Clinical Pharmacology & Therapeutics最新文献

Digital patients and in silico clinical trials offer new opportunities to advance pharmacological research, clinical development, and regulatory decision-making by enabling the simulation of treatment effects prior to testing in patients. Among the most promising of these opportunities is the combined use of quantitative systems pharmacology (QSP) and causal inference (CI), two complementary paradigms that, together, address different but interlocking aspects of causal reasoning in drug development: one grounded in mechanistic simulation, the other in data-driven estimation. Mechanistic quantitative systems pharmacology (QSP) models generate physiologically interpretable predictions by encoding biological knowledge—such as compartmental flows, receptor kinetics, and feedback loops—typically in systems of differential equations. As such, QSP addresses the question: given specified mechanistic assumptions, how does the system behave under intervention? Complementing this mechanistic lens, causal inference approaches start from observed data and estimate the effects of interventions using formal causal frameworks and comparative outcome analysis. These methods explicitly address confounding, multicausality, and clinical heterogeneity, asking instead: given the data, what are the causal effects of intervening in the real world? These paradigms reflect fundamentally different representations of causality—mechanism-driven versus data-driven—two complementary lenses on the same underlying goal of understanding and predicting intervention effects. This distinction raises a central question: how can causality be consistently represented, inferred, and validated across modeling approaches, and what is required to integrate them to support robust and credible decision-making?

Immunotherapy has transformed cancer treatment, yet its efficacy in gastrointestinal (GI) cancers and inflammatory diseases remains limited, underscoring the need for more effective immunomodulatory strategies. Multiple biomaterial-based delivery platforms, including liposomes, polymeric nanoparticles, viral vectors, and inorganic nanocarriers, have been employed to enhance immune regulation and therapeutic transport. However, their clinical translation is hindered by intrinsic limitations such as immunogenicity, variable biocompatibility, nonspecific tissue distribution, payload instability, and challenges in scalable and reproducible manufacturing. These constraints highlight the need for safer and more clinically adaptable delivery systems. Extracellular vesicles (EVs), as naturally derived nanoscale carriers, have emerged as promising therapeutic tools due to their unique advantages, such as biocompatibility, low immunogenicity, precise targeting, and multifunctional molecular delivery. This review synthesizes recent advances in EV-based immunotherapeutic strategies for GI cancers and diseases, emphasizing approaches relevant to clinical pharmacology. Key innovations include EV vaccines, modulation of the immune microenvironment using cell source-specific EVs, and engineered EVs for the targeted delivery of cytokines, nucleic acids, or immune checkpoint inhibitors. Emerging applications involving EV-mediated transport of CAR constructs, oncolytic agents, and gene-editing tools further broaden their therapeutic potential. In conclusion, EV-based therapies offer transformative approaches through multimodal immune modulation and precision drug delivery to GI diseases. However, standardization, large-scale manufacturing, targeted delivery, and addressing tumor heterogeneity with personalized EV engineering will be crucial for clinical success in GI diseases. Overcoming these barriers will accelerate EV integration into next-generation immunotherapy and precision oncology.

Concomitant congestive heart failure (CHF) and diabetes mellitus (DM) patients frequently are treated with polypharmacy, thus potentially increasing the risk of drug-drug interactions (pDDIs). Conventional DDI screening methods often fail to distinguish clinically significant interactions from recommended therapeutic combinations. This study assessed the prevalence, severity, and clinical consequences of pDDIs in hospitalized patients with CHF and DM. This ambispective, cross-sectional study included 144 hospitalized patients with CHF and DM. We used the Micromedex and Lexicomp databases to check prescriptions. We classified clinically relevant DDIs and therapeutic combinations requiring routine monitoring by severity. 207 pDDIs were identified, of which 84.1% were moderate and 15.9% severe. Polypharmacy was a major risk factor for the DDI burden, and over seven drugs were associated with significantly more interactions than fewer drugs (P < 0.001). Polypharmacy was an independent predictor of clinically significant DDI (AOR 3.42; 95% CI 1.78-6.59) in the multivariate analysis. The most commonly seen combinations (statin-ezetimibe and sulfonylurea-metformin therapy) are guideline-recommended and do not need to be avoided. Clinical pharmacists and prescribing clinicians performing context-aware interpretation of DDI alerts by differentiating between guideline-recommended therapeutic combinations requiring monitoring and truly actionable interactions can reduce alert fatigue, preserve evidence-based therapy, and support safe prescribing in complex CHF-DM patients.

Demonstrating pharmacological effects in early-phase oncology clinical trials remains challenging, largely due to the lack of robust pharmacodynamic markers. Lipopolysaccharide (LPS) is used as an immune challenge agent in healthy participants to study drugs for autoimmune conditions. We hypothesized that the intravenous LPS challenge model could be implemented in oncology drug studies in healthy participants to evaluate in vivo pharmacodynamics of new drugs. This open-label study included 13 healthy males who received a single intravenous dose of 1 or 2 ng/kg LPS. Blood and bone marrow samples were collected pre- and post-LPS for bulk RNA sequencing, multiplex cytokine measurements, immunophenotyping, and immunohistochemistry of bone marrow biopsies. Differentially expressed genes were integrated with the OpenTargets Platform and the COSMIC database to assess their tractability and relation to cancer hallmarks. The LPS challenge and bone marrow sampling were safe and well tolerated. In peripheral blood, LPS induced a rapid, transient inflammatory response, with myeloid cell activation and enrichment of inflammation-related pathways. Conversely, bone marrow showed an attenuated cellular response and activation of hematopoietic and cell cycle-related transcriptional programs. We identified 91 LPS-induced oncology-relevant targets in blood and 24 targets in bone marrow. The oncogenic transcription factor MYC was upregulated in the bone marrow, which was confirmed at the protein level by a dose-dependent increase in c-Myc expression. Our findings highlight the potential of immune challenges in healthy participants to pharmacodynamically profile novel oncology agents, with the aim of generating early pharmacological data ahead of pivotal clinical trials in cancer patients.

Repurposing drugs whose clinical safety has been established offers a valuable approach to reduce the cost and time associated with the development of new drugs for malaria. Here, we investigate the potential to repurpose the anticancer kinase inhibitor berzosertib for the treatment of malaria, by assessing whether a predicted efficacious human dose for malaria is within the clinically established safety and tolerability profile. First, an oral dose fractionation study was performed in a Plasmodium falciparum-infected humanized mouse model to evaluate the antimalarial pharmacokinetic-pharmacodynamic properties of berzosertib. The activity of berzosertib was shown to be strongly dependent on the time in which the concentrations remain above the minimum parasiticidal concentration (MPC). A human physiologically based pharmacokinetic model was then developed and used to simulate the exposure-response relationships and determine the effective human dose which fulfilled the target pharmacokinetic profile of sustained concentrations above the MPC. Our prediction indicates that six daily intravenous doses of 75 mg of berzosertib will be required for the treatment of an uncomplicated blood stage malaria infection, which is comparatively lower than the clinical maximum tolerated dose for cancer therapy, which was identified as 440 mg administered intravenously twice weekly over a 21-day cycle for up to 18 weeks. Despite berzosertib's potential for repurposing, further preclinical development is necessary to ensure it meets the standard of current and emerging antimalarials.

Identifying ideal candidates for cancer therapies is challenging, especially with multiple oncogenic variants involved. In head and neck squamous cell carcinoma, the PI3Kα-AKT-mTOR and HRAS-MAPK pathways are frequently dysregulated. This study demonstrates how a quantitative systems pharmacology (QSP) model can help optimize clinical trial design by guiding patient selection and dosing strategies based on oncogenic genotypes. A QSP model was developed to capture the experimentally observed dynamics of the HRAS and PI3K pathways across five molecularly defined patient cohorts in the KURRENT-HN Phase I/II trial (NCT 04997902). The model assessed which genotypes would benefit the most from combination therapy with tipifarnib (farnesyl transferase inhibitor) and alpelisib (PIK3CA inhibitor). Simulation results identified the PIK3CA gain of function (GOF) as the genotype most likely to benefit. Virtual population analysis of PIK3CA GOF with dose escalation to 600 mg b.i.d. tipifarnib and 250 mg q.d. alpelisib suggested that a higher tipifarnib dose could enhance tumor response, potentially due to significant dependency of the PIK3CA-mutant cells on mTORC1 signaling. These simulations were consistent with the clinical data. This key dependency can be targeted by tipifarnib by blocking farnsylation of RHEB, an essential activator of mTORC1. Vpop responders showed that reduced intracellular mTOR activity in simulations increased the likelihood of tumor volume reduction. Global sensitivity analysis identified compensatory feedback, tumor proliferation rate, and PI3K-mTOR crosstalk as key determinants of tumor response. This novel QSP application exemplifies an innovative bottom-up modeling approach to support patient selection and dosing strategies for future clinical studies.

Validation of new approach methodologies increasingly requires developers to define a context of use (CoU) before establishing scientific credibility, constraining complex in vitro models. This CoU first strategy can impose a structural limitation on validation. We propose a two-phase framework that separates scientific validation from context-specific qualification, bridged by a shared data repository. Under this structure, the model's scientific capabilities and limitations inform the range of potential applications, rather than being constricted to the first CoU selected.

Pediatric drug development has achieved remarkable success in the last 20 years with over 1,000 products studied in pediatric patients. This success has been driven in part by an increased understanding of pediatric disease processes. The aspect that has been largely overlooked is the potential adverse effect of new drugs on pediatric developmental processes. The realization of this risk comes with the understanding that we could not predict another thalidomide worldwide tragedy even 70 years later.

IMPAACT 2001 (NCT02651259) evaluated the safety and pharmacokinetics (PK) of once-weekly isoniazid and rifapentine in pregnant and postpartum women for tuberculosis prevention. Rifapentine (RPT) and desacetyl rifapentine (desRPT) PK data were collected in maternal plasma, breast milk, infant cord blood, and infant plasma. Longitudinal maternal plasma and breast milk RPT and desRPT concentrations were characterized using a multicompartment PK model with biophase effect compartments (NONMEM v7.4). Rate parameters describing drug entry into breast milk from plasma and breast milk-to-plasma ratio parameters were estimated. For mother-infant pairs, maternal plasma PK was simulated for the last RPT dose pre-delivery (with HIV and partum status covariates), and concentration ratios (observed infant cord blood or plasma relative to simulated maternal plasma) were calculated at the infant PK sample collection time. For RPT and desRPT, derived equilibration half-lives were 0.568 hr and 0.439 hr, and estimated concentration ratios of breast milk to maternal plasma were 0.0236 and 0.0411, respectively, indicating rapid equilibration between maternal plasma and breast milk and a minimal extent of drug excretion into breast milk. Maximum relative infant dose was estimated at <1%. Within 72 hours of maternal dosing, median concentration ratios of observed infant cord blood and plasma to predicted maternal plasma were approximately 0.280 and 0.612 for RPT and 0.493 and 1.83 for desRPT, respectively. RPT crosses the placenta and is measurable in infant plasma at concentrations unlikely to be clinically significant. Breast milk concentrations are very low, supporting safe use of RPT in breastfeeding women.

The analgesic effectiveness of several opioids depends on CYP2D6-mediated metabolism to more active opioid receptor agonists. CYP2D6 activity is impacted by genetic variability and medications that inhibit CYP2D6. We conducted a pragmatic, multi-center, randomized, controlled trial to determine whether CYP2D6-guided opioid prescribing improves chronic pain. Participants were adults recruited from primary care or pain specialty clinics with at least 3 months of chronic pain treated or considered for treatment with tramadol, hydrocodone, or codeine. Participants were randomized to the usual care or CYP2D6-guided arm, in which CYP2D6 pharmacogenetic testing was performed. Healthcare professionals were recommended to avoid tramadol, hydrocodone, or codeine in patients with CYP2D6 intermediate or poor or ultra-rapid (for safety) metabolizer phenotypes based on either genotype or drug interactions. The primary outcome was the change in composite pain intensity scores from baseline to 3 months. Of 1048 enrolled participants, 282 had an actionable CYP2D6 phenotype. In this group, CYP2D6-guided recommendations did not improve the change in composite pain intensity scores from baseline to 3 months compared with usual care (mean [SD] -0.8 [2.4] vs. -0.7 [2.3]) or improve drug-gene concordance (26.8% vs. 27.1%) in the CYP2D6-guided versus usual care arm, respectively. In an exploratory analysis, participants whose therapy changed to align with their CYP2D6 phenotype had significantly improved pain scores. In conclusion, incorporating CYP2D6-guided recommendations for opioid therapy into routine practice did not change prescribing sufficiently to assess its impact on chronic pain control, suggesting that EHR-integrated CYP2D6 guidance is insufficient to change opioid prescribing for chronic pain.