Models for Assessing Drug Absorption and Metabolism

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MDCK cells also are ideal for transfection and overexpression experiments with human transporters and receptors due to the lack of P-glycoprotein [ 68 , 71 ]. Immobilized artificial membranes IAMs were already used very early on for lipophilicity determination and are gaining interest again in recent years for direct permeability measures [ 25 ]. IAMs are also intensively used in the measurement of the volume of distribution to mimic in vivo binding to phospholipids and phospholipid bilayers membranes.

Therefore, IAMs are discussed in more detail in the following section. The parallel artificial membrane permeability assay PAMPA [ 73 ] is a cheap and fast in vitro alternative to cellular-based assay systems. Another advantage of PAMPA over conventional cell-based assays is the ability to selectively measure passive permeability, while in cell-based systems influence of membrane transporters cannot be left out. PAMPA assays can be readily applied in high throughput processes or scales and different variants exist to address ionic and H-bonding with membranes that influence permeability and complement the use of Caco-2 and other cell assays [ 73 ].

Bermejo and colleagues also showed a significant correlation between Caco-2, in situ rat perfusion, and PAMPA assay data underlining applicability of the method for ADME assessment [ 75 ]. Although high-throughput applications of newly developed and standardized techniques allow gathering of an exorbitant amount of data, it is crucial to also cor relate the physicochemical and biomimetic properties to structural features of the compound.

This will facilitate the development of QSPRs and allows the construction of in silico models ultimately guiding the medicinal chemistry efforts [ 25 ]. When dealing with oral administration, it is important to note that the drug is not only confronted with the hurdles of solubility and permeability in the absorption process but is also facing metabolizing mechanisms i. These include but are not limited to P-glycoproteins, uridine diphosphate glucuronosyltransferase, and mainly cytochrome Ps CYP [ 24 ]. This will be discussed more deeply in the Metabolism section.

Permeability has a direct influence on the drug absorption rate and, as discussed, despite the several in vitro cellular models available e. However, we are far from a model that can predict overall permeability and, the current status, rather focuses on individual compartments and tissues, such as the gastrointestinal GI tract, skin, buccal membrane, and the blood-brain barrier BBB. The work of Shen et al.


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Log BB can be calculated by the log of the ratio between the concentration within the brain C brain by the bloodstream concentration C blood of the determined chemical entitiy. Oral bioavailability is defined as the amount of drug that reaches the site of action after oral administration and is influenced by factors like drug solubility and dissolution, chemical and enzymatic stability in the gastric and intestinal lumen, interacting luminal contents food , gastrointestinal transit time, enterocyte permeability, and intestinal and hepatic metabolism [ 24 ].

Recently, bioavailability has been also described as the rate and speed of the drug to reach systemic bloodstream, considering the initial formulation as the starting point. Oral administration includes a pharmaceutical phase—prior to PK and PD phases—that comprises disintegration and dissolution of the dosage form. When using oral dosage forms, the shape and chemical composition e.

Absorption of Drugs - Pharmacokinetics - Pharmacology Lect 2

Following the pharmaceutical phase, absorption is the first step in the pharmacokinetic phase and is defined as the movement of the drug from the site of administration to the bloodstream. The main properties determining the rate of oral absorption for small molecules are permeability and solubility [ 87 ]. As such, the rate of dissolution and ionization, which are described by the Noyes-Whitney and Henderson-Hasselbalch equation, respectively, is the key factors in lead optimization for oral administration and is complemented by lipophilicity as an additional factor influencing membrane permeation and solubility of the compound [ 31 ].

Dissolution can be expressed by a function of the aqueous solubility of a compound, the surface area of the administered tablet or the particles in other solid formulation , and a specific dissolution rate constant. Altering any of these parameters directly affects the dissolution profile [ 26 ]. While solubility is an endpoint value indicating the amount of a compound that is soluble in a solvent, dissolution describes the kinetic process of a compound being solved in a solvent [ 88 ].

On the other hand, ionization reflects if a compound is present in the charged or uncharged state and is at least influenced by two major parameters. The physicochemical property responsible for ionization is the pKa and describes the ionization state of that entity at a given pH. It is also referred to as aqueous ionization constant [ 30 ]. The determination of the ionization state of a compound in the gastrointestinal system stomach, jejunum, ileum, and colon is crucial for absorption since it not only influences the solubility of a compound but also the lipophilicity and permeability [ 26 , 30 , 89 ].

While charged molecules easily dissolve in aqueous systems GI tract , they do not permeate membranes via passive diffusion and are reliant on active transport.

Models for Assessing Drug Absorption and Metabolism

The contrary is true for uncharged molecules, which pass biological membranes passively but show low solubility in aqueous solutions. Mechanisms of drug absorption include passive diffusion, active transport, and receptor-mediated endocytosis, which are influenced by different factors and can themselves influence the bioavailability. Similar to model and prediction, the absorption of a drug is a complex process, which is influenced not only by the physicochemical properties of drugs themselves but also by the physiological state of the tissue in question.

As such, there are a large number of prediction models available, which were generated based on the physicochemical properties involved in the absorption process, such as membrane permeability and drug solubility. These models can help formulation scientists to optimize drugs with poor absorption due to low aqueous solubility. Initial absorption models can be separated into dispersion and compartmental models [ 91 ].

Disposition

While dispersion models treat the GI as a continuous system, with variable pH and surface area, compartmental models take into account physiological factors such as transporters. The compartmental absorption transit CAT was one of the first models to regard distinct physiological properties, such as the minimal absorption in the stomach and colon, while assuming some mathematical simplifications, such as the instant dissolution of the drug and linear kinetics [ 92 ]. ACAT also considers the gastrointestinal tract as nine subsections, each with unique physicochemical properties, such as pH, allowed solubility, particle size, and permeability [ 93 ].

Novel developments have included other absorption routes other than the GI, which have been recently included in commercially available software, such as oral absorption for the development of sublingual zolpidem tablets [ 94 ]. After being absorbed and entering the circulatory system, the drug moves reversibly between different compartments within the body, which is described as distribution and influenced by several physicochemical properties of the drug and biological factors of the body.

Interestingly, binding to plasma proteins can also prolong the drug action by releasing the drug over a longer period of time. It is also important to note that the influence of lipophilicity on plasma protein binding is hypothesized to be higher for acidic compounds than for bases, meaning that negative charges contribute highly to plasma protein binding and prevent tissue binding, which leads to diminished volumes of distribution V d , Eq.

The V d is the amount of drug that is freely available in the blood, thus not bound to plasma proteins or other components [ 25 , 97 , 98 ]. V d is an apparent volume that increases proportionally to the extravascular drug binding and not an anatomically defined volume. Consequently, extensive drug binding outside the bloodstream leads to increasing values of volume distribution.

Volume distribution V d is defined by the ratio between the amount of drug in the body A and the drug concentration in plasma C, comprising both free drug and protein-bound drug :. The parameter describing protein binding is the plasma protein affinity constant K i. In the beginning, stationary phases in RP- HPLC were either silica-based or polymer-based but both had difficulties to reproduce log P and log D values despite several additives in the mobile phases [ 99 ].

A method to address plasma protein binding is the use of HSA and other plasma proteins e. Both techniques represent good assay systems to model in vivo V d in high-throughput scale [ 98 ]. Problems with HPLC techniques, which are also true for biomimetic phases, include the lack of a gold standard that is needed to calibrate and later standardize results to make a comparison possible [ 25 ]. In vitro standard methods for unbound plasma fraction calculation include equilibrium dialysis and ultrafiltration among several others as the two most commonly used methods and are considered the gold standard for binding assessment [ 26 ].

Apart from protein binding, tissue binding is also involved in the distribution of the compound. In silico models to predict the V d are often based on lipophilicity and solubility descriptors, which correlate with the fractions of the drugs that are either bound to plasma proteins or freely available. Expanding these studies, the work of Lombardo and Jing generated a set of models to predict the V d in the steady state V ss , using a dataset of diverse compounds [ ].

They compared models generated by linear PLS with nonlinear Random Forest models, recommending the latter, with 33 descriptors, as the optimal method for V ss prediction. The V d of drugs is greatly influenced by binding to plasma proteins with several machine-learning—based models generated to predict this interaction. Additionally, global quantitative models using an array of classification and regression models using physicochemical and molecular descriptors derived from a dataset of compounds were shown to correctly classify the binding status of the test set compounds and could be used as a prescreening [ ].

Another recent QSAR study using an extensively curated training set of diverse pharmaceuticals aimed to predict plasma protein-bound fractions fb using models generated by six machine-learning algorithms with 26 molecular descriptors [ ]. This study is particularly interesting where the applicability domain is concerned allowing to differentiate whether the classification derives from un- favorable regions. Complementarily, the prediction of the drug passage through the blood-ocular barrier was described to be an important factor to evaluate volume distribution in this organ [ ].

Volume of distribution is also closely related to half-life and clearance parameters. As the V d is a relative measurement of the free concentration of drug in the blood, this same amount could be excreted by kidneys in the glomerular filtration clearance. Consecutively, the rate of clearance discussed below in Excretion section directly influences the amount of available drug. Naturally, the concentration of free drug that can bind its molecular target is related to the therapeutic dosage and the half-life of the administered drug as seen in Eq. Half-life definition.

Half-life is calculated by a ratio between the Napierian logarithm multiplied by the volume of distribution V d and renal clearance CL :. Drug metabolism normally involves enzymatic modification or degradation of the compound to facilitate excretion via one of the major clearance organs: liver, kidney, spleen, or bile.

While phase I enzymatic reactions include modifications such as oxidation, hydrolysis, and reduction to either introduce a functional group to the molecule or make it accessible, phase II reactions are conjugation mechanisms e. Thus, isozymes of the CYP family and efflux transporters such as P-glycoprotein and members of the multidrug resistance transporter MRP family are highly involved in the metabolism of drugs as well as drug-drug interactions, which are a major attrition cause.

Interestingly, when CYP3A4 is expressed, usually P-glycoprotein is as well [ 8 , 10 , 14 , 24 , ]. An approximation for metabolic behavior analysis is the use of either liver microsomes or S9 fractions although also recombinantly expressed proteins are partially in use [ 24 , 26 ]. When available, the 3D structure of those proteins could be employed in molecular docking and molecular dynamics simulations aiming to predict the binding affinity of drugs or drug candidates aiming the estimation of a PK profile [ ].

Models for assessing drug absorption and metabolism

The metabolism prediction combines mathematical models to predict whether the target compound could be a substrate of a specific enzyme in combination with metabolism site predictions. Usually, those initial predictions are followed by molecular docking simulations and quantum mechanics simulations due to the dependency of electronics structure from both substrate and enzyme in catalyzed reaction [ , ].

Nowadays, several attempts have been made to develop in silico models for predicting drug metabolism, specifically site-of-metabolism SOM , and quite often are also converted into online server prediction tools for general use, for instance, the FAst MEtabolizer FAME model, which was generated from a diverse chemical datasets of more than 20, molecules and their respective experimentally determined metabolism sites.

FAME prediction rates were comparable to other metabolism site predictors focused on specific enzyme families, despite using only seven chemical descriptors [ ]. A more refined version was later updated to include the atomic solvent accessible surface area, which is independent of 3D coordinates, slightingly improving the overall prediction accuracy for different CYP isoforms [ ]. Excretion is guided by one of the major clearance organs, and the assessment of clearance behavior sometimes involves isolated organs or tissues [ 24 ].

Humans rely on the kidney clearance as a major route for xenobiotic excretion, despite other available routes such as feces, bile, sweat, and breath. The excretion pathways directly impact the concentration of available drugs and are often measured in terms of half-life and the initial administered dose. The renal clearance of a drug is another important parameter, which is usually employed to predict drug excretion. Experimentally, clearance is defined by the drug concentration drug along a defined time of renal excretion by a linear equation Eq.

Equation for renal clearance. The model performed with high accuracy, despite the highly diverse initial dataset employed for its generation, which points the importance of those models in early steps of the drug-discovery pipeline.

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This model approach was further refined by using support vector machine and increasing the number of relevant descriptors [ ]. Their partial least-square models resulted in a highly accurate model derived from compounds [ ]. Together with the hERG channel and CYP3A4, P-gp is one of the most widely studied antitarget, where its inhibition could bring consequences for several processes, such as the absorption, distribution, and excretion of drugs. Alternatively, an approach to predict P-glycoprotein inhibition using molecular interaction fields, derived from a literature collection of more than structures, generated a pharmacophore model for competitive P-gp inhibition [ ].

The most recent reported studies involving prediction of drug clearance, both from human and rat hepatic in vitro systems, were based on microsomes, with a recent emphasis on the use of hepatocytes. Recent PBPK models benefit from the large amount of available ADME data not only to aid the drug discovery process and dose regiment selection but also to guide the risk assessment for regulatory reviews [ ].

PBPK models are compartmentalized representations of the different organs, and each compartment can be described by a specific tissue volume and blood flow rate, which communicates with the blood venous and arterial. The use of drug-dependent parameters includes, but is not limited to, physicochemical properties, solubility and permeability values, and also the role of individual enzymes and transporters in the metabolism.

Those parameters can be determined in vitro or calculated from the compound structure with other in silico approaches, which allows the early use of PBPK in the DDD the bottom-up approach.

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The dosing time-dependent model considering the interaction between repaglinide with rifampicin was able to predict repaglinide plasma concentrations along a day. During recent years, larger molecules LM have gained in significance and popularity, due to achievements and approvals, as new molecular entities. From a historical perspective, drug discovery and development of LMs are heavily delayed in comparison to SMs with their first approved entity happening in the s [ ].

At about the same time, two major inventions allowed huge progress in pharmacokinetics assessment of small molecules, contributing to smaller drop-out rates in later DDD stages [ ]. The main differences between small and large molecules, despite the molecular weight, the number of heavy atoms, and torsions, can be found in the physicochemical properties, such as permeability, oral bioavailability, stability, specificity, and immunogenicity [ , ].

New parameters, unique for large molecules, are also of interest, such as the physical particle size and the hydrodynamic radius, which has a dramatic effect on the absorption. Both parameters are related to the overall shape and correlate well with MW for globular proteins, but not necessarily for unstructured or highly modified entities. As a result, biologics are normally administered parenterally, only targeting extracellular structures; they are also more likely to trigger an immune response; and their production costs are considerably higher [ ].

Interestingly, with the exception of the costs, these disadvantages can potentially be circumvented by appropriate delivery systems, for example, nanoparticle-based delivery to facilitate membrane permeation. Other parameters, such as charges, which were previously modeled by pKa in case of small molecules, are heavily heterogeneous in LMs.

Overall protein charge often influences the biologic excretion [ ], since negatively charged molecules undergo less renal filtration disregarding size effect [ ]. While representing difficulties in the development of new molecular entities, the aforementioned properties also offer special advantages that small molecules cannot cover.

The increasing effort and development of new technologies, driven by the belief in higher success rates, enabled the latest advances in the field [ ]. Several other biologics are currently in use, namely monoclonal antibodies mAbs and bispecific antibodies bsAbs , as example agents that activate or enhance the immunologic response. Of special interest in cancer therapy is a subclass of bsAbs, so-called bispecific T-cell engager BiTEs , which can recruit CD3 cells at the tumor site by binding to both cell types thereby directing the immunological response [ ].

Other interesting examples for biologics comprise hormones e. While such a broad spectrum of molecule classes offers also a wide range of treatments, at the same time, it exacerbates the need for new developments since every molecule type exhibits different properties. In the field of predicting the biologics activity against specific targets, classical modeling tools, such as Monte Carlo sampling, genetic algorithms, docking, and molecular dynamics simulation, were adapted or even developed anew to accommodate the specifics as extensively reviewed by [ , ].

On the other hand, the absence of standard techniques to assess ADME properties hampers the PK profiling and thus further development [ ]. In fact, the current knowledge of LM pharmacokinetics is even impaired compared to the basic knowledge of ADME principles for small molecules in the s [ ]. To begin with, the route of administration between them can differ, which leads to different mechanisms of absorption and first-pass metabolism. Furthermore, LMs are not metabolized by CYPs but can still trigger the release of pro-inflammatory cytokines leading to heavy side effects known as cytokine storm [ , ].

Unfortunately, up until now, most of the evaluation of those factors is only addressed on in vivo level systems, which are not suited for HTS, are expensive and labor intensive, and require longer bioethics evaluation. In this regard, the development of in vitro and in silico methods to evaluate ADME should be a high-profile goal. The main difficulties in PK profiling lie in the high costs and comparable low throughput of in vivo models. The extensive use of animals in DDD also raises ethical issues and is further affected since animal models not always translate readily to the humans, especially in terms of metabolism [ , ].

Furthermore, the advent of combinatorial chemistry coupled with HTS for efficacy evaluation leads to an explosion in the number to an extent that the classical PK assays could not compensate [ 29 , 47 ]. In vitro PK screens are supposed to offer a solution to the problem by complementing in vivo assessment to reduce costs while increasing efficiency, but they also suffer from shortcomings.

Models for assessing drug absorption and metabolism [1996]

In general, one must distinguish between two main forms of in vitro systems: static and dynamic models. Only dynamic models are suited for PK evaluation because they allow variation of compound concentrations, a key factor in pharmacokinetics. In this sense, diffusion-based dynamic in vitro models offer a solution but still are quite limited in terms of high throughput and costs. An alternative was presented by Lockwood and colleagues in the form of a 3D-printed fluidic device utilizing trans-well technique generating dynamic in vitro PK profiles also applicable for HTS infrastructure [ ].

First, in the past, pharmaceutical companies as well as academic laboratories were not that concerned with ADMET assessment in the early stages of drug discovery hit and lead generation and only addressed PK from preclinical stages on forward. Today, almost all pharmaceutical big-players have shifted pharmacokinetic profiling to discovery phases. However, only the future will tell whether those changes will yield fruit.

Another important aspect, recently addressed by the work of Ferreria and Andricopulo [ ], is the importance of translating those models into well-structured and user-friendly online platforms that can be accessed and used by the drug discovery community.

Still, the efficacy and reliability of computer simulations increase permanently and drastically, and many see a future of solely virtual drug discovery. Thankfully, these failures resulted in the consequence of addressing safety and efficacy concern earlier in the drug discovery process, for instance, via in vitro screens to assess metabolic stability and absorption properties and diminish failure rates later on [ 13 ].

The authors would like to thank Prof Dr. Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3. Help us write another book on this subject and reach those readers. MDS offers the following services Predict gastrointestinal absorption, using Caco2 cells. Rapid screening in multi well plates to detection, to quickly identify those compounds that can cross the GI tract into the blood stream.

Mechanism studies, evaluating flux rate and permeability constants from the apical to basolateral, and basolateral to apical directions Evaluate drug metabolism and drug interactions, using human and animal tissue models, such as primary hepatocytes. Determine the metabolite profile of drug candidates using hepatocytes, liver and small intestine microsomes and S9, microsomes containing a single enzyme. Predict drug interactions due to inhibition or induction of drug metabolizing enzymes.

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