden J, Bouwstra JA, El Ghalbzouri A. Improved epidermal barrier formation in human skin models by chitosan modulated dermal matrices. PLoS One 2017;12:e0174478.  Back to cited text no. 77
    
78.
Khalil RM, El Arini SK, AbouSamra MM, Zaki HS, El-Gazaerly ON, Elbary AA. Development of lecithin/chitosan nanoparticles for promoting topical delivery of propranolol hydrochloride: Design, optimization and in-vivo evaluation. J Pharm Sci 2021;110:1337-48.  Back to cited text no. 78
    
79.
Cojocaru C, Samoila P, Pascariu P. Chitosan-based magnetic adsorbent for removal of water-soluble anionic dye: Artificial neural network modeling and molecular docking insights. Int J Biol Macromol 2019;123:587-99.  Back to cited text no. 79
    
80.
Sai N, Nimal TR, Baranwal GR, Suresh MK, Anju CP, Kumar VA, et al. Preparation, characterization and efficacy of lysostaphin-chitosan gel against Staphylococcus aureus. Int J Biol Macromol 2018;110:157-66.  Back to cited text no. 80
    
81.
Dadou SM, El-Barghouthi MI, Alabdallah SK, Badwan AA, Antonijevic MD, Chowdhry BZ. Effect of protonation state and N-acetylation of chitosan on its interaction with xanthum gum: A molecular dynamics simulation study. Mar Drugs 2014;15:298.  Back to cited text no. 81
    


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