**2. Lead selection**

During the Lead Selection stage of drug discovery, candidate chemical series and potential lead compounds are screened for preferential physiochemical properties and metabolic stability. Promising chemical entities are then investigated *in vivo* in rodents (N = 3-5) via cassette dosing [5] in order to generate PK data. With recent advances in microsampling techniques [6] and automated blood sampling systems [7], robust PK data can be generated in a limited number of rodents resulting in reliable IVIVC models. Pharmacokinetic data can then be predicted across multiple species to evaluate and identify the chemical series and chemical entities that are ideal candidates for optimization.

Most chemical entities are substrates of *in vivo* enzymatic metabolic reactions that modify the chemical structure to clear the exogenous compound over time. Metabolizing enzymes are primarily expressed in the liver but are also found in the intestines, lungs, and other various organs. Traditionally, there are two phases of drug metabolism that exist to transfrom lipophilic compounds into hydrophilic products that are more readily eliminated from systemic circulation. Phase 1 biotransformations are primarily oxidative; however, reduction, hydrolysis, and hydration reactions are also observed. Phase 1 reactions are primarily carried out by microsomal expressed cytochrome P450s (CYP450), flavin monooxygenases, aldehyde oxidase, and monoamine oxidase in the hepatocytes and microsome preparations from hepatocytes (a fragment of endoplasmic reticulum and attached ribosomes obtained by the centrifugation of homogenized cells). Phase 2 biotransformations are conjugative and occur within the S9 fractions (harvested from a mixture of unfractionated microsomes and cytosol by the centrifugation [9000 g] of homogenized cells) and hepatocytes. The primary conjugative reactions are glucuronidation, sulfation, methylation, acetylation, glutathione conjugation, and amino acid conjugation.

### **2.1 Metabolic stability**

The industry standard for screening the metabolic stability of a compound or compound-series is via the substrate depletion approach and the determination of half-life in rodent, nonrodent and human. Briefly, the metabolic stability, or intrinsic clearance (Clint) is assessed by incubating the compound at a concentration assumed to be below the Km for P450 metabolism. The *in vitro* Clint can be assessed in multiple species hepatocytes, hepatic S9 fractions and/or microsomes [8]. A series of samples are collected and analyzed over the time course in order to determine the percentage of compound remaining. The resulting half-life (t1/2) is then appropriately converted to an activity (Clint, mg/mL/Kg), taking into account the ratio of protein content to liver mass (e.g., mammalian microsomal protein, 45 mg protein/gm liver), and the ratio of liver mass to total animal or body mass (e.g., human, 20 gm/Kg body weight); the principles of allometry apply in this context of scaling protein content and P450 activity (physiological parameter) versus organ-to-body mass ratio (anatomical parameter) [9]. The intrinsic clearance *in vitro* PK parameter may be generated from a number of subcellular fractions, including microsomes, S9 fraction, cytosol and mitochondria (e.g., MAO metabolism), as well as from whole cell incubations employing freshly isolated or cryopreserved hepatocytes. The high throughput nature of the aforementioned metabolic stability assay enables the rapid generation of *in vitro* PK parameters (t1/2 and Clint) with each cycle of medicinal chemistry. With the continued innovation of mass spectroscopy and the liquid chromatogram coupled mass spectrometry (LC/MS) bioanalytical techniques, the contemporary biotransformation laboratory and scientist can now identify metabolites in the same intrinsic clearance assay, thus elucidating the relevant pathway of metabolism under kinetically controlled conditions. Alternatively, the same subcellular fraction and whole cell metabolism experiment may be employed to determine all relevant pathways and under conditions where compound(s) concentrations have been elevated appreciably above the anticipated KM for a particular drug metabolizing enzyme (DME). The merits of either approach will be discussed in a subsequent section.

#### **2.2 Soft spot analysis/metabolite identification**

The medicinal chemist utilizes the metabolic stability data generated during the Lead Selection and Lead Optimization stages of discovery as a tool to understand the impact of structural modifications within one or more chemical series or a lead series, respectively. Depending on the subcellular fraction employed, the Clint informs the chemist as to ensuing stabilization of the compound or series to oxidative (or reductive) metabolism via P450 (e.g., microsomes or S9 fractions). In addition to the t1.2 and Clint data produced from *in vitro* screening, the incubations also provide an opportunity to determine the site of metabolism on the molecule, or elucidate the so-called metabolic "soft-spot". From P450 mediated oxidation, to direct glucuronidation and sulfation, to ester and amide hydrolysis, small molecule drug candidates possess physico-chemical properties that are perfectly suited to drug metabolism-mediated, hepatic clearance. Typically, the soft-spot analysis is performed in the same subcellular fraction or hepatocyte system employed in the intrinsic clearance assessment. With recent improvements in electrospray ionization liquid chromatography tandem mass spectrometry (ESI-LC–MS/MS) bioanalysis, the DMPK scientist is able to associate one or more predominate sites of metabolism (e.g., oxidation, reduction, hydrolysis, conjugation) with the clearance of a compound or series *in vitro*, and under the same kinetically controlled conditions of the metabolic stability assessment (i.e., ≤ 1 μM). The described soft-spot analysis construct enables a concerted execution of both the Clint assessment (substrate depletion) as well as the identification of the principal metabolites produced *in vitro* in "one plate". Whether in concert with the Clint assessment or as a discrete stand-alone in vitro experiment, innovation in mass spectroscopy hardware and software has enabled significant means of metabolite detection and structural elucidation, the results of which commonly augment the nonclinical pharmacology reports and summaries within an Investigational New Drug application (IND).

Still, the DMPK scientist may seize on the opportunity to determine the soft-spot(s) of metabolism to simultaneously assess the extent and range of *Drug Metabolism in Drug Discovery and Preclinical Development DOI: http://dx.doi.org/10.5772/intechopen.97768*

biotransformation likely produced in multiple species during preclinical and clinical development. Access to study samples (e.g., plasma, urine, bile, feces, organ tissue) presents the opportunity to survey the metabolites produced in the rodent and nonrodent species selected for development, comparing to that observed in human hepatocytes and hepatic (e.g.) subcellular fractions. In order to provide a comparative analysis to the *in vivo* metabolism picture, the DMPK scientist will produce in parallel a set of *in vitro* experiments in rodent and nonrodent, where the compound or preclinical candidate is incubated at concentrations assumed to exceed the Km for most drug metabolizing enzymes (e.g., ≥25 μM ≤50 μM). Importantly, *in vitro* incubations of subcellular fractions should be fortified with appropriate cofactors (or co-enzymes) to "fuel" the relevant catalytic activities of select enzymes and at excess (1-2 mM): P450 (NADPH, or NADPH-regenerating system), FMO (NADPH), UGT (UDPGA), SULT (PAPS), NAT (acetyl-CoA), GST (GSH).

### **2.3** *In vivo-in vitro* **correlation**

The *in vitro* hepatic clearance of a compound (within a series) is a valuable PK parameter for the medicinal chemist and the DMPK scientist. Because the intrinsic clearance (Clint, mL/min/kg) describes the unrestricted, unscaled clearance of a compound, the medicinal chemist may utilize Clint to gauge the impact of structural alterations in the series on P450 metabolism (oxidation or reduction in microsomes); the primary goal of which to stabilize a compound or chemical series towards hepatic clearance, thus increasing the *in vitro* half-life. The value of the Clint PK parameter is in its correlation to a plasma clearance (CLp), as typically determined in a rodent (e.g., Sprague–Dawley rat) or nonrodent (e.g., beagle dog) species during the hit-to-lead and later in the lead optimization stages of discovery. Establishing an in *vitro:in vivo* correlation (IVIVC) between predicted hepatic clearance (CLH) and CLp serves two purposes: (1) validation of the CLint screening approach for the ensuing lead optimization stage of discovery, and (2) establishes the nonclinical species for *in vivo* PK screening and the species predictive of human hepatic clearance. The selection of appropriate *in vitro* and *in vivo* PK screening approaches during early discovery provides a mechanism for an iterative medicinal chemistry optimization of one or more chemical series, with the goal of predicting human PK parameters.

## **3. Lead optimization**

Once a lead series is selected, further *in vitro* and *in vivo* testing is conducted on a fewer number of compounds, illuminating metabolism and PK (and pharmacologic) attributes for select compounds. This stage of drug discovery is known as Lead Optimization. From an *in vitro* perspective, potential DDIs are identified with CYP450 inhibition and induction assays, and reaction phenotyping assays. Soft spot analysis is performed to identify areas liable to biotransformation, metabolic identification of potential *in vivo* metabolites, and drug transporters are identified for which the candidate entities are a substrate. Rodent and nonrodent PK studies are conducted in order to optimize the exposure and disposition (PK) of the lead series while determining the nonclinical pharmacologic effects of the lead series in select rodent and/or nonrodent disease models. These data are then used to establish preliminary exposure-effect relationships (PK-PD). The exposure data collected from rodent (e.g.) models of efficacy are particularly valuable and provide a critical assessment of dose-exposure relationships of the lead series in anticipation of advancing into the single- and repeat-dose tolerability assessments prior to

candidate selection. At minimum this exposure information guides the discovery team to the dose range required for a tolerability screening assessment; ideally, the efficacy model exposure assessments guide the selection of the dosing frequency required to maintain exposure during the repeat-dose tolerability assessment.

The Food and Drug Administration's (FDA) January 2020 guidance on clinical drug interaction studies states that "clinically relevant DDIs between an investigational drug and other drugs should therefore be: (1) defined during drug development as part of the sponsor's assessment of the investigational drug's benefits and risks; (2) understood via nonclinical and clinical assessment at the time of the investigational drug's approval; (3) monitored after approval; and (4) communicated in the labeling." Furthermore, the FDA defines the goals of studies that evaluate P450 enzyme- and transporter-mediated DDIs to be: (1) determine whether the investigational drug alters the pharmacokinetics of other drugs; (2) determine whether other drugs alter the pharmacokinetics of the investigational drug; (3) determine the magnitude of changes in pharmacokinetic parameters; (4) determine the clinical significance of the observed or expected DDIs; and (5) inform the appropriate management and prevention strategies for clinically significant DDIs [10].

Additionally, the FDA also provided guidance for *in vitro* drug interaction studies (P450 and transporter) in January 2020. This guidance provides the framework for designing and conducting *in vitro* experiments in order to assess potential clinical DDIs. The CYP450 experiments are to: (1) determine which CYP450 enzyme the drug entity is a substrate of (reaction phenotyping); (2) determine if the drug entity is a CYP450 inhibitor; and (3) determine if the drug entity is a CYP450 inducer. Metabolite investigations may be warranted on a case-by-case basis. If the metabolite is pharmacologically active and contributes ≥50% of the overall activity, then reaction phenotyping analyses should be conducted. Inhibition studies are to be conducted if the total exposure/area under the curve (AUC) of the metabolite is ≥25% of the parent or if the metabolite is more polar than the parent entity and the AUC of the metabolite is greater than or equal to the parent. Transporter studies are to investigate if the drug entity is a substrate of efflux pumps (P-glycoprotein [P-gp] and breast cancer resistance protein [BCRP]), hepatic transporters (OATP1B1 and OATP1B3), and renal transporters (OAT, OCT, and MATE) [11].

#### **3.1 Reaction phenotyping**

Having assembled relative *in vitro* pharmacokinetic data (e.g., Clint) and elucidated metabolism pathways for a compound or preclinical candidate, it's incumbent upon the DMPK scientist to identify the particular human drug metabolizing enzymes that are contributing to the *in vitro* clearance in an effort to identify and/ or manage latent drug–drug interaction potential that exist. To identify such victim drug–drug interaction (DDI) potential, [12] the DMPK scientist employs a variety of recombinantly expressed drug metabolizing enzymes, notably P450 enzymes to determine the extent any one enzyme contributes to the clearance of a compound [13]. The fraction-metabolized (fm) term is often employed within the context of P450 mediated metabolism, but more recently applied to the growing number preclinical candidates observed to be non P450 substrates (e.g., UGT). Correlating the *in vitro* microsomal (e.g.) clearance to contributions from any one or more P450 enzymes, most notably P450 1A2, 2B6, 2C8, 2C9, 2C19, 2D6, and 3A4, is the goal of so-called reaction phenotyping. Importantly, this reaction phenotyping of drug clearance involves the comparative metabolism (and intrinsic clearance thereof) of the preclinical compound by the recombinant enzyme to what is observed in the subcelluar fraction; in the case of P450 or UGT, that can be accomplished in hepatic microsomes or S9 fractions. Two industrial standard approaches involve the

#### *Drug Metabolism in Drug Discovery and Preclinical Development DOI: http://dx.doi.org/10.5772/intechopen.97768*

generation of relative activity factors (RAF method) and intersystem extrapolation factors (ISEF) to adequately relate a single recombinant enzyme activity to human liver microsomes bearing a full complement of expressed P450 enzymes. Each of these methods, while instrumental in arriving at the fm value for a particular drug candidate, fall short in estimating the impact of polymorphically expressed enzymes (e.g., P450 2D6, 2C9, 2C19, UGT1A1) in the metabolism-mediated clearance of a compound. More recently, and as a result of pharmacogenomics and pharmacogenetics clinical research and impact, the DMPK scientist can gain access to subcellular fractions obtained from sparse or densely genotyped individual liver donors.

The importance of linking a particular biotransformation reaction to one or more metabolites is of particular interest during preclinical development. Whether in terms of pharmacology (e.g., P450 mediated production of an active metabolite), drug safety (e.g., UGT mediated production of an acyl glucuronide) or confirming multiple enzymes producing the same pathway (e.g., risk mitigation of a clinically relevant DDI), the use of recombinantly expressed enzymes are critical in mapping the range of metabolites observed in human hepatocytes or subcellular fractions to specific drug metabolizing enzymes. At the elevated concentrations employed in the generation of metabolite(s) *in vitro*, there is limited kinetic value to these experiments and should be viewed as informative in nature and restricted to the metabolite ID and structure elucidation exercise previously described.
