Novel Technologies Enable Mechanistic Understanding and Modeling of Drug Exposure and Response
Year: 2020
Type: article
Abstract: Historically, the development of sensitive and specific methodologies for detection and measurement of drugs and their metabolites in biological fluids ushered in the field of pharmacokinetics, which in turn led to a new understanding of the pharmacological and toxicological effects of drugs and their metabolites. The principles of pharmacokinetics are central to the research and practice of modern clinical pharmacology. Today, new technologies and methodologies continue to advance the field of clinical pharmacology, bringing fresh insights into the mechanisms that underlie pharmacokinetic principles. In this issue of Clinical Pharmacology and Therapeutics, four articles that rely heavily on contemporary technologies and methodologies further advance our understanding of the mechanisms involved in drug absorption, distribution, metabolism, and excretion (ADME) and enable mechanistic pharmacokinetic modeling (Figure 1). Targeted proteomic methods (liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS)) are used in two of the studies to understand the tissue distribution of drugs in healthy and diseased individuals and to identify the mechanistic underpinnings of human disease using targeted proteomic methods quantitatively report the protein levels of key membrane transporters in microvessels that form the blood brain barrier (BBB). The microvessels were extracted and purified from brain tissues harvested from healthy individuals and patients with glioblastoma as well as from rodents. The BBB forms a protective barrier for the brain in part because of expression of the efflux transporters, P-glycoprotein (P-gp), and breast cancer resistance protein (BCRP), which repel the entry of environmental toxins, drugs, and other xenobiotics into the central nervous system (CNS), where they may potentially cause neurotoxicity. Reduced expression levels of these transporters in the BBB have been implicated in both the etiology and progression of various neurodegenerative diseases. In their study, Bao et al.1 show that the levels of transporter protein expression in BBB isolated from brain samples from adults are high, but extremely variable, and are significantly reduced in BBB extracted from human glioblastoma tumors. In addition, they find significant differences in the expression levels of macronutrient transporters (e.g., the glucose transporter, GLUT1) in BBB from glioblastoma compared with BBB from healthy brain tissue. Such differences may contribute to differences in energy production in healthy brain compared with brain tumors and to the pathological sequalae associated with glioblastoma. In addition, Bao et al. describe enormous species differences in expression levels of BBB transporters, suggesting that typical rodent models of the human brain may not reliably predict BBB penetration of drugs. Collectively, this paper has important implications for drug delivery to the CNS, and in particular to glioblastoma, a primary cancer of the brain with few treatment options. Similarly, the study of Vildhede et al.2 presented in this issue uses targeted LC-MS/MS to quantitatively measure the protein levels of drug transporters in liver samples from healthy individuals and those with hepatic steatosis and nonalcoholic steatohepatitis (NASH). Again, significant disease-related changes in the protein expression levels of key drug transporters are noted in liver samples from patients with NASH, providing crucial information relevant to understanding and predicting hepatic drug levels, targeting, and metabolism in patients with this common disease. The results are consistent with those of Drozdzik et al.,3 who not only find that expression levels of transporters are significantly different in livers from patients with NASH but that livers from patients with other diseases that affect liver function also show changes in transporter expression. These kinds of data can be incorporated into physiologically-based pharmacokinetic (PBPK) models to predict disease-induced changes in drug levels and exposure in patients with NASH and other diseases that affect liver function. Indeed, Vildhede et al. perform PBPK modeling and predict NASH-associated changes in drug levels and exposure. Collectively, these studies significantly add to the power of PBPK modeling to mechanistically predict drug absorption and disposition in diverse individuals, including those of different races and ethnicities as well as those harboring various diseases. As reviewed in the article by Taskar et al.,4 PBPK modeling is now commonly used and accepted by regulatory authorities to predict pharmacokinetics and drug–drug interactions (DDIs). Importantly, PBPK models for drugs that are eliminated by drug-metabolizing enzymes and, in particular, cytochrome P450s have been extensively used to predict DDIs. In sharp contrast, PBPK models that incorporate information on transporters to predict transporter-mediated DDIs are much less common, and the development of such models represents an active research area in clinical pharmacology. In their excellent review, Taskar et al. begin with a brief overview of transporters that play an important role in drug absorption, distribution, and elimination and may work in concert with drug-metabolizing enzymes. This overview is supplemented with clearance principles that include drug interactions with both enzymes and transporters. From there, the review describes the use of PBPK modeling for substrates and inhibitors of transporters and provides a rich set of case studies depicting the use of PBPK models submitted to regulatory agencies to support predictions of transporter-mediated DDIs, nonlinear pharmacokinetics, ethnic differences in drug exposure, and kidney, hepatic, and intestinal DDIs. The examples alone represent a wealth of information for clinical pharmacologists to use in educational activities and to understand how PBPK modeling is changing the landscape of drug development sciences. The article ends with a section describing challenges and opportunities for the field of PBPK modeling of transporter-mediated DDIs, specifically highlighting current efforts focused on the discovery and validation of metabolic biomarkers for transporter-mediated DDIs. In fact, the discovery and validation of clinical biomarkers for DDIs mediated by both drug transporters and metabolizing enzymes represents an active field of investigation in clinical pharmacology, which has been greatly advanced by metabolomic methods. The paper of H. Zhang et al.5 presents elegant research on the discovery and validation of a urinary biomarker, testosterone glucuronide (TG) normalized to androsterone glucuronide (AG), to assess the activity of UDP-glucuronosyltransferase 2B17 (UGT2B17), a highly variable drug-metabolizing enzyme involved in glucuronide conjugation of endogenous and synthetic steroids. Using targeted metabolomics with LC/MS/MS in pediatric patients, the investigators show that TG/AG ratio appropriately associates with age-related and sex-related changes in UGT2B17 as well as copy number variation, supporting the use of this biomarker for predicting the activity of UGT2B17 during drug development and treatment. For DDIs mediated by transporters, drug development scientists rely extensively on in vitro testing with cells or membrane vesicles overexpressing certain drug transporters. Such systems are critical in identifying drugs that are substrates, inhibitors, and inducers of transporters and have been used to quantitatively predict drug absorption, distribution, and elimination as well as transporter-mediated DDIs. However, major gaps exist that make pharmacokinetic predictions from limited in vitro data unreliable. These include a poor understanding of how human disease and other factors modulate the expression levels of transporters and enzymes. Even when the information on expression levels is available, methods and tools need to be established that promote its incorporation into computational mechanistic models that predict not only systemic drug and metabolite levels but also their levels in relevant tissues and cells. This issue of Clinical Pharmacology and Therapeutics presents four highly relevant articles that advance our understanding of the effect of disease on expression levels of transporters involved in drug absorption, disposition, and response and our ability to incorporate this information reliably into mechanism-based predictive models.
DOI: 10.1002/cpt.1822
Author: Kathleen M. Giacomini
Institution: University of California, San Francisco
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W3016778748