Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulations. Sheila Annie Peters

in the kidney (nephrotoxicity). Perpetrators of renal DDIs are usually acids or bases with high therapeutic doses and inhibit the renal uptake transporters OATs 1 and 3 or OCT2. Probenecid inhibits the renal OATs and causes drug–drug interactions when coadministered with hydrophilic acids that are predominantly cleared by the kidney, such as penicillin, famotidine, and chlorothiazide (Inotsume et al., 1990; Ayrton and Morgan, 2001; Ho and Kim, 2005; Shitara et al., 2009). Decreases in renal function with age and disease will impact the extent of drug interaction.

      Complex drug–drug interactions (DDIs) with potential involvement of multiple elimination pathways and interaction mechanisms is challenging. When a transporter and an enzyme are involved in DDI, then the net effect depends on whether the inhibitor or the substrate of the enzyme is dependent on the transporter for access to the enzyme. Understanding the mechanisms underlying complex DDIs are challenged by the lack of specific probe substrates for transporters (Jones et al., 2020). Rifampicin through its activation of the nuclear receptor, PXR, induces CYP3A4. Additionally, it also inhibits OATP1B1 uptake transporter. Thus, a single dose of rifampicin caused an 8‐fold increase in the in the exposure of atorvastatin (Lau et al., 2007) However, multiple doses of rifampicin caused a decrease in the exposure of atorvastatin, due to induction (Backman et al., 2005). When multiple inhibitors inhibit multiple enzymes, the overall effect is additive, but if they inhibit the same enzyme, then the net effect defaults to the most potent inhibitor.

2.4 IN VITRO METHODS TO EVALUATE DRUG–DRUG INTERACTIONS

      A candidate drug must be evaluated for its potential to be a perpetrator or victim of DDI with likely coadministered drugs. In vitro methods that aid this assessment focus on reversible/irreversible inhibition or induction mediated by an enzyme or a transporter.

2.4.1 Candidate Drug as a Potential Perpetrator

      Determination of IC ₅₀ and K _i for reversible enzyme‐ or transporter‐mediated inhibition: A commonly used measure of inhibitory potential of a drug is the inhibitor concentration required to inhibit 50% of the metabolic rate of a probe substrate (IC₅₀ ) in vitro. IC₅₀ can be converted to absolute inhibition constant (K_i ) using the Cheng–Prusoff equation (Yung‐Chi and Prusoff, 1973). For a competitive enzyme inhibition, K_i is given as IC₅₀ /(1+[S/K_m ]), where S is the substrate concentration and K_m is the Michaelis–Menten constant for the substrate. Thus, when measurement of IC₅₀ is done at substrate concentrations that are less than or approaching the K_m, IC₅₀ is less than K_i for a reversible enzyme inhibition. When the substrate concentration exceeds its K_m, IC₅₀ will overestimate K_i . For noncompetitive inhibition, IC₅₀ = K_i, and enzyme inhibition is independent of substrate concentration. Assumption of the mechanism of inhibition is thus necessary to get K_i from IC₅₀ . The in vitro models employed to study enzyme inhibition are hepatocytes or microsomes, depending on the role of phase II enzymes in the metabolism of the substrate. The advantage of these systems is the presence of enzymes in proportions found in vivo. A disadvantage of these systems is that only partial inhibition can be observed for substrates lacking specificity. In these cases, recombinant systems can be useful. Partial inhibition is also attributable to poor solubility of the inhibitor. The preferred in vitro systems for quantifying inhibitory potency of transporter inhibitors are Caco‐2 cells (do not distinguish between transporters), HEK, or MDCK transfected cells for uptake transporters and membrane vesicles for efflux transporters. The inhibitor and substrate concentrations used in the in vitro assays should be clinically relevant. If the inhibitor concentrations in the assay are in excess of the clinical concentrations, the inhibitory potential determined would overestimate the risk in vivo. If the substrate concentration is different from clinically relevant plasma concentrations, then the relative contribution of different enzymes involved in the substrate metabolism could be different from that in vivo, making the in vitro IC₅₀ irrelevant.

      Determination of IC ₅₀ shift, maximum inactivation rate (k _inact ), and inactivator concentration at half‐maximal k _inact (K _I ) for TDI: The clinical value of in vitro models that address TDI of drug‐metabolizing enzymes has been reviewed (Venkatakrishnan et al., 2005; Grimm et al., 2009). IC₅₀ shift (Obach et al., 2007) and projected IC₅₀ (Atkinson et al., 2005) assays are generally the first screening methods to assess TDI. Determination of the kinetic parameters k_inact and K_I (Atkinson et al., 2005; Obach et al., 2007) is often carried out for promising drug candidates. The test compound is preincubated with an enzyme source (usually pooled human liver microsomes) in at least five different inhibitor concentrations to get the pseudo first‐order rate constant (k_obs ), which is related to k_inact and K_I by the Michaelis–Menten equation:

(2.1)

k_inact and K_I can be obtained either by a nonlinear regression of equation 2.1 or by plotting the Kitz–Wilson plot (1/k_obs vs. 1/[I]). Different in vitro approaches to evaluate MBI have been reviewed (Grime et al., 2009). The quasi‐irreversible MIC generated by coordinate bond formation to iron in the heme is identifiable by the absorbance of Soret peak at 450–455 nm by difference spectra scanning (Franklin, 1991) in a dual‐beam spectrophotometer. Human liver microsomes or recombinant enzyme can be incubated with the test compound. Nitrogen‐based MICs are further characterized by the reduction of the Soret absorbance on oxidation by potassium ferricyanide (Watanabe et al., 2007; Grimm et al., 2009). A key parameter that is necessary for the evaluation of TDI is the CYP degradation rate constant (k_deg ) for the isoform that is inactivated. However, measured values vary widely (Yang et al., 2008). Intestinal inhibition needs to be considered for the TDI observed for CYP3A4 enzyme (Galetin et al., 2006).

      Determination of E _max and IC ₅₀ for CYP induction: Any in vitro system employed to evaluate induction potential of a candidate drug should retain activity for a considerable length of time. In vitro systems employed to study enzyme induction are cultured human hepatocytes (LeCluyse et al., 2000; Kato et al., 2005; Luo et al., 2005), immortalized hepatocytes (Mills et al., 2004), minimally derived cell lines (Aninat et al., 2006) like HepaRG cells (Kanebratt and Andersson, 2008), reporter gene assays (El‐Sankary et al., 2001; Persson et al., 2006; Sinz et al., 2006), and ligand binding assays. The classic method that is widely used in the pharmaceutical industry and accepted by the FDA to measure induction is incubation of test compound with cultured human hepatocytes in which changes in mRNA expression and/or enzyme activity of target genes are measured and compared to untreated control hepatocytes (Chu et al., 2009). Advantages of hepatocytes are many. They contain native receptors and transporters; target genes are in their native context with full complement of regulatory elements. The disadvantage comes from the interdonor variability in CYP levels, which is difficult to distinguish from actual variability in induction response across human population. Methods using in vitro data to predict induction potential have been reviewed (Fahmi and Ripp, 2010). Focusing on CYP3A induction, an Innovation and Quality (IQ) Induction Working Group (WG) used a large clinical dataset to highlight the variability in in vitro parameters due differences in protocols, reagents, donors, analysis (methods and instrumentation) and to recommend in vitro evaluation of induction in three separate human donors (Kenny et al. 2018). If unbound maximum plasma concentrations or dose of the perpetrator are not known, the highest concentration tested in vitro is limited by aqueous solubility or cytotoxicity.

2.4.2