Section 3 Occular Drug Delivery

#### **Chapter 4**

### Ocular Drug Delivery System: Barrier for Drug Permeation, Method to Overcome Barrier

*Prakriti Diwan, Rajendra Jangde, Sulekha Khunte, Harish Bhardwaj and Preeti K. Suresh*

#### **Abstract**

The physiological and anatomical barriers are major obstacles in the field of ocular drug delivery systems. The barriers involve nasolacrimal drainage, blinking, cornea, sclera, and blood-aqueous barriers whereas dynamic barriers involve conjunctival blood flow, lymphatic clearance and tear drainage. These barriers are showing natural protective functions, as well as limiting drug entry into the eye. Nanocarriers have been found to be effective at overcoming the problems and associated with conventional ophthalmic dosage forms. In this chapter emphasizes overcome to barriers and discusses advanced novel techniques used in the field of ocular drug delivery systems including nano dispersion systems, nanomicelles, lipidic nanocarriers, polymeric nanoparticles, liposomes, noisome, and dendrimer, have been investigated for improved permeation and effective targeted drug delivery to various ophthalmic site.

**Keywords:** ocular drug delivery, inflammation, conjunctiva, microphages, aqueous humor

#### **1. Introduction**

The eyes are one of the most important and complex sensory organs; they serve as a gateway for collecting and transmitting extraneous images to the brain as signals via the optic nerve. They maintain a relationship between the body and our surroundings through this action. Various disorders, such as inflammations or bacterial and viral infections, affect the eye's behavior. Such kind of disorders affecting the anterior eye tissues can be smoothly treated with high doses of drugs. However, diseases affecting the posterior tissue of the eye are tough to teach and treat. Age-related disorders including macular decay, glaucoma, diabetic macular edema, and proliferative vitreoretinopathies are some of familiar posterior eye diseases that can lead to vision loss if left untreated.

The complex structures of the eyes provide a high level of drug/treatment resistance. A thorough understanding of ocular anatomy, physiology, and barriers is essential for providing effective treatment for diseases affecting both anterior and posterior ocular tissues. This assists with clarifying the difficulties related with drug conveyance to eye. In this part, we give a point by point depiction of visual life systems and physiology as well as the blockade those stances difficulties to sedate conveyance.

### **2. Structure of the eye**

The eye can be partitioned into two divisions the front fragment and the back section in structure of eye (**Figure 1**). The front fragment stays alive of the cornea, conjunctiva, watery humor, iris, ciliary body, and translucent focal point. These elaborate roughly 33% of the front of the eye. The leftover part for example back section made out of the sclera, choroid, Bruch's layer, retinal shade epithelium (RPE), nonpartisan retina, and glassy humor. An exhaustive depiction of the life systems and physiology of the eye is introduced beneath.

#### **2.1 Cornea**

The cornea is delicate, straight, smooth, vascularized, generally deciduous, and the most meticulous tissue in the body. The curved and rounded design allows it to be inserted directly into the external environment. The cornea develops in the white part of the eye called the sclera and the translucent tissue called the conjunctiva. The limitation of the cornea from which it connects to the sclera is known as the annulus. Limbus is strangely angiogenic and is thought to be a reservoir of pluri potent juvenile microorganisms. The surface of the cornea, which is not covered by the external environment, is invaded by the tear film, and its inner surface is mainly in contact with liquid called liquid humor. The thickness of the cornea often increases from the center to the periphery. These are seen in the twist of the cornea, which is huge in the center and has the smallest ring.

**Figure 1.** *Structure of human eye.*

#### *DOI: http://dx.doi.org/10.5772/intechopen.105401 Ocular Drug Delivery System: Barrier for Drug Permeation, Method to Overcome Barrier*

Corneal shape, surface perfection, straightforwardness, and refractive list manage the optical properties of the cornea. The corneal stroma is fixed with an almost homogeneous and methodical course of action of collagen fitters (distance across 25, 35 nm). Such a course of action is wanted to be answerable for forestalling and canceling dissipated light obstruction from the occurrence beam of light on collagen. The unequivocal plan and capacity of strands permit the light beams to ignore the cornea with next to no impedance. Corneal perfection is constrained by the corneal epithelium and tear film. Any redirection from the typical engineering of corneal collagen strands and nonappearance of tear movie inclusion causes dry eye and dissipating of episode light beams and prompts loss of corneal forms, straightforwardness, and perfection.

As mentioned above, the cornea is deeply attached to soft spots that are 300,400 times thicker than the skin [1]. Tactile nerves, long ciliary nerves, and well-thoughtout involuntary nerve cords persuade the panniculus tissue. The ciliary nerve of the trigeminal nerve of the eye provides the tactile nerve to the membrane. The marginal nerve ring is formed by the longciliary nerve, which sheds its myelin sheath (at certain short distances) after entering the cornea, infiltrates the Bowman layer and then annihilate it with vane cells. These nerve cords further radiate anteriorly cross into the stroma, building the sub epithelial plexus [2]. Damage or loss of the corneal epithelium reveals external climate-sensitive areas. Causes extreme visual distress [3]. The typical cornea has no venous supply. Therefore, this tissue is treated as one of the internal tissues in the body along with ligaments and focal points. Internally, corneal epithelial cells and endothelial cells are metabolically dynamic and are effectively involved in injury healing. Both cell layers receive blood parts and various necessities from the veins of the internal and external carotid corridors that make up the conduit around the marginal cornea [4, 5]. Aqueous humor provides the cornea with the glucose it needs and a limited amount of oxygen. Most of the oxygenation of the cornea is evident in the opening to the air where the oxygen taken up by the lacrimal layer diffuses into the corneal epithelial cells. This openness of the saturated tear layer on the surface of the cornea is essential for oxygenation and maintenance of integrity and virtue. The histological section shows that the cornea is composed of six different layers, specifically the corneal epithelium, Bowman's layer, stromal, DNA layer, Descemet's membrane, and endothelium. The corneal epithelium is composed of 5–6 layers of discrete squamous epithelial cells that are not cornealized. The various epithelial layers of the cornea are composed of several layers of flat cells and wing cells and a single layer of basal cells. Various corneal epithelial cells are formed by rectangular parallelepiped basal cells with a tight cross-sectional structure that prevents tears from entering the intercellular space. After separation, these cells become delicately smooth as they migrate to the surface of the cornea. Electron microscopy shows that the outer layer of flat epithelial cells is sporadic and that rim-like disruption of the cell membrane is shown as microplica (**Figure 2**) [6]. This collapse expands the contact area. Microprica is covered with a very fine, tightly packed, charged sugar-coated layer that aids in scattering.

The fluid spreads evenly over the entire corneal surface at each strabismus. Flatepithelial cells are revived every 710 days by pluri potent microorganisms in the cornea llimbus [7]. More established cells are shed after their tight interface is destroyed by the tear film. The new cell layer controls tight junctions with the help of vane cells, which form lateral junctions between cells [8, 9]. Epithelial cells are connected to a 0.05 atm thick basement membrane consisting of columnar epithelium and are approximately 20 cm high. The presence of tight cells overlaps between the cell and the framework structure, and they are deeply impervious. Basal cells are the major corneal epithelial cells suitable for mitosis [10]. The Bowman's layer is a band of undefined fibrous material

**Figure 2.** *Histology of corneal layer of the eye.*

with a thickness of 81 Skm that simply resides beneath the base of corneal epithelial cells. This layer is non-regenerative, forming a boundary between the stromaof the cornea and epithelial cells and maintaining the shape of the cornea. It has been hypothesized that it is associated with re-epithelialization [11]. The Bowman layer is composed of short type I collagen fibrils embedded in the proteoglycan lattice. The stroma of the cornea intervenes between the Bowman layer and the Descemet's membrane and occupies 95% of the thickness of the cornea. The stroma contains corneal keratocytes, fibroblasts, brain tissue, and Schwann cells. Most of the interstitium is composed of collagen fibrils, mainly type I collagen [12]. These fibrils extend from one end of the annulus to the next and are calculated symmetrically at 90° in the anterior interstitium and symmetrically in the posterior interstitium. Collagen fibrils maintain the cornea with mechanical strength. The posterior interstitial fibrils are more tightly coordinated than the anterior, which clearly contributes to the mechanical and bipodal strength of the cornea [13]. The Dua's layer is a highly detailed, cell-free solid layer that resides on the Descemet's membrane [14]. Its physiological function in the cornea has not yet been investigated. The Descemet's membrane layer is 10 pm thick, the surface is undefined and huge. Upon invasion, this layer is alternative, homogeneous, cell-free and gradually transforms into Descemet's membrane [15, 16]. This layer is thrown into many creases that appear as stripes, because of awry enlarging of the back stroma, and underlying limitations name by the limbus.

The corneal endothelium is the most profound monolayers of cells, which is nonmitotic and in direct association with fluid humor, while endothelium control itself, stroma leftover in a deliquescent state to give corneal clearness [17]. This monolayer has characterized penetrability to particle transition, which is important to construct an osmotic angle. Endothelial cell numbers by and large downfall with age. During sickness, cells help in size (polymegathism) and show shape variety (polymorphism) to make up for the spaces made by declining cells.

#### **2.2 Conjunctiva**

Histologically, conjunctiva is primarily comprised shallow multifaceted epithelium and a fundamental stroma [18]. Conjunctival epithelial cells holds less close intercellular intersections with transepitlielial electric obstruction in the scope of 0.7S to −1.5 Kf2cm' [19, 20]. Conjunctiva moreover contains pores of 5.5 nm sweep [21].

#### *DOI: http://dx.doi.org/10.5772/intechopen.105401 Ocular Drug Delivery System: Barrier for Drug Permeation, Method to Overcome Barrier*

Implanted inside the conjunctival epithelium are challis cells, mucous organs, organs of Manz and the sepulchers of Henle [22]. Challis cells are matched in electrolyte, liquid and bodily fluid discharge [23] to plan the tear film. Transportation of challis cells in various areas of conjunctiva persue a particular request: mediocre > predominant > nasal > fleeting conjunctiva [24]. The apical surface of conjunctiva shows out foldings called microvilli and microplicae [25]. During tear film part emission the conjunctival apical layer cells are breakdown alongside secretory granules and different parts into the tear film. Conjunctival apical out foldings might advance in expanding surface region, offer help or balance out and attach with tear film [25].

The conjunctiva is a thin, highly vascularized, opaque, mucous-draining tissue that covers the inner layers of the upper and lower eyelids [26]. It reflects off the eye as a faint, transparent tissue on the sclera and extends toward the annulus of the cornea. This tissue is very fast with efferent, afferent, tangible nerves, and also lymphoid tissue. The total surface area of the conjunctiva is many times the total surface area of the cornea [27, 28]. Due to its versatile nature, the conjunctiva promotes eye and eyelid movement. The lamina propria of tears combined with a small amount of aqueous humor protects the internal visual tissue from the external climate. Depending on the area, thickness, and angiogenesis, this tissue can be broadly divided into three types: eyelid, eyelid, and hamburger conjunctiva. The upper and lower eyelids are lined inside by a medium palpebral conjunctiva, with a small portion of the conjunctiva called the bulbous conjunctiva adjacent to the sclera. The bulbar conjunctiva is very light, standard on the cornea, and has open eyes with an air interface and is exposed to the outside climate (**Figure 3**). The eyelids and parts of the eyeball are connected by a small piece of tissue called the vestibular conjunctiva. The vestibular and volar conjunctiva are rich in veins and composed of heterogeneous tissue [26].

Conjunctival stroma is parleyed between the foremost epithelium and back sclera. This layer is equiped with blood and lymph vessels and innervated with sensitive spots. This is interlaced with mature lymphocytes, overwhelmingly with T cells comparative with B cells. Mucosa-connected lymphoid tissue assumes a urgent part in the resistant

#### **Figure 3.**

*Diagrammatic portrayal of front office of natural eye with shut eye covers showing three particular conjunctivas (striking layers). (1) Forniceal conjunctiva, (2) bulbar conjunctiva, (3) palpebral conjunctiva, (4) upper eye top, (5) watery humor, (6) lower eye lid, (7) cornea, (8) sclera and (9) lacrimal gland,(10) translucent focal point and (11) iris ciliary body.*

reaction [29]. It is made out of lymphatic cells present underneath the epithelium, which achieve a safe reaction against antigens by emitting dissolvable antibodies. Lymphocytes are circled over particular vascular framework alluded high endothelial venules [30–32]. Electron micrograph of the tear film surface of the conjunctival epithelium. It shows (a) the mucous layer (m) and (b) the sugar coating (studs) present in the micropreca. (A) In this movie, we used various mucins, MUC1 (red), MUC4 (blue) and MUC1 (green). These protrude as solid bars to form the sugar coating from the microvilli of the epithelial cells. (B) The higher emphasis in the area (a) indicates fine fibers extending into the sugar coating (sharp stones), probably film-related mucin atoms. In the cytoplasm of microplica, actin fibers extend to the surface film, from which layered mucin emerges (giant bolts). (C) Small immunoelectron confinement of the immune agent H185 that senses carbohydrate epitopes on the layer-related mucin MUC16 [25].

#### **2.3 Fluid humor**

Watery humor is an optically clear, gently basic visual liquid that is developing from plasma by epithelial cells of ciliary body [33]. Three distinct cycles dissemination, ultra filtration and dynamic emission, commit to the compound organization and arrangement of fluid humor. It is anticipated that the whole watery humor is supplanted in roughly 100 min [34]. This liquid comprises somewhat less protein, egg whites and y-globulins, than plasma. Moreover, glucose, lactic corrosive, ascorbic corrosive and immunoglobulin G are similarly present [35, 36]. Fluid humor supplies supplements and an oxygen to the visual a vascular tissue, alluded as cornea and focal point. It clean up squanders items, macrophages, blood and other trash from the back of the cornea and front of the focal point. Additionally, it assumes a vital part in keeping up with the shape and interior afflictions of the eyeball over creation of intraocular pressure. The liquid humor that is assembled and drained into the posterior eye ignores the pupil in the anterior chamber. It is drained into the venous bloodstream through the trabecular meshwork and Schlemm's canal (**Figure 4**). Approximately 510% of the aqueous humor is used to secure the uveal scleral pathway [37, 38].

*DOI: http://dx.doi.org/10.5772/intechopen.105401 Ocular Drug Delivery System: Barrier for Drug Permeation, Method to Overcome Barrier*

#### **2.4 Iris—ciliary body**

The iris and the ciliary body are two distinct tissues with distinct physical and physiological properties. Their life systems and physiology are explained combined for simple mindfulness and close physical limits. The iris is placed at the back of the cornea and appears as a ciliary body spirit. The iris is made up of three layers: endothelium, stroma, and epithelium, according to histology. The iris creates a small roundabout opening or gap in the bleeding edge of the focus point, known as the student, which helps to control the amount of light that reaches the retina. Each ciliary body has a ciliary cycle, which conveys a fibrovascular core that is in continual contact with the ciliary body's stroma. Blood flow from the choroidal veins in the front to the back. The blood from the ciliary body of the eye is drained from the border of the vortex vein. The ciliary body is physically adjacent to the iris and is in charge of adjusting three vital capacities in the eye: it secretes watery humour, which runs before the focal point and is depleted out of the eye through tubules known as the trabecular meshwork and schlemm channel close to the intersection of cornea and iris (ii) this tissue also contains smooth muscles, which demonstrate through zonular filaments on the glasslike focal point to acclimate centigrade (iii) it can coordinate in emptying fluid humor out of the eye into the bordering trabecular meshwork by broadening smooth muscle strands and ligaments.

#### **2.5 Focal point**

The focal point is clear, internal, non-innervated and biconvex. It is situated behind the student and iris with the help of the ciliary body's zonular strands [39]. The foremost focal point is covered with fluid humor and the back with glassy humor. The focal point film (otherwise called the case) keeps up with latent trade of metabolic substrates and waste through straightforward dissemination [40] checked by their size and charge [41–44]. The focal point comprises of four explicit parts: the case, epithelium, cortex (fiber cell mass) and core. Additionally, it controls light passage into the eye and its refraction.

The case is an undisturbed, uncomplicated, versatile foundation layer that embodies the entire focus and provides the framework underlying the focus of the eye. Container thickness varies between species and within similar assemblies. For example, in mice, rodents, and rabbits, the container thickness is approximately 10 μm, 13 μm, and 14 μm, respectively. The thickness of the human front shell ranges from 25 to 30 μm compared to 14:00 on the back [45, 46]. A layer of casing protects the focus from direct contact with adjacent visual tissue and aqueous fluids. In addition, it acts as a sea of developmental factors, sequestering them [47–50] and forming a defensive barrier against microbial attack [51–53]. The arrival of developing factors from the container supports the improvement and isolation of focal cells. The instrument used to give the focal point its shape and surface curvature is fluffy. A tall, columnar monolayer of epithelium lies beneath the foremost focal vessel. The epithelium is not present behind the focal point in contact with the vitreous liquid. The cortex lies below the vessel and later in the epithelium of focus. It contains 68.6 of water [54]. The focal cortex is composed of newly assembled filaments that make up most of the focal point. The strands are tightly packed, the newly assembled filaments make up the nucleated organelles [2, 55, 56], and with age the strands shift towards the focal point (**Figure 5**) [46]. During this

**Figure 5.** *Histology of lens.*

development period, filaments lose organelles and nuclei, providing focus directness. The preparation of these filaments in adult focus involves four pointed stellar programs [57]. Entanglement between adjacent fiber cells can cause interlocks in deeper focal strands [57, 58]. Further migration to the focal cortex near the nucleus, the cells appear in the form of edgy hexagons. The locking system works together to even out the pressure during operation, perhaps preventing the fiber cells from slipping against each other.

The focal point core is exceptionally gotten by its area. It contains 63.4% water [54] and is started by testimony of old fiber cells that movement to the middle from the fringe. As the consequence of gathering of old cells in this zone, it turns out to be exceptionally thick and denser.

#### **2.6 Sclera**

The sclera, commonly referred to as the white of the eye, is a highly vascularized and flexible tissue beneath the conjunctiva and dilated cornea. The optic nerve exits the eye posteriorly through a scleral opening surrounded by a complex, gridlike, filamentous tissue network called the cribriform plate. The sclera (the top layer of the sclera) provides the expected nutrition to the sclera. The sclera occupies about 80% of the skin of the eye and the rest is composed of the inner cornea. Its physical area, the anterior sclera (near the annulus), thickens as it moves towards the equator, near and around the optic nerve, towards the back of the eye. It will decrease [59]. The sclera is composed of a sloppy tissue of filaments emanating from the dura mater of the focal sensory system. In this type of plan, all visible light frequencies are scattered and appear dazzlingly white in the shadows. At the interface between the sclera

*DOI: http://dx.doi.org/10.5772/intechopen.105401 Ocular Drug Delivery System: Barrier for Drug Permeation, Method to Overcome Barrier*

and the cornea, changes to true standard changes occur sporadically from nowhere, leading directly to such dark white scleral changes.

Similar collagen strands are available in the cornea yet are coordinated in a standard example that provisions straightforwardness to the tissue, hydrated sclera filaments stay obscure while corneal strands do not require water and become straightforward. The corneal endothelium serves to its straightforwardness by the droping out of water. Sclera, being the layer of the eyeball, is presenting to visit changes of outer alongside of intraocular pressure. Width of the sclera at tropical zone goes from 25 to 230 nm and at the fluid measurement of the sclera goes from 20 to 80 nm [60, 61].

#### **2.7 Choroid**

Choroid is put between fringe sclera and internal retinal epithelium. It is famously vascularized and innervated tissue fixing melanocytes alongside bodily fluid like extracellular liquid. It comprises of four particular parts: from external to internal suprachoroid, haller's layer, sattler's layer and Bruch's film. Suprachoroid is made by 6–10 layers, around 30 qm in which lay out the connection point between external sclera and internal [62]. The suprachoroid supported anteriorly with supraciliary and last posteriorly up to the optic nerve. This locale is exceptionally aroused with nerve strands and ganglion yet no vasculature. Meager lamellar filaments, in touch to one another, related the choroid and sclera. This procedure develops a small space between these tissues called the space on or around the choroid. This space is scarce as it moves backwards towards the macula.

The vascular layer below the supra choroid lamina is composed of three specific vascular layers, which are narrow and the distance within the lumen is continuously reduced. These blood vessels are surrounded by pigmented melanocytes and non-pigmented fibroblasts, reaching the maximum amount of choroid. Blood vessels, fibroblasts, and melanocytes appear in discreet amounts of choroidal stroma. The thickness of melanocytes increases from concentration to the periphery. Choroidal vessels are suggested by the luminal distance across the site as follows: Haller layer with larger outer blood vessels. Currently being tracked on a medium-sized vessel at Sattler's Layer. Well-traced choroid capillaries with narrowed blood vessels. Choroidal blood flow are also high in other visual tissues and the brain [63, 64]. The enlarged choroidal blood circulation provides an additional supply and diffusion of oxygen within the retina of the brain. Metabolic waste from the retina is carried away by changes in intraocular temperature caused by the visual cycle. This accelerated circulatory system also seems to predict sections when monitoring intraocular pressure [65–67]. Bruch's film is the last and deepest choroidal layer that covers the RPE [68]. Also known as the vitreous membrane. It is a thin, pentahedral, multifaceted, cell-free, layered structure initiated by the interaction of villous capillaries with RPE. Bruch's film grows from behind the eye, such as the optic nerve, to the oraserrata of the iris, with a continuous decrease in thickness from behind the eye to the edge. This film separates RPE and villous capillaries. Retinal cell associations include seven unique types of cells, including RPE cells. These are photoreceptor cells (poles and cones), squamous cells, amacrine cells, interstitial cells, bipolar cells, ganglion cells and glial cells (**Figure 6**).

**Figure 6.**

*Layers and histology of normal adult human retina.*

#### **2.8 Retinal pigment epithelium**

Each individual eye contains approximately 3.5 million RPE cells [69], which attach to each other to form a narrow intersection (closed zone). The retina epithelium (RPE) contains non-dividing cells that form the monolayer that covers the retina of the cerebrum, but these cells can proliferate under masochistic conditions and are therefore isolated. I have not. It provides confirmation to the internal visual tissue and secretes large amounts of growth factors (vascular endothelial enhancer factor, ciliaryneurotrophin factor, and platelet engineering enhancer factor). This monolayer monitors visual resistance and protects against oxidative damage by releasing immune modulatory cytokines [70, 71]. RPE cells mediate several compounds such as superoxide dismutase, catalase, glutathione, and melanin clay. RPE supports the function of villous capillaries and photoreceptors and plays a fundamental role in patience. Therefore, its presence is important for the adjustment of visual boundaries [72]. Its capabilities include regulation of priotoceptors to external parts, retinoid processing, management of the visual cycle, and maintenance of sub retinal complex ecological variables [73–75].

#### **2.9 Neural retina**

Geologically, the retina is fashioned into macula, optic plate, fovea and fringe retina. The macula or are acentral is round 5 mm in thickness and positioned kind of three mm farfar from the optic circle. Macula accepts its call from the yellow carotenoid color ationxanthophylls, because the macula lutea. The focal factor of the macula serves a huge locale of visible sharpness [76] and is known as the fovea. The fovea has the maximum noteworthy thickness of confined and extended cone receptors to misrepresent mild location [77]. The focal factor of the fovea is vascular as much as 500 atm and the bloods deliver to this locale offers from chorio capillaries. Retinal fleeting veins encase the fovea. The outside layers of the fovea is thick and contain cores of photoreceptor cells. The relaxation fringe retina, bodily one layer of ganglionic cells,

#### *DOI: http://dx.doi.org/10.5772/intechopen.105401 Ocular Drug Delivery System: Barrier for Drug Permeation, Method to Overcome Barrier*

are to be had outside the fleeting retinal courses. Most outside retina receives its blood conveyances from the choroidal course, whilst standard retinal go with the drift satisfies inward retinal blood deliver [78, 79]. The inner arranging of the eyeball is made from mild-sensitive mind cells, alluded the mind retina, which talk tactile records to the cerebrum and interplay with the out of doors climate. These tactile nerves emerge from the focal sensory system [80, 81]. Brain retina is framed of round 7.7 million poles and five million cones (**Figure 7**) [82, 83]. The photoreceptor cells made from bars and cones. These cones basically ability to capture and extra de over the photons right into a nerve signal [84]. Retinal pole cells are accountable for keeping apart colorings in exquisite mild alien though cone cells has a tendency in keeping apart exceptionally contrasting shading in stupid mild. Biggest portions of cones are discovered with inside the fovea; whilst the bars are appropriated during the retina bar the focal fovea. The cones and bars are interconnected with among neurons named bipolar cells. Visual records is passing directly to ganglion cells through the a macrine cells, which cross approximately as an extension. Ganglionic cells talk records symptoms and symptoms to the focal sensory system, as an example mind. During power transmission Muller's telephonesassist in directing the community microenvironment for visible working.

**Figure 7.** *Diagrammatic representations of rod and cone cells of neural retina.*

#### **2.10 Vitreous humor**

The glassy humor is a straightforward, dismal, coagulated mass that consume the space in the eye between the focal point and the retina. It is encased by a layer of collagen named the glassy film (or hyaloid film or glassy cortex) isolating it from the remainder of the eye. It makes up four-fifths of the volume of the eyeball. The glassy humor is liquid like close by the middle, and gel-like close by the edges.

The glassy humor is in touch with the glassy film overlying the retina. Collagen fibrils associate with the glassy at the optic nerve circle and the oraserrata, (where the retina closes anteriorly), at the Wieger-band (the dorsal side of the focal point). The glassy likewise unequivocally connects to the focal point case, retinal vessels, and the macula, the region of the retina which gives better detail and focal vision [85].

Thickness of the liquid shrinkage with developing age. As this occurs, foremost watery humor might penetrate into the back glassy, bringing about a pulling impact at the association point of retina and glassy liquid. Moreover, this might source the arrival of cells into the liquid, which show up as floaters, and if a huge pulling impact is created it might pull off or withdraw the retina [86].

#### **2.11 Hindrances to visual medication conveyance**

Ophthalmic medications are accessible as arrangements, suspensions and balms, which are fundamentally directed topically. Elective courses of medication conveyance to the eye are oral, intravenous, intra vitreal, sub conjunctival and peri ocular infusions or inserts [87]. A challenge task in drug conveyance is to defeated visual boundaries and convey sedates definitively to the designated visual tissue. Drugs are meddling from arriving at designated visual tissues by static and dynamic natural visual boundaries. These obstructions can be ordered by their physical area and their practical properties. Generally, these obstructions can be named foremost and back section boundaries.

#### **3. Front portion hindrances**

There are two assortments of boundaries, for example static or dynamic boundaries. Static boundaries cover cornea, conjunctiva, iris-ciliary body, focal point, and blood watery boundary and efflux siphons propose on cell surface, for example, multidrug-safe proteins: penetrability glycoprotein. Conjunctival lymph, blood stream, inverse directional progression of fluid humor and tear creation together cover under the powerful boundaries. These obstructions are shown exhaustively underneath.

#### **3.1 Cornea**

The life systems and physiology of the cornea have been delineated before in this section. Cornea go about as a multifaceted mechanical obstruction to keep away from exogenous substances, including topically applied drugs, from infiltrating further into visual tissues. Expanding size influences the conjunctival penetrability. Bulbar and palpebral do not show a huge contrast [88]. Penetrability correlation that the conjunctiva is two times as porous as the sclera, while is roughly multiple times more penetrable than cornea. The epithelial pore size and thickness are two and multiple times more noteworthy in cornea and sclera individually. An anticipated complete paracellular space in conjunctiva is multiple times higher than that of cornea. External corneal

#### *DOI: http://dx.doi.org/10.5772/intechopen.105401 Ocular Drug Delivery System: Barrier for Drug Permeation, Method to Overcome Barrier*

layer, with 90% epithelium cells, convey this profoundly lipophilic. Mature corneal epithelial cells go about as a district for drug ingestion since they are firmly limited by cell bond, for example, Occludin, ZO1 and ZO2 [89]. These proteins tenaciously seal cells and establish a tight convergence for paracellular drug transport across the cornea.

Drug transport across films by a variety of devices, specifically (I) paracells, (II) transcellular, (iii) dynamic, (iv) carrier-mediated, and (v) receptor-mediated transport. Paracellular scattering of ionic (polar) particles is hampered by the narrow convergence of the cornea, but lipophilic preparations can be beneficially diffused through the transcellular portion across the lipophilic cornea. Bowman's shift does not have a significant impact on countering drugs with a wider distribution. Hydrophilic stroma is a rate-determining disorder of fat-soluble molecules, preventing more important visual media. In the case of a superbly lipophilic and almost hydrophilic combination, the epithelium and stroma act as a reservoir. Descemet'smembrane film is not involved in blocking drug intake on a regular basis. The corneal monolayer endothelium forms a cellular barrier between the stroma and the aqueous humor. This boundary mediates a flawed narrow intersection that promotes the free development of macromolecules between stroma and body fluids [90]. The distribution of drug intake through the cornea usually occurs in the aqueous humor. Subatomic size does not play an important role in transport through the corneal epithelium, but ionization of the mixture reduces transcellular dispersal. Therefore, particle physicochemical properties, ionization resistance, and visual medicine pH play important roles in maintaining and determining visual medicine porosity in the corneal epithelium 4. Vernal kerato conjunctivitis is a severely helpless conjunctivitis that impairs the dignity of the corneal epithelium and limits its prosperity. During this condition, eosinophils transport cytotoxic proteins such as myc-sponsored restricted protein (MBP1), eosinophil peroxidase, eosinophil-specific neurotoxin, and eosinophil-cationic proteins into the tears and the corneal epithelium. Reverse rotation of the epithelium [91–95].

Corneal ulcers are caused by the sensation of corneal fibroblasts by neutrophils. These fibroblasts are involved in the deterioration of nearby corneal collagen through various components such as disease putrefactive factors, interleukin 4, and interleukin 13 [96–98]. Therefore, the cornea loses an undeniable collagen strategy that can affect the corneal barrier properties against locally controlled drugs. Corneal fibroblasts are involved in the reverse rotation of collagen and can change parts of the basal layer to metastasize corneal ulcers. Tears from patients with vernal kerato conjunctivitis destroy type IV collagen and laminin, dominate part of the tornado protective layer, and accordingly lead to the development of corneal ulcers [99–102]. Conjunctiva Conjunctival tissue continues as a permeability limit to topically applied drugs. One of the limit properties can be connected with its transepithelial electric protection [103]. Various courses of prescription osmosis, for instance, paracellular, transcellular, dynamic and endocytic courses accept a key part in visual medicine transport through conjunctiva for topically applied drug things. The paracellular course of medicine immersion is noticeably bound in view of the presence of tight crossing points at the epithelial surface, which is the rate limiting development for drug maintenance [104]. The presence of secretory organs presented inside the conjunctiva could have a shock on drug osmosis [105, 106]. Tear creation is a careful response of the eye considering topically applied xenobiotics. A limit reduces the prescription concentration and bioavailability at the conjunctival apical surface. Physicochemical properties of the meds, for instance, hydrophilicity and subnuclear weight were found to accept a fundamental part in drug infiltration across conjunctiva. Test results explained that hydrophilic prescriptions with under 20 kDa nuclear

burdens are vulnerable comparing higher subnuclear weight drug particles. By and large, conjunctiva has more conspicuous paracellular permeability than the cornea for proteins (insulin, subnuclear weight 5.8 kDa) and peptides (pamino clonidine subnuclear weight 245.1 Da) through its greater surface locale and defective vasculature [107, 108]. On the other hand, lipophilic prescription ingestion through the transcellular course has unequivocally higher responsibility relative with hydrophilic meds. As the conjunctiva has esterase activity [109], it could go probably as another impediment for drug transport. During the wiped out conditions the conjunctiva could expand and cultivate fibrosis as consequence of high proportions of collagen creation.

#### **3.2 Blood liquid limit**

Endothelial cells of iris/ciliary veins and the non-pigmented ciliary epithelium together arrangement the blood liquid limit (BAB) in the front piece of the eye. This prevention shapes tight convergences at the cell level and stays aware of the exchanging of solutes between the preeminent and back visual region. Thus, it hinders some vague medication entrance into more profound visual tissues by functioning as a boundary. Cell tight intersections blocks vague medication passage into internal visual tissues and proceed as an obstruction. BAB assists with managing visual straightforwardness and keeps up with synthetic substances structure [110, 111]. An ideal BAB conveys deficient obstruction usefulness; for instance, 40 kDa horseradish peroxidase arrives at watery humor through fenestrated ciliary vessels, which are associated with controlling penetration of plasma proteins, into the fluid humor. The vast majority of the medications arriving at watery humor are clear out into the fundamental course by means of the iris blood vessels. In this way, drugs in fluid humor are consumed by the iris colors and crash into the iris blood dissemination [112, 113]. Then again, little lipophilic medication atoms that withdraw tight intersections and cross BAB can be disposed of more quickly than the bigger hydrophilic particles. Accordingly, drug infiltration to more profound visual tissues is halted.

#### **3.3 Efflux pump**

Multidrug obstruction in cells might be to some degree created because of cell layer joined with efflux carrier proteins. These efflux siphons live from the adenosine triphosphate-restricting tape (ABC) superfamily and are effectively taken an interest in effluxing exogenous xenobiotics and cater insurance to the cell. Penetrability glycoprotein (P-gp) and multidrug obstruction protein (MRP) are two efflux proteins that are key members in drug efflux at the cell level, initiated a decreased intracellular medication fixation.

#### **4. Front section dynamic boundaries**

#### **4.1 Tear seepage**

Ophthalmic medication items are fundamentally managed by skin course into the visual circular drive. A huge segment (around 90%) of regulated drug is lost as the aftereffect of a precorneal obstruction—attack nasolacrimal conduits. Likewise, different variables that required into diminish drug focuses in the parkway incorporate tear weakening, drug restricting to tear proteins and sped up freedom.

*DOI: http://dx.doi.org/10.5772/intechopen.105401 Ocular Drug Delivery System: Barrier for Drug Permeation, Method to Overcome Barrier*

Ordinarily, the advertised droppers are acclimated to convey a volume of effective drop from 20 to 50 μL. From the applied portion, the precorneal pocket contains just 7–10 μL by supplanting tear, which is as of now present in the precorneal pocket [114]. Such overabundance managed portion might be lost by means of spillage from the precorneal pocket or waste through the nasolacrimal pipe [115]. Precorneal tear waste washes the topically imparted drops/arrangements in the initial 15–30 s, causing a genuine decrease in drug contact time and finely lessening visual bioavailability <5 [116–118].

#### **4.2 Conjunctival lymph and blood stream**

The conjunctiva is immensely vascularized and equipped with lymph vessels. Topically applied drugs into the precorneal pocket may be essentially clear out into the fundamental and lymph dispersal while crossing the conjunctiva [119], achieving lower drug invasion to more significant visual tissue. Conjunctival blood and lymph stunningly limit the vehicle of sodium fluorescein from showing up at the inside retinal tissues [120, 121], exhibiting the effective work in drug end.

#### **4.3 Watery humor**

Watery humor produced by the ciliary body overflowing towards the cornea, the alternate way to the medicine entrance applied topically. Topically sedates, what separate across the front corneal epithelial may be also described by liquid humor stream. Meds may be exhausted out through trabecular meshwork into the channel of Schlemm. Greatest hydrophilic meds are seen as discard by watery humor using sub therapeutic levels in the inside visual tissues [122].

BAB activity can be abandoned due to immunological visual deterioration and can prevent reasonable vulnerabilities of experts on various subjects. Intravitreal monitored fluorescein-tested serum protein (FRSA) enters the main chamber after 5 min in the eye, but not in the control/abiotic eye [121]. It is worth noting that the basic fluorescein sodium particles actually pass through the BAB, but not the FRSA. The FRSA scattered in the first room seeks a two-step plan until it appears in the previous room in 23 h, generally with exorbitant obsession (Cmax). After that, the fluorescence continued to decrease. These preliminary results indicate that the student lost the boundaries of the BAB obstacle and lost dosing to the main chamber. In another audit, Kongetal. It has been shown that basic point-edge glaucoma damages the BAB and mediates irritation in the anterior part of the eye. Inconsistent reports between patients with severe terminal glaucoma (PACG) and patients with refractory PACG assume that patients with exceptional PACG have higher BAB damage than patients with consistent PACG [123]. It has been speculated that a significant increase in intra orbital load may be the causative variable for distinguishing BAB in the eye of PACG patients.

#### **5. Posterior segment staticbarriers**

#### **5.1 Sclera**

The sclera is one of the peripheral layers of the eye that cater security and forestalls the passage of exogenous substances to the back visual tissues. Porousness across sclera predominantly relies upon specific standards of the saturating particle, like atomic range, physicochemical properties and surface charge. Drug porousness across sclera steadily diminishes with expanding lipophilicity and sub-atomic span. Thus, particles with higher lipophilicity and bigger sub-atomic range might be kept from penetration through watery scleral pores. The contrary surface charge of the atom further restricts its pervasion across sclera. Drug atoms become caught in the pores of sclera causing low porousness presumably through connections with contrarily charged pores and proteoglycan network [3]. Scleral thickness varies as per their physical area. It is exceptionally wide posterior segment, close to the optic nerve because of its extensiveness; the back sclera has extremely low porousness for drug particles.

Bruch's shiftIn a sense, Bruch's film is concerned with directing the trading of dietary supplements, water, metabolic waste, oxygen, and biomolecules between the choroidal bloodstream and the retina [124]. Bruch's layer thickness increases with age, resulting in calcification, high cross-linking of collagen fibers, and turnover of glycosaminoglycans. The contrast in the thickness of this layer can also affect the permeability of the film from the sclera to the retinal tissue. Also, most medicines can enter the choroid before reaching Bruch's layer. In age-related conditions such as macular degeneration, extracellular edema and accumulation of edema result in oxidative pressure and inflammation of the Bruch layer. As extracellular flots and jets grow, the transport of supplements and drug atoms decreases. This may contribute to an increase in the metabolic pressure of the option [6, 123].

#### **5.2 Blood retina obstruction**

Blood retinal obstruction (BRB) consists of medial and lateral BRBs. The outer BRB is composed of tight junctions of RPE cells, while the inner BRB contains endothelial cells such as retinal hair. Tight junctions of the external BRB release retinal photoreceptors from the RPE [125, 126]. Two astrocytes and Muller cells provide the practical help expected at these narrow intersections. Together, these two cell types maintain the exchange of substances between the outer choroid and the inner retina. It supports astrocytes, catches up honestly, improves the occlusive properties of retinal endothelial blood vessels [127], and increases the safety of inward retinal cells so that they do not boil the flowing particles during retinal proliferation. The absence of windows on RPE and retinal endothelial cells is indistinguishable from blood brain obstruction and indicates potential drug distribution. RPE gives dissemination of tiny atoms like CO2, O2 and lipophilic particles to inward retinal tissues from the choroid. In this way, the boat of atoms might be intervened by receptor or energy/ATPsubordinate liquid stage pinocytosis. Thus, drug entrance is profoundly controlled because of the presence of tight intersections. Likewise, RPE go about as a siphon by getting dried out the sub retinal space. This includes keeping up with the ordinary neuro retinal pairing with RPE [128]. The siphon working is vital in light of the fact that there is no solid bond between the portions of outer brain retina and RPE. The inter photoreceptor framework, a gel-like thick arrangement happens between retinal cells, cause exceptionally powerless bonds. During the ischemic circumstances, retinal cement powers decline inside the space of minutes [129].

#### **5.3 Efflux siphons**

Transmembrane efflux siphons, for example, P-gp or MRP revealed on apical and basal sides of human RPE have been accounted for [130]. Articulation of P-gp on

*DOI: http://dx.doi.org/10.5772/intechopen.105401 Ocular Drug Delivery System: Barrier for Drug Permeation, Method to Overcome Barrier*

rodent retinal endothelial cells has additionally announced. Additionally, the presence of P-gp, MRP4 and bosom disease opposition.

protein in retinal vasculature of post pregnancy mouse was accounted for. The outflow of P-gp in retinal astrocytes was additionally been accounted for [131]. As of late, unique articulation levels of MRP1, MRP4 and MRP5 efflux proteins in human RPE cells have additionally been accounted for [63]. At cell level, these efflux siphons are energetically associated with diminishing the intracellular medication focuses and play out a significant obstruction to tranquilize conveyance.

#### **5.4 Choroidal blood and lymph circulation**

Choroid is an exceptionally vascularized tissue and outfitted with lymph vessels. This tissue has biggest blood vasculature (−80%) of the all out visual blood supply co-connected with iris-ciliary body and retina [7]. Lipophilic medication atoms are effectively depleted into the choroidal and fundamental dissemination, then, at that point, section into the inward visual tissues. This limited medication might prompt sub remedial medication fixation in retinal tissues. This limited medications entry might prompt sub helpful medication focus in retinal tissues. Studies with hydrophilic medications, for example, sodium fluorescein communicated extremely low end through choroid [8].

#### **6. Ocular drug delivery systems**

Visual medication conveyance framework there are principle issue of fast and exhaustive end of regular eye drops from the eye [9]. This issue brings about loss of enormous measure of medication. Just hardly any measure of medication enters the corneal layer and enter to inward tissue of eye [10, 11]. The primary explanation of medication misfortune incorporates lachrymal waste and medication weakening by tears [13]. This super smoothness diminishes the visual bioavailability and cause undesirable harmful and incidental effect. Yet, late biomaterials and nanotechnology headways have started a significant development in investigation into biodegradable microparticles and nanoparticles, hydrogel, and visual inserts, all of which can contain visual pharmacologic specialists, considering further developed prescription conveyance. Besides, supported discharge drug treatments might diminish the in general financial effect of visual sicknesses by lessening the aftereffects related with current clinical therapies [14]. Visual medication conveyance is progressing dangerously fast, on account of consistently advancing ways to deal with designated conveyance (**Table 1**).

#### **6.1 Nanoparticles and microparticles**

Nanoparticles and microparticles particulate polymeric medication conveyance frameworks comprise of miniature and nanoparticles. The prevalent size of microparticles for the ophthalmic organization in regards to 5–10 mm, over this size, a scratching sensation inside the eye might result after visual application. Microspheres and nanoparticles recommend excited drug transporters for ophthalmic application. The medication restricting depends on the physicochemical properties of the nano-or miniature molecule polymer. There after best medication restricting to those particles, the medication retention inside the eye is worked on widely when contrasted with eye drops. Particulates like nanocapsules, nanoparticles, nanosuspensions and so forth are wont to work on the bioavailability of visually applied drugs [15].


#### **Table 1.**

*Showcased product of ocular drug delivery system.*

#### **6.2 Fake tear inserts**

In 1981 Merck, Sharp and Dohme was fostered an instrument for supported discharge alluded to as counterfeit tear. Industrially Lacrisert is out there inside the market. It contains hydroxyl propyle cellulose without additives used in the treatment of dry eye [132].

#### **6.3 Visual inserts**

In conventional arrangement of medication conveyance like suspension, arrangement and balm have various drawbacks. Visual additions limit the matter happen in customary framework. These are strong measurement structure have high ability to hold required convergence of medication in to designated tissue. A few examples of visual supplements are Dexamethasone, Pilocarpine nitrate, Tropicamide and Timolol Maleate [17].

#### Benefits of visual additions

A few benefits of visual addition which might be summed up as follows:


*DOI: http://dx.doi.org/10.5772/intechopen.105401 Ocular Drug Delivery System: Barrier for Drug Permeation, Method to Overcome Barrier*

#### **6.4 Liposome**

Liposomes are spherical phospholipids vesicles made up of one or more concentric bilayers arranged in a ring. They are non-toxic and biodegradable in nature, owing to the fact that they are made up of lipids that are similar to those found in biological membranes. Liposomes have the ability to entrap both hydrophilic and hydrophobic compounds in the aqueous compartments or the lipid layer due to their amphiphilic nature. Liposomes have a positive, negative, or neutral surface charge depending on their composition. Surface charge is one of the most important factors in liposome stability because it reduces the rate of aggregation and fusion during storage. The fact that the corneal epithelium is thinly coated with negatively charged mucin may explain why positively charged liposomes appear to be more important. Negatively charged liposomes are more effective than positively charged liposomes because of lowering of IOP over a longer period of time. The different structures and shape of liposome are classified;1. Multilamella vesicle (MLV): 520 lipid bilayer and very 5000 nm measurements 2. Oligolamera vesicle (OLV): 25 lipid bilayer and 1,001,000 nm measurements 3. Polyvascular vesicle (MVV): Very 1000 nm in diameter. 4. Single-layer vesicle (ULV): Single-layer of lipid Thesecan also be further categorized based on size potential 1. An SUV (more discreet single-layer vesicle) sized at 2040 nm. 2. MUV (medium-sized single-layer vesicle) with a size of 4080 nm. 3. 1,001,000 nm size LUV (larger single layer vesicle) 4. GUV (giant single-layer vesicle) with a size exceeding 1000 nm.

#### **6.5 Collagen shield**

Collagens have amazing biocompatibility and security in view of its natural attributes and for the most part used in clinical application since biodegradability and feeble antigenecity. Collagen safeguard was first ready from procine sclera tissues since they bear a collagens equal arrangement thereto of natural eye. The safeguards are hydrated before the inclusion into the eye commonly the medication is stacked into the medication answer for a time of at some point before application.

Collagen safeguard regularly produce a few uneasiness and obscured vision and are not fit every understanding. For improvement of this issue, Kaufman found a substitution idea of medication conveyance by combination of collagen safeguard particles and gets in-tuned with focal points named collasomes. Collasomes could be ingrained under the eyelid and limit the obscured vision issue [26].

#### **6.6 Mucosal adhesive dose structure**

In fact, mucosal adherent dose structures for visual delivery present many difficulties [24]. This approach relies on vehicles containing polymers that can bind to conjunctival mucin via non-covalent bonds. Mucoadhesive polymers are conventional polymeric hydrophilic colloids and contain several hydrophilic, practical compounds that can establish electrostatic bonds, similar to carboxyls, hydroxyls, amides, and sulfates. The bioadhesive metric structure showed higher drug bioavailability than the traditional dose structure. The effect of polyacrylicacid as a bioadhesive polymer on the visual bioavailability of timolol was evaluated. It has also been used to improve the visual bioavailability of progesterone [133].

#### **6.7 Microemulsion**

Microemulsions have local properties and a clear design. They are finished by automatic emulsification and disinfected directly. These assemblies have high drug dissolving capacity and excellent robustness. Due to these properties, it has excellent bioavailability. The mechanism of action of the drug is assimilation. Microemulsions contain nanospheres inside that hold the drug substance on the outer surface of the cornea and reduce drug waste [134].

#### **6.8 Solution and suspension**

These are liquid assemblies containing the active ingredients used in visual drug delivery. The drug substance must remain on the surface of the eye or in the internal area of the eye after passing through the cornea or conjunctiva. These arrangements even have some inconveniences such as unfortunate bioavailability and potency. Issues related to layout planning safety, bioavailability, and visual maintenance seasons are addressed through the work of a wide range of analysts by increasing thickness, using additives, and changing the pH of these definitions [28, 135].

#### **6.9 Sol to gel systems**

In 1980s a substitution idea delivering a gel set up was suggested by specialists. This thought of medication conveyance to eye is generally utilized in light of the fact that it expands the consistency and diminished the seepage of medication from cornea. Along these lines the bioavailability of medication naturally expanded. The in-situ gelling frameworks are regularly impacted by temperature, pH or particle enactment. In trial examination of bunny eye examiner found the in-situ gelling framework gives better and delay of medication contrast with the conventional eye drops [18].

### **7. Conclusion**

The broad add visual medication conveyance during the sooner time frame. It's been plan, to expand the span of topically applied drugs inside the corneal and conjunctiva area. A few new methodologies like nanoparticles, liposome, contact focal points, visual supplements, collagen safeguard, set up initiated gel arrangement, non corneal course of visual medication dissemination, and nanoparticles-based polymeric arrangements and gels are being created by the drug science. An exhaustive depiction of the eye life structures and physiology, alongside its obstruction properties are given in this part. Perusers will be educated with various visual tissues, their physical areas, work and their static and dynamic hindrance nature. The natural eyes are fundamental and biggest delicate and profoundly watched organs in the body. These are extremely confounded organs and which are immediate contact with the outside conditions, this organ is truly helpless against injury and subsequently impermeable to exogenous synthetic compounds. In addition, over the top tear creation in view of reaction to outer upgrades or remedial specialists and furthermore expanded flickering rate due to adding to shield. These security capacities normally created and go about as an obstruction to remotely applied drugs, particularly when these are designated to tissues at the rear of the eye. Articulation of medication efflux siphons at a cell level squares drug section into the sick cell and afterward presents serious

#### *Ocular Drug Delivery System: Barrier for Drug Permeation, Method to Overcome Barrier DOI: http://dx.doi.org/10.5772/intechopen.105401*

difficulties to visual medication conveyance. The physicochemical properties of the medication and conveyance framework can be arched relying upon the designated eye tissue. For the back eye conveyance, different courses of medication organization, for example, periocular infusions/inserts, are successfully utilized to defeat visual static and dynamic boundaries. Accordingly, information on the different visual tissues alongside the dynamic drug and physicochemical boundary properties is vital. The data gave in this part might facilitate the improvement of an ideal definition for designated conveyance.

#### **Author details**

Prakriti Diwan, Rajendra Jangde\*, Sulekha Khunte, Harish Bhardwaj and Preeti K. Suresh University Institute of Pharmacy, Pt. Ravishankar Shukla University, Raipur, C.G., India

\*Address all correspondence to: rjangdepy@gmail.com

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Section 4

## *In Silico* Drug Development

#### **Chapter 5**

## Computer-Aided Drug Design and Development: An Integrated Approach

*Neelima Dhingra*

#### **Abstract**

Drug discovery and development is a very time- and resource-consuming process. Comprehensive knowledge of chemistry has been integrated with information technology to streamline drug discovery, design, development, and optimization. Computer-aided drug design is being utilized to expedite and facilitate hit identification, hit-to-lead selection, and optimize the absorption, distribution, metabolism, excretion, and toxicity profile. Regulatory organizations and the pharmaceutical industry are continuously involved in the development of computational techniques that will improve the effectiveness and efficiency of the drug discovery process while decreasing the use of animals, cost, and time and increasing predictability. The present chapter will provide an overview of computational tools, such as structurebased and receptor-based drug designing, and how the coupling of these tools with a rational drug design process has led to the discovery of small molecules as therapeutic agents for numerous human disease conditions duly approved by the Food and Drug Administration. It is expected that the power of CADD will grow as the technology continues to evolve.

**Keywords:** drug discovery, Cheminformatics, CADD, success stories

#### **1. Introduction**

*"This is a fantastic time to be doing drug discovery. We have an incredible wealth of knowledge that has been generated over the past few years."*

Drug discovery and development (DD&D) is a lengthy and complex process that takes around 12–15 years and costs up to multi-million dollars for a drug to reach the market. Interdisciplinary DD&D begins with the identification and validation of a suitable drug target, followed by a hit to lead discovery and optimization, and finally preclinical and clinical studies. Despite the huge investments and time incurred for the discovery of new drugs, the success rates are too low that only five out of 10,000 compounds make their way to reach human testing after preliminary evaluation in animals and only one of five compounds reaches final clinical studies. Further, a majority (40–60%) of the drug failure has been observed at a later stage

of the DD&D process due to a lack of optimal pharmacokinetic properties, that is, absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox). This all suggested and urged the need to develop new methodologies to facilitate and expedite the DD&D process [1].

Advances in computational techniques and parallel hardware support have enabled computer-aided drug design (*in silico*) methods, that are being used by leading pharmaceutical companies and research groups to speed up the DD&D process. It entails:


Broadly classified computational methods include combinatorial chemistry; high throughput screening; virtual screening; pharmacophore modeling structure-based drug design (drug-target docking); ligand-based drug design (pharmacophore, a 3-D spatial arrangement of chemical features essential for biological activity)—quantitative structure-activity and structure-property relationships; quantum mechanics and *in silico* evaluation methods [2]. The commonly and widely used CADD approaches are structure-based and ligand-based drug design approaches, to identify suitable lead molecules in the drug discovery process. The structure-based drug design (SBDD) relies on the three-dimensional (3D) structure of the target receptor and its active sites to understand the molecular interaction between the receptor and ligand. While the ligand-based-drug design (LBDD) depends on the knowledge of ligands interacting with the given target receptor. A few marginally active or better compounds may be found, and then chemical similarity searching techniques are used to find more compounds that can be assayed. Finding some of the more active compounds further computationally techniques are applied to identify more potent compounds with favorable ADME/T [2, 3].

The application of rational drug design as an integral part of CADD provides useful insights into the understanding of the binding affinity and molecular interaction between target and ligand. Additionally, lead identification in pharmaceutical research has been facilitated by the availability of the super-computing facility, parallel processing, and advanced programs, algorithms, and tools. Furthermore, recent advancements in artificial intelligence (AI) and machine learning methods have greatly aided in analyzing, learning, and explaining the pharmaceutical-related big data in the drug discovery process [4].

#### **2. Structure-based drug design**

#### **2.1 Docking**

Understanding the principles by which small-molecule (ligands) recognize and interact with a receptor (macromolecules target) is of great importance in the


#### **Table 1.**

*Molecular docking approaches.*

pharmaceutical research and development (R&D) process. The availability and systematic use of the three-dimensional (3D) structure of the therapeutic target proteins and exploration of the binding site cavity form the basis of structure-based drug design (SBDD). This specific and fast approach identifies the lead molecules followed by their optimization and helps to understand disease at a molecular level. Some of the common methods employed in SBDD include structure-based virtual screening (SBVS), molecular docking, and molecular dynamics (MD) simulations. In case of the non-availability of structural information of the desired target, another computational approach, that is, homology modeling can be utilized to build the model of the needed target [5].

Molecular docking is one of the most commonly used techniques in SBDD, because of its ability to predict with a significant degree of accuracy the conformation of smallmolecule ligands within the applicable target binding site. Two approaches are primarily used to perform molecular docking, the simulation approach, and the shape complimentary approach. The first approach utilizes computer simulations wherein the energy profiling is assessed for ligand-target docked conformer. Whereas, the second approach employs the practice that calculates surface complementarity between ligand and target. The brief and main properties of both approaches are described in **Table 1** [6].

#### **2.2 Methodology**

It is a cyclic process with basic steps of the preparation of target structure, binding site prediction, ligand preparation using generated library of synthesized compounds, molecular docking, binding free energy calculations, analysis based on these scoring functions, and molecular dynamic simulation. In the last few decades, advancement in structural elucidation techniques, such as X-ray and NMR, has increased the availability of protein structure deposits in protein data bank (PDB). However, some of the target protein structures have not been solved to date and computational techniques, such as comparative homology modeling, ab initio modeling and threading are successful in interpreting the structures of such proteins from their sequences. After the target selection, it's important to identify the ligand-binding

site, a prerequisite step for carrying out specific docking. The information on the binding sites can be obtained from the X-ray crystallographic structures of proteins co-crystallized with substrates or inhibitors or through site-directed mutagenesis study. In the absence of the experimental information about the binding site of many proteins, plenty of software and webservers such DoGSite Scorer, CASTp, DEPTH, and NSiteMatch, allows us to predict the putative binding sites of the isolated and purified protein [7]. For a particular selected prepared protein, a library of the compounds retrieved from chemical databases, ZINC database, DrugBank, PubChem, or synthesized molecules in the research laboratories can be tested. Also, it preferred to perform the docking on drug-like compounds being filtered using Lipinski's rule of five and ADMET (absorption, distribution, metabolism, excretion, and toxicity) parameters, including risk parameters, such as acute rat toxicity, carcinogenicity, hepatotoxicity, and mutagenicity [8].

#### **2.3 Interpretation and correlation**

The ultimate purpose of the SBDD is to visualize ligands with specific electrostatic (charge distribution) and stereochemical (bulky or small) attributes to achieve high receptor-binding affinity. First, developed algorithms for molecular docking in 1980, enabled us to understand crucial molecular events, such as ligand-binding modes and their corresponding intermolecular interactions that stabilize the ligand-receptor complex [9]. The availability of 3D protein (macromolecular) structures further facilitated a careful inspection of the binding site topology, as well as the presence of cavities, clefts, and sub-pockets. Investigations arrange and rank the docked compounds based on the specific scoring functions of ligand-receptor complexes after quantitative predictions of their binding energetics [9]. Molecular docking programs identify the most likely binding conformations after cyclical steps of exploring the large conformational space representing various potential binding modes and accurate prediction of the interaction energy coupled with each of these predicted binding conformations [10].

These molecular modeling procedures are followed by the synthesis of the most promising compounds, and their biological properties evaluations using diverse *in vitro* and *in vivo* experimental protocols. Once the active compounds have been identified, the 3D structures of such ligand-receptor complexes from docking studies allow the observation of numerous intermolecular features supporting the process of molecular recognition. Structural descriptions of these complexes are helpful in investigating the binding conformations, key intermolecular interactions, characterization of unknown binding sites, mechanistic studies, and the elucidation of ligandinduced conformational changes [11]. Then biological activity data can be justified and correlated to structural information once a ligand-receptor complex has been determined.

In this way, the SBDD process in CADD starts over with new steps to design ligand by incorporating necessary molecular modifications for efficient or increased affinity toward the binding site. And selective adjustments of a validated drug target by highaffinity ligands ultimately lead to the desired pharmacological and therapeutic effects by interfering with specific cellular processes. The flexibility of the target receptor always remains an essential aspect to be considered throughout the modeling phase, as substantial conformational changes can occur upon its binding with the ligand. To address the issue, two modified approaches flexible docking (flexible-ligand and flexible-protein search docking) and molecular dynamics were further tried and

*Computer-Aided Drug Design and Development: An Integrated Approach DOI: http://dx.doi.org/10.5772/intechopen.105003*


**Table 2.**

*List of software tools for docking and their algorithms [14].*

explored. Stochastic method, systematic method, and simulation method are the three commonly used algorithms for ligand flexibility in the case of the flexibleligand search docking method, whereas flexible-protein docking usually depends on molecular dynamic (MD) and Monte Carlo (MC) methods [12].

#### **2.4 Application and tools**

SBDD as a computational technique has greatly helped many pharmaceutical industries and medicinal chemists in the discovery of several drugs available on the market. Few of them include the discovery of amprenavir as a potential inhibitor of the human immune deficiency virus (HIV) protease using homology protein modeling and MD simulations; norfloxacin a commonly used antibiotic against urinary tract infection using SBVS, isoniazid (antituberculosis drug) an enoyl-acyl-ACP reductase (InhA) inhibitor discovered through SBVS and pharmacophore modeling, and flurbiprofen, a nonsteroidal anti-inflammatory (NSAID) drug used against rheumatoid and osteoarthritis targeting cyclooxygenase-2 (COX-2) discovered through molecular docking approach, etc. [13]. Some of the freely available software tools for docking study are enlisted in **Table 2** summarizing their algorithms, scoring functions, and advantages.

#### **3. Ligand-based drug design**

Ligand-based drug design is an extensively used approach in computer-aided drug designing, especially when the three-dimensional structure of the target receptor is not available. The information derived from a set of active compounds against a particular target receptor can be used in the identification of structural and physicochemical properties accountable for the given biological activity, based on the fact that structural resemblances correspond to similar biological functions. Pharmacophore modeling [15] and quantitative structure-activity relationships (QSARs) [16] are some of the conventional techniques used in ligand-based virtual screening (LBVS).

Generated pharmacophore model elucidates the spatial arrangement of chemical features in ligands that are essential for interaction with the target receptor. H-bond donors/acceptors, hydrophobic areas, aromatic ring systems, and +ve/−ve charged ionizable groups are some of the chemical features used in pharmacophore modeling. Ligands with different scaffolds but the similar spatial arrangement of the abovementioned key interacting functional moieties can be identified using pharmacophore-based virtual screening [15]. The conformation of the active molecules within the target binding site can be integrated into the pharmacophore model for further application in QSAR studies in the molecular alignment stage.

#### **3.1 Quantitative structure-activity relationships (QSARs)**

Quantitative structure-activity relationship (QSAR) analysis is the most commonly utilized LBDD approach and correlates the variations in the bioactivity of the compounds with the changes in molecular structure. A statistically significant model is being constructed using these correlation studies, and the final model can be utilized to predict the biological activity of new molecules [17]. They are widely used in the drug discovery process in the hit to lead identification or lead optimization.

The technique was developed more than 50 years ago by Hansch and Fujita (1964), where affinities of ligands to their binding sites, rate, and inhibition constants were correlated with other biological endpoints with the atomic, group, or molecular properties, such as lipophilicity, electronic parameters, polarizability, and steric properties (Hansch analysis), or with some structural features (Free-Wilson analysis) [18]. But, the limitation of the classical approach in designing a new molecule on account of the lack of understanding of the 3D structure of the molecules urged the scientists to look for an alternative. So, an extension to the existing classical Hansch and Free-Wilson approaches emerged as 3D-QSAR, which exploits the three-dimensional properties of the ligands to predict their biological activities. Since then and until now, QSAR continues an efficient approach for building mathematical models to find a statistically significant correlation between the chemical structure and continuous (pIC50, pEC50, Ki, etc.) or categorical/binary (active, inactive, toxic, nontoxic, etc.) biological/toxicological property using regression and classification techniques, respectively [19].

In the last decades, QSAR has undergone several transformations, ranging from the dimensionality of the molecular descriptors (**Table 3**) and different methods (**Table 4**) for finding a correlation between the chemical structures and the biological property. Based on how the descriptors are derived, QSAR can be classified into six different types (**Table 5**) [20].

Depending on the quantity of dataset, QSARs can be classified and designed as local or global. A local QSAR is trained on a small and congeneric series of chemical structures, whereas a large and structurally diverse set of chemicals are used for


#### **Table 3.**

*Types of descriptors in different QSAR.*

#### *Computer-Aided Drug Design and Development: An Integrated Approach DOI: http://dx.doi.org/10.5772/intechopen.105003*


#### **Table 4.**

*QSAR classification based on the methodology.*


#### **Table 5.**

*QSAR classification based on parameter correlated.*

global QSAR [21]. The development of the QSAR model follows a general workflow as shown in **Figure 1**, starting from the collection of the dataset, its curation, and preparation, followed by the generation and selection of chemical descriptors to be used as independent variables. Collected data is divided into training and test sets, and the QSAR model is built using a training set consisting of chemical structures and their related experimental data. The chemical structures are represented by chemical descriptors, which are used as independent variables in the model. Statistically significant model (s) are built within the defined applicability domains (AD) based on field-based or atom-based approaches. In field QSAR, the molecule atoms are treated as fields and classified as either H-bond acceptor, H-bond donor, electrostatic or stearic. On the other hand, in atom-based QSAR, molecule atoms are considered spheres and can predict the results for H-bond donor/acceptor, non-polar/hydrophobic, negative/positive ionic, or electron-withdrawing groups. Both methods can predict the change in activity by the fields or spheres in molecules with various types of substitution [22].

Validation is the process to access the reliability and relevance of a particular approach, method, or process established for a defined purpose. Certainly, it is possible to calculate a large number of descriptors using various software tools, however, one cannot ignore the risk of chance correlations with the higher number of variables incorporated in the final model as compared to the number of compounds for which the model has been constructed. And with diverse optimization procedures, one can try to get models that can fit the experimental data well, but there is always a chance of overfitting. Thus, all indicate the necessity to validate the developed models for their robustness and predictivity. And QSAR models are fundamentally judged for their predictivity, which represents how well they are capable to forecast the endpoint values of molecules being not employed to develop the correlation. QSAR models can be validated using two major strategies: (i) internal validation based on the training set molecules, and (ii) external validation using the test set compounds after splitting the whole data set into training and test sets. Validation and analysis are characterized in terms of statistical parameters, such as regression values (R2),

**Figure 1.** *Workflow of ligand based drug designing.*

variance ratios (F), standard deviations (SD), root mean square error of the test set (RMSE), anticipated activities of the test set (Q2), and Pearson-R, were used to validate and analyze the model generated [18].

#### **3.2 Tools**

Quantitative structure-activity relationships (QSAR) have been applied for decades in the development of new drugs. Although a QSAR does not completely eliminate the trial-and-error factor involved in the development of a new drug, it


**Table 6.**

*Software's and their features [14].*

*Computer-Aided Drug Design and Development: An Integrated Approach DOI: http://dx.doi.org/10.5772/intechopen.105003*

certainly decreases the number of compounds synthesized by facilitating the selection of the most promising examples. A number of successful QSAR studies have been carried out using various platforms and helped in building models for predicting chemical, biological and toxicological activities. Some of these are listed in **Table 6**.

#### **4. In silico 5-AlPHA reductase activity prediction using SBDD**

Benign prostatic hyperplasia (BPH), a common condition of males over the age of 50 is characterized by enlargement of the prostate and results in urinary obstructions. Dihydrotestosterone (DHT), the 5α-reduced metabolite of testosterone (T) (**Figure 2**) has been implicated as a causative factor in this progression of BPH. 5-alpha reductase (5AR) a membrane-bound NADPH-dependent enzyme is responsible for the irreversible conversion of androgen T into DHT in the prostate. And inhibition of 5AR represents a logical treatment for controlling the BPH by diminishing the concentration of DHT in the prostate and is expected to improve the pathology of this disease [23].

In the past few decades, numerous nonsteroidal and steroidal compounds have been prepared as competitive or noncompetitive inhibitors of 5AR [24, 25]. In 1992,

**Figure 2.** *Action of 5-alpha reductase (5AR).*

**Figure 3.** *Clinically approved 5-alpha reductase inhibitors.* Finasteride (MK-906) was approved as the first 5-alpha reductase inhibitor (5ARI) in the United States for the clinical management of BPH. It is a competitive inhibitor of 5AR type-2 and at the clinical doses of 5 mg/day, it forms a stable complex with a 10-fold higher affinity than type 1 and decreases the prostatic DHT level and prostate volume by 70–90%, in human beings. Unlike finasteride, another molecule, that is,

#### **Figure 4.**

*Novel 5-alpha reductase inhibitors synthesized in our laboratory.*


**Table 7.** *Docking score of novel 5ARIs.* *Computer-Aided Drug Design and Development: An Integrated Approach DOI: http://dx.doi.org/10.5772/intechopen.105003*

dutasteride (**Figure 3**) a nonselective competitive inhibitor of both isozymes. 5AR type-1 and type-2 were approved in 2002 by the USA for the symptomatic treatment of BPH [26].

Since then, only finasteride and dutasteride are being used, but their long-term treatment has been found to be associated with the development of decreased libido, impotence or ejaculatory dysfunction, or, breast enlargement while rashes, insulin resistance, type 2 diabetes, kidney dysfunction, and other metabolic dysfunctions [26]. Thus, the identification of highly efficacious and selective inhibitors of 5AR for use in the treatment of BPH has engendered considerable interest from research groups in several laboratories. Attempts have been made in our laboratory to synthesize and characterize novel 5ARIs (**ND-1 to ND-6**) (**Figure 4**).

Structure-based drug designing approach, that is, docking (*in silico*) was performed on the newly synthesized compounds (**ND-1 to ND-6**) against PDBID: 4AT0 of 5AR receptor, using extra precision GRIP docking feature in BIOPREDICTA module available in the molecular design suite of Vlife MDS software package, version 4.6. Reference drug finasteride (FN) known to possess an affinity for the 5AR receptor was also included in the docking studies for comparing the docking results. Studies have shown that all the synthesized compounds have been found to bound better with

#### **Figure 5.**

*(a, c, and e) Grip docked pose of standard drug FN, ND-1, and ND 3 into the active binding site of 5AR receptor; (b, d, and f) 2D representation of the FN, ND-1, and ND 3 with active amino residues of 5AR receptor.*

the 5AR receptor affording dock score from −59.75 to −74.91 than reference, finasteride −57.09 (**Table 7**).

The docking score has been observed in order ND3 > ND1 > ND2 > ND6 > ND4 and ND5. Further, similar docking behavior has been observed among all the synthesized compounds in comparison to FN, by interacting hydrophobically with common amino acid residues THR175A, ALA59A, ALA63A, SER471A, and ALA264A. Additional aromatic interactions have also been observed only in ND-6 for amino acid HIS268A. Among all the newly synthesized derivatives ND-1 to ND-6, **ND-3** has been found to bound best with the 5AR receptor affording the highest D-score of −74.91 than FN. This high score of −74.91 can be attributed to its strong hydrogen bonds between NO2 at the p-position of the benzene ring with amino acid residue GLY241A and GLY245A. The binding pose of the fitted ligands was visualized extending deep into the active site pocket and are presented in the **Figure 5a**–**f**.

### **5. Conclusion**

In the pharmaceutical industries, Research and Development is undergoing a lot of technological changes, and there is pressure to make the investment pay off. There is a massive demand to sensibly use the big amount of chemical and biologicalrelated data produced in the process. Careful use of chemoinformatics techniques and software is becoming crucial in drug discovery success and an evolving field with many facets. In the past few years, it has led to the discovery of small-molecule therapeutic agents with activity directed against target proteins critical in the presentation of numerous disease conditions and/or have supported their clinical evaluation. Coupling more sophisticated computer software and hardware technologies with a rational drug design process has become an indispensable tool for the development of effective therapies and it yields information that is not easy to obtain in laboratory analysis, and, furthermore, is typically (much) less costly, save time, money, and resources. It is expected that the power of CADD will grow as the technology continues to evolve.

*Computer-Aided Drug Design and Development: An Integrated Approach DOI: http://dx.doi.org/10.5772/intechopen.105003*

#### **Author details**

Neelima Dhingra University Institute of Pharmaceutical Sciences, Panjab University, Chandigarh, India

\*Address all correspondence to: neelimad08@gmail.com

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

### **References**

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#### **Chapter 6**

## Machine Learning and Artificial Intelligence in Therapeutics and Drug Development Life Cycle

*Subhomoi Borkotoky, Amit Joshi, Vikas Kaushik and Anupam Nath Jha*

#### **Abstract**

In recent years, the pharmaceutical business has seen a considerable increase in data digitization. With digitization, however, comes the challenge of obtaining, analyzing, and applying knowledge to solve complex clinical problems. Artificial intelligence (AI), which entails a variety of advanced tools and networks that can mimic human intellect, can overcome such challenges with traditional pharmaceutical development. Artificial intelligence and machine learning have a vast role in therapeutic development, including the prediction of drug target and properties of small molecules. By predicting the 3D protein structure, AI techniques, such as Alpha Fold, can help with structure-based drug development. Machine learning algorithms have been utilized to anticipate the properties of small molecules based on their chemical structure. Many researches have shown the importance of using *in silico* predictive ADMET (absorption, distribution, metabolism, excretion, and toxicity) models to speed up the discovery of small compounds with enhanced efficacy, safety, and dosage. This chapter discusses various roles of these methods in the development of effective therapeutics.

**Keywords:** ADMET, deep learning, neural network, virtual screening, QSAR

#### **1. Introduction**

The goal of the drug development process is to find bioactive molecules that can help in disease therapy [1, 2]. The drug discovery life cycle has multiple steps: drug target identification, target validation, hit identification, lead optimization, preclinical development, clinical trial, approval, and postmarketing monitoring (**Figure 1A**). Because going through all of these stages of developing a new drug can cost between \$1 and \$2 billion and take 10–17 years, drug discovery is a big issue in the pharmaceutical business [3]. To speed up the drug discovery process, a considerable number of developments were made in the 1990s using combinatorial and high-throughput screening (HTS) approaches. These approaches were widely used since they allowed for the quick synthesis and screening of vast libraries, but no meaningful success was achieved, and little progress was made toward the discovery of new compounds. To

 *Flowchart of events taken place in (A) drug discovery lifecycle and (B) computer aided drug discovery.* 

aid the discovery process, a combination of modern computer approaches, biological research, and chemical synthesis was developed, and this combined approach increased the scope of discovery. The term "computer-aided drug design" (CADD) was eventually used to describe the use of computers in drug discovery. Computeraided drug design (CADD) is one of the most widely utilized approach for reducing drug development costs and time. CADD is a specialized field, in which various computational approaches are employed to mimic receptor–drug interactions in order to identify binding affinities ( **Figure 1B** ). The approach, however, is not just for studying chemical interactions and predicting binding affinity; it can be used for everything from designing compounds with desired physiochemical features to managing digital databases of chemicals. CADD is a wide term that encompasses both structure- and ligand-based drug developments. Virtual screening (VS) is a computational method for screening large databases of compounds that has successfully supplemented HTS in drug discovery. The fundamental purpose of VS is to make it feasible to quickly and cheaply screen enormous virtual chemical databases for potential leads for synthesis and future study [ 4 ]. Computer-assisted virtual screenings have been a widely used method for estimating various types of ligands to bind with target over time [ 5 – 8 ]. Additionally, in order to investigate atomistic level of protein/ nucleic acid-small molecule interactions, one of the widely utilized computational biophysics tools is molecular dynamics (MD) simulation [ 9 , 10 ]. MD simulation finds its relevance in shedding lights on the conformational ensembles either of the small molecule or of the target. The technique is seldom utilized to capture the dynamics of proteins and/or to check the stability of modeled protein structures enabling CADD for designing efficient inhibitors [ 11 , 12 ]. It is also leveraged to investigate a comparative binding of small molecule to different proteins along with complementing experimental observations [ 13 ].

 The recent expansion of make-on-demand libraries to billions of synthesizable molecules has piqued the interest of the drug-discovery community, as such massive databases allow access to previously unexplored chemical realms. The introduction of ultralarge libraries, on the other hand, has revealed substantial limitations of traditional docking techniques, which typically work on the scale of millions of molecules

*Machine Learning and Artificial Intelligence in Therapeutics and Drug Development Life Cycle DOI: http://dx.doi.org/10.5772/intechopen.104753*

#### **Figure 2.**

*Biomolecular analysis core scheme for drug discovery via AI/ML.*

at a high cost of computation. This aspect depicts CADD as a very useful process, in which only a small portion of the highest-scoring compounds often leaving low scoring but potential compounds are considered for experimental examination. Artificial intelligence (AI) and machine learning (ML) aided approaches provide a low-cost, high-reliability solution to a variety of problems (**Figure 2**), from protein three-dimensional (3D) structure prediction to physiochemical property calculation and bioactivity prediction to ultralarge docking [14, 15].

#### **2. Artificial intelligence (AI) and machine learning (ML)**

The ultimate goal of artificial intelligence (AI) is to train computer programs with human-like intellect. For this, AI uses computers to learn human behaviors, such as learning, judgment, and decision-making by simulating human intelligent behavior with computers. The term artificial intelligence was first proposed in 1956 at a conference at Dartmouth University; however, the major AI-related research started since the end of the twentieth century. AI has provided enormous economic benefits to humanity and has helped all parts of life, while also considerably promoted social growth and ushered in a new era of social development [16, 17]. Both the volume and the multidimensionality of data have increased dramatically as a result of the advent of numerous high-throughput technologies. Big data is both a requirement and a key component for AI to improve its recognition rate and accuracy [16].

Machine learning (ML), a branch of AI, is the use of an algorithm that improves its performance by learning from data. Machine learning, according to Arthur Samuel, is described as a computer's ability to analyze without being explicitly programmed [16, 18]. Supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning are the four types of machine learning algorithms [16]. In supervised learning, the test data are trained with a labeled dataset to predict the type

or value of new data, whereas unsupervised learning uses unlabeled data based on the input pattern. Support vector machine (SVM), linear discrimination, and decision tree are some of the types of supervised learning algorithms, whereas k-clustering and principal component analysis are the examples of unsupervised learning algorithms. Semisupervised learning combines the benefits of both supervised and unsupervised learning. It can be useful if there exist unlabeled data and collecting the labeled data is a time-consuming procedure. Reinforcement learning seeks to solve a problem through a hit-and-trial strategy, including feedback and decisions, with the ultimate goal of increasing total reward [16, 18, 19]. Deep learning (DL), a subset of machine learning, is one of the most cutting-edge areas of research and development in practically every scientific and technical discipline. Many problems that normal ML algorithms could not solve, such as image recognition and speech recognition, can be solved with the help of DL methods. DL methods also have immense role in the drug discovery pipelines, including drug activity prediction, target identification, and lead molecule discovery. The foundations of DL are frequently implicated in neural network systems, where they are employed to develop systems capable of complicated data recognition, interpretation, and production [20].

#### **3. AI and ML in protein structure modeling**

Drug design is based on the idea of creating compounds with a regulated interaction profile against a variety of target and off-target proteins in an organism. To understand the mode of action of a candidate drug, three-dimensional (3D) details are of paramount importance. Despite the availability of a variety of experimental methods to decipher the 3D structure of proteins, such as X-ray crystallography, nuclear magnetic resonance (NMR), and cryo-electron microscopy, the sequence– structure gap, in which protein sequences vastly outnumber the number of corresponding 3D structures, frequently causes problems. In such cases, protein structure prediction methods come as a remedy [21, 22].

Over the past 30 years, AI and ML methods have been used to predict protein structure. AI programs have helped to assess and identify most accurate models. To compare the predicted models to known crystal structures, these programs are trained utilizing numerous numerically represented atomic parameters from the models, such as bond lengths, inter-residue interactions, physiochemical properties, and so on. The Critical Assessment of Protein Structure Prediction (CASP) contests have been held biannually since 1994 for the blind evaluation of cutting-edge methods for predicting three-dimensional (3D) protein structures from protein sequences [23, 24]. For the cases where a template is not available for modeling, two approaches are considered: fragment-based assembly and *ab initio* folding. Fragment-based assembly is advantageous than *ab initio* folding due to its higher accuracy and higher capability [25].

With near-experimental precision, Alphabet's DeepMind won the 13th edition of CASP in 2018 with its latest artificial intelligence (AI) system, AlphaFold [25]. The 3D structure prediction by assembling the most probable fragments by AlphaFold is done by using co-evolution analysis of a multiple sequence alignment and using deep neural networks (DNNs) to discover coevolutionary patterns in protein sequences as contact distributions and transform them into protein-specific statistical energy potentials [23]. DeepFragLib, a fragment library constructed utilizing deep contextual learning techniques to give high-quality, native-like fragments for every segment *Machine Learning and Artificial Intelligence in Therapeutics and Drug Development Life Cycle DOI: http://dx.doi.org/10.5772/intechopen.104753*


#### **Table 1.**

*Few examples of applications of AI–ML methods in protein structure methods.*

of a protein for the efficient assembly of near-native conformations, is another example of AI breakthrough in the field of structure prediction. **Table 1** represent applications of some AI-ML approaches for protein structure prediction/quality assessment.

#### **4. ML and AI in physiochemical property calculation**

In the biopharmaceutical sectors, the effective and precise forecasting of molecular characteristics of drug compounds is indeed a fundamental component of rationalized compound synthesis. Current techniques span from basic atom summation through bond energy additions, paired interatomic configurations, and more sophisticated machine learning systems capable of representing aggregate reactions among several particles or bonds. In addition, simple correlation force fields show predictive performance comparable to reference energy sources determined utilizing density functional theory with hybrid exchange-correlation functional for steadystate geometric models; even so, properly accounting for the collaborative many-body connections is required for advancing the "magic formula" of compound accuracy of 1 kcal/mol for both the steady-state and out-of-equilibrium topologies [32]. In the years 2010–2012, the initial machine learning (ML) methods for molecular modeling relied on tiny datasets with quantum mechanical (QM) features for 102–103 molecule systems. It is believed that the chemical compound space has 1060–10,100 molecular systems. Chemical spaces have grown in size and complexity during the previous decade. Data are being generated at an astonishing rate owing to large-scale QM and MD methodologies, as well as developments in high-throughput studies [33].

Machine learning models predict small molecule's properties based on their chemical structure. Because of their ease of interpretation and effectiveness on small datasets, linear models were initially used. However, over time, nonlinear models were developed to capture more complex relationships between structure and activity. The nonlinear approaches include support vector machines, recursive partitioning methods, and deep learning methods. With the availability of standardized large-scale data, deep-learning-based techniques for ADMET (absorption,

distribution, metabolism, excretion, and toxicity) prediction are showing growing promise and utility. The ability to identify small compounds with increased efficacy, safety, and dosage is considerably aided by understanding ADMET characteristics. In terms of consistency and predictive performance, the graph convolutional DNN (GCNN) technique is proposed to be superior to existing approaches such as random forest (RF), Cubist, and support vector machine (SVM) for calculating ADMET characteristics [34]. The AI-based ADMET predictors utilize cellular permeability data from a diverse class of molecules generated by different cell lines. To predict acid dissociation constant of compounds, artificial neural network (ANN)-based models, graph kernels, and kernel-ridge-based models have been used. To predict the solubility of the compounds, undirected graph recursive neural networks and GCNN have been used. GCNN methods are also used to predict cytotoxicity, which is one of the important properties used in drug discovery to avoid toxic effects [35]. The latest ANN studies support immunoinformatics and chemoinformatics analysis for novel vaccine and drug discovery [11]. Examples of the AI-based tools for molecular property calculation are DeepTox (www.bioinf.jku.at/research/DeepTox), Chemputer (https://zenodo.org/record/1481731), and ORGANIC (https://github.com/aspuruguzik-group/ORGANIC) [35].

#### **5. ML and AI in bioactivity prediction**

In order to prioritize compounds for synthesis and/or biological evaluation, quantitative structure-activity relationship (QSAR) modeling has been used [36]. The goal of QSAR models is to find a mathematical relationship between the physicochemical qualities of substances, which are represented by molecular descriptors, and their biological activity. These models are important in drug optimization because they provide a preliminary *in silico* assessment of key qualities such as activity, selectivity, and toxicity of candidate compounds. In QSAR modeling, AI/ML techniques (such as RF, SVM, Naïve Bayesian, and ANN) have been widely used. Among these techniques, the RF algorithm has been regarded as a gold standard in QSAR studies [23]. In the case of bioactivity prediction, DL approaches have shown improved performance compared with ML [35]. Few examples of AI-based tools to determine bioactivity are WDL-RF (integration of DL and RF) (https://zhanglab.ccmb.med.umich. edu/WDL-RF/), pairwiseMKL (multiple-kernel-learning-based method) (https:// github.com/aalto-ics-kepaco), and DeepMalaria [37] (DL based).

#### **6. ML and AI in drug–target interactions**

To fully comprehend a drug's efficacy and usefulness, it is important to determine how it interacts with a receptor or target. Drug-protein interactions have recently been a hot topic in drug repurposing research [38]. ML algorithms have become the advanced approach for estimation of drug–target interactions due to the huge amount of obtainable drugs and target information in huge datasets, advancing as well as innovative computer networking, and inherent characteristics of different types of deep learning. A vast number of proteins have indeed been sequenced, and numerous molecules have now been synthesized since the advent of sequencing technology, high-throughput technologies, and computer-aided drug design methods. Actual information has been organized, and multiple databases have been developed based

*Machine Learning and Artificial Intelligence in Therapeutics and Drug Development Life Cycle DOI: http://dx.doi.org/10.5772/intechopen.104753*


#### **Table 2.**

*Few examples of applications of AI–ML methods in drug–protein interactions.*

on existing related efforts and acquired expertise. The majority of data in these sources is open to the public and free to download, thus providing a strong data basis for using deep learning to solve drug-target contact predictions issues. PubChem presently comprises 109 million chemicals and is the world's biggest database with open access to chemical characterization. PubChem has grown in importance as a source of chemical knowledge for researchers, learners, and the general public. Artificial intelligence can be used to train deep learning models for drug discovery using known drug data [39]. Several ML techniques have been used to predict drug– target interactions including SVM, DL, DNN, convolutional neural network (CNN), etc. The *de novo* drug design approach has been frequently employed to create therapeutic compounds in recent years. The old approach of *de novo* drug design is being phased out in favor of emerging DL methodologies, which have the advantages of less complicated synthesis routes and easier prediction of novel molecule bioactivity [35]. However, classical algorithms cannot be completely ignored, as studies point out that the classical algorithms show higher and more stable performance than the machinelearning-based methods at different similarity levels of training sets. Hence, many tools have been developed combining both classical and deep learning models [40]. Few of the tools and servers available for finding drug-protein interactions using AI and ML methods are mentioned in **Table 2**.

#### **7. Conclusion**

Although AI is frequently portrayed as a magic wand that can provide flawless output regardless of the quality of the input, it is not the solution to every problem. The ultimate goal of using AI and machine learning approaches to drug development challenges is to bring the best drugs to market. Throughout the drug discovery process, the combined effort of different AI methods allows for a better understanding and design of novel inputs [45]. The AI-based applications are getting more intelligent, cost-effective, and time-efficient while increasing efficacy, because of more precise algorithms, more powerful supercomputers, and significant private and public investment in the sector [20]. To properly leverage AI in drug development, one must increase the quality of decisions we make regarding compounds that are

progressed to clinical trials. However, in many circumstances, the data available to make those decisions are not totally sufficient for this purpose [46]. Since the entire success of AI depends on the availability of a substantial amount of data, we need to conduct trials more efficiently, which can be supported by computational methods [35, 46]. Major challenges faced by AI methods include data accuracy and availability, reproducibility, model appropriateness, etc. Despite the challenges, AI is projected to advance the field of personalized/precision medicine to the point where it becomes regular practice even in the treatment of minor illnesses in the future [47]. By 2028, AI is expected to save the pharmaceutical industry more than US\$70 billion in drug discovery costs [48]. With more clinical data and improved AI calculations, AI is projected to improve many elements of drug discovery and development and will eventually become the standard computer-assisted technique for drug discovery.

### **Acknowledgements**

ANJ would like to acknowledge Department of Science and Technology (DST-SERB File no. CRG/2020/001829), Government of India, for providing computational facilities.

### **Author details**

Subhomoi Borkotoky1 \*, Amit Joshi1,2, Vikas Kaushik2 and Anupam Nath Jha<sup>3</sup> \*

1 Department of Biotechnology, Invertis University, Bareilly, UP, India

2 School of Bio-Engineering and Bio-Sciences, Lovely Professional University, Phagwara, Punjab, India

3 Department of Molecular Biology and Biotechnology, Tezpur University, Tezpur, Assam, India

\*Address all correspondence to: drsubhomoi.b@invertis.org and anjha@tezu.ernet.in

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Machine Learning and Artificial Intelligence in Therapeutics and Drug Development Life Cycle DOI: http://dx.doi.org/10.5772/intechopen.104753*

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### *Edited by Juber Akhtar, Badruddeen, Mohammad Ahmad and Mohammad Irfan Khan*

Drug authentication is generally governed by critical procedures. This book presents a comprehensive overview of drug design, highlighting the steps involved from the discovery phase to product approval. Chapters address such topics as drug development processes, pharmaceutical nanoformulations and cosmeceuticals for topical use, ocular drug delivery systems, and drug development involving computeraided drug design, machine learning, and artificial intelligence.

Published in London, UK © 2022 IntechOpen © miluxian / iStock

Drug Development Life Cycle

Drug Development Life Cycle

*Edited by Juber Akhtar, Badruddeen,* 

*Mohammad Ahmad and Mohammad Irfan Khan*