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

      To evaluate a compound’s potential to be a victim of DDI, it is necessary to identify the specific enzymes involved in its metabolism. A common experimental approach to reaction phenotyping (Harper and Brassil, 2008; Zhang et al., 2009) is the use of cDNA‐expressed recombinant enzyme systems, in which the test compound is incubated with a panel of individually expressed human recombinant enzymes. A typical panel of CYP enzymes includes CYPs 1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1, 3A4, and 3A5. Using the known CYP abundances^{60, 61}, the percentage contribution of individual CYPs to the overall oxidative metabolism of a drug candidate (f_m,CYP ) can be estimated:

      (2.2)

CL_int,i is the intrinsic clearance of the ith CYP isoform in individually expressed recombinant enzyme. The application of this method to estimate the relative contributions of the individual UGT enzymes to the overall glucuronidation rate is not so feasible today as the relative abundances of the various UGT enzyme activities in human liver are not well‐established. Altenatively, reaction phenotyping experiments are also done by incubating the test compound in hepatocytes, microsomes, or some other in vitro preparation, using normal tissues as the enzyme source with inclusion of selective chemical or immunoinhibitors of specific enzymatic pathways. By performing a series of incubations with various inhibitors, and comparing the relative rates of metabolism, one can identify which inhibitor reduces the overall metabolism to the greatest extent and thereby uncover the metabolic pathway that contributes the most to the clearance of a compound. The use of hepatocytes guarantees the full range of phase I and phase II enzymes but is limited by the availability of specific inhibitors to some enzymes (example, UGT enzymes). The percentage contribution of an enzyme isoform to the metabolism of a victim drug provides a measure of the extent of its dependence on that isoform (f_m,CYP ). In addition, a good assessment of victim potential requires knowledge of the fractions eliminated in bile and urine in order to get the fraction of compound metabolized (f_m ).

2.5 SOURCES OF UNCERTAINTY

      In vitro data are associated with uncertainty due to knowledge gaps in system parameters or scalars. The data may also vary considerably between laboratories due to differences in donors and assays. When an NCE is a perpetrator of drug interaction, the unbound drug concentration at the site of interaction is uncertain, if the compound is a transporter substrate and/or highly bound to plasma proteins. This is especially true for efflux transporter inhibition or for CYP induction, where the driving concentrations are intracellular drug concentrations. The uncertainty in driving concentrations are even higher when these processes occur in the gut. In early development, the therapeutic dose of the perpetrator NCE drug is not identified and can add to the uncertainty in the DDI risk assessment. Sources of uncertainty are listed below:

      NCE as victim

       Contribution of gut for CYP3A and UGT substrates, fg;

       Fraction metabolized, fm

       fm,CYP

      NCE as perpetrator

       In vitro interaction parameters (Ki, kinact, KI, EC50 )

       In vivo relevance of transporters in determining the intracellular concentrations of inhibitors that are substrates for uptake and/or efflux transporters

       Protein binding of highly plasma‐bound drugs

       Final dose

2.6 THERAPEUTIC PROTEIN–DRUG INTERACTION

Under inflammatory conditions, proinflammatory cytokines are produced locally around the pathological areas and are circulated to activate inflammatory responses in distal tissues (Wu and Lin, 2019). Cytokines like tumor necrosis factor‐α (TNF‐α), interleukin‐1β (IL‐1β), interleukin‐6 (IL‐6), interferon‐γ (IFN‐γ), and transforming growth factor‐β (TGF‐β) have been shown to reduce the expression of CYP1A2, CYP2C8, and CYP3A4 in cultured human hepatocytes. Given that CYP enzymes and drug transporters share certain common regulatory pathways, the effects of cytokines on the regulation of CYP enzymes may also be applicable to transporters (P‐gp). Aberrant expression of these drug‐processing proteins is observed in several animal models of human inflammatory diseases like type 1 diabetes, rheumatoid arthritis, inflammatory bowel disease, metabolic disorders, and several neurodegenerative diseases. Cytokine‐induced gene regulation is probably driven by altered activities of various transcription factors, including nuclear factor‐κB (NF‐κB) and nuclear receptors. Thus, a therapeutic protein acting as a pro‐inflammatory cytokine or a cytokine modulator could potentially impact CYP enzyme and transporter function. There are still knowledge gaps in this area (13). TP–drug interactions have been evaluated from in vitro studies, animal studies, and/or clinical settings. An in vitro assessment of drug interaction between a small molecule and a cytokine could be initiated, if justified based on biology. Measuring cytokine levels in clinical study can provide supportive evidence for the DDI potential. FDA scientists (Jing et al., 2020) reported that about a third of the FDA‐approved drugs have TP labels that contain PK‐related DDI information derived from at least one study method. More than half of these evaluations showed no interaction, and for the remaining, no dose adjustment was recommended (Jing et al., 2020). For an antibody–drug conjugate (ADC), the interaction with small molecule drugs potentially involves not only the antibody component but also the small molecule drug component. However, since the free small molecule component may not be at high enough concentrations to act as a perpetrator; only the victim interaction must be evaluated. FDA has recently released a draft guidance to determine the need for DDI studies for a therapeutic protein (USFDA 2020c).

KEYWORDS

Cytokines:Cytokines are a large group of proteins, peptides, or glycoproteins secreted by specific cells of immune system. They act as signaling molecules and mediate and regulate immunity, inflammation, and hematopoiesis.Mechanism‐based inhibition:MBI is a potential mechanism for TDI, where a more inhibitory metabolite relative to parent, causes the inactivation of a CYP by a protein or heme adduct formation.Reaction phenotyping:is the estimation of the relative contributions of specific enzymes to the metabolism of a test compoundTime‐dependent inhibition (TDI):TDI is a collective term that refers to the increase in the extent of inhibition of the substrate, when the inhibitor is incubated with the enzyme prior to the addition of the substrate in vitro or during the dosing period in vivo.

REFERENCES

      1 Aninat, C. et al. (2006). Expression of cytochromes P450, conjugating enzymes and nuclear receptors in human hepatoma HepaRG cells. Drug Metabolism and Disposition 34 (1): 75–83. https://doi.org/10.1124/dmd.105.006759.

      2 Atkinson, A., Kenny, J.R., and Grime, K. (2005). Automated assessment of time‐dependent inhibition of human cytochrome P450 enzymes using liquid chromatography‐tandem mass spectrometry analysis. Drug Metabolism and Disposition 33 (11): 1637–1647. https://doi.org/10.1124/dmd.105.005579.

      3 Ayrton, A. and Morgan, P. (2001). Role of transport proteins in drug absorption, distribution and excretion. Xenobiotica 31 (8–9): 469–497. https://doi.org/10.1080/00498250110060969.

      4 Backman, J.T. et al. (2005). Rifampin markedly decreases and gemfibrozil increases the plasma concentrations of atorvastatin and its metabolites. Clinical Pharmacology and Therapeutics 78 (2): 154–167. https://doi.org/10.1016/j.clpt.2005.04.007.

      5 Bendayan, R. et al. (1990). Effect