Clinical Pharmacology & Therapeutics最新文献

Rifampicin is a prototypical clinical inducer used in drug-drug interaction (DDI) studies. However, the clinical relevance and predictability of rifampicin-mediated hepatic transporter induction remain poorly defined. Conventional hepatocyte culture models fail to capture clinically relevant regulation of transporters and drug-metabolizing enzymes (DMEs), particularly at the protein level. In this study, we characterized a liver tissue chip (LTC) as a new approach methodology (NAM) for long-term transporter- and DME-mediated induction risk assessment. Transporter-certified® cryopreserved primary human hepatocytes from three donors were cultured in the LTC under recirculating flow with or without 10 μM rifampicin for 7 days. The LTC facilitated simultaneous mRNA-protein quantification for transporters and mRNA-protein-activity profiling for cytochrome P450s (CYPs) within individual chips across treatment and control conditions. In addition, extracellular bile acid profiling and intracellular rifampicin concentrations were measured by LC-MS/MS. Rifampicin induced multiple transporters and enzymes at the protein level, including organic anion transporting polypeptide (OATP)2B1, P-glycoprotein, CYP3A4, CYP2C19, and uridine 5'-diphospho-glucuronosyltransferase (UGT)1A4. Induction patterns differed between transporter mRNA and protein in many cases, suggesting regulatory mechanisms beyond PXR-mediated activation. Matched mRNA-protein-activity measurements of five CYP enzymes also revealed discrepancies in rifampicin-mediated induction magnitude. Rifampicin altered bile acid composition, increasing taurine-conjugated bile acids and inducing bile acid-related genes. Measured intracellular rifampicin concentrations differed substantially from experimentally employed media concentrations. Overall, these findings demonstrate that the LTC enables integrated assessment of mRNA, protein, and activity measurements in addition to bile acid profiles and intracellular drug exposure, which may improve mechanistic understanding of induction processes.

Romidepsin (RMD), an FDA-approved histone deacetylase inhibitor for treating cancer, demonstrates potent latency-reversing activity in vitro but has yielded suboptimal outcomes in kick-and-kill HIV-cure trials when administered as a 5 mg/m² infusion. We hypothesized that distinct pharmacokinetic targets exist between the pharmacodynamic goals of cytotoxicity for cancer therapy and latency reversal for HIV cure. To address this question, we designed an empirical modeling framework incorporating patient pharmacokinetics as a New Approach Methodology. Using clonal- and primary cell models of HIV-1 latency with readouts for different stages of latency-reversing activity, we performed exposure-response and dose-fractionation studies to define pharmacokinetic drivers of efficacy in latency, then designed tailored dosing strategies to meet these targets in static- and dynamic-culture systems, including a hollow-fiber model. Across all systems, the only consistent pharmacokinetic parameter driving latency-reversing activity, was RMD exposure time, with maximal activity achieved in cells exposed to ~4-6 ng/mL RMD for ≥ 20 hours (AUCs of ~80-120 ng*hour/mL). With current clinical-dosing strategies achieving AUCs of 240-600 ng*hour/mL, the same outcome occurred only when RMD concentrations greatly exceeded a cytotoxicity threshold, suggesting response was a byproduct of cytotoxic processes rather than targeted latency reversal. These findings indicate that conventional oncologic dosing strategies for RMD might not be directly applicable to HIV-1 latency reversal, offering a mechanistic explanation for suboptimal clinical outcomes. We provide proof-of-concept for RMD's limited clinical efficacy and propose a "low and slow" dosing strategy targeting time-driven, toxicity-minimized drug exposure, demonstrating how in vitro pharmacokinetic/pharmacodynamic investigations applying clinical pharmacokinetic data may inform dose optimization for clinical trials of latency reversal.

Dengue virus (DENV) infection is a significant global health threat. Mosnodenvir (JNJ-64281802) is a pan-serotype small molecule antiviral blocking DENV replication. A Phase IIa study (NCT05048875) evaluated the antiviral activity, safety, and pharmacokinetics of mosnodenvir as prophylaxis against DENV-3 in a controlled human infection model (CHIM). A combined population pharmacokinetic (PopPK) and viral dynamic model (VDM) was developed to estimate the prophylactic effect of mosnodenvir and to inform dose selection. The PopPK model is a two-compartment model based on plasma pharmacokinetic data from three Phase I studies and Cohort 1 of the Phase IIa study. Mosnodenvir exposure metrics were estimated for participants receiving high, medium, or low doses. A VDM with an immune response compartment adequately described DENV-3 RNA profiles for placebo participants and was subsequently extended by including a sigmoid E_max relationship with average mosnodenvir concentration at steady state (C_avg,ss) to capture the antiviral effect on virion production rate. Simulations supported daily and weekly mosnodenvir dosing regimens to further characterize the pharmacokinetic/pharmacodynamic relationship in Cohort 2. Based on the complete Phase IIa study data, the basic reproductive number for DENV-3 in this CHIM was estimated at 21.7, and the typical C_avg,ss required to achieve complete virus eradication was estimated to be 1,410 ng/mL. In conclusion, the combined PopPK and VDM approach effectively informed dose selection and characterized the pharmacokinetic-antiviral activity relationship, supporting the development of mosnodenvir as a prophylactic treatment for dengue infection.

The Clinical Pharmacogenetics Implementation Consortium (CPIC) TPMT/NUDT15 Pharmacogene Curation Expert Panel (PCEP) conducted a comprehensive review of clinical, laboratory, and computational evidence to determine the clinical function assignments for TPMT and NUDT15 star alleles. These genes are critical for the metabolism of thiopurines, which are widely used in the treatment of cancer and autoimmune disorders. Standardized allele function assignment is essential for predicting metabolizer phenotypes and pharmacogenetics-guided thiopurine dosing. The work presented here includes the first designation of decreased function alleles for both TPMT and NUDT15, reflecting new clinical data that demonstrate partial loss of enzymatic activity and reduced dose tolerance. The panel also reclassified several alleles previously assigned uncertain or unknown function. The functional assignments were informed by a standardized framework incorporating clinical data, such as thiopurine tolerance and toxicity, as well as in vitro protein activity, ex vivo enzymatic measurements, and in silico variant effect prediction tools. These updates enhance the precision of genotype-to-phenotype mapping and support more personalized thiopurine therapy across diverse patient populations.

Obeticholic acid (OCA), a synthetic analog of chenodeoxycholic acid, was approved in 2016 for the treatment of primary biliary cholangitis. Early clinical trials revealed elevated liver biomarkers in healthy subjects receiving supratherapeutic OCA doses (100-250 mg). OCA was also evaluated as a treatment for metabolic dysfunction-associated steatotic liver disease (MASLD) but was not approved by the FDA due to liver safety concerns. In this in silico study, we investigated mechanisms of OCA-associated liver injury in virtual healthy and MASLD populations receiving supratherapeutic and therapeutic (10-25 mg) doses, respectively. OCA and metabolite exposures in plasma, sinusoidal blood, liver, and gut compartments were simulated using a physiologically based pharmacokinetic model. In the virtual MASLD population, exposures were increased 2-, 5-, and 10-fold in plasma, sinusoidal, and/or liver compartments relative to baseline. Mechanistic parameters relevant to OCA-mediated liver injury, including bile acid transporter inhibition and mitochondrial dysfunction, were incorporated into the DILIsym model. Predicted liver injury was reported as evaluation of drug-induced serious hepatotoxicity (eDISH) plots, and elevations in alanine aminotransferase, aspartate aminotransferase, and total hepatic bile acids. DILIsym simulations recapitulated liver biomarker elevations observed at supratherapeutic OCA doses in healthy subjects and predicted biomarker increases in the MASLD population under conditions of 5- and 10-fold increased exposures relevant to this population. Bile acid transporter inhibition alone reproduced simulated biomarker elevations, whereas mitochondrial uncoupling alone predicted increased biomarkers only at the highest exposures. Results suggest that DILIsym modeling would have predicted the liver safety concerns that led to withdrawal of OCA from the US market.

Adverse events (AEs) of small molecule kinase inhibitors (SMKIs) at therapeutic doses in cancer patients are largely unpredictable in phase I-III studies and clinical use, despite extensive preclinical toxicity testing under good laboratory practice conditions. To address this gap, we developed a machine learning (ML) framework to predict the occurrence and time to onset of clinical AEs caused by SMKIs using on-/off-target engagement and tissue/cell selectivity. The analysis included 1939 unique AEs from 3,433 patients treated with 16 SMKIs. On-/off-target engagement was evaluated by linking the inhibition (Ki) constants and expression of 442 kinase targets to SMKI exposure, expressed as dose-normalized AUC across plasma and 36 tissues. For each AE, we constructed random survival forest models composed of ensembles of binary decision trees, evaluated predictive accuracy using the concordance index, and applied variable importance (VIMP) measures to identify kinase targets potentially responsible for tissue-specific AEs. The final models successfully predict the most common AEs (rash, nausea, fatigue, headache) along with the most severe hematological AEs (neutropenia, leukopenia, lymphopenia, thrombocytopenia, anemia). VIMP analyses highlighted previously unrecognized kinases potentially involved in tissue-specific AE profiles. External validation using data from a Phase II neratinib monotherapy trial demonstrated strong model performance, yielding Pearson correlation coefficients (PCCs) ≥ 0.87 between predicted and observed AE incidences. These findings show that integrating exposure, on-/off-target engagement, and tissue-specific selectivity enables robust prediction of the likelihood of SMKI-associated AEs for both blood- and organ-related toxicities, offering a scalable approach at both the patient- and population-level.

We previously developed a mechanistic model framework integrating physiologically based pharmacokinetic model (LeiCNS-PK3.0), binding kinetics, and receptor dynamics, to predict regional human central nervous system (CNS) receptor occupancy (RO), using brain extracellular fluid (brain_ECF) concentrations. Positron emission tomography (PET)-RO is a non-invasive biomarker to estimate, and validate human CNS RO. However, for highly lipophilic drug buprenorphine brain_ECF concentrations-based RO predictions deviated substantially from PET-RO. This mismatch and the lipophilicity driven brain cell membrane (brain_M) accumulation motivated us to investigate relevance of brain_ECF vs. brain_M concentrations for PET-RO. We identified human studies reporting plasma PK and CNS PET-RO for D2 receptor ligands spanning lipophilicities. Population PK parameters, derived via model fitting, served as inputs to the mechanistic framework to simulate brain_ECF and brain_M-based regional RO and compared with PET-RO. We systematically assessed the impact of lipophilicity, receptor dynamics, endogenous ligands, and schizophrenia-related physiological changes on simulations accuracy. Brain_M concentrations showed better agreement with PET-RO than brain_ECF for five of eight D2 ligands. However, neither improvement nor brain_M partitioning showed a clear trend with logP; $K_p^M$ substitution yielded modest improvement. Receptor internalization reduced simulated RO and enhanced PET agreement (remoxipride/haloperidol), independent of lipophilicity. Endogenous dopamine and schizophrenia-related changes had negligible effects. This study demonstrated that brain_M concentrations offer improved PET-RO alignment for a subset of compounds, independent of lipophilicity suggesting role of other drug-specific factors in brain_M accumulation or lack thereof. The integrated mechanistic framework offers potential to enhance CNS drug treatment, subject to further development via qMRI, hMRI, MRS, and controlled clinical studies.

Zavegepant nasal spray, a calcitonin gene-related peptide receptor antagonist indicated for acute treatment of migraine with or without aura in adults, is a substrate for organic anion transporting polypeptide 1B3 (OATP1B3), Na⁺/taurocholate cotransporting polypeptide (NTCP), P-glycoprotein (P-gp), and cytochrome P450 3A4 (CYP3A4). In clinical drug-drug interaction (DDI) studies, oral zavegepant exposure increased in the presence of multiple-dose (MD) rifampin or itraconazole; however, no interaction was observed between intranasal (IN) zavegepant and itraconazole. A physiologically based pharmacokinetic (PBPK) model was developed and validated using in vitro, human mass balance, and observed clinical zavegepant pharmacokinetic data and was employed to assess clinical DDI mechanisms and further support zavegepant nasal spray labeling recommendations. PBPK modeling indicated that DDI observed between itraconazole and oral zavegepant is primarily driven by intestinal P-gp inhibition, and observed DDI with MD rifampin primarily results from hepatic uptake inhibition. Modeling predicted an increase in plasma exposure (AUC ratio [AUCR] = 2.39) of IN zavegepant with single-dose (SD) rifampin; IN zavegepant plasma exposure was also predicted to increase with MD rifampin (AUCR = 2.11). Predicted IN zavegepant exposure change with SD cyclosporine A was 1.58-fold (AUCR) when assuming 90:10 proportion for OATP1B3:NTCP contribution to hepatic uptake. CYP3A inducers, carbamazepine and efavirenz, were predicted to have minimal effect on IN zavegepant PK. Overall, PBPK modeling and observed PK data support that CYP3A/P-gp inhibitors and inducers do not produce clinically relevant modulation in IN zavegepant plasma exposure; however, OATP1B3/NTCP inhibitors can significantly increase IN zavegepant exposure, and coadministration should be avoided.

Hyponatremia poses risks in type 2 diabetes, yet comparative electrolyte safety across glucose-lowering therapies remains unclear. We evaluated the risk of incident hyponatremia associated with sodium-glucose cotransporter 2 (SGLT2) inhibitors, glucagon-like peptide-1 receptor agonists (GLP-1 RAs), and dipeptidyl peptidase-4 (DPP-4) inhibitors in real-world practice. Using electronic health records from 155 healthcare organizations in the TriNetX Global Collaborative Network, we conducted multicenter active-comparator, new-user target trial emulations with 1:1 propensity score matching. For each pairwise comparison, adults with type 2 diabetes initiating either drug class, with no record of either class during the preceding 365 days, were included. The primary outcome was incident hyponatremia (ICD-10-CM E87.1 or serum sodium ≤ 135 mmol/L); sensitivity outcomes included diagnosis-only, laboratory-only, and stricter laboratory thresholds. Among more than 300,000 matched pairs per comparison, SGLT2 inhibitors and GLP-1 RAs were each associated with a 15% lower hazard of hyponatremia than DPP-4 inhibitors (hazard ratio [HR], 0.850; 95% confidence interval [CI], 0.842-0.858 and HR, 0.847; 95% CI: 0.838-0.856). The lower risk persisted at stricter sodium thresholds and across clinically relevant subgroups, including patients with heart failure, chronic kidney disease, or high-risk medications use. SGLT2 inhibitors and GLP-1 RAs were broadly comparable (HR, 0.962; 95% CI: 0.952-0.971). Positive and negative control outcomes were consistent with expectations. Overall, SGLT2 inhibitors and GLP-1 RAs were associated with a lower risk of incident hyponatremia than DPP-4 inhibitors. These real-world findings provide reassurance regarding the electrolyte safety of SGLT2 inhibitors and GLP-1 RAs, complementing their established cardiorenal benefits.

Clinical pharmacology and pharmacometrics teams are now being encouraged to implement the ICH M15 framework in drug development to support model-informed decisions in regulatory submissions. Although model performance is an important component of model evaluation, regulatory confidence also depends on the intended context of use (CoU) and the supporting level of evidence. This is particularly relevant for physiologically based pharmacokinetic (PBPK) modeling, where increasing regulatory application has strengthened the need for structured approaches to evaluate predictive performance, extrapolation, uncertainty, and model applicability. The Simcyp Simulator (V19R1) was recently qualified by the EMA for prediction of CYP-mediated drug-drug interactions (DDIs) involving six enzymes (CYP1A2, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP3A4/5), competitive inhibition, and mechanism-based inhibition for both victim and perpetrator drugs across three CoUs. Qualification data demonstrated consistent predictive performance across the three CoUs, with average fold errors close to unity, absolute average fold errors ranging from 1.23 to 1.25, and 98.8% of 246 predictions falling within 2-fold of observed values. The opinion predates ICH M15; however, it does not provide either a mapping to its credibility concepts or an operational basis for extending platform-level evidence to a specific investigational drug. We address this gap with an explicit mapping of the qualification opinion onto ICH M15 credibility concepts and an integrated approach linking qualification evidence, drug-specific validation, predictive performance, uncertainty characterization, and prospective application, illustrating how confidence in PBPK-supported regulatory decisions can be established within an ICH M15-aligned framework.