Category: 3. Business

Introduction

Lorlatinib is a potent third-generation anaplastic lymphoma kinase (ALK) and c-ros oncogene 1 (ROS1) tyrosine kinase inhibitor (TKI) approved for the treatment of ALK-positive non-small cell lung cancer (NSCLC).^1,2 It is especially effective against central nervous system (CNS) metastases, addressing a key limitation of earlier ALK inhibitors.¹ Given to its significant clinical efficacy, lorlatinib is widely used in oncology practice.³ However, cancer patients are frequently exposed to polypharmacy, and concomitant medications may precipitate drug–drug interactions (DDIs) that alter lorlatinib’s therapeutic efficacy and safety profile.^4–6 Therefore, a detailed investigation of its DDIs is essential to better understand and manage its clinical pharmacology.

DDIs can substantially alter systemic exposure to anticancer agents, potentially resulting in subtherapeutic efficacy or increased toxicity.⁷ Clinical and pharmacokinetic studies of several tyrosine kinase inhibitors have shown that coadministration with strong CYP3A inhibitors such as ketoconazole and itraconazole, or with potent inducers such as rifampicin, can markedly alter systemic exposure and, in some cases, increase the risk of adverse events or necessitate dose adjustment.^8,9 For instance, imatinib exposure increased by approximately 40% when combined with ketoconazole,¹⁰ brigatinib exposure nearly doubled with itraconazole,¹¹ and clinical reports have described itraconazole-related toxicity in patients receiving osimertinib.¹² As with other TKIs metabolized primarily by CYP3A, lorlatinib is prone to significant pharmacokinetic alterations when co-administered with CYP3A modulators.

For lorlatinib, co-administration with strong CYP3A4 inhibitors, such as itraconazole, has been shown to increase plasma AUC by approximately 42% and C_max by about 24%.¹³ Conversely, potent CYP3A4 inducers, including rifampicin, dramatically reduce lorlatinib exposure to approximately 15% of control levels.¹⁴ These pharmacokinetic alterations translate into clinically relevant consequences, as co-administration of lorlatinib with ritonavir has been associated with rapid elevations in liver function tests (LFTs) within 24–72 hours,^15,16 whereas co-administration with rifampicin has led to severe but self-limiting increases in transaminase levels in healthy volunteers.¹⁴ Such findings emphasize that CYP3A-mediated DDIs not only modulate lorlatinib plasma concentrations but may also precipitate adverse reactions, highlighting the importance of investigating its metabolic characteristics both in vitro and in vivo.

Current evidence indicates that lorlatinib undergoes extensive biotransformation in vivo, primarily mediated by cytochrome P450 (CYP) 3A4 for oxidative Phase I metabolism and uridine 5′-diphospho-glucuronosyltransferase (UGT) 1A4 for N-glucuronidation in Phase II metabolism.^17,18 While minor contributions from CYP2C8, CYP2C19, CYP3A5, and UGT1A3 have not been detected, the relative contributions of these enzymes remain to be clarified.^3,19 This gap hampers a comprehensive understanding of its DDI liability.

To address this gap, in vitro liver microsomal systems are often employed.²⁰ Liver microsomes are extensively utilized in metabolic stability assays because they are easy to handle and contain a comprehensive array of drug-metabolizing enzymes. These enzymes encompass major CYP450 isoforms, flavin-containing monooxygenases, carboxylesterases, epoxide hydrolases, as well as UGTs.^21,22 This composition enables liver microsomes to closely mimic in vivo metabolic processes.^23,24 When combined with selective enzyme inhibitors, they can be used to identify the specific enzymes responsible for the major metabolic pathways of a compound, thereby aiding in the prediction of potential DDIs in humans.⁷ Similar integrated strategies have been successfully applied to other anticancer agents, such as the TKIs canertinib²⁵ and amdizalisib, a PI3Kδ inhibitor,²⁶ facilitating dose optimization and informed management of potential DDIs. It is suggested to provide additional justification for studying the metabolism of lorlatinib, since it is the most recent ALK inhibitor approved by the FDA for NSCLC. Furthermore, the study by Zapata Dongo R. et al proposes that lorlatinib could overcome up to 53 amino acid mutations in ALK and might even be repurposed for other neoplasms.²⁷ This context increases the relevance of the present work, as it could provide solid foundations to explain the efficacy of lorlatinib in NSCLC. Therefore, a systematic investigation into lorlatinib’s in vitro and in vivo metabolism is warranted, not merely to advance pharmacokinetic insights but also to establish the rationale for its safe and effective use in patients.

Accordingly, in the present study, we combined in vitro liver microsomal assays with in vivo pharmacokinetic experiments in rats to clarify the metabolic disposition of lorlatinib and to better predict its interaction potential in clinical settings. For the first time, we investigated interspecies variation in the in vitro metabolic stability of lorlatinib using human liver microsomes (HLM) and rat liver microsomes (RLM) to provide a comprehensive characterization of its disposition. We used an ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC–MS/MS) method to determine the concentrations of lorlatinib and its metabolite M2a. Building on this foundational in vitro analysis, our investigation extended to in vivo studies. We determined its pharmacokinetic profile after a single oral dose in rats, evaluated its drug-drug interaction potential with potent CYP3A4 inhibitors (itraconazole, voriconazole) and inducers (rifampicin, carbamazepine), and quantified its tissue distribution with an emphasis on brain penetration and major organ biodistribution. Collectively, these objectives sought to establish a foundational understanding of lorlatinib’s metabolic fate, systemic exposure, interaction risks, and tissue accessibility.

Materials and Methods

Drugs and Reagents

Lorlatinib (purity 98%, CAS No. 1454846–35-5) was supplied by Aladdin Industrial Corporation (Shanghai, China). Voriconazole (≥98%, CAS No. 137234–62-9), itraconazole (≥98%, CAS No. 84625–61-6), rifampicin (97%, CAS No. 13292–46-1), and carbamazepine (>99%, CAS No. 298–46-4) were also purchased from Aladdin. Gefitinib (≥99%, CAS No. 184475–35-2), obtained from the same vendor, served as the internal standard (IS). Methanol and acetonitrile of analytical grade were obtained from Aladdin, while ultrapure water was produced in-house using a Pall purification system. All other reagents were of analytical grade.

Experimental Animals

Male Sprague–Dawley (SD) rats (200 ± 20 g, 6–8 weeks old) were provided by the Experimental Animal Center of Southern Medical University (Guangzhou, China; license No. SCXK 2021–0041). Animals were housed under controlled laboratory conditions (25 ± 2 °C, 50 ± 5% relative humidity, 12 h light/dark cycle) and acclimated for one week before the start of the study. Rats were fasted for 12 h prior to drug administration. All animal procedures were conducted in compliance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals. Ethical approval was granted by the Ethics Committee of Southern Medical University (Approval No. SMUL202410030), and all protocols were reviewed and approved by the Institutional Animal Care and Use Committee of Southern Medical University.

Chromatography and Conditions

UHPLC–MS/MS analysis was carried out on an Agilent 1290 Infinity II LC system coupled with an Agilent 6495 triple quadrupole mass spectrometer. Separation was achieved using an Eclipse Plus C18 column (2.1 × 50 mm, 1.8 μm; Agilent) maintained at 30 °C. The mobile phase consisted of acetonitrile (solvent A) and 0.1% formic acid in water (solvent B), delivered at a flow rate of 0.3 mL/min. The gradient program was as follows: 0–0.5 min, 10% A; 0.5–3.0 min, 10–80% A; 3.0–4.0 min, 80% A; 4.0–4.1 min, 80–10% A; and 4.1–5.0 min, 10% A, with a total runtime of 5 min. Detection was performed on an Agilent Jet Stream electrospray ionization (AJS-ESI) source in positive ion mode. The monitored ion transitions were lorlatinib m/z 407 → 228 (fragmentor 166 V, collision energy 30 eV), IS m/z 447 → 128 (fragmentor 166 V, collision energy 20 eV), and M2a m/z 393 → 180 (fragmentor 166 V, collision energy 20 eV). Source conditions were set as follows: gas temperature, 200 °C; nebulizer pressure, 20 psi; gas flow, 14 L/min; capillary voltage, 3000 V; and corona current, 0.13 μA. Data collection and processing were conducted using MassHunter software (version B.10.1.67, Agilent). The UHPLC-MS/MS method validation details, including accuracy, precision, and linearity, are provided in the Supplementary Tables 1–4. The ultra-performance liquid chromatography–quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) system from Waters was used to analyze the metabolite M2a, and representative spectra and chromatograms are included in the Supplementary Figures 1 and 2.

In vitro Experiments

Enzyme Kinetics

Human liver microsomes (HLM) and rat liver microsomes (RLM) were supplied by IPHASE BIOSCIENCES Co., Ltd. (Suzhou, China). Incubations were carried out in a final volume of 190 μL containing 100 mM PBS (pH 7.4), lorlatinib at various concentrations, and microsomal protein (0.05 mg/mL). For RLM, lorlatinib concentrations ranged from 0.001 to 4 mM (0.001, 0.01, 0.05, 0.25, 1, 1.5, 2, 2.5, 3, and 4 mM), while for HLM, the range was 0.001–4 mM (0.001, 0.01, 0.05, 0.10, 0.25, 1, 1.5, 2, 2.5, and 4 mM). After a 5-min equilibration at 37 °C, reactions were initiated by the addition of 12 μL of NADPH regenerating system (10 μL Solution A + 2 μL Solution B). Mixtures were incubated for 60 min and then terminated by adding 600 μL of acetonitrile containing the internal standard (IS, 50 ng/mL). Samples were vortexed briefly, centrifuged at 13,000 rpm for 30 min at 4 °C, and 100 μL of the supernatant was analyzed by UHPLC–MS/MS for lorlatinib metabolite quantification and determination of Michaelis–Menten kinetics (K_m). All assays were conducted using the IPHASE I Metabolic Stability Research Kit (IPHASE BIOSCIENCES, Suzhou, China) following the manufacturer’s protocol.

Enzyme Phenotyping

CYP450 phenotyping of lorlatinib was conducted in pooled human (HLM) and rat liver microsomes (RLM) using the IPHASE CYP450 Metabolic Phenotype Research Kit (Chemical Inhibition Method, V1.5; IPHASE Biosciences, Suzhou, China). Each incubation (200 μL total volume) contained 100 mM PBS (pH 7.4), microsomal protein (0.5 mg/mL), an NADPH-regenerating system (kit solutions A and B), lorlatinib as substrate, and a selective CYP isoform inhibitor. Substrate concentrations were 10 μM for HLM and 1 μM for RLM, approximating their respective K_m values. Lorlatinib was prepared from a 10 mM DMSO stock, with the final solvent content maintained below 1% (v/v).

The following inhibitors were used at isoform-selective concentrations: α-naphthoflavone (200 μM, CYP1A2), sertraline (25 μM, CYP2B6), montelukast (500 μM, CYP2C8), sulfaphenazole (1 μM, CYP2C9), nootkatone (25 μM, CYP2C19), quinidine (10 μM, CYP2D6), and ketoconazole (1 μM, CYP3A4). Reaction mixtures were pre-equilibrated at 37 °C for 5 min (without microsomes), after which HLM or RLM were added to initiate the reaction. Incubations were carried out at 37 °C for isoform-specific times (CYP1A2, 30 min; CYP2B6, 30 min; CYP2C8, 5 min; CYP2C9, 20 min; CYP2C19, 30 min; CYP2D6, 10 min; CYP3A4, 10 min). Reactions were terminated with three volumes of ice-cold acetonitrile containing internal standard, and protein was removed by centrifugation. The relative contributions of individual CYP isoforms were assessed using the substrate depletion approach, whereby the rate of lorlatinib disappearance in the presence of selective CYP inhibitors was measured to estimate enzymatic activity, following the kit protocol.

In vivo Pharmacokinetic Experiments

Serum Sample Preparation

A 100 μL aliquot of plasma was combined with 300 μL of an acetonitrile solution containing 50 ng/mL internal standard (IS) and vortex-mixed for 3 minutes. Lorlatinib was extracted by centrifuging the mixture at 13,000 rpm for 20 minutes at 4 °C. Subsequently, 320 μL of the upper organic layer was transferred to a 1.5-mL microcentrifuge tube and evaporated to dryness under a gentle stream of nitrogen at –70 °C. The dried residue was reconstituted in 100 μL of a 50:50 (v/v) acetonitrile/water solution followed by vortex mixing for 3 minutes. After a second centrifugation step (13,000 rpm, 20 minutes, 4 °C), 70 μL of the supernatant was collected into a vial, and 1 μL was injected into the UHPLC–MS/MS system for analysis.

Tissue Sample Pretreatment

For tissue samples, approximately 50 mg of tissue was weighed and homogenized in 0.45 mL of ice-cold physiological saline (0.9%). The homogenate was centrifuged at 13,000 rpm for 20 minutes at 4 °C to obtain the supernatant. A 100 μL aliquot of the supernatant was then treated with 500 μL of acetonitrile containing 50 ng/mL IS, vortex-mixed, and centrifuged. From the resulting mixture, 520 μL of the organic phase was transferred to a 1.5-mL microcentrifuge tube and evaporated to dryness at –70 °C. In the pharmacokinetic study, this homogenization and extraction procedure was consistently repeated across all tissue samples.

PK Study

Forty-eight male Sprague-Dawley (SD) rats were randomly divided into nine groups (n = 6 per group) and received the following treatments: control group, single dose of lorlatinib (10 mg/kg); voriconazole group, single dose of voriconazole (20 mg/kg) administered 30 min before the experiment; itraconazole group, single dose of itraconazole (20 mg/kg) administered 30 min before the experiment; rifampicin group, daily dose of rifampicin (50 mg/kg) administered over 7 days; carbamazepine group, daily dose of carbamazepine (50 mg/kg) administered over 7 days; low-dose group, single dose of lorlatinib (5 mg/kg); and high-dose group, single dose of lorlatinib (50 mg/kg). All drug solutions were made up in 0.5% carboxymethylcellulose sodium. The voriconazole and itraconazole groups received 10 mg/kg lorlatinib orally 30 min after oral administration of voriconazole and itraconazole. Following 7 consecutive days of daily oral gavage with 50 mg/kg rifampicin or carbamazepine, animals in the rifampicin and carbamazepine groups were administered a single dose of lorlatinib (10 mg/kg) orally on the 8th day. The dosing regimens for CYP inhibitors were selected according to established rodent pharmacokinetic studies,^28–30 and the 7-day pretreatment for rifampicin and carbamazepine was adopted to achieve stable enzyme induction as supported by previous reports.^31,32

To determine absolute bioavailability, six SD rats were weighed and received a single dose of lorlatinib (1 mg/kg) via caudal intravenous injection. Thereafter, a 100-μL blood sample was harvested in 1.5-mL heparinized tubes through the retroorbital plexus at 0.083, 0.167, 0.25, 0.333, 0.5, 1, 2, 4, 6, 8, 12, and 24 h. Plasma was collected by centrifuging at 6,000 rpm at 4 °C for 10 min before transferring to another 1.5-mL Eppendorf microcentrifuge tube. Samples were stored at −80 °C before further analysis.

Tissue Distribution Study

Forty-eight male Sprague-Dawley rats were randomly assigned to eight experimental groups (n = 6 per group) and their body weights were recorded. Each animal was administered a single oral dose of lorlatinib (10 mg/kg). At predetermined time points (0.0833, 0.25, 0.5, 1, 2, 4, 6, and 24 h) after administration, the rats were euthanized by cervical dislocation. The heart, liver, spleen, lungs, kidneys, stomach, cecum, colon, and brain were promptly excised. Each tissue was perfused with ice-cold physiological saline (0.9%) to remove residual blood, gently blotted dry on filter paper, and subsequently stored at −80 °C pending further analysis.

Molecular Docking Analysis

The three-dimensional structure of CYP3A4 (PDB ID: 00009bv5) was obtained from the Protein Data Bank (https://www.rcsb.org/). The structures of lorlatinib (PubChem CID: 71731823), voriconazole (PubChem CID: 71616), and itraconazole (PubChem CID: 55283) were retrieved from PubChem (https://pubchem.ncbi.nlm.nih.gov/). Protein preprocessing was performed in PyMOL by removing chains B and C, the co-crystallized ligand, and all water molecules. Hydrogen atoms were subsequently added using AutoDock Tools 1.5.6. Semi-flexible molecular docking was carried out with AutoDock Vina, generating up to 20 docking conformations for each ligand. Docking poses were clustered with a root mean square deviation (RMSD) threshold of 0–2 Å, and the representative pose with the lowest binding energy from the most populated cluster was selected. The final binding conformations were visualized and analyzed using PyMOL.

Statistical Analysis

The mean plasma concentration–time profile was generated using Prism 10.0 (GraphPad Software Inc., San Diego, USA). Pharmacokinetic (PK) parameters for lorlatinib were derived by non-compartmental analysis (NCA) implemented in Drug and Statistics Software (DAS, version 2.0). All statistical comparisons were conducted with SPSS 19.0 using one-way ANOVA followed by Dunnett’s post hoc test. A p-value less than 0.05 was considered statistically significant.

The extent of lorlatinib penetration into target tissues was assessed by calculating the total tissue-to-plasma partition coefficient (Kp), defined as the ratio of the area under the concentration–time curve (AUC) in tissue to that in plasma, using the following equation:

The absolute bioavailability (F) of lorlatinib in rats was derived using the formula:

where AUC _oral and AUC _iv represent the area under the concentration-time curve from 0 to infinity after oral and intravenous administration of lorlatinib, respectively. Similarly, Dose _oral and Dose _iv represent the oral and intravenous administration doses, respectively.

Results

In vitro Metabolism

Metabolite Identification

An UHPLC-MS/MS method for the quantification of lorlatinib in plasma and tissue homogenates was developed and validated. The method specificity was enhanced through optimization of mass spectrometric conditions in Supplementary Tables 1–4. Acetonitrile was selected as the protein precipitation solvent, which resulted in minimal endogenous interference and efficient extraction of the analyte. The total analytical run time was approximately 4 min, with retention times of approximately 2.283, 2.122, and 2.051 min for lorlatinib, metabolite M2a, and the IS, respectively.

Kinetic Analysis of Lorlatinib

The incubation of lorlatinib with RLM and HLM for preliminary kinetic analysis produced an atypical metabolite formation curve for M2a. The observed trend was inconsistent with classical Michaelis-Menten kinetics but aligned with a model of substrate inhibition, where a drug, at elevated concentrations, impedes its own metabolism by binding to a non-catalytic inhibitory site on the enzyme³³ (Figure 1a and b).

Figure 1 Kinetics analysis of lorlatinib demethylated (M2a) in rat liver microsomes (RLM) (a) and human liver microsomes (HLM) (b). The microsomal enzyme incubation assay was performed as indicated in the methods section. For RLM and HLM, Km and Vmax for lorlatinib demethylated (M2a) were calculated using GraphPad Prism 10.0 software using the following equation to describe substrate inhibition. Date points represent mean ± SD of three experiments performed in triplicate.

The enzyme kinetic parameters, K_m, V_max, C_lint, and K_i values, are shown in Table 1. The metabolism of lorlatinib in RLM showed a low K_m value (0.035±0.013 mM) and a Vmax of 2389.40±1595.00 pmol/mg/min. Although the maximal velocity was moderate, the markedly reduced K_m resulted in a high intrinsic clearance (CL_int = 68.27 μL/min/mg protein), reflecting strong catalytic efficiency. Substrate inhibition was observed with a K_i of 0.339 mM, suggesting that excessive substrate concentrations could suppress the metabolic rate in rats.In the meantime,the metabolism of lorlatinib in HLM showed a higher V_max (8704 ± 2839.00 pmol/mg/min) but was accompanied by a much higher K_m (2.552 ± 0.509 mM), leading to a considerably lower CL_int (3.41 μL/min/mg protein) (Table 1). Substrate inhibition was also detected, with a K_i of 0.638 mM. Compared with RLM, the higher K_m in HLM indicates reduced substrate affinity, which may translate into slower metabolic turnover and greater in vivo accumulation of the parent drug. Such interspecies differences suggest that human metabolic capacity for lorlatinib is relatively limited, which may influence systemic exposure and tolerability in patients. These findings underscore the importance of considering species-specific metabolic profiles when extrapolating preclinical data to clinical settings.

Table 1 Kinetic Parameters of Lorlatinib Metabolism in Rat Liver Microsomes (RLM) and Human Liver Microsomes (HLM)

Enzyme Phenotyping

To further investigate the interspecies differences in CYP-mediated metabolism between RLM and HLM, we assessed the inhibitory effects of selective chemical inhibitors. As shown in Figure 2, the major metabolic pathways indeed differed between RLM and HLM. CYP3A exhibited the highest inhibition rates in both systems (62.5% in RLM and 70.8% in HLM), indicating that it is the primary enzyme involved in lorlatinib metabolism. In contrast, the contributions of other CYP isoforms varied substantially between the two species. In our study, CYP2C19 displayed a moderate contribution in RLM, whereas its role was negligible in HLM. These findings suggest that the observed differences are mainly attributable to interspecies variation in CYP expression profiles and kinetic parameters.

Figure 2 Comparative inhibition of lorlatinib metabolism in rat and human liver microsomes. (a) Relative inhibition rates of lorlatinib metabolism in rat liver microsomes (RLM) by selective CYP inhibitors. (b) Relative inhibition rates of lorlatinib metabolism in human liver microsomes (HLM) by selective CYP inhibitors. (c) Comparison of overall inhibition patterns between RLM and HLM analyzed by one-way ANOVA. Data are presented as mean ± SD (n = 3). p < 0.05 indicates significant difference between groups.

In vivo Metabolism

Effects of CYP3A4 Inhibitors on the PK of Lorlatinib

The mean plasma concentration-time profiles of lorlatinib following co-administration with CYP3A4 inhibitors are presented in Figure 3, and the corresponding pharmacokinetic parameters were summarized and compared in the Table 2 and Supplementary Figure 3. Pretreatment with either voriconazole or itraconazole resulted in a significant increase in the systemic exposure of lorlatinib. The mean ± standard deviation (SD) of the mean C_max concentrations were 565.74 ± 171.36 ng/mL and 508.16 ± 124.05 ng/mL in the voriconazole and itraconazole groups, respectively, compared with 468.02 ± 165.79 ng/mL in the control group, representing increases of 20.88% and 8.58%, respectively. Notably, voriconazole led to a significant increase of 120.39% in the AUC_(0–24h), with values rising from 3,162.09 ± 1,491.41 ng/mL in the control group to 6,969.08 ± 2,844.69 ng/mL (P = 0.009; 95% CI 1012.472 to 6601.514). Itraconazole caused a 15% increase in the AUC_(0–24h) to 3,636.34 ± 1,223.35 ng h/mL (P = 0.693; 95% CI −2066.2 to 3014.7). These results indicate that voriconazole has a more potent inhibitory effect on lorlatinib metabolism than itraconazole. Furthermore, lorlatinib clearance in the voriconazole and itraconazole groups was 0.002 ± 0.001 L/h and 0.003 ± 0.001 L/h, respectively, with a notable reduction of approximately 50% and 25% from that in the control groups (0.004 ± 0.003 L/h). The apparent volume of distribution (V_z/F) values for lorlatinib in the voriconazole and itraconazole groups was respectively 70.27% and 48.65% lower than that in the control group.

Table 2 The Main Pharmacokinetic Parameters of Lorlatinib After Co-Administration with Voriconazole and Itraconazole (20 mg/kg, n = 6)

Figure 3 The mean plasma concentration time profile of lorlatinib after oral administration of voriconazole and itraconazole (20 mg/kg, n = 6). The detailed plasma concentration-time profiles of lorlatinib during the initial 2 h period following administration was also showed. Error bars represent ± SD for concentrations.

In addition, the value of mean residence time from time zero to infinity (MRT_(0–∞)) decreased from 11.72 ± 5.56 h in the control group to 8.11 ± 0.53 h (P = 0.14, 95% CI −1.3 to 8.5) and 6.79 ± 1.37 h (P = 0.045, 95% CI 0.1 to 9.8) in the voriconazole and itraconazole groups, respectively. These results suggest that voriconazole and itraconazole inhibit the metabolism of lorlatinib, enhancing its bioavailability and systemic exposure by suppressing CYP3A4 activity.

Effects of CYP3A4 Inducers on the PK of Lorlatinib

Following the administration of lorlatinib with or without the CYP3A4 inducers rifampicin and carbamazepine, the mean plasma concentration-time profiles were assessed (Figure 4), and the pharmacokinetic parameters were compared and analyzed (Table 3 and Supplementary Figure 4). Administration of 50 mg/kg rifampicin daily over 7 days prior to a single 10 mg/kg dose of lorlatinib led to significant reductions in lorlatinib exposure levels. The C_max, AUC_(0–24h), and AUC_(0-∞) were 140.08 ± 79.42 ng/mL, 723.59 ± 235.72 ng h/mL, and 755.59 ± 243.07 ng h/mL, respectively. These values indicate substantial decreases of 70.07%, 77.12%, and 80.31%, respectively, compared with administration of lorlatinib without CYP3A4 inducers. Furthermore, there was a marked increase in the lorlatinib clearance (0.004 ± 0.003 L/h to 0.015 ± 0.006 L/h; Table 3) when administered in the presence of rifampicin. Therefore, co-administration of rifampicin significantly altered the PK profile of lorlatinib, highlighting the substantial inductive effect of rifampicin on CYP3A4 activity.

Table 3 The Main Pharmacokinetic Parameters of Lorlatinib After Co-Administration with Rifampicin and Carbamazepine (n = 6)

Figure 4 The mean plasma concentration time profile of lorlatinib after oral administration of multiple doses of rifampicin and carbamazepine for consecutive 7 days (50 mg/kg, n = 6). The detailed plasma concentration-time profiles of lorlatinib during the initial 2 h period following administration was also showed. Error bars represent ±SD for concentrations.

Daily administration of 50 mg/kg carbamazepine over 7 consecutive days prior to administration of a single 10 mg/kg dose of lorlatinib resulted in a modest reduction in the plasma exposure of lorlatinib. C_max was 375.82 ±146.20 ng/mL in the carbamazepine group compared with 468.02 ± 165.79 ng/mL in the control group, a reduction of approximately 19.70%. The AUC_(0–24h) in the carbamazepine group was 1,953.51 ± 853.11 ng h/mL, which represents a 38.22% reduction from that in the control group (3,162.09 ± 1,491.41 ng h/mL). The AUC_(0-∞) also showed a decrease of 30.41%, with values of 2,670.38 ± 2,052.47 ng h/mL in the carbamazepine group versus 3,837.13 ± 2,157.00 ng h/mL in the control group. Carbamazepine co-administration also increased lorlatinib clearance from 0.004 ± 0.003 L/h to 0.006 ± 0.003 L/h. However, this difference was not statistically significant (P = 0.694, 95% CI 0.0043 to 0.0080), suggesting that carbamazepine has a less pronounced induction effect on CYP3A4 activity than rifampicin.

Effect of Dose Escalation on the PK of Lorlatinib

The plasma concentration–time profiles of lorlatinib following oral administration at 5, 10, and 50 mg/kg in rats are shown in Figure 5, with pharmacokinetic parameters summarized in Table 4. Dose escalation from 5 to 50 mg/kg produced a dose-dependent trend in systemic exposure.

Table 4 The Main Pharmacokinetic Parameters of Lorlatinib After Oral Administration of Low, Medium and High Dose (5 mg/kg, 10 mg/kg and 50 mg/Kg) in Rats (n = 6)

Figure 5 The mean plasma concentration time profile of lorlatinib after oral administration of low, medium and high dose (5 mg/kg, 10 mg/kg and 50 mg/kg) in rats. The detailed plasma concentration-time profiles of lorlatinib during the initial 2 h period following administration was also showed. Error bars represent ± SD for concentrations.

The AUC_(0–24h) values at the low, medium, and high doses were 1,780.60 ± 573.68, 3,162.09 ± 1,491.41, and 24,756.38 ± 5,906.92 ng h/mL, respectively, and the corresponding C_max values were 218.29 ± 72.67, 468.02 ± 165.79, and 2,484.98 ± 1,363.21 ng/mL. While lorlatinib exposure increased approximately dose-proportionally at the lower doses, the 50 mg/kg dose exhibited a pronounced deviation from proportionality. Mean clearance values decreased from 0.003 ± 0.001 and 0.004 ± 0.003 L/h at the lower doses to 0.001 ± 0.001 L/h at the highest dose, consistent with reduced clearance at higher systemic exposure. These results indicate that lorlatinib pharmacokinetics in rats demonstrate a dose-dependent trend toward nonlinearity at 50 mg/kg, potentially reflecting partial saturation of metabolic enzymes or transporter-mediated processes. However, due to species differences, these findings cannot be directly extrapolated to humans, and identifying the dose at which nonlinear metabolism occurs in patients requires further studies with broader dose ranges and more rigorous experimental design.

Absolute Oral Bioavailability

The mean plasma concentration–time curve of lorlatinib following a 1 mg/kg intravenous dose in rats is presented in Figure 6, with the corresponding pharmacokinetic parameters summarized in Table 5. After oral administration of lorlatinib, the plasma concentration increased sharply and the C_max of 468.02 ng/mL was reached at 1.36 h. The half-life (t_1/2) was 8.82 h. Following intravenous administration, lorlatinib rapidly distributed to tissues and organs, resulting in a short t_1/2. The maximum plasma concentration of lorlatinib was 2,245.55 ng/mL, with a t_1/2 of 4.05 h. The AUC_(0–24h) for the plasma concentration-time curve of lorlatinib was 3,162.09 ± 1,491.41 and 3,789.05 ± 2,316.59 ng h/mL for oral and intravenous administration, respectively. The absolute oral bioavailability of lorlatinib in rats was determined to be 8.58%, suggesting that lorlatinib may suffer from incomplete absorption in the gastrointestinal tract and/or could be extensively metabolized during first-pass metabolism.

Table 5 The Main Pharmacokinetic Parameters of Lorlatinib After Intravenous (1 mg/Kg) and Oral (10 mg/Kg) Administration in Rats (n = 6)

Figure 6 The mean plasma concentration time profile of lorlatinib after intravenous administration (1 mg/kg) (n = 6). The detailed plasma concentration-time profiles of lorlatinib during the initial 2 h period following administration was also showed. Error bars represent ± SD for concentrations.

Tissue Distribution

The tissue distribution of lorlatinib in rats after oral administration of 10 mg/kg is shown in Figure 7, with mean concentrations at different time points and corresponding tissue partition coefficients summarized in Table 6. Lorlatinib was detectable in all examined tissues as early as 0.083 h, indicating rapid systemic distribution. At 0.25 h, the highest concentration was observed in the stomach (22,850.67 ng/g), followed by the liver (3,893.61 ng/g), colon (2,772.92 ng/g), kidney (2,408.37 ng/g), heart (1,922.79 ng/g), lung (1,842.36 ng/g), cecum (1,498.22 ng/g), and spleen (1,323.12 ng/g). These results highlight the stomach as a major site of drug retention and the liver as the key organ for metabolism in rats.

Table 6 Mean Tissue Concentrations at Various Time Points and Tissue Partition Coefficients Following a Single 10 mg/kg Oral Dose of Lorlatinib (n = 6)

Figure 7 The mean drug concentrations in various tissues at different time points after oral administration at a dose of 10 mg/kg lorlatinib in rats (n = 6). Error bars represent ± SD for concentrations.

Notably, lorlatinib has an exceptional capacity to penetrate the blood-brain barrier, as evidenced by a brain concentration of 774.29 ng/g and a brain-to-plasma ratio of 0.82. This underscores the potential contribution of its lipophilic nature to its in vivo disposition. The tissue partition coefficients detailed in Table 6 indicate that lorlatinib rapidly distributes to highly perfused organs in rats, including the liver (liver-to-plasma ratio = 4.82), kidneys (kidney-to-plasma ratio = 2.54), brain, and lungs (lung-to-plasma ratio = 1.77). This is consistent with its physicochemical properties and the expected distribution for lorlatinib.

Molecular Docking

To corroborate the inhibitory effects of itraconazole and voriconazole on lorlatinib metabolism, we performed molecular docking studies. As depicted in Figure 8, lorlatinib as well as both azoles were predicted to occupy the catalytic pocket of CYP3A4. Itraconazole established hydrogen bonds with ARG-372 (3.5 Å and 2.3 Å), ARG-212 (2.8 Å) and ILE-300 (3.2 Å), whereas voriconazole interacted with ASP-76 (2.8 Å), ARG-106 (2.3 Å), THR-224 (2.5 Å), and LEU-221 (3.4 Å). Lorlatinib also showed interactions with THR-224 (3.3 Å), GLY-109 (2.6 Å), and PHE-108 (3.2 Å). The calculated binding energies were −10.63 kcal/mol for voriconazole, −8.82 kcal/mol for lorlatinib, and −7.80 kcal/mol for itraconazole. Collectively, these docking data demonstrate strong CYP3A4 binding by itraconazole and voriconazole, offering a plausible mechanistic basis for their observed inhibition of lorlatinib metabolism in vivo. Furthermore, detailed two-dimensional ligand–protein interaction diagrams of CYP3A4 with lorlatinib, itraconazole, and voriconazole are provided in the Supplementary Figures 5–7. These 2D diagrams comprehensively illustrate all types of ligand–enzyme interactions, including hydrogen bonds, hydrophobic contacts, and π-related interactions, highlighting the specific residues involved in binding.

Figure 8 Molecular docking of lorlatinib and azoles with CYP3A4. (a) Voriconazole, (b) lorlatinib, and (c) itraconazole were docked into the CYP3A4 catalytic pocket. Key hydrogen bond interactions are indicated. The binding energies were −10.63, −8.82, and −7.80 kcal/mol, respectively, supporting strong CYP3A4 affinity of the azoles consistent with their inhibitory effects in vivo.

Discussion

This study provides a comprehensive characterization of lorlatinib’s pharmacokinetics, integrating in vitro metabolism with in vivo disposition. Notably, this is the first study to employ M2a as a representative marker metabolite to characterize lorlatinib metabolism in liver microsomes. M2a was selected because it exhibited a stable and reproducible response in our assays, whereas among the three known in vitro metabolites reported in the FDA documentation,³⁴ M6 is formed exclusively via CYP3A4 and M1a through UGT-mediated conjugation, both showing responses below the quantification limit, making M2a the most suitable indicator of overall microsomal metabolic activity (Supplementary Figures 1 and 2).

In this study, it was first observed that lorlatinib undergoes rapid metabolism in RLM, as evidenced by the low K_m value of 0.035 mM and the high C_Lint of 68.27 pmol/mg/min, suggesting efficient metabolic processing in rats and a high enzyme-substrate binding affinity. In contrast, HLM exhibited a significantly higher K_m of 2.552 mM and a lower C_Lint of 3.41 μL/min/mg protein, indicating a reduced affinity for the substrate and slower metabolic clearance in humans. These metabolic differences not only suggest that lorlatinib metabolism is much slower in humans than in rats, but also imply the potential for drug accumulation in plasma and tissues, particularly when the drug is highly lipophilic (Supplementary Table 5). Such accumulation could increase the risk of toxicity due to increased systemic exposure and may lead to higher accumulation in highly perfused organs.³⁵ Additionally, the significant differences observed in K_m and CL_int between rats and humans also highlight the importance of understanding species-specific metabolic processes. These differences underscore the necessity of carefully interpreting preclinical pharmacokinetics to avoid overestimating clearance in animal models, which may not accurately predict human drug behavior.³⁶

Therefore, to further explore the metabolic differences caused by species-specific metabolism of lorlatinib in vivo, the contribution of various cytochrome P450 (CYP) enzymes was assessed using probe substrate assays in both HLM and RLM. While CYP3A4 remains the primary enzyme in both rats and humans, notable divergence was observed for CYP2C19. In rat liver microsomes, CYP2C19 contributed substantially to lorlatinib metabolism, whereas in human liver microsomes its activity was almost negligible. These interspecies differences not only affect overall metabolic clearance but may also influence the relative impact of CYP-specific inhibitors or inducers, emphasizing the importance of integrating species-specific enzyme phenotyping into pharmacokinetic assessments.

The trends observed in our study were consistent with clinical findings reported by Zhou et al and Umehara et al, where rifampicin markedly reduced lorlatinib exposure due to CYP3A induction, while itraconazole increased systemic exposure through potent CYP3A inhibition.^13,14 Interestingly, unlike in humans, the inhibitory effects of CYP3A inhibitors on lorlatinib metabolism differed markedly in rats, with voriconazole exerting a stronger impact than itraconazole. Although itraconazole is generally recognized as a potent time-dependent CYP3A4 inhibitor in clinical settings,³⁷ our findings are consistent with a previous pharmacokinetic study reporting a greater effect of voriconazole in vivo.³⁸ This apparent discrepancy can be explained by differences in their inhibitory spectrum and pharmacokinetic characteristics. Voriconazole, a broad-spectrum triazole, is known to strongly inhibit CYP2C19 and CYP2C9 and moderately inhibit CYP3A4.³⁷ Its high oral bioavailability and relatively low plasma protein binding facilitate higher free drug exposure in rats, thereby enhancing CYP3A4 inhibition.^39–41 By contrast, itraconazole, despite being a strong CYP3A4 inhibitor, has poor solubility, pH-dependent absorption, and extremely high protein binding (~99%), which limit the free drug concentration available to act on CYP3A4.⁴² Consistently, in silico predictions from SwissADME aligned with these physicochemical differences, further supporting their divergent inhibitory effects observed in vivo (Supplementary Table 5). These factors, together with potential differences in transporter interactions and species-specific pharmacokinetics, likely account for the more modest inhibitory effect of itraconazole observed in our study.

Notably, human clinical pharmacology data show that lorlatinib is not only a substrate of CYP3A4 but also exhibits time-dependent inhibition and induction of CYP3A, which may further complicate its interaction profile in vivo.⁴³ Importantly, in a Phase I human study, co-administration of itraconazole (200 mg/day) with a single dose of lorlatinib (100 mg) increased lorlatinib AUC _(0-∞) by ~141.8% and C_max by ~124.4% compared with lorlatinib alone, confirming itraconazole as a strong clinical inhibitor of CYP3A4.¹³ Collectively, these findings emphasize the need for caution when extrapolating rat data to humans and suggest that voriconazole may pose a greater inhibitory risk in preclinical models, whereas itraconazole remains the more potent inhibitor in clinical practice.

Molecular docking analysis provided mechanistic support for these observations. Voriconazole exhibited stronger binding affinity for the CYP3A4 active site compared with lorlatinib, indicating its ability to effectively compete for the binding pocket and suppress metabolism. In contrast, itraconazole showed slightly weaker affinity but formed multiple hydrogen bonds, consistent with its moderate inhibitory effect in microsomal assays. Together, these results suggest that co-administration with CYP3A inhibitors can substantially increase lorlatinib exposure, although the extent of interaction may vary depending on the inhibitor and the experimental system. Since most inducers exert their effects by activating nuclear receptors like pregnane X receptor (PXR) and constitutive androstane receptor (CAR), which upregulate CYP3A4 expression at the transcriptional level rather than directly binding to the CYP3A4 active site, molecular docking simulations of lorlatinib with inducers were not conducted in this study.⁴⁴

Therefore, we focused on studying the effects of co-administration with inducers in rats, specifically assessing how these compounds influence lorlatinib’s pharmacokinetics. In comparison to lorlatinib administered alone, co-administration with rifampicin significantly reduced the C_max, AUC_(0–24h), and AUC_(0-∞) by 70.07%, 77.12%, and 80.31%, respectively. These findings are consistent with their underlying mechanisms. Rifampicin, a potent PXR activator, strongly induces CYP3A4 and intestinal P-gp, while carbamazepine activates both PXR and CAR, leading to broader but less selective induction.⁴⁵ Taken together, these results highlight co-administration of lorlatinib with strong CYP3A4 inducers such as rifampicin or carbamazepine is strictly contraindicated.

Building on the significant role of CYP3A4 in lorlatinib metabolism observed in our pharmacokinetics studies, we next examined its dose-dependent pharmacokinetic behavior in rats. To this end, lorlatinib was administered at 5, 10, and 50 mg/kg to evaluate systemic exposure. These results indicate a trend toward nonlinear pharmacokinetics at 50 mg/kg, which may be related to partial saturation of CYP3A4-mediated metabolism; however, confirmation of nonlinear kinetics would require further evaluation across a broader dose range. Because of species differences in clearance, protein binding, and bioavailability, direct milligram-per-kilogram conversion between humans and rats is inappropriate.^46,47

To further investigate this possibility under conventional exposure conditions, we next evaluated the tissue distribution of lorlatinib in rats at 10 mg/kg. The analysis was performed at key time points (0.083, 0.25, 0.5, 1, 2, 4, 6, and 24 hours post-administration) designed to characterize lorlatinib concentrations in various tissues across the pre-peak, peak, and post-peak phases of plasma concentration. Lorlatinib levels in the stomach peaked at 22,850.67 ng/g at 0.25 h, reflecting efficient dissolution and initial absorption, while the subsequent decline in liver concentrations (3,893.61 → 47.90 ng/g) suggested its central role in clearance, likely through metabolism and high hepatic blood flow.⁴⁸ Similarly, lorlatinib concentrations in the cecum and colon decreased markedly, consistent with the intestine contributing to drug elimination via both metabolic and efflux mechanisms.⁴⁹ These findings, together with the calculated oral bioavailability of 8.58%, indicate that extensive first-pass processes in the liver and intestine substantially limit systemic exposure. Notably, most tissues exhibited a secondary peak at 2–4 h, which may reflect redistribution from lipophilic tissues, whereas the delayed peak in the cecum and colon is more plausibly explained by gastrointestinal transit and reabsorption.^50,51 Such redistribution and delayed gastrointestinal transit and reabsorption could contribute to gastrointestinal adverse effects commonly observed in NSCLC patients receiving lorlatinib.⁵²

An assessment of lorlatinib’s distribution and behavior across tissues was carried out using the partition coefficient (Kp) to evaluate tissue-to-plasma ratios.⁵³ Among all tissues, the brain distribution is of particular clinical relevance, given the high incidence of brain metastases in NSCLC patients. Lorlatinib achieved a peak brain concentration of 774.29 ng/g at 0.25 hours, corresponding to a brain-to-plasma ratio of 0.82, which demonstrates efficient penetration across the blood–brain barrier.⁵⁴ This property distinguishes lorlatinib from earlier-generation ALK inhibitors such as crizotinib, which exhibits a much lower brain-to-plasma ratio of 0.23.⁵⁵ Notably, interspecies differences were also observed, with wild-type mice showing a brain-to-plasma ratio of approximately 0.5–0.6, whereas in humans a cerebrospinal fluid-to-plasma concentration ratio of 0.77 has been reported.^56,57 Although the clinical value is derived from cerebrospinal fluid rather than brain tissue directly, these data are broadly consistent with our findings and support lorlatinib’s strong central nervous system penetration across species.

Additionally, transporter interactions may significantly influence lorlatinib disposition. Lorlatinib has been reported as a substrate of P-gp/ABCB1 (and to a lesser extent ABCG2) at the blood–brain barrier,⁵⁶ while in vitro studies suggest potential inhibition of uptake transporters such as OCT1, OAT3, and OATPs;³⁴ however, definitive evidence for the role of hepatic uptake transporters in vivo remains limited. Collectively, these observations indicate that in vivo drug–drug interactions cannot be attributed solely to CYP3A4 modulation, and transporter-mediated processes likely act in concert with metabolism, warranting further mechanistic evaluation. Although the present study focused primarily on Phase I oxidative metabolism, future investigations should also examine Phase II pathways, such as UGT1A4-mediated conjugation, to achieve a more complete understanding of lorlatinib clearance. Moreover, because NSCLC patients receive lorlatinib once daily, single-dose pharmacokinetic studies have inherent limitations, and the impact of chronic dosing—particularly given lorlatinib’s dual role as a both a time-dependent inhibitor and an inducer of CYP3A—merits further study to assess potential time-dependent changes in systemic exposure.^3,43

In conclusion, this study provides novel insights into the metabolism, pharmacokinetics, and tissue distribution of lorlatinib, emphasizing the central role of CYP3A4 in its disposition and drug–drug interaction potential. A more comprehensive understanding of lorlatinib’s pharmacokinetic profile will support the optimization of treatment strategies and guide personalized therapy to maximize both safety and efficacy.

Conclusion

This study comprehensively evaluated the metabolism, pharmacokinetics, and tissue distribution of lorlatinib in rats using both in vitro and in vivo approaches. Lorlatinib was found to undergo extensive CYP3A4-mediated metabolism, exhibiting low oral bioavailability and nonlinear pharmacokinetics. Coadministration with CYP3A4 inhibitors markedly increased systemic exposure, whereas inducers significantly reduced it, highlighting a high potential for drug–drug interactions. Lorlatinib was widely distributed across major organs and showed efficient brain penetration, consistent with its clinical efficacy against CNS metastases. These findings provide mechanistic insights into lorlatinib disposition and underscore the need for careful management of CYP3A4-mediated interactions in clinical practice.

Abbreviations

ALK, Anaplastic Lymphoma Kinase; ANOVA, Analysis of Variance; ARG, Arginine; AUC, Area Under the Curve; BBB, Blood–Brain Barrier; CAR, Constitutive Androstane Receptor; CNS, Central Nervous System; CSF, Cerebrospinal Fluid; CYP, Cytochrome P450; DDI, Drug–Drug Interaction; DMSO, Dimethyl Sulfoxide; FMO, Flavin-Containing Monooxygenase; HLM, Human Liver Microsomes; i.v., Intravenous; UHPLC–MS/MS, Ultra-High-Performance Liquid Chromatography–Tandem Mass Spectrometry; LFT, Liver Function Test; MRT, Mean Residence Time; NADPH, Nicotinamide Adenine Dinucleotide Phosphate; NSCLC, Non-Small Cell Lung Cancer; P-gp, P-Glycoprotein; PK, Pharmacokinetics; PXR, Pregnane X Receptor; QC, Quality Control; ROS1, c-ros Oncogene 1; SD, Standard Deviation; TKI, Tyrosine Kinase Inhibitor; t₁_/2, Elimination Half-Life; T_max, Time to Reach Maximum Concentration; UGT, UDP-Glucuronosyltransferase; V_max, Maximum Velocity (of an Enzymatic Reaction); V_z/F, Volume of Distribution.

Data Sharing Statement

The data supporting the findings of this study are available from the corresponding author upon reasonable request.

Ethics Statement

All animal experiments were conducted in compliance with the Guide for the Care and Use of Laboratory Animals and approved by the Institutional Animal Care and Use Committee of Southern Medical University (Approval No. SMUL202410030).

Acknowledgments

The authors thank the staff of the Experimental Animal Center of Southern Medical University for their support with animal care and handling. We also acknowledge the technical assistance provided by the Clinical Pharmacy Center for UHPLC-MS/MS measurements.

Author Contributions

Cong Xie and Tongshu Guan contributed equally to this work and share first authorship. Yilei Li and Ping Zheng are co-corresponding authors. Conceptualization: Cong Xie and Tongshu Guan, Yilei Li and Ping Zheng; Methodology: Cong Xie and Tongshu Guan; Investigation: Jin Huang, Jiayu Chen; Formal analysis: Jin Huang, Jiayu Chen; Data curation: Jin Huang, Jiayu Chen; Writing – original draft: Cong Xie and Tongshu Guan; Writing – review & editing: Yilei Li and Ping Zheng; Supervision: Yilei Li and Ping Zheng; Funding acquisition: Cong Xie. All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This work was supported by the National Natural Science Foundation of China [82204499], the China International Medical Foundation [Z-2021-46-2101], Science and Technology Program of Guangzhou [2025A04J4279] and the Science and Technology Projects in Guangzhou [202201010876].

Disclosure

The authors report no conflicts of interest in this work.

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54. Bauer TM, Shaw AT, Johnson ML, et al. Brain Penetration of Lorlatinib: cumulative Incidences of CNS and Non-CNS Progression with Lorlatinib in Patients with Previously Treated ALK-Positive Non-Small-Cell Lung Cancer. Target Oncol. 2020;15(1):55–65. doi:10.1007/s11523-020-00702-4

55. Tang SC, Nguyen LN, Sparidans RW, Wagenaar E, Beijnen JH, Schinkel AH. Increased oral availability and brain accumulation of the ALK inhibitor crizotinib by coadministration of the P-glycoprotein (ABCB1) and breast cancer resistance protein (ABCG2) inhibitor elacridar. Int J Cancer. 2014;134(6):1484–1494. doi:10.1002/ijc.28475

56. Li W, Sparidans RW, Wang Y, et al. P-glycoprotein (MDR1/ABCB1) restricts brain accumulation and cytochrome P450-3A (CYP3A) limits oral availability of the novel ALK/ROS1 inhibitor lorlatinib. Int J Cancer. 2018;143(8):2029–2038. doi:10.1002/ijc.31582

57. Sun S, Pithavala YK, Martini JF, Chen J. Evaluation of Lorlatinib Cerebrospinal Fluid Concentrations in Relation to Target Concentrations for Anaplastic Lymphoma Kinase (ALK) Inhibition. J Clin Pharmacol. 2022;62(9):1170–1176. doi:10.1002/jcph.2056

Reynolds Consumer Products (REYN) has recently seen some movement in its stock price. Investors might be curious about how the company’s fundamentals stack up and whether the current valuation offers opportunity or risk in today’s market.

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Reynolds Consumer Products has seen its share price slip over 12% since the start of the year, with a one-year total shareholder return of -18% reflecting fading momentum despite a modest bounce in recent months. While the business continues to generate steady growth, the market’s risk appetite for the stock appears softer than it was last year. This suggests investors are still weighing up the balance between stability and opportunity.

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Reynolds Consumer Products’ last close of $23.42 sits noticeably below the most-followed narrative’s fair value estimate of $26.25. This gap highlights growing expectations for future profitability and revenue growth, despite recent share price volatility.

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This article by Simply Wall St is general in nature. We provide commentary based on historical data and analyst forecasts only using an unbiased methodology and our articles are not intended to be financial advice. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your financial situation. We aim to bring you long-term focused analysis driven by fundamental data. Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material. Simply Wall St has no position in any stocks mentioned.

Companies discussed in this article include REYN.

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Amazon strategised about keeping the public in the dark over the true extent of its datacentres’ water use, a leaked internal document reveals.

The biggest owner of datacentres in the world, Amazon dwarfs competitors Microsoft and Google and is planning a huge increase in capacity as part of a push into artificial intelligence. The Seattle firm operates hundreds of active facilities, with many more in development despite concerns over how much water is being used to cool their vast arrays of circuitry.

Amazon defends its approach and has taken steps to manage how efficient its water use is, but it has faced criticism over transparency. Microsoft and Google regularly publish figures for their water consumption, but Amazon has never publicly disclosed how much water its server farms consume.

When designing a campaign for water efficiency, the company’s cloud computing division chose to account for only a smaller water usage figure that does not include all the ways its datacentres use water so as to minimise the risk to its reputation, according to a leaked memo seen by SourceMaterial and the Guardian.

Amazon as a whole consumed 105bn gallons of water in total in 2021, as much as 958,000 US households, which would make for a city bigger than San Francisco, according to the memo.

Asked about the leaked document, Amazon spokesperson Margaret Callahan described it as “obsolete” and said it “completely misrepresents Amazon’s current water usage strategy”.

“A document’s existence doesn’t guarantee its accuracy or finality,” she said. “Meetings often reshape documents or reveal flawed findings or claims.” Callahan would not elaborate on which strategic elements of the document were “obsolete”.

The memo was dated one month before Amazon Web Services (AWS), the company’s cloud computing division, debuted a new sustainability campaign in November 2022 called “Water Positive”, with a commitment to “return more water than it uses” by 2030.

In the memo, ahead of the campaign’s launch, executives grappled with whether to include public disclosures about “secondary” use – water used in generating the electricity to power its datacentres.

They warned that full transparency was “a one-way door” and advised keeping AWS’s projections confidential, even as they feared that their advice could invite accusations of a cover-up. “Amazon hides its water consumption” was one negative headline the authors anticipated.

Callaghan said efficiency savings have already been achieved and pointed out that other companies also don’t count secondary water use.

Executives opted to use only the relatively smaller figure of primary use, 7.7bn gallons per year, roughly equivalent to 11,600 Olympic swimming pools, when calculating progress towards internal targets because of “reputational risk”, fearing bad publicity if the full scale of Amazon’s consumption was revealed, the document shows. Ultimately as part of the campaign for water efficiency, Amazon aimed to cut its estimated 7.7bn gallon primary consumption to 4.9bn by 2030 – without addressing secondary use.

Using the higher of two water usage estimates, the one that would include secondary use, “would double the size and budget” of the campaign “without addressing meaningful operational, regulatory or reputational risks”, they warned, adding that there was “no focus from customers or media” on water used for electricity.

“We may decide to release water volumes in the future,” the document said. “But … we should only do so if the lack of data undermines the programme or is required by regulators.”

Scientists balked at the selective disclosure and the choice not to include secondary use of water in the total.

“In environmental science, it is standard practice to include both to more accurately capture the true water cost of datacentres,” said Shaolei Ren, associate professor of electrical and computer engineering at the University of California, Riverside.

Amazon’s Water Positive campaign is still active and does not take into account secondary use, while the company continues to keep its current overall water consumption confidential.

As US tech companies ride the wave of AI investment and pursue greater heights of computational power, the $2.4tn corporation is building new datacentres in some of the world’s driest areas, SourceMaterial and the Guardian revealed in April.

Feeling water positive

In November 2022, Amazon Web Services debuted its new Water Positive sustainability campaign, with a commitment to “return more water than it uses by 2030”. The campaign only applies to Amazon Web Services. The wider Amazon group, including the world’s biggest online retail business, has an overall water consumption that is far higher, 105bn gallons per year.

“The models referenced in this document were preliminary and unvetted,” said Amazon’s Callahan, who declined to provide any alternative figures.

The document’s authors advised the company not to release data about the wider company, but they also warned that selective disclosure could lead to accusations of a cover-up. There was “reputational risk of publicly committing to a goal for only a portion of Amazon’s direct water footprint”, they wrote. They even suggested negative headlines that might result including “Amazon disappoints, failing to take full responsibility for water”.

“It would be better if they could own up to it,” said a current Amazon software developer, who asked to remain anonymous for fear of retaliation. “Even if they said it was a low priority, at least that would be honest.”

In a sustainability report in August, AWS claimed it had achieved 53% of its Water Positive goal. The division’s plan for reaching the target relies mostly on “water replenishment” projects, some in partnership with Water.org, a non-profit organisation co-founded by actor Matt Damon. The strategy document refers to these projects as “offsets”, describing initiatives like using Amazon computer technology to help utilities prioritise which pipes to fix in order to minimise leaks.

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But of the $109m AWS planned to spend on offsets, around half would have been spent anyway, either to meet regulatory requirements or because the projects would help AWS operations by making water more available, the document shows. Experts said this amounted to incomplete accounting.

“Regardless of what sort of offsetting or replenishment you do, it doesn’t necessarily nullify the water footprints of your own operations,” said Tyler Farrow, standards manager at the Alliance for Water Stewardship. “Calling your operations water positive or water neutral is misleading.”

Amazon’s Callahan said that the “replenishment spending”, which other tech companies also undertake, is a voluntary, not a regulatory, requirement.

“We’ve expanded well beyond what was imagined in the document because it’s the right thing to do for the world and for the communities in which we operate,” she said.

Amazon is also engineering industry standards to downplay its water use and avert scrutiny, said Nathan Wangusi, a former water sustainability manager at the company.

The corporation has funded efforts by the Nature Conservancy and the World Resources Institute non-profits, alongside LimnoTech, a consultancy, “to create a globally accepted methodology for quantifying the benefit of watershed restoration projects”.

Responding to questions from SourceMaterial, all three organisations defended their integrity and independence, insisting that Amazon had no undue influence on any methodologies they had created.

“They spend a lot of time creating methodologies that are used to obfuscate the water footprint,” Wangusi said, referring to Amazon.

Callahan said Wangusi’s claim was “contradicted by facts”. “Amazon’s water use reporting is based on third-party assured data from actual utility bills, not estimates or self-reporting,” she said. Wangusi’s claim, though, was not about Amazon’s water-use reporting, but about measuring the effects of water offsets.

Callahan said these efforts were “standard practice” and that Amazon’s “customers expect us to hold ourselves accountable to credible guidance and best practices”.

As well as choosing not to disclose water use from electricity generation, Amazon has estimated its larger “indirect” water footprint, the document shows. This extra usage, which falls under a classification known as “scope 3”, includes water for production and construction – in Amazon’s case, mostly irrigation of cotton plantations supplying its fashion brands, and vegetables for its grocery arm, Amazon Fresh.

Here, too, Amazon decided to keep its consumption confidential, even though “indirect water use represents roughly 90% of Amazon’s total water footprint”, according to the document.

AWS avoided establishing targets for indirect water use because that figure would be “much more significant for the rest of Amazon, especially in the agricultural supply chain, and the team does not want to establish a standard for addressing scope 3 water use that the rest of Amazon would need to follow, given the larger resource implications”, the authors wrote.

“You don’t need to obscure or obfuscate,” said Wangusi, who believes he was “hounded out” of Amazon for criticising the company’s approach. (Amazon declined to comment on his departure.)

“It doesn’t make you more profitable,” he said. “It makes you less trustworthy.”

Introduction

Cardiovascular disease (CVD) remains the leading cause of global mortality, posing a significant burden on healthcare systems worldwide.^1,2 Despite advances in management strategies, including revascularization and pharmacotherapy, critical challenges persist. These include: (1) Irreversible myocardial cell damage: Myocardial cell apoptosis is a key mechanism underlying myocardial injury post-infarction.^3,4 (2) Inflammasome imbalance: Excessive inflammatory responses following cardiovascular injury, exacerbate tissue damage.^5,6 (3) Insufficient tissue repair: Functional angiogenesis and endothelial-pericyte interactions are crucial for effective repair.⁷ This multifactorial complexity is likely linked to heterogeneous activation of the tissue microenvironment, receptor expression patterns, and downstream signaling events. So, it is highlighting the urgent need for novel therapeutic targets that can precisely modulate these pathological processes.

The tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) and its death receptor 5 (DR5) pathway, initially characterized for its anti-cancer potential, has emerged as a key regulatory axis in CVD. Intriguingly, current evidence reveals a complex, context-dependent “double-edged sword” role for TRAIL-DR5 signaling in the cardiovascular system.^8–11 It can exacerbate injury by promoting apoptosis and inflammation in conditions like myocardial infarction,^5,12 preclinical studies confirm that elevated TRAIL levels correlate with increased apoptosis, and DR5 is upregulated in ischemic myocardium, amplifying cell death signals.¹³ Otherwise, high glucose or inflammatory cytokines boost TRAIL and DR5 expression, promoting apoptosis in cardiomyocytes and endothelial cells, accelerating plaque instability, and enhancing fibrosis.^14,15 In Alzheimer’s-related vascular impairment, TRAIL-DR5 activation may damage the neurovascular unit via apoptosis.¹⁶ Clinically, high serum TRAIL in acute stroke patients predicts poorer short-term outcomes, underscoring its pathological relevance.¹⁷

Paradoxically, however, it also demonstrates protective effects by enhancing vascular stability, resolving inflammation, and facilitating repair in other contexts.^5,18–20 Animal studies also show that blocking TRAIL-DR5—eg, with sDR5-Fc fusion protein—attenuates viral myocarditis and COVID-19-related cardiovascular complications.^14,21 In summary, this functional dichotomy suggests immense therapeutic potential but also underscores the risk of unintended consequences if targeted without a deep understanding of its nuanced biology.

While several reviews have extensively covered the role of TRAIL-DR5 in oncology, a comprehensive synthesis focusing on its dual and often contradictory roles across various cardiovascular diseases is notably lacking. Existing literature on this topic is fragmented, often focusing on a single disease entity, and has not adequately addressed the mechanistic basis for its opposing functions or the translational barriers specific to CVD. Therefore, this review is designed to fill this gap. We aim to provide a systematic and updated examination of the TRAIL-DR5 signaling axis, with a specific focus on: its intricate molecular mechanisms; its multifaceted and paradoxical roles in key CVDs (AMI, heart failure, atherosclerosis, atrial fibrillation); the latest advances in targeted therapeutic strategies, including agonists, inhibitors, and combination therapies.

By integrating these aspects, this review seeks to offer a foundational resource for understanding the TRAIL-DR5 pathway in CVD and to propel the development of novel, effective, and safe therapeutic interventions.

Molecular Mechanism of TRAIL-DR5

TRAIL Ligands and Their Receptor Family

TRAIL is a member of the TNF superfamily and functions as a type II transmembrane protein. This protein can be proteolytically cleaved into a soluble trimeric form. It is predominantly secreted by immune cells, such as natural killer (NK) cells and T lymphocytes, playing a critical role in immune surveillance.^22–24 The receptor family of TRAIL comprises two types of receptors with opposing functions: death receptors (DR4/DR5) and decoy receptors (DcR1/DcR2/OPG). These receptors collectively regulate the biological activities of TRAIL.^25,26

Both DR4 and DR5 possess an intracellular Death Domain (DD), which upon TRAIL binding induces receptor trimerization. This process recruits Fas-associated death domain (FADD) protein and caspase-8/10 to form the death-inducing signaling complex (DISC), thereby activating the caspase cascade and ultimately leading to apoptosis.^25,27 While the two receptors exhibit partial functional redundancy, certain tumors may preferentially rely on either DR4 or DR5 for mediating apoptosis.^28,29

Decoy receptors modulate the biological activity of TRAIL by binding to it without transmitting apoptotic signals. Owing to the absence of an intracellular domain, DcR1 is tethered to the cell membrane via glycosyl phosphatidylinositol (GPI) and competitively inhibits TRAIL from binding to DR4/DR5.^30,31 DcR2, which contains an incomplete intracellular death domain, suppresses Caspase activation while activating pro-survival pathways such as NF-κB.^31,32 Osteoprotegerin (OPG), the sole soluble receptor, primarily binds to TRAIL, thereby preventing its interaction with membrane-bound receptors and playing a critical role in bone metabolism and the tumor microenvironment.³³

TRAIL-DR5-Mediated Signaling Pathway

At present, the signal pathways mediating apoptosis by TRAIL-DR5 can be mainly divided into three categories: exogenous apoptotic pathway, mitochondrial apoptotic pathway and other cross-signaling pathways (Figure 1).

Figure 1 TRAIL-DR5-mediated apoptosis signaling pathway. Activation of DR4 and DR5 by TRAIL induces the extrinsic apoptosis pathway, which is triggered by a variety of stimuli and leads to the release of proapoptotic proteins from the mitochondria, thereby initiating the mitochondria-mediated apoptotic process. The two pathways interact upon the activation of caspase-8. Specifically, caspase-8 can activate initiator caspases such as caspase-3, which subsequently induces apoptosis. Alternatively, caspase-8 can also activate the mitochondrial mechanism of apoptosis through the cleavage and activation of Bid. In addition to the aforementioned classical apoptotic signaling pathways, the TRAIL-DR5 signaling axis also activates a range of additional signaling mechanisms that facilitate cell survival, proliferation, migration, or inflammatory responses. For instance, within the DR5 complex, the recruitment of receptor-interacting protein kinase 1 (RIPK1) constitutes a pivotal event in non-apoptotic signaling. Furthermore, non-apoptotic signals mediated by DR5 can promote inflammatory responses and cell survival via the NF-κB pathway. In addition, there are complex cross-interactions among various signaling pathways, including protein kinase B (Akt) and mitogen-activated protein kinases (MAPK), such as extracellular signal-regulated kinase 1/2 (ERK1/2).

DR5-Mediated Apoptosis Pathway

As a homotrimeric ligand, TRAIL specifically binds to the DR5 receptor on the surface of the cell membrane. This binding is highly selective, but the affinity of DR5 for TRAIL may be affected by the glycosylation status of the receptor or the cell type. TRAIL binding induces the formation of a homotrimer of the DR5 receptor, leading to a conformational change in its intracellular DD, which subsequently recruits the adaptor protein FADD to interact with the DD of DR5 via its C-terminal DD. The N-terminal Death Effector Domain (DED) of FADD further recruits DED containing pro-caspase 8 or pro-caspase 10 to form the DISC complex.³⁴ In DISC, pro-caspase 8 is activated via a proximity-induced self-cleavage mechanism to generate the enzymatically active caspase 8 (or caspase 10). Activated caspase 8 clears downstream effector caspases (such as caspase 3, 6, and 7), triggering their activation, and directly degrades key cellular structural proteins (such as lamin and cytoskeletal proteins) and DNA repair enzymes (such as PARP), leading to cell apoptosis.^35,36

In some cases, activated caspase 8 specifically cleans BH3-only protein Bid to generate a truncated form of tBid.³⁷ Through its BH3 domain, tBid interacts with pro-apoptotic proteins of the Bcl-2 family, such as Bax/Bak, inducing their oligomerization and formation of pores in the outer mitochondrial membrane.³⁸ Mitochondrial outer membrane permeabilization (MOMP) leads to the release of cytochrome c (Cyt c) from the mitochondrial membrane space to the cytoplasm, which may be accompanied by the release of other pro-apoptotic factors (such as Smac/DIABLO).³⁹ Cyt c in the cytoplasm binds to Apaf-1 and forms apoptosome in the presence of dATP, which recruits and activates caspase-9.⁴⁰ Activated caspase 9 further clears downstream effector caspases, such as Caspase-3/7, to execute apoptosis. This process significantly amplifies apoptotic signals through a protease cascade. In some cells, even in the absence of caspase 9, the mitochondrial pathway can induce apoptosis through other effector molecules, such as caspase 2 or the alternative pathway.⁴¹

TRAIL-DR5-Mediated Non-Apoptotic Signal Transduction Pathways

In addition to the classical apoptotic signaling pathways described above, the TRAIL-DR5 signaling axis activates a range of other signaling mechanisms that promote cell survival, proliferation, migration, or inflammatory responses. For example, in the DR5 complex, the recruitment of receptor-interacting protein kinase 1 (RIPK1) is a central event in non-apoptotic signaling. The antibody drug AMG655 (Conatumumab) could activate RIPK1 in the DR5 complex in either sensitive or resistant cells, thereby triggering pro-survival and pro-proliferation effects.⁴² NK (c-Jun N-terminal kinase) has been shown to promote the transcription of DR5 by phosphorylating transcription factors such as c-Jun. 7-methoxy-esculetin significantly up-regulates the expression of DR5 mRNA and protein by activating JNK pathway, thereby enhancing the killing effect of TRAIL on colon cancer cells.⁴³ Similarly, 6-MS, a chemotherapeutic agent, upregulates DR5 expression through JNK-dependent oxidative stress pathway and enhances TRAIL-induced apoptosis in liver cancer cells.⁴⁴ In addition, basal ERK activity can inhibit DR5 expression, while ERK inhibition (eg, PD98059) or ERK gene knockdown can significantly enhance the expression of DR4 and DR5 by relieving the negative regulation of DR5 by ERK.^45,46 In glioblastoma, the combination of ERK inhibitor and TRAIL enhanced apoptosis by upregulating DR5.⁴⁷

DR5 non-apoptotic signaling can also mediate inflammatory responses and cell survival through the NF-κB pathway. In DcR2 knockdown tumor cells, TRAIL stimulation significantly enhanced the activity of NF-κB, suggesting a direct or indirect association between DR5 signaling and NF-κB.⁴⁸ In pancreatic β cells, glucocorticoid-induced upregulation of TRAIL and DR5 expression activates NF-κB and is accompanied by the release of pro-apoptotic proteins (such as BAX, caspase-8/3) and inflammatory mediators, which further leads to cellular dysfunction.⁴⁹ In addition, studies have shown that TRAIL-induced ERK and p38 activities are significantly enhanced after knocking down DcR2, suggesting that DR5 signaling may cooperate with other receptors to regulate the MAPK pathway.⁴⁸

Paradoxical Role of TRAIL-DR5 Signaling Axis in Cardiovascular Diseases

Recent studies have shown that circulating TRAIL may have important value in the prognosis evaluation of cardiovascular diseases. A prospective cohort study in the elderly population⁵⁰ found that plasma TRAIL level was negatively correlated with all-cause mortality, especially for cardiovascular death, while non-cardiovascular mortality did not show statistically significant association. Although the research in this field is still in the early stage, TRAIL and its death receptor DR5 have shown potential value, such as a new biomarker for cardiovascular events. However, the existing evidence mainly comes from observational studies, and it is still necessary to verify its clinical transformation value through large-scale cohorts and further clarify its specific regulatory mechanism in different cardiovascular disease subtypes (Figure 2, Table 1 and Table 2).

Table 1 Summary of the Roles in Various Cardiovascular Cells

Table 2 The Paradoxical Role of the TRAIL-DR5 Signaling Axis in Cardiovascular Diseases

Figure 2 Paradoxical role of TRAIL-DR5 signaling axis in cardiovascular diseases. The TRAIL-DR5 signaling axis presents a complex and contradictory role in cardiovascular disease. Studies have shown that circulating TRAIL levels correlate with cardiovascular disease prognosis: reduced serum TRAIL levels in patients with acute myocardial infarction (AMI) correlate with poor prognosis, whereas soluble DR5 (sDR5) has a significant predictive potency for long-term mortality risk, suggesting its potential as a novel biomarker. Notably, peripheral blood immune cell TRAIL expression is upregulated during AMI, whereas cardiomyocyte DR5 upregulation enhances apoptosis susceptibility and exacerbates ischemia-reperfusion injury. In heart failure (HF), elevated plasma TRAIL was associated with reduced mortality and may exert a protective effect by inhibiting apoptosis or promoting cell proliferation, but elevated DR5 levels were associated with deterioration of left ventricular function, demonstrating an inverse ligand-receptor regulatory feature. In atherosclerosis, TRAIL action shows tissue specificity: in vascular endothelial/smooth muscle cells it may induce apoptosis to promote plaque instability, whereas in macrophages it inhibits inflammation and improves cholesterol metabolism. Circulating TRAIL levels are reduced after ablation in patients with atrial fibrillation (AF), and elevated sDR5 is associated with the risk of AF recurrence, suggesting its involvement in the electrical remodeling process. However, the existing evidence mainly originates from observational studies, and further validation of its clinical value through large-scale cohorts is needed in the future, as well as elucidation of the tissue-specific regulatory mechanisms and bidirectional roles of the signaling pathways (pro-apoptotic and non-apoptotic effects), to provide a theoretical basis for precision therapy.

Dual Role of the TRAIL-DR5 Axis in Acute Myocardial Infarction (AMI)

AMI is a serious cardiovascular disease caused by acute complete or incomplete occlusion of the coronary artery, leading to myocardial ischemia and necrosis. Current studies have shown that the role of TRAIL-DR5 signaling axis in AMI involves multiple pathophysiological links, including the regulation of myocardial cell apoptosis, neutrophil-mediated inflammatory response and ischemia-reperfusion injury. A systematic screening study of 92 cardiovascular and inflammation-related biomarkers⁵⁸ found that soluble DR5 (sDR5) had the strongest predictive power for long-term all-cause mortality risk in patients with AMI. This finding suggests that high or low levels of circulating DR5 may be associated with poor prognosis. In addition, serum TRAIL levels within 72 hours after AMI onset were significantly negatively correlated with peak creatine kinase (CK-MB) and also negatively correlated with B-type natriuretic peptide (BNP), suggesting that it may play a protective role by antagonizing the ventricular remodeling process.⁵⁷ These evidences suggest that reduced serum TRAIL levels after myocardial infarction may be detrimental to prognosis. However, these changes in circulating TRAIL may not be specific to the heart and may also be associated with other ischemic events such as ischemic stroke.⁶⁹ In addition, although many studies have shown that TRAIL is down-regulated in the serum of AMI patients,^58,59 the expression level of TRAIL in peripheral blood mononuclear cells (PBMCs) (mainly CD4⁺ and CD14⁺) is up-regulated in the acute phase of AMI patients,⁷⁰ indicating that immune cells may be an important source of TRAIL in AMI.

In addition, the high expression of DcR2 in normal cardiomyocytes may inhibit the pro-apoptotic effect of TRAIL, while the up-regulation of DR5 may enhance the sensitivity to apoptosis in I/R injury.⁷¹ During AMI, myocardial ischemia and hypoxia cause TRAIL to bind to DR5, activate the caspase cascade, induce cardiomyocyte apoptosis, and aggravate myocardial injury.⁵¹

Heart Failure (HF)

HF is a complex clinical syndrome characterized by the inability of the heart to pump blood efficiently to meet systemic metabolic demands or to maintain pumping function only when filling pressure is elevated. The causes of heart failure include coronary heart disease, hypertension, cardiomyopathy, etc. According to a clinical study on non-ischemic cardiomyopathy, the plasma TRAIL concentration of patients was increased, which was positively correlated with left ventricular diastolic diameter. The expression of TRAIL was detected in the ento-myocardial biopsy of patients, and the TRAIL gene in the peripheral blood leukocytes of patients was up to the surface.⁷² In addition, a study of 351 patients with advanced HF found that elevated sTRAIL levels were associated with a 70% reduction in all-cause mortality, possibly by exerting a protective effect through inhibition of apoptosis or promotion of cell proliferation, associated with β-blocker use.⁶⁰ Other studies using proteomics in two community-based elderly cohorts, PIVUS cohort (n=901, median age 70.2 years) and ULSAM cohort (n=685, median age 77.8 years), found that soluble DR5 was associated with deterioration of left ventricular systolic function. It is a risk factor for the development of heart failure, but the specific mechanism is not clear.⁶¹ However, the study had a limitation that the sample size was not large enough to determine causality. A clinical study on heart failure with preserved ejection fraction (HFpEF) found that plasma TRAIL was determined to be negatively correlated with prognosis, while soluble DR5 was positively correlated.⁶² Circulating DR5 is elevated in heart failure patients with poor left ventricular ejection fraction and diastolic function, but it is positively correlated with disease incidence.⁶¹ In contrast, a prospective observational study showed no difference in TRAIL levels in heart failure patients treated with cardiac resynchronization therapy, and TRAIL levels did not predict mortality.⁷³ These studies indicate that decreased ligand levels and increased nuclear receptor levels may be associated with worse prognosis in the disease, suggesting a possible beneficial role for TRAIL signaling.

The activation of TRAIL pathway has been implicated in the development and progression of HF, but the mechanism by which TRAIL exerts cardio-protection has not been fully elucidated. One idea is that higher levels of TRAIL may reflect the need to resolve inflammation due to TRAIL-induced apoptosis,⁶² but data from animal models actually suggest the opposite. Injection of recombinant TRAIL significantly reduced myocardial fibrosis and apoptosis and, therefore, prevented more relevant cardiac structural changes in a mouse model of cardiomyopathy.⁷⁴ In this study, it was proposed that, in contrast to its pro-apoptotic effect, it may be the result of triggering non-apoptotic signals in normal cells (promoting survival, migration, and proliferation of primary vascular smooth muscle cells.^54,75 Therefore, TRAIL and TRAIL receptors may serve as potential biomarkers for HF and predict the prognosis and mortality of patients; However, more studies are needed to confirm these.

Atherosclerosis

Atherosclerosis refers to the thickening and hardening of the arterial wall, loss of elasticity and narrowing of the lumen, and the appearance of yellow atheroma of lipids accumulated on the intima of the artery. TRAIL and its receptors, such as DR5, are closely related to the pathological process of atherosclerosis. In response to perivascular cuff injury, Trail^−/− mice had reduced neointimal hyperplasia compared with Trail ^+/+ mice, and recombinant TRAIL delivery restored neointimal thickening,⁵⁵ a finding supported by in vitro studies using human VSMCS.⁵⁶ These findings suggest that TRAIL may contribute to the development of early atherosclerosis. In addition, meta-analysis showed that changes in circulating TRAIL or DR5 levels may be associated with mortality or risk of cardiovascular events in patients with atherosclerosis, suggesting its possibility as a potential biomarker.¹⁵ In patients with chronic kidney disease, a 24-month follow-up study found that low levels of circulating TRAIL were associated with the emergence of new atherosclerotic plaques.⁶⁴ However, TRAIL levels measured in coronary arteries from patients with stable angina or a positive noninvasive ischemia test showed an inverse correlation with TRAIL levels in the necrotic core of atherosclerotic plaques, and TRAIL levels were also reduced in the fibrofatty component of atherosclerotic plaques, although the decrease was small.⁷⁶ Preclinical studies have also shown that TRAIL may play a protective role in atherosclerosis by inhibiting intra-plaque inflammation or regulating cell survival, such as reducing intra-plaque macrophage infiltration or enhancing fibrous cap stability.¹³ DcR1 and DcR2 may regulate TRAIL signaling through competitive binding and affect the balance between cell survival and death in the plaque.³⁵ TRAIL-deficient macrophages are more inflammatory, have poor exocytosis, impaired cholesterol processing, and reduced migration, which are hallmarks of macrophage dysfunction in lesions and accelerate atherosclerosis.⁵³ In contrast, pretreatment with exogenous TRAIL increased lipid uptake and foam cell formation, and resulted in macrophage apoptosis. There may be cross-regulation of TRAIL receptor signaling with other inflammatory pathways, such as NF-κB or TLR pathways. For example, TRAIL-activated FADD complex can promote NF-κB signaling, which in turn affects the expression of inflammatory factors (such as IL-6 and TNF-α) and aggravates the progression of atherosclerosis.⁷⁷ At the same time, TRAIL may affect monocyte infiltration by regulating endothelial cell function (such as adhesion molecule expression) and participate in early plaque formation.⁷⁸ In diabetic Apoe-/- or Trail-/ -apoe -/ -mice, TRAIL protein, TRAIL gene therapy, or TRAIL bone marrow transplantation attenuated the development of atherosclerosis, reduced the content of macrophages in the vessel wall, and reduced inflammation.^53,63 In conclusion, TRAIL activates the apoptotic signaling pathway by binding to death receptors, but its role in atherosclerosis may be tissue specific. In vascular endothelial cells or smooth muscle cells, TRAIL may promote plaque instability (such as fibrous cap thinning) by inducing apoptosis; While in immune cells (such as macrophages), TRAIL may inhibit the release of proinflammatory factors, thereby reducing the local inflammatory response.

Atrial Fibrillation (AF)

AF refers to the loss of regular and orderly atrial electrical activity, which is replaced by rapid and disordered fibrillation waves. It is a serious disorder of atrial electrical activity. At this time, the atrium loses effective contraction and relaxation, and blood is easy to stasis in the atrium, which increases the risk of thrombosis. AF promotes tissue fibrosis and is an important cause of AF recurrence, drug resistance and complications.^79,80 A prospective observational study found that circulating TRAIL levels were reduced in patients with successful ablation of AF,⁶⁵ in contrast, patients with acute episodes of AF had lower circulating TRAIL levels and increased TRAIL levels after sinus rhythm maintenance.⁶⁶ In addition, abnormal TRAIL receptor expression may impair the immune system’s ability to clear damaged cardiomyocytes, leading to abnormal electrical and structural remodeling.²⁴ Some clinical studies have reported that TRAIL levels were decreased in patients with AF after electrical cardioversion and during six-month follow-up when TRAIL concentrations were measured on the cardiac gradient (coronary sinus concentration minus aortic root concentration), indicating that the gradient was negatively correlated with AF recurrence, but there was no difference in plasma TRAIL levels.⁶⁸ In addition, a cardiovascular biomarker screening study found that sDR5 is one of the markers associated with AF in patients with AF. DR5 lacks the intracellular domain required for initiating signaling, so circulating DR5 levels are considered to be negatively correlated with DR5 activation at the tissue level. Therefore, DR5 is considered as a risk factor for AF.⁶⁷ However, based on the current research, whether TRAIL and its receptors can be used as prognostic factors or biomarkers needs to be further elucidated.

Intervention Strategies Targeting TRAIL-DR5 Signaling Axis in Cardiovascular Related Diseases

The clinical relevance of many of the current studies on the cardiovascular effects of TRAIL-DR suggests both protective and detrimental effects depending on the study and the disease. This may vary depending on the type of cardiovascular disease being studied. Interventional strategies targeting the TRAIL-DR5 axis have potential for multidirectional regulation in cardiovascular disease, such as agonists to remove diseased cells through selective activation of apoptotic pathways; inhibitory interventions to protect the myocardium by blocking pathological signaling; and combination therapies to enhance the efficacy of therapy through multi-target synergy. However, there are no drugs on the market for this target yet (Figure 3 and Table 3).

Table 3 Intervention Strategies Targeting the TRAIL-DR5 Signaling Axis

Figure 3 Intervention strategies targeting TRAIL-DR5 signaling axis in cardiovascular related diseases. Targeting the TRAIL-DR5 signaling axis has demonstrated multidimensional intervention potential in cardiovascular disease therapy, with strategies that can be categorized into agonist activation, inhibitory blockade, and combination therapies, which need to be combined with the stage of the disease and the cell type to achieve precise modulation. Agonist development focuses on selective clearance of diseased cells: monoclonal antibodies (eg, Drozitumab) and multivalent ligands (eg, MEDI3039) induce apoptosis in inflammatory cells by enhancing DR5 cross-linking, but cardiac targeting needs to be optimized to minimize off-target effects; small-molecule agonists (eg, Bioymifi) are highly utilized orally but may activate the ERK1/2 pathway and lead to myocardial hypertrophy; engineered DR5-scFv sEVs clear pro-inflammatory macrophages by targeted delivery, but clinical translation requires validation of specificity. Inhibitory interventions protect the myocardium by blocking TRAIL-DR5 signaling in myocardial ischemia or heart failure, eg, sDR5-Fc competitive binding of TRAIL improves cardiac function, and DR5 inhibitors reduce apoptosis in the infarct zone by 45%, but timing of blockade needs to be weighed to avoid impaired inflammatory clearance. Combination therapy enhances efficacy by synergistically modulating multiple pathways such as apoptosis and autophagy, eg, the kinase inhibitor NT157 enhances the apoptotic effect of TRAIL, and chloroquine inhibits autophagy and synergistically promotes apoptosis, but the threshold of intervention needs to be finely tuned.

Agonist Development – Enhancing Pro-Apoptotic Signaling

Monoclonal antibodies against DR5, such as Drozitumab, induce apoptosis by activating death receptor signaling pathways and have shown selective killing potential especially in cancer therapy, but their application in cardiovascular diseases still needs to be optimized for improved efficacy.⁸¹ Multivalent ligands, such as MEDI3039. MEDI3039 is a multivalent ligand. By enhancing the cross-linking efficiency of the DR5 receptor, it significantly improves its activation ability, thereby demonstrating stronger pro-apoptotic activity in preclinical models. Its effect does not depend on the classic FADD/caspase-8 pathway, but promotes apoptosis through an atypical RIPK1-dependent signaling pathway, and can still effectively activate DR5 especially in drug-resistant cells.⁸² This class of drugs can selectively remove inflammatory cells in atherosclerotic plaques and reduce plaque instability. In addition, the bifunctional protein SRH-DR5-B achieves the synergistic effect of DR5 receptor-mediated tumor cell apoptosis and vascular targeting by fusing the specific peptide of VEGFR2, providing a new idea for the treatment of abnormal vascular proliferation in cardiovascular diseases.⁸²

Small-molecule DR5 agonists, such as Bioymifi, selectively activate DR5 and promote cell apoptosis by mimicking the natural TRAIL domain-activated receptor. Their advantages are high oral bioavailability and strong penetration, and they have shown the potential to enhance stem cell activity in intestinal organoid models.⁸⁸ In addition, studies have shown that the combination of Bioymifi and chemotherapy drugs (such as doxorubicin) can synergistically enhance the sensitivity of vascular endothelial cells and smooth muscle cells to apoptosis, especially in inhibiting vascular remodeling.^13,15 Studies have demonstrated that the treatment of cardiomyocytes with TRAIL or Bioymifi can promote cardiomyocyte hypertrophy through the activation of the ERK1/2 signaling pathway and transactivation of the epidermal growth factor receptor (EGFR). This form of hypertrophy is characterized in animal models by increased heart weight, thickened ventricular walls, and improved contractile function, which suggests that it may represent a physiological compensatory response rather than a pathological condition. Nevertheless, further research is required to determine whether prolonged activation of these pathways leads to pathological cardiac remodeling.⁵² Treatment of wild-type mice with MD5-1 (agonizing mDR5 mAb) resulted in an increase in heart weight and cardiomyocyte area, in part through activation of the epidermal growth factor receptor.⁵² Increased ventricular fractional shortening was also observed with DR5 activation.⁵² Engineered DR5 agonists, such as DR5-scFv sEVs, can specifically induce apoptosis in DR5-positive cells, and this targeting has been validated in cancer therapy.⁸³ In cardiovascular disease, this technology may have applications to eliminate pathologic cells, such as proatherogenic macrophages or hyperproliferating vascular smooth-muscle cells, thereby slowing plaque progression. Small molecule DR5 agonists have shown many therapeutic potentials in cardiovascular diseases, but their translational application still needs further mechanistic studies and clinical trials.

Inhibitory Intervention: Blocking the Pro-Apoptotic Pathway

sDR5-Fc inhibits apoptotic signaling by competitively binding TRAIL and blocking its interaction with membrane-bound DR5. In a model of myocardial I/R injury, sDR5-Fc exerts cardioprotective effects by reducing cardiomyocyte apoptosis and inflammatory responses.^5,59 Similar strategies have also shown potential to inhibit excessive inflammatory responses in the treatment of severe COVID-19 patients.²¹ In addition, targeted silencing of TRAIL or DR5 mRNA by siRNA delivered by liposomes or viral vectors can down-regulate the levels of pro-apoptotic proteins. For example, silencing TRAIL or DR5 significantly alleviated podocyte injury induced by high glucose in diabetic nephropathy models.¹⁴ Similarly, administration of recombinant TRAIL or adenoviral TRAIL resulted in a significant reduction in cardiac fibrosis and apoptosis compared with control diabetic animals.⁷⁴ Knockdown of CHOP by siRNA resulted in a 60% decrease in DR5 expression and a 2.3-fold increase in cell survival in a model of cardiac apoptosis induced by eugenol combined with TRAIL.⁸⁴ In the rat model of acute myocardial infarction, treatment with DR5 inhibitor reduced the apoptotic cells in the infarct area by 45%, while improving the left ventricular ejection fraction by 28%.¹² Blockade of DR5 using sDR5-fc in heart failure models prevents myocardial cell death and inflammation, preserves ejection fraction and fractional shortening, reduces fibrosis, and prevents ventricular wall thinning, as observed in rodents, pigs, and monkeys.⁵⁹ In addition, inhibition of the TRAIL-DR5 pathway improved cardiac function by reducing neutrophil infiltration and inflammatory factor release in a myocardial ischemia model.⁵ This implies that the activation of TRAIL signaling in the heart may be detrimental under certain circumstances, and that blocking TRAIL signaling may be used as a potential therapeutic approach.

Combined Therapeutic Strategy: Multi-Pathway Synergistic Regulation

Recent studies have shown that kinase inhibitors, such as NT157, enhance TRAIL-induced apoptosis by up-regulating DR5 expression. In the glioblastoma model, NT157 combined with TRAIL significantly activated caspase-3 and inhibited tumor growth, suggesting that it may enhance apoptosis sensitivity through a similar mechanism in cardiovascular diseases.⁴⁷ In addition, MAPK pathway inhibitors (such as PD98059) further amplify TRAIL-DR5 signaling-mediated apoptotic effects by regulating ERK and JNK phosphorylation levels.^46,85 In the atherosclerosis model, MAPK signaling affects plaque stability by regulating DR5 expression, suggesting that combined targeting of autophagy and apoptosis pathways may become a new therapeutic strategy.⁸⁹ However, MAPK inhibitors may present a “double-edged sword” effect. For example, in KRAS-mutant pancreatic cancer, although MAPK inhibition up-regulates TRAIL, but down-regulates DR4/DR5, it is necessary to combine DR5 stabilizer (such as tetrandrine) to enhance the efficacy.⁸⁶ In addition, JNK pathway activation can up-regulate the expression of DR5 and enhance TRAIL-induced apoptosis. For example, 7-methoxy-aesculin enhances DR5 expression through the JNK pathway and promotes tumor cell death in combination with TRAIL. A similar mechanism may be applied to myocardial protection.⁴³ In addition, the RAS-RAF-MEK-ERK pathway is associated with the progression of cardiovascular diseases, and the combination of MEK inhibitors and TRAIL-DR5 activators may synergistically inhibit pathological cell proliferation or inflammation.⁹⁰ Although combination with kinase inhibitors (eg, JNK, ERK, or MEK inhibitors) can enhance efficacy by regulating DR5 expression or signal transduction, it is necessary to balance pro-apoptotic and cytoprotective effects. Future studies need to further explore tissue-specific mechanisms and develop precise combined treatment regimens.

There is a cross-regulation mechanism between TRAIL-DR5 signaling pathway and autophagy. For example, activated caspase-8 can cleave autophagy-related proteins (such as Beclin-1) and inhibit autophagy to promote apoptosis-led cell death.¹⁹ TRAIL-DR5 activation may up-regulate autophagy-related genes (such as LC3) through JNK or p38 MAPK signaling to induce protective autophagy to antagonized apoptosis.⁴⁵ DR5 activation partly promotes the expression of anti-apoptotic proteins (such as Bcl-2) through NF-κB, while Bcl-2 can bind to Beclin-1 to inhibit autophagy, forming an apoptosis-autophagy negative feedback loop.⁹¹ It is worth noting that TRAIL-DR5 also interacts with autophagy in some disease models. For example, in the atherosclerotic model, excessive activation of TRAIL-DR5 in intraplaque macrophages leads to increased apoptosis, while autophagy inhibition (eg, mTOR activation) exacerbates lipid accumulation and inflammation; Conversely, enhanced autophagy may remove oxidized LDL and stabilize the plaque,⁹² suggesting that combined regulation of the two may be more effective in stabilizing the plaque. During myocardial ischemia-reperfusion injury, autophagy enhances the removal of damaged mitochondria and protects the myocardium. Excessive activation of TRAIL-DR5 during reperfusion leads to apoptosis, while autophagy may alleviate injury by inhibiting ROS production.^5,21 In advanced cardiovascular diseases, such as heart failure, excessive autophagy may lead to excessive degradation of cardiomyocytes and impaired contractile function. For example, autophagy-related genes (such as SPAG5) affect endothelial cell survival by regulating the PI3K/Akt/mTOR pathway, and their abnormal expression may accelerate the process of atherosclerosis,⁹³ suggesting that myocardial cell autophagy dysregulation (excessive or insufficient) and TRAIL-DR5-mediated apoptosis synergistically promote myocardial fibrosis and deterioration of systolic function. Late autophagy inhibitors (such as chloroquine) can enhance the pro-apoptotic effect of TRAIL-DR5, which is also suitable for cardiovascular pathological states with apoptosis resistance.⁸⁷ Of course, the threshold of apoptosis and autophagy varies with cell type and disease stage. Excessive inhibition of autophagy may aggravate cell death, while excessive activation of TRAIL-DR5 may induce unexpected inflammatory responses. Most of the existing studies are based on animal models. It is necessary to further explore the interaction mechanism and therapeutic window of apoptosis and autophagy in human tissues.

In conclusion, the intervention strategy targeting TRAIL-DR5 signaling axis has multi-directional regulatory potential in cardiovascular diseases: agonists eliminate diseased cells by selectively activating apoptotic pathways; Inhibitory intervention protects myocardium by blocking pathological signals. Combination therapy enhances the efficacy through multi-target synergy. In the future, it is necessary to further explore the specific mechanism of DR5 signaling in cardiovascular cell types and optimize drug delivery systems to improve targeting.

Challenges and Future Directions

Currently, serum DR5/TRAIL levels are associated with outcomes of cardiovascular events, such as atherosclerotic plaque stability or degree of myocardial damage, but there are significant individual differences in sensitivity and specificity. Although pre-clinical studies have suggested that TRAIL concentration may be a prognostic indicator of CVD,¹³ actual clinical data are still contradictory. The pathological mechanisms of different cardiovascular diseases (for example, myocardial infarction, heart failure, and arrhythmia) are different, and the role of TRAIL signaling may be opposite. Age, gender, and comorbidities (such as diabetes and hypertension) can also affect the level of TRAIL and its biological effects. For example, inhibition of the TRAIL-DR5 pathway can reduce cardiac ischemia-reperfusion injury,⁵ but there is a lack of consistency in the association of TRAIL levels with disease severity or short-term prognosis in different patients. In addition, biomarkers such as TRAIL, sDR5, and OPG exhibit alterations across various diseases—including cancer, autoimmune disorders, and infections—and therefore lack cardiac specificity. Currently, there are no standardized methods or threshold values for their detection, limiting their utility in clinical classification and prognosis. Existing biomarker discovery approaches predominantly rely on single-omics data (eg, genomics or proteomics) and lack integration across multiple omics layers, which compromises the specificity of identified biomarkers.^27,94 In the future, large-scale longitudinal studies combined with multi-omics technologies (such as proteomics, metabolomics and epiomics, etc) are needed to screen dynamic marker combinations to accurately evaluate the relationship between pathway activity and disease progression, so as to verify the clinical value of TRAIL/DR5 related markers.⁹⁵

TRAIL or DR5 agonists and antagonists require precise targeting of cardiac tissues to minimize systemic side effects, such as immunosuppression and hepatotoxicity. Conventional systemic administration methods are susceptible to off-target effects, including the activation of apoptotic pathways in non-cardiac tissues. Furthermore, current delivery systems, such as recombinant proteins, antibodies, and small molecules, face challenges related to stability, half-life, and tissue permeability.^34,51 For example, TRAIL can be combined with chemotherapeutic agents to enhance anti-apoptotic effects, but the penetration efficiency of nanocrystals in ischemic myocardium is insufficient.⁵ In the future, responsive nanocarriers, such as pH or ROS-sensitive, can be developed for precise release of TRAIL agonists in the cardiac microenvironment^34,51 or combined with single-cell imaging techniques to optimize delivery pathways, such as enhancing vascular stability through endothelial-pericyte interactions.^18,96

In addition to the above-mentioned obstacles in clinical translation, there is also a need for research on specific mechanisms. There are multiple TRAIL receptors (DR4, DR5, DcR1, DcR2, OPG), with different affinities, expression levels and downstream signaling pathways. A single-targeting strategy may be ineffective or even harmful. Different cell subsets in the cardiovascular system (such as cardiomyocytes, endothelial cells, fibroblasts, and immune cells) have significant heterogeneity in response to TRAIL-DR5 signaling. For example, endothelial cells are the main source of TRAIL in the healthy circulation, but their ability to secrete is impaired under ischemic conditions.¹⁸ DR5 activation may promote macrophage apoptosis to reduce inflammation, but induce myocardial cell apoptosis to aggravate cardiac function damage. Currently, single-cell transcriptome, epiomics and spatial omics techniques can be used to reveal ① the epigenetic regulation mechanism of DR5 expression in specific cell subsets (such as diabetes-associated inflammatory macrophages).^13,97 ② the interaction network between TRAIL signaling and key angiogenic pathways (such as VEGF or Notch) during cardiac repair.^18,98 However, the integration of existing single-cell data still faces the challenge of insufficient standardization, and it is necessary to establish cross-platform analysis methods.

The function of TRAIL-DR5 pathway is also regulated by post-translational modifications such as ubiquitination and phosphorylation. For example, ASB3-mediated ubiquitination of DR5 may affect its apoptotic signaling efficiency, but the specificity of this mechanism in cardiovascular cells has not been tested.⁴⁵ Gene editing technologies such as CRISPR-Cas9 or base editing can be applied to construct cardiovascular disease models such as cardiomyocyte-specific DR5 knockout mice, to resolve the dual role of TRAIL signaling in pathological conditions (pro-apoptotic or pro-repair) or to screen for key factors regulating DR5 stability (such as de-ubiquitinizing enzyme USP9X), and to explore the role of TRAIL signaling in the pathogenesis of cardiovascular disease. Providing new targets for drug design.^26,81 In addition, the role of non-coding Rnas (such as miR-155) in the regulation of TRAIL-DR5 pathway is also worth further exploration.⁹⁹

The efficacy of TRAIL-DR5 pathway is significantly affected by patients’ comorbidities, such as diabetes and chronic kidney disease. For example, high glucose environment in diabetic patients with CVD can enhance TRAIL-DR5-mediated endothelial cell apoptosis while inhibiting its proangiogenic function.^13,18 In the future, it is necessary to establish a patient stratification system based on multi-dimensional biomarkers (genetic variation, metabolic characteristics, imaging phenotypes), and develop concomitant diagnostic tools (such as circulating tumor necrosis factor receptor detection) to guide individualized medicine.

In addition, TRAIL-DR5 has a “double-edge sword” effect in the cardiovascular system: moderate activation in the early stage can clear diseased cells (such as pro-inflammatory macrophages), but excessive activation leads to myocardial cell loss and aggravation of injury, and promotes repair and improves angiogenesis in the later stage.^18,26 Spatiotemporal specific intervention strategies (such as the use of DR5 neutralizing antibodies to reduce myocardial apoptosis in the acute phase, and gene editing or small molecule activators to enhance the pro-angiogenic function of TRAIL in the recovery phase) can be used to achieve local signaling regulation.^100,101 In addition, combination therapies (eg, TRAIL agonists combined with antifibrotic agents) may synergistically improve cardiac remodeling. Artificial intelligent-driven multi-omics models can predict a patient’s individualized treatment window.⁹⁴

In conclusion, the potential of TRAIL-DR5 pathway in the treatment of cardiovascular diseases has not been fully released, and the translational barriers need to be overcome through the interdisciplinary (such as biomaterials, computational biology, and clinical medicine). Future research should focus on: (1) developing highly sensitive biomarkers and targeted delivery systems; (2) Unraveling the mechanisms of cell subpopulation-specificity and validating novel targets; (3) Constructing a dynamic intervention strategy to achieve the balance between efficacy and safety. The ultimate goal is to achieve a paradigm shift from “broad spectrum treatment” to “precision intervention” and provide innovative solutions for complex cardiovascular diseases.

Conclusions

The TRAIL-DR5 signaling axis exhibits a context-dependent dual role in cardiovascular diseases, acting both as a promoter of apoptosis/injury and a mediator of anti-inflammatory/protective effects. This functional dichotomy is influenced by cell-type specificity, dynamic microenvironmental changes, and crosstalk with other signaling pathways such as NF-κB, MAPK, and autophagy. Throughout this review, we have highlighted its complex involvement in key cardiovascular conditions—including acute myocardial infarction, heart failure, atherosclerosis, and atrial fibrillation—where TRAIL and DR5 levels often serve as prognostic biomarkers with contradictory implications.

Therapeutic strategies targeting this axis are multifaceted: DR5 agonists (such as, monoclonal antibodies, multivalent ligands, and small molecules) show promise in selectively eliminating pathological cells; inhibitory approaches (such as, sDR5-Fc, siRNA) protect against excessive apoptosis and inflammation; and combination therapies (eg, with kinase or autophagy inhibitors) enhance efficacy through synergistic pathway modulation. However, the clinical translation of these interventions remains challenging due to issues of specificity, delivery, and contextual signaling outcomes. Future research should prioritize: (1) Elucidating the mechanisms underlying cell-type-specific responses to TRAIL-DR5 signaling; (2) Exploring its interactions with immune homeostasis and alternative cell death modalities; (3) Developing spatiotemporally controlled delivery systems to balance therapeutic efficacy with safety. Advances in multi-omics profiling, single-cell technologies, and biomarker validation will be essential to transition from broad-spectrum treatments toward precision interventions, ultimately enabling individualized therapeutic strategies for complex cardiovascular diseases.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This research was funded by the medical science and technology research in Henan Province (SBGJ202103095), the Science and technology research project of Henan Province (252102311022), the Key project of National Natural Science Foundation (U22A20382), the Key Research and Development Project of Henan Province (241111312200), the China Postdoctoral Science Foundation (2021M701061).

Disclosure

The authors declare that they have no competing interests.

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The AI bubble might pop, but Kong CEO Augusto “Aghi” Marietti told Business Insider he thinks it’ll be worth it.

AI companies will ultimately need the massive infrastructure projects they’re now spending so much on to build, he said.

“We’re in this new builders era where it’s a very singular moment where we are going to probably deploy more capex and more capital for enabling the AI era, and we need it,” Marietti told Business Insider.

Marietti said that energy-related issues are likely to be the primary bottleneck that stunts AI growth. Business Insider has documented how AI companies are so desperate for power for their large data centers that some are building self-contained supplies.

“We don’t have the energy we need to power all the GPUs in the following year,” he said.

Wall Street, however, is concerned about the sustainability of the capex spending craze by leading AI startups and other Big Tech companies, which is generating all kinds of bubble talk. A Business Insider analysis found that Amazon, Microsoft, Meta, and Google could spend an estimated $320 billion on capex, primarily for AI-related needs.

OpenAI CEO Sam Altman said in August that he agrees AI could be in a bubble phase, echoing others who have warned that the spending cannot be sustained. Some economists say capex spending is so high right now that it is propping up the entire US economy.

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Like Altman and others, Marietti compared the current spending to the building of railroads in the US in the 19th century. AI optimists argue that AI, like the railroads, will fundamentally transform the economy, and therefore, massive expenditures are needed to lay the groundwork for what’s to come.

“Some railroads were deployed ahead of time, but then all the railroads got used,” he said. “I think in AI, we’re just deploying ahead of time, and eventually something will blow up for a little bit, but we would eventually need the infrastructure that we’re deploying anyways.”

OpenAI President Greg Brockman has suggested that soon, every person will want their own GPU, a level of demand that would require massive expansion by his company and others.

Marietti said even “a down moment” won’t stop what’s coming down the tracks.

“After that, we’ll still use all the infrastructure that we build,” he said. “We still use the railroads that we deployed 150 years ago ahead of time.”

Deep Robotics, a Hangzhou-based start-up, is facing a severe talent shortage, hindering its efforts to commercialise the results of its research and development, according to its chief technology officer (CTO).

Li Chao, who is also a co-founder of the robot maker, said at the Bund Summit Financial Forum in Shanghai on Friday that highly skilled professionals were very much needed to help Deep Robotics take its business forward.

He added that top talent in the algorithm area was in strong demand, which the company needed to fine-tune its humanoid robots to better serve clients.

Do you have questions about the biggest topics and trends from around the world? Get the answers with SCMP Knowledge, our new platform of curated content with explainers, FAQs, analyses and infographics brought to you by our award-winning team.

“Robots are [penetrating] every manufacturing sector,” Li said. “As a company, we must seize the opportunity to make our products not only usable but also reliable in some industrial scenarios.”

Deep Robotics has launched the DR02 industrial-grade robot capable of operating reliably under all weather conditions. Photo: Handout alt=Deep Robotics has launched the DR02 industrial-grade robot capable of operating reliably under all weather conditions. Photo: Handout>

Deep Robotics was founded by CEO Zhu Qiuguo in 2017, when he was an associate professor at Zhejiang University.

The company is part of an unofficial group of start-ups dubbed the “Six Little Dragons of Hangzhou”, alongside artificial intelligence developer DeepSeek, video game studio Game Science, brain-machine interface innovator BrainCo, 3D interior design software developer Manycore and robot maker Unitree Robotics.

The six firms are widely seen as future stars, boosting Beijing’s ambitions of building China into a global technology powerhouse.

DeepSeek’s breakthroughs in large-language models sparked a trillion-dollar global rout in Nvidia and US tech stocks in January. The firm’s two powerful AI models were built at a fraction of the cost and computing power used by foreign firms. But their performance proved to be on par with OpenAI’s GPT model.

“The ‘Little Dragons’ are the envy of the country’s tech industries and, technically, they can attract all kinds of talent because of their reputation,” said Ding Haifeng, a consultant at financial advisory firm Integrity in Shanghai. “Top start-ups are eager to accelerate the transition of their research into commercial applications. Consequently, they are actively chasing more qualified professionals to strengthen their overall capabilities.”

CTO Li said Deep Robotics was also consolidating tie-ups with overseas partners in markets like Asia-Pacific and the Middle East to speed up its global expansion.

Earlier this month, the company launched the DR02, an industrial-grade robot capable of operating reliably under all weather conditions.

The 1.75-metre, 65kg humanoid robot features a waterproof body and frame. With wide thermal tolerance, it can endure rain, humidity and dust.

The humanoid robot sector is one of the areas that both the US and China are looking to gain an advantage in amid escalating trade tensions between the world’s two largest economies.

China has an edge in supply chain and scale as it manufactures high-quality, cost-efficient components, according to analysts.

The mainland was now home to nearly 100 humanoid robot makers, accounting for more than 70 per cent of the global ­market, according to Lu Hancheng, former director of the Shenzhen-based Gaogong Robot Industry Research Institute.

This article originally appeared in the South China Morning Post (SCMP), the most authoritative voice reporting on China and Asia for more than a century. For more SCMP stories, please explore the SCMP app or visit the SCMP’s Facebook and Twitter pages. Copyright © 2025 South China Morning Post Publishers Ltd. All rights reserved.

Copyright (c) 2025. South China Morning Post Publishers Ltd. All rights reserved.

Gold and silver prices increased on Saturday in both global and local markets after a six-day pause, driven by a rise in international bullion rates.

In the international bullion market, gold gained $18 per ounce, reaching $4,113. Following the global trend, the price of 24-carat gold in local markets rose by Rs1,800 per tola, bringing it to Rs433,662, while the price of 10 grams increased by Rs1,543 to Rs371,795.

Similarly, silver prices also rose, with the rate per tola increasing by Rs57 to Rs5,124, and 10 grams climbing by Rs49 to Rs4,393.

Read: SBP injects Rs4.25tr via OMOs

Traders attributed the increase to fluctuations in the global bullion market, which directly influenced domestic precious metal rates.

On Friday, gold prices continued their downward trajectory, mirroring trends in the international market, where the precious metal struggled to recover despite slightly softer-than-expected US inflation data that bolstered expectations of a Federal Reserve rate cut next week.

According to the rates issued by the All-Pakistan Gems and Jewellers Sarafa Association, the price of gold per tola fell by Rs2,000, settling at Rs431,862, while the price of 10 grams declined by Rs1,714 to Rs370,252.

Read more: Gold prices drop sharply in Pakistan following global decline

The fall marks the first weekly loss in nearly 10 weeks as global investors adjusted their positions ahead of next week’s US monetary policy announcement.

On Thursday, the yellow metal had already recorded a sharp drop of Rs3,500 per tola, bringing local prices down from recent highs.

The consistent downward pressure reflects international market sentiment, where gold has been trading in a narrow band after heavy profit-taking earlier in the week.

 

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