Artificial Neural Networks in Drug Transport Modeling and Simulation–I

Artificial Neural Networks in Drug Transport Modeling and Simulation–I

CHAPTER 11 Artificial Neural Networks in Drug Transport Modeling and SimulationeI Matthew MacPherson1, Jeffrey Burgess2, Brain McMillan3, Todd Daviau3...

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Artificial Neural Networks in Drug Transport Modeling and SimulationeI Matthew MacPherson1, Jeffrey Burgess2, Brain McMillan3, Todd Daviau3, Srinivas M. Tipparaju2 1 Department of Chemical Engineering, College of Engineering, University of South Florida, Tampa, FL, USA; 2Department of Pharmaceutical Sciences, College of Pharmacy, University of South Florida, Tampa, FL, USA; 3CoreRx, Inc., Clearwater, FL, USA

1. INTRODUCTION The human body is susceptible to a wide range of diseases, which can affect any of its complex systems. Differing diseases require unique therapies to improve the patient’s quality of life. The patient’s chief complaint, past medical history, family history, and social history are all contributing factors when diagnosing the patient’s ailment and subsequent treatment. On the basis of the classification of the disease and available treatment options, the patient and doctor will decide upon the most appropriate and beneficial form of therapy [2]. Some of the most common forms of pharmaceutical therapies prescribed are in the form of liquids, creams, gels, or solids. The location of the disease and patient preference dictate which dosage forms will be used. The physical and chemical properties of the selected dosage form each have their own advantages and disadvantages. For example, liquid dosage forms have a high bioavailability, which may provide an effective treatment of the disease; however, they also have stability issues that require rigid shelf-life protocols. Conversely, solid dosages forms, such as capsules, tablets, or powders, tend to have few stability issues while having various complications that lead to reduced bioavailability [3]. To understand how pharmaceutics aid in the therapy of a patient’s disease, two diseases will be discussed by briefly detailing causes and complications of the disease, the typical dosage form used to treat the disease, the mechanism by which the treatment acts, the bioavailability, complications with the treatment, and why the treatment was designed in its selected form.

1.1 Ocular Drug Transport: Glaucoma Treatment Glaucoma is a small group of eye disorders that typically corresponds to an increase in intraocular pressure and can result in a loss of vision via optic Artificial Neural Network for Drug Design, Delivery and Disposition. Copyright © 2016 Elsevier Inc. All rights reserved.

CONTENTS 1. Introduction .. 221 1.1 Ocular Drug Transport: Glaucoma Treatment ........... 221 1.2 Solid Oral Tablet Drug Transport: Hypercholesterolemia Treatment ........... 224

2. Why Are ANNs, Modeling and Simulation of Drug Transport Useful?.......... 227 3. ANNs and Their Helping Hand in the Discovery of New Drugs.... 228 3.1 Parameters Used to Design the Transportation Model .................. 228 3.1.1 Wetting and Disintegration .....229 3.1.2 Dissolution ..........229

3.2 Absorption .......... 230 3.3 Gastrosimulators on the market.......... 232




Artificial Neural Networks in Drug Transport Modeling and SimulationeI

3.3.1 Intellipharm™ ....232 3.3.2 GastroPlus™ ......233 3.3.3 PK-SimÒ .............233

4. Applicability to Researchd Review of Scholarly Works............ 235 4.1 Prediction of Human Intestinal Absorption of Drug Compounds from Molecular Structure............. 235 4.2 Avascular Tumor Growth Dynamics and the Constraints of Protein Binding for Drug Transportation.... 235 4.3 Future Pharmaceutical Research Applications ........ 236

5. The Use of ANNs during Formulation Design ........... 236 5.1 Formulation Development for a BCS Class II Drug .................... 237 5.2 Modeling of Sotalol for Pediatrics Based on an Adult Model .................. 237 5.3 Feasibility of Formulating a Controlled-Release Danoprevir .......... 238

6. Conclusions .. 239

nerve damage. It is categorized into primary, secondary, or congenital glaucoma; each of which can be further subdivided into open angle or closed angle. Primary glaucoma is considered inherited, secondary is considered the result of another complication, and congenital is considered to be glaucoma that is present within the first few months of life. Furthermore, open-angle glaucoma is caused by increased intraocular pressure alone whereas closed-angle glaucoma arises from the blockage of the trabecular meshwork, the major drainage structure of the aqueous humor. Therefore, open-angle glaucoma is very difficult to diagnose and its symptoms are silent until late in the disease progression. Conversely, closed-angle glaucoma is associated with sudden symptoms with sudden relief, which can occur at any point in the disease progression [4]. Ophthalmic medications must penetrate many barriers to treat these disorders. The first of these barriers is diffusion through the hydrophobic conjunctiva, cornea, and/or sclera. The drug must then enter the hydrophilic aqueous humor and solubilize. Depending upon the drug, its mechanism of action may occur within the aqueous humor or the bloodstream. Drugs that solubilize in the blood need to enter the trabecular meshwork and travel along Schlemm’s canal, which is a channel that surrounds the eye and distributes contents within the aqueous humor and into the bloodstream. This distribution typically requires the following drug properties: n n n n n n

pH of 4.5e9.0 Osmolality of 200e600 mOsm/kg Uniform particle size Non-protein bound Hydrophilic and hydrophobic regions Balanced pKa (ionized and un-ionized states)

These anatomical factors must be accounted for in drug design and give rise to many alternative drug delivery forms, such as eye drops and gels [5] (Figure 1). The most common forms of eye medications include solutions, suspensions, ointments, and gels, each of which has a unique set of advantages and disadvantages. Solutions and suspensions are the most common dosage forms for ophthalmic drug delivery. They are simple to produce in large quantities because of the lack of extra distribution factors to take into consideration. The active pharmaceutical ingredient is simply dissolved into a solution with other excipients that are necessary for patient safety and comfort. Suspensions are similar; however, the ingredients are not all dissolved and need to be shaken to obtain a uniform concentration. The main disadvantage of these formulations is that most of the drug is lost during administration because of the absorption capacity of the eye. The solution or suspension will either be flushed out by the normal physiological function of the eye or lost because of noncompliance with proper administration technique. This issue gives rise to the need

1. Introduction

FIGURE 1 Diagram illustrating ocular drug absorption using a transverse sectioning of the eye to portray the anterior and posterior chambers. Adapted from Dipiro et al. [4].

for enhanced drug delivery systems used to treat ophthalmic disorders, such as glaucoma. Ointments, creams, and gels are semisolids, which can provide various benefits over liquid dosage forms. These added benefits are derived from the base chosen, of which there are four common classes: absorption bases, oleaginous bases, water-removable bases, and water-soluble bases. Absorption bases will uptake water and create a water-in-oil emulsion. Oleaginous bases create an occlusive gel layer when applied. Water-removable bases create ointments and creams that can easily be washed off. Finally, water-soluble bases are commonly used to incorporate solid substances into a greaseless cream [3]. These specific uses of ointments, creams, and gels can make them very specialized and not always ideal for uniform drug delivery. In addition, drug-dosage form designers must take other requirements into consideration, such as sterility, shelf life, aesthetic properties, etc., which can




Artificial Neural Networks in Drug Transport Modeling and SimulationeI

complicate the production process. Therefore, the application of an artificial neural network (ANN) could truly progress the designs of future ophthalmic drug delivery.


Solid Oral Tablet Drug Transport: Hypercholesterolemia Treatment

Hypercholesterolemia, or the state of having elevated blood cholesterol, is a contributing factor to the development of atherosclerosis and increases the patient’s risk of suffering from a heart attack or stroke. The onset of hypercholesterolemia is classified as primary or secondary, in which primary is independent of health and lifestyle and secondary is dependent. Familial hypercholesterolemia (primary) is a genetic deficiency in which the gene that specifies the function of low-density lipoprotein (LDL) receptors is mutated. This is an issue because nearly 70% of all LDL, also known as “bad cholesterol,” is removed from the body by LDL-receptor-dependent pathways. Secondary hypercholesterolemia is caused by a range of social and lifestyle choices or other health complications. Social and lifestyle choices may include highcalorie, high-cholesterol, or high-triglyceride and saturated fat diets. Highcalorie diets increase the production of very low-density lipoproteins, which are highly converted to LDL. High-cholesterol diets reduce the formation of LDL receptors, decreasing the ability to remove LDL from the body. Lastly, high-triglyceride and saturated fat diets increase cholesterol synthesis, thus decreasing LDL receptor activity. Health complications derived from other diseases can be either direct or indirect. Diseases that directly affect the development of hypercholesterolemia include diabetes mellitus, hypothyroidism, nephrotic syndrome, and liver disease. Indirect complications may arise from medications treating other disorders. These medications include beta blockers, estrogens, and protease inhibitors [6]. There are multiple pathways or treatments available to aid in the therapy of hypercholesterolemia, most of which are in solid dosage forms. Figure 2 provides a summary of therapies typically used, their mechanisms, and some common side effects. For discussion purposes, we will focus on one type of therapydstatins. HMG-CoA (3-hydroxy-3-methyl-glutaryl-CoA) reductase is an enzyme that plays an important role in the biosynthesis of cholesterol. Inhibiting its activity, the primary mechanism of statins, leads to increased hepatic LDL receptor activity and corresponds to subsequent elimination of circulating LDL cholesterol (a more detailed mechanism can be found in Figure 3). The degree of hepatic response is dependent on the type of statin (Lova-, Prava-, Simva-, Fluva-, Atorva-, or Rosuva-), the level of dosing, and the individual patient. The increase of dosing (2-fold) typically correlates with an approximate 6% further

1. Introduction

FIGURE 2 Summary of hypercholesterolemia therapies [7].

Cholesterol Manufacturing

25 step metabolic process (occurs in the liver)

Acetyl CoA is generated by the breakdown of fatty acids

HMG-CoA reductase is the primary regulatory site for biosynthesis


Under normal circumstances negative feedback is used to shutdown biosynthesis

High levels of circulating LDL usually shutdown production

While in “off” mode, the liver produces more LDL receptors for detection of circulating LDL


Insufficient production of LDL receptors

High levels of LDL not detected

HMG-CoA reductase does not shutdown production of LDL

Statin Therapy

Given orally

Act by inhibiting HMG-CoA reductase

Lessens production of LDL

Increases time in “off” mode, thus increases LDL receptor production time

FIGURE 3 Statin therapy mechanism [1].

reduction of LDL in the circulation. Along with LDL-lowering effects, statin therapies often result in an increase of high-density lipoprotein (HDL), or “good cholesterol”, by 5e10% [7]. The mechanism of absorption for orally administered solid dosage forms is fairly similar. The process begins once the patient inserts the tablet into their mouth and swallows. For normal-release tablet designs, such as atorvastatin (ATV), disintegration and dissolution of the tablet will not initiate until it

Life long therapy




Artificial Neural Networks in Drug Transport Modeling and SimulationeI

FIGURE 4 Diagram illustrating solid oral dosage form absorption and metabolism (gut and hepatic). Adapted from van de Waterbeemd and Gifford [9].

reaches the patient’s gut; therefore, drug release will not occur. Once in the gut, gastric juices break down the tablet matrix and dissolve the tablet components, including the drug. As illustrated in Figure 4, the solubilized drug would then permeate through the gut wall and into the portal vein. The portal vein then carries the drug into the liver and releases the bioavailable drug into the body. At each interchange, the drug may be susceptible to metabolism, and in consequence, lowering the amount of active drug available for therapeutic use. The resulting amount of drug exiting from the liver is referred to as its bioavailability [8]. ATV specifically is only available on the market as a regular-release tablet; thus, it travels on a similar path as previously described. Upon its arrival into the patient’s stomach, ATV is dissolved and begins its transport through the gut wall. The pathway from the gastrointestinal tract to the portal vein involves multiple complex transport mechanisms. ATV is primarily transported by passive diffusion, but it could be absorbed via protein-mediated transports [10]. The protein-mediated transport (uptake or efflux) is thought to contribute to the reported low bioavailability of ATV. Cytochrome P450 is known to have an oxidative effective on multiple orally administered drugs when being absorbed through the gut wall, which may metabolize ATV, resulting in its elimination [11]. Further gut metabolism occurs because of binding with CYP3A4, the most common CYP isoenzyme in the intestine. Successfully transported ATV into the portal vein continues its journey to the liver, where it again

2. Why Are ANNs, Modeling and Simulation of Drug Transport Useful?

becomes susceptible to further metabolism because the liver contains CPY3A4. The bioavailability of ATV is most commonly reported as 14% and is most thought to be due to gut wall and membrane interactions, not hepatic interactions [10]. Lennernäs assumes that ATV is fully absorbed from the gut because of ATV’s high solubility and permeability and found that the losses due to hepatic first-pass effect were too small to explain the 14% bioavailability. After some testing, it appears as though ATV is extracted from the gut via enzymatic transport, which has not fully been explained [10]. Although Figure 4 seems to portray a fairly simple transportation of the drug from the mouth to the therapeutic site, the route is much more complex and challenging. Each membrane has its own anatomy, which is often unique to every patient, heavily increasing the importance of the drug’s chemistry and pharmaceutical design to produce the highest efficacy. Such factors include the drug’s solubility in the gut, permeability through the gut membranes, permeability variances throughout the gut wall, regional pH differences, luminal and mucosal enzymes, intestinal motility, and more [12]. It is the complex interrelationships between the aforementioned factors that make the prediction of oral drug absorption very difficult to model and even more difficult to correlate between in silico and in vitro models with in vivo processes [12].

2. WHY ARE ANNs, MODELING AND SIMULATION OF DRUG TRANSPORT USEFUL? As previously mentioned, the measuring of drug absorption through the gut wall involves a very complex, expensive, and time-consuming process. Potential drug candidates are typically synthesized and respectively screened in vitro for pharmacological properties. In vivo animal studies have been used to produce intestinal absorption data, and subsequent ex vivo human data, to create models, but they come at a cost. The purchase, or synthesis, of drugs and time costs for animal studies prove to have a large affinity for draining the budget, whereas the models produced yield variable results [13,14]. The results achieved through such correlations stem from the use of solutions or suspensions, rather than the actual dosage form (e.g., a tablet), because of the ability of delivering the dose to the animal subject. By neglecting the dosage form, the results would not hold data that incorporated the design of the tablet, the particle size of the drug, or the solubility of the drug. With the discovery of new drugs increasing, and with most of them being Biopharmaceutics Classification System (BCS) Class IV (low solubility, low permeability), the demand for an efficient model is growing [13]. Many models have been reported for a multitude of pharmaceutical applications, such as pharmacokinetics; metabolism; and permeation across the bloodebrain barrier, through the skin, and into the eye, but most are limited because of their structural and chemical design [14].




Artificial Neural Networks in Drug Transport Modeling and SimulationeI



Developing a model for predicting drug absorption via oral solid dosage forms, continuing from discussions in previous sections, consists of designing a precise representation of the anatomical structures and the correct layout of the system, or the actual sequences of nodes (structures) and connections (including weights, or contribution values) within the ANN [14]. Construction of the anatomical features does not only refer to their physical appearance but also to their response when stimulated by their surroundings. The structure is designed to mathematically represent its structural features, volume, and chemical responses. To correctly characterize the structures, multiple characteristics must be used to encompass the structures’ unique behaviors and contribution to the system [14]. Once the structures have been designed, the proper layout of the nodes and connections of the ANN must be organized to represent the anatomical system [14]. This includes primary, secondary, tertiary, and quaternary connections, which must be made between the structures. To simplify, layers of structures can be made. Layers consist of similar structures that yield similar output responses [15]. After the structures and layers have been organized to best represent the body system, the weights for each connection are determined by utilizing an algorithm that results in the best approximation of absorption for this design. By harnessing the power of a neural network, the system learns from its experiences, or propagating the error backward into the ANN, and optimizes the connection weights by calculating error between actual and expected results [15,16]. The weights are most often transformed by a constant value and its total contribution to the system error. For multiple layered systems, such as the system described for oral solid dosage form absorption, error is also fed back between each layer to optimize each sublayer of the total ANN [15]. It is because of these features that the pharmaceutical industry is allowed to utilize ANNs in product screening and development. The ability to recognize, respond, and learn from its experiences allows the ANN to predict outcomes when newly acquired data are input into the algorithm. Although the results may not be completely accurate, ANNs are very useful screening tools, thus reducing future costs of animal and human testing [16].


Parameters Used to Design the Transportation Model

The absorption process of drug from the gut encompasses many different processes, some of which are concurrent and some consequent. The processes that oral dosage forms undergo include tablet disintegration, powder wetting,

3. ANNs and Their Helping Hand in the Discovery of New Drugs

dissolution, degradation, adsorptive or complex binding, and permeation through the gut wall. Some are very difficult to model because of their short lifespan or because of the processes occurring in parallel. For instance, disintegration and wetting happen extremely fast, whereas drug release and dissolution occur simultaneously, as a result, making them particularly hard to differentiate [13].

3.1.1 Wetting and Disintegration Entering the gut of the patient, gastric fluids bombard the tablet and begin assaulting the tablet matrix. The tablet design is an essential part of the wetting and disintegration process. The design of the tablet can allow for immediate drug release, delayed release, or extended release. The release profiles are dependent on the excipients chosen by the formulator and the manufacturing process variables [17]. Figure 5 shows various excipients that could be used to design the aforementioned release matrices. For the sake of simplicity, the remainder of the discussion will consider an immediately released drug; therefore, wetting and disintegration will not be factored into ANN because of the speed at which the matrix releases the drug, as mentioned previously, in comparison to the rest of the process.

3.1.2 Dissolution The drug is available for dissolution after the tablet matrix has disintegrated and the drug particles are in suspension within the gastric fluid. Once the drug is dissolved into solution, the drug would migrate to the gut wall and begin transporting through it. This sounds much easier than it actually plays out to be. Dissolution must overcome several challenges, including inherent drug properties and pH dependencies [22]. Some of these challenges are overcome by excipient selection or novel drug delivery systems, and in some cases they are not overcome at all. The solubility of the drug can be best explained by the NoyeseWhitney equation (NWE): dc ¼ Kðcs  ct Þ dt

FIGURE 5 Excipients for desired drug release profile [18e21].





Artificial Neural Networks in Drug Transport Modeling and SimulationeI

where dc/dt is the dissolution rate, K is a constant, cs is the solubility of the drug in an unstirred environment, and ct is the solubility of the drug in a stirred environment. Using Eqn (1) and Fick’s first law of diffusion, dM dc ¼ SD dt dx


where dM/dt is the mass transported of a period of time, S is the surface area available for diffusion, D is the diffusion coefficient, dc is the difference in concentration levels, and dx is the diffusion distance. The solubility of a drug can be further defined by incorporating the NernsteBrunner equation (not shown) and divided by the bulk volume to uncover some of the most critical factors that promote or inhibit a drug’s solubility and thus its absorption into the body. The final equation looks like the following: dc SD ¼ ðcs  ct Þ dt Vh


where V is the bulk volume of the fluid and h is the thickness of the unstirred diffusion boundary layer [22]. Equation (3) defines the parameters needed to design a model for dissolution within the ANN for drug absorption. The parameters detailed in Figure 6 should be considered.



The absorption calculation of the ANN may be the most complex, as apparent from the study conducted by Wessel et al., in which each of their 86 compounds were characterized, including molecular geometries, by 162 descriptors each. In general, once the drug is in solution and at the absorption area, such as the gut as is the case with orally administered tablets such as ATV, the transfer across the intestinal mucosa is the next step. This action can be characterized by using a flux equation, J ¼ Peff  SA  ðC2  C1 Þ

FIGURE 6 Factors affecting drug solubility.


3. ANNs and Their Helping Hand in the Discovery of New Drugs

FIGURE 7 Molecule absorption diagram of a drug of concentration level C1 across a membrane to a concentration level of C2.

where J represents the rate of diffusion across the intestinal mucosa, Peff is the effective permeability constant, SA is the surface area, and (C2  C1) is the concentration difference across the membrane as shown in Figure 7. Many of the parameters that must be described include the size, shape, and charge of the drug molecule, which describe the molecule’s lipophilicity, thus describing its potential of crossing the intestinal membrane [13]. Un-ionized molecules, either weak bases or weak acids, innately have a greater lipophilicity than ionized molecules. No matter if the molecule ionized is a hydrogen acceptor or donor, too many of either are not good [8]. Correlations explaining this were shown in works conducted by Lipinski et al. [23] and further detailed by Ungell et al. [24] when they showed that lipophilicity is the primary determinant in predicting the extent of membrane permeation, especially in the case of the molecules being in an unstirred layer of fluid [8]. Parameters also describe anatomical characterizations, including transport mechanisms embedded in the junctions of the intestinal mucosa, such as facilitated and active transport proteins, whereas the mucosal structure descriptions must also include the effects of drug efflux or the rejection of molecules to completely pass through the membrane, the “leakiness” of the membrane, and the pH changes within the membrane [13]. For most setups, in vitro and in situ models are used to predict the outcomes of diffusion. In vitro studies are primarily conducted by utilizing Caco-2 monolayer cell models whereas in situ studies are performed on segmented rat intestine using Ussing chambers and by utilizing perfusion techniques [8,13]. Caco-2 cells are derived from human colon carcinoma cells and operate, very closely, to those of the human intestine, thus allowing researchers to




Artificial Neural Networks in Drug Transport Modeling and SimulationeI

simulate absorption in a laboratory setting rather than a complex in vivo study [8]. Correlations are developed using in vitro and in situ methods as detailed by Doluisio et al. [25] and again by Sim and Back [26], where they utilized rat gut lumen and monitored for drug disappearance by measuring the incoming and outgoing arterial fluid flows for drug content [8]. There are countless experiments that go into creating successful models to predict absorptive tendencies of the human gastrointestinal tract, and these are just a couple of simple examples of that, but a perfect correlation to direct human studies has yet to be developed. There are many companies that have developed effective models and offer software to aid in the pharmaceutical development of new drugs.


Gastrosimulators on the market

The potential of ANNs to aid in the development of new products, as well as troubleshoot current products, has opened the doors to the most talented programmers. Systems such as Intellipharm™, GastroPlus™, and PK-SimÒ have been developed to aid the pharmaceutical industry in those feats.



Intellipharm™ simulates drug dissolution, absorption, and pharmacokinetics by modeling segments of the intestine, along with its peristaltic motion, to show the time dependency on drug solubility, volume, and transport or absorption. For each time domain, Intellipharm™ has incorporated absorption rate constants to predict the pharmacokinetics of the drug. Intellipharm™ claims that its software provides insight into many of the market’s drug selection and developmental challenges. Some of the areas at which they believe they can aid developers are 1. Drug characterization, including solubility, absorption rate constants, drug clearance, and volume of distribution 2. Effects of drug plasma concentrations on the parameters just listed 3. Developing a desired drug release profile using their controlled drug delivery program 4. Particle size effects on drug release and release profiles 5. Correlations to simulation and experimental results 6. Determining if particle size reduction or solubility will be enough to achieve the desired absorption or drug plasma concentration levels. Intellipharm™ utilizes the NWE, discussed previously, and many other geometric and chemical parameters to determine the outcomes to their listed claims. Along with the list above, Intellipharm™ can also aid in determining content uniformity to aid in troubles with ideal mixing [27].

3. ANNs and Their Helping Hand in the Discovery of New Drugs

3.3.2 GastroPlus™ GastroPlus™ aids in the areas of intravenous, oral, oral cavity, ocular, intranasal, and pulmonary absorption, pharmacokinetics, and pharmacodynamics in human and animals. Along with the claims made by Intellipharm™, GastroPlus™ has supplied a more vast software package that can aid developers by 1. Understanding behavior of drugs in animals and humans 2. Understanding the effects of food 3. Analyzing the effects of influx and efflux transporters in multiple anatomical tissues 4. Modeling initial human and animal dosage ranges, tissue metabolism, and targeted tissue concentrations 5. Conducting virtual population studies 6. Tracking metabolites 7. Predicting drug-to-drug interactions 8. Developing in vitro dissolution methods 9. Assisting with quality by design (QbD) 10. And more. GastroPlus™ has recently expanded the ability of their program by including models for in vivo precipitation, in vitro to in vivo extrapolation across all tissues, and updated enzyme and transporter expressions. They have also updated their software to predict effects in infants. With the growth and increases in the amount of attention on ocular and pulmonary systems in recent years, GastroPlus™ has also developed models for metabolism, drug disposition, and physiology in those systems [28]. The increased sophistication of GastroPlus™ has been used by companies such as Unilever Research, Vertex Pharmaceuticals, Pfizer, and Johnson & Johnson [28].

3.3.3 PK-Sim®

Bayer Technology Services has recently released a revision to PK-SimÒ, which was mainly focused on their incorporation of the compartmentalistic factors of the intestinal system. This advance was aimed at delivering a more precise tool for predicting the absorption of orally administered drugs, both solid and solution. By including physiological features, PK-SimÒ has detailed the intestinal mucosa, which optimally allows it to model the transport processes and gut wall metabolism more accurately. It does this by including the following parameters: 1. Adding 12 compartments that detail the gut lumen and characterize the compartments by an array of properties including surface area and pH




Artificial Neural Networks in Drug Transport Modeling and SimulationeI

2. Including liquid volume and drug in solution data for each compartment 3. Solid dosage form dissolution pathways d transport and dissolution along the gut lumen 4. Additional compartmentalization of the intestinal mucosa (11 compartments), which includes enterocytes, interstitial, and vascular space A schematic of how the above-mentioned program would be modeled can be seen in Figure 8. Although the small and large intestine would both need to be designed, only the small intestine is shown. As seen in Figure 8, by compartmentalizing the small and large intestine (large intestine not shown), the amount of error can be minimized. This also drives home the point made earlier that deriving the connections to each system only through biology is extremely difficult and would require large amounts of time and money. Using ANN-derived programs such as PK-SimÒ more efficiently helps in predicting drug transport with new and current drugs [29,30].

FIGURE 8 Structure schematic showing the compartmentalization of the small intestine. Adapted from Thelen et al. [29,30].

4. Applicability to ResearchdReview of Scholarly Works

4. APPLICABILITY TO RESEARCHdREVIEW OF SCHOLARLY WORKS ANNs offer the ability to learn or be trained; they do not require programming. This ability gives rise to performing linear and nonlinear functions. Harnessing the ability to perform nonlinear functions, ANNs are capable of approximating and describing data sets that contain unknown relationships. This can be useful in many aspects of research design and methodology. The following sections will discuss some examples of these research applications in greater detail [31].

4.1 Prediction of Human Intestinal Absorption of Drug Compounds from Molecular Structure Wessel, Jurs, Tolan, and Muskal utilized an ANN to predict the conditions in which a drug compound would be absorbed by the human intestine. This was accomplished by training the ANN to categorize molecules based upon their known topological properties (atom counts, bond counts, molecular weights, and two-dimensional structures), electronic properties (atomic charges, partial atomic charges, and dipole moments), and geometric properties (moments of inertia, surface area, and volume). This ANN was trained by loading a data set of 86 drug-like compounds into the network; it could then predict the intestinal absorption of new molecules on the basis of its knowledge of these molecules. The result was a computer-generated model of quantitative structureeproperty relationships (QSPR) among molecules, which could be compared to actual intestinal absorption data. QSPRs can then be used to model physiochemical properties (including intestinal absorption), chromatographic properties, spectroscopic properties, and toxicity properties of various compounds. This specific ANN could predict intestinal absorption of new compounds with an error rate of 16%, which is a fair result given the structural diversity of this data set. Therefore, this ANN and models similar to it could be tremendously influential in the future drug of design or other molecular studies [14].

4.2 Avascular Tumor Growth Dynamics and the Constraints of Protein Binding for Drug Transportation In addition to drug design, ANNs may be useful in the prediction of therapeutic effectiveness of certain drugs. Kazmi, Hossain, Phillips, Al-Mamun, and Bass used an ANN to predict the conditions in which tumor growth was favorable; therefore, this network could then determine whether or not a drug therapy would be effective. Their ANN was trained with known cell environmental properties, such as growth factors, inhibitory factors, number of neighboring cells, oxygen concentrations, hydrogen concentrations, and glucose concentrations. These properties were all assigned weights to which newer testing




Artificial Neural Networks in Drug Transport Modeling and SimulationeI

samples could be compared. Their study was limited because of the price and scarcity of oncology medication; they only tested for the effectiveness of one drug, tirapazamine. However, they were able to predict whether or not it would be an effective treatment and conclude that avascular tumor growth is highly dependent upon the microenvironments within and surrounding single cancerous cells. Models such as this may prove to be useful in future drug design clinical practice [32].


Future Pharmaceutical Research Applications

The use of ANNs appears to be highly beneficial for future pharmaceutical research and will likely be applied to many formulation designs. As identified by Sutariya, Groshev, Sadana, Bhatia, and Pathak, there are many pharmaceutical research applications for ANNs, including drug modeling, dosage design, protein structure and function prediction, pharmacokinetics and pharmacodynamics modeling, in vitro/in vivo correlations, enantiomeric relationships, quantitative structureeactivity relationships, quantitative structureeproperty relationships, prediction of drug permeability to various organs, and prediction of therapeutic doses and dose schedules [33].



In the development of new drugs, the U.S. Food and Drug Administration (FDA) has set checkpoints or stages of development that must be taken before their approval: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

Drug discovery Clinical testing Investigational New Drug (IND) application Phase I clinical trials (20e80 human subjects) Phase II clinical trials (hundreds of human subjects) Phase III clinical trials (thousands of human subjects) FDA review New Drug Application (NDA) Drug labeling Application review Facility inspection Final approval

Formulation design is performed in Step 2; therefore, it will be the only checkpoint discussed in this section [34]. Once a drug has been identified, Step 1, the heavy task of assessing the drug and developing an appropriate mode of administration begins. Most often than not, drug discoveries lend many forms of the molecule to be potential

5. The Use of ANNs during Formulation Design

candidates. Therein lies the challenge of assessing each candidate for biopharmaceutical properties, which primarily lead to their solubility and permeability [35]. To achieve the ability to rank or classify the candidate molecules more easily, Amidon et al. devised the Biopharmaceutical Classification System (BCS), which categorizes drugs on the basis of their aqueous solubility and gastrointestinal permeability [35,36]. The BCS is divided into four categories: 1. 2. 3. 4.

Class Class Class Class

Idhigh solubility and high permeability IIdlow solubility and high permeability IIIdhigh solubility and low permeability IVdlow solubility and low permeability

In the sections that follow, three areas will be detailed by summarizing works performed by researchers from the university level as well as by corporations such as Roche and Pfizer.

5.1 Formulation Development for a BCS Class II Drug Kuentz et al. (2006) utilized an updated version of the compartmental absorption and transit (CAT) model, the “advanced” CAT (GastroPlus™), to predict the absorption in the intestine and progressively through the digestive tract to provide information on the factors that most affected the oral bioavailability of their drug. GastroPlus™ used a set of default compartmental parameters and differential equations that were characterized by methods discussed previously, including pH values in each segment of the intestine for the fasted and fed states. The results of the simulation provided Kuentz et al. with the information required to orchestrate a more efficient design of experiments (DoE) using a beagle dog. For a drug that initially profiled as being a BCS Class II, which usually need the assistance of a sophisticated drug delivery system, but simulations revealed that bioavailability was not dependent on solubility or particle size, as confirmed by the factorial DoE; therefore, a standard capsule with surfactant is suitable for delivery [35].

5.2 Modeling of Sotalol for Pediatrics Based on an Adult Model Formulating drugs is primarily accomplished with the adult patient in mind. Most in vitro and in silico models have been designed to simulate the correlation to the human adult. Khalil and Laer (2013) have recognized the increased desire for pediatric research, understanding that the pediatric anatomy requires alternative dosing ranges and weight-based ratios compared with adult drug formulations. Khalil and Laer point out that there have only been eight reported full pediatric models designed, six of which are for intravenous delivery (10 drugs) and two for oral delivery (6 drugs)done was also evaluated for the neonate. The purpose of their study was to design a full model for sotalol,




Artificial Neural Networks in Drug Transport Modeling and SimulationeI

which ranged from adults to neonates, as well as observing changes in the model across the pediatric zone. The authors utilized SymcorÒ, simulation software not described in previous sections, and PK-SimÒ to model drug absorption and disposition throughout the body and incorporate acquired data that described the anatomical and physiological characteristics of their patients while correlating results with age. The simplest model to construct was an intravenous adult model, which was transferred to an oral delivery model by maintaining the same parameters as in the intravenous model but with the addition of intestinal-specific information. Lastly, the model was then scaled to fit the physiological parameters of a child; therefore, it needed to incorporate the metabolic and clearance pathways to predict the outcome of a solatrol dosing. Both simulators had options for small children that could be integrated into the adult model. To predict how the model would operate within a large population of data, the software conducted a virtual simulation, testing 100 virtual subjects from each subset of age groups, races, genders, and dosing ranges. Results from both simulators reported that the models accurately predicted the outcomes within the adult and pediatric age ranges, as well as under gender, sex, and dosing categories, as compared with in vivo data. Unfortunately, the models were unable to predict outcomes related to neonates because of fluctuating absorption profiles [37].


Feasibility of Formulating a Controlled-Release Danoprevir

To develop a carrier for the drug and ensure that it delivers the correct therapy, the formulation scientist must assess each optiondcapsule, tablet, immediate release, modified release, etc. Within each option, the formulation will change depending on how each ingredient interacts with the drug and the body. To accelerate this process, ANNs can simulate various drug and formulation types before testing in vitro or in vivo, giving a better understanding of how the drug operates [35e39]. Danoprevir is a potent selective inhibitor of the hepatitis C virus that was evaluated by Reddy et al. [38] as a monotherapy; results concluded that dosing every 8 h rather than the baseline treatment of 12 h. However, these results imply that more frequent administration of the same dose would need to occur, which decreases the chance of patient treatment adherence [38]. To circumvent the challenge of decreased adherence with the new dosing regimen, Reddy et al. have proposed a new controlled release (CR) version. To evaluate the potential of the new delivery system, GastroPlus™ was utilized to predict the feasibility of CR danoprevir. Danoprevir is a BCS Class IV drug with 1e2% drug availability, found in monkey pharmacokinetic studies, because of rapid cytochrome 3A metabolism.


Using the advanced compartmental absorption and transit model for the human and monkey models, while incorporating fasting and fed conditions to estimate food effects, dosage forms (solutions and powders (capsules)) were targeted to deliver immediately (immediate-release tablet) and to areas of the colon (CR). The models predicted that the formulation of CR danoprevir would be challenging because of the rapid hepatic metabolism and low bioavailability from the colon at 37% or less. The level of uncertainty related to dissolution rates, precipitation time, and intestinal transportation was also concerning. Clinical results confirmed the challenge presented by the simulation, showing that the CR formulation would require 4 h of delivery to the colon, which is unfeasible [38].

6. CONCLUSIONS Pharmaceutical companies and university researchers have been striving to make advancements in pharmaceutical science for years, whether it is by creating new therapies or by advancing current ones. The main goal is to give the patients the opportunity to lead a less restricted life. The only problem with the current state of affairs is the time and resources it takes, and ultimately the cost, to make any notable progress. With the assistance of ANNs, advances in research are more accessible in less time and with a smaller dent in the pocketbook. The ability to assess molecules for toxicity and therapeutic ranges without doing much work on the laboratory bench-top, the ability to assess multiple delivery systems to determine the most efficacious method, and the ability to scale dosage ranges for multiple age groups of varying ethnicities, genders, and health conditions are within a finger-click away. ANNs are paving the way for future pharmaceutical growth and opening the door for companies to achieve their goal of delivering efficacious molecules to market.

Acknowledgments The authors acknowledge the funding received by Florida High Tech Corridor.

References [1] Adams M, Leland Jr NH, Bostwick PM. Pharmacology for nurses: a pathophysiologic approach. Upper Saddle River (NJ): Pearson/Prentice Hall; 2008. [2] Baerheim A. The diagnostic process in general practice: has it a two-phase structure? Fam Pract 2001;18:243e6. [3] Lloyd J, Allen V, Popovich NG, Ansel HC. Ansel’s pharmaceutical dosage forms and drug delivery systems. 9th ed. Philidelphia (PA): Lippincott Williams & Wilkins, a Wolters Kluwer Business; 2011. [4] Dipiro J, Talbert R, Yee G, Matzke G, Wells B, Posey L. Pharmacotherapy e a pathophysiological approach. 8th ed. New York: McGraw-Hill; 2011.




Artificial Neural Networks in Drug Transport Modeling and SimulationeI


Eaton S. Optimized ophthlamics: advances in the medical treatment of ocular disease. Cornell University Veterinary Specialists.


Porth C, Matfin G. Pathophysiology: concepts of altered health states. Wolters Kluwer Health/Lippincott Williams & Wilkins; 2009.


Longo D, Fauci A, Kasper D, Hauser S, Jameson J, Loscalzo J. Harrison’s principles of internal medicine. New York: McGraw Hill; 2011.


Pang KS. Modeling of intestinal drug absorption: roles of transporters and metabolic enzymes. Drug Metab Dispos 2003;31:1507e19.


van de Waterbeemd H, Gifford E. ADMET in silico modelling: towards prediction paradise? Nat Rev Drug Discov 2003;2:192e204.

[10] Lennernäs H. Clinical pharmacokinetics of atorvastatin. Clin Pharmacokinet 2003;42: 1141e60. [11] Lau YY, Okochi H, Huang Y, Benet LZ. Pharmacokinetics of atorvastatin and its hydroxy metabolites in rats and the effects of concomitant rifampicin single doses: relevance of first-pass effect from hepatic uptake transporters, intestinal and hepatic metabolism. Drug Metab Dispos 2006;34:1175e81. [12] Dokoumetzidis A, Valsami G, Macheras P. Modelling and simulation in drug absorption processes. Xenobiotica 2007;37:1052e65. [13] Paterson FJaR. Physiologically based in silico models for the prediction of oral drug absorption. In: Ehrhardt C, Kim K-J, editors. Drug absorption studies: in situ, in vitro, and in silico models. New York: Springer; 2008. p. 486e509. [14] Wessel MD, Jurs PC, Tolan JW, Muskal SM. Prediction of human intestinal absorption of drug compounds from molecular structure. J Chem Inf Comput Sci 1998;38:726e35. [15] Gershenson C. Artificial neural networks for beginners. arXiv preprint cs/0308031. 2003. [16] Ilic M, Ðuris J, Kovacevic I, Ibric S, Parojcic J. In vitro e in silico e in vivo drug absorption model development based on mechanistic gastrointestinal simulation and artificial neural networks: nifedipine osmotic release tablets case study. Eur J Pharm Sci 2014;62:212e8. [17] Marais AF, Song M, de Villiers MM. Effect of compression force, humidity and disintegrant concentration on the disintegration and dissolution of directly compressed furosemide tablets using croscarmellose sodium as disintegrant. Trop J Pharm Res 2003;2:125e35. [18] Bühler V. Pharmaceutical technology of BASF excipients. Germany: BASF The Chemical Company; 2008. [19] Eastman C-A-P enteric coating material (cellulose acetate phthalate or cellacefate, NF). Kingsport (TN): Eastman Chemical Company; 2003. p. 1e6. [20] Hydroxypropyl cellulose USP/NF, EP, NP technical data sheet. In: HPC N, editor. Joetsu City (Japan); 2013. [21] Tableting excipients and co-processing technology for APIs. JRS Pharma GMBH & Co. KG: Patterson (NY). [22] Siepmann J, Siepmann F, Florence AT. Factors influencing oral drug absorption and bioavailability. In: Siepmann J, Florence AT, editors. Modern pharmaceutics: basic principles and systems. New York: Informa Healthcare; 2009. p. 117e54. [23] Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 2001;46:3e26. [24] Ungell AL, Nylander S, Bergstrand S, Sjöberg Å, Lennernäs H. Membrane transport of drugs in different regions of the intestinal tract of the rat. J Pharm Sci 1998;87:360e6.


[25] Doluisio JT, Billups NF, Dittert LW, Sugita ET, Swintosky JV. Drug absorption I: an in situ rat gut technique yielding realistic absorption rates. J Pharm Sci 1969;58:1196e200. [26] Sim S, Back D. Intestinal absorption of oestrone, oestrone glucuronide and oestrone sulphate in the rat in situeII. Studies with the doluisio technique. J Steroid Biochem 1986;24:1085e9. [27] Software product description and manual: INTELLIPHARMTM PKCR software and INTELLIPHARMTM CU software. Niantic (CT): Intellipharm, LLC; 2007. [28] GastroPlus™ product brochure. Lancaster (CA): Simulations Plus, Inc.; 2012. [29] Thelen K, Coboeken K, Willmann S, Burghaus R, Dressman JB, Lippert J. Evolution of a detailed physiological model to simulate the gastrointestinal transit and absorption process in humans, part 1: oral solutions. J Pharm Sci 2011;100:5324e45. [30] Thelen K, Coboeken K, Willmann S, Dressman JB, Lippert J. Evolution of a detailed physiological model to simulate the gastrointestinal transit and absorption process in humans, part II: extension to describe performance of solid dosage forms. J Pharm Sci 2012;101:1267e80. [31] Lek S, Guegan J. Artificial neural networks as a tool in ecological modelling, an introduction. Ecol Model 1999;120:65e73. [32] Kazmi N, Hossain M, Phillips R, Al-Mamun M, Bass R. Avascular tumour growth dynamics and the constraints of protein binding for drug transportation. J Theor Biol 2012;313: 142e52. [33] Sutariya V, Groshev A, Sandana P, Bhatia D, Pathak Y. Artificial neural network in drug delivery and pharmaceutical research. Open Bioinforma J 2013;7:49e62. [34] FDA drug approval process in, United States food and drug administration. [35] Kuentz M, Nick S, Parrott N, Rothlisberger D. A strategy for preclinical formulation development using GastroPlus™ as pharmacokinetic simulation tool and a statistical screening design applied to a dog study. Eur J Pharm Sci 2005;27:91e9. [36] Pandey P, Hamey R, Bindra DS, Huang Z, Mathias N, Eley T, et al. From bench to humans: formulation development of a poorly water soluble drug to mitigate food effect. AAPS Pharm Sci Tech 2014;15:407e16. [37] Khalil F, Läer S. Physiologically based pharmacokinetic models in the prediction of oral drug exposure over the entire pediatric age RangedSotalol as a model drug. AAPS J 2014;16: 226e39. [38] Reddy MB, Connor A, Brennan BJ, Morcos PN, Zhou A, McLawhon P, et al. Physiological modeling and assessments of regional drug bioavailability of danoprevir to determine whether a controlled release formulation is feasible. Biopharm Drug Dispos 2011;32: 261e75. [39] Zhang H, Xia B, Sheng J, Heimbach T, Lin T-H, He H, et al. Application of physiologically based absorption modeling to formulation development of a low solubility, low permeability weak base: mechanistic investigation of food effect. AAPS Pharm Sci Tech 2014;15: 400e6.