**4.4 Metabolic stability**

Metabolic stability defines the liability of a drug compound to its metabolism. It is determined by estimating the disappearance of the drug substrate in a relevant *in vitro* system over a particular period. Further, metabolic stability data gives information on the secondary pharmacokinetic parameters such as bioavailability and half-life of the drug. Therefore, optimizing metabolic stability plays a vital role in the drug discovery and development phase [60]. These obtained parameters explain the drug's pharmacological and toxicological profile and shed light on the patient adherence to the drug. **Figure 4** shows the different stages in the metabolic stability study.

Metabolic stability studies are performed at a drug concentration less than Km value, where enzymatic reactions follow the first-order process. While dealing with an unknown Km value, 1 μM concentration of a drug is recommended. In general, the metabolic system is incubated with the drug substrate for a specified period at 37°C. The disappearance of the drug substrate is monitored at individual time points using a

#### **Figure 4.**

*Metabolic stability of drug substance. This assessment involves the incubation of the drug with microsomes in 100 mM potassium phosphate buffer. The addition of 1 mM NADPH solution followed by incubation at 37°C initiates the metabolic reaction. The disappearance of the drug substrate is monitored at individual time points using the analytical technique. Plotting the natural log of peak area ratio with time yields a straight line, where the slope of the line gives the elimination rate constant that helps in predicting the intrinsic clearance. The conversion scaling factors involves in the correlation between the clearance values obtained from the* in vitro *intrinsic clearance data and* in vivo *clearance values.*

suitable analytical technique. Testosterone or DL-propranolol is added as a positive control to ensure the adequate execution of the assay. A negative control without NADPH is included to ascertain drug loss due to thermal degradation. Negative controls could also serve as matrix controls if they lack the drug or responsible enzyme. Plotting natural log of peak area ratio (drug substance peak area/internal standard peak area) with time yields a straight line, where the slope of the line gives the elimination rate constant (k).

The following equation determines half-life (t1/2)

$$t\_{\frac{1}{2}} = \frac{0.693}{k}$$

Metabolic stability studies derive various parameters that include half-life, intrinsic clearance, and total hepatic clearance. These parameters can be calculated using "well-stirred" and "parallel tube" approaches. In the "well-stirred" approach, the liver is characterized by a single compartment where the intracellular free concentration of drugs in hepatocytes is in equilibrium with the free concentration of drug in blood eliminating from the liver. Whereas in a "parallel tube" approach, the liver comprises numerous parallel tubes, in which the enzymes are evenly distributed. In every tube, the intracellular free concentration of drug in hepatocytes is in equilibrium with the free concentration of the drug in blood [61].

The whole liver CLint is determined by using *in vitro* half-life:

$$\text{Whole liver CL}\_{\text{int}} = \frac{0.693 \times \text{liver weight}}{\text{invent}\_{\ddagger} \times \text{amount of the liver in inclusion} \times \text{fraction unbounded to microonal protein}}$$

According to the "well-stirred" model, hepatic clearance (*CLH*) and hepatic extraction ratio (*EH*) are given by: [62].

$$\mathrm{CL\_H} = \frac{\mathrm{Q\_H} \times \mathrm{CL\_{int}}}{\mathrm{Q\_H} + \mathrm{CL\_{int}}}$$

$$E\_H = \frac{\mathrm{CL\_H}}{\mathrm{Q\_H}}$$

where QH is the hepatic blood flow.

#### **4.5 Enzyme kinetics in drug metabolism using HLM**

It is essential to determine the enzymes involved in the metabolism process and their respective kinetic parameters throughout the drug discovery process. Enzyme kinetics involves studying reaction rates affected by different experimental variables such as enzyme concentration, substrate concentration, enzyme activators, enzyme inhibitors, temperature, pH, and ionic strength [63, 64]. Chakraborty et al. demonstrated the effect of pH, temperature, pressure and dwell time on enzyme inhibition kinetics in pineapple puree and concluded that the temperature had the highest impact on enzyme inactivation [65]. Further, they elucidate the role of polymorphism in determining drug clearance and aid in predicting drug–drug interactions associated with metabolites [66].

The CYP enzyme family metabolizes numerous xenobiotics, thus making it an integral part of drug–drug interactions [67]. Inhibition studies predict most P450

In Vitro *Drug Metabolism Studies Using Human Liver Microsomes DOI: http://dx.doi.org/10.5772/intechopen.108246*

oxidations and drug–drug interactions, owing to their competitive Michaelis–Menten kinetics. Models with a single binding site are explained by competitive, noncompetitive, and uncompetitive inhibition, or activation of the enzyme, whereas some CYP3A4 oxidations tend to demonstrate unusual kinetics [67, 68]. Michaelis– Menten kinetics determines the enzyme kinetic constants such as Km and Vmax. The reaction velocity (V), i.e., the formation rates of metabolites with a fixed amount of HLM, is given by: [69].

$$\mathbf{V} = \frac{\mathbf{V}\_{\text{max}} \, \mathbf{C}}{\mathbf{K}\_{\text{m}} + \mathbf{C}}$$

where C depicts the initial drug concentration, Vmax gives the maximum reaction velocity of the enzyme, and Km represents the Michaelis–Menten constant.

Intrinsic clearance (CLint) is defined as the ratio of the rate of product formation to the substrate concentration and can be ascertained by using the Km and Vmax values [70].

$$\mathbf{CL}\_{\text{int}} = \frac{\mathbf{o}}{[\mathbf{S}]} = \frac{\mathbf{V}\_{\text{max}}}{\mathbf{K}\_{\text{m}} + [\mathbf{S}]}$$

where [S] is the substrate concentration, Vmax gives the maximum reaction velocity of the enzyme,

and *Km* represents the Michaelis–Menten constant.

When the concentration of the substrate is considerably lower than the Km value, then intrinsic clearance is augmented to total clearance [70]. Then, the above equation is simplified to:

$$\mathrm{CL}\_{\mathrm{int}} \approx \frac{\mathrm{V}\_{\mathrm{max}}}{\mathrm{K}\_{\mathrm{m}}}$$

In cases where more than one CYP is involved in a drug metabolism reaction, a biphasic relationship is observed between Vmax (maximal reaction velocity) and [S] (substrate concentration). It can be explained by using a two-enzyme model [8]:

$$\mathbf{V} = \frac{\mathbf{V}\_{\max \mathbf{1} \cdot [\mathbf{S}]}}{\mathbf{K}\_{\mathbf{m1}} + [\mathbf{S}]} + \frac{\mathbf{V}\_{\max \mathbf{2} \cdot [\mathbf{S}]}}{\mathbf{K}\_{\mathbf{m2}} + [\mathbf{S}]}$$

Where Km1 and Km2 are high-affinity and low-affinity component constants, Vmax1 and Vmax2 are the maximal velocities of the enzymes for high and low-affinity components, respectively.

Atypical kinetics are elucidated through a particular enzyme by binding more than one drug molecule concomitantly or through other active site interactions [71, 72]. Hence, it is essential to analyze kinetics as an *in vivo* or *in vitro* effect to prevent an erroneous prediction of intrinsic clearance and ultimately impact the *in vivo* clearance [72]. Eadie-Hofstee plots are used to resolve multiple-enzyme kinetics when Michaelis–Menten plots fail to be beneficial [73]. It is essential to model the nonadditive interactions to understand multidrug cocktails usage [74]. Further, it is necessary to consider the non-specific binding in enzyme kinetics. The chances of these events are high, resulting in the *in vitro* clearance values being closer to the measured values. However, if the non-specific binding is absent, the obtained values of intrinsic clearance will be lower than the real-time *in vivo* clearance [75].

Electrochemical methods determine enzyme kinetics where electron transfers are involved. The catalytic activity of the cytochrome P450 enzyme is electro analyzed as the catalytic cycle requires electron transfer. Electroanalysis paves the way for multicomponent studies entailing many drugs to describe interactions under the mutual influence or drug interference, which in turn is manifested by an alteration in the kinetic constants of enzymatic catalysis [76]. Novel microfluidic tools and detection methods have made the high throughput measurement of enzyme kinetics possible using droplet-based optofluidic systems [77]. A nanochannel-array enzyme reactor has been developed to comprehend the basics of enzymatic reactions restricted to nano-spaces and also gives an outreach to design productive enzyme reactors [78].

Many *in vitro* systems like HLM, human hepatocytes, recombinantly expressed CYP enzymes, S9 fractions and human liver slices are used to determine the intrinsic clearance of the drug for speculating the *in vivo* clearance by estimating the kinetic parameters, Km and Vmax values. HLM is a well-established in vitro system for studying drug metabolism through CYP450 kinetics due to its low cost, ease of use, and commercial availability [79]. CYP activity levels differ in different microsomal preparations. This variation between different donor's preparations can be used to understand the effects of age, sex, and genotype on CYP-regulated kinetics [22].

#### **4.6 Glutathione conjugation assay**

The tripeptide, L-γ-glutamyl-L-cysteinyl-glycine, known as Glutathione (GSH), is a low molecular mass, thiol-reducing compound, synthesized from L-glutamate, L-cysteine, and glycine amino acids [80]. The cysteine sulfhydryl group (-SH) is responsible for reduction and conjugation reactions for eliminating reactive electrophiles and enhancing a lipophilic compound's solubility [81, 82]. GSH is usually found at concentrations between 1 and 10 mM and involves scavenging reactive oxygen species and detoxifying foreign compounds [83, 84]. The glutathione conjugation is followed by a series of metabolic and transport phases that eventually leads to the mercapturic acid formation (S-conjugates of N-acetylcysteine) [85, 86]. The formed mercapturic acid is more polar and, thus, easily excreted in urine [87]. Glutathione S-transferases (GSTs) include drug-metabolizing enzymes responsible for catalyzing glutathione conjugation with many foreign compounds [88]. They belong to the superfamily of Phase II enzymes and exist as dimeric proteins [89, 90].

The human GST is present in the cytosol, is expressed in the liver, and is subdivided into classes α, п, and μ. In a human liver, 80–90% of the GST is present in the form of GST-α [91]. The expression of these GST-α enzymes may lead to resistance toward anticancer drugs, and thus, GSTs can also be used as markers for malignant tumors [92, 93]. The main functions of GSTs are redox signaling, antioxidation, and detoxification of many cancer drugs [94–96]. The detoxification phenomenon is not always valid, and in some instances, GSH S-conjugates were observed to be toxic [97]. Christoph Englert et al. reported that nanocarriers' coupling to glutathione aided in effectively crossing the blood–brain barrier [98].

#### **4.7 Glucuronidation and its kinetics**

Glucuronidation reaction results in the conjugation of glucuronic acid obtained from uridine diphosphate-glucuronic acid (UDPGA) to compounds that contain hydroxyl, carboxyl, thiol, amino, and acidic functional groups by UDPglucuronosyltransferase enzymes (UGTs) [99, 100]. UGTs are abundant in the

liver and intestine [101]. These membrane-bound enzymes of the endoplasmic reticulum account for the metabolism of more than 35% of drugs [102, 103]. Human UGT enzymes are categorized into four families, namely: UGT1, UGT2, UGT3, and UGT8. These enzymes are further classified into UGT 1A, 2A, and 2B isoforms depending upon the structure of the gene and the analogy of sequence. The isoforms expressed in the liver of UGT1A: UGT1A1, UGT1A3, UGT1A4, UGT1A5, UGT1A6, and UGT1A9 [104, 105] and UGT2B isoforms: UGT2B4, UGT2B7, UGT2B10, UGT2B11, UGT2B15, UGT2B17 and UGT2B28 [104]. The lumen of the endoplasmic reticulum (ER) serves as an active site for UGTs, and its membrane allows substrates, cofactors, and products to diffuse [106]. The latent action of the UGTs in microsomal incubations can be removed by distorting the barrier. Alamethicin disrupts the barrier by forming pores in the membrane and permits entry to the enzyme, causing no impact on the membrane's structure or its intrinsic catalytic activity [107]. Glucuronidation is a detoxification reaction as it enhances the compound's polarity and facilitates the excretion of compounds through urine and bile [103, 108]. It is necessary to comprehend the involvement of UGTs in the drug's metabolism as it aids in averting drug–drug interactions and adverse drug reactions [109].

In the first stage, the microsomes are activated in 0.1 M potassium phosphate buffer (pH 7.4) pre-incubated with 50 μg/mL concentration of alamethicin on ice for 30 min. The drug is incubated for 5 min at 37°C with 0.1 M potassium phosphate buffer (pH 7.4), 4 mM MgCl2and the activated HLM with the final concentration of 0.5 mg/mL. The metabolic reaction is initiated by adding 5 mM UDPGA and incubating this mixture at 37°C for predetermined time points. A well-known UGT substrate is included as a positive control, and a mixture without UDPGA is used as a negative control to assess the formation of metabolites other than glucuronidation metabolism [110, 111]. The addition of ice-cold extraction solvents (Acetonitrile or methanol) terminates the reaction. The samples are centrifuged, and the supernatant is collected for further analysis using a suitable analytical technique like HPLC [112–114] or LC–MS/MS [115–117].

Kinetic analyses were performed with HLM and commercially available UGTs. The elucidation of the kinetics of glucuronidation has a significant influence on the credibility of the predicted *in vitro* clearance value [118]. Michaelis–Menten kinetic model is used for the determination of the kinetics of glucuronidation via kinetic constants, Km (Michaelis constant, the concentration of substrate when the reaction rate is 50% of Vmax) and Vmax (the maximum rate of reaction when the substrate saturates all the active sites of the enzymes). Kinetics of glucuronidation for several drugs like NSAIDs [119], olanzapine [120], serotonin [121], and ursolic acid [122] were determined using this model. The substrate inhibition equation used is:

$$\nu = \frac{\mathbf{V}\_{\text{max}} \ [\mathbf{S}]}{\mathbf{K}\_{\text{s}} + [\mathbf{S}] + [\mathbf{S}]^2 \zeta\_{\text{si}}}.$$

where v is the reaction rate, [S] is the substrate concentration, Vmax is the maximum velocity, Ks is the substrate affinity constant, and Ksi is the substrate inhibition constant [123].

The hill equation is used to determine the sigmoidal kinetics:

$$\nu = \frac{\mathbf{V}\_{\text{max}} \mathbf{S}^{\text{n}}}{\mathbf{S}\_{50}^{\text{n}} + \mathbf{S}^{\text{n}}}$$

where S<sup>n</sup> <sup>50</sup> is the substrate concentration leading to 50% of Vmax, and n is the Hill coefficient [124].

Eadie–Hofstee plots and Lineweaver-Burk plots determine the model to be selected for the kinetic analyses using nonlinear regression analysis for fitting the experimental data [125, 126]. A straight line in the plot signifies the Michaelis–Menten model's usage. In contrast, if a hook in the upper panel is obtained, it represents the usage of the substrate inhibition model [110, 127].

### **5. Conclusion**

After the drug's oral administration, the drug undergoes various processes like absorption, distribution, metabolism, and excretion. Metabolism of most of the drugs is carried out by CYP and UGT enzymes, which are abundant in the liver. *In vivo* and *in vitro* studies are often used for drug metabolism studies. *In vivo* studies involve screening serum, urine and feces, whereas *in vitro* drug metabolism studies are carried out using HLM, human hepatocytes, and recombinantly expressed cytochrome P450 enzymes. HLM are widely used as it offers several advantages like high throughput screening, ease in storage, economic, and simplicity and convenience in their usage. Metabolism studies play a significant role in identifying, characterizing, and quantifying potential metabolites of a particular drug, thereby elucidating the drug metabolism pathway. Metabolism may convert a drug into a toxic metabolite or render an inactive drug into its active form. Metabolic stability helps predict the metabolic clearance of the drug and thereby helps in dosage adjustment and the frequency of administration of a drug. Therefore, conducting drug metabolism studies using HLM at the drug discovery stage helps screen the potential leads with optimized pharmacological properties.
