Evaluation of drug-drug interactions in drug metabolism: Differences and harmonization in guidance/guidelines

Disease progression modeling with temporal realignment: An emerging approach to deepen knowledge on chronic diseases

The recent development of the first disease-modifying drug for Alzheimer's disease represents a major advancement in dementia treatment. Behind this breakthrough is a quarter century of research efforts to understand the disease not by a particular symptom at a given moment, but by long-term sequential changes in multiple biomarkers. Disease progression modeling with temporal realignment (DPM-TR) is an emerging computational approach proposed with this biomarker-based disease concept. By integrating short-term clinical observations of multiple disease biomarkers in a data-driven manner, DPM-TR provides a way to understand the progression of chronic diseases over decades and predict individual disease stages more accurately. DPM-TR has been developed primarily in the area of neurodegenerative diseases but has recently been extended to non-neurodegenerative diseases, including chronic obstructive pulmonary, autoimmune, and ophthalmologic diseases. This review focuses on opportunities for DPM-TR in clinical practice and drug development and discusses its current status and challenges.

Mechanistic Study of Icaritin-Induced Inactivation of Cytochrome P450 2C9

Icaritin (ICT) is a prenylflavonoid derivative that has been approved by National Medical Products Administration for the treatment of hepatocellular carcinoma. This study aims to evaluate the potential inhibitory effect of ICT against cytochrome P450 (CYP) enzymes and to elucidate the inactivation mechanisms. Results showed that ICT inactivated CYP2C9 in a time-, concentration-, and NADPH-dependent manner with Ki = 1.896 μM, Kinact = 0.02298 minutes-1, and Kinact/Ki = 12 minutes-1 mM-1, whereas the activities of other CYP isozymes was minimally affected. Additionally, the presence of CYP2C9 competitive inhibitor, sulfaphenazole, superoxide dismutase/catalase system, and GSH all protected CYP2C9 from ICT-induced activity loss. Moreover, the activity loss was neither recovered by washing the ICT-CYP2C9 preincubation mixture nor the addition of potassium ferricyanide. These results, collectively, implied the underlying inactivation mechanism involved the covalent binding of ICT to the apoprotein and/or the prosthetic heme of CYP2C9. Furthermore, an ICT-quinone methide (QM)-derived GSH adduct was identified, and human glutathione S-transferases (GST) isozymes GSTA1-1, GSTM1-1, and GSTP1-1 were shown to be substantially involved in the detoxification of ICT-QM. Interestingly, our systematic molecular modeling work predicted that ICT-QM was covalently bound to C216, a cysteine residue located in the F-G loop downstream of substrate recognition site (SRS) 2 in CYP2C9. The sequential molecular dynamics simulation confirmed the binding to C216 induced a conformational change in the active catalytic center of CYP2C9. Lastly, the potential risks of clinical drug-drug interactions triggered by ICT as a perpetrator were extrapolated. In summary, this work confirmed that ICT was an inactivator of CYP2C9. SIGNIFICANCE STATEMENT: This study is the first to report the time-dependent inhibition of CYP2C9 by icaritin (ICT) and the intrinsic molecular mechanism behind it. Experimental data indicated that the inactivation was via irreversible covalent binding of ICT-quinone methide to CYP2C9, while molecular modeling analysis provided additional evidence by predicting C216 as the key binding site which influenced the structural confirmation of CYP2C9's catalytic center. These findings suggest the potential of drug-drug interactions when ICT is co-administered with CYP2C9 substrates clinically.

Change of metformin concentrations in the liver as a pharmacological target site of metformin after long-term combined treatment with ginseng berry extract

Metformin as an oral glucose-lowering drug is used to treat type 2 diabetic mellitus. Considering the relatively high incidence of cardiovascular complications and other metabolic diseases in diabetic mellitus patients, a combination of metformin plus herbal supplements is a preferrable way to improve the therapeutic outcomes of metformin. Ginseng berry, the fruit of Panax ginseng Meyer, has investigated as a candidate in metformin combination mainly due to its anti-hyperglycemic, anti-hyperlipidemic, anti-obesity, anti-hepatic steatosis and anti-inflammatory effects. Moreover, the pharmacokinetic interaction of metformin via OCTs and MATEs leads to changes in the efficacy and/or toxicity of metformin. Thus, we assessed how ginseng berry extract (GB) affects metformin pharmacokinetics in mice, specially focusing on the effect of the treatment period (i.e., 1-day and 28-day) of GB on metformin pharmacokinetics. In 1-day and 28-day co-treatment of metformin and GB, GB did not affect renal excretion as a main elimination route of metformin and GB therefore did not change the systemic exposure of metformin. Interestingly, 28-day co-treatment of GB increased metformin concentration in the livers (i.e., 37.3, 59.3% and 60.9% increases versus 1-day metformin, 1-day metformin plus GB and 28-day metformin groups, respectively). This was probably due to the increased metformin uptake via OCT1 and decreased metformin biliary excretion via MATE1 in the livers. These results suggest that co-treatment of GB for 28 days (i.e., long-term combined treatment of GB) enhanced metformin concentration in the liver as a pharmacological target tissue of metformin. However, GB showed a negligible impact on the systemic exposure of metformin in relation to its toxicity (i.e., renal and plasma concentrations of metformin).

The Effect of Oral Letermovir Administration on the Pharmacokinetics of a Single Oral Dose of P-Glycoprotein Substrate Digoxin in Healthy Volunteers

Letermovir is a human cytomegalovirus (CMV) terminase inhibitor approved in the United States, Canada, Japan, and the European Union for prophylaxis of CMV infection and disease in CMV-seropositive, allogeneic, hematopoietic stem-cell transplant recipients. In vitro, letermovir is a substrate and potential modulator of P-glycoprotein. The potential of letermovir to alter the pharmacokinetics of digoxin (a P-glycoprotein substrate) upon coadministration in healthy subjects was therefore investigated in a phase 1 trial (EudraCT: 2011-004516-39). Oral letermovir 240 mg was administered twice daily for 12 days with a single oral digoxin 0.5-mg dose on day 7; after a washout period, oral digoxin 0.5 mg was administered on day 35 (sequence 1). The period order was reversed after a 28-day washout for sequence 2. Pharmacokinetics and safety were evaluated. The presence of steady-state letermovir reduced digoxin area under the plasma concentration-time curve from administration until last quantifiable measurement by 12% and maximum plasma concentration by 22% compared with digoxin alone; digoxin half-life and elimination rate remained similar in both conditions. The between-subject variability of digoxin maximum plasma concentration was higher with letermovir than without (42% vs 31%) and similar for digoxin area under the plasma concentration-time curve in both periods. No specific safety or tolerability concerns were identified. Overall, letermovir had no clinically relevant effect on concomitant administration with digoxin.

Interaction of Phytocompounds of Echinacea purpurea with ABCB1 and ABCG2 Efflux Transporters

Preparations of Echinacea purpurea (E. purpurea) are widely used for the management of upper respiratory infections, influenza, and common cold, often in combination with other conventional drugs. However, the potential of phytochemical constituents of E. purpurea to cause herb-drug interactions via ABCB1 and ABCG2 efflux transporters remains elusive. The purpose of this study was to investigate the impact of E. purpurea-derived caffeic acid derivatives (cichoric acid and echinacoside) and tetraenes on the mRNA and protein expression levels as well as on transport activity of ABCB1 and ABCG2 in intestinal (Caco-2) and liver (HepG2) cell line models. The safety of these compounds was investigated by estimating EC₂₀ values of cell viability assays in both cell lines. Regulation of ABCB1 and ABCG2 protein in these cell lines were analyzed after 24 h exposure to the compounds at 1, 10, and 50 μg/mL. Bidirectional transport of 0.5 μg/mL Hoechst 33342 and 5 μM rhodamine across Caco-2 monolayer and profiling for intracellular concentrations of the fluorophores in both cell lines were conducted to ascertain inhibition effects of the compounds. Cichoric acid showed no cytotoxic effect, while the EC₂₀ values of tetraenes and echinacoside were 45.0 ± 3.0 and 52.0 ± 4.0 μg/mL in Caco-2 cells and 28.0 ± 4.3 and 62.0 ± 9.9 μg/mL in HepG2 cells, respectively. In general, the compounds showed heterogeneous induction of ABCB1 with the strongest 3.6 ± 1.2-fold increase observed for 10 μg/mL tetraenes in Caco-2 cells (p < 0.001). However, the compounds did not induce ABCG2. None of the phytocompounds inhibited significantly net flux of the fluorophores across Caco-2 monolayers. Overall, tetraenes moderately induced ABCB1 but not ABCG2 in Caco-2 and HepG2 cells while no compound significantly inhibited activity of these transporters at clinically relevant concentration to cause herb-drug interactions.

Evaluation of Methods to Assess CYP3A Induction Risk in Clinical Practice Using in Vitro Induction Parameters

Established guidelines have recommended a number of methods based on in vitro data to assess the CYP3A induction risk of new chemical entities in clinical practice. In this study, we evaluated the predictability of various assessment methods. We collected in vitro parameters from a variety of literature that includes data on 19 batches of hepatocytes. Clinical CYP3A induction was predicted using 3 direct approaches-the fold-change, basic model, and mechanistic static models-as well as 5 correlation approaches, including the relative induction score (RIS) and the relative factor (RF) method. These predictions were then compared with data from 30 clinical inductions. Collected in vitro parameters varied greatly between hepatocyte batches. Direct assessment methods using fixed cut-off values provided a lot of false predictions due to hepatocyte variability, which can overlook induction risk or lead to needless clinical drug-drug interaction (DDI) studies. On the other hand, correlation methods with the cut-off values set for each batch of hepatocytes accurately predicted the induction risk. Among these, the AUC_u/inducer concentrations for half the maximum induction (EC₅₀) and the RF methods which use the area under the curve (AUC) of the unbound inducers for calculating induction potential showed an especially good correlation with clinical induction. Correlation methods were better at predicting clinical induction risk than the other methods, regardless of hepatocyte variability. The AUC_u/EC₅₀ and the RF methods in particular had a small number of false predictions, and can therefore be used to assess induction risk along with the other correlation methods recommended in guidelines.

INTEDE: interactome of drug-metabolizing enzymes

Drug-metabolizing enzymes (DMEs) are critical determinant of drug safety and efficacy, and the interactome of DMEs has attracted extensive attention. There are 3 major interaction types in an interactome: microbiome-DME interaction (MICBIO), xenobiotics-DME interaction (XEOTIC) and host protein-DME interaction (HOSPPI). The interaction data of each type are essential for drug metabolism, and the collective consideration of multiple types has implication for the future practice of precision medicine. However, no database was designed to systematically provide the data of all types of DME interactions. Here, a database of the Interactome of Drug-Metabolizing Enzymes (INTEDE) was therefore constructed to offer these interaction data. First, 1047 unique DMEs (448 host and 599 microbial) were confirmed, for the first time, using their metabolizing drugs. Second, for these newly confirmed DMEs, all types of their interactions (3359 MICBIOs between 225 microbial species and 185 DMEs; 47 778 XEOTICs between 4150 xenobiotics and 501 DMEs; 7849 HOSPPIs between 565 human proteins and 566 DMEs) were comprehensively collected and then provided, which enabled the crosstalk analysis among multiple types. Because of the huge amount of accumulated data, the INTEDE made it possible to generalize key features for revealing disease etiology and optimizing clinical treatment. INTEDE is freely accessible at: https://idrblab.org/intede/.